Diss/01_tex/Diss_JohZ.bib

@article{Ye2012,
abstract = {As demand for proactive real-time transportation management systems has grown, major developments have been seen in short-time traffic forecasting methods. Recent studies have introduced time series theory, neural networks, genetic algorithms, etc., to short-time traffic forecasting to make forecasts more reliable, efficient and accurate. However, most of these methods can only deal with data recorded at regular time intervals, thereby restricting the range of data collection tools to loop detectors or other equipment that generate regular data. The study reported here represents an attempt to expand on several existing time series forecasting methods to accommodate data recorded at irregular time intervals, thus ensuring these methods can be used to obtain predicted traffic speeds through intermittent data sources such as the GPS. The study tested several methods using the GPS data from 480 Hong Kong taxis. The results show that the best performance is obtained using a neural network model with acceleration information predicted by ARIMA model.},
author = {Ye, Qing and Szeto, W. Y. and Wong, S. C.},
doi = {10.1109/TITS.2012.2203122},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2012{\_}Short-Term Traffic Speed Forecasting Based on Data Recorded at Irregular Intervals{\_}ITS.pdf:pdf},
isbn = {9781424476572},
issn = {15249050},
journal = {IEEE Transactions on Intelligent Transportation Systems},
keywords = {Autoregressive integrated moving average (ARIMA),Holt's method,combined forecasting,exponential smoothing method,irregularly spaced time series data,neural network,short-term traffic speed forecasting},
number = {4},
pages = {1727--1737},
title = {{Short-term traffic speed forecasting based on data recorded at irregular intervals}},
volume = {13},
year = {2012}
}
@book{Hettlich,
author = {Hettlich, Frank and Karpfi, Christian},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/07{\_}Math/Book{\_}2008{\_}Mathematik{\_}Lehrbuch{\_}Springer.pdf:pdf},
isbn = {9783642449185},
title = {{Mathematik}}
}
@article{Ekberg2016,
abstract = {Todays vehicle industry is strictly controlled by environmental legislations. The vehicle industry is spending much money on reducing the fuel consumption and fulfilling the emission requirements to make sales possible in different regions in the world. Before introducing a vehicle on the market, it is tested according to standardized driving cycles to specify the vehicle pollutant emissions and fuel consumption. These cycles allow some deviation from the reference vehicle speed during tests, e.g. NEDC allows deviations of ±2 km/h and ±1 s. This paper uses dynamic programming to find fuel optimal velocity profiles, given the allowed deviations of ±2 km/h and ±1 s from reference speed during drive cycle test. By taking advantage of the allowed deviation, the fuel consumption can be reduced by up to 16.56 {\%} according to model results, running NEDC if gear selections are unrestricted (i.e. using automatic gearbox), and up to 5.90 {\%} if changing gears according to the specifications in the drive cycle. Two different optimization goals are investigated, minimum amount of mass fuel consumed and best mileage.},
author = {Ekberg, Kristoffer and Eriksson, Lars and Sivertsson, Martin},
doi = {10.1016/j.ifacol.2016.08.095},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2016{\_}Cycle Beating - An Analysis of the Boundaries During Vehicle Testing{\_}IFAC{\_}Schweden.pdf:pdf},
issn = {24058963},
journal = {IFAC-PapersOnLine},
keywords = {Cycle Beating,Dynamic Programming},
number = {11},
pages = {657--664},
title = {{Cycle Beating - An Analysis of the Boundaries During Vehicle Testing}},
volume = {49},
year = {2016}
}
@article{Pion2012,
author = {Pion, Dipl Olivier and Henze, Roman and K{\"{u}}{\c{c}}{\"{u}}kay, Prof Ferit},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2011{\_}Fingerprint des Fahrers zur Adaption von Assistenzsystemen{\_}IfF{\_}TU Braunschweig.pdf:pdf},
journal = {GI-Jahrestagung},
pages = {833--842},
title = {{Fingerprint des Fahrers zur Adaption von Assistenzsystemen}},
year = {2012}
}
@article{Elektrotechnik1999,
author = {Elektrotechnik, Von Der Fakult{\"{a}}t and Stuttgart, Der Universit{\"{a}}t and Ebersp{\"{a}}cher, Markus},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/02{\_}Dissertation/Diss{\_}1999{\_}Prognose der Eigenschaften stochastischer Prozesse mittels Neuronaler Netze mit spezifischen Anwendungen in der Kommunikationstechnik.pdf:pdf},
title = {{mittels Neuronaler Netze mit spezifischen Anwendungen in der Kommunikationstechnik}},
year = {1999}
}
@article{Lastname,
author = {Lastname, Firstname},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/02{\_}Dissertation/Diss{\_}2018{\_}zzz{\_}Leitner{\_}THI{\_}marked{\_}JZ.pdf:pdf},
title = {{Some Very Important Research}}
}
@article{,
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Presentation{\_}2015{\_}HEV{\_}Symposium/Kasper{\_}Universitaet-Magdeburg{\_}HEV{\_}2015{\_}Vortrag.pdf:pdf},
title = {{Kompakte Leistung vor Ort mit leichten und hochintegrierten Radnabenmotoren neuer Architektur Gliederung}},
year = {2015}
}
@article{Gmbh2015,
author = {Gmbh, Evonik Litarion},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Presentation{\_}2015{\_}HEV{\_}Symposium/Modlinger{\_}Evonik{\_}HEV{\_}2015{\_}Vortrag.pdf:pdf},
title = {{Separatorb{\"{a}}ndern f{\"{u}}r gro{\ss}- Die Evonik Litarion ist Teil des Produktionsverbunds in Kamenz}},
year = {2015}
}
@article{Chu2011,
author = {Chu, Liang and Wang, Yanbo},
doi = {10.1109/MEC.2011.6025781},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2011{\_}A Method for a Driver Substitute in Intelligent Driving System Based on Simulation.pdf:pdf},
isbn = {9781612847221},
journal = {Proceedings 2011 International Conference on Mechatronic Science, Electric Engineering and Computer, MEC 2011},
keywords = {Driver model,Driver-Vehicle-Environment,Fuzzy control,Intelligent Driving system,PID control},
pages = {1594--1597},
title = {{A method for a driver substitute in intelligent driving system based on simulation}},
year = {2011}
}
@article{Alanis2009,
abstract = {In this paper, we propose a high order neural network (HONN) trained with an extended Kalman filter based algorithm to predict wind speed. Due to the chaotic behavior of the wind time series, it is not possible satisfactorily to apply the traditional forecasting techniques for time series; however, the results presented in this paper confirm that HONNs can very well capture the complexity underlying wind forecasting; this model produces accurate one-step ahead predictions.},
author = {Alanis, a Y and Ricalde, L J and Sanchez, E N},
doi = {10.1109/IJCNN.2009.5178893},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2009{\_}High Order Neural Networks for wind speed time series prediction{\_}Mexico.pdf:pdf},
isbn = {9781424435531},
issn = {1098-7576},
journal = {2009 International Joint Conference on Neural Networks},
pages = {76--80},
title = {{High Order Neural Networks for wind speed time series prediction}},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5178893},
year = {2009}
}
@article{Kneip,
author = {Kneip, Alois and Abteilung, Statistische},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/06{\_}Skripte-Vorlesungen/Script{\_}2008{\_}Nichtparametrische Statistik{\_}Uni Bonn{\_}Alois Kneip.pdf:pdf},
title = {{Nichtparametrische Statistik Inhalt}}
}
@article{Funfgeld2016,
author = {Funfgeld, Sebastian and Holzapfel, Marc and Frey, Michael and Gauterin, Frank},
doi = {10.1109/IVS.2016.7535571},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2016{\_}Driver State Estimation for Prediction of Vehicle States Within Control Systems{\_}IV{\_}IEEE.pdf:pdf},
isbn = {9781509018215},
journal = {IEEE Intelligent Vehicles Symposium, Proceedings},
number = {Iv},
pages = {1386--1391},
title = {{Driver state estimation for prediction of vehicle states within control systems}},
volume = {2016-Augus},
year = {2016}
}
@article{Palazzi2017,
abstract = {Despite the advent of autonomous cars, it's likely - at least in the near future - that human attention will still maintain a central role as a guarantee in terms of legal responsibility during the driving task. In this paper we study the dynamics of the driver's gaze and use it as a proxy to understand related attentional mechanisms. First, we build our analysis upon two questions: where and what the driver is looking at? Second, we model the driver's gaze by training a coarse-to-fine convolutional network on short sequences extracted from the DR(eye)VE dataset. Experimental comparison against different baselines reveal that the driver's gaze can indeed be learnt to some extent, despite i) being highly subjective and ii) having only one driver's gaze available for each sequence due to the irreproducibility of the scene. Eventually, we advocate for a new assisted driving paradigm which suggests to the driver, with no intervention, where she should focus her attention.},
archivePrefix = {arXiv},
arxivId = {1611.08215},
author = {Palazzi, Andrea and Solera, Francesco and Calderara, Simone and Alletto, Stefano and Cucchiara, Rita},
doi = {10.1109/IVS.2017.7995833},
eprint = {1611.08215},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2017{\_}Learning Where to Attend Like a Human Driver{\_}IV2017.pdf:pdf},
isbn = {9781509048045},
journal = {IEEE Intelligent Vehicles Symposium, Proceedings},
number = {Iv},
pages = {920--925},
title = {{Learning where to attend like a human driver}},
volume = {1},
year = {2017}
}
@book{Rooch,
author = {Rooch, Aeneas},
doi = {10.1007/978-3-658-20640-6},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2014{\_}Statistik f{\"{u}}r Ingenieure{\_}Rooch.pdf:pdf},
isbn = {9783642548567},
title = {{Statistik f{\"{u}}r Ingenieure}}
}
@article{Li2016,
abstract = {Naturalistic driving recordings are important for understanding the driver behavior. Driver behavior events of interest in these recordings, such as driver confusion and stress, are important for studying driver behavior and develop the next generation advanced driver assistant systems (ADASs). Unfortunately, such events are rare cases in the naturalistic driving data. Manual annotation is usually required to extract such events from a large data set. This study investigates the idea of using drivers' physiological signals to help with the manual annotation process. The proposed framework uses the unsupervised cluster algorithm, density-based spatial clustering of applications with noise (DBSCAN), to cluster the physiological data into three classes: “Normal”, “Event” and “Noise”. We define three types of driver behavior events of interest in our real-world driving data, and evaluate the recall rate using the data classified in the “Event” cluster. High recall rate at 75{\%} is achieved on average. We also evaluate the reduced effort for the annotator by estimating the viewing time compression rate, which is reduced by half when we set the fast forward rate in non “Event” segment to 5 times of normal speed.},
author = {Li, Nanxiang and Misu, Teruhisa and Miranda, Alexandre},
doi = {10.1109/ITSC.2016.7795971},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2016{\_}Driver Behavior Event Detection for Manual Annotation by Clustering of the Driver Physiological Signals{\_}0429.pdf:pdf},
isbn = {9781509018895},
journal = {IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC},
pages = {2583--2588},
title = {{Driver behavior event detection for manual annotation by clustering of the driver physiological signals}},
year = {2016}
}
@book{Jeschke2013,
address = {Berlin, Heidelberg},
doi = {10.1007/978-3-642-33389-7},
editor = {Jeschke, Sabina and Isenhardt, Ingrid and Hees, Frank and Henning, Klaus},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2012{\_}Automation, Communication and Cybernetics in Science and Engineering 2011-2012.pdf:pdf},
isbn = {978-3-642-33388-0},
publisher = {Springer Berlin Heidelberg},
title = {{Automation, Communication and Cybernetics in Science and Engineering 2011/2012}},
url = {http://link.springer.com/10.1007/978-3-642-33389-7},
year = {2013}
}
@book{Duffy2012,
author = {Duffy, Vincent G.},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2016{\_}Advances in Applied Digital Human Modeling and Simulation{\_}AHFE16.pdf:pdf},
isbn = {9781439870327},
title = {{Advances in Applied Human Modeling and Simulation}},
url = {http://books.google.com/books?hl=en{\&}lr={\&}id=l1BJ6XTBfo8C{\&}oi=fnd{\&}pg=PP1{\&}dq={\%}22agile+development{\%}22+and+(method+or+process+or+improvement)+and+(personality+or+{\%}22human+factors{\%}22)+-+-+-{\&}ots=94jWVxzGVk{\&}sig=l0oRCTVVyvndtwsmMvC8Tph6zmk{\%}5Cnhttp://books.google.se},
year = {2012}
}
@article{Fuller2005,
abstract = {Taylor [Taylor, D.H., 1964. Drivers' galvanic skin response and the risk of accident. Ergonomics 7, 439-451] argued that drivers attempt to maintain a constant level of anxiety when driving which Wilde [Wilde, G.J.S., 1982. The theory of risk homeostasis: implications for safety and health. Risk Anal. 2, 209-225] interpreted to be coupled to subjective estimates of the probability of collision. This theoretical paper argues that what drivers attempt to maintain is a level of task difficulty. N{\"{a}}{\"{a}}t{\"{a}}nen and Summala [N{\"{a}}{\"{a}}t{\"{a}}nen, R., Summala, H., 1976. Road User Behaviour and Traffic Accidents. North Holland/Elsevier, Amsterdam, New York] similarly rejected the concept of statistical risk as a determinant of driver behaviour, but in so doing fell back on the learning process to generate a largely automatised selection of appropriate safety margins. However it is argued here that driver behaviour cannot be acquired and executed principally in such S-R terms. The concept of task difficulty is elaborated within the framework of the task-capability interface (TCI) model, which describes the dynamic interaction between the determinants of task demand and driver capability. It is this interaction which produces different levels of task difficulty. Implications of the model are discussed regarding variation in performance, resource allocation, hierarchical decision-making and the interdependence of demand and capability. Task difficulty homeostasis is proposed as a key sub-goal in driving and speed choice is argued to be the primary solution to the problem of keeping task difficulty within selected boundaries. The relationship between task difficulty and mental workload and calibration is clarified. Evidence is cited in support of the TCI model, which clearly distinguishes task difficulty from estimates of statistical risk. However, contrary to expectation, ratings of perceived risk depart from ratings of statistical risk but track difficulty ratings almost perfectly. It now appears that feelings of risk may inform driver decision making, as Taylor originally suggested, but not in terms of risk of collision, but rather in terms of task difficulty. Finally risk homeostasis is presented as a special case of task difficulty homeostasis. {\textcopyright} 2005 Elsevier Ltd. All rights reserved.},
author = {Fuller, Ray},
doi = {10.1016/j.aap.2004.11.003},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2005{\_}Towards a general theory of driver behaviour{\_}Fuller{\_}TheoryofDrivingBehavior.pdf:pdf},
isbn = {0001-4575 (Print)$\backslash$n0001-4575 (Linking)},
issn = {00014575},
journal = {Accident Analysis and Prevention},
keywords = {Driver capability,Driving behaviour,Mental effort,Risk perception,Task demand,Task-capability interface},
number = {3},
pages = {461--472},
pmid = {15784200},
title = {{Towards a general theory of driver behaviour}},
volume = {37},
year = {2005}
}
@book{Mitschke1990,
address = {Berlin, Heidelberg},
author = {Mitschke, Manfred},
doi = {10.1007/978-3-642-86470-4},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}1990{\_}Dynamik der Kraftfahrzeuge - Band C Fahrverhalten{\_}Mitschke.pdf:pdf},
isbn = {978-3-642-86471-1},
publisher = {Springer Berlin Heidelberg},
title = {{Dynamik der Kraftfahrzeuge}},
url = {http://link.springer.com/10.1007/978-3-642-86470-4},
year = {1990}
}
@book{Aronsson2006,
abstract = {The objective of the study was to gain in-depth knowledge of speed relationships for ur-ban streets. The speed characteristics were examined using a number of methods for data collection. Throughout the research, a special focus was placed on capturing the influ-ence on driver speed of interactions with pedestrians, cyclists and other road users, called side-friction events in this study. First, driver behaviour and travel time data was collected from field and driving simula-tor studies for a range of street types and traffic conditions. The collected data was used to calibrate a microscopic traffic simulation model. Production runs with this model were performed for various traffic conditions. Second, aggregated speed data was col-lected at the link level, i.e. the macro level, for three street types. In combination with street site variables, speed and flow data was analysed using multiple regression tech-niques with space mean speed as dependent variable. This analysis was also performed for average travel speed data produced by microscopic traffic simulation.},
author = {Aronsson, Karin F M},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/02{\_}Dissertation/Diss{\_}2006{\_}Speed characteristics of urban streets based on driver behaviour studies and simulation{\_}KTH Schweden{\_}K Aronsson.pdf:pdf},
isbn = {9789185539130},
issn = {1653-4468},
keywords = {Aronsson,behaviour studies and simulation,speed characteristics of urban},
pages = {1--130},
title = {{Speed characteristics of urban streets based on driver behaviour studies and simulation}},
year = {2006}
}
@article{Falcone2007,
abstract = {In this paper, a model predictive control (MPC) approach for controlling an active front steering system in an autonomous vehicle is presented. At each time step, a trajectory is assumed to be known over a finite horizon, and an MPC controller computes the front steering angle in order to follow the trajectory on slippery roads at the highest possible entry speed. We present two approaches with different computational complexities. In the first approach, we formulate the MPC problem by using a nonlinear vehicle model. The second approach is based on successive online linearization of the vehicle model. Discussions on computational complexity and performance of the two schemes are presented. The effectiveness of the proposed MPC formulation is demonstrated by simulation and experimental tests up to 21 m/s on icy roads},
author = {Falcone, P and Borrelli, F and Asgari, J and Tseng, H E and Hrovat, D},
doi = {10.1109/TCST.2007.894653},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2007{\_}Predictive Active Steering Control for Autonomous Vehicle Systems{\_}Falcone{\_}CST.pdf:pdf},
isbn = {1063-6536},
issn = {1063-6536},
journal = {Control Systems Technology, IEEE Transactions on},
keywords = {Active steering,Computational modeling,Control systems,Mobile robots,Predictive models,Roads,Testing,autonomous vehicle systems,autonomous vehicles,computational complexity,model predictive control,nonlinear optimization,nonlinear vehicle model,position control,predictive active steering control,predictive control,remotely operated vehicles,robot dynamics,stability,steering systems,vehicle dynamics control,vehicle stability},
number = {3},
pages = {566--580},
title = {{Predictive Active Steering Control for Autonomous Vehicle Systems}},
volume = {15},
year = {2007}
}
@book{Dorrestijna,
author = {Dorrestijn, Jesse},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/02{\_}Dissertation/Diss{\_}2016{\_}Stochastic{\_}Convection{\_}Parametzerization{\_}Jesse{\_}TUDelft.pdf:pdf},
isbn = {9789402802757},
title = {{Stochastic Convection Parameterization}}
}
@article{Ag2014,
author = {Ag, Daimler},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Presentation{\_}2015{\_}HEV{\_}Symposium/Mohrdieck{\_}Daimler{\_}HEV{\_}2015{\_}Vortrag.pdf:pdf},
pages = {1--24},
title = {{Responsibility for our Blue Planet}},
year = {2014}
}
@article{Nickel2003,
author = {Nickel, M and Hugemann, W},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2003{\_}L{\"{a}}ngs-und Querbeschleunigungen im Alltagsverkehr{\_}Nickel{\_}Hugemann.pdf:pdf},
journal = {EVU Conference, {\ldots}},
keywords = {Fahrverhalten,L{\"{a}}ngsbeschleunigung,Querbeschleunigung},
title = {{L{\"{a}}ngs-und Querbeschleunigungen im Alltagsverkehr}},
url = {http://www.unfallrekonstruktion.de/pdf/evu{\_}2003{\_}german.pdf},
year = {2003}
}
@article{Probabilistic2016,
author = {Probabilistic, F O R and Of, Modeling and Behavior, Driver},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Presentation{\_}2016{\_}MICROSCOPIC BEHAVIOR MODELS - FOR PROBABILISTIC MODELING OF DRIVER BEHAVIOR{\_}ICTS{\_}TimWheeler.pdf:pdf},
title = {{Microscopic Behavior Models}},
year = {2016}
}
@article{Maybank1996,
author = {Maybank, SJ and Worrall, AD and Sullivan, GD},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}1996{\_}A filter for visual tracking based on a stochastic model for driver behaviour.pdf:pdf},
journal = {Computer Vision—ECCV'96},
title = {{A filter for visual tracking based on a stochastic model for driver behaviour}},
url = {http://link.springer.com/chapter/10.1007/3-540-61123-1{\_}168},
year = {1996}
}
@article{Amata2009,
abstract = {We investigate the driving behavior differences at unsignalized intersections between expert and nonexpert drivers. By analyzing real-world driving data, significant differences were seen in pedal operations but not in steering operations. Easing accelerator behaviors before entering unsignalized intersections were especially seen more often in expert driving. We propose two prediction models for driving behaviors in terms of traffic conditions and driver types: one is based on multiple linear regression analysis, which predicts whether the driver will steer, ease up on the accelerator, or brake. The second predicts driver decelerating intentions using a Bayesian network. The proposed models could predict the three driving actions with over 70{\%} accuracy, and about 50{\%} of decelerating intentions were predicted before entering unsignalized intersections.},
author = {Amata, Hideomi and Miyajima, Chiyomi and Nishino, Takanori and Kitaoka, Norihide and Takeda, Kazuya},
doi = {10.1109/ITSC.2009.5309718},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2009{\_}Prediction Model of Driving Behavior Based on Traffic Conditions and Driver Types{\_}ITS{\_}IEEE.pdf:pdf},
isbn = {978-1-4244-5519-5},
journal = {12th International IEEE Conference on Intelligent Transportation Systems},
keywords = {Multi-Sensor Fusion,Simulation and Modeling,Statistical Modeling},
pages = {1--6},
title = {{Prediction model of driving behavior based on traffic conditions and driver types}},
url = {http://ieeexplore.ieee.org/document/5309718/},
year = {2009}
}
@article{Thesis2016a,
author = {Thesis, Master and Science, Computer},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/05{\_}Abschlussarbeiten/MA{\_}2016{\_}Increasing Electric Vehicle Range with a Recommendation App providing Context-Specific Trip Rankings{\_}Pichler{\_}JKU{\_}Linz.pdf:pdf},
title = {{Increasing Electric Vehicle Range with a Recommendation App providing Context- Specific Trip Rankings}},
year = {2016}
}
@article{Erreichbarkeit,
author = {Erreichbarkeit, Steuerbarkeit},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/07{\_}Math/Formulary{\_}2012{\_}Systemtheorie{\_}RWTH.pdf:pdf},
title = {{Regelungsnormalform steuerbar}}
}
@book{Winner,
author = {Winner, Hermann and Hakuli, Stephan and Wolf, Gabriele},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2009{\_}Handbuch{\_}Fahrerassistenzsysteme.pdf:pdf},
isbn = {9783834802873},
title = {{No Title}}
}
@book{Siebertz2010,
abstract = {Die statistische Versuchsplanung (Design of Experiment, DoE) ist ein Verfahren zur Analyse von (technischen) Systemen. Dieses Verfahren ist universell einsetzbar und eignet sich sowohl zur Produkt- als auch zur Prozessoptimierung, insbesondere dann, wenn viele Einflussgr{\"{o}}{\ss}en zu ber{\"{u}}cksichtigen sind. Hauptanliegen der Autoren ist es, die Planung und Durchf{\"{u}}hrung von systematischen Versuchsreihen mit engem Praxisbezug darzustellen. Industriespezifische Probleme illustrieren sie anhand zahlreicher Fallbeispiele.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Siebertz, Karl and van Bebber, David and Hochkirchen, Thomas},
doi = {10.1007/978-3-642-05493-8},
eprint = {arXiv:1011.1669v3},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2017{\_}Statistische Versuchsplanung{\_}Siebertz{\_}Springer.pdf:pdf},
isbn = {978-3-642-05492-1},
issn = {0009-286X},
pmid = {25246403},
title = {{Statistische Versuchsplanung}},
url = {http://link.springer.com/10.1007/978-3-642-05493-8},
year = {2010}
}
@article{Versionskontrolle2016,
author = {Versionskontrolle, Verteilte},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2016{\_}ProGIT-de{\_}German.pdf:pdf},
title = {{Git}},
year = {2016}
}
@article{Dorrestijn,
author = {Dorrestijn, Jesse and Dorrestijn, Jesse},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/02{\_}Dissertation/Diss{\_}2016{\_}Stochastic{\_}Convection{\_}Parametzerization{\_}Jesse{\_}TUDelft{\_}Doc.pdf:pdf},
pages = {9--10},
title = {{by Stellingen door}}
}
@book{KarinMullerVolkerDittmannWolfgangSchubert2011,
author = {{Karin M{\"{u}}ller Volker Dittmann Wolfgang Schubert}, Herausgeber and {Rainer Mattern}, Herausgeber},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2011{\_}Fehlverhalten als Unfallfaktor-Kriterien u Methoden der Risikobeurteilung{\_}Tagungsband{\_}Potsdam.pdf:pdf},
isbn = {978-3-7812-1859-8},
number = {September},
title = {{Fehlverhalten als Unfallfaktor-Kriterien und Methoden der Risikobeurteilung in Potsdam}},
url = {www.kirschbaum.de},
year = {2011}
}
@book{MarquesdeSa2003,
author = {{Marques de S{\'{a}}}, J. P.},
doi = {10.1007/978-3-662-05804-6},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2003{\_}Applied{\_}Statistics{\_}Springer.pdf:pdf},
isbn = {9783662058060},
pages = {452},
pmid = {13146529},
title = {{Applied Statistics using SPSS, STATISTICA and MATLAB}},
year = {2003}
}
@article{Kennelb,
author = {Kennel, Dr.-Ing Ralph and Hoffmann, Thomas},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/05{\_}Abschlussarbeiten/MA{\_}2017{\_}ThHoffmann{\_}Identifikation von Nutzerverhalten sowie Nutzerverhalten f{\"{u}}r ein Elektrofahrzeug.pdf:pdf},
keywords = {{\textless}Mehrere Suchschl{\"{u}}ssel{\textgreater}},
title = {{Masterarbeit Identikation von Nutzerverhalten sowie Parameter f{\"{u}}r ein Elektrofahrzeug}}
}
@article{Brookhuis2009,
abstract = {Introduction The aim of this study was to investigate the effects of providing travel information to drivers about a traffic jam ahead and a potential detour or short-cut. Two groups of participants, native and non-native Dutch speakers were requested to drive in a driving simulator under both calm and dense traffic conditions. Method Travel-information was presented by means of three nomadic systems; in visual mode on a PDA and on a mobile phone via SMS, and through a mobile phone in auditory mode via the (simulator mock-up) vehicle's audio system. Results The results showed that with regard to usability the SMS message was evaluated worse than the other two systems, while with respect to cognitive processing, SMS caused more subjective (i.e. experienced) workload than the other two systems. Native participants believed any information-providing system to be less useful than nonnative participants did. All participants remembered more of the information when traffic was dense whereas natives remembered more than non-natives. With regard to performance and safety, driving performance was better when traffic was calm, as compared to dense traffic; however, compensation was shown by lowering driving speed in the latter condition. After participants were provided with travel information, their driving performance with respect to the consequences of distraction differed between systems. Conclusion The auditory information provision system allowed the best driving performance; the other two systems required the participants to look away from the road (too) long compromising safety, while reading an SMS took longer than scanning a PDA. {\textcopyright} European Conference of Transport Research Institutes (ECTRI) 2009.},
author = {Brookhuis, K. A. and Dicke, M.},
doi = {10.1007/s12544-009-0007-4},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2009{\_}The effects of travel information presentation through nomadic systems on driver behaviour.pdf:pdf},
isbn = {1254400900074},
issn = {18670717},
journal = {European Transport Research Review},
keywords = {Driving performance,Driving simulator,Nomadic systems,Travel information},
number = {2},
pages = {67--74},
title = {{The effects of travel information presentation through nomadic systems on driver behaviour}},
volume = {1},
year = {2009}
}
@article{Co2006,
author = {Co, Der},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2007{\_}3 F Anforderungsoptimierung f{\"{u}}r Getriebe und Komponenten{\_}TU Braunscheig.pdf:pdf},
title = {{Anforderungsoptimierung f{\"{u}}r Getriebe und Komponenten}},
volume = {109},
year = {2006}
}
@article{Preuße2001,
author = {Preu{\ss}e, Christian and Keller, Helmut and Hunt, Kenneth J.},
doi = {10.1524/auto.2001.49.12.540},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2001{\_}Fahrzeugf{\"{u}}hrung durch ein Fahrermodell.pdf:pdf},
issn = {01782312},
journal = {At-Automatisierungstechnik},
number = {12},
pages = {540--546},
title = {{Fahrzeugf{\"{u}}hrung durch ein Fahrermodell}},
volume = {49},
year = {2001}
}
@book{Kuckartz2013,
abstract = {We consider the motion of a spinning relativistic particle in external electromagnetic and gravitational fields, to first order in the external field, but to an arbitrary order in spin. The noncovariant spin formalism is crucial for the correct description of the influence of the spin on the particle trajectory. We show that the true coordinate of a relativistic spinning particle is its naive, common coordinate {\$}\backslashr{\$}. Concrete calculations are performed up to second order in spin included. A simple derivation is presented for the gravitational spin-orbit and spin-spin interactions of a relativistic particle. We discuss the gravimagnetic moment (GM), a specific spin effect in general relativity. It is shown that for the Kerr black hole the gravimagnetic ratio, i.e., the coefficient at the GM, equals unity (just as for the charged Kerr hole the gyromagnetic ratio equals two). The equations of motion obtained for relativistic spinning particle in external gravitational field differ essentially from the Papapetrou equations.},
address = {Wiesbaden},
archivePrefix = {arXiv},
arxivId = {gr-qc/9809069},
author = {Kuckartz, Udo and R{\"{a}}diker, Stefan and Ebert, Thomas and Schehl, Julia},
doi = {10.1007/978-3-531-19890-3},
eprint = {9809069},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2013{\_}Statistik{\_}Eine verstaendliche Einfuehrung{\_}Lehrbuch.pdf:pdf},
isbn = {978-3-531-19889-7},
issn = {15320464},
number = {6},
pages = {1202--1216},
pmid = {22995208},
primaryClass = {gr-qc},
publisher = {VS Verlag f{\"{u}}r Sozialwissenschaften},
title = {{Statistik}},
url = {http://link.springer.com/10.1007/978-3-531-19890-3},
volume = {45},
year = {2013}
}
@article{Nie2016,
abstract = {Modeling of decision-making behavior for discretionary lane-changing execution (DLCE) is fundamental to both movement simulation and controlling design of automatic vehicles. The existing gap acceptance models ingored the nonlinearity of drivers' DLCE decision-making behavior. Therefore, this study tries to analyze and simulate the DLCE decision-making behavior using the real trajectory data. First, a threshold of the lane-changer's lateral velocity is introduced to identify the starting point of DLCE process based on vehicle trajectories from the NGSIM data set. In the following, the empirical analysis based on traffic state variables at the instant of accepting DLCE event are presented, which prove the necessity of modeling DLCE decision-making behavior with machine learning method. Then, we propose a DLCE decision-making model using the Support Vector Machine (SVM). For verifying the prediction performance, the proposed model is compared with the Nagel's model based on the NGSIM data set. The comparison results indicate that the proposed model using SVM outperforms the Nagel's model in predicting the DLCE decision.},
author = {Nie, Jianqiang and Zhang, Jian and Wan, Xia and Ding, Wanting and Ran, Bin},
doi = {10.1109/ITSC.2016.7795631},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2016{\_}Modeling of Decision-Making Behavior for Discretionary Lane-Changing Execution{\_}0134.pdf:pdf},
isbn = {9781509018895},
journal = {IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC},
pages = {707--712},
title = {{Modeling of decision-making behavior for discretionary lane-changing execution}},
year = {2016}
}
@article{Irmscher2004,
abstract = {A driver model is presented that accounts for individual driver behavior and allows driver classification or behavior for common driving tasks. Typical driver errors can be modeled by means of parameters of the driver controller and by influencing the driving course. This is illustrated for some typical driver types and driving maneuvers.},
author = {Irmscher, Marita and Ehmann, Martin},
doi = {10.4271/2004-01-0451},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2004{\_}Driver Classification using ve-DYNA Advanced Driver{\_}Irmscher{\_}Ehmann.pdf:pdf},
isbn = {0768013194},
journal = {SAE Paper},
number = {724},
title = {{Driver Classification Using Ve-Dyna Advanced Driver}},
volume = {2004-01-04},
year = {2004}
}
@article{Mallwitz2015,
author = {Mallwitz, Prof Regine},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Presentation{\_}2015{\_}HEV{\_}Symposium/Mallwitz{\_}TU-Braunschweig{\_}HEV{\_}2015{\_}Vortrag.pdf:pdf},
title = {{Kompakte Leistungselektronik f{\"{u}}r Elektrofahrzeuge Kompakte Leistungselektronik f{\"{u}}r Elektrofahrzeuge}},
year = {2015}
}
@article{Bengler2014,
abstract = {This contribution provides a review of fundamental goals, development and future perspectives of driver assistance systems. Mobility is a fundamental desire of mankind. Virtually any society strives for safe and efficient mobility at low ecological and economic costs. Nevertheless, its technical implementation significantly differs among societies, depending on their culture and their degree of industrialization. A potential evolutionary roadmap for driver assistance systems is discussed. Emerging from systems based on proprioceptive sensors, such as ABS or ESC, we review the progress incented by the use of exteroceptive sensors such as radar, video, or lidar. While the ultimate goal of automated and cooperative traffic still remains a vision of the future, intermediate steps towards that aim can be realized through systems that mitigate or avoid collisions in selected driving situations. Research extends the state-of-the-art in automated driving in urban traffic and in cooperative driving, the latter addressing communication and collaboration between different vehicles, as well as cooperative vehicle operation by its driver and its machine intelligence. These steps are considered important for the interim period, until reliable unsupervised automated driving for all conceivable traffic situations becomes available. The prospective evolution of driver assistance systems will be stimulated by several technological, societal and market trends. The paper closes with a view on current research fields.},
author = {Bengler, Klaus and Dietmayer, Klaus and Farber, Berthold and Maurer, Markus and Stiller, Christoph and Winner, Hermann},
doi = {10.1109/MITS.2014.2336271},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2014{\_}Three Decades of Driver Assistance Systems - Review and Future Perspectives{\_}Bengler{\_}TUM.pdf:pdf},
isbn = {1939-1390 VO - 6},
issn = {19391390},
journal = {IEEE Intelligent Transportation Systems Magazine},
number = {4},
pages = {6--22},
pmid = {1546147},
title = {{Three decades of driver assistance systems: Review and future perspectives}},
volume = {6},
year = {2014}
}
@article{Deutschland2008,
author = {Deutschland, Bundesrepublik and Gmbh, Robert Bosch},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/04{\_}Patente/22571 Y3 - DE102006039583A1 20236493.pdf:pdf},
isbn = {1111111111},
number = {19},
pages = {1--8},
title = {{22571 Y3 - De102006039583a1 20236493}},
volume = {1111111111},
year = {2008}
}
@article{Wheeler2016,
author = {Wheeler, Tim A and Robbel, Philipp and Kochenderfer, Mykel J},
doi = {10.1109/TITS.2016.2603007},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2016{\_}Analysis of Microscopic Behavior Models for Probabilistic Modeling of Driver Behavior{\_}0213.pdf:pdf},
isbn = {9781509018895},
issn = {1524-9050},
journal = {Intelligent Transportation Systems Conference},
keywords = {Advanced Vehicle Safety Systems,Driver Assistance Systems,Simulation and Modeling},
pages = {1604--1609},
title = {{Analysis of Microscopic Behavior Models for Probabilistic Modeling of Driver Behavior}},
year = {2016}
}
@book{Amditis2007,
abstract = {Critical Issues in Driver Interactions with Intelligent Transport Systems},
address = {London},
author = {Amditis, Angelos and Bailly, B{\'{e}}atrice and Baumann, Martin and Bekiaris, Evangelos and Bellet, Thierry and Bengler, Klaus and Cacciabue, P. Carlo and Carsten, Oliver and Chao, Kevin C. and Cody, Delphine and Engstr{\"{o}}m, Johan and Fuller, Ray and Georgeon, Olivier and Gordon, Timothy and Hollnagel, Erik and Inagaki, Toshiyuki and Janssen, Wiel and J{\"{u}}rgensohn, Thomas and Krems, Josef F. and Macchi, Luigi and Marzani, Stefano and Meyenobe, Pierre and Montanari, Roberto and Nilsson, Lena and Panou, Maria and Papakostopoulos, Vassilis and Parker, Dianne and Peters, Bj{\"{o}}rn and Polychronopoulos, Aris and Re, Cristina and Saad, Farida and Summala, Heikki and Tango, Fabio and Vaa, Truls and van der Horst, Richard and Weir, David H.},
doi = {10.1007/978-1-84628-618-6},
editor = {Cacciabue, P. Carlo},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2007{\_}Modelling driver behavior in automotive environments{\_}Carlo Cacciabue.pdf:pdf},
isbn = {978-1-84628-617-9},
number = {May 2005},
pages = {21677},
publisher = {Springer London},
title = {{Modelling Driver Behaviour in Automotive Environments}},
url = {http://link.springer.com/10.1007/978-1-84628-618-6},
year = {2007}
}
@article{Thesis2016,
author = {Thesis, Master and Science, Computer},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/05{\_}Abschlussarbeiten/MA{\_}2016{\_}Increasing Electric Vehicle Range with a Recommendation App providing Context-Specific Trip Rankings{\_}UniLinz{\_}Pichler{\_}Benj.pdf:pdf},
title = {{Increasing Electric Vehicle Range with a Recommendation App providing Context- Specific Trip Rankings}},
year = {2016}
}
@book{Ziegel1987,
abstract = {Bridging the gap between physics and astronomy textbooks, this book provides step-by-step physical and mathematical development of fundamental astrophysical ...},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Ziegel, Eric and Press, William and Flannery, Brian and Teukolsky, Saul and Vetterling, William},
booktitle = {Technometrics},
doi = {10.2307/1269484},
eprint = {arXiv:1011.1669v3},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2007{\_}Numerical Recipes - The art of scientific compution - third edition.pdf:pdf},
isbn = {0521431085},
issn = {00401706},
number = {4},
pages = {501},
pmid = {7879318},
title = {{Numerical Recipes: The Art of Scientific Computing}},
url = {http://www.jstor.org/stable/1269484?origin=crossref},
volume = {29},
year = {1987}
}
@article{MAUK2011,
abstract = {Dieses Buch beschreibt ein System zur Pr{\"{a}}diktion energetisch relevanter Gr{\"{o}}{\ss}en (z.B. Fahrzeuggeschwindigkeit, Antriebsleistung) im Kraftfahrzeug entlang der vorausliegenden Fahrstrecke. Das System erfasst die gew{\"{u}}nschte Gr{\"{o}}{\ss}e st{\"{a}}ndig und erlernt ihren Verlauf bereits nach wenigen Befahrungen einer Strecke. Auf Basis dieses gelernten Wissens wird beim erneuten Befahren eine stochastisch optimale Pr{\"{a}}diktion der Gr{\"{o}}{\ss}e erstellt. Da die Pr{\"{a}}diktion verschiedenen Unsicherheiten unterliegt, wird au{\ss}erdem ein G{\"{u}}tema{\ss} berechnet, das die Verl{\"{a}}sslichkeit der Pr{\"{a}}diktion quantifiziert. Zwei Anwendungen werden n{\"{a}}her betrachtet: Durch Lernen des Energieverbrauchs entlang der Strecke kann eine Reichweitenpr{\"{a}}diktion realisiert werden. Das G{\"{u}}tema{\ss} gibt dabei den Schwankungsbereich einer konkreten Pr{\"{a}}diktion an. Ferner erm{\"{o}}glicht die Pr{\"{a}}diktion der Geschwindigkeit und der Antriebskraft eine verbrauchsoptimierte vorausschauende Betriebsstrategie f{\"{u}}r Hybridfahrzeuge. Anhand des G{\"{u}}tema{\ss}es kann eine solche Betriebsstrategie entscheiden, wann eine vorausschauende Optimierung erfolgversprechend ist.},
author = {MAUK, T.},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/02{\_}Dissertation/Diss{\_}2011{\_}Selbstlernende, zuverl{\"{a}}ssigkeitsorientierte Pr{\"{a}}diktion energetisch relevanter Gr{\"{o}}{\ss}en im Kraftfahrzeug{\_}TobiasMauk{\_}UniStuttgart.pdf:pdf},
isbn = {9783816931232},
pages = {105},
title = {{Selbstlernende, zuverl{\"{a}}ssigkeitsorientierte Pr{\"{a}}diktion energetisch relevanter Gr{\"{o}}{\ss}en im Kraftfahrzeug}},
year = {2011}
}
@article{Toque2016,
abstract = {{A considerable number of studies have been undertaken on using smart card data to analyse urban mobility. Most of these studies aim to identify recurrent passenger habits, reveal mobility patterns, reconstruct and predict passenger flows, etc. Forecasting mobility demand is a central problem for public transport authorities and operators alike. It is the first step to efficient allocation and optimisation of available resources. This paper explores an innovative approach to forecasting dynamic Origin-Destination (OD) matrices in a subway network using long Short-term Memory (LSTM) recurrent neural networks. A comparison with traditional approaches, such as calendar methodology or Vector Autoregression is conducted on a real smart card dataset issued from the public transport network of Rennes Métropole, France. The obtained results show that reliable short-term prediction (over a 15 minutes time horizon) of OD pairs can be achieved with the proposed approach. We also experiment with the effect of taking into account additional data about OD matrices of nearby transport systems (buses in this case) on the prediction accuracy.{\}}, keywords={\{}demand forecasting;optimisation;public transport;recurrent neural nets;resource allocation;smart cards;traffic engineering computing;France;LSTM recurrent neural networks;Rennes Metropole;forecasting dynamic public transport origin-destination matrices;long-short term memory recurrent neural networks;mobility demand forecasting;mobility patterns;passenger flow prediction;prediction accuracy;public transport authorities;recurrent passenger habit identification;resource allocation;resource optimisation;smart card data;subway network;urban mobility;Data models;Forecasting;Predictive models;Public transportation;Recurrent neural networks;Smart cards}},
author = {Toq{\'{u}}e, Florian and C{\^{o}}me, Etienne and Mahrsi, Mohamed Khalil El and Oukhellou, Latifa},
doi = {10.1109/ITSC.2016.7795689},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2016{\_}Forecasting Dynamic Public Transport Origin-Destination Matrices with Long-Short Term Memory Recurrent Neural Networks{\_}0526.pdf:pdf},
isbn = {9781509018895},
journal = {IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC},
pages = {1071--1076},
title = {{Forecasting dynamic public transport origin-destination matrices with long-short term memory recurrent neural networks}},
year = {2016}
}
@article{Liebner2013,
abstract = {Predicting turn and stop maneuvers of potentially errant drivers is a basic requirement for advanced driver assistance systems for urban intersections. Previous work has shown that an early estimate of the driver's intent can be inferred by evaluating the vehicle's speed during the intersection approach. In the presence of a preceding vehicle, however, the velocity profile might be dictated by car-following behavior rather than by the need to slow down before doing a left or right turn. To infer the driver's intent under such circumstances, a simple, real-time capable approach using a parametric model to represent both car-following and turning behavior is proposed. The performance of two alternative parameterizations based on observations at an individual intersection and a generic curvature-based model is evaluated in combination with two different Bayes net classification algorithms. In addition, the driver model is shown to be capable of predicting the future trajectory of the vehicle.},
author = {Liebner, Martin and Klanner, Felix and Baumann, Michael and Ruhhammer, Christian and Stiller, Christoph},
doi = {10.1109/MITS.2013.2246291},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2013{\_}Velocity-Based Driver Intent Inference at Urban Intersections in the Presence of Preceding Vehicles{\_}BMW.pdf:pdf},
isbn = {1939-1390},
issn = {1939-1390},
journal = {IEEE Intelligent Transportation Systems Magazine},
number = {2},
pages = {10--21},
title = {{Velocity-based driver intent inference at urban intersections in the presence of preceding vehicles}},
volume = {5},
year = {2013}
}
@article{Blaschke2015,
abstract = {Kurzfassung Die F{\"{a}}higkeit, die Absicht eines Fahrers fr{\"{u}}hzeitig zu erkennen, bietet enormes Potential zur Verbesserung vorhandener sowie zur Entwicklung zuk{\"{u}}nftiger Fahrerassistenzsysteme. W{\"{a}}-ren Kraftfahrzeuge in der Lage, das vom Fahrer intendierte Fahrman{\"{o}}ver zu erkennen, k{\"{o}}nn-ten sie {\"{u}}ber m{\"{o}}gliche Gefahren informieren oder auch unterst{\"{u}}tzend eingreifen. In der hier beschriebenen Studie wurde-ausgehend von psychologischen Handlungsmodel-len-ein Fahrversuch durchgef{\"{u}}hrt, der Aufschluss {\"{u}}ber typische Datenmuster der Fahrma-n{\"{o}}ver "Abbiegen" und "{\"{U}}berholen" lieferte. Die Analyse des Fahrerverhaltens und verschie-dener Umgebungsdaten wenige Sekunden vor dem eigentlichen Fahrman{\"{o}}ver erm{\"{o}}glichte das Aufstellen eines Fuzzy-Logic-Systems, das in 93{\%} der Fahrten das richtige Fahrman{\"{o}}-ver vorhersagen konnte. Der zeitliche Vorlauf vor dem Abbiegen bzw. dem Spurwechsel beim {\"{U}}berholen betrug durchschnittlich 3,8 Sekunden, was ausreichend Zeit f{\"{u}}r einen Ein-griff oder die Warnung des Fahrers l{\"{a}}sst. Die Ergebnisse stimmen zuversichtlich, die Absicht des Fahrers mit ausreichend zeitlichem Vorlauf erkennen zu k{\"{o}}nnen. 1. Auf dem Weg zur optimalen Assistenz Betrachtet man die Entwicklung von Fahrerassistenz-und Fahrerinformationssystemen, ist nicht nur eine zunehmende Komplexit{\"{a}}t durch die rasanten Fortschritte im Bereich der Mik-roprozessoren und Sensoren festzustellen, auch die Art bzw. die sensorische Grundlage wandelt sich zunehmend. Obwohl Autofahren schon l{\"{a}}nger als Interaktion zwischen dem Fahrer, dem Fahrzeug und der Umwelt verstanden wird, wird der Fahrer erst jetzt immer mehr bei der Entwicklung von Assistenzsystemen ber{\"{u}}cksichtigt. Bei den ersten Fahrerassistenzsystemen, wie z.B. dem ABS, stellte dies kein Problem dar, da diese allein mit Informationen aus dem Fahrzeug auskamen, also keine Interaktionen (mit der Umwelt oder dem Fahrer) f{\"{u}}r die Regelung ber{\"{u}}cksichtigen mussten. Neuere Assistenzsysteme beziehen aber neben den Fahrzeugdaten zus{\"{a}}tzlich die Umge-bungsbedingungen mit ein. Beispielsweise werden bei Spurhaltesystemen einerseits die Markierungen auf der Stra{\ss}e (Umwelt), andererseits die Geschwindigkeit und der Lenkrad},
author = {Blaschke, Dipl-Psych C and Schmitt, Dipl-Ing J and F{\"{a}}rber, B},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2015{\_}Fahrmanoever-Praediktion ueber CAN-Bus Daten{\_}UniBW{\_}Faerber{\_}VDI{\_}2015165.pdf:pdf},
title = {{VDI-Berichte Nr Fahrman{\"{o}}ver-Pr{\"{a}}diktion {\"{u}}ber CAN-Bus Daten}},
year = {2015}
}
@article{Cruz2016,
abstract = {"Vehicle congestion is a serious concern in metropolitan areas. Some policies have been adopted in order to soften the problem: construction of alternative routes, encouragement for the use of bicycles, improvement on public transportation, among others. A practice that might help is carpooling. Carpooling consists in sharing private vehicle space among people with similar trajectories. Although there exist some software initiatives to facilitate the carpooling practice, none of them actually provides some key facilities such as searching for people with similar trajectories. The way in which such a trajectory is represented is also central. In the specific context of carpooling, the use of Points of Interest (POI) as a method for trajectory discretization is rather relevant. In this paper, we consider that and other assumptions to propose an innovative approach to generate trajectory clusters for carpooling purposes, based on Optics algorithm. We also propose a new similarity measure for trajectories. Two experiments have been performed in order to prove the feasibility of the approach. Furthermore, we compare our approach with K-means and Optics. Results have showed that the proposed approach has results similar for Davies-Boulding index (DBI)."},
author = {Cruz, Michael O. and Macedo, Hendrik and Guimar{\~{a}}es, Adolfo},
doi = {10.1109/BRACIS.2015.36},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2015{\_}Grouping similar trajectories for carpooling purposes{\_}CIS{\_}Brazil.pdf:pdf},
isbn = {9781509000166},
journal = {Proceedings - 2015 Brazilian Conference on Intelligent Systems, BRACIS 2015},
keywords = {Carpooling,trajectory clustering,trajectory similarity},
pages = {234--239},
title = {{Grouping similar trajectories for carpooling purposes}},
year = {2016}
}
@article{Rasmussen1983,
author = {Rasmussen, Jens and Member, Senior},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}1983{\_}Skills-rules-knowledge-Rasmussen-seg.pdf:pdf},
number = {3},
pages = {257--266},
title = {{Paper{\_}1983{\_}Skills-rules-knowledge-Rasmussen-seg}},
year = {1983}
}
@book{Hornich1936,
abstract = {Diese Formelsammlung enth{\"{a}}lt die wichtigsten mathematischen Formeln f{\"{u}}r Mathematiker, Naturwissenschaftler und Ingenieure. Sie entspricht in ihrer Zusammensetzung dem heutigen Bedarf in Wissenschaft und Technik und ist bestimmt f{\"{u}}r den Gebrauch in Schulen, Hochschulen und in der Praxis. Aus dem Inhalt: Arithmetik, Algebra, Geometrie, Koordinatensysteme, spezielle Funktionen, Differentialrechnung, Integralrechnung und Integraltafel.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Hornich, H.},
booktitle = {Monatshefte f{\"{u}}r Mathematik und Physik},
doi = {10.1007/BF01708013},
eprint = {arXiv:1011.1669v3},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/07{\_}Math/Book{\_}2014{\_}Mathematische Formelsammlung{\_}Lothar Papula.pdf:pdf},
isbn = {978-3-8348-0757-1},
issn = {0026-9255},
number = {1},
pages = {A4--A4},
pmid = {15003161},
title = {{Mathematische Formelsammlung}},
url = {http://link.springer.com/10.1007/BF01708013},
volume = {45},
year = {1936}
}
@phdthesis{Altmannshofera,
author = {Altmannshofer, Simon},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/02{\_}Dissertation/Diss{\_}2018{\_}Robuste Parametersch{\"{a}}tzung f{\"{u}}r Elektrofahrzeuge{\_}Altmannshofer{\_}THI.pdf:pdf},
title = {{Robuste Parametersch{\"{a}}tzung f{\"{u}}r Elektrofahrzeuge}}
}
@article{Quantmeyer,
author = {Quantmeyer, F and Scherler, S},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Presentation{\_}2015{\_}HEV{\_}Symposium/Scherler{\_}Ostfalia{\_}HEV{\_}2015{\_}Vortrag.pdf:pdf},
title = {{Entwurf und Erprobung des Fahrzeugmanagements f{\"{u}}r Elektrofahrzeuge mit dezentralen Direktantrieben Inhalt}}
}
@article{Lohrer2016,
author = {Lohrer, J{\"{u}}rgen and Lienkamp, Markus},
doi = {10.1007/s41104-016-0012-2},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2016{\_}An approach for predicting vehicle velocity in combination with driver turns{\_}Springer Journal.pdf:pdf},
isbn = {4110401600122},
issn = {2365-5127},
journal = {Automotive and Engine Technology},
keywords = {intelligent transportation systems {\'{a}},profile {\'{a}} trip prediction,speed},
number = {1-4},
pages = {27--33},
title = {{An approach for predicting vehicle velocity in combination with driver turns}},
url = {http://link.springer.com/10.1007/s41104-016-0012-2},
volume = {1},
year = {2016}
}
@article{Rill2001,
abstract = {Skript},
author = {Rill, G.},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/06{\_}Skripte-Vorlesungen/Script{\_}2001{\_}Fahrzeugdynamik{\_}FH{\_}Regensburg{\_}Prof.Rill.pdf:pdf},
title = {{Fahrzeugdynamik}},
url = {http://homepages.fh-regensburg.de/{~}rig39165/},
year = {2001}
}
@book{Grzesik2009,
abstract = {Dissertation},
author = {Grzesik, Axel},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/02{\_}Dissertation/Diss{\_}2009{\_}Physiologiebasierte Simulation des Bremsverhaltens von Fahrzeugf{\"{u}}hrern{\_}Uni Ilmenau{\_}Axel Grzesik.pdf:pdf},
isbn = {9783939473480},
keywords = {Bremsen,Fahrer,Fahrermodellierung,Verhalten,behavior,braking,driver,driver modeling},
title = {{Physiologiebasierte Simulation des Bremsverhaltens von Fahrzeugf{\"{u}}hrern}},
year = {2009}
}
@article{Park2011,
abstract = {Prediction of the traffic information such as flow, density, speed, and travel time is important for traffic control systems, optimizing vehicle operations, and the individual driver. Prediction of future traffic information is a challenging problem due to many dynamic contributing factors. In this paper, various methodologies for traffic information prediction are investigated. We present a speed prediction algorithm, NNTM-SP (Neural Network Traffic Modeling-Speed Prediction) that trained with the historical traffic data and is capable of predicting the vehicle speed profile with the current traffic information. Experimental results show that the proposed algorithm gave good prediction results on real traffic data and the predicted speed profile shows that NNTM-SP correctly predicts the dynamic traffic changes.},
author = {Park, Jungme and Li, Dai and Murphey, Yi L. and Kristinsson, Johannes and McGee, Ryan and Kuang, Ming and Phillips, Tony},
doi = {10.1109/IJCNN.2011.6033614},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2011{\_}Real Time Vehicle Speed Prediction using a Neural Network Traffic Model{\_}IEEE{\_}JCNN.pdf:pdf},
isbn = {978-1-4244-9635-8},
issn = {2161-4393},
journal = {The 2011 International Joint Conference on Neural Networks},
pages = {2991--2996},
title = {{Real time vehicle speed prediction using a Neural Network Traffic Model}},
url = {http://ieeexplore.ieee.org/document/6033614/},
year = {2011}
}
@article{Schneider2009,
author = {Schneider, Joerg Henning},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/02{\_}Dissertation/Diss{\_}2009{\_}Model. u Erkennung von Fahrsituationen u Fahrman{\"{o}}vern fuer sicherheitsrelevante FAS{\_}JSchneider{\_}Chemnitz.pdf:pdf},
title = {{Modellierung und Erkennung von Fahrsituationen und Fahrmanoevern fuer sicherheitsrelevante Fahrerassistenzsysteme}},
year = {2009}
}
@book{Findeisen2007,
abstract = {In view of the rapid changes in requirements, it has became necessary to place at the reader's disposal a book dealing with basic and advanced concepts and techniques for the monitoring and control of chemical and biochemical processes, as well as with the aspects of the implementation of these different robust techniques. To make the ideas covered in this book accessible to a larger audience, the authors attempted to present a balanced view of the theoretical and practical issues of control systems. Different cases are presented to illustrate the controller and observer design procedures and.},
archivePrefix = {arXiv},
arxivId = {arXiv:1211.2549v2},
author = {Findeisen, R. and Allg{\"{o}}wer, F. and Biegler, L.T.},
booktitle = {Lecture Notes in Control and Information Sciences},
doi = {10.1007/978-3-642-13812-6},
eprint = {arXiv:1211.2549v2},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2010{\_}Automotive Model Predictive Control.pdf:pdf},
isbn = {354043240X},
issn = {01708643},
number = {1},
pages = {85233},
pmid = {19886812},
title = {{Lecture Notes in Control and Information Sciences: Preface}},
volume = {358},
year = {2007}
}
@article{Vinet2010,
abstract = {We study a family of "classical" orthogonal polynomials which satisfy (apart from a 3-term recurrence relation) an eigenvalue problem with a differential operator of Dunkl-type. These polynomials can be obtained from the little {\$}q{\$}-Jacobi polynomials in the limit {\$}q=-1{\$}. We also show that these polynomials provide a nontrivial realization of the Askey-Wilson algebra for {\$}q=-1{\$}.},
address = {New York, USA},
archivePrefix = {arXiv},
arxivId = {1011.1669},
author = {Vinet, Luc and Zhedanov, Alexei},
doi = {10.1088/1751-8113/44/8/085201},
eprint = {1011.1669},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2001{\_}Estimation with Applications To Tracking and Navigation - Theory Algorithms and Software.pdf:pdf},
isbn = {047141655X},
issn = {1098-6596},
month = {nov},
pages = {0--471},
pmid = {25246403},
publisher = {John Wiley {\&} Sons, Inc.},
title = {{A "missing" family of classical orthogonal polynomials}},
url = {http://doi.wiley.com/10.1002/0471221279 http://arxiv.org/abs/1011.1669 http://dx.doi.org/10.1088/1751-8113/44/8/085201},
volume = {9},
year = {2010}
}
@article{Schulz2008,
author = {Schulz, Alexandra and Fr{\"{o}}ming, Robert},
doi = {10.1007/BF03222040},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2008{\_}Analyse des Fahrerve3rhaltens zur Darstellung adaptiver Eingriffsstrategien von Assistenzsystemen.pdf:pdf},
issn = {0001-2785},
journal = {ATZ - Automobiltechnische Zeitschrift},
number = {12},
pages = {1124--1131},
title = {{Analyse des Fahrerverhaltens zur Darstellung adaptiver Eingriffs-strategien von Assistenzsystemen}},
url = {http://link.springer.com/10.1007/BF03222040},
volume = {110},
year = {2008}
}
@book{LinoGuzella2007,
abstract = {This book analyzes the longitudinal behavior of road vehicles only.$\backslash$nIts main$\backslash$n$\backslash$nemphasis is on the analysis and minimization of the energy consumption.$\backslash$n$\backslash$nOther aspects that are discussed are drivability and performance.$\backslash$n$\backslash$nThe starting point for all subsequent steps is the derivation of simple$\backslash$nyet$\backslash$n$\backslash$nrealistic mathematical models that describe the behavior of vehicles,$\backslash$nprime$\backslash$n$\backslash$nmovers, energy converters, and energy storage systems. Typically,$\backslash$nthese models$\backslash$n$\backslash$nare used in a subsequent optimization step to synthesize optimal vehicle$\backslash$n$\backslash$nconfigurations and energy management strategies.$\backslash$n$\backslash$nExamples of modeling and optimization problems are included in Appendix$\backslash$n$\backslash$nI. These case studies are intended to familiarize the reader with$\backslash$nthe$\backslash$n$\backslash$nmethods and tools used in powertrain optimization projects.},
author = {{Lino Guzella}, Antonio Sciarretta},
doi = {10.1007/978-3-642-35913-2},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2013{\_}Vehicle Propulsion Systems{\_}Guzzella.pdf:pdf},
isbn = {9783642359125},
keywords = {EV-HEV,control algorithm,fuel comsumption,vehicle},
number = {August 2014},
pages = {338},
title = {{Vehicle Propulsion Systems}},
volume = {2},
year = {2007}
}
@article{Funfgeld2017,
abstract = {Predictive control is a popular approach for further improving the efficiency and performance of vehicular systems enabling intelligent systems behavior appropriate to the driving situation. To calculate such control strategies, the future vehicle dynamics or subsequent states have to be predicted. We introduce a stochastic framework based on an explanatory model and stochastic processes to predict future vehicle dynamics with road network data. The distributions of the future states are approximated using sequential Monte Carlo simulation. The proposed approach enables stochastic forecasts incorporating uncertain driver behavior and available road data. Parameter inference is shown for exemplary real-drive test data, and predictive performance is evaluated using commonly used reference models. The results show that the explanatory model provides more specific information than time-series models do, still considering the uncertainty in the driver's behavior or the situation. The framework can be applied with predictive control algorithms enabling intelligent control of vehicular systems. Furthermore, the framework or parts of it may be usable for other applications like predicting behavior of traffic participants or general characterization of driver behavior.},
author = {Funfgeld, Sebastian and Holzapfel, Marc and Frey, Michael and Gauterin, Frank},
doi = {10.1109/TIV.2017.2723823},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2017{\_}Stocastic Forecasting of Vehicle Dynamics Using Sequential Monte Carlo Simulation.pdf:pdf},
issn = {2379-8904},
journal = {IEEE Transactions on Intelligent Vehicles},
number = {2},
pages = {1--1},
title = {{Stochastic Forecasting of Vehicle Dynamics Using Sequential Monte Carlo Simulation}},
url = {http://ieeexplore.ieee.org/document/7968503/},
volume = {2},
year = {2017}
}
@article{Communications1958,
author = {Communications, Short and Constraint, References and Pawley, See},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}1976{\_}A solution for the best rotation to relate{\_}Kabsch.pdf:pdf},
number = {6},
pages = {922--923},
title = {{j2a . . . . {\~{}}m,( {\~{}} W,,X.kX,,j+ {\&}j)}},
year = {1958}
}
@book{Jager2003,
address = {Berlin, Heidelberg},
author = {Jager, Willi and {Hans-Joachim Krebs}},
doi = {10.1007/978-3-642-55753-8},
editor = {J{\"{a}}ger, Willi and Krebs, Hans-Joachim},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/07{\_}Math/Book{\_}2003{\_}Mathematics-Key Technology for the Future{\_}Springer.pdf:pdf},
isbn = {978-3-642-62914-3},
publisher = {Springer Berlin Heidelberg},
title = {{Mathematics — Key Technology for the Future}},
url = {http://link.springer.com/10.1007/978-3-642-55753-8},
year = {2003}
}
@article{SchmidlBakktechn2011,
author = {{Schmidl Bakktechn}, Stephan and {Maurer Priv -Doz Dipl-Ing Wolfgang Berger Ass Dipl-Ing Philippe Nitsche}, Peter J},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/05{\_}Abschlussarbeiten/MA{\_}2011{\_}SSchmidl{\_}Untersuchung des Fahrverhaltens in unterschiedl. Kurvenradien bei trockener Fahrbahn{\_}Wien{\_}AT.pdf:pdf},
title = {{Masterarbeit f{\"{u}}r das Fachgebiet VERKEHRSWESEN Betreuung: Untersuchung des Fahrverhaltens in unterschiedlichen Kurvenradien bei trockener Fahrbahn}},
year = {2011}
}
@article{Mozaffari2015,
abstract = {The main goal of the current study is to take advantage of advanced numerical and intelligent tools to predict the speed of a vehicle using time series. It is clear that the uncertainty caused by temporal behavior of the driver as well as various external disturbances on the road will affect the vehicle speed, and thus, the vehicle power demands. The prediction of upcoming power demands can be employed by the vehicle powertrain control systems to improve significantly the fuel economy and emission performance. Therefore, it is important to systems design engineers and automotive industrialists to develop efficient numerical tools to overcome the risk of unpredictability associated with the vehicle speed profile on roads. In this study, the authors propose an intelligent tool called evolutionary least learning machine (E-LLM) to forecast the vehicle speed sequence. To have a practical evaluation regarding the efficacy of E-LLM, the authors use the driving data collected on the San Francisco urban roads by a private Honda Insight vehicle. The concept of sliding window time series (SWTS) analysis is used to prepare the database for the speed forecasting process. To evaluate the performance of the proposed technique, a number of well-known approaches, such as auto regressive (AR) method, back-propagation neural network (BPNN), evolutionary extreme learning machine (E-ELM), extreme learning machine (ELM), and radial basis function neural network (RBFNN), are considered. The performances of the rival methods are then compared in terms of the mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), median absolute percentage error (MDAPE), and absolute fraction of variances (R2) metrics. Through an exhaustive comparative study, the authors observed that E-LLM is a powerful tool for predicting the vehicle speed profiles. The outcomes of the current study can be of use for the engineers of automotive industry who have been seeking fast, accurate, and inexpensive tools capable of predicting vehicle speeds up to a given point ahead of time, known as prediction horizon (HP), which can be used for designing efficient predictive powertrain controllers.},
author = {Mozaffari, Ladan and Mozaffari, Ahmad and Azad, Nasser L.},
doi = {10.1016/j.jestch.2014.11.002},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2015{\_}Vehicle speed prediction via a sliding-window time series analysis and an evolutionary least learning machine .pdf:pdf},
issn = {22150986},
journal = {Engineering Science and Technology, an International Journal},
keywords = {Intelligent tools,Predictive control,Sliding window time series forecasting,Speed prediction,Vehicle powertrains},
number = {2},
pages = {150--162},
publisher = {Elsevier Ltd},
title = {{Vehicle speed prediction via a sliding-window time series analysis and an evolutionary least learning machine: A case study on San Francisco urban roads}},
url = {http://dx.doi.org/10.1016/j.jestch.2014.11.002},
volume = {18},
year = {2015}
}
@article{,
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}1968{\_}Psychologische Aspekte der Unfallverh{\"{u}}tung.pdf:pdf},
pages = {238--251},
title = {{Paper{\_}1968{\_}Psychologische Aspekte der Unfallverh{\"{u}}tung}},
volume = {251},
year = {1968}
}
@book{Johannsen1993,
abstract = {The objective of this case study was to obtain some first-hand information about the functional consequences of a cosmetic tongue split operation for speech and tongue motility. One male patient who had performed the operation on himself was interviewed and underwent a tongue motility assessment, as well as an ultrasound examination. Tongue motility was mildly reduced as a result of tissue scarring. Speech was rated to be fully intelligible and highly acceptable by 4 raters, although 2 raters noticed slight distortions of the sibilants /s/ and /z/. The 3-dimensional ultrasound demonstrated that the synergy of the 2 sides of the tongue was preserved. A notably deep posterior genioglossus furrow indicated compensation for the reduced length of the tongue blade. It is concluded that the tongue split procedure did not significantly affect the participant's speech intelligibility and tongue motility.},
author = {Johannsen, Gunnar},
booktitle = {Springer-Verlag Berlin Heidelberg},
doi = {10.1007/978-3-642-46785-1},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}1993{\_}Mensch-Maschine-Systeme{\_}Johannsen.pdf:pdf},
isbn = {978-3-642-46786-8},
issn = {0717-6163},
keywords = {Adolescence,Adolescencia,Adolescent,Adolescent Behavior,Adolescent Behavior: psychology,Adult,Agresiones al cuerpo,Attachment to the body,Attaque au corps,Autolesiones deliberadas,Automutilation d{\'{e}}lib{\'{e}}r{\'{e}}e,Body Piercing,Body Piercing: psychology,Body Piercing: statistics {\&} numerical data,Body image,CUERPO,Chile,Chile: epidemiology,Cosmetic Techniques,Deliberate self-harm,Epidemiologic Methods,Female,Humans,Image corporelle,Imagen corporal,JUVENTUD,MODIFICACIONES CORPORALES,Male,Motivation,Movement,Risk-Taking,Self Mutilation,Self Mutilation: physiopathology,Self Mutilation: ultrasonography,Sex Distribution,Speech Articulation Tests,Speech Intelligibility,Tattooing,Tattooing: psychology,Tattooing: statistics {\&} numerical data,Tongue,Tongue: injuries,Tongue: physiopathology,Tongue: ultrasonography,aesthetics,and on cor-,as none were found,autoinjury and health,body,complications did not,complications from inserting a,constituci{\'{o}}n del yo,control postural- estabilizaci{\'{o}}n- v{\'{i}}as,corporal modifications,corps,cuerpo,culturas juveniles,cultures juv{\'{e}}niles,epidural,esth{\'{e}}tique,est{\'{e}}tica,find any reports of,high resolution images,if neuraxial anes-,ing with neuraxial anesthesia,jeunesse,juvenile cultures,juventud,mecanismos de anteroalimentaci{\'{o}}n y,modificacio -,needle through a,nes corporales,perforaci{\'{o}}n corporal,piel,pr{\'{a}}ctica autolesiva,psicoan{\'{a}}lisis,research,retroalimentaci{\'{o}}n,risks management,segunda piel,sensitivas y motoras,spinal,sustainable reconstruction,tattoo,tattooing,tattoos,tatuaje,the literature on tattoos,was reviewed to see,youth},
number = {1},
pmid = {15003161},
title = {{Mensch-Maschine-Systeme}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/15003161{\%}5Cnhttp://cid.oxfordjournals.org/lookup/doi/10.1093/cid/cir991{\%}5Cnhttp://www.scielo.cl/pdf/udecada/v15n26/art06.pdf{\%}5Cnhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84861150233{\&}partnerID=tZOtx3y1{\%}5Cnhttp://},
year = {1993}
}
@book{Freyer2008,
abstract = {In dieser Arbeit wird ein situationsadaptives, nutzerzentriertes Fahrermodell durch konsequente Vernetzung von Fahrerassistenzsystemen vorgestellt, das Adaptive Cruise Control (ACC) in Spurwechselsituationen nachhaltig verbessert. In umfangreichen Fahrversuchen im {\"{o}}ffentlichen Stra{\ss}enverkehr konnten signifikante Ver{\"{a}}nderungen im Fahrverhalten der Probanden mit und ohne ACC anhand subjektiver und objektiver Merkmale festgestellt werden. Aufbauend auf diesen Erkenntnissen wurde ein ganzheitliches, konsistentes Fahrermodell f{\"{u}}r Spurwechselvorg{\"{a}}nge mit Hilfe von Methoden der Fuzzy Logic erarbeitet, welches das menschliche Fahrverhalten mit unscharfen Wahrnehmungs- und Entscheidungsprozessen abbildet. In weiteren Fahrversuchen wurde dieses Fahrermodell validiert und die Wirksamkeit anhand von verbesserter Systemakzeptanz und einem nat{\"{u}}rlicheren Fahrverhalten mit ACC nachgewiesen.},
author = {Freyer, J{\"{o}}rn},
doi = {10.1515/9783111360225.3},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/02{\_}Dissertation/Diss{\_}2008{\_}Vernetzung von Fahrerassistenzsystemen zur Verbesserung des Spurwechselverhaltens von ACC{\_}J{\"{o}}rn Freyer.pdf:pdf},
isbn = {9783111360225},
pages = {198},
title = {{Vernetzung von Fahrerassistenzsystemen zur Verbesserung des Spurwechselverhaltens von ACC}},
year = {2008}
}
@article{Burkhard2009,
author = {Burkhard, Hans-dieter},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/06{\_}Skripte-Vorlesungen/Script{\_}2009{\_}Moderne Methoden der KI{\_}Maschinelles Lernen.pdf:pdf},
pages = {1--14},
title = {{Moderne Methoden der KI: Maschinelles Lernen}},
year = {2009}
}
@article{Bocker2014,
author = {B{\"{o}}cker, Prof Joachim},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/06{\_}Skripte-Vorlesungen/Script{\_}2014{\_}Antriebe f{\"{u}}r umweltfreundliche Fahrzeuge.pdf:pdf},
title = {{Brennstoffzellen}},
year = {2014}
}
@article{Reimann,
author = {Reimann, Michael},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/05{\_}Abschlussarbeiten/StudWork{\_}2007{\_}Simulationsmodelle im Verkehr{\_}Reimann.pdf:pdf},
title = {{StudWork{\_}2007{\_}Simulationsmodelle im Verkehr{\_}Reimann}}
}
@book{Yeboah2015,
abstract = {Land use (LU) maps are an important source of information in academia and for policy-makers describing the usage of land parcels. A large amount of effort and monetary resources are spent on mapping LU features over time and at local, regional, and global scales. Remote sensing images and signal processing tech-niques, as well as land surveying are the prime sources to map LU features. However, both data gathering approaches are financially expensive and time con-suming. But recently, Web 2.0 technologies and the wide dissemination of GPS-enabled devices boosted public participation in collaborative mapping projects (CMPs). In this regard, the OpenStreetMap (OSM) project has been one of the most successful representatives, providing LU features. The main objective of this paper is to comparatively assess the accuracy of the contributed OSM-LU features in four German metropolitan areas versus the pan-European GMESUA dataset as a ref-erence. Kappa index analysis along with per-class user's and producers' accuracies are used for accuracy assessment. The empirical findings suggest OSM as an alternative complementary source for extracting LU information whereas exceeding 50 {\%} of the selected cities are mapped by mappers. Moreover, the results identify which land types preserve high/moderate/low accuracy across cities for urban LU mapping. The findings strength the potential of collaboratively collected LU 37 features for providing temporal LU maps as well as updating/enriching existing inventories. Furthermore, such a collaborative approach can be used for collecting a global coverage of LU information specifically in countries in which temporal and monetary efforts could be minimized.},
author = {Yeboah, Godwin and Seraphim, Alvanides},
booktitle = {OpenStreetMap in GIScience: Experiences, Research, Applications},
doi = {10.1007/978-3-319-14280-7},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2015{\_}OpenStreetMap in GIScience{\_}Arsanjani{\_}Springer.pdf:pdf},
isbn = {978-3-319-14279-1},
number = {JANUARY},
pages = {1--20},
title = {{Route Choice Analysis of Urban Cycling Behaviors Using OpenStreetMap: Evidence from a British Urban Environment}},
url = {http://link.springer.com/10.1007/978-3-319-14280-7},
year = {2015}
}
@article{Rauch2009,
abstract = {Onter dem Begnff des "Sltuatlonsbewusstselns" wlrd Im Allgemelnen die Fahlgkelt  einer Person verstanden, hoch dynamische bzw. komplexe Situationen umfassenq  wahrzunehmen und sle nchtlg zu Interpretleren. Dies soil sle befahlgen, angemessed  In ihnen agieren und auf sie reagieren zu konnen. In der vorliegenden Arbeit wird  dlskutlert, Inwlewelt dleses Im Berelch der Luftfahrt entwlckelte Konzept auf de{\~{}}  Fahrkontext ubertragen werden kann. Zudem wlrd gepruft, ob die ganglged  IMethoden zur Erfassung in diesem Bereich geeignet sind. Basierend auf  bestehenden Deflnltlonen und Modellen werden zwel wesentllche Merkmale vorl  {\S}Ituatlonsbewusstseln deflnlert: Antlzlpatlve Prozesse der Handlungsplanung sowle  {\~{}}ontrollierende Prozesse der Handlungsabsicherung. Diese sollen es ermoglichen,1  jederzeit das eigene Verhalten an Veranderungen der Situation anzupassen.  Die methodischen Oberlegungen zeigen, dass die bestehenden Ansatze zur  Erfassung von Situationsbewusstsein fOr die Anwendung im Fahrkontext nicht  ausrelchend slnd. Die In der Luftfahrt hauflg elngesetzten Befragungsverfahred  Ihaben den Nachteil, dass sie nur das explizit berichtbare Wissen einer Person Ober  die Situation abbilden konnen. Das Fahren stellt dagegen eine primar implizit  gesteuerte Handlung dar. Daher wlrd eln neues Messmodell entwlckelt, das vermehrt  Verhaltensma Be als Indlkatoren fiir SltlJatlonsbewlJsstseln verwendet. Als  Ontersuchungsparadlgma wlrd hlerfur zusatzllch zur t-ahraufgabe elne  INebenaufgabe elngefuhrt. Dies ermogllcht es, Sltuatlonsbewusstseln uber das  Verhalten in einer konkreten Aufgabe messbar zu machen und die beide{\~{}}  postullerten Prozesse der Handlungsplanung und -abslcherung vonelnander zq  trennenJ  {\~{}}ituationsbewusstsein wird in diesem Zusammenhang als wesentliche  Voraussetzung fur elne flexible Anpassung der Pnonslerung von Fahr- und  INebenaufgabe an die aktuellen Kontextbedingungen verstanden. In eine{\~{}}  antlzlpatlven Prozess der Handlungsplanung 1st zunachst elne  {\S}Ituatlonselnschatzung erforderllch, um zu entschelden, ob uberhaupt elne  Zuwendung zu einer Nebenaufgabe staltfinden kann. Diese muss Wissen um die  Anforderungen der Situation, notwendige Reaktionen sowie die Antizipation der  wahrschelnllchen Sltuatlonsentwlcklung belnhalten. Wahrend der  INebenaufgabenbeschaftigung muss zudem sichergestellt werden, dass eventuelle  Anderungen der Situationsentwicklung bemerkt werden, die zu einer  Verhaltensanpassung fuhren mussen und damlt elne Onterbrechung der  INebenaufgabe erforderlich machen. Dabei handelt es sich Oberwiegend u{\~{}}  Prozesse der Handlungsabsicherung.  Im Rahmen der Arbeit wird eine spezielle Versuchsanordnung in der Fahrsimulatiod  entwickelt, die es ermoglicht, das Situationsbewusstsein eines Fahrers Ober de{\~{}}  Omgang mlt elner Nebenaufgabe zu prufen. Dazu werden dem Fahrer vor  pnterschledllch anspruchsvollen Sltuatlonen Aufgaben angeboten. Der Fahrer muss  sich innerhalb eines vorgegebenen Intervalls entscheiden, ob und wie lange er die  Aufgabe bearbelten mochte. MaBe fur elnen sltuatlonsbewussten Omgang mlt der  INebenaufgabe stellen die Anpassung des Bedien-, Fahr- sowie des Blickverhaltens  an die Anforderungen der Situation dar. Zusatzllch werden die Auswlrkungen auf dl{\~{}}  Fahrsicherheit betrachtetJ  Zur PrOfung der Hypothesen wurden zwel Studlen durchgefOhrt: In Studle 1 wlrd eln{\S}  kOnstliche, stark extern gesteuerte, nur abbrechbare Nebenaufgabe eingesetzt. 1nl  Studie 2 soil ein Fahrerinformationssystem mit hierarchischer MenOstruktur bedien{\~{}}  werden. Diese Aufgabe kann jederzeit unterbrochen und wieder fortgesetzt werden.  pie Ergebnisse verdeutlichen, dass Fahrer durchaus in der Lage sind,  sltuallonsbewusst mlt elner Nebenaufgabe umzugehen. Dies zelgt slch In  angemessenen Entscheidungen, bei hohen Anforderungen seitens der Fahraufgab{\S}  die Nebenaufgabe auszulassen bzw. erst verzogert zu beginnen oder sie vor einer  kritischen Situation zu unterbrechen. Wahrend der Nebenaufgabenbearbeitung  selbst werden kurze Kontrollbllcke zurOck zur Fahraufgabe ausgefOhrt. Sle dlenen  der Uberwachung der Situationsentwicklung und werden in ihrer Frequenz undl  pauer den Anforderungen der Situation angepasst (die Ergebnisse zum  !3lickverhalten werden im Rahmen dieser Arbeit nur grob dargestellt - FOr detailliert{\S}  Auswertungen wird auf die Arbeit von Metz (in preparation) verwiesen). Di{\~{}}  Indlvlduelle Bedlenstrategle erwelst slch von generellen Elnstellungen gegenuber der  !3eschaftigung mit Nebenaufgaben und deren Risikoeinschatzung abhangig.  Weiterhin konnen situationsabhangige, personenabhangige undl  nebenaufgabenabhanglge Faktoren Identlflzlert werden, die die Fahrslcherhelt Im  !Jmgang mit Nebenaufgaben beim Fahren gefahrden.  Anhand der Ergebnlsse wlrd eln 3-Ebenen-Prozess-Modell vonl  Sltuatlonsbewusstseln Im Umgang mlt Nebenaufgaben belm Fahren entwlckelt, daij  sog. PDC-Modell. Es beschreibt eine Obergeordnete Planungsebene, auf der  generelle Strategien fOr die Beschaftigung mit Nebenaufgaben festgelegt werden  ("Planning"). Die Entscheidungsebene beinhaltet eine Einschatzung der aktuellen  Situation, ob eine kurzfristige Abwendung zu einer Nebenaufgabe moglich is{\~{}}  ("Declslon"). Auf der Kontrollebene schlleBllch wlrd wahrend der  Nebenaufgabenbeschaftlgung die Sltuallonsentwlcklung welter Oberwacht undl  gegebenenfalls Verhaltensanpassungen vorgenommen ("Control").  per dargestellte Untersuchungsansatz stellt eine Erweiterung der Methoden zur  !Jntersuchung von Situationsbewusstsein dar. Er ermoglicht eine eindeutig{\~{}}  Abgrenzung des Begnffs zu anderen Konzepten, wle Antlzlpallon, Aufmerksamkelt,  Workload oder Gefahrenwahrnehmung. Die Nebenaufgabe wlrd hler zunachst alij  methodisches Mittel gesehen. DarOber hinaus erlaubt die Methode, konkret{\~{}}  {\~{}}andlungsempfehlungen zur Aufrechterhaltung von Situationsbewusstsein bei der  !3eschaftigung mit Fahrerinformationssystemen abzuleiten.  pie Arbeit wurde im Rahmen des Projekts "Das Konzept des Situationsbewusstsein{\S}  und seine Implikationen fOr die Fahrsicherheit" im Auftrag der Forschungsvereinigungl  Automobiltechnik e.V. (FAT) und der Bundesanstalt fOr StraBenwesen (BASt)  durchgefuhrt. Die Ergebnlsse des Prolekts slnd In elnem Abschlussbencht  zusammengefasst, der In der FAT-Schnftenrelhe erschlenen 1st (Rauch et aI., 2008)1},
author = {Rauch, N.},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/02{\_}Dissertation/Diss{\_}2009{\_}Ein verhaltensbasiertes Messmodell zur Erfassung von Situationsbewusstsein im Fahrkontext{\_}Uni Wuerzburg{\_}Rauch.pdf:pdf},
journal = {En.Scientificcommons.Org},
title = {{Ein verhaltensbasiertes Messmodell zur Erfassung von Situationsbewusstsein im Fahrkontext}},
url = {http://en.scientificcommons.org/48118909},
year = {2009}
}
@article{ScJavierAntonioOlivaAlonsoGuatemala2016,
author = {{Sc Javier Antonio Oliva Alonso Guatemala}, by M},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/02{\_}Dissertation/Diss{\_}2017{\_}Model-based Prognostics for Energy-Constrained Mobile Systems Operating in Stochastic Environments.pdf:pdf},
keywords = {electric vehicles,machine learning,prognostics},
title = {{Model-based Prognostics for Energy-Constrained Mobile Systems Operating in Stochastic Environments Applied to the Remaining Driving Range Estimation of Electric Vehicles}},
year = {2016}
}
@book{Systeme,
author = {Systeme, Komponenten},
booktitle = {Praxis},
doi = {10.1007/978-3-8348-8619-4},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2012{\_}Handbuch{\_}Fahrerassistenzsysteme{\_}ATZ{\_}Vieweg{\_}Teubner.pdf:pdf},
isbn = {9783834814579},
title = {{Handbuch systeme}}
}
@article{Mensch,
author = {Mensch, Gesamtsystems},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2006{\_}PELOPS - overview BMW.pdf:pdf},
title = {{Was ist PELOPS? Das Modell PELOPS}}
}
@article{Ag2015,
author = {Ag, Volkswagen},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Presentation{\_}2015{\_}HEV{\_}Symposium/Stobbe{\_}Volkswagen{\_}HEV{\_}2015{\_}Vortrag.pdf:pdf},
title = {{Brennstoffzellenentwicklung im Volkswagen-Konzern Braunschweiger Hybridsymposium 2015}},
year = {2015}
}
@article{Kasper2012,
author = {Kasper, Dietmar},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/02{\_}Dissertation/Diss{\_}2013{\_}Erkennung von Fahrmanoevern mit objektorientierten Bayes-Netzen in Autobahnszenarien{\_}Daimler{\_}Kasper.pdf:pdf},
pages = {155},
title = {{Erkennung von Fahrman{\"{o}}vern mit objektorientierten Bayes-Netzen in Autobahnszenarien}},
year = {2012}
}
@article{Ikami2011,
abstract = {The dynamical characteristics of driving behavior may change due to various reasons, such as the increase of experience, fatigue, and change of driving condition. In the design of a driver-assisting system that exploits a mathematical model of the driving behavior, the online adaptation mechanism for the driving behavior model must be developed and implemented. This paper presents an online parameter estimation scheme for the Probability weighted ARX (PrARX) model, which is a class of a hybrid dynamical system model, and is known to capture the complex characteristics of the driving behavior together with an explicit understanding of the drivers' motion control and decision making aspects. Since the parameter estimation for the PrARX model is originally based on a steepest descent manner, it is quite natural to extend it to the online version. The proposed method is first demonstrated using artificial data, and then applied to the online modeling of the driving behavior.},
author = {Ikami, Norimitsu and Okuda, Hiroyuki and Tazaki, Yuichi and Suzuki, Tatsuya and Takeda, Kazuya},
doi = {10.1109/ITSC.2011.6082882},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2011{\_}Online Parameter estimation of Driving Behavior using Probability-Weighted ARX Models{\_}ITS{\_}IEEE.pdf:pdf},
isbn = {9781457721984},
issn = {2153-0009},
journal = {IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC},
pages = {1874--1879},
title = {{Online parameter estimation of driving behavior using probability-weighted ARX models}},
year = {2011}
}
@article{Cascade-correlation1997,
author = {Cascade-correlation, Recurrent and Chunking, Neural Sequence},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}1997{\_}Long short-term memory{\_}Hochreiter{\_}MIT{\_}sec.pdf:pdf},
number = {8},
pages = {1--32},
title = {{Paper{\_}1997{\_}Long short-term memory{\_}Hochreiter{\_}MIT{\_}sec}},
volume = {9},
year = {1997}
}
@book{Roth2017,
author = {Roth, Michael},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/02{\_}Dissertation/Diss{\_}2017{\_}Advanced Kalman Filtering Approaches to Bayesian State Estimation{\_}MichaelRoth{\_}Linkoeping.pdf:pdf},
isbn = {9789176855782},
title = {{Advanced Kalman Filtering Approaches to Bayesian State Estimation}},
year = {2017}
}
@book{Hornich1936a,
abstract = {Diese Formelsammlung enth{\"{a}}lt die wichtigsten mathematischen Formeln f{\"{u}}r Mathematiker, Naturwissenschaftler und Ingenieure. Sie entspricht in ihrer Zusammensetzung dem heutigen Bedarf in Wissenschaft und Technik und ist bestimmt f{\"{u}}r den Gebrauch in Schulen, Hochschulen und in der Praxis. Aus dem Inhalt: Arithmetik, Algebra, Geometrie, Koordinatensysteme, spezielle Funktionen, Differentialrechnung, Integralrechnung und Integraltafel.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Hornich, H.},
booktitle = {Monatshefte f{\"{u}}r Mathematik und Physik},
doi = {10.1007/BF01708013},
eprint = {arXiv:1011.1669v3},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2014{\_}Mathematische{\_}Formelsammlung{\_}Lothar{\_}Papula.pdf:pdf},
isbn = {978-3-8348-0757-1},
issn = {0026-9255},
number = {1},
pages = {A4--A4},
pmid = {15003161},
title = {{Mathematische Formelsammlung}},
url = {http://link.springer.com/10.1007/BF01708013},
volume = {45},
year = {1936}
}
@incollection{Cai2008,
abstract = {Y. Cai, I. Pavlyshak, J. Laws, R. Magargle and J. Hoburg, “Augmented Privacy with Vertual Humans,” Digital Human Modeling Lecture Notes in Computer Science, Volume 4650/2008, pp176-193, {\textcopyright} Springer- Verlag Berlin Heidelberg, 2008.},
author = {Cai, Yang and Pavlyshak, Iryna and Laws, Joseph and Magargle, Ryan and Hoburg, James},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-540-89430-8_10},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2008{\_}Digital Human Modeling - Trends in Human Algorithms{\_}LecutreNotes{\_}Springer.pdf:pdf},
isbn = {3540894292},
issn = {03029743},
keywords = {3D scan,Feature recognition,Human body,Privacy,Security},
pages = {176--193},
pmid = {1000198484},
title = {{Augmented Privacy with Virtual Humans}},
url = {http://link.springer.com/10.1007/978-3-540-89430-8{\_}10},
volume = {4650 LNAI},
year = {2008}
}
@article{DeCauwer2017,
abstract = {Limited driving range remains one of the barriers for widespread adoption of electric vehicles (EVs). To address the problem of range anxiety, this paper presents an energy consumption prediction method for EVs, designed for energy-efficient routing. This data-driven methodology combines real-world measured driving data with geographical and weather data to predict the consumption over any given road in a road network. The driving data are linked to the road network using geographic information system software that allows to separate trips into segments with similar road characteristics. The energy consumption over road segments is estimated using a multiple linear regression (MLR) model that links the energy consumption with microscopic driving parameters (such as speed and acceleration) and external parameters (such as temperature). A neural network (NN) is used to predict the unknown microscopic driving parameters over a segment prior to departure, given the road segment characteristics and weather conditions. The complete proposed model predicts the energy consumption with a mean absolute error (MAE) of 12–14{\%} of the average trip consumption, of which 7–9{\%} is caused by the energy consumption estimation of the MLR model. This method allows for prediction of energy consumption over any route in the road network prior to departure, and enables cost-optimization algorithms to calculate energy efficient routes. The data-driven approach has the advantage that the model can easily be updated over time with changing conditions.},
author = {{De Cauwer}, Cedric and Verbeke, Wouter and Coosemans, Thierry and Faid, Saphir and {Van Mierlo}, Joeri},
doi = {10.3390/en10050608},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2017{\_}A Data-Driven Method for Energy Consumption Predicition and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions{\_}UniBrussel.pdf:pdf},
isbn = {3226292838},
issn = {19961073},
journal = {Energies},
keywords = {Electric vehicle (EV),Energy consumption,Prediction,Routing},
number = {5},
title = {{A data-driven method for energy consumption prediction and energy-efficient routing of electric vehicles in real-world conditions}},
volume = {10},
year = {2017}
}
@article{Ericsson2006,
abstract = {Today, driver support tools intended to increase traffic safety, provide the driver with convenient information and guidance, or save time are becoming more common. However, few systems have the primary aim of reducing the environmental effects of driving. The aim of this project was to estimate the potential for reducing fuel consumption and thus the emission of CO2through a navigation system where optimization of route choice is based on the lowest total fuel consumption (instead of the traditional shortest time or distance), further the supplementary effect if such navigation support could take into account real-time information about traffic disturbance events from probe vehicles running in the street network. The analysis was based on a large database of real traffic driving patterns connected to the street network in the city of Lund, Sweden. Based on 15 437 cases, the fuel consumption factor for 22 street classes, at peak and off-peak hours, was estimated for three types of cars using two mechanistic emission models. Each segment in the street network was, on a digitized map, attributed an average fuel consumption for peak and off-peak hours based on its street class and traffic flow conditions. To evaluate the potential of a fuel-saving navigation system the routes of 109 real journeys longer than 5 min were extracted from the database. Using Esri's external program ArcGIS, Arcview and the external module Network Analysis, the most fuel-economic route was extracted and compared with the original route, as well as routes extracted from criterions concerning shortest time and shortest distance. The potential for further benefit when the system employed real-time data concerning the traffic situation through 120 virtual probe vehicles running in the street network was also examined. It was found that for 46{\%} of trips in Lund the drivers spontaneous choice of route was not the most fuel-efficient. These trips could save, on average, 8.2{\%} fuel by using a fuel-optimized navigation system. This corresponds to a 4{\%} fuel reduction for all journeys in Lund. Concerning the potential for real-time information from probe vehicles, it was found that the frequency of disturbed segments in Lund was very low, and thus so was the potential fuel-saving. However, a methodology is presented that structures the steps required in analyzing such a system. It is concluded that real-time traffic information has the potential for fuel-saving in more congested areas if a sufficiently large proportion of the disturbance events can be identified and reported in real-time. {\textcopyright} 2006 Elsevier Ltd. All rights reserved.},
author = {Ericsson, Eva and Larsson, Hanna and Brundell-Freij, Karin},
doi = {10.1016/j.trc.2006.10.001},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2006{\_}Optimizing-route-choice-for-lowest-fuel-consumption-Potential-effects-of-a-new-driver-support-tool{\_}Transportation-Research.pdf:pdf},
isbn = {0968-090X},
issn = {0968090X},
journal = {Transportation Research Part C: Emerging Technologies},
keywords = {Driving pattern,Fuel consumption,Navigation system,Probe vehicle,Street classification,Street types,Traffic disturbance,Traffic flow},
number = {6},
pages = {369--383},
pmid = {23603630},
title = {{Optimizing route choice for lowest fuel consumption - Potential effects of a new driver support tool}},
volume = {14},
year = {2006}
}
@book{Takeda2009,
address = {Boston, MA},
doi = {10.1007/978-0-387-79582-9},
editor = {Takeda, Kazuya and Erdogan, Hakan and Hansen, John H. L. and Abut, Huseyin},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2009{\_}In-Vehicle Corpus and Signal Processing for Dirver Behavior.pdf:pdf},
isbn = {978-0-387-79581-2},
publisher = {Springer US},
title = {{In-Vehicle Corpus and Signal Processing for Driver Behavior}},
url = {http://link.springer.com/10.1007/978-0-387-79582-9},
year = {2009}
}
@article{Hybrid2015,
author = {Hybrid, Symposium and Danzer, Christoph},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Presentation{\_}2015{\_}HEV{\_}Symposium/Danzer{\_}IAV{\_}HEV{\_}2015{\_}Vortrag.pdf:pdf},
pages = {1--21},
title = {{Systematische Triebstrangsynthese f{\"{u}}r effiziente Elektrofahrzeuge Heutige Batterie-Elektrofahrzeuge}},
year = {2015}
}
@article{Rizzoni1999,
abstract = {Hybridizing automotive drivetrains, or using more than one type of$\backslash$nenergy converter, is considered an important step toward very low$\backslash$npollutant emission and high fuel economy. The automotive industry and$\backslash$ngovernments in the United States, Europe, and Japan have formed$\backslash$nstrategic initiatives with the aim of cooperating in the development of$\backslash$nnew vehicle technologies. Efforts to meet fuel economy and exhaust$\backslash$nemission targets have initiated major advances in hybrid drivetrain$\backslash$nsystem components, including: high-efficiency high-specific power$\backslash$nelectric motors and controllers; load-leveling devices such as$\backslash$nultracapacitors and fly-wheels; hydrogen and direct-methanol fuel cells;$\backslash$ndirect injection diesel and Otto cycle engines; and advanced batteries.$\backslash$nThe design of hybrid electric vehicles is an excellent example of the$\backslash$nneed for mechatronic system analysis and design methods. If one is to$\backslash$nfully realize the potential of using these technologies, a complete$\backslash$nvehicle system approach for component selection and optimization over$\backslash$ntypical driving situations is required. The control problems that arise$\backslash$nin connection with hybrid power trains are significant and pose$\backslash$nadditional challenges to power-train control engineers. The principal$\backslash$naim of the paper is to propose a framework for the analysis, design, and$\backslash$ncontrol of optimum hybrid vehicles within the context of energy and$\backslash$npower flow analysis. The approaches and results presented in the paper$\backslash$nare one step toward the development of a complete toolbox for the$\backslash$nanalysis and design of hybrid vehicles},
author = {Rizzoni, Giorgio and Guzzella, Lino and Baumann, Bernd M.},
doi = {10.1109/3516.789683},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}1999{\_}Unified modeling of hybrid electric vehicle drivetrains{\_}Rizzoni{\_}ASME{\_}IEEE.pdf:pdf},
isbn = {1083-4435 VO  - 4},
issn = {10834435},
journal = {IEEE/ASME Transactions on Mechatronics},
number = {3},
pages = {246--257},
pmid = {9252316},
title = {{Unified modeling of hybrid electric vehicle drivetrains}},
volume = {4},
year = {1999}
}
@article{Kedar-Dongarkar2012,
abstract = {Real time monitoring of some key dynamical parameters of a vehicle provide critical information about the driving styles and expectations of vehicle drivers. Some of these key dynamical parameters include vehicle acceleration, braking, speeding index and throttle activity index. This paper presents a simple classifier that uses the estimated values of the above parameters to classify a driver into one of three categories, aggressive, moderate and conservative. The proposed classifier is computationally more efficient compared to other conventional classifiers, such as K-nearest neighbor algorithm, and hidden Markov model. Also, it filters the reference data set in an intelligent fashion. In a dual-power vehicle, such as a hybrid electric vehicle, this kind of classifier can be used to develop an optimum shift schedule, or an optimum engine on-off strategy, and estimate the available amount of regenerative energy. {\textcopyright} 2012 Published by Elsevier Ltd.},
author = {Kedar-Dongarkar, Gurunath and Das, Manohar},
doi = {10.1016/j.procs.2012.01.077},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/06{\_}Paper/2019{\_}03{\_}Ilmenau{\_}DE/01{\_}Literature/2012 - Kedar-Dongarkar - Driver Classification for Optimization of Energy Usage in a Vehicle.pdf:pdf},
isbn = {1877-0509},
issn = {18770509},
journal = {Procedia Computer Science},
keywords = {Driver classification,K-Nearest Neighbor algorithm,Powertrain signals,Principal component analysis},
pages = {388--393},
title = {{Driver classification for optimization of energy usage in a vehicle}},
url = {http://dx.doi.org/10.1016/j.procs.2012.01.077},
volume = {8},
year = {2012}
}
@article{Gindele2015,
abstract = {Estimating and predicting traffic situations over time is an essential capability for sophisticated driver assistance systems and autonomous driving. When longer prediction horizons are needed, e.g., in decision making or motion planning, the uncertainty induced by incomplete environment perception and stochastic situation development over time cannot be neglected without sacrificing robustness and safety. Building consistent probabilistic models of drivers interactions with the environment, the road network and other traffic participants poses a complex problem. In this paper, we model the decision making process of drivers by building a hierarchical Dynamic Bayesian Model that describes physical relationships as well as the driver's behaviors and plans. This way, the uncertainties in the process on all abstraction levels can be handled in a mathematically consistent way. As drivers behaviors are difficult to model, we present an approach for learning continuous, non-linear, context-dependent models for the behavior of traffic participants. We propose an Expectation Maximization (EM) approach for learning the models integrated in the DBN from unlabeled observations. Experiments show a significant improvement in estimation and prediction accuracy over standard models which only consider vehicle dynamics. Finally, a novel approach to tactical decision making for autonomous driving is outlined. It is based on a continuous Partially Observable Markov Decision Process (POMDP) that uses the presented model for prediction.},
author = {Gindele, Tobias and Brechtel, Sebastian and Dillmann, R{\"{u}}diger},
doi = {10.1109/MITS.2014.2357038},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2015{\_}Gindele - Learning driver behavior models from traffic observations for decision making and planning.pdf:pdf},
issn = {1939-1390},
journal = {IEEE Intelligent Transportation Systems Magazine},
number = {1},
pages = {69--79},
title = {{Learning Driver Behavior Models from Traffic Observations for Decision Making and Planning}},
volume = {7},
year = {2015}
}
@article{Endisch2013,
author = {Endisch, Christian},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/06{\_}Skripte-Vorlesungen/Script{\_}2014{\_}Systemidentifikation{\_}Neuronale{\_}Netze{\_}Endisch.pdf:pdf},
title = {{Systemidentifikation in der Mechatronik}},
year = {2013}
}
@book{Chen2017,
author = {Chen, Weidong},
doi = {10.1007/978-3-319-48036-7},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2017{\_}Intelligent Autonomous Systems 14.pdf:pdf},
isbn = {978-3-319-48035-0},
title = {{Intelligent Autonomous Systems 14}},
url = {http://link.springer.com/10.1007/978-3-319-48036-7},
volume = {531},
year = {2017}
}
@article{Park2009,
abstract = {Previous research has shown that current driving conditions and driving style have a strong influence over a vehicle's fuel consumption and emissions. This paper presents a methodology for inferring road type and traffic congestion (RT{\&}amp;TC) levels from available onboard vehicle data and then using this information for improved vehicle power management. A machine-learning algorithm has been developed to learn the critical knowledge about fuel efficiency on 11 facility-specific drive cycles representing different road types and traffic congestion levels, as well as a neural learning algorithm for the training of a neural network to predict the RT{\&}amp;TC level. An online University of Michigan-Dearborn intelligent power controller (UMDIPC) applies this knowledge to real-time vehicle power control to achieve improved fuel efficiency. UMDIPC has been fully implemented in a conventional (nonhybrid) vehicle model in the powertrain systems analysis toolkit (PSAT) environment. Simulations conducted on the standard drive cycles provided by the PSAT show that the performance of the UMDIPC algorithm is very close to the offline controller that is generated using a dynamic programming optimization approach. Furthermore, UMDIPC gives improved fuel consumption in a conventional vehicle, alternating neither the vehicle structure nor its components.},
author = {Park, Jungme and Chen, Zhihang and Kiliaris, Leonidas and Kuang, Ming L. and Masrur, M. Abul and Phillips, Anthony M. and Murphey, Yi Lu},
doi = {10.1109/TVT.2009.2027710},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2009{\_}Intelligent vehicle power control based on machine learning of optimal control parameters and prediction of road type and traffic congestion.pdf:pdf},
isbn = {0769527957},
issn = {00189545},
journal = {IEEE Transactions on Vehicular Technology},
keywords = {Fuel economy,Machine learning,Road type and traffic congestion (RT{\&}TC) level pre,Vehicle power management},
number = {9},
pages = {4741--4756},
title = {{Intelligent vehicle power control based on machine learning of optimal control parameters and prediction of road type and traffic congestion}},
volume = {58},
year = {2009}
}
@article{Klingebiel2012,
author = {Klingebiel, Wolfgang},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/06{\_}Skripte-Vorlesungen/Script{\_}2008{\_}Statistik 2{\_}Uni Karlsruhe{\_}Prof{\_}Holzmann.pdf:pdf},
isbn = {9783531169408},
number = {4},
pages = {1--59},
title = {{Statistik 2}},
volume = {08},
year = {2012}
}
@book{Mahlisch2009,
abstract = {Filter Synthesis for Simultaneous Minimization of Detection, Association, and State Uncertainties in Automotive Environment Perception with Heterogeneous Sensor Data},
author = {M{\"{a}}hlisch, Mirko},
booktitle = {Booksgooglecom},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/02{\_}Dissertation/Diss{\_}2009{\_}Filtersynthese zur simultanen Minimierung von Existenz- Assoziations- und Zustandsunsicherheiten in der Fahrzeugumfelderfassung mit heterogenen Sensordaten.pdf:pdf},
isbn = {3941543032},
pages = {224},
title = {{Filtersynthese zur simultanen Minimierung von Existenz-, Assoziations- und Zustandsunsicherheiten in der Fahrzeugumfelderfassung mit heterogenen Sensordaten}},
url = {http://vts.uni-ulm.de/doc.asp?id=7188},
year = {2009}
}
@article{Fischer2011,
author = {Fischer, Rainer and Butz, Torsten and Ehmann, Martin and Vockenhuber, Mario},
doi = {10.1365/s35148-011-0220-z},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2011{\_}Fahrermodell zur virtuellen Regelsystementwicklung{\_}Fischer{\_}Butz{\_}Ehmann{\_}Vockenhuber.pdf:pdf},
issn = {0001-2785},
journal = {ATZ - Automobiltechnische Zeitschrift},
number = {12},
pages = {946--949},
title = {{Fahrermodell zur virtuellen Regelsystementwicklung}},
url = {http://www.springerlink.com/index/10.1365/s35148-011-0220-z},
volume = {113},
year = {2011}
}
@book{Russell2009,
abstract = {The long-anticipated revision of this {\#}1 selling book offers the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications.Intelligent Agents. Solving Problems by Searching. Informed Search Methods. Game Playing. Agents that Reason Logically. First-order Logic. Building a Knowledge Base. Inference in First-Order Logic. Logical Reasoning Systems. Practical Planning. Planning and Acting. Uncertainty. Probabilistic Reasoning Systems. Making Simple Decisions. Making Complex Decisions. Learning from Observations. Learning with Neural Networks. Reinforcement Learning. Knowledge in Learning. Agents that Communicate. Practical Communication in English. Perception. Robotics.For computer professionals, linguists, and cognitive scientists interested in artificial intelligence.},
archivePrefix = {arXiv},
arxivId = {arXiv:gr-qc/9809069v1},
author = {Russell, Stuart and Norvig, Peter},
booktitle = {Pearson},
doi = {10.1017/S0269888900007724},
eprint = {9809069v1},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2009{\_}Artificial Intelligence{\_}A Modern Approach{\_}3rd-Edition.pdf:pdf},
isbn = {9780136042594},
issn = {0269-8889},
pages = {1152},
pmid = {20949757},
primaryClass = {arXiv:gr-qc},
title = {{Artificial Intelligence: A Modern Approach, 3rd edition}},
url = {http://portal.acm.org/citation.cfm?id=1671238{\&}coll=DL{\&}dl=GUIDE{\&}CFID=190864501{\&}CFTOKEN=29051579{\%}5Cnpapers2://publication/uuid/4B787E16-89F6-4FF7-A5E5-E59F3CFEFE88},
year = {2009}
}
@article{Robnik-Sikonja2003,
author = {Robnik-Sikonja, Marko and Kononenko, Igor},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2003{\_}Theoretical and Empirical Analysis of ReliefF and RReliefF{\_}Robnik{\_}Springer.pdf:pdf},
journal = {Journal of Machine Learning Research},
keywords = {attribute evaluation,classification,feature selection,regression,relief algorithm},
pages = {23--69},
title = {{Theoretical and empirical analysis of ReliefF and RRiefF}},
volume = {53},
year = {2003}
}
@book{Jurgensohn2001,
address = {Berlin, Heidelberg},
author = {J{\"{u}}rgensohn, Thomas and {Klaus-Peter Timpe}},
doi = {10.1007/978-3-642-56721-6},
editor = {J{\"{u}}rgensohn, Thomas and Timpe, Klaus-Peter},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2001{\_}Kraftfahrzeugfuehrung{\_}J{\"{u}}rgensohn{\_}Timpe.pdf:pdf},
isbn = {978-3-642-62639-5},
publisher = {Springer Berlin Heidelberg},
title = {{Kraftfahrzeugf{\"{u}}hrung}},
url = {http://link.springer.com/10.1007/978-3-642-56721-6},
year = {2001}
}
@article{Ralph,
abstract = {Die Themenstellung dieser Masterarbeit ist die Entwicklung eines vorausschauenden Funkti-onsalgorithmus, der dem Fahrer eine optische und haptische Empfehlung f{\"{u}}r ein m{\"{o}}glichst z{\"{u}}giges Fahren anzeigt. Eingangsgr{\"{o}}{\ss}en sind: • Pr{\"{a}}diktive Strecken-Daten aus der Navigation mit folgenden Informationen-Tempolimits, Kurven und Steigungen-Kreuzungen, Abzweigungen, Stoppstellen und Ampeln-Traffic-Online-Informationen und weitere Car2X-Daten • Vorderfahrzeuginformationen mit Abstand und Relativgeschwindigkeit aus Radarsenso-ren und Kamera • Ego-Fahrzeugdaten auf Basis von fahrzeuginternen Sensoren Im Rahmen der Masterarbeit soll ein Anzeige-und Funktionskonzept entwickelt werden und eine erste prototypische Darstellung in einem Versuchsfahrzeug erfolgen. Diese prototypische Darstellung soll dann durch interne Probanden bewertet werden. Durch Patrick Herrmann sind folgende Punkte zu bearbeiten: • Literatur-und Patentrecherche zu bestehenden Systemen • Konzeptentwicklung f{\"{u}}r eine optische und haptische Anzeige • Prototypische Umsetzung des erarbeitenden Konzeptes in einem Versuchsfahrzeug • Probandenstudie mit internen Testfahrern und Auswertung erster Tendenzen • Weiterentwicklung des Konzeptes auf Basis der Ergebnisse der Studie},
author = {Ralph, Ing and Kennel, M},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/05{\_}Abschlussarbeiten/MA{\_}2015{\_}PHermann{\_}PR{\"{A}}DIKTIVE BESCHLEUNIGUNGSEMPFEHLUNG{\_}TUM.pdf:pdf},
number = {August 2014},
title = {{Pr{\"{a}}diktive Beschleunigungsempfehlung}}
}
@article{Kheyfets2010,
author = {Kheyfets, Julie},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2016{\_}EUROPEAN VEHICLEICCT MARKET STATISTICS{\_}EU-pocketbook{\_}2015-16.pdf:pdf},
pages = {2010--2010},
title = {{Resumo : P{\'{a}}ginas 139 – 149}},
year = {2010}
}
@article{Rezaei2015,
abstract = {In model predictive control, knowledge about the future trajectories of the set points or disturbances is used to optimize the overall system performance, Camacho and Bordons (2007). For hybrid electric vehicles, by predicting the future Driver's Desired Velocity (DDV), fuel economy, or emissions can be improved, Debert et al. (2010). For predicting DDV, different approaches have been suggested, for example, artificial neural networks, Fotouhi et al. (2011), statistical methods, or methods based on GPS and Geographical Information Systems(GIS), Keulen et al. (2009). In this work, some of these approaches are introduced and autoregressive methods with GPS/GIS information are evaluated.},
author = {Rezaei, Amir and Burl, Jeffrey B.},
doi = {10.1016/j.ifacol.2015.10.037},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2015{\_}Prediction of Vehicle Velocity for Model Predictive Control{\_}TU Michigan.pdf:pdf},
issn = {24058963},
journal = {IFAC-PapersOnLine},
keywords = {Model predictive control,Time series,Velocity prediction},
number = {15},
pages = {257--262},
publisher = {Elsevier B.V.},
title = {{Prediction of vehicle velocity for model predictive control}},
url = {http://dx.doi.org/10.1016/j.ifacol.2015.10.037},
volume = {28},
year = {2015}
}
@article{Kramer2013,
author = {Kramer, Ulrich and Bielefeld, F H and Dresden, Safe},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/06{\_}Skripte-Vorlesungen/Script{\_}2013{\_}Fahrermodellierung{\_}Fahrerassistenz{\_}Prof.Kramer.pdf:pdf},
pages = {1--16},
title = {{Fahrermodellierung und Fahrermodellierung und Fahrerassistenz Fahrerassistenz Spurhaltung beim Autofahren}},
year = {2013}
}
@article{Sautermeister2017,
author = {Sautermeister, Stefan and Falk, Max and Baker, Bernard and Gauterin, Frank and Vaillant, Moritz},
doi = {10.1109/TITS.2017.2762829},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2017{\_}Influence of Measurement and Prediction Uncertainties on Range Estimation for Electric Vehicles.pdf:pdf},
issn = {15249050},
journal = {IEEE Transactions on Intelligent Transportation Systems},
keywords = {Batteries,Estimation,Mechanical power transmission,Predictive models,Range estimation,Resistance,Uncertainty,Vehicles,battery state estimation,electric vehicle,energy prediction.,error propagation,reachability,uncertainty analysis},
pages = {1--12},
title = {{Influence of Measurement and Prediction Uncertainties on Range Estimation for Electric Vehicles}},
year = {2017}
}
@book{Roth2008,
author = {Roth, Peter M and Bischof, Horst},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/02{\_}Dissertation/Diss{\_}2009{\_}Machine Learning Techniques for Time Series Classification{\_}Botsch Michael{\_}TUM.pdf:pdf},
isbn = {9783867279505},
title = {{Machine Learning Techniques for Multimedia}},
year = {2008}
}
@book{Krengel,
author = {Krengel, Ulrich},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/07{\_}Math/Book{\_}2005{\_}Einf{\"{u}}hrung in die Wahrscheinlichkeitstheorie und Statistik{\_}Ulrich Krengel.pdf:pdf},
isbn = {9783834800633},
title = {{Ulrich Krengel Einf{\"{u}}hrung in die Wahrscheinlichkeits-theorie und Statistik}}
}
@article{Automobilentwicklung2011,
author = {Automobilentwicklung, In},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Presentation{\_}2011{\_}BMW Fahrermodelle In Automobilentwicklung und Simulation{\_}DGLR{\_}FKFS{\_}Tagung.pdf:pdf},
title = {{In Automobilentwicklung und Simulation .}},
year = {2011}
}
@book{Hellstrom2007,
abstract = {The power to mass ratio of a heavy truck causes even moderate slopes to have a significant influence on the motion. The velocity will inevitable vary within an interval that is primarily determined ...},
author = {Hellstr{\"{o}}m, Erik},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/02{\_}Dissertation/Diss{\_}2007{\_}Look-ahead control of heavy trucks utilizing road topography{\_}Hellstr{\"{o}}m{\_}Link{\"{o}}ping.pdf:pdf},
isbn = {9789185831586},
issn = {02807971},
keywords = {Control Engineering,Reglerteknik},
number = {1319},
title = {{Look-ahead Control of Heavy Trucks utilizing Road Topography}},
url = {http://liu.diva-portal.org/smash/record.jsf?pid=diva2:23829},
year = {2007}
}
@article{Hochreiter1997,
abstract = {Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient-based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O(1). Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.},
archivePrefix = {arXiv},
arxivId = {1206.2944},
author = {Hochreiter, Sepp and Schmidhuber, J{\"{u}}rgen},
doi = {10.1162/neco.1997.9.8.1735},
eprint = {1206.2944},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}1997{\_}Long short-term memory{\_}Hochreiter{\_}MIT.pdf:pdf},
isbn = {08997667 (ISSN)},
issn = {08997667},
journal = {Neural Computation},
number = {8},
pages = {1735--1780},
pmid = {9377276},
title = {{Long Short-Term Memory}},
volume = {9},
year = {1997}
}
@article{Kuge2000,
abstract = {A method for detecting drivers' intentions is essential to facilitate operating mode transitions between driver and driver assistance systems. We propose a driver behavior recognition method using Hidden Markov Models (HMMs) to characterize and detect driving maneuvers and place it in the framework of a cognitive model of human behavior. HMM-based steering behavior models for emergency and normal lane changes as well as for lane keeping were developed using a moving base driving simulator. Analysis of these models after training and recognition tests showed that driver behavior modeling and recognition of different types of lane changes is possible using HMMs.},
author = {Kuge, Nobuyuki and Yamamura, Tomohiro and Shimoyama, Osamu and Liu, Andrew},
doi = {10.4271/2000-01-0349},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2000{\_}A Driver Behavior Recognition Method Based on a Driver Model Framework{\_}Nissan.pdf:pdf},
isbn = {0096-736X},
issn = {0096736X},
journal = {Structure},
number = {Idm},
pages = {469--476},
title = {{A Driver Behavior Recognition Method Based on a Driver Model Framework}},
url = {http://web.mit.edu/amliu/www/Papers/SAE2000{\_}Kuge.pdf},
volume = {109},
year = {2000}
}
@article{Zitzewitz2012,
author = {Zitzewitz, Moritz Von},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/05{\_}Abschlussarbeiten/BA{\_}2012{\_}Android Smartphone als Fahrzeug Datenlogger{\_}TUM{\_}EI{\_}Zitzewitz.pdf:pdf},
title = {{Android Smartphone als Fahrzeug Datenlogger Android Smartphone as Vehicle Data Recorder Erkl{\"{a}}rung}},
year = {2012}
}
@article{Kristan2011,
abstract = {We propose a novel approach to online estimation of probability density functions, which is based on kernel density estimation (KDE). The method maintains and updates a non-parametric model of the observed data, from which the KDE can be calculated. We propose an online bandwidth estimation approach and a compression/revitalization scheme which maintains the KDE's complexity low. We compare the proposed online KDE to the state-of-the-art approaches on examples of estimating stationary and non-stationary distributions, and on examples of classification. The results show that the online KDE outperforms or achieves a comparable performance to the state-of-the-art and produces models with a significantly lower complexity while allowing online adaptation. {\textcopyright} 2011 Elsevier Ltd. All rights reserved.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Kristan, Matej and Leonardis, Ale{\v{s}} and Sko{\v{c}}aj, Danijel},
doi = {10.1016/j.patcog.2011.03.019},
eprint = {arXiv:1011.1669v3},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2011{\_}Multivariate online kernel density estimation with Gaussian kernels{\_}Kristan{\_}PatternRecognition.pdf:pdf},
isbn = {1841690643 (hardcover); 1841690651 (paperback)},
issn = {00313203},
journal = {Pattern Recognition},
keywords = {Gaussian mixture models,Kernel density estimation,Online models,Probability density estimation},
number = {10-11},
pages = {2630--2642},
pmid = {25246403},
title = {{Multivariate online kernel density estimation with Gaussian kernels}},
volume = {44},
year = {2011}
}
@article{Kraus2012,
abstract = {Die Mobilit{\"{a}}t stellt eines der wichtigsten Mittel zur Wahrung menschlicher Bed{\"{u}}rfnisse dar. Individuelle Unabh{\"{a}}ngigkeit misst hier dem Automobil einen gro{\ss}en Stellenwert zu. Der Mensch in seiner Aufgabe als Fahrzeugf{\"{u}}hrer fungiert dabei als Teil des Gesamtsystems Fahrer-Fahrzeug-Umwelt. Er sieht sich im allt{\"{a}}glichen Stra{\ss}enverkehr regelm{\"{a}}{\ss}ig mit di- versen Situationen konfrontiert. Diese k{\"{o}}nnen einen monotonen und unterfordernden Cha- rakter, wie z.B. lange Autobahnfahrten, aufweisen oder den Fahrer bei der Fahrzeugbedie- nung oder der Informationsaufnahme {\"{u}}berfordern. Beide Situationsklassen kennzeichnen sich durch eine geringe Fahrerperformance und bergen demzufolge ein erhebliches Gefah- renpotential in sich. Es werden daher komfort- und sicherheitsorientierte Assistenzsysteme in die Fahrzeuge integriert, um den Fahrer in den genannten Situationsklassen zu unterst{\"{u}}t- zen und damit zu einer Reduktion der Unfallzahlen beizutragen. Komfortsysteme, die den Fahrer in unterfordernden Situationen entlasten sollen, werden meist {\"{u}}ber h{\"{o}}herklassige Fahrzeugmodelle in den Markt eingebracht. Wohingegen in der Vergangenheit vorwiegend Assistenzsysteme der Fahrzeugl{\"{a}}ngsf{\"{u}}hrung, wie der Tempomat oder das ACC-System, ihrenWeg in Serienfahrzeuge schafften, dringen zunehmend Syste- me in den Markt, die sich auch mit der Querf{\"{u}}hrung oder sogar mit der kompletten Fahrzeug- f{\"{u}}hrungsaufgabe in Stausituationen besch{\"{a}}ftigen. Derartige Systeme erfordern keinerlei Be- dient{\"{a}}tigkeiten vom Fahrer, dieser findet sich in einer rein {\"{u}}berwachenden, beobachtenden Rolle wieder. Jedoch bedingt die Verbreitung derartiger Systeme die Akzeptanz durch den Fahrzeugf{\"{u}}hrer, welche wiederum auch vom technischen Verhalten der Systeme abh{\"{a}}ngt. In diesem Zusammenhang spielt der erstmals in der Robotik in den 1970er-Jahren beobachtete Effekt des „Uncanny Valley“ eine Rolle, der sich auf das Akzeptanzverhalten von nonverba- len und technisch simulierten Systemen bezieht. Demnach steigt die Akzeptanz mit dem Grad der Menschen{\"{a}}hnlichkeit des Systems, jedoch nicht stetig linear, sondern mit einem starken Einbruch innerhalb einer gewissen Spanne. Wird diese {\"{u}}berschritten, so kann eine h{\"{o}}heres Akzeptanzniveau erreicht werden, als bei Systemen mit rein artifiziellem Verhalten. Der Applikationsprozess bei dem die Reglerparameter letztendlich bestimmt werden, ist je- doch sehr ressourcenintensiv und kann durch die inter- und intraindividuellen Unterschiede der Applikationsingenieure im Ergebnis differieren.},
author = {Kraus, Sven Bernd},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/02{\_}Dissertation/Diss{\_}2012{\_}Fahrverhaltensanalyse{\_}zur{\_}Parametrierung{\_}situationsadaptiver{\_}Fahrzeugfuehrungssysteme.pdf:pdf},
title = {{Fahrverhaltensanalyse zur Parametrierung situationsadaptiver Fahrzeugf{\"{u}}hrungssysteme}},
year = {2012}
}
@article{Fox2009,
author = {Fox, Emily B},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/02{\_}Dissertation/Diss{\_}2009{\_}Bayesian nonparametric learning of complex dynamical phenomena - Fox.pdf:pdf},
pages = {270},
title = {{Bayesian Nonparametric Learning of Complex Dynamical Phenomena}},
year = {2009}
}
@book{Hippler2011,
address = {Berlin, Heidelberg},
author = {Hippler, Horst},
doi = {10.1007/978-3-642-23662-4},
editor = {Hippler, Horst},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2011{\_}Ingenieurspromotion - Starken und Qualit{\"{a}}tssicherung{\_}.pdf:pdf},
isbn = {978-3-642-23661-7},
keywords = {Apress},
publisher = {Springer Berlin Heidelberg},
series = {acatech DISKUSSION},
title = {{Ingenieurpromotion — St{\"{a}}rken und Qualit{\"{a}}tssicherung}},
url = {www.acatech.de http://link.springer.com/10.1007/978-3-642-23662-4},
year = {2011}
}
@article{Hallac2016,
abstract = {As automotive electronics continue to advance, cars are becoming more and more reliant on sensors to perform everyday driving operations. These sensors are omnipresent and help the car navigate, reduce accidents, and provide comfortable rides. However, they can also be used to learn about the drivers themselves. In this paper, we propose a method to predict, from sensor data collected at a single turn, the identity of a driver out of a given set of individuals. We cast the problem in terms of time series classification, where our dataset contains sensor readings at one turn, repeated several times by multiple drivers. We build a classifier to find unique patterns in each individual's driving style, which are visible in the data even on such a short road segment. To test our approach, we analyze a new dataset collected by AUDI AG and Audi Electronics Venture, where a fleet of test vehicles was equipped with automotive data loggers storing all sensor readings on real roads. We show that turns are particularly well-suited for detecting variations across drivers, especially when compared to straightaways. We then focus on the 12 most frequently made turns in the dataset, which include rural, urban, highway on-ramps, and more, obtaining accurate identification results and learning useful insights about driver behavior in a variety of settings.},
archivePrefix = {arXiv},
arxivId = {1708.04636},
author = {Hallac, David and Sharang, Abhijit and Stahlmann, Rainer and Lamprecht, Andreas and Huber, Markus and Roehder, Martin and Sosi{\v{c}}, Rok and Leskovec, Jure},
doi = {10.1109/ITSC.2016.7795670},
eprint = {1708.04636},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2016{\_}Driver Identification Using Automobile Sensor Data from a Single Turn{\_}0113.pdf:pdf},
isbn = {9781509018895},
journal = {IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC},
pages = {953--958},
title = {{Driver identification using automobile sensor data from a single turn}},
year = {2016}
}
@article{Held2011,
author = {Held, Pascal and Gonter, Mark and Bauer, Colin and Kruse, Rudolf and Steinbrecher, Matthias},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/05{\_}Abschlussarbeiten/MA{\_}2011{\_}PHeld{\_}Sch{\"{a}}tzen verdeckter Fahrereigenschaften auf Basis des Fahrverhaltens.pdf:pdf},
title = {{Otto-von-Guericke Universit{\"{a}}t Magdeburg Sch{\"{a}}tzen verdeckter Fahrereigenschaften auf Basis des Fahrverhaltens}},
year = {2011}
}
@article{NgBoyleProfessor2016,
author = {{Ng Boyle Professor}, Linda},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Presentation{\_}2016{\_}Guest talk Prof. Linda Boyle - Modeling the effects of drivers adaptive behavior on system safety.pdf:pdf},
title = {{Modeling the effects of drivers' adaptive behavior on system safety}},
year = {2016}
}
@article{,
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/06{\_}Skripte-Vorlesungen/Paper{\_}2015{\_}ProductOverview{\_}veDYNA{\_}Professional Real-Time Vehicle - Dynamics Simulation Environment.pdf:pdf},
title = {{Professional Real-Time Vehicle Dynamics Simulation Environment More efficiency in component and}}
}
@article{Wang2017,
abstract = {Analysis and recognition of driving styles are profoundly important to intelligent transportation and vehicle calibration. This paper presents a novel driving style analysis framework using the primitive driving patterns learned from naturalistic driving data. In order to achieve this, first, a Bayesian nonparametric learning method based on a hidden semi-Markov model (HSMM) is introduced to extract primitive driving patterns from time series driving data without prior knowledge of the number of these patterns. In the Bayesian nonparametric approach, we utilize a hierarchical Dirichlet process (HDP) instead of learning the unknown number of smooth dynamical modes of HSMM, thus generating the primitive driving patterns. Each primitive pattern is clustered and then labeled using behavioral semantics according to drivers' physical and psychological perception thresholds. For each driver, 75 primitive driving patterns in car-following scenarios are learned and semantically labeled. In order to show the HDP-HSMM's utility to learn primitive driving patterns, other two Bayesian nonparametric approaches, HDP-HMM and sticky HDP-HMM, are compared. The naturalistic driving data of 18 drivers were collected from the University of Michigan Safety Pilot Model Deployment (SPDM) database. The individual driving styles are discussed according to distribution characteristics of the learned primitive driving patterns and also the difference in driving styles among drivers are evaluated using the Kullback-Leibler divergence. The experiment results demonstrate that the proposed primitive pattern-based method can allow one to semantically understand driver behaviors and driving styles.},
archivePrefix = {arXiv},
arxivId = {1708.08986},
author = {Wang, Wenshuo and Xi, Junqiang and Zhao, Ding},
eprint = {1708.08986},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/06{\_}Paper/2019{\_}03{\_}Ilmenau{\_}DE/01{\_}Literature/2017 - Wang - Driving Style Analysis Using Primitive Driving Patterns With Bayesian Nonparametric Approaches.pdf:pdf},
number = {July},
pages = {1--13},
title = {{Driving Style Analysis Using Primitive Driving Patterns With Bayesian Nonparametric Approaches}},
url = {http://arxiv.org/abs/1708.08986},
year = {2017}
}
@article{Kennela,
author = {Kennel, Ing Ralph and Schmid, Michael},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/05{\_}Abschlussarbeiten/MA{\_}2017{\_}MSchmid{\_}Modellbasierte Fahrzeugdynamik und Fahrerverhaltenspr{\"{a}}diktion Anahnd der Fahrumgebung.pdf:pdf},
title = {{Modellbasierte Fahrzeugdynamik-und Fahrerverhaltenspr{\"{a}}diktion anhand der Fahrumgebung}}
}
@article{Doktoringenieur1982,
author = {Doktoringenieur, Grades and Prof, Magdeburg Gutachter and Prof, Bernd Michaelis and Prof, Ulrich Jumar and Brandmeier, Thomas},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/02{\_}Dissertation/Diss{\_}2013{\_}Muehlfeld{\_}Fahrstiladaptive{\_}Auslegung{\_}integraler{\_}Sicherheitssysteme{\_}am{\_}Bsp{\_}reversiblen{\_}Gurtstraffers.pdf:pdf},
title = {{Fahrstiladaptive Auslegung integraler Sicherheitssysteme am Beispiel des reversiblen Gurtstraffers}},
year = {1982}
}
@article{GrundherrzuAltenthanundWeiyherhaus2010,
abstract = {Kurzfassung Die vorliegende Arbeit besch{\"{a}}ftigt sich mit Betriebsstrategievarianten f{\"{u}}r ein Hybridfahr-zeug, das in naher Zukunft in Serie produziert werden soll. Ziel der Untersuchungen ist es, einen geeigneten Algorithmus zu finden, der die Zielkonflikte zwischen den Anfor-derungen Kraftstoffeffizienz, Qualit{\"{a}}t des Fahrverhaltens und Komponentenbelastungen optimal aufl{\"{o}}st. Da geringe Entwicklungs-und Herstellkosten erstrebenswert sind, ist zudem die Komplexit{\"{a}}t der Algorithmen im Serienprozess ein wichtiges Bewertungskri-terium. F{\"{u}}r die Untersuchungen wird das ausgew{\"{a}}hlte Fahrzeug in einem Simulationsmodell abgebildet. Dazu werden in ein bestehendes Modell die Hybridkomponenten Getriebe, E-Maschinen mit Leistungselektronik und Hochvoltspeicher integriert und validiert. Alle Betriebsstrategieans{\"{a}}tze werden im Simulationsmodell umgesetzt und ausgewertet: Als Ausgangspunkt dient der Ansatz Online-Optimierung, da dieser unter allen derzeit umsetzbaren Algorithmen die h{\"{o}}chste Kraftstoffeffizienz erm{\"{o}}glicht. F{\"{u}}r die angestreb-ten Untersuchungen wird das Prinzip weiterentwickelt, um es sowohl auf das ausge-w{\"{a}}hlte Fahrzeug und Getriebe als auch auf das erweiterte Spektrum an Anforderungen anzupassen. So entsteht eine L{\"{o}}sung, die unter Einhaltung der Anforderungen an Fahr-verhalten und Komponentenbelastungen den niedrigsten Kraftstoffverbrauch erzielt. Ein Nachteil der erarbeiteten L{\"{o}}sung ist ihre hohe Komplexit{\"{a}}t. Ein weiterer Schritt ana-lysiert daher, welche Betriebspunkte die Online-Optimierung ausw{\"{a}}hlt und leitet aus den erkannten Mustern eine vereinfachte, erfahrungsbasierte Regelstrategie ab.},
author = {ohanne {Grundherr zu Altenthan und Weiyherhaus}},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/02{\_}Dissertation/Diss{\_}2010{\_}Ableitung heuristischen Betriebsstrategie fuer ein Hybridfahrzeug aus einer OnlineOptimierung {\_}TUM.pdf:pdf},
title = {{Ableitung einer heuristischen Betriebsstrategie f{\"{u}}r ein Hybridfahrzeug aus einer Online-Optimierung}},
url = {https://mediatum.ub.tum.de/doc/810285/810285.pdf},
year = {2010}
}
@article{Thrun2002,
abstract = {Predicting the binding mode of flexible polypeptides to proteins is an important task that falls outside the domain of applicability of most small molecule and protein−protein docking tools. Here, we test the small molecule flexible ligand docking program Glide on a set of 19 non-$\alpha$-helical peptides and systematically improve pose prediction accuracy by enhancing Glide sampling for flexible polypeptides. In addition, scoring of the poses was improved by post-processing with physics-based implicit solvent MM- GBSA calculations. Using the best RMSD among the top 10 scoring poses as a metric, the success rate (RMSD ≤ 2.0 {\AA} for the interface backbone atoms) increased from 21{\%} with default Glide SP settings to 58{\%} with the enhanced peptide sampling and scoring protocol in the case of redocking to the native protein structure. This approaches the accuracy of the recently developed Rosetta FlexPepDock method (63{\%} success for these 19 peptides) while being over 100 times faster. Cross-docking was performed for a subset of cases where an unbound receptor structure was available, and in that case, 40{\%} of peptides were docked successfully. We analyze the results and find that the optimized polypeptide protocol is most accurate for extended peptides of limited size and number of formal charges, defining a domain of applicability for this approach.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Thrun, Sebastian},
doi = {10.1145/504729.504754},
eprint = {arXiv:1011.1669v3},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2006{\_}PROBABILISTIC ROBOTICS{\_}Thrun{\_}Stanford{\_}Early Draft.pdf:pdf},
isbn = {9788578110796},
issn = {00010782},
journal = {Communications of the ACM},
number = {3},
pages = {1999--2000},
pmid = {25246403},
title = {{Probabilistic robotics}},
url = {http://portal.acm.org/citation.cfm?doid=504729.504754},
volume = {45},
year = {2002}
}
@article{Jeske2012,
abstract = {In recent years, a trend of using real-time traffic data for navigation has developed. Google Navigation and Waze, for instance, generate traffic data from movement profiles of smartphones. In this paper we tackle the question to which extent it is possible for Google and Waze to track the smartphone and its owner. Furthermore, we show how wireless access points and smartphones acting like wireless access points can be located around the world. In addition to the privacy issue, we examine whether the authenticity of traffic data can be guaranteed. We demonstrate in practice how hackers can take control of navigation systems and, in the case of a wide distribution of floating car data, can actively control the traffic flow. At the end we present a practical protocol preventing such attacks and at the same time preserving the user's privacy. The protocol has been implemented on different hardware platforms and benchmark results are given.},
author = {Jeske, Tobias},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2007{\_}Floating Car Data from Smartphones - What Google and Waze Know About You and How Hackers Can Control Traffic.pdf:pdf},
journal = {Media.Blackhat.Com},
pages = {12},
title = {{Floating Car Data from Smartphones: What Google and Waze Know About You and How Hackers Can Control Traffic}},
url = {https://media.blackhat.com/eu-13/briefings/Jeske/bh-eu-13-floating-car-data-jeske-wp.pdf},
year = {2012}
}
@book{Mitschke2014,
abstract = {Diese Neuausgabe wurde umfassend bearbeitet; dabei wurden die Grundlagen f{\"{u}}r die modernen aktiven Systeme in das Standardwerk {\"{u}}ber Antrieb und Bremsung, Schwingungen und Fahrverhalten integriert. Das Buch gibt einen Einblick in die Theorie des Gesamtfahrzeuges mit den auf das Kraftfahrzeug wirkenden St{\"{o}}rungen. Auch das Wechselspiel Fahrzeug/Insassen wird einbezogen. Die Theorie wird anwendbar durch eine F{\"{u}}lle von Fahrzeugdaten in Tabellen- oder Diagrammform und durch viele Rechenbeispiele. Die Diskussion der Ergebnisse f{\"{u}}hrt zu Vorschl{\"{a}}gen f{\"{u}}r die Verbesserung von Kraftfahrzeugen.},
author = {Mitschke, Manfred and Wallentowitz, Henning},
doi = {10.1007/978-3-658-05068-9},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2014{\_}Dynamik der Kraftfahrzeuge - 5 Auflage{\_}Mitschke und Wallentowitz.pdf:pdf},
isbn = {978-3-658-05067-2},
title = {{Dynamik der Kraftfahrzeuge}},
url = {http://link.springer.com/10.1007/978-3-658-05068-9},
year = {2014}
}
@article{Reghunath2014,
abstract = {Availability of road navigation data and route pattern details to the vehicle controller allows the use of predictive algorithms to obtain optimal performance from the vehicle. Conventionally, in the automated transmissions, gear position values are decided from predefined maps depending on the load demand and vehicle velocity at that instant. Due to the instantaneous decisions taken to get the gear position, minor changes in terrain sometimes might cause multiple unwanted gear shifts. The paper presents the concept of predictive optimal gear shifting strategy, utilizing the route information from the vehicle navigation system and vehicle state. Route terrain information is processed to analyze the vehicle behavior at future route gradient segments. Several categories of vehicle behavior are identified and at each decision point, the driving state is classified into one of these categories. Each},
author = {Reghunath, Sreenath K and Sharma, Deepak and Athreya, Ashwini S},
doi = {10.4271/2014-01-1743.Copyright},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2014{\_}Optimal Gearshift Strategy using Predictive Algorithm for Fuel Economy Improvement{\_}Reghunath{\_}SAE.pdf:pdf},
issn = {0148-7191},
journal = {SAE International},
number = {2014-01-1743},
title = {{Optimal Gearshift Strategy using Predictive Algorithm for Fuel Economy Improvement}},
year = {2014}
}
@book{Hyotyniemi2001,
abstract = {Multivariate statistical methods are powerful tools for analysis and manipulation of large data sets. This report introduces the most important statistical tools that can be used for multivariate regression in a general framework.},
author = {Hy{\"{o}}tyniemi, Heikki},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/06{\_}Skripte-Vorlesungen/Script{\_}2001{\_}MULTIVARIATE REGRESSION{\_}Helsinki{\_}University{\_}Heikki Hy{\"{o}}tyniemi.pdf:pdf},
isbn = {9512255871},
keywords = {canonical correlation analysis and regression,chemometrics,cluster analysis,component analysis,data analysis,factor analysis,independent,linear,multivariate statistical methods,neural networks,orthogonal least squares,partial least squares,principal component analysis and,regression,ridge regression,subspace identification},
pages = {1 -- 217},
title = {{Multivariate Regression Techniques and Tools}},
year = {2001}
}
@article{Rehder2016,
abstract = {To assure a safe, comfortable and especially a cooperative driving experience while driving semi, highly or even fully automated, anticipation of the driving behavior of other traffic participants is needed. Because of the amount of different traffic situations and influence factors on the task of driving, due to uncertainties in environmental sensor measurements, and as a consequence of variable and individual driving styles, probabilistic models in combination with machine learning techniques are often applied to learn driving behavior from data. In this paper, with the help of a simulator study, the driving behavior of a subject group is examined regarding their intention to change lane on highways. The simulator is set up as a partially automated driving system that takes discrete maneuver wishes as input (lane change left or lane change right). If the traffic situation permits it, the requested maneuvers is executed automatically by the system. This generates ground truth labels that are being used to train a lane change intention classifier. The results show that the approach is able to predict upcoming lane changes at an average of more than 3.5 seconds in advance.},
author = {Rehder, Tobias and Muenst, Wolfgang and Louis, Lawrence and Schramm, Dieter},
doi = {10.1109/ITSC.2016.7795661},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2016{\_}Learning Lane Change Intentions through Lane Contentedness Estimation from Demonstrated Driving{\_}0576.pdf:pdf},
isbn = {9781509018895},
journal = {IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC},
keywords = {Advanced Vehicle Safety Systems,Cooperative Techniques and Systems,Driver Assistance Systems},
pages = {893--898},
title = {{Learning Lane Change Intentions through Lane Contentedness Estimation from Demonstrated Driving}},
year = {2016}
}
@article{Berk2017,
author = {Berk, Mario and Straub, Daniel},
doi = {10.4271/2017-01-0050.Copyright},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2017{\_}Bayesian Test Design for Reliability Assessments of Safety-Relevant Environment Sensors Considering Dependent Failures{\_}MBerk{\_}MKroll{\_}AUDI.pdf:pdf},
title = {{Bayesian Test Design for Reliability Assessments of Safety- Relevant Environment Sensors Considering Dependent Failures Background : Reliability Assessment of Automotive Environment Perception}},
year = {2017}
}
@article{Macadam1996,
abstract = {This paper demonstrates the use of elementary neural networks for modelling and representing driver steering behaviour in path regulation control tasks. Areas of application include uses by vehicle simulation experts who need to model and represent specific instances of driver steeringcon- trol behaviour, potential on-board vehicle technologies aimed at representing and tracking driver steering control behaviour over time, and use by human factors specialists interested in representing or classifying specific families of driver steering behaviour. Example applications are shown for data obtained from a driver/vehicle numerical simulation, a basic driving simulator, and an experimental on-road test vehicle equipped with a camera and sensor processing system.},
author = {Macadam, Charles C. and Johnson, Gregory E.},
doi = {10.1080/00423119608968955},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}1996{\_}Application of Neural Networks and Preview Sensors for Representing Driver Steering Control Behaviour.pdf:pdf},
issn = {00423114},
journal = {Vehicle System Dynamics},
number = {1},
pages = {3--30},
title = {{Application of elementary neural networks and preview sensors for representing driver steering control behaviour}},
volume = {25},
year = {1996}
}
@article{Romera2016,
author = {Romera, E and Bergasa, L.{\~{}}M. and Arroyo, R},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2016{\_}Need Data for Driver Behaviour Analysis. Presenting the Public UAH-DriveSet{\_}0048.pdf:pdf},
isbn = {9781509018895},
journal = {Proc. of the 19th IEEE International Conference on Intelligent Transportation Systems},
keywords = {Data Mining and Data Analysis,Driver Assistance Systems,Sensing, Vision, and Perception},
pages = {387--392},
title = {{Need Data for Driver Behaviour Analysis? {\{}Presenting{\}} the Public {\{}UAH-DriveSet{\}}}},
year = {2016}
}
@article{Camera1972,
author = {Camera, A N Eye-mark and Usedriver, F O R and Studies, Behaviour},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}1972{\_}Technical Note - An eye-mark camera for use in driver behaviour studies.pdf:pdf},
number = {June 1971},
pages = {101--103},
title = {{Paper{\_}1972{\_}Technical Note - An eye-mark camera for use in driver behaviour studies}},
volume = {10},
year = {1972}
}
@article{Chains2018,
archivePrefix = {arXiv},
arxivId = {arXiv:1808.10705v1},
author = {Chains, Markov},
eprint = {arXiv:1808.10705v1},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2018{\_}Bayesian Classifier for Route Prediction with Markov Chains{\_}IBM{\_}ITSC2018{\_}Hawaii.pdf:pdf},
title = {{Bayesian Classifier for Route Prediction with}},
year = {2018}
}
@article{Yang2012,
abstract = {— Feature selection is of considerable importance in data mining and machine learning, especially for high dimensional data. In this paper, we propose a novel nearest neighbor-based feature weighting algorithm, which learns a feature weighting vector by maximizing the expected leave-one-out classification accuracy with a regularization term. The algorithm makes no parametric assumptions about the distribution of the data and scales naturally to multiclass problems. Experiments conducted on artificial and real data sets demonstrate that the proposed algorithm is largely insensitive to the increase in the number of irrelevant features and performs better than the state-of-the-art methods in most cases.},
author = {Yang, Wei and Wang, Kuanquan and Zuo, Wangmeng},
doi = {10.4304/jcp.7.1.161-168},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2012{\_}Neighborhood component Feature Selection for High-Dimensional Data{\_}Yang{\_}JournalofComputers.pdf:pdf},
issn = {1796203X},
journal = {Journal of Computers},
keywords = {Feature selection,Feature weighting,Nearest neighbor},
number = {1},
pages = {162--168},
title = {{Neighborhood component feature selection for high-dimensional data}},
volume = {7},
year = {2012}
}
@article{Munchen,
author = {M{\"{u}}nchen, Technische Universit{\"{a}}t and Walch, Florian and Walch, Florian and Cremers, Prof Daniel},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/05{\_}Abschlussarbeiten/MA{\_}2016{\_}Deep Learning for Image-Based Localization{\_}Walch{\_}TUM.pdf:pdf},
title = {{Master ' s Thesis in Informatics Deep Learning for Image-Based Localization Master ' s Thesis in Informatics Deep Learning for Image-Based Localization Deep Learning f{\"{u}}r bildbasierte Lokalisierung}}
}
@article{Ramalingam2013,
author = {Ramalingam, Geetha},
doi = {10.4271/2016-28-0223.Copyright},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2016{\_}Advanced driver assistance systems{\_}Paul{\_}SAE.pdf:pdf},
isbn = {9783939163374},
issn = {0148-7191},
journal = {Human Factors},
pages = {8--14},
title = {{Advanced Driver Assistance Systems}},
volume = {2013},
year = {2013}
}
@article{Kucukay,
author = {K{\"{u}}c{\"{u}}kay, F.},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/06{\_}Skripte-Vorlesungen/Script{\_}2006{\_}Mein Fahrzeug mein pers{\"{o}}nlicher Assistent{\_}Kuecuekay{\_}Vormittag.pdf:pdf},
title = {{Mein Fahrzeug - mein pers{\"{o}}nlicher Assistent}}
}
@book{Radke2013,
abstract = {Die vorliegende Arbeit besch{\"{a}}ftigt sich mit der energieoptimalen L{\"{a}}ngsf{\"{u}}hrung von Kraftfahrzeugen, die vorausschauend bekannte Streckendaten nutzt, um eine maximal energieeffiziente Fahrstrategie nach Fahrerwunsch zu realisieren. Zur L{\"{o}}sung dieses Optimierungsproblems wird ein ressourceneffizienter Algorithmus entwickelt und in einem eingebetteten Fahrerassistenzsystem zur automatisierten L{\"{a}}ngsf{\"{u}}hrung prototypisch zum Einsatz gebracht. Das System erzielt eine Kraftstoffeinsparung von etwa 10{\%}.},
author = {Radke, Tobias},
doi = {http://dx.doi.org/10.5445/KSP/1000035819},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/02{\_}Dissertation/Diss{\_}2013{\_}Energieoptimale L{\"{a}}ngsf{\"{u}}hrung v Kraftfahrzeugen durch Einsatz vorausschauender Fahrstrategien{\_}Radke{\_}KIT.pdf:pdf},
isbn = {9783731500698},
issn = {1869-6058},
keywords = {"Kraftstoffverbrauch,Betriebsstrategie,Fahrerassistenz,Fahrstrategie,L{\"{a}}ngsf{\"{u}}hrung,Optimalsteuerung",Pr{\"{a}}diktion},
title = {{Energieoptimale L{\"{a}}ngsf{\"{u}}hrung von Kraftfahrzeugen durch Einsatz vorausschauender Fahrstrategien}},
url = {http://digbib.ubka.uni-karlsruhe.de/volltexte/1000035819},
year = {2013}
}
@article{Albers2013,
author = {Albers, A and Heinrich, D and Brezger, F},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2015{\_}Methoden zum Vergleich und zur Kombination von Fahrermodellen (Lastuntersuchungen Fhzantrieb{\_}Heinrich.pdf:pdf},
pages = {1--12},
title = {{Methoden zum Vergleich und zur Kombination von Fahrer-modellen vor dem Hintergrund von Lastuntersuchungen im Fahrzeugantriebsstrang}},
year = {2013}
}
@book{Hutchison2007,
abstract = {P{\'{a}}g. 51 es una actualizaci{\'{o}}n del libro de Wasserman que hace el propio autor},
author = {Hutchison, David and Mitchell, John C},
doi = {10.1007/978-3-540-73133-7},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2007{\_}Statistical Network Analysis - Models, Issues, and New Directions.pdf:pdf},
isbn = {978-3-540-73132-0},
title = {{Statistical Network Analysis: Models, Issues, and New Directions}},
url = {http://link.springer.com/10.1007/978-3-540-73133-7},
volume = {4503},
year = {2007}
}
@article{Kucukay2010,
abstract = {Zentrales Element der Methodik ist der so genannte 3F-Parameterraum, der bereits in um- fangreichen Messungen erfasst wurde und dar{\"{u}}ber hinaus in der Simulation abgebildet wird. Die Achsen des 3F-Parameterraums werden durch die Eigenschaften von Fahrer, Fahrzeug und Fahrumgebung definiert.},
author = {K{\"{u}}{\c{c}}{\"{u}}kay, Ferit},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/06{\_}Skripte-Vorlesungen/Script{\_}2010{\_}3F-Methodik{\_}dt{\_}3F-Methode, Requirement Engineering (Anforderungsermittlung).pdf:pdf},
keywords = {3F-Methodik,Anforderungsermittlung,Fahrstrecke,Fahrweise,Fahrzeugbeladung,Kundenverhalten},
title = {{3F-Methode , Requirement Engineering ( Anforderungsermittlung ) Repr{\"{a}}sentative Anforderungen}},
year = {2010}
}
@article{Sander2010,
author = {Sander, Marcel and Meister, Thorsten and K{\"{u}}c{\"{u}}kay, Prof F},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Presentation{\_}2015{\_}HEV{\_}Symposium/Sander{\_}TU-Braunschweig{\_}HEV{\_}2015{\_}Vortrag.pdf:pdf},
number = {April},
pages = {1--39},
title = {{Identification of the driving behaviour with electric vehicles regarding power requirements and the interaction with the driving environment}},
year = {2010}
}
@article{,
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/06{\_}Skripte-Vorlesungen/Script{\_}2014{\_}Tutorial - Bayesian Filtering and Smoothing{\_}Saerkkae.pdf:pdf},
title = {{Tutorial : Bayesian Filtering and Smoothing}},
year = {2014}
}
@article{Lattemann2004,
abstract = {Predictive Cruise Control (PCC) is a system that enhances and works in combination with the existing Conventional Cruise Control. Based on elevation information captured in a 3D map and a predictive algorithm, PCC allows the vehicle speed to vary around the cruise control set speed within a defined speed band in an effort to reduce fuel consumption. As fuel consumption is a major portion of a truck's life cycle costs (LCC) and cruise control is used extensively in the United States and Canada, PCC can significantly reduce the truck's LCC.},
author = {Lattemann, F and Neiss, K and Terwen, S and Connolly, T},
doi = {10.4271/2004-01-2616},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2004{\_}The Predictive Cruise Control – A System to Reduce Fuel Consumption of Heavy Duty Trucks{\_}Lattemann{\_}SAE.pdf:pdf},
isbn = {0768013194},
issn = {0096-736X},
journal = {SAE transactions},
number = {724},
pages = {139--146},
title = {{The predictive cruise control: A system to reduce fuel consumption of heavy duty trucks}},
url = {http://cat.inist.fr/?aModele=afficheN{\&}cpsidt=16972791},
volume = {113},
year = {2004}
}
@article{Hamann2015,
author = {Hamann, Harry},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Presentation{\_}2015{\_}HEV{\_}Symposium/Hamann{\_}Volkswagen{\_}HEV{\_}2015{\_}Vortrag.pdf:pdf},
title = {{Development of an Energy Management Strategy for a Series-Parallel Hybrid Introduction Series-Parallel Hybrid Energy Management Strategy Drivetrain Efficiency Evaluations}},
year = {2015}
}
@article{Bauer2010,
author = {Bauer, Colin and Gonter, Mark and Rojas, R},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2010{\_}Fahrerspezifische Analyse des Fahrverhaltens zur Parametrierung aktiver Sicherheitssysteme.pdf:pdf},
journal = {Sicherheit durch Fahrerassistenz},
keywords = {parametrierung aktiver sicherheitssysteme,rerspezifische analyse des fahrverhaltens,zur},
pages = {1--11},
title = {{Fahrerspezifische Analyse des Fahrverhaltens zur Parametrierung aktiver Sicherheitssysteme}},
url = {http://www.ftm.mw.tum.de/uploads/media/28{\_}bauer.pdf},
year = {2010}
}
@article{McNew2012,
abstract = {We present a data-driven method for predicting driver behavior of sufficiently low complexity to be implemented in an automotive context. In this work, we develop a method to predict the driver's intended cruising speed as they launch from a stopped position. Our goal is to make this prediction in spite of highly modal driving by the driver (i.e. they drive in either an aggressive or relaxed manner). To reduce complexity and improve prediction, we do not try to calculate the hidden variables causing the modal driving or try to predict the vehicle's entire trajectory through filtering. We instead formulate a supervised learning problem to estimate the cruise speed directly. First we segment the trajectories into launch, cruise, and deceleration behavioral segments based on vehicle state and environment. Within each of these behavioral segments, we extract a low dimensional feature set which can be used to learn a model for predicting cruise speed under modal driving. In particular, a dynamical model is fit to the launch sequence data and then the coefficients of the model are used as regressors for a Nadaraya-Watson estimator. The method is implemented real-time in a vehicle, and results show that for a single road type, prediction error is significantly lower than other standard prediction methods. A key point of this paper is that our simpler prediction technique can yield good prediction results over long time scales with low complexity by predicting goal states directly rather than predicting the evolution of the vehicle state in time. {\&}copy; 2012 IEEE.},
author = {McNew, John Michael},
doi = {10.1109/ITSC.2012.6338762},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2012{\_}Predicting Cruising Speed through Data-driven Driver Modeling{\_}ITSC{\_}IEEE.pdf:pdf},
isbn = {9781467330640},
issn = {2153-0009},
journal = {IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC},
keywords = {Data Mining and Analysis,Driver Assistance Systems,Human Factors},
pages = {1789--1796},
title = {{Predicting cruising speed through data-driven driver modeling}},
year = {2012}
}
@article{Manz,
author = {Manz, Holger and Bruna, Michal and Thiel, Michael and Ag, Volkswagen and E-mobilit{\"{a}}t, Einleitung and Schwelle, Der},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Presentation{\_}2015{\_}HEV{\_}Symposium/Manz{\_}Volkswagen{\_}HEV{\_}2015{\_}Vortrag.pdf:pdf},
title = {{Batteriesysteme “ Made in Braunschweig ” - Aspekte einer neuen Technologie f{\"{u}}r einen Fahrwerkstandort}}
}
@book{Treiber2013,
abstract = {This textbook provides a comprehensive and instructive coverage of vehicular traffic flow dynamics and modeling. It makes this fascinating interdisciplinary topic, which to date was only documented in parts by specialized monographs, accessible to a broad readership. Numerous figures and problems with solutions help the reader to quickly understand and practice the presented concepts. This book is targeted at students of physics and traffic engineering and, more generally, also at students and professionals in computer science, mathematics, and interdisciplinary topics. It also offers material for project work in programming and simulation at college and university level. The main part, after presenting different categories of traffic data, is devoted to a mathematical description of the dynamics of traffic flow, covering macroscopic models which describe traffic in terms of density, as well as microscopic many-particle models in which each particle corresponds to a vehicle and its driver. Focus chapters on traffic instabilities and model calibration/validation present these topics in a novel and systematic way. Finally, the theoretical framework is shown at work in selected applications such as traffic-state and travel-time estimation, intelligent transportation systems, traffic operations management, and a detailed physics-based model for fuel consumption and emissions.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Treiber, M. and Kesting, A.},
booktitle = {Traffic Flow Dynamics: Data, Models and Simulation},
doi = {10.1007/978-3-642-32460-4},
eprint = {arXiv:1011.1669v3},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2010{\_}Verkehrsdynamik und -simulation{\_}Treiber{\_}Kesting.pdf:pdf},
isbn = {9783642324604},
issn = {15529924},
pages = {1--503},
pmid = {21831745},
title = {{Traffic flow dynamics: Data, models and simulation}},
url = {http://link.springer.com/10.1007/978-3-642-05228-6},
year = {2013}
}
@article{Sagberg2015,
abstract = {Objective: The aim of this study was to outline a conceptual framework for understanding driving style and, on this basis, review the state-of-the-art research on driving styles in relation to road safety.Background: Previous research has indicated a relationship between the driving styles adopted by drivers and their crash involvement. However, a comprehensive literature review of driving style research is lacking.Method: A systematic literature search was conducted, including empirical, theoretical, and methodological research, on driving styles related to road safety.Results: A conceptual framework was proposed whereby driving styles are viewed in terms of driving habits established as a result of individual dispositions as well as social norms and cultural values. Moreover, a general scheme for categorizing and operationalizing driving styles was suggested. On this basis, existing literature on driving styles and indicators was reviewed. Links between driving styles and road safety were identified and individual and sociocultural factors influencing driving style were reviewed.Conclusion: Existing studies have addressed a wide variety of driving styles, and there is an acute need for a unifying conceptual framework in order to synthesize these results and make useful generalizations. There is a considerable potential for increasing road safety by means of behavior modification. Naturalistic driving observations represent particularly promising approaches to future research on driving styles.Application: Knowledge about driving styles can be applied in programs for modifying driver behavior and in the context of usage-based insurance. It may also be used as a means for driver identification and for the development of driver assistance systems. },
author = {Sagberg, Fridulv and Selpi and {Bianchi Piccinini}, Giulio Francesco and Engstr{\"{o}}m, Johan},
doi = {10.1177/0018720815591313},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/06{\_}Paper/2019{\_}03{\_}Ilmenau{\_}DE/01{\_}Literature/2015 - A Review of Research on Driving Styles and Road Safety.pdf:pdf},
isbn = {0018-7208},
issn = {15478181},
journal = {Human Factors},
keywords = {driver behavior,driver profiling,driving habit,driving pattern},
number = {7},
pages = {1248--1275},
pmid = {26130678},
title = {{A review of research on driving styles and road safety}},
volume = {57},
year = {2015}
}
@article{Ag,
author = {Ag, Volkswagen},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Presentation{\_}2015{\_}HEV{\_}Symposium/Ulrich{\_}Volkswagen{\_}HEV{\_}2015{\_}Vortrag.pdf:pdf},
title = {{Der virtuelle Pr{\"{u}}fstand Simulationsmodell eines Elektroantriebs-Pr{\"{u}}fstandes zur Absicherung der Messdateng{\"{u}}te in der Applikation Beitrag reduzieren Messtechnik- Bewertungs-}}
}
@article{Fotouhi2011,
author = {Fotouhi, A and Jannatipour, M},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2011{\_}vehiclesVelocityTimeSeriesPredictionUsingNeuralNetworks{\_}fotouhi.pdf:pdf},
keywords = {neural networks,prediction,time series,vehicle,velocity},
number = {1},
pages = {21--28},
title = {{Paper{\_}2011{\_}vehiclesVelocityTimeSeriesPredictionUsingNeuralNetworks{\_}fotouhi}},
volume = {1},
year = {2011}
}
@book{Fahrmeir,
author = {Fahrmeir, Ludwig},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2009{\_}Statistik und ihre Anwendungen - Regression - Modelle, Methoden und Anwendungen.pdf:pdf},
isbn = {9783642343322},
title = {{Ludwig Fahrmeir Thomas Kneib Stefan Lang Brian Marx}}
}
@article{Kretschmer2006,
abstract = {Unmittelbar vor einem {\"{U}}berholvorgang muss der Fahrzeugf{\"{u}}hrer in k{\"{u}}rzester Zeit eine Vielzahl von Informationen erfassen oder absch{\"{a}}tzen und geeignet verarbeiten, um daraus eine {\"{U}}berholentscheidung ableiten zu k{\"{o}}nnen. Gerade unge{\"{u}}bte Fahrer sind innerhalb dieses Entscheidungsprozesses {\"{u}}berfordert, was h{\"{a}}ufig zu kritischen Fahrsituationen f{\"{u}}hrt. Das hier vorgestellte Fahrerassistenzsystem erkennt aus Fahrzeugdaten, wie z. B. Lenkwinkel, L{\"{a}}ngsbeschleunigung und Abstand zum voraus fahrenden Fahrzeug, den Beginn eines {\"{U}}berholvorgangs und pr{\"{a}}diziert daraus die Dauer dieses Fahrman{\"{o}}vers. Die Erkennung des {\"{U}}berholbeginns sowie die Vorhersage des Fahrerverhaltens geschehen dabei zu einem Zeitpunkt, bei dem sich das Fahrzeug noch nicht in einer verkehrskritischen Situation befindet, so dass der Fahrzeugf{\"{u}}hrer bei einer erkannten Verkehrsgef{\"{a}}hrdung fr{\"{u}}hzeitig gewarnt werden kann.},
author = {Kretschmer, M. and K{\"{o}}nig, L. and Neubeck, J. and Wiedemann, J.},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2006{\_}Erkennung und Pr{\"{a}}diktion des Fahrerverhaltens w{\"{a}}hrend eines {\"{U}}berholvorgangs{\_}Uni Stuttgart.pdf:pdf},
journal = {2. Tagung Aktive Sicherheit durch Fahrerassistenz},
title = {{Erkennung und Pr{\"{a}}diktion des Fahrerverhaltens w{\"{a}}hrend eines {\"{U}}berholvorgangs}},
year = {2006}
}
@article{Blome2015,
author = {Blome, Frank},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Presentation{\_}2015{\_}HEV{\_}Symposium/Franke{\_}Blome{\_}Deutsche{\_}ACCUmotive{\_}HEV{\_}2015{\_}Vortrag.pdf:pdf},
title = {{Current status and future challenges for automotive energy storage production}},
year = {2015}
}
@inproceedings{Reif2007,
author = {Reif, Konrad},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2007{\_}Fahrzustandsschaetzung{\_}auf{\_}Basis{\_}eines{\_}nichtlinearen{\_}Zweispurmodells.pdf:pdf},
pages = {682--687},
title = {{auf Basis eines nichtlinearen}},
volume = {109},
year = {2007}
}
@article{Trautmann2010,
abstract = {ur detaillierten Untersuchung der Wirkung von Fahrzeug-Sicherheitsfunktionen in kritischen Fahrsituationen und der Ver{\"{a}}nderung des F ahrzeugzustandes {\"{u}}ber die Fahrzeuglebensdauer wird gegenw{\"{a}}rtig durch die FSD Fahrzeugsystemdaten GmbH ein mehrj{\"{a}}hriger Feldtest mit ca. 2000 Probanden vorbereitet (DDS 21 - Defect Dete ction Study). Ein zentrales Element f{\"{u}}r das hierzu neu entwickelte Datenaufzeichnungsger{\"{a}}t ist die fahrerindividuelle Adaption von Ausl{\"{o}}sekriterien. Damit soll einerseits durch Vermeidung unpassender Ausl{\"{o}}sungen der, bedingt du rch die Videoaufzeichnung der Gesamtsituation beschr{\"{a}}nkte, Speicherplatz optimal gen utzt werden. Andererseits soll aber sichergestellt sein, dass auch f{\"{u}}r Fahrer mit "geringen" Schwellen die relevanten Situat ionen nicht durch zu hohe Grenzwerte {\"{u}}bersehen werden. Gel{\"{o}}st wird dieser Zielkonflikt durch die Nutzung von Regelkreisen f{\"{u}}r die relevanten physikalischen Gr{\"{o}}{\ss}en der L{\"{a}}ngs- und Querdynamik. Durch Vorgabe eines Sollwertes f{\"{u}}r eine bestimmte Anzahl an Vergleichsereignissen (z. B. Bremsungen) kann anhand des Vergleichs mit dem Istwert eine Adaption der f{\"{u}}r die Ausl{\"{o}}sung verwendeten Schwellwerte, innerhalb fahrphysikalisch sinnvoller Grenzen, unter Verwendung eines konventionellen Regleransatzes erfolgen. Die Herausforderung f{\"{u}}r die Entwicklung besteht dann in der Wahl geeigneter Reglerparameter. Um diese abzuleiten und auch Erfahrungen mit der Methodik zu gewinnen, erfolgten einst{\"{u}}ndige Testfahrten im Realverkehr mit 13 Personen. Dabei konnten relevante Unterschiede zwischen den Fahrern eindeutig ermittelt werden. Neben der Nutzu ng f{\"{u}}r das Aufzeichnungsger{\"{a}}t liefern diese Daten auch wichtige Hinweise f{\"{u}}r die fahreradaptive Auslegung von Fahrerassistenzsystemen.},
author = {Trautmann, Toralf and M{\"{u}}ller, Burkhard and Staffetius, Tino and B{\"{o}}nninger, J{\"{u}}rgen and van Calker, J{\"{o}}rg},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2010{\_}Fahrerindividuelle Erkennung von fahrdynamischen Grenzwerten{\_}TU Dresden.pdf:pdf},
journal = {T{\"{U}}V-S{\"{u}}d Tagung - Sicherheit durch Fahrerassistenz},
number = {April},
pages = {9},
title = {{Fahrerindividuelle Erkennung von fahrdynamischen Grenzwerten}},
url = {http://mediatum.ub.tum.de/doc/1142254/1142254.pdf},
volume = {4},
year = {2010}
}
@article{Ziegmann2017,
abstract = {Driver behavior is a key factor in vehicle energy demand calculation
required for vehicle optimization strategies and range calculations of
electric vehicles. Thereby the vehicle speed resulting from the driver
demand has a major influence on the energy consumption.
Thus, if the individual speed profile of a driver can be predicted
accurately, the energy demand of a vehicle for a given route can be
estimated, enabling better range calculation and optimization
strategies. However, the driver behavior depends on multidimensional
input factors, varies from driver to driver and can change over time.
In this paper a learning approach is proposed to predict the individual
speed profile. This approach takes environmental influences on the
driver behavior into account. Different artificial neural network models
and a Kernel regression approach for driver velocity prediction are
investigated. The learned models are evaluated on real drive data from
different drivers on a specified route. Results show that Long
Short-term Memory networks can predict the driver behavior very
accurately, leading to small prediction error.},
author = {Ziegmann, Johannes and Shi, Jieqing and Schnorer, Tobias and Endisch, Christian},
doi = {10.1109/IVS.2017.7995770},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2017{\_}Analysis of Individual Driver Velocity Prediction Using Data-Driven Driver Models with Environmental Features{\_}IV2017{\_}JZ.pdf:pdf},
isbn = {9781509048045},
issn = {1931-0587},
journal = {IEEE Intelligent Vehicles Symposium, Proceedings},
number = {Iv},
pages = {517--522},
title = {{Analysis of individual driver velocity prediction using data-driven driver models with environmental features}},
year = {2017}
}
@book{Bishop2006,
abstract = {The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications. This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory. The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information. Christopher M. Bishop is Deputy Director of Microsoft Research Cambridge, and holds a Chair in Computer Science at the University of Edinburgh. He is a Fellow of Darwin College Cambridge, a Fellow of the Royal Academy of Engineering, and a Fellow of the Royal Society of Edinburgh. His previous textbook "Neural Networks for Pattern Recognition" has been widely adopted. Coming soon: *For students, worked solutions to a subset of exercises available on a public web site (for exercises marked "www" in the text) *For instructors, worked solutions to remaining exercises from the Springer web site *Lecture slides to accompany each chapter *Data sets available for download},
author = {Bishop, Christopher M.},
edition = {1},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2006{\_}Pattern Recognition and Machine Learning{\_}Bishop.pdf:pdf},
isbn = {978-1-4939-3843-8},
issn = {978-0-387-31073-2},
keywords = {Machine Learning},
mendeley-tags = {Machine Learning},
pages = {XX, 738},
publisher = {Springer-Verlag New York},
title = {{Pattern Recognition and Machine Learning}},
url = {https://www.springer.com/de/book/9780387310732},
year = {2006}
}
@article{Lin2014,
abstract = {With the help of various positioning tools, individuals' mobility behaviors are being continuously captured from mobile phones, wireless networking devices and GPS appliances. These mobility data serve as an important foundation for understanding individuals' mobility behaviors. For instance, recent studies show that, despite the dissimilarity in the mobility areas covered by individuals, there is high regularity in the human mobility behaviors, suggesting that most individuals follow a simple and reproducible pattern. This survey paper reviews relevant results on uncovering mobility patterns from GPS datasets. Specially, it covers the results about inferring locations of significance for prediction of future moves, detecting modes of transport, mining trajectory patterns and recognizing location-based activities. The survey provides a general perspective for studies on the issues of individuals' mobility by reviewing the methods and algorithms in detail and comparing the existing results on the same issues. Several new and emergent issues concerning individuals' mobility are proposed for further research. {\textcopyright} 2013 Elsevier B.V. All rights reserved.},
author = {Lin, Miao and Hsu, Wen Jing},
doi = {10.1016/j.pmcj.2013.06.005},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2013{\_}Mining GPS data for mobility patterns - A survey.pdf:pdf},
isbn = {1574-1192},
issn = {15741192},
journal = {Pervasive and Mobile Computing},
keywords = {GPS data,Mobile computing,Mobility pattern,Ubiquitous computing},
pages = {1--16},
publisher = {Elsevier B.V.},
title = {{Mining GPS data for mobility patterns: A survey}},
url = {http://dx.doi.org/10.1016/j.pmcj.2013.06.005},
volume = {12},
year = {2014}
}
@article{Schattenberg2002,
author = {Schattenberg, Kay},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/02{\_}Dissertation/Diss{\_}2002{\_}Phil{\_}Fahrzeugf{\"{u}}hrung und gleichzeitige Nutzung von Fahrerassistenzsystemen und Fahrerinformationssystemen{\_}Aachen.pdf:pdf},
title = {{Fahrzeugf{\"{u}}hrung und gleichzeitige Nutzung von Fahrerassistenz- und Fahrerinformationssystemen Untersuchungen zur sicherheitsoptimierten Gestaltung und}},
year = {2002}
}
@article{Vehicles,
author = {Vehicles, Hybrid and Sattler, Martin and Wurzberger, Philip},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Presentation{\_}2015{\_}HEV{\_}Symposium/Sattler{\_}Schaeffler{\_}HEV{\_}2015{\_}Vortrag.pdf:pdf},
title = {{Electric Axles for Electric and Hybrid Vehicles}}
}
@article{Lass2014,
abstract = {Zunehmende Vernetzung, Konvergenz und Integration lassen Bedrohungen der Standard-IT auch in der Industrie- und Automatisierungstechnik an Bedeutung gewinnen. Gleichzeitig sind klassische Methoden der Informationssicherheit nicht ohne Weiteres auf die Fabrik zu {\"{u}}bertragen. Spezifische Systeme, Anwendungen und Prozesse erfordern Modifikationen, nicht zuletzt konzeptioneller Art. Das Vorgehensmodell „IT-Grundschutz“ des Bundesamtes f{\"{u}}r Sicherheit in der Informationstechnik (BSI) kann hier mit zus{\"{a}}tzlicher Anpassungsarbeit eine Grundabdeckung bieten. Y3  - 16.05.2013},
author = {Lass, Sander and Fuhr, David},
doi = {10.1007/978-3-658-04019-2},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2014{\_}Assistenzsystem f{\"{u}}r mehr Kraftstoffeffizienz{\_}Porsche.pdf:pdf},
isbn = {9783658040192},
issn = {18688519},
journal = {Productivity Management},
keywords = {ISMS,ISO 27001,IT security,Security management},
number = {3},
pages = {13--16},
title = {{IT-sicherheit in der fabrik}},
volume = {19},
year = {2014}
}
@article{Brundell-Freij2005,
abstract = {Driving patterns (i.e., speed, acceleration and choice of gears) influence exhaust emissions and fuel consumption. The aim here is to obtain a better understanding of the variables that affect driving patterns, by determining the extent they are influenced by street characteristics and/or driver-car categories. A data set of over 14,000 driving patterns registered in actual traffic is used. The relationship between driving patterns and recorded variables is analysed. The most complete effect is found for the variables describing the street environment: occurrence and density of junctions controlled by traffic lights, speed limit, street function and type of neighbourhood. A fairly large effect is found for car performance, expressed in terms of the power-to-mass ratio. For elderly drivers, the average speed systematically decreases for all street types and stop time systematically increases on arterials. The results have implications for the assessment of environmental effects through appropriate street categorisation in emission models, as well as the possible reduction of environmental effects through better traffic planning and management, driver education and car design. {\textcopyright} 2005 Published by Elsevier Ltd.},
author = {Brundell-Freij, Karin and Ericsson, Eva},
doi = {10.1016/j.trd.2005.01.001},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2005{\_}Influence-of-street-characteristics-driver-category-and-car-performance-on-urban-driving-patterns{\_}Transportation-Research.pdf:pdf},
isbn = {1361-9209},
issn = {13619209},
journal = {Transportation Research Part D: Transport and Environment},
keywords = {Car type,Driving behaviour,Speed behaviour,Traffic conditions},
number = {3},
pages = {213--229},
title = {{Influence of street characteristics, driver category and car performance on urban driving patterns}},
volume = {10},
year = {2005}
}
@article{Pardalos2010,
abstract = {We are developing a dual panel breast-dedicated PET system using LSO scintillators coupled to position sensitive avalanche photodiodes (PSAPD). The charge output is amplified and read using NOVA RENA-3 ASICs. This paper shows that the coincidence timing resolution of the RENA-3 ASIC can be improved using certain list-mode calibrations. We treat the calibration problem as a convex optimization problem and use the RENA-3s analog-based timing system to correct the measured data for time dispersion effects from correlated noise, PSAPD signal delays and varying signal amplitudes. The direct solution to the optimization problem involves a matrix inversion that grows order (n3) with the number of parameters. An iterative method using single-coordinate descent to approximate the inversion grows order (n). The inversion does not need to run to convergence, since any gains at high iteration number will be low compared to noise amplification. The system calibration method is demonstrated with measured pulser data as well as with two LSO-PSAPD detectors in electronic coincidence. After applying the algorithm, the 511keV photopeak paired coincidence time resolution from the LSO-PSAPD detectors under study improved by 57{\%}, from the raw value of 16.30.07 ns FWHM to 6.920.02 ns FWHM (11.520.05 ns to 4.890.02 ns for unpaired photons).},
archivePrefix = {arXiv},
arxivId = {1111.6189v1},
author = {Pardalos, Panos M.},
doi = {10.1080/10556781003625177},
eprint = {1111.6189v1},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2004{\_}Convex optimization{\_}Boyd{\_}CambridgeUniversity.pdf:pdf},
isbn = {9780521833783},
issn = {1055-6788},
journal = {Optimization Methods and Software},
month = {jun},
number = {3},
pages = {487--487},
pmid = {20876008},
title = {{Convex optimization theory}},
url = {https://web.stanford.edu/{~}boyd/cvxbook/bv{\_}cvxbook.pdf http://www.tandfonline.com/doi/abs/10.1080/10556781003625177},
volume = {25},
year = {2010}
}
@book{Schauffele2013,
abstract = {Seit Anfang der 1970er Jahre ist die Entwicklung von Kraftfahrzeugen gepr{\"{a}}gt von einem rasanten Anstieg des Einsatzes von Elektronik und Software. Dieser Trend h{\"{a}}lt bis heute an und wird getrieben von steigenden Kunden- und Umweltanforderungen. Nahezu alle Funktionen des Fahrzeugs werden inzwischen elektronisch gesteuert, geregelt oder {\"{u}}berwacht. Die Realisierung von Funktionen durch Software bietet einzigartige Freiheitsgrade beim Entwurf. In der Fahrzeugentwicklung m{\"{u}}ssen jedoch Randbedingungen wie hohe Zuverl{\"{a}}ssigkeits- und Sicherheitsanforderungen, lange Produktlebenszyklen, begrenzte Kostenrahmen, kurze Entwicklungszeiten und zunehmende Variantenvielfalt ber{\"{u}}cksichtigt werden. In diesem Spannungsfeld steht Automotive Software Engineering. Dieses Fachbuch enth{\"{a}}lt die Grundlagen sowie zahlreiche Anregungen und praktische Beispiele zu Prozessen, Methoden und Werkzeugen, die zur sicheren Beherrschbarkeit von elektronischen Systemen und Software im Fahrzeug beitragen. Die 4. Auflage wurde hinsichtlich des AUTOSAR-Standards {\"{u}}berarbeitet und aktualisiert.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Sch{\"{a}}uffele, J{\"{o}}rg and Zurawka, Thomas},
doi = {10.1007/978-3-8348-2470-7},
eprint = {arXiv:1011.1669v3},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2013{\_}Automotive Software Engineering - Grundlagen, Prozesse, methoden und Werkzeuge effizient einsetzen - 5 Auflage.pdf:pdf},
isbn = {978-3-8348-2469-1},
issn = {1098-6596},
keywords = {den raschen und sicheren,die arbeitswei-,die komplexe technik heutiger,einen immer gr{\"{o}}{\ss}er,kraftfahrzeuge und motoren macht,mtz-fachbuch,notwendig,se von komponenten oder,systemen zu verstehen,um die funktion und,werdenden fundus an informationen,zugriff auf},
pages = {346},
pmid = {25246403},
title = {{Automotive Software Engineering}},
url = {http://download.springer.com.eaccess.ub.tum.de/static/pdf/832/bok{\%}253A978-3-8348-2470-7.pdf?auth66=1392984831{\_}edb6aaa5ebde5e7ff1deb9b1c03317d2{\&}ext=.pdf},
year = {2013}
}
@article{Schneiter,
author = {Schneiter, Mani and Vehicles, Electric},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Presentation{\_}2015{\_}HEV{\_}Symposium/Schneiter{\_}Ballard{\_}HEV{\_}2015{\_}Vortrag.pdf:pdf},
title = {{The Case for Heavy Duty Fuel Cells}}
}
@article{Fahrzeugsysteme,
author = {Fahrzeugsysteme, Fachgebiet and Maschinen, Fachgebiet Elektrische and Leistungselektronik, Fachgebiet and Antriebstechnik, Elektrische},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Presentation{\_}2015{\_}HEV{\_}Symposium/Kuhl{\_}Uni-Kassel{\_}HEV{\_}2015{\_}Vortrag.pdf:pdf},
title = {{Kalorimetrische Wirkungsgradmessungen A New Approach to Calorimetric Efficiency Measurements for Optimizing Battery Electric Vehicle Drives}}
}
@article{Liu2015,
abstract = {This paper presents a new way to evaluate vehicle speed profile aggressiveness, quantify it from the perspective of the rapid speed fluctuations, and assess its impact on vehicle fuel economy. The speed fluctuation can be divided into two portions: the large-scale low frequency speed trace which follows the ongoing traffic and road characteristics, and the small-scale rapid speed fluctuations normally related to the driver's experience, style and ability to anticipate future events. The latter represent to some extent the driver aggressiveness and it is well known to affect the vehicle energy consumption and component duty cycles. Therefore, the rapid speed fluctuations are the focus of this paper. Driving data collected with the GPS devices are widely adopted for study of real-world fuel economy, or the impact on electrified vehicle range and component duty cycles. However, the accompanying signal noise poses a challenge, and needs to be separated from realistic rapid speed fluctuations. Filtering is commonly used, but aggressive smoothing technique can lead to loss of useful driving information. In contrast, mild smoothing technique can lead to under-filtering and inclusion of redundant information. The main contribution of this paper is a proposed metric, denoted as “Ripple Aggressiveness”, to quantify the rapid speed fluctuations over a drive cycle based on the Fourier analysis. This metric allows assessment of the filtering level, and detection of over-filtering, or under-filtering. The data used to develop and demonstrated the new technique are from the 2001∼2002 Southern California Household Travel Survey, 2010∼2012 California Household Travel Survey, 2001∼2005 Michigan Road Departure Crash Warning System Field Operational Tests, and EPA dynamometer drive schedules. The newly developed metric is correlated with fuel economy using the vehicle simulation, and the results show a positive correlation between Ripple Aggressiveness and vehicle fuel consumption. Hence the metric can also be used as a surrogate to quantify the driver aggressiveness. CITATION:},
author = {Liu, Zifan and Ivanco, Andrej and Filipi, Zoran},
doi = {10.4271/2015-01-1213},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2015{\_}Quantification of Drive Cycle's Rapid Speed Fluctuations using Fourier Analysis{\_}SAE.pdf:pdf},
issn = {2167-4205},
journal = {SAE International Journal of Alternative Powertrains},
number = {1},
title = {{Quantification of Drive Cycle's Rapid Speed Fluctuations Using Fourier Analysis}},
volume = {4},
year = {2015}
}
@article{Santin2016,
abstract = {Automotive cruise control systems are used to automatically maintain the speed of a vehicle at a desired speed set-point. It has been shown that fuel economy while in cruise control can be improved using advanced control methods. The objective of this paper is to validate an Adaptive Nonlinear Model Predictive Controller (ANLMPC) implemented in a vehicle equiped with standard production Powertrain Control Module (PCM). Application and analysis of Model Predictive Control utilizing road grade preview information has been reported by many authors, namely for commercial vehicles. The authors reported simulations and application of linear and nonlinear MPC based on models with fixed parameters, which may lead to inaccurate results in the real world driving conditions. The significant noise factors are namely vehicle mass, actual weather conditions, fuel type, etc. In the ANLMPC approach, the vehicle and fuel model parameters are adapted automatically, so accuracy of the prediction is ensured. The adaptation is implemented by a Recursive Least Square (RLS) algorithm and the numerical robustness is improved by adopting Bierman's implementation with exponential/directional forgetting, and with suitable RLS stopping condition. The ANLMPC has been validated in real world driving conditions running in a production PCM module of a Sport Utility Vehicle (SUV), showing up to 2.4{\%} fuel economy improvement in average compared to the production cruise controller with the same time of arrival. It has been confirmed that the ANLMPC can be run in a standard PCM module with single precision arithmetic, together with its other powertrain control functions.},
author = {Santin, Ondrej and Pekar, Jaroslav and Beran, Jaroslav and D'Amato, Anthony and Ozatay, Engin and Michelini, John and Szwabowski, Steven and Filev, Dimitar},
doi = {10.4271/2016-01-0155},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2016{\_}Cruise Controller with Fuel Optimization Based on Adaptive Nonlinear Predictive Control{\_}Santin{\_}SAE.pdf:pdf},
issn = {1946-4622},
journal = {SAE International Journal of Passenger Cars - Electronic and Electrical Systems},
number = {2},
pages = {2016--01--0155},
title = {{Cruise Controller with Fuel Optimization Based on Adaptive Nonlinear Predictive Control}},
url = {http://papers.sae.org/2016-01-0155/},
volume = {9},
year = {2016}
}
@book{Cramer2008a,
abstract = {Statistic},
author = {Cramer, Erhard and Kamps, Udo},
doi = {10.1007/978-3-540-77761-8},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2014{\_}Grundlagen der Wahrscheinlichkeitsrechnung und Statistik{\_}Cramer{\_}Springer.pdf:pdf},
isbn = {978-3-54077760-1},
pages = {325},
title = {{Grundlagen der Wahrscheinlichkeitsrechnung und Statistik - Ein Skript f{\"{u}}r Studierende der Informatik, der Ingenieur- und Wirtschaftswissenschaften}},
year = {2008}
}
@article{Xu2017,
author = {Xu, Donghao and Zhao, Huijing and Guillemard, Franck and Geronimi, Stephane},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2017{\_}Scene-Aware Driver State Understanding in Car-Following Behaviors{\_}IV2017{\_}Zhao.pdf:pdf},
isbn = {9781509048038},
number = {Iv},
pages = {1490--1496},
title = {{Scene-aware driver state understanding in car-following behaviors}},
year = {2017}
}
@article{Ekstrom2005,
author = {Ekstr{\"{o}}m, Pa},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/05{\_}Abschlussarbeiten/StudWork{\_}2005{\_}A Simulation Toolbox for Sensitivity Analysis.pdf:pdf},
journal = {Master's},
number = {February},
pages = {41},
title = {{Eikos A Simulation Toolbox for Sensitivity Analysis}},
url = {http://scholar.google.com/scholar?hl=en{\&}btnG=Search{\&}q=intitle:Eikos+A+Simulation+Toolbox+for+Sensitivity+Analysis{\#}0},
year = {2005}
}
@article{Vogele2016,
author = {V{\"{o}}gele, Ulrich and Endisch, Christian},
doi = {10.4271/2016-01-0121.Copyright},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2016{\_}Predictive Vehicle Velocity Control using Dynamic Traffic Information{\_}V{\"{o}}gele{\_}SAE.pdf:pdf},
journal = {SAE Technical Paper},
title = {{Predictive Vehicle Velocity Control Using Dynamic Traffic Information}},
year = {2016}
}
@article{Data2015,
author = {Data, Temporal and Spiegel, Stephan},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/02{\_}Dissertation/Diss{\_}2015{\_}Time Series Distance Measures - Stephan Spiegel - TU Berlin.pdf:pdf},
keywords = {Distanzma{\ss}e,Mustererkennung,Wissensextraktion,Zeitreihen,data mining,distance measures,machine learning,maschinelles Lernen
time series,pattern recognition},
title = {{Time Series Distance Measures: Segmentation, Classification, and Clustering of Temporal Data}},
year = {2015}
}
@article{Sharp2005,
abstract = {The article is about steering control of cars by drivers, concentrating on following the lateral profile of the roadway, which is presumed visible ahead of the car. It builds on previously published work, in which it was shown how the driver's preview of the roadway can be combined with the linear dynamics of a simple car to yield a problem of discrete-time optimal-linear-control-theory form. In that work, it was shown how an optimal ‘driver' of a linear car can convert the path preview sample values, modelled as deriving from a Gaussian white-noise process, into steering wheel displacement commands to cause the car to follow the previewed path with an attractive compromise between precision and ease.Recognizing that real roadway excitation is not so rich in high frequencies as white-noise, a low-pass filter is added to the system. The white-noise sample values are filtered before being seen by the driver. Numerical results are used to show that the optimal preview control is unaltered by the inclusion of the low-pass filter, whereas the feedback control is affected diminishingly as the preview increases. Then, using the established theoretical basis, new results are generated to show time-invariant optimal preview controls for cars and drivers with different layouts and priorities. Tight and loose controls, representing different balances between tracking accuracy and control effort, are calculated and illustrated through simulation. A new performance criterion with handling qualities implications is set up, involving the minimization of the preview distance required. The sensitivities of this distance to variations in the car design parameters are calculated. The influence of additional rear wheel steering is studied from the viewpoint of the preview distance required and the form of the optimal preview gain sequence. Path-following simulations are used to illustrate relatively high-authority and relatively low-authority control strategies, showing manoeuvring well in advance of a turn under appropriate circumstances.The results yield new insights into driver steering control behaviour and vehicle design optimization. The article concludes with a discussion of research in progress aimed at a further improved understanding of how drivers control their vehicles. },
author = {Sharp, R. S.},
doi = {10.1243/095440605X31896},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2005{\_}Driver steering control and a new perspective{\_}McRuer.PDF:PDF},
issn = {09544062},
journal = {Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science},
keywords = {Automobile,Driving,Handling qualities,Optimal control,Preview},
number = {10},
pages = {1041--1051},
title = {{Driver steering control and a new perspective on car handling qualities}},
volume = {219},
year = {2005}
}
@article{SimonAltmannshofer2015,
abstract = {Kurzfassung Es existieren verschiedene Sch{\"{a}}tzalgorithmen f{\"{u}}r die Bestimmung der Fahrzeugmasse und der Fahrwiderst{\"{a}}nde. Neben gew{\"{o}}hnlichen Verfahren werden spezielle Methoden vorgestellt, die auf bestimmte Eigenschaften des vorliegenden Problems angepasst sind. Um die Verfahren zu vergleichen, werden Fahrversuche durchgef{\"{u}}hrt und eine Methode zur Ermittlung von Referenzwerten f{\"{u}}r die Fahrwiderst{\"{a}}nde vorgestellt.},
author = {{Simon Altmannshofer}, Dipl.-Ing and Martin, Jan},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2015{\_}Robuste, onlinef{\"{a}}hige Sch{\"{a}}tzung von Fahrzeugmasse und Fahrwiderst{\"{a}}nden{\_}AUTOREG{\_}SimonAltmannshofer{\_}THI.pdf:pdf},
title = {{Robuste, onlinef{\"{a}}hige Sch{\"{a}}tzung von Fahrzeugmasse und Fahrwiderst{\"{a}}nden}},
year = {2015}
}
@book{Back2005,
author = {Back, Michael},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/02{\_}Dissertation/Diss{\_}2009{\_}Praediktive Antriebsregelung zum energieoptimalen Betrieb von Hybridfahrzeugen{\_}UniStuttgart.pdf:pdf},
isbn = {3866440316},
keywords = {Dynamische P,Hybridfahrzeuge,Pr{\"{a}}diktive Regelung},
title = {{Pr{\"{a}}diktive Antriebsregelung zum energieoptimalen Betrieb von Hybridfahrzeugen}},
year = {2005}
}
@article{Hackl2014,
author = {Hackl, Christoph},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/07{\_}Math/Formulary{\_}2014{\_}BDGEA - Bewegungssteuerung durch geregelte elektrische Antriebe{\_}CHackl.pdf:pdf},
pages = {1--8},
title = {{Grundlagen linearer Regelungstechnik}},
year = {2014}
}
@article{Miller2011,
author = {Miller, Kurt Tadayuki},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/02{\_}Dissertation/Diss{\_}2011{\_}Bayesian Nonparametric Latent Feature Models - Kurt T Miller.pdf:pdf},
keywords = {Engineering–Electrical Engineering and Computer Sc},
title = {{Bayesian Nonparametric Latent Feature Models}},
year = {2011}
}
@article{Filtering2013,
author = {Filtering, Bayesian},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2013{\_}Bayesian Filtering and Smoothing{\_}Cambridge{\_}Saerkkae.pdf:pdf},
title = {{Book{\_}2013{\_}Bayesian Filtering and Smoothing{\_}Cambridge{\_}Saerkkae}},
year = {2013}
}
@article{Altmannshofer2016,
author = {Altmannshofer, Simon and Endisch, Christian and Martin, Jan and Gerngross, Martin and Limbacher, Reimund},
doi = {10.1109/IVS.2016.7535443},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2016{\_}Robust Estimation of Vehicle Longitudinal Dynamics Parameters{\_}Altmannshofer.pdf:pdf},
isbn = {9781509018215},
journal = {IEEE Intelligent Vehicles Symposium, Proceedings},
number = {Iv},
pages = {566--571},
title = {{Robust estimation of vehicle longitudinal dynamics parameters}},
volume = {2016-Augus},
year = {2016}
}
@article{Mauermann2004,
author = {Mauermann, Dirk},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/05{\_}Abschlussarbeiten/DA{\_}2004{\_}DMauermann{\_}Echtzeitsimualtion detaillierter Fahr- und Antriebsstrangdynamik{\_}DLR.pdf:pdf},
journal = {Simulation},
title = {{Echtzeitsimulation detaillierter Fahr- und Antriebsstrangdynamik Vorwort}},
year = {2004}
}
@article{Ericsson2000,
abstract = {Although it is known that driving patterns strongly affect the emission of pollutants from vehicles, existing empirical knowledge about driving patterns is limited. The first-step in this project was to find relevant parameters for describing driving patterns. These served as a basis for investigating variations in such patterns. An experimental study was carried out to compare driving patterns between and within different street-types, drivers and traffic conditions. Data were analysed using general factorial analysis of variance. Driving patterns showed very significant differences between street type and driver, and these factors had significant impact on all the parameters employed. The effect of street type was generally higher than the driver effect. Average speed and deceleration levels were lower at peak hours compared to off-peak hours. Men had higher acceleration levels than women generally and specially on one street type. The study showed no major differences in average speed for gender except for one street type where men drove faster than women. The knowledge attained in this study may be a step towards a better knowledge of driving patterns and their variation, and may provide possibilities of changing driving patterns and thus exhaust emissions from vehicles. Knowledge about driving patterns is also an essential part in efforts to improve models to calculate emission from traffic in urban environment. (C) 2000 Elsevier Science Ltd. All rights reserved.},
author = {Ericsson, Eva},
doi = {10.1016/S1361-9209(00)00003-1},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2000{\_}Variability-in-urban-driving-patterns{\_}Transportation{\_}Lund.pdf:pdf},
isbn = {1361-9209},
issn = {13619209},
journal = {Transportation Research Part D: Transport and Environment},
keywords = {Driver behaviour,Exhaust emission,Speed profile characterisation,Street environment},
number = {5},
pages = {337--354},
title = {{Variability in urban driving patterns}},
volume = {5},
year = {2000}
}
@article{Rothengatter1992,
author = {Rothengatter, Talib},
doi = {10.1007/BF02684467},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}1982{\_}The effects of police surveillance and law enforcement on driver behaviour.pdf:pdf},
issn = {01433887},
journal = {Current Psychological Research},
number = {3},
pages = {349--358},
title = {{The effects of police surveillance and law enforcement on driver behaviour}},
volume = {2},
year = {1992}
}
@article{Figueroa2017,
author = {Figueroa, Nadia and Medina, Jose and Billard, Aude},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/06{\_}Skripte-Vorlesungen/Script{\_}2017{\_}Modeling time series with HMM.pdf:pdf},
title = {{Modeling time series with hidden Markov models}},
year = {2017}
}
@article{Mensing2011,
abstract = {To reduce fuel consumption in the transportation sector research focuses mainly on the development of more efficient drive train technologies and alternative drive train designs. Another and immidiately applicable way found to reduce fuel consumption in road vehicles is to change vehicle operation such that system efficiency is maximized. The concept of Eco-driving refers to the change of driver behavior in a fuel saving way or more generally in an energy saving way. In this paper system efficiency of a vehicle is optimized using a dynamic programming optimization approach. Given a drive cycle a so called `eco-drive cycle' is identified in which a vehicle performs the same distance with the same stops in equivalent time, while consuming less fuel.},
author = {Mensing, Felicitas and Trigui, Rochdi and Bideaux, Eric},
doi = {10.1109/VPPC.2011.6042993},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2011{\_}Vehicle trajectory optimization for application in eco-driving{\_}Mensing{\_}VPPC.pdf:pdf},
isbn = {978-1-61284-248-6},
issn = {Pending},
journal = {2011 IEEE Vehicle Power and Propulsion Conference},
pages = {1--6},
title = {{Vehicle trajectory optimization for application in ECO-driving}},
url = {http://ieeexplore.ieee.org/document/6042993/},
year = {2011}
}
@article{Fursov,
author = {Fursov, Mikhail and Okonechnikov, Konstantin},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/06{\_}Skripte-Vorlesungen/Script{\_}2016{\_}Windows batch tutorial.pdf:pdf},
title = {{About this tutorial}}
}
@article{Zambou2004,
abstract = {INVENT -STA L{\"{a}}ngsregler},
author = {Zambou, Nathan and Richert, Felix and Schlo{\ss}er, A. and Abel, D. and Sandk{\"{u}}hler, D.},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2004{\_}Modellgest{\"{u}}tzte Pr{\"{a}}diktive Regelung zur L{\"{a}}ngsf{\"{u}}hrung von Kraftfahrzeugen im niedrigen Geschwindigkeitsbereich.pdf:pdf},
issn = {00835560},
journal = {VDI Berichte},
number = {1828},
pages = {361--370},
title = {{Modellgest{\"{u}}tzte Pr{\"{a}}diktive Regelung zur L{\"{a}}ngsf{\"{u}}hrung von Kraftfahrzeugen im niedrigen Geschwindigkeitsbereich}},
year = {2004}
}
@article{Orner2015,
author = {Orner, Dipl Markus},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Presentation{\_}2015{\_}HEV{\_}Symposium/Orner{\_}FKFS{\_}HEV{\_}2015{\_}Vortrag.pdf:pdf},
title = {{Einsatzzweckbasierte Antriebsstrang- und Reichweitenauslegung f{\"{u}}r Elektrofahrzeuge}},
year = {2015}
}
@article{Quiroga1998,
abstract = {The paper describes a new methodology for performing travel time studies using global positioning system (GPS) and geographic information system (GIS) technologies. It documents the data collection, data reduction, and data reporting procedures, as well as analyses that illustrate the capabilities of the GPS/GIS methodology. The data collection procedure uses GPS receivers to automatically collect time, local coordinates, and speed at regular sampling periods, for example every one second. The data reduction procedure filters and aggregates GPS data to compute travel time and speed along highway segments. The data reporting procedure uses a GIS-based management information system to define queries, tabular reports, and color coded maps to document travel time data along these highway segments. These procedures have been implemented in three metropolitan areas in Louisiana: Baton Rouge, Shreveport, and New Orleans. In these cities, more than 180 000 segment travel time and speed records were derived between 1995 and 1996 from nearly three million GPS data points collected on 30 000 miles of travel time runs along 300 miles of urban highways. The three analyses included in the paper to assist in the process of understanding the GPS/GIS methodology are the following: segment lengths, sampling rates, and central tendency. The segment length analysis examines the effect of using different highway segment lengths and shows that relatively short segments (0.2-0.5 miles long) are needed to detect localized traffic effects. These traffic disturbances become visible only when segment lengths are at most half the length of the associated disturbance. This means that traditional link-based segments, which are typically longer than 0.5 miles, are not sufficient to characterize localized effects properly. The sampling rate analysis addresses the effect of collecting GPS data at different sampling periods and shows that for a segment to have GPS data associated with it, the GPS sampling period should be smaller than half the shortest travel time associated with the segment. The analysis also shows a tradeoff between sampling rates and segment speed reliability, and emphasizes the need for even shorter GPS sampling periods (1-2s) in order to minimize errors in the computation of segment speeds. The central tendency analysis compares harmonic mean speeds and median speeds and shows that median speeds are more robust estimators of central tendency than harmonic mean speeds.},
author = {Quiroga, Cesar A. and Bullock, Darcy},
doi = {10.1016/S0968-090X(98)00010-2},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}1998{\_}Travel time studies with global positioning and geographic information systems - an integrated methodology.pdf:pdf},
isbn = {0968-090X},
issn = {0968090X},
journal = {Transportation Research Part C: Emerging Technologies},
keywords = {1,accuracy,been used to measure,errors,geographic information systems,gis,global positioning systems,gps,harmonic mean speed,median speed,nition,problem de,sampling rates,segment speed,segmentation,the license plate technique,travel time,travel time studies,two techniques have traditionally},
number = {1-2},
pages = {101--127},
pmid = {754767},
title = {{Travel time studies with global positioning and geographic information systems: an integrated methodology}},
volume = {6C},
year = {1998}
}
@book{Kohler2014,
address = {Wiesbaden},
author = {Kohler, Tom P.},
doi = {10.1007/978-3-658-05012-2},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2014{\_}Pr{\"{a}}diktives Leistungsmanagement in Fahrzeugbordnetzen{\_}Kohler.pdf:pdf},
isbn = {978-3-658-05011-5},
publisher = {Springer Fachmedien Wiesbaden},
title = {{Pr{\"{a}}diktives Leistungsmanagement in Fahrzeugbordnetzen}},
url = {http://link.springer.com/10.1007/978-3-658-05012-2},
year = {2014}
}
@article{Pichler2015,
author = {Pichler, Benjamin and Riener, Andreas},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2015{\_}An Interactive Exploration Tool for Detailed E-Vehicle Range Analysis{\_}Pichler{\_}Riener.pdf:pdf},
isbn = {9783110443332},
journal = {Mensch und Computer 2015 - Workshopband},
keywords = {battery drain,car-sharing network,electric vehicles,exploration tool,range-influencing factors},
title = {{An Interactive Exploration Tool for Detailed E-Vehicle Range Analysis}},
year = {2015}
}
@article{Kennel,
author = {Kennel, Dr.-Ing Ralph and Shi, Jieqing},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/05{\_}Abschlussarbeiten/MA{\_}2017{\_}JShi{\_}Driver Behavior Prediction Using Machine Learning Algorithms.pdf:pdf},
title = {{Driver Behavior Prediction Using Machine Learning Algorithms}}
}
@article{Roesener2016,
author = {Roesener, Christian and Fahrenkrog, Felix and Uhlig, Axel and Eckstein, Lutz},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2016{\_}A Scenario-Based Assessment Approach for Automated Driving by Using Time Series Classification of Human-Driving Behaviour{\_}0104.pdf:pdf},
isbn = {9781509018895},
pages = {1360--1365},
title = {{A Scenario-Based Assessment Approach for Automated Driving by Using Time Series Classification of Human-Driving Behaviour}},
year = {2016}
}
@article{Grauers2016,
abstract = {Fuel saving potential of hybrid electric vehicles (HEVs) depends mainly on driving cycle and on sizing of powertrain components. Since a complete driving cycle, representing the whole life usage of a vehicle, is very long it is time consuming to predict the fuel saving potential, especially if many different types of HEVs should be analyzed. This paper presents an energy based method to quickly screen different types of HEVs for many and long driving cycles, in order to find interesting candidates for deeper and more accurate analysis. The technique used also allows to derive the fuel consumption analytically, and thus it is a very effective tool to explain the main fuel savings mechanisms of different types of HEVs and how they are influenced by the driving cycle. Some of the simplifications will lead to errors, but since the sign of the main errors are known it is still easy to draw several clear conclusions using the method.},
author = {Grauers, Anders and Upendra, Karthik},
doi = {10.1016/j.ifacol.2016.08.093},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2016{\_}Energy based method to analyse fuel saving potential of hybrid vehicles for different driving cycles{\_}IFAC{\_}Schweden.pdf:pdf},
issn = {24058963},
journal = {IFAC-PapersOnLine},
keywords = {Driving cycle,Energy,Fuel saving,Hybrid vehicles,Propulsion,Sizing},
number = {11},
pages = {641--648},
title = {{Energy based method to analyse fuel saving potential of hybrid vehicles for different driving cycles}},
volume = {49},
year = {2016}
}
@book{Schaffer2011,
abstract = {Users of multimodal systems have to choose between different interaction strategies. Thereby the number of interaction steps to solve a task can vary across the available modalities. In this work we introduce such a task and present empirical data that shows that strategy selection of users is affected by modality specific shortcuts. The system under investigation offered touch screen and speech as input modalities. We introduce a first version of an ACT-R model that uses the architectures-inherent mechanisms production compilation and utility learning to identify modality-specific shortcuts. A simple task analysis is implemented in declarative memory. The model reasonably accurate matches the human data. In our further work we will try to get a better fit by extending the model with further influence factors of modality selection like speech recognition errors. Further the model will be refined concerning the cognitive processes of speech production and touch screen interaction. {\textcopyright} 2011 Springer-Verlag.},
archivePrefix = {arXiv},
arxivId = {9780201398298},
author = {Schaffer, Stefan and Schleicher, Robert and M{\"{o}}ller, Sebastian},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-642-21799-9},
eprint = {9780201398298},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2011{\_}Digital Human Modeling - ICDHM2011{\_}Proceedings.pdf:pdf},
isbn = {978-3-642-21798-2},
issn = {03029743},
keywords = {Automated Usability Evaluation,Multimodal HCI,User Modeling},
pages = {337--346},
pmid = {4520227},
title = {{Digital Human Modeling}},
url = {http://link.springer.com/content/pdf/10.1007/978-3-642-21799-9.pdf{\%}5Cnhttp://www.scopus.com/inward/record.url?eid=2-s2.0-79960314544{\&}partnerID=tZOtx3y1},
volume = {6777},
year = {2011}
}
@article{Julier1996,
abstract = {In this paper we describe a new approach for generalised nonlinear filtering. We show that the technique is more accurate, more stable, and far easier to implement than an extended Kalman filter. Several examples are provided, including the application of the new filter to problems involving discontinuous functions.},
author = {Julier, Simon J. and Uhlmann, Jeffrey K.},
doi = {10.1371/journal.pone.0006243},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}1996{\_}A general method for approximating nonlinear transformations of probability distributions{\_}SJulier.pdf:pdf},
issn = {19326203},
journal = {Upublished},
pages = {1--27},
pmid = {19603074},
title = {{A general method for approximating nonlinear transformations of probability distributions}},
url = {http://www.smpp.northwestern.edu/savedLiterature/JulierUhlmannUnscented.pdf},
year = {1996}
}
@article{Deml2007,
abstract = {Um einen Beitrag zur Definition und Pr{\"{a}}diktion des Konstrukts „Fahrstil“ zu leisten, wurde zun{\"{a}}chst eine Literaturstudie und darauf aufbauend eine eigene empirische Studie durchge- f{\"{u}}hrt. Es konnte gezeigt werden, dass unabh{\"{a}}ngige Beobachter den Fahrstil einer Person relativ zuverl{\"{a}}ssig einsch{\"{a}}tzen k{\"{o}}nnen. Die Probanden assoziieren dabei mit einem „sportli- chen“ Fahrer eine technisch versierte Person, die h{\"{o}}here L{\"{a}}ngs- und Querbeschleuni- gungswerte zeigt, tendenziell dichter auff{\"{a}}hrt und h{\"{a}}ufiger {\"{u}}berholt. Au{\ss}erdem wurde das Beschleunigungsverhalten in singul{\"{a}}ren Verkehrssituationen (z.B. Ampelstart, Abfahren von Autobahnen) analysiert und M{\"{o}}glichkeiten zur situations{\"{u}}bergreifenden Pr{\"{a}}diktion des Fahr- stils aufgezeigt, die sowohl f{\"{u}}r die Fahrermodellierung als auch f{\"{u}}r die fahrstil-adaptive Aus- legung von Mensch-System-Schnittstellen relevant sind.},
author = {Deml, B. and Freyer, J. and F??rber, B.},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2007{\_}Ein Beitrag zur Pr{\"{a}}diktion des Fahrstils{\_}Deml und Freyer{\_}VDI.pdf:pdf},
isbn = {9783180920153},
issn = {00835560},
journal = {VDI Berichte},
number = {2015},
pages = {47--59},
title = {{Ein Beitrag zur Pr??diktion des Fahrstils}},
year = {2007}
}
@article{Petersen2007,
abstract = {These pages are a collection of facts (identities, approxima- tions, inequalities, relations, ...) about matrices and matters relating to them. It is collected in this form for the convenience of anyone who wants a quick desktop reference .},
archivePrefix = {arXiv},
arxivId = {math/0608522},
author = {Petersen, Kaare Breandt and Pedersen, Michael Syskind},
doi = {10.1111/j.1365-294X.2006.03161.x},
eprint = {0608522},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/07{\_}Math/Book{\_}2012{\_}matrixcookbook.pdf:pdf},
isbn = {0962-1083 (Print)$\backslash$r0962-1083 (Linking)},
issn = {09621083},
journal = {Citeseer},
keywords = {acknowledgements,and suggestions,bill baxter,christian rish{\o}j,contributions,derivative of,derivative of inverse matrix,determinant,di erentiate a matrix,douglas l,esben,matrix algebra,matrix identities,matrix relations,thank the following for,theobald,we would like to},
number = {4},
pages = {1--66},
pmid = {17284204},
primaryClass = {math},
title = {{The Matrix Cookbook}},
volume = {16},
year = {2007}
}
@article{Hackl2012,
abstract = {High-gain adaptive control and its applications in mechatronics are discussed. The high-gain adaptive controllers are presented and developed for “minimum-phase” systems with relative degree one or two, known sign of the high-frequency gain, bounded disturbances and state dependent, functional perturbations. System identification or parameter estimation is not required for controller implementation. Structural system knowledge is sufficient. The robust controllers guarantee tracking with prescribed asymptotic or transient accuracy and, in combination with a proportional-integral internal model, may assure steady state accuracy. Finally, the controllers are applied for speed and position control of stiff and flexible industrial servo-systems and it is shown that high-gain adaptive position control with prescribed transient accuracy of rigid revolute joint robotic manipulators is feasible, if the inertia matrix is known},
author = {Hackl, Christoph M.},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/02{\_}Dissertation/Diss{\_}2012{\_}Contributions to high-gain adaptive{\_}Hackel{\_}TUM.pdf:pdf},
keywords = {Hackl},
title = {{Contributions to high-gain adaptive control in mechatronics}},
url = {https://mediatum.ub.tum.de/doc/1084562/file.pdf},
year = {2012}
}
@article{Strecker1997,
abstract = {Herk{\"{o}}mmliche Computer erledigen exakt berechenbare, routinehafte Aufgaben schneller und zuverl{\"{a}}ssiger als der Mensch. Einige typisch menschliche F{\"{a}}higkeiten (z. B. die Gesichtserkennung) stellen die konventionelle Informationsverarbeitung dagegen vor gro{\ss}e Schwierigkeiten. Herk{\"{o}}mmliche Algorithmen scheitern, sobald die vorausgesetzte Bildqualit{\"{a}}t nicht gegeben ist. Der Mensch erkennt dagegen Gesichter problemlos auch unter erschwerten Bedingungen (Dunkelheit, Nebel). Es liegt also nahe zu fragen, nach welchen Prinzipien das menschliche Gehirn organisiert ist und auf welche Weise es die sensorischen Informationen der Sinne verarbeitet. Vor diesem Hintergrund ist die Entwicklung K{\"{u}}nstlicher Neuronaler Netze (KNN) zu sehen: KNN imitieren die Organisations- und Verarbeitungsprinzipien des menschlichen Gehirns. Aus betriebswirtschaftlicher Sicht stellen KNN neue Probleml{\"{o}}sungsverfahren aus dem Forschungsgebiet der K{\"{u}}nstlichen Intelligenz dar, die das {\"{o}}konomische Modellierungsinstrumentarium erweitern und sich besonders f{\"{u}}r komplexe, nicht-konservative Aufgabenstellungen eignen. Gegen{\"{u}}ber traditionellen Verfahren aus der Statistik und dem Operations Research zeichnen sich KNN durch Lernf{\"{a}}higkeit, Fehlertoleranz, Robustheit und Generalisierungsf{\"{a}}higkeit aus. Betriebliche Anwendungsfelder finden sich insbesondere in den Bereichen Pr{\"{u}}fung und Beurteilung, Prognose, Klassenbildung und Optimierung. Der vorliegende Beitrag soll praxisorientiert einen {\"{U}}berblick {\"{u}}ber den Aufbau und die Funktionsweise von KNN geben und damit einen Einstieg in die Thematik erm{\"{o}}glichen. Ausgehend von den biologischen Grundlagen werden die statischen und dynamischen Kernkomponenten von KNN definiert und die prinzipiellen Informationsverarbeitungsprozesse erl{\"{a}}utert. Ein {\"{U}}berblick {\"{u}}ber die typischen Eigenschaften von KNN bildet den Abschlu{\ss} des Beitrags.},
author = {Strecker, Stefan},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/06{\_}Skripte-Vorlesungen/Script{\_}1997{\_}K{\"{u}}nstliche Neuronale Netze - Aufbau und Funktionsweise{\_}Uni{\_}Mainz.pdf:pdf},
isbn = {3-8272-9531-9},
journal = {Lehrstuhl f{\"{u}}r Allg. BWL und Wirtschaftsinformatik, Johannes Gutenberg-Universit{\"{a}}t},
number = {10},
pages = {27},
title = {{K{\"{u}}nstliche Neuronale Netze – Aufbau und Funktionsweise}},
url = {http://wi.uni-giessen.de},
year = {1997}
}
@article{Martinez2016,
abstract = {The progressive integration of Advanced Driver Assistant Systems (ADAS) into vehicles has contributed significantly to increasing safety and comfort levels of the driver. The need to adapt the vehicle to the preferences and requirements of the driver leads to the development of individualized ADAS. Automatic identification of the driver is a key factor in the design of these systems. In this work, a driver identification model with impostor detection capability is proposed. This approach is based on non-intrusive information from driving behavior signals, and an extreme learning machine (ELM) network. The performance of the system is evaluated on the basis of groups of different number of known drivers, and possible impostor drivers. Identification rates are greater than 80{\%} for every group category tested, and still above 90{\%} for groups of two and three drivers. The impostor detection rate is above 80{\%} when the car has a single genuine driver. This rate decays in inverse proportion to the number of authorized drivers, but it is greater than 50{\%} in all cases.},
author = {Mart{\'{i}}nez, M. V. and Echanobe, J. and {Del Campo}, I.},
doi = {10.1109/ITSC.2016.7795582},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2016{\_}Driver Identification and Impostor Detection Based on Driving Behavior Signals{\_}0531.pdf:pdf},
isbn = {9781509018895},
journal = {IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC},
pages = {372--378},
title = {{Driver identification and impostor detection based on driving behavior signals}},
year = {2016}
}
@article{Roesky2015,
author = {Roesky, Ole and Mummel, Jan},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Presentation{\_}2015{\_}HEV{\_}Symposium/Roesky{\_}TLK-Thermo{\_}HEV{\_}2015{\_}Vortrag.pdf:pdf},
title = {{Auswirkungen von Ladeverlusten auf die Ladestrategien von Elektrofahrzeugen 24.}},
year = {2015}
}
@article{Lemieux2015,
abstract = {Global optimization of the energy consumption of dual power source vehicles such as hybrid electric vehicles, plug-in hybrid electric vehicles, and plug in fuel cell electric vehicles requires knowledge of the complete route characteristics at the beginning of the trip. One of the main characteristics is the vehicle speed profile across the route. The profile will translate directly into energy requirements for a given vehicle. However, the vehicle speed that a given driver chooses will vary from driver to driver and from time to time, and may be slower, equal to, or faster than the average traffic flow. If the specific driver speed profile can be predicted, the energy usage can be optimized across the route chosen. The purpose of this paper is to research the application of Deep Learning techniques to this problem to identify at the beginning of a drive cycle the driver specific vehicle speed profile for an individual driver repeated drive cycle, which can be used in an optimization algorithm to minimize the amount of fossil fuel energy used during the trip.},
archivePrefix = {arXiv},
arxivId = {1510.07208},
author = {Lemieux, Joe and Ma, Yuan},
doi = {10.1109/VPPC.2015.7353037},
eprint = {1510.07208},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2014{\_}Vehicle Speed Prediction using Deep Learning{\_}Michigan.pdf:pdf},
isbn = {9781467376372},
journal = {arXiv cs.LG},
keywords = {Neural Networks,Stacked Auto Encoders,Traffic Prediction,—Deep Learning},
pages = {07208},
title = {{Vehicle Speed Prediction using Deep Learning}},
url = {http://arxiv.org/abs/1510.07208},
volume = {10},
year = {2015}
}
@book{Siebertz2010a,
address = {Berlin, Heidelberg},
author = {Siebertz, Karl and van Bebber, David and Hochkirchen, Thomas},
doi = {10.1007/978-3-642-05493-8},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2010{\_}Statistische Versuchsplanung{\_}Kap 10 Sensitivit{\"{a}}tsanalyse.pdf:pdf},
isbn = {978-3-642-05492-1},
publisher = {Springer Berlin Heidelberg},
title = {{Statistische Versuchsplanung}},
url = {http://link.springer.com/10.1007/978-3-642-05493-8},
year = {2010}
}
@article{Kavraki2010,
author = {Kavraki, Lydia E},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/06{\_}Skripte-Vorlesungen/Script{\_}2009{\_}Geometric methods in structural computational biology{\_}Kavraki{\_}RiceUniversity.pdf:pdf},
keywords = {Biological Products,geometrics,structure},
pages = {1--183},
title = {{Geometric Methods in Structural Computational Biology}},
url = {http://cnx.org/content/col10344/1.6/},
year = {2010}
}
@book{Commission2005,
author = {Commission, European and Of, Security and Citizen, T H E},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2005{\_}Proceedings of the international workshop on modelling driver behaviour in automotive environments.pdf:pdf},
isbn = {9289496282},
title = {{Proceed I N Gs of Th E I N Tern a Ti on a L W Ork Sh Op on M Od Elli N G D Ri Ver Beh a Vi Our I N a Utom Oti Ve}},
year = {2005}
}
@article{Haberstroh2013,
abstract = {The contemporary economic and working environment is more complex and turbulent than ever before. On the one hand, enterprises must succeed in turbulent and fast changing global markets. On the other hand, traditional models of the regular employee have been substituted by dynamic biographies. Nowadays, individuals are required to refresh their knowledge and modify their skills constantly and for a long working life while organizations have to use efficient instruments for the flexible transfer of work-related knowledge. These enhanced requirements of individual qualification and competency development conflict with the increasing time pressure of the economic and everyday life. This paper firstly analyzes lifelong learning and continuous competency development as essential requirements in a modern working environment. The socioeconomic dilemma, Time for Learning Processes vs. Time Pressure, however, shows that in the tightened conditions of today's economy the fulfilment of these requirements can only be obtained by innovative forms of work-integrated learning. Based on these results the paper finally describes the concept of Microtraning as one example of an efficient method of work-integrated learning and powerful measures to face the dilemma described},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Haberstroh, Max and Klingender, Max and Huang, Qihui and Hauck, Eckart and Henning, Klaus},
doi = {10.1007/978-3-642-33389-7},
eprint = {arXiv:1011.1669v3},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2010{\_}User adaptive design of Active Vehicle Safety Systems with Regard to the driver behavior of elderly drivers.pdf:pdf},
isbn = {978-3-642-33388-0},
issn = {1098-6596},
keywords = {1 purpose of the,active vehicle safety systems,age based constraints,elderly drivers,elderly people,for,like worsening sight,most elder drivers suffer,most important vehicle for,study,the car is the,the maintenance of mobility,today},
pmid = {25246403},
title = {{Automation, Communication and Cybernetics in Science and Engineering 2011/2012}},
url = {http://link.springer.com/10.1007/978-3-642-33389-7},
year = {2013}
}
@article{Kramer,
author = {Kramer, Ulrich},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2008{\_}Kraftfahrzeugfuehrung{\_}Ulrich{\_}Kramer.pdf:pdf},
title = {{Kraftfahrzeug-f{\"{u}}hrung}}
}
@article{Aachen2015,
author = {Aachen, Forschunggesellschaft Kraftfahrwesen and Br{\"{o}}ckerhoff, Markus},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2014{\_}PELOPS{\_}whitepaper{\_}RWTH{\_}Aachen{\_}FKA.pdf:pdf},
number = {201},
title = {{PELOPS White Paper}},
url = {file:///Users/baumann/Documents/Mendeley Desktop/pelops{\_}whitepaper-1.pdf{\%}5Cnhttp://www.fka.de/pdf/pelops{\_}whitepaper.pdf},
year = {2015}
}
@article{Hoyes1996,
abstract = {Risk homeostasis theory (RHT) suggests that changes made to the intrinsic risk of environments are negated in one of three ways: behavioural adjustments within the environment, mode migration, and avoidance of the physical risk. To date, this three-way model of RHT has little empirical support, whilst research findings on RHT have at times been diametrically opposed. A reconciliation of apparently opposing findings might be possible by suggesting that extrinsic compensation fails to restore previously existing levels of actual risk in cases where behavioural adjustments within the environment are incapable of negating intrinsic risk changes. This paper reports a study in which behavioural adjustments within the physical risk- taking environment are capable of reconciling target with actual risk. The results provide positive support for RHT in the form of overcompensation for the intrinsic risk change on specific driver behaviours.},
author = {Hoyes, Thomas W. and Stanton, Neville A. and Taylor, R. G.},
doi = {10.1016/0925-7535(96)00007-0},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2008{\_}Risk{\_}homeostasis{\_}theory{\_}A{\_}study{\_}of{\_}intrinsic{\_}compensation{\_}Hoyes{\_}et{\_}al.pdf:pdf},
isbn = {0925-7535},
issn = {09257535},
journal = {Safety Science},
number = {1-3},
pages = {77--86},
title = {{Risk homeostasis theory: A study of intrinsic compensation}},
volume = {22},
year = {1996}
}
@book{Liebl2014,
author = {Liebl, Johannes and Lederer, Matthias and Rohde-Brandenburger, Klaus and Biermann, Jan-Welm and Roth, Martin and Sch{\"{a}}fer, Heinz},
doi = {10.1007/978-3-658-04451-0},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2014{\_}Energiemanagement{\_}im{\_}Kraftfahrzeug.pdf:pdf},
isbn = {978-3-658-04450-3},
title = {{Energiemanagement im Kraftfahrzeug}},
url = {http://link.springer.com/10.1007/978-3-658-04451-0},
year = {2014}
}
@article{Chatzikomis2009,
abstract = {The use of closed-loop driver models is important$\backslash$nfor accurate vehicle simulations and in active safety$\backslash$nsystems evaluation. In this paper we present a combined$\backslash$nlongitudinal-lateral controller that is regulating the steering$\backslash$nangle and throttle/brake levels by previewing the path ahead$\backslash$nof the vehicle. The lateral steering controller is using, as$\backslash$ninput, the heading and position deviation between the vehicle$\backslash$nand the road. The controller is using fixed gains with$\backslash$na simple gain scheduling based on the vehicle's speed. The$\backslash$nlongitudinal speed controller is using the curvature of the$\backslash$npath ahead of the vehicle to determine the appropriate velocity$\backslash$nof the vehicle. The longitudinal-lateral controller is$\backslash$ntested by driving a double-lane change (ISO 3888-2) and$\backslash$na lap around a racing track.},
author = {Chatzikomis, C. I. and Spentzas, K. N.},
doi = {10.1007/s10010-009-0112-5},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2009{\_}A path-following driver model with longitudinal and lateral control of vehicles motion.pdf:pdf},
issn = {00157899},
journal = {Forschung im Ingenieurwesen/Engineering Research},
number = {4},
pages = {257--266},
title = {{A path-following driver model with longitudinal and lateral control of vehicle's motion}},
volume = {73},
year = {2009}
}
@article{Shalev-shwartz2016,
author = {Shalev-shwartz, Shai},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Presentation{\_}2016{\_}deep-learning-for-autonomous-driving{\_}jku{\_}linz{\_}SHochreiter.pdf:pdf},
title = {{Deep Learning for Autonomous Driving}},
year = {2016}
}
@article{Christens2008,
abstract = {In diesem Beitrag wird zun{\"{a}}chst das Verkehrssimulationsprogramms PELOPS vorgestellt. Dabei wird detailliert auf das in PELOPS enthaltene Fahrermodell eingegangen. Das Modell ist in der Lage, sowohl einspuriges Folgeverhalten, als auch das Fahrverhalten beispielsweise bei Spurwechselman{\"{o}}vern realistisch abzubilden. Nach der Beschreibung des Modells wird die Methodik zur Entwicklung und Implementierung neuer Fahrermodellfunktionen erl{\"{a}}utert. Hierbei werden die Vor-und Nachteile bei der Erfassung des Fahrerverhaltens anhand von Induktionsschleifen, Videodektionsanlagen, Fahrsimulatoren oder Messfahrzeugen beschrie-ben. Abschlie{\ss}end umrei{\ss}t der Beitrag zwei Applikationsm{\"{o}}glichkeiten des Fahrermodells: Zum einen den Einsatz des Fahrermodells im Fahrsimulator und zum anderen die Integration des Fahrermodells als Reglers im Fahrzeug. 1 Das Verkehrsflusssimulationsprogramm PELOPS Das mikroskopische, fahrzeugorientierte Verkehrsflusssimulationsprogramm PELOPS (Pro-gramm zur Entwicklung l{\"{a}}ngsdynamischer, mikroskopischer Prozesse in systemrelevanter Umgebung) wurde an der Forschungsgesellschaft Kraftfahrwesen mbH Aachen (fka) in Zu-sammenarbeit mit der BMW AG entwickelt (Ludmann, 1989 und Diekamp 1995). Es wird heute von der fka vertrieben und gepflegt. Das Konzept von PELOPS besteht in der Verkn{\"{u}}pfung detaillierter submikroskopischer Fahr-zeugmodelle mit mikroskopischen verkehrstechnischen Modellen, die sowohl eine Untersu-chung des l{\"{a}}ngsdynamischen Fahrzeugverhaltens als auch eine Analyse des Verkehrsablaufs erm{\"{o}}glichen. Im Gegensatz zu klassischen in der Automobilindustrie angewandten Simulati-onswerkzeugen, die in der Regel nur ein Teilsystem oder ein einzelnes isoliertes Gesamtfahr-zeug abbilden, verfolgt der Ansatz in PELOPS daher die Simulation der drei wesentlichen Elemente des Verkehrs -Strecke/Umwelt, Fahrer und Fahrzeug -mit ihren Wechselwirkun-gen. In einer modularen Programmstruktur werden die genannten Elemente modelliert und durch Schnittstellen abgegrenzt (vgl. Bild 1). Das Umweltmodell erlaubt bei Bedarf eine detaillierte Beschreibung der Einfl{\"{u}}sse einer stati-on{\"{a}}ren Verkehrsumgebung. Sowohl der Verlauf der Stra{\ss}e in horizontaler und vertikaler Richtung {\"{u}}ber Radien und {\"{U}}berg{\"{a}}nge, als auch die Anzahl und die Breite der Spuren wird angegeben. Zus{\"{a}}tzlich zu diesen geometrischen Daten k{\"{o}}nnen Verkehrszeichen sowie},
author = {Christens, F and Huang, Q},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2008{\_}Das Fahrermodell im Verkehrsflusssimulationsprogram PELOPS - Modellierung und Applikationsm{\"{o}}glichkeiten.pdf:pdf},
isbn = {9783183028221},
journal = {Fahrermodellierung in Wissenschaft und Wirtschaft.2. Berliner Fachtagung Fahrermodellierung, 19. - 20. Juni 2008},
pages = {128--144},
title = {{Das Fahrermodell im Verkehrsflusssumilationsprogramm PELOPS - Modellierung und Applikationsm{\"{o}}glichkeiten}},
year = {2008}
}
@article{Liu2016,
abstract = {Copyright {\textcopyright} 2016 SAE International.The 48V mild hybrid technology is emerging as a very attractive option for high-volume vehicle electrification. Compared to high-voltage hybrids, the 48V system has a potential of achieving competitive fuel economy with significantly lower incremental costs. While previous studies of 48V mild hybrid systems discussed vehicle configuration, power management strategy and electric machine design, quantitative assessment of fuel economy under real-world conditions remains an open topic. Objectives of this paper are to propose a methodology for categorizing real-world cycles based on driver aggressiveness, and to subsequently analyze the impact of driving patterns on fuel saving potentials with a 48V mild hybrid system. Instead of using the certification test cycles to evaluate the fuel economy, real-world cycles are extracted from 2001-2003 Southern California Household Travel Survey. Subsequently, a consistent energy management strategy is implemented into the vehicle simulation and the real-world fuel consumption reductions are quantified for different levels of driver aggressiveness.},
author = {Liu, Zifan and Ivanco, Andrej and Filipi, Zoran S.},
doi = {10.4271/2016-01-1166},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2016{\_}Impacts of Real-World Driving and Driver Aggressiveness on Fuel Consumption of 48V Mild Hybrid Vehicle{\_}SAE.pdf:pdf},
issn = {2167-4205},
journal = {SAE International Journal of Alternative Powertrains},
number = {2},
pages = {2016--01--1166},
title = {{Impacts of Real-World Driving and Driver Aggressiveness on Fuel Consumption of 48V Mild Hybrid Vehicle}},
url = {http://papers.sae.org/2016-01-1166/},
volume = {5},
year = {2016}
}
@misc{,
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/02{\_}Dissertation/Diss{\_}2004{\_}Optimaltheoretische Modellierung und Identifizierung von Fahrereigenschaften{\_}Torsten Butz{\_}TU Darmstadt.pdf:pdf},
title = {{Butz.Pdf}}
}
@article{Fischer2008,
abstract = {Die Modellierung des Fahrerverhaltens spielt f{\"{u}}r die modellbasierte Entwicklung und den Test von Fahrdynamikregelsystemen und Fahrerassistenzsystemen eine wichtige Rolle. Eben-so gro{\ss}e Bedeutung hat eine robuste und exakte Geschwindigkeitsregelung bei Verbrauchs-berechnungen f{\"{u}}r neue Antriebskonzepte. In diesem Beitrag wird ein Fahrermodell vorge-stellt, das f{\"{u}}r beide Anwendungsbereiche einsetzbar ist. Durch die strikte Trennung sowohl von L{\"{a}}ngs-und Querf{\"{u}}hrung als auch von Sollwertgenerierung und eigentlicher Regelung ergeben sich zus{\"{a}}tzliche Einsatzm{\"{o}}glichkeiten in Anwendungen, bei denen nur Teilfunktiona-lit{\"{a}}ten des Fahrermodells ben{\"{o}}tigt werden. Bei der Sollwertgenerierung werden ein Ge-schwindigkeitsprofil f{\"{u}}r die L{\"{a}}ngsregelung und eine Sollposition f{\"{u}}r die Querregelung be-rechnet. Die L{\"{a}}ngsregelung erfolgt mit einem pr{\"{a}}zisen Geschwindigkeitsregler, dessen Struk-tur das nichtlineare Verhalten von Verbrennungsmotor und Antriebsstrang ber{\"{u}}cksichtigt. Bei der Querregelung kommt ein nichtlinearer Positionsregler zum Einsatz, der durch geeignete Wahl der Reglerparameter verschiedene Eigenschaften und Fahrfehler von menschlichen Fahrern abbilden kann. Ergebnisse von Anwendungen des Fahrermodells in Handlingunter-suchungen, bei der Validierung von Fahrdynamikregelsystemen und zur Verbrauchsberech-nung zeigen die Vielseitigkeit des modularen Modells.},
author = {Fischer, R and Butz, T and Ehmann, M and Irmscher, M},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2000{\_}Fahrermodellierung{\_}fuer{\_}Fahrdynamik{\_}und{\_}Verbrauchsberechnungen{\_}Fischer{\_}Irmscher{\_}etall.pdf:pdf},
isbn = {9783183028221},
journal = {Fahrermodellierung in Wissenschaft und Wirtschaft.2. Berliner Fachtagung Fahrermodellierung, 19. - 20. Juni 2008},
pages = {1--14},
title = {{Fahrermodellierung f{\"{u}}r Fahrdynamik und Verbrauchsberechnungen}},
year = {2008}
}
@book{Spitzenposition,
author = {Spitzenposition, Diese and Information, Mehr and Gmbh, Mahle},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2004{\_}Dynamik der Kraftfahrzeuge - 4 Auflage{\_}Mitschke und Wallentowitz.pdf:pdf},
isbn = {9783662068038},
title = {{Der 50}}
}
@article{Yi2016,
abstract = {{\textcopyright} 2016 IEEE. A good level of situation awareness is critical for vehicle lane change decision making. In this paper, a Data- Driven Situation Awareness (DDSA) algorithm is proposed for vehicle environment perception and projection using machine learning algorithms in conjunction with the concept of multiple models. Firstly, unsupervised learning (i.e., Fuzzy C-Mean Clustering (FCM)) is drawn to categorize the drivers' states into different clusters using three key features (i.e., velocity, relative velocity and distance) extracted from Intelligent Driver Model (IDM). Statistical analysis is conducted on each cluster to derive the acceleration distribution, resulting in different driving models. Secondly, supervised learning classification technique (i.e., Fuzzy k-NN) is applied to obtain the model/cluster of a given driving scenario. Using the derived model with the associated acceleration distribution, Kalman filter/prediction is applied to obtain vehicle states and their projection. The publicly available NGSIM dataset is used to validate the proposed DDSA algorithm. Experimental results show that the proposed DDSA algorithm obtains better filtering and projection accuracy in comparison with the Kalman filter without clustering.},
author = {Yi, Dewei and Su, Jinya and Liu, Cunjia and Chen, Wen Hua},
doi = {10.1109/ITSC.2016.7795677},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2016{\_}Data-Driven Situation Awareness Algorithm for Vehicle Lane Change{\_}0108.pdf:pdf},
isbn = {9781509018895},
journal = {IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC},
keywords = {Clustering and classification,Filtering and prediction,Lane change,NGSIM dataset},
pages = {998--1003},
title = {{Data-driven situation awareness algorithm for vehicle lane change}},
year = {2016}
}
@book{Siebenpfeiff,
author = {Siebenpfeiff, Wolfgang},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2015{\_}Fahrerassistenzsysteme und Effiziente Antriebe.pdf:pdf},
isbn = {9783658081607},
title = {{Fahrerassistenzsysteme und Effi ziente Antriebe}}
}
@article{Markteinsteigern,
author = {Markteinsteigern, U N D},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/06{\_}Skripte-Vorlesungen/Paper{\_}2016{\_}Self{\_}driving{\_}TAXI.pdf:pdf},
pages = {36--41},
title = {{Autonomes Fahren M{\"{a}}rkte, Treiber und Gesch{\"{a}}ftsmodelle}}
}
@article{Ehmann,
author = {Ehmann, Martin and Butz, Torsten},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2004{\_}Raceline Optimierung und Fahrermodellierung f{\"{u}}r die Simulation von Rennfahrzeugen in Echtzeit{\_}Ehmann{\_}Butz.pdf:pdf},
pages = {1--12},
title = {{Raceline Optimierung und Fahrermodellierung f u ¨ r die Simulation von Rennfahrzeugen in Echtzeit Einf u ¨ hrung Echtzeitf ¨ ahige Rennfahrzeugsimulation}}
}
@article{Rhode,
author = {Rhode, Stephan},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/02{\_}Dissertation/Diss{\_}2016{\_}Robust and Regularized Algorithms for Vehicle Tractive Force Prediction and Mass Estimation{\_}Rhode Stephan KIT.pdf:pdf},
keywords = {system identification, Kalman filter, errors-in-va},
title = {{Tractive Force Prediction and Mass Estimation}}
}
@book{Provence2013,
abstract = {Innovative transportation technologies will be a vital component of any future sustainable society. Gathering over 50 authoritative, peer-reviewed entries from the Encyclopedia of Sustainability Science and Technology, Transportation Technologies for Sustainability covers a broad range of transportation-related sustainability research, from vehicle design and technology to mass transit systems. State-of-the-art chapters describe key developments in intelligent vehicle technology, including vision sensors, driver status monitoring, and vehicle motion control, while international experts present the latest research in electric, hybrid, and fuel cell vehicles. Leaders in the mass transit field assess a broad spectrum of alternatives in both small and large urban areas. This valuable collection is an essential reference for undergraduate and graduate students, researchers, policymakers, and industry experts.},
author = {Provence, Salon De and Outline, Article and Description, Estimation Process and Model, Four-wheel Vehicle and Results, Experimental and Perspectives, Future},
doi = {10.1007/978-1-4614-5844-9},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2013{\_}Transportation Technologies for Sustainability{\_}Driver{\_}Assistance{\_}System, Biologically Inspired.pdf:pdf},
isbn = {978-1-4614-5843-2},
issn = {03861112},
pages = {338--466},
title = {{Transportation Technologies for Sustainability}},
url = {http://link.springer.com/10.1007/978-1-4614-5844-9},
year = {2013}
}
@article{Brunner,
author = {Brunner, David and Cramer, Heiko and Gabler, Anja and Winzig, Stefan},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Presentation{\_}2014{\_}Grundlagen{\_}Grundlagen Pr{\"{a}}diktive Streckendaten - PSD{\_}Brunner.pdf:pdf},
title = {{Grundlagen Pr{\"{a}}diktive Streckendaten}}
}
@article{,
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Presentation{\_}2015{\_}HEV{\_}Symposium/Prasser{\_}BMW{\_}HEV{\_}2015{\_}Vortrag.pdf:pdf},
title = {{INTELLIGENTE BETRIEBSSTRATEGIE DER BMW eDRIVE PLUG-IN HYBRIDE.}},
year = {2015}
}
@article{Gindele2015a,
author = {Gindele, Tobias and Brechtel, Sebastian and Dillmann, R{\"{u}}diger},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2015{\_}Learning driver behavior models from traffic observations for decision making and planning{\_}KIT{\_}Gindele.pdf:pdf},
number = {January},
pages = {69--79},
title = {{Learning Driver Behavior Models from Traffic Observations for Decision}},
year = {2015}
}
@phdthesis{Altmannshofer,
author = {Altmannshofer, Simon},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/02{\_}Dissertation/Diss{\_}2018{\_}Robuste Parametersch{\"{a}}tzung f{\"{u}}r Elektrofahrzeuge{\_}Altmannshofer{\_}THI{\_}final.pdf:pdf},
title = {{Robuste Parametersch{\"{a}}tzung f{\"{u}}r Elektrofahrzeuge}}
}
@article{Meinel2012,
author = {Meinel, Jan},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/02{\_}Dissertation/Diss{\_}2012{\_}Spezifische Effekte visueller und kognitiver Ablenkung bei der Kraftfahrzeugf{\"{u}}hrung{\_}UniBerlin.pdf:pdf},
title = {{Spezifische Effekte visueller und kognitiver Ablenkung bei der Kraftfahrzeugf{\"{u}}hrung}},
year = {2012}
}
@book{Cramer2008,
abstract = {Statistic},
author = {Cramer, Erhard},
doi = {10.1007/978-3-540-77761-8},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2017{\_}Grundlagen der Wahrscheinlichkeitsrechnung und Statistik{\_}Cramer{\_}Kamps.pdf:pdf},
isbn = {978-3-540-77760-1},
title = {{Grundlagen der Wahrscheinlichkeitsrechnung und Statistik}},
url = {http://link.springer.com/10.1007/978-3-540-77761-8},
year = {2008}
}
@article{Universit1997,
author = {Universit, Technische},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/05{\_}Abschlussarbeiten/DA{\_}1997{\_}MV{\"{o}}gel{\_}AdvancedDriver{\_}Fahrbahnmodellierung und Kursregelung f{\"{u}}r ein echtzeitf{\"{a}}higes Fahrdynamikprogramm.pdf:pdf},
title = {{DA{\_}1997{\_}MV{\"{o}}gel{\_}AdvancedDriver{\_}Fahrbahnmodellierung und Kursregelung f{\"{u}}r ein echtzeitf{\"{a}}higes Fahrdynamikprogramm}},
year = {1997}
}
@article{Vockenhuber2011,
abstract = {Simulation of the full vehicle dynamics is an efficient means for function development and validation as well as calibration of traction control systems for four-wheel drive vehicles. Simulation models for vehicle, control systems and environment with a suitable level of detail are used to investigate different layout variants of the drivetrain on various tracks. This contribution outlines a driver model which enables considering the influence of different driving styles. Various human driver types are depicted by specific controller parameterization or definition of reference values for longitudinal and lateral vehicle guidance. Thus, apart from the calibration of control system electronics also realistic load spectra for durability computations of mechanical components can be determined via simulation.},
author = {Vockenhuber, Mario and Powertrain, Magna and Fischer, Rainer and Butz, Torsten and Ehmann, Martin and Dynaware, Tesis},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2011{\_}Abstimmung von Traktionsregelsystemen f{\"{u}}r Allradfahrzeuge mit Hilfe eines virtuellen Fahrermodells{\_}Vockenhuber{\_}Tesis.pdf:pdf},
number = {MiL},
title = {{Abstimmung von Traktionsregelsystemen f{\"{u}}r Allradfahrzeuge mit Hilfe eines virtuellen Fahrermodells}},
year = {2011}
}
@article{Rehbinder2001,
author = {Rehbinder, Henrik and Martin, Clyde},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}1990{\_}A Control Theoretic Model of Driver Steering Behavior.pdf:pdf},
keywords = {computational model,control theoretic biomechanics,elbow,forearm,muscle activations},
pages = {741--748},
title = {{A control theoretic model of the forearm}},
volume = {34},
year = {2001}
}
@article{Fotouhi2011a,
author = {Fotouhi, A and Jannatipour, M},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2011{\_}Vehicle's Velocity Time Series Prediction Using Neural{\_}AutomotiveEngineering.pdf:pdf},
keywords = {neural networks,prediction,time series,vehicle,velocity},
number = {1},
pages = {21--28},
title = {{Paper{\_}2011{\_}Vehicle's Velocity Time Series Prediction Using Neural{\_}AutomotiveEngineering}},
volume = {1},
year = {2011}
}
@article{Magiera2016,
abstract = {As a result of the static and dynamic instabilities of a Powered-Two-Wheeler, the rider performs a highly demanding control task. Rider safety strongly depends on the individual abilities and skills of the rider. To improve the riders' skill level and reduce riding errors, safety trainings are well established. Additionally, safety systems and recently also advanced rider assistance systems help to avoid or mitigate accidents. While conventional rider training is limited to a small number of training scenarios in a controlled environment, safety systems are typically limited to specific situations (collision warning) or physical limits of the vehicle (ABS). We propose a method to identify riding errors based on a statistical rider model for cornering scenarios and estimate a personal rider skill score. The intention is to extend rider skill training beyond organized events and towards personal self-training. Automatic scoring of the cornering skill level has to take into account the high variability in speed and local curvature as well as the variety of different traffic situations that may be encountered during a ride. We suggest to split the complex driving task e.g. riding along a winding road, into smaller control tasks e.g. roll-into-corner, stable lean, and roll-out-of-corner and analyze them separately first, then their sequence and transitions. We evaluate various approaches based on Hidden Markov models that can split a complex task into smaller segments and show indicators for rider skill based on the best segmentation model.},
author = {Magiera, N. and Janssen, H. and Heckmann, M. and Winner, H.},
doi = {10.1109/ITSC.2016.7795583},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2016{\_}Rider Skill Identification by Probabilistic Segmentation into Motorcycle Maneuver Primitives{\_}0126.pdf:pdf},
isbn = {9781509018895},
issn = {978-1-5090-1889-5},
journal = {IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC},
pages = {379--386},
title = {{Rider skill identification by probabilistic segmentation into motorcycle maneuver primitives}},
year = {2016}
}
@article{Wimmmer2008,
abstract = {Die Inhalte des ersten Bandes beziehen sich u.a. auf Fragen des wissenschaftlichen Schreibens, des Zitierens von gedruckten Publikationen und Internetquellen, des Studierens und Forschens mit digitalen Medien, des Wissens- und Projektmanagements, des Pr{\{}{\"{a}}{\}}sentierens wissenschaftlicher Arbeiten sowie auf institutionelle und hochschuldidaktische Dimensionen, erfahrungsreflexive und subjektbezogene Aspekte, Metadaten und kognitive Werkzeuge.},
author = {Wimmmer, Mag Petra and Zauchner, Sabine},
doi = {10.1007/978-3-322-85517-6},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2015{\_}Einf{\"{u}}hrung in das wissenschaftliches Arbeiten mit Citavi 4.pdf:pdf},
isbn = {3896764136},
journal = {Arbeit},
number = {1},
pages = {1--101},
title = {{Einf{\"{u}}hrung in das wissenschaftliche Arbeiten}},
volume = {2015},
year = {2008}
}
@article{Althoff2008,
author = {Althoff, Dipl.-Ing Matthias and Stursberg, Ing Olaf},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Presentation{\_}2008{\_}Algorithmische Ans{\"{a}}tze zur Beurteilung des Fahrverhaltens autonomer Fahrzeuge{\_}TUM LSR{\_}Vortrag{\_}Stursberg.pdf:pdf},
number = {September},
title = {{Algorithmische Ans{\"{a}}tze zur Beurteilung des Fahrverhaltens autonomer Fahrzeuge}},
url = {http://www.lsr.ei.tum.de/},
year = {2008}
}
@article{Division,
author = {Division, Powertrain},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Presentation{\_}2015{\_}HEV{\_}Symposium/Weber{\_}Conti{\_}HEV{\_}2015{\_}Vortrag.pdf:pdf},
title = {{High Voltage Power Electronics Innovations in Response to Future Challenges}}
}
@article{Rempis2004,
author = {Rempis, Christian},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/06{\_}Skripte-Vorlesungen/Script{\_}2004{\_}fahrassistenzsysteme{\_}KFZ{\_}rempis{\_}2003.pdf:pdf},
journal = {System},
title = {{Fahrassistenzsysteme im Kfz Fachbereich Design und Informatik}},
year = {2004}
}
@article{Michel2015,
author = {Michel, Thomas},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Presentation{\_}2015{\_}HEV{\_}Symposium/Michel{\_}FEV{\_}HEV{\_}2015{\_}Vortrag.pdf:pdf},
title = {{Predictive Hybrid Strategy Using Car-2-X Communication}},
year = {2015}
}
@article{Knoll2015,
author = {Knoll, Markus and Ernst, Sebastian},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/05{\_}Abschlussarbeiten/BA{\_}2015{\_}Big{\_}DATA{\_}Bachelorthesis{\_}MKnoll.pdf:pdf},
number = {September},
title = {{Big Data-Eine analytische Betrachtung f{\"{u}}r die Aggregateentwicklung bei der AUDI AG zur Erlangung des akademisches Grades eines}},
year = {2015}
}
@book{Food2012,
archivePrefix = {arXiv},
arxivId = {UCD-ITS-RR-09-08},
author = {Food, Guelph and Network, Canadian Dairy},
doi = {10.1007/978-1-4419-0851-3},
eprint = {UCD-ITS-RR-09-08},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2012{\_}Encyclopedia of Sustainability Science and Technology{\_}Dairy Cattle Breeding{\_}Genetic Evaluation.pdf:pdf},
isbn = {978-0-387-89469-0},
issn = {09596526},
pmid = {12960964},
title = {{Encyclopedia of Sustainability Science and Technology}},
url = {http://link.springer.com/10.1007/978-1-4419-0851-3},
year = {2012}
}
@article{Majjad1998,
author = {Majjad, R. and Kiencke, U. and Kramer, S.},
doi = {10.1016/S1474-6670(17)42174-4},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}1999{\_}Modeling and performance analysis of a hybrid driver model{\_}KIT.pdf:pdf},
isbn = {4972160845},
issn = {14746670},
journal = {IFAC Proceedings Volumes},
keywords = {control systems,discrete event systems,driver model,hybrid simulation,modelling},
number = {1},
pages = {37--42},
title = {{Modeling and Performance Analysis of a Hybrid Driver Model}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S1474667017421744},
volume = {31},
year = {1998}
}
@article{Vogele2018,
author = {V{\"{o}}gele, Ulrich and Ziegmann, Johannes and Endisch, Christian},
doi = {10.1109/ITSC.2017.8317668},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2017{\_}Driver Adaptive Predictive Velocity Control{\_}V{\"{o}}gele{\_}ITSC.pdf:pdf},
isbn = {9781538615256},
journal = {IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC},
keywords = {Driver Adpative,Longitudinal Vehicle Control,Multi-Criteria Optimization,Predictive Velocity Control},
pages = {1--6},
title = {{Driver adaptive predictive velocity control}},
volume = {2018-March},
year = {2018}
}
@article{Fachtagung2006,
author = {Fachtagung, Berliner},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Presentation{\_}2006{\_}BerlinerFachtagung{\_}Fahrermodellierung{\_}LueMoe{\_}Einfaedeln{\_}BFFM2006.pdf:pdf},
title = {{Fahrermodellierung}},
year = {2006}
}
@book{Schramm2010,
abstract = {Einspurmodelle gestatten es, ohne gro{\ss}en Modellierungs- und Parametrierungsaufwand bereits zu aussagekr{\"{a}}ftigen Ergebnisse im Rahmen einer Simulation des Fahrverhaltens von Kraftfahrzeugen zu kommen. In diesem Kapitel werden daher einige lineare und nichtlineare Einspurmodelle beschrieben.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Schramm, Dieter and Hiller, Manfred and Bardini, Roberto},
doi = {10.1007/978-3-540-89315-8},
eprint = {arXiv:1011.1669v3},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2013{\_}Modellbildung und Simulation der Dynamik von Kraftfahrzeugen.pdf:pdf},
isbn = {978-3-540-89313-4},
issn = {0717-6163},
pmid = {15003161},
title = {{Modellbildung und Simulation der Dynamik von Kraftfahrzeugen}},
url = {http://link.springer.com/10.1007/978-3-540-89315-8},
year = {2010}
}
@article{Fischer2011a,
author = {Fischer, Rainer and Butz, Torsten and Ehmann, Martin and Vockenhuber, Mario},
doi = {10.1365/s35148-011-0220-z},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2011{\_}Magna{\_}Powertrain{\_}Fahrermodell{\_}zur{\_}virtuellen{\_}Regelsystementwicklung.pdf:pdf},
issn = {0001-2785},
journal = {ATZ - Automobiltechnische Zeitschrift},
number = {12},
pages = {946--949},
title = {{Fahrermodell zur virtuellen Regelsystementwicklung}},
url = {http://www.springerlink.com/index/10.1365/s35148-011-0220-z},
volume = {113},
year = {2011}
}
@article{Dr-IngSawodny2015,
author = {{Dr-Ing Sawodny}, Univ-Prof O and {Eckhard Arnold}, Dr-Ing},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/07{\_}Math/Script{\_}2015{\_}Numerische L{\"{o}}sung von Optimalsteuerungsproblemen{\_}Uni{\_}Stuttgart.pdf:pdf},
number = {0},
title = {{is y s Institut f{\"{u}}r Systemdynamik Numerische L{\"{o}}sung von Optimalsteuerungsproblemen}},
url = {http://www.ampl.com},
volume = {49},
year = {2015}
}
@article{Julier1997a,
abstract = {The Kalman Filter (KF) is one of the most widely used methods for tracking and estimation due to its simplicity, optimality, tractability and robustness. However, the application of the KF to nonlinear systems can be difficult. The most common approach is to use the Extended Kalman Filter (EKF) which simply linearizes all nonlinear models so that the traditional linear Kalman filter can be applied. Although the EKF (in its many forms) is a widely used filtering strategy, over thirty years of experience with it has led to a general consensus within the tracking and control community that it is difficult to implement, difficult to tune, and only reliable for systems which are almost linear on the time scale of the update intervals. In this paper a new linear estimator is developed and demonstrated. Using the principle that a set of discretely sampled points can be used to parameterize mean and covariance, the estimator yields performance equivalent to the KF for linear systems yet generalizes elegantly to nonlinear systems without the linearization steps required by the EKF. We show analytically that the expected performance of the new approach is superior to that of the EKF and, in fact, is directly comparable to that of the second order Gauss filter. The method is not restricted to assuming that the distributions of noise sources are Gaussian. We argue that the ease of implementation and more accurate estimation features of the new filter recommend its use over the EKF in virtually all applications.},
author = {Julier, Simon J. and Uhlmann, Jeffrey K.},
doi = {10.1117/12.280797},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}1997{\_}A new extension of the kalman filter to nonlinear systems{\_}SJulier{\_}JUhlmann.pdf:pdf},
issn = {0277786X},
number = {July 1997},
pages = {182},
pmid = {5098},
title = {{New extension of the Kalman filter to nonlinear systems}},
url = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.280797},
year = {1997}
}
@article{Themann2014,
abstract = {Predictive and energy efficient driving styles considerably reduce fuel$\backslash$nconsumption and emissions of vehicles. Vehicle-to-vehicle and$\backslash$nvehicle-to-infrastructure (V2X) communication provide information useful$\backslash$nto further optimize fuel economy especially in urban conditions. This$\backslash$nwork summarizes an optimization approach integrating V2X information in$\backslash$nthe optimization of longitudinal dynamics. Besides the dimensions$\backslash$ndistance and velocity also the dimension time is reflected in discrete$\backslash$ndynamic programming, which is based on a three-dimensional state space.$\backslash$nUpcoming signal states of traffic signals are reflected in the$\backslash$noptimization to implement an efficient pass through at intersections.$\backslash$nFurthermore, simulated average driving behavior defines a reference for$\backslash$noptimized velocity trajectories. This excludes optimization results$\backslash$nstrongly deviating from average behavior. The approach is implemented in$\backslash$na vehicle in a real-time capable way. In a field test the vehicle$\backslash$napproaches a V2X traffic light and the optimization reduces fuel$\backslash$nconsumption by up to 15 {\%} without increasing travel time.},
author = {Themann, P. and Krajewski, R. and Eckstein, L.},
doi = {10.1109/IVS.2014.6856411},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2014{\_}Discrete dynamic optimization in automated driving systems to improve energy efficiency in cooperative networks{\_}Themann{\_}IV.pdf:pdf},
isbn = {978-1-4799-3638-0},
issn = {1931-0587},
journal = {2014 IEEE Intelligent Vehicles Symposium Proceedings},
number = {Iv},
pages = {370--375},
title = {{Discrete dynamic optimization in automated driving systems to improve energy efficiency in cooperative networks}},
url = {http://ieeexplore.ieee.org/document/6856411/},
year = {2014}
}
@article{Herpel2008,
abstract = {The application of environment sensor systems in modern - often called ldquointelligentrdquo - cars is regarded as a promising instrument for increasing road traffic safety. Based on a context perception enabled by well-known technologies such as radar, laser or video, these cars are able to detect threats on the road, anticipate emerging dangerous driving situations and take proactive actions for collision avoidance. Besides the combination of sensors towards an automotive multi-sensor system, complex signal processing and sensor data fusion strategies are of remarkable importance for the availability and robustness of the overall system. In this paper, we consider data fusion approaches on near-raw sensor data (low-level) and on pre-processed measuring points (high-level). We model sensor phenomena, road traffic scenarios, data fusion paradigms and signal processing algorithms and investigate the impact of combining sensor data on different levels of abstraction on the performance of the multi-sensor system by means of discrete event simulation.},
author = {Herpel, T. and Lauer, C. and German, R. and Salzberger, J.},
doi = {10.1109/ICSENST.2008.4757100},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2008{\_}Multi-sensor data fusion in automotive applications{\_}IEEE.pdf:pdf},
isbn = {978-1-4244-2176-3},
issn = {2156-8065},
journal = {2008 3rd International Conference on Sensing Technology},
keywords = {automotive,environment perception,intelligent cars,multi-sensor data fusion,simulation},
pages = {206--211},
title = {{Multi-sensor data fusion in automotive applications}},
year = {2008}
}
@book{Wallentowitz2010,
abstract = {In den letzten drei Jahrzehnten ist der Anteil der Elektronik in Kraftfahrzeugen dramatisch gestiegen. Die Anteile werden immer gr{\"{o}}{\ss}er und der Trend h{\"{a}}lt, getrieben von steigenden Kunden- und Umweltanforderungen, ungebremst an. Bald wird der Wertanteil der Elektronik am Gesamtfahrzeug bei 20 Prozent liegen. Nahezu alle Funktionen des Fahrzeugs werden heute elektronisch gesteuert, geregelt oder {\"{u}}berwacht. Ausgehend von den physikalisch/technischen Grundlagen der Elektronik und Bauelemente werden Funktion und Anwendung von Komponenten und Systemen in Motor und Fahrwerk in Bordnetz, Fahrerassistenzsystemen, Infotainment und Multimedia gezeigt. Kapitel {\"{u}}ber Softwareentwicklung, Beleuchtung, Passive Sicherheit und Diagnose runden den Inhalt ab.},
author = {Wallentowitz, Henning and Reif, Konrad},
booktitle = {2},
doi = {Book-WallentowitzReif-06},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2006{\_}Handbuch Kraftfahrzeugelektronik - Grundlagen, Komponenten, Systeme, Anwendungen.pdf:pdf},
isbn = {3834807001},
keywords = {undefined},
pages = {724},
title = {{Handbuch Kraftfahrzeugelektronik: Grundlagen- Komponenten- Systeme- Anwendungen}},
year = {2010}
}
@article{Hamada2013,
author = {Hamada, Ryunosuke},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/05{\_}Abschlussarbeiten/MA{\_}2013{\_}Applying Nonparametric Bayesian Approach to Non-homogeneous Multiple Time Series towards Prediction of Driving Operations.pdf:pdf},
title = {{Master's Thesis Applying Nonparametric Bayesian Approach to Non-homogeneous Multiple Time Series towards Prediction of Driving Operations}},
year = {2013}
}
@article{Jeske2013,
author = {Jeske, Tobias},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Presentation{\_}2013{\_}Floating Car Data from Smartphones - What Google and Waze Know About You and How Hackers Can Control Traffic.pdf:pdf},
journal = {Proc. of the BlackHat Europe},
pages = {1--53},
title = {{Floating Car Data from Smartphones : What Google And Waze Know About You and How Hackers Can Control Traffic Black Hat | Europe Agenda • Introduction • Protocol Analysis Google Protocol Waze Protocol Privacy Authenticity / Attack Requirements Zero-Knowled}},
year = {2013}
}
@article{Deml2008,
author = {Deml, Barbara and Neumann, Hendrik and Andre, M and W, Hans Joachim},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2008{\_}Implementierung eines Fahrermodells in die Simulationsumgebung eines autonomen 'Fahrzeugs{\_}UniBW.pdf:pdf},
keywords = {cognitive automobile,driver model,simulation environment},
number = {2020},
pages = {1--8},
title = {{Implementierung eines Fahrermodells in die Simulationsumgebung eines autonomen Fahrzeugs}},
volume = {68},
year = {2008}
}
@article{Hua2009,
abstract = {Contemporary biological technologies produce extremely high-dimensional data sets from which to design classifiers, with 20,000 or more potential features being common place. In addition, sample sizes tend to be small. In such settings, feature selection is an inevitable part of classifier design. Heretofore, there have been a number of comparative studies for feature selection, but they have either considered settings with much smaller dimensionality than those occurring in current bioinformatics applications or constrained their study to a few real data sets. This study compares some basic feature-selection methods in settings involving thousands of features, using both model-based synthetic data and real data. It defines distribution models involving different numbers of markers (useful features) versus non-markers (useless features) and different kinds of relations among the features. Under this framework, it evaluates the performances of feature-selection algorithms for different distribution models and classifiers. Both classification error and the number of discovered markers are computed. Although the results clearly show that none of the considered feature-selection methods performs best across all scenarios, there are some general trends relative to sample size and relations among the features. For instance, the classifier-independent univariate filter methods have similar trends. Filter methods such as the t-test have better or similar performance with wrapper methods for harder problems. This improved performance is usually accompanied with significant peaking. Wrapper methods have better performance when the sample size is sufficiently large. ReliefF, the classifier-independent multivariate filter method, has worse performance than univariate filter methods in most cases; however, ReliefF-based wrapper methods show performance similar to their t-test-based counterparts. {\textcopyright} 2008 Elsevier Ltd. All rights reserved.},
archivePrefix = {arXiv},
arxivId = {f},
author = {Hua, Jianping and Tembe, Waibhav D. and Dougherty, Edward R.},
doi = {10.1016/j.patcog.2008.08.001},
eprint = {f},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2008{\_}Performance of feature-selection methods in the classification of high-dimension data{\_}Hua{\_}PatternRecognition.pdf:pdf},
isbn = {0031-3203},
issn = {00313203},
journal = {Pattern Recognition},
keywords = {Classification,Feature selection,Microarray},
number = {3},
pages = {409--424},
pmid = {19169416},
title = {{Performance of feature-selection methods in the classification of high-dimension data}},
volume = {42},
year = {2009}
}
@article{Steyvers2015,
author = {Steyvers, Mark},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/06{\_}Skripte-Vorlesungen/Script{\_}2015{\_}Advanced Matlab - Exploratory Data Analysis and Computational Statistics - Uni California - Mark Steyvers.pdf:pdf},
title = {{Advanced Matlab : Exploratory Data Analysis and Computational Statistics}},
year = {2015}
}
@article{Grabocka,
abstract = {Objectives The primary scope of the second work package is to develop methods which involve statistical prediction in the realm of three ecological ICT solutions. Eco-driving: refers to the adaptation of driving behaviors and is addressed by analyzing large amounts of driving behavior data. The detection of inefficient behaviors will be conducted by calibrating the predictive models with the help of fuel consumption estimations. Distributed data mining: refers to the elaborations of statistical models which operate in a decentralized mode. Such topologies are inherent to the nature of inter-vehicular (V2V) communication, where centralized decision-making is not feasible. REDUCTION aims at developing distributed statistical models per vehicle, which share intelligence within a neighborhood.},
author = {Grabocka, Josif and Khan, Umer and Schmidt-Thieme, Lars},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2015{\_}poster{\_}Predictive Analytics Models for Energy-Efficient Driving and Driver-Behaviour Adaption.pdf:pdf},
pages = {78},
title = {{REDUCTION-WP2: Predictive Analytics Models for Energy-Efficient Driving and Driver-Behaviour Adaptation}}
}
@article{Julier1997,
abstract = {A significant problem in tracking and estimation is the consistent transformation of uncertain state estimates between Cartesian and spherical coordinate systems. For example, a radar system generates measurements in its own local spherical coordinate system. In order to combine those measurements with those from other radars, however, a tracking system typically transforms all measurements to a common Cartesian coordinate system. The most common approach is to approximate the transformation through linearisation. However, this approximation can lead to biases and inconsistencies, especially when the uncertainties on the measurements are large. A number of approaches have been proposed for using higher order transformation models, but these approaches have found only limited use due to the often enormous implementation burdens incurred by the need to derive Jacobians and Hessians. This paper expands a method for nonlinear propagation which is described in a companion paper 3 . A disc...},
author = {Julier, Simon J. and Uhlmann, Jeffrey K.},
doi = {10.1117/12.277178},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}1997{\_}A consistent, debiased method for converting between polar and cartesian coordinate systems{\_}SJulier{\_}JUhlmann.pdf:pdf},
issn = {0277786X},
number = {June 1997},
pages = {110--121},
title = {{{\textless}title{\textgreater}Consistent debiased method for converting between polar and Cartesian coordinate systems{\textless}/title{\textgreater}}},
url = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=927450},
year = {1997}
}
@article{Sciarretta2004,
abstract = {In this paper, a model-based strategy for the real-time load control of parallel hybrid vehicles is presented. The aim is to develop a fuel-optimal control which is not relying on the a priori knowledge of the future driving conditions (global optimal con- trol), but only upon the current system operation. Themethodology developed is valid for those problem that are characterized by hard constraints on the state—battery state-of-charge (SOC) in this ap- plication—and by an arc cost—fuel consumption rate—which is not an explicit function of the state. A suboptimal control is found with a proper definition of a cost function to be minimized at each time instant. The ”instantaneous” cost function includes the fuel energy and the electrical energy, the latter related to the state con- straints. In order to weight the two forms of energy, a new def- inition of the equivalence factors has been derived. The strategy has been applied to the “Hyper” prototype of DaimlerChrysler, obtained from the hybridization of the Mercedes A-Class. Simulation results illustrate the potential of the proposed control in terms of fuel economy and in keeping the deviations of SOC at a low level},
author = {Sciarretta, Antonio and Back, Michael and Guzzella, Lino},
doi = {10.1109/TCST.2004.824312},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2004{\_}Optimal control of parallel hybrid electric vehicles{\_}Sciaretta{\_}CST.pdf:pdf},
isbn = {1063-6536},
issn = {10636536},
journal = {IEEE Transactions on Control Systems Technology},
keywords = {Cost optimal control,Dynamic programming,Fuel optimal control,Road vehicle control,Suboptimal control},
number = {3},
pages = {352--363},
title = {{Optimal control of parallel hybrid electric vehicles}},
volume = {12},
year = {2004}
}
@article{Ghahramani1996,
abstract = {Linear systems have been used extensively in engineering to model and control the behavior of dynamical systems. In this note, we present the Expectation Maximization (EM) algorithm for estimating the parameters of linear systems (Shumway and Stoffer, 1982). We also point out the relationship between linear dynamical systems, factor analysis, and hidden Markov models.},
author = {Ghahramani, Zoubin and Hinton, Geoffrey E.},
doi = {10.1080/00207177208932224},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}1996{\_}Parameter estimation for linear dynamical systems{\_}Ghahramani{\_}UniToronto.pdf:pdf},
isbn = {0018-9294 (Print)},
issn = {00207179},
journal = {Technical Report},
number = {CRG-TR-96-2},
pages = {1--6},
pmid = {17605350},
title = {{Parameter Estimation for Linear Dynamical Systems}},
url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.131.8274{\&}rep=rep1{\&}type=pdf},
volume = {6},
year = {1996}
}
@article{Sun2015,
abstract = {The performance and practicality of predictive energy management in hybrid electric vehicles (HEVs) are highly dependent on the forecast of future vehicular velocities, both in terms of accuracy and computational efficiency. In this brief, we provide a comprehensive comparative analysis of three velocity prediction strategies, applied within a model predictive control framework. The prediction process is performed over each receding horizon, and the predicted velocities are utilized for fuel economy optimization of a power-split HEV. We assume that no telemetry or on-board sensor information is available for the controller, and the actual future driving profile is completely unknown. Basic principles of exponentially varying, stochastic Markov chain, and neural network-based velocity prediction approaches are described. Their sensitivity to tuning parameters is analyzed, and the prediction precision, computational cost, and resultant vehicular fuel economy are compared.},
author = {Sun, C and Hu, X and Moura, S J and Sun, F},
doi = {10.1109/TCST.2014.2359176},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2014{\_}Velocity Predictors for Predictive Energy Management in Hybrid Electric Vehicles{\_}Sun IEEE.pdf:pdf},
isbn = {1063-6536},
issn = {1063-6536},
journal = {IEEE Transactions on Control Systems Technology},
keywords = {Artificial neural network (NN),Artificial neural networks,Batteries,Energy management,Fuels,System-on-chip,comparison,energy management systems,fuel economy,fuel economy optimization,hybrid electric vehicle,hybrid electric vehicle (HEV),hybrid electric vehicles,model predictive control (MPC),model predictive control framework,neural nets,neural network,optimisation,power-split HEV,predictive control,predictive energy management,stochastic Markov chain,vehicular fuel economy,velocity prediction,velocity prediction strategy},
number = {3},
pages = {1197--1204},
title = {{Velocity Predictors for Predictive Energy Management in Hybrid Electric Vehicles}},
volume = {23},
year = {2015}
}
@article{Walcott-Bryant2016,
abstract = {Driving in developing cities presents numerous challenges. Traffic
congestion and traffic accidents are the most visible challenges which
are caused by different underlying factors. Two chief factors are poorly
planned and maintained roadway infrastructure and the decisions made by
the drivers. Drivers are constantly forced to negotiate road hazards,
like potholes, unlabeled speed bumps as well as moving obstacles, like
pushcarts, motorcycles, and animals. Current Usage Based Insurance (UBI)
models do not include the context which in many cities may be paramount
to understanding driver behavior. This article presents the
Context-based Driver Score (CDS) model as a unified model for scoring a
driver based on a unique formulation of context that includes road
quality. We demonstrate the CDS model on a real-world use case in
Nairobi, Kenya, where waste-collection trucks were instrumented with
smartphones in order to collect inertial and telematic data. We present
an analysis of the CDS model and driver behaviors in contexts that
include weather, time-of-day, and road quality. Our results show that
the distribution of driving behaviors, like harsh braking and swerving,
vary greatly based on the context and the definition of the CDS model.
Ultimately, this work aims to extend the utility and scalability of UBI
models in order to make them more suitable for deployment in developing
cities.},
author = {Walcott-Bryant, Aisha and Tatsubori, Michiaki and Bryant, Reginald E. and Oduor, Erick and Omondi, Samuel and Osebe, Samuel and Wamburu, John and Bent, Oliver},
doi = {10.1109/ITSC.2016.7795626},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2016{\_}Harsh Brakes at Potholes in Nairobi. Context-Based Driver Behavior in Developing Cities{\_}0642.pdf:pdf},
isbn = {9781509018895},
journal = {IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC},
pages = {675--681},
title = {{Harsh brakes at potholes in Nairobi: Context-based driver behavior in developing cities}},
year = {2016}
}
@article{,
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2017{\_}EUROPEAN VEHICLEICCT MARKET STATISTICS{\_}EU-pocketbook{\_}2016-17.pdf:pdf},
title = {{European Vehicle Market Statistics}},
url = {http://eupocketbook.theicct.org},
year = {2017}
}
@book{Ia-Hev2013,
author = {Ia-Hev},
booktitle = {Annual Report},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2015{\_}HEVSymposium{\_}BS{\_}Identification of driving behaviour with electric vehicles regarding power requirements and the interaction with drv environment{\_}Sander.pdf:pdf},
isbn = {9783937655321},
number = {0},
title = {{Hybrid and Electric Vehicles}},
volume = {44},
year = {2013}
}
@article{Rempe2016,
author = {Rempe, Felix and Huber, Gerhard and Bogenberger, Klaus},
doi = {10.1109/ITSC.2016.7795876},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2016{\_}Travel Time Prediction in Partitioned Road Networks Based on Floating Car Data{\_}0306.pdf:pdf},
isbn = {9781509018895},
pages = {1982--1987},
title = {{Travel Time Prediction in Partitioned Road Networks Based on Floating Car Data}},
year = {2016}
}
@article{Optimale,
author = {Optimale, Ist-zustand},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/06{\_}Skripte-Vorlesungen/Script{\_}2010{\_}3F-Methodik{\_}Anforderungsoptimierung.pdf:pdf},
title = {{3F-Methodik}}
}
@article{Keulen2009,
abstract = {Deceleration rates have considerable influence on the fuel economy of hybrid electric vehicles. Given the vehicle characteristics and actual/measured operating conditions, as well as upcoming route information, optimal velocity trajectories can be constructed that maximize energy recovery. To support the driver in tracking of the energy optimal velocity trajectory, automatic cruise control is an important driver aid. In practice, perfect tracking of the optimal velocity trajectory is often not possible. An Adaptive Cruise Control (ACC) system is employed to react to the actual traffic situation. The combination of optimal velocity trajectory construction and ACC is presented as Predictive Cruise Control (PCC).},
author = {Keulen, Thijs Van and Naus, Gerrit and Jager, Bram De},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2009{\_}Predictive Cruise Control in Hybrid Electzric Vehicles{\_}Eindhoven.pdf:pdf},
journal = {World Electric vehicle Journal},
keywords = {energy consumption,hev,hybrid electric vehicle,optimization,regenerative braking,truck},
pages = {494--504},
title = {{Predictive Cruise Control in Hybrid Electric Vehicles}},
volume = {3},
year = {2009}
}
@article{Ericsson2001,
abstract = {This study is aimed at finding independent measures to describe the dimensions of urban driving patterns and to investigate which properties have main effect on emissions and fuel-use. 62 driving pattern parameters were calculated for each of 19 230 driving patterns collected in real traffic. These included traditional driving pattern parameters of speed and acceleration and new parameters of engine speed and gear-changing behaviour. By using factorial analysis the initial 62 parameters were reduced to 16 independent driving pattern factors. Fuel-use and emission factors were estimated for a subset of 5217 cases using two different mechanistic instantaneous emission models. Regression analysis on the relation between driving pattern factors and fuel-use and emission factors showed that nine of the driving pattern factors had considerable environmental effects. Four of these are associated with different aspects of power demand and acceleration, three describe aspects of gear-changing behaviour and two factors describe the effect of certain speed intervals. {\textcopyright} 2001 Elsevier Science Ltd. All rights reserved.},
author = {Ericsson, Eva},
doi = {10.1016/S1361-9209(01)00003-7},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2001{\_}Independent driving pattern factors and their influence on fuel-use and exhaust emission factors.pdf:pdf},
isbn = {1361-9209},
issn = {13619209},
journal = {Transportation Research Part D: Transport and Environment},
keywords = {Driving pattern factors,Emission factors,Fuel consumption,Gear changing,Power demand},
number = {5},
pages = {325--345},
title = {{Independent driving pattern factors and their influence on fuel-use and exhaust emission factors}},
volume = {6},
year = {2001}
}
@inproceedings{Wang2011,
abstract = {The performance of a vehicle control strategy, in terms of fuel economy improvement and emission reduction, is strongly influenced by driving conditions and drivers' driving styles. The term of `driving conditions' here means the traffic conditions and road type, which is usually indicated by standard driving cycles, say FTP 75 and NEDC; the term of `driving styles' here relates to the drivers' behavior, especially how drivers apply pressure on acceleration and brake pedal. To realize optimal fuel economy, it is ideal to obtain the information of future driving conditions and drivers' driving styles. This paper summarizes the methods and parameters that have been utilized to attain this end as well as the results. Based on this study, methods and parameters can be better selected for further improvement of driving conditions prediction and driving style recognition based hybrid electric vehicle control strategy.},
author = {Wang, Rui and Lukic, Srdjan M.},
booktitle = {2011 IEEE Vehicle Power and Propulsion Conference, VPPC 2011},
doi = {10.1109/VPPC.2011.6043061},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/06{\_}Paper/2019{\_}03{\_}Ilmenau{\_}DE/01{\_}Literature/2011 - Review of driving conditions prediction and driving style recognition based control algorithms for hybrid electric vehicles.pdf:pdf},
isbn = {9781612842486},
issn = {Pending},
keywords = {control strategy,driving condition prediction,driving style recognition,hybrid electric vehicle},
title = {{Review of driving conditions prediction and driving style recognition based control algorithms for hybrid electric vehicles}},
year = {2011}
}
@article{Kruger1999,
author = {Kr{\"{u}}ger, Hans-peter},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}1999{\_}Bewertung von Fahrzeugeigenschaften - vom Fahrgef{\"{u}}hl zum Fahrergef{\"{u}}hl{\_}W{\"{u}}rzburg.pdf:pdf},
pages = {1--15},
title = {{Bewertung von Fahrzeugeigenschaften – vom Fahrgef{\"{u}}hl zum Fahrergef{\"{u}}hl}},
volume = {22},
year = {1999}
}
@article{Horst2015,
author = {Horst, Tobias L{\"{o}}sche-ter},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Presentation{\_}2015{\_}HEV{\_}Symposium/Loesche-ter{\_}Horst{\_}Volkswagen{\_}HEV{\_}2015{\_}Vortrag.pdf:pdf},
title = {{Holistic CO 2 -analysis of powertrains and fuels}},
year = {2015}
}
@article{Fox2010,
abstract = {In this article, we explored a Bayesian nonparametric approach to learning Markov switching processes. This framework requires one to make fewer assumptions about the underlying dynamics, and thereby allows the data to drive the complexity of the inferred model. We began by examining a Bayesian nonparametric HMM, the sticky HDPHMM, that uses a hierarchical DP prior to regularize an unbounded mode space. We then considered extensions to Markov switching processes with richer, conditionally linear dynamics, including the HDP-AR-HMM and HDP-SLDS. We concluded by considering methods for transferring knowledge among multiple related time series. We argued that a featural representation is more appropriate than a rigid global clustering, as it encourages sharing of behaviors among objects while still allowing sequence-specific variability. In this context, the beta process provides an appealing alternative to the DP.},
author = {Fox, Emily B and Sudderth, Erik B and Jordan, Michael I and Willsky, Alan S},
doi = {10.1109/MSP.2010.937999},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2010{\_}Bayesian nonparametric methods for learning markov switching processes.pdf:pdf},
journal = {IEEE Signal Processing Magazine},
keywords = {HMM},
mendeley-tags = {HMM},
number = {November},
pages = {43--54},
title = {{Bayesian Nonparametric Learning of Markov Switching Processes}},
url = {https://ieeexplore.ieee.org/document/5563110},
year = {2010}
}
@article{Wang2016,
abstract = {Driving styles have a great influence on vehicle fuel economy, active safety, and drivability. To recognize driving styles of path-tracking behaviors for different divers, a statistical pattern-recognition method is developed to deal with the uncertainty of driving styles or characteristics based on probability density estimation. First, to describe driver path-tracking styles, vehicle speed and throttle opening are selected as the discriminative parameters, and a conditional kernel density function of vehicle speed and throttle opening is built, respectively, to describe the uncertainty and probability of two representative driving styles, e.g., aggressive and normal. Meanwhile, a posterior probability of each element in feature vector is obtained using full Bayesian theory. Second, a Euclidean distance method is involved to decide to which class the driver should be subject instead of calculating the complex covariance between every two elements of feature vectors. By comparing the Euclidean distance between every elements in feature vector, driving styles are classified into seven levels ranging from low normal to high aggressive. Subsequently, to show benefits of the proposed pattern-recognition method, a cross-validated method is used, compared with a fuzzy logic-based pattern-recognition method. The experiment results show that the proposed statistical pattern-recognition method for driving styles based on kernel density estimation is more efficient and stable than the fuzzy logic-based method.},
archivePrefix = {arXiv},
arxivId = {1606.01284},
author = {Wang, Wenshuo and Xi, Junqiang and Li, Xiaohan},
doi = {10.1049/iet-its.2017.0379},
eprint = {1606.01284},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/06{\_}Paper/2019{\_}03{\_}Ilmenau{\_}DE/01{\_}Literature/2016 - Wang - statistical pattern recognition for driving styles based on bayesian probability and kernel density estimation.pdf:pdf},
isbn = {0000000000},
issn = {1573-4978 (Electronic)$\backslash$r0301-4851 (Linking)},
pages = {1--10},
pmid = {20842443},
title = {{Statistical Pattern Recognition for Driving Styles Based on Bayesian Probability and Kernel Density Estimation}},
url = {http://arxiv.org/abs/1606.01284{\%}0Ahttp://dx.doi.org/10.1049/iet-its.2017.0379},
year = {2016}
}
@book{Trzesniowski,
author = {Trzesniowski, Michael},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2010{\_}Rennwagentechnik{\_}Grundlagen, Konstruktion, Komponenten, Systeme{\_}2 Auflage.pdf:pdf},
isbn = {9783834808578},
title = {{No Title}}
}
@article{Nr,
author = {Nr, Reihe},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2000{\_}FAT{\_}Erg{\"{a}}nzende Auswertungen zur subjektiven und objektiven Beurteilung des Fahrverhaltens von PKW{\_}Riedel A.pdf:pdf},
number = {169},
title = {{Erg{\"{a}}nzende Auswertungen}}
}
@inproceedings{Hei2007,
address = {Wiesbaden},
author = {Hei, Bernd and Ersoy, Metin},
doi = {10.1007/978-3-8348-9151-8},
editor = {Hei{\ss}ing, Bernd and Ersoy, Metin},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2007{\_}Fahrwerkhandbuch{\_}Grundlagen, Fahrdynamik, Komponenten, Systeme, Mechatronik, Perspektiven.pdf:pdf},
isbn = {978-3-8348-0105-0},
publisher = {Vieweg},
title = {{Fahrwerkhandbuch}},
url = {http://link.springer.com/10.1007/978-3-8348-9151-8},
year = {2007}
}
@book{Beucher2005,
abstract = {Dieses Buch gibt eine Einf{\"{u}}hrung in die grundlegenden Begriffe und Werkzeuge der Wahrscheinlichkeitsrechnung. Zentrale Begriffe und Methoden der angewandten mathematischen Statistik werden beschrieben, und weitergehende statistische Verfahren wie die Varianz- und Regressionsanalyse oder nichtparametische Verfahren werden diskutiert. Moderne Techniken wie die Monte-Carlo-Methode und wichtige Anwendungsgebiete aus dem ingenieurwissenschaftlichen Bereich werden vorgestellt. Alle Themen werden weitestgehend unter Verwendung von MATLAB bearbeitet. Dies erm{\"{o}}glicht die Diskussion praxisorientierter Beispiele, die meist nicht analytisch behandelt werden k{\"{o}}nnen, und es erh{\"{o}}ht die Verst{\"{a}}ndlichkeit der Thematik durch die M{\"{o}}glichkeiten der grafischen Visualisierung. Die verwendeten MATLAB-Programme werden ausf{\"{u}}hrlich kommentiert und dem Leser als Begleitsoftware auf der Homepage des Autors zur Verf{\"{u}}gung gestellt. Das Buch enth{\"{a}}lt {\"{u}}ber 100 {\"{U}}bungsaufgaben mit vollst{\"{a}}ndigen L{\"{o}}sungen. Das Werk eignet sich f{\"{u}}r Studierende aller ingenieurwissenschaftlichen und naturwissenschaftlichen Fachrichtungen an Universit{\"{a}}ten und Fachhochschulen.},
address = {Berlin/Heidelberg},
author = {Beucher, Ottmar},
doi = {10.1007/b138960},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2007{\_}Wahrscheinlichkeitsrechnung und Statistik mit MATLAB{\_}Beucher.pdf:pdf},
isbn = {3-540-23416-0},
pages = {XII, 509},
publisher = {Springer-Verlag},
title = {{Wahrscheinlichkeitsrechnung und Statistik mit MATLAB}},
url = {http://link.springer.com/10.1007/b138960},
year = {2005}
}
@book{Chacon,
author = {Chacon, Scott and Ben, Straub},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2014{\_}ProGit-en.105.pdf:pdf},
title = {{Pro Git}}
}
@article{Untersuchung2006,
abstract = {Die klassische Fahrverhaltensanalyse beruht auf der Untersuchung vieler verschiedener Man{\"{o}}ver und Fahrzust{\"{a}}nde. Die dabei gesammelten Informationen sind teilweise redundant. In diesem Beitrag der Daimler-Chrysler AG wird eine Metho-dik zur Fahrverhaltensanalyse beschrieben, die mit Hilfe eines einfachen Simulationsmodells und anhand weniger Fahr-man{\"{o}}ver eine {\"{a}}hnlich vollst{\"{a}}ndige Bewertung erm{\"{o}}glicht wie die klassische Fahrverhaltensanalyse.},
author = {Untersuchung, Der and Die, Fahrzust{\"{a}}nde},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2006{\_}Einspurmodell f{\"{u}}r die Fahrdynamiksimulation und -analyse{\_}ATZ.pdf:pdf},
title = {{ENTWICKLUNG Fahrdynamik Die Autoren Einspurmodell f{\"{u}}r die Fahrdynamiksimulation und-analyse}},
volume = {108},
year = {2006}
}
@article{Sicherheits-,
abstract = {Die Sicherheits-und Assistenzsysteme werden immer st{\"{a}}rker vernetzt, um das Autofahren mit innovativen Funktionen noch sicherer zu machen, Unf{\"{a}}lle ganz zu verhindern oder zumindest in ihrer Schwere zu mildern. Audi setzt auch im A6 einen neuen Meilenstein: In Situationen, bei denen eine Kollision wahrscheinlich ist, kann "Audi pre sense plus" eine Teilbremsung einleiten. Ist die Kollision unvermeidbar, kann sich nach der Teilbremsung eine Vollverz{\"{o}}gerung anschlie{\ss}en. Damit wird die St{\"{a}}rke des Aufpralls verringert. Eine Ziel-bremsung unterst{\"{u}}tzt den Fahrer in Situationen, in denen er-trotz vorangegangener optischer und akusti-scher Warnung-nur unzureichend bremst. 204 Audi A6 FAhrZEUgSIchErh EIT},
author = {Sicherheits-, Die and Ziel, Eine},
doi = {10.1365/s35778-010-048},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2011{\_}Fahrerassistenz und integrale Sicherheit{\_}Botsch{\_}Audi{\_}ATZ.pdf:pdf},
pages = {204--207},
title = {{Fahrerassistenz und integrale sicherheit}}
}
@book{Beyer1999,
abstract = {Dieses Lehrbuch f{\"{u}}hrt den Anwender der Mathematik, insbesondere Ingenieure und Naturwissenschaftler, in die Wahrscheinlichkeitsrechnung und mathematische Statistik ein. - Inhaltliche Schwerpunkte sind zuf{\"{a}}llige Ereignisse, Wahrscheinlichkeit, Zufallsgr{\"{o}}{\ss}en, Stichproben, statistische Sch{\"{a}}tzverfahren, statistische Pr{\"{u}}fverfahren sowie Regressions- und Korrelationsanalyse. Zahlreiche Beispiele sowie Aufgaben mit L{\"{o}}sungen bieten M{\"{o}}glichkeiten zur Kontrolle des erworbenen Wissens.},
address = {Wiesbaden},
author = {Beyer, Otfried and Hackel, Horst and Pieper, Volkmar and Tiedge, J{\"{u}}rge},
doi = {10.1007/978-3-322-94870-0},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}1999{\_}Wahrscheinlichkeitsrechnung und mathematische Statistik{\_}Beyer.pdf:pdf},
isbn = {978-3-519-00229-1},
pages = {264},
publisher = {Vieweg+Teubner Verlag},
series = {Mathematik f{\"{u}}r Ingenieure und Naturwissenschaftler},
title = {{Wahrscheinlichkeitsrechnung und mathematische Statistik}},
url = {http://link.springer.com/10.1007/978-3-322-94870-0},
year = {1999}
}
@book{Nollau1997,
abstract = {Dieses Lehr- und Aufgabenbuch zur Wahrscheinlichkeitsrechnung und mathematischen Statistik wendet sich an Studierende der Wirtschaftswissenschaften sowie der Ingenieur- und Naturwissenschaften. Inhalt und Aufbau orientieren sich an dem vielf{\"{a}}ltig erprobten Konzept, mathematische Begriffe, Definitionen, Aussagen und Verfahren unmittelbar und ausf{\"{u}}hrlich an Beispielen zu erl{\"{a}}utern. Zahlreiche {\"{U}}bungsaufgaben (mit L{\"{o}}sungen im Anhang) unterst{\"{u}}tzen den Leser bei der Aneignung dieses Wissensgebietes - als Erg{\"{a}}nzung zu Lehrveranstaltungen und im Selbststudium. Zugunsten hoher Verst{\"{a}}ndlichkeit und unmittelbarer N{\"{a}}he zu praktischen Fragestellungen wird bewu{\ss}t auf mathematische Beweise verzichtet. Eine {\"{u}}bersichtliche Darstellung mit zahlreichen Abbildungen und Tabellen erleichtert den Zugang zu diesem aktuellen Gebiet der Mathematik.},
address = {Wiesbaden},
author = {Nollau, Volker and Partzsch, Lothar and Storm, Regina and Lange, Claus},
doi = {10.1007/978-3-322-87374-3},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}1997{\_}Wahrscheinlichkeitsrechnung und Statistik in Beispielen und Aufgaben{\_}Nollau.pdf:pdf},
isbn = {978-3-8154-2073-7},
pages = {271},
publisher = {Vieweg+Teubner Verlag},
title = {{Wahrscheinlichkeitsrechnung und Statistik in Beispielen und Aufgaben}},
url = {http://link.springer.com/10.1007/978-3-322-87374-3},
year = {1997}
}
@phdthesis{Pavlidis2007,
author = {Pavlidis, Panos},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/05{\_}Abschlussarbeiten/StudWork{\_}2007{\_}Werkzeuge f{\"{u}}r die Verkehrssimulation{\_}Uni{\_}Karlsruhe.pdf:pdf},
pages = {1--18},
title = {{Werkzeuge f{\"{u}}r die Verkehrssimulation}},
url = {file:///Users/baumann/Documents/Mendeley Desktop/FAS2007{\_}Pavlidis{\_}Fahrerassistenzsysteme{\_}Ausarbeitung.pdf},
year = {2007}
}
@book{Von,
author = {Von, Weiterentwicklung},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2015{\_}Der Fahrer im 21 Jahrhundert{\_}VDI.pdf:pdf},
title = {{Der Fahrer im 21 Jahrhundert}}
}
@article{Uncu2007,
abstract = {Feature selection is one of the most important issues in the research fields such as system modelling, data mining and pattern recognition. In this study, a new feature selection algorithm that combines feature wrapper and feature filter approaches is proposed in order to identify the significant input variables in systems with continuous domains. The proposed method utilizes functional dependency concept, correlation coefficients and K-nearest neighbourhood (KNN) method to implement the feature filter and feature wrappers. Four feature selection methods independently select the significant input variables and the input variable combination, which yields best result with respect to their corresponding evaluation function, is selected as the winner. This is similar to the basic information fusion notion of integrating the information collected from different sources. All of the four feature selection methods are performed in two stages: (i) pre-selection, (ii) selection. Two of the four feature selection methods utilize KNN method for evaluating the candidates. These two methods use sequential forward and sequential backward search mechanism, respectively, in pre-selection stage. Whereas, the third feature selection method uses correlation coefficients in the pre-selection stage. It is common to have outliers and noise in real-life data. In order to make the proposed feature selection algorithm noise and outlier resistant, approximate functional dependencies are used by utilizing membership values that inherently cope with uncertainty in the data. Thus, the fourth feature selection method makes use of approximate functional dependencies to evaluate candidates in pre-selection stage. All of these four methods apply KNN method with exhaustive search strategy in order to find the most suitable input variable combination with respect to a performance measure. {\textcopyright} 2006 Elsevier Inc. All rights reserved.},
author = {Uncu, {\"{O}}zge and T{\"{u}}rkşen, I. B.},
doi = {10.1016/j.ins.2006.03.022},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/03{\_}Paper/Paper{\_}2007{\_}A novel feature selection approach - Combining feature wrappers and filters{\_}Uncu{\_}InformationSciences.pdf:pdf},
isbn = {0020-0255},
issn = {00200255},
journal = {Information Sciences},
keywords = {Approximate functional dependency,Cluster validity index,Feature filters,Feature wrappers,Fuzzy discretization},
number = {2},
pages = {449--466},
title = {{A novel feature selection approach: Combining feature wrappers and filters}},
volume = {177},
year = {2007}
}
@misc{Pink2014,
author = {Pink, Oliver and Nordbruch, Stefan},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/04{\_}Patente/22571 X1 - DE102013210941A1 20236491.pdf:pdf},
pages = {1--13},
title = {{Verfahren und Vorrichtung zum Betreiben eines Fahrzeugs}},
year = {2014}
}
@article{AndreasJorg2008,
abstract = {This thesis addresses the problem of rating the components and the operation of hybrid powertrains. This will be shown exemplarily on the optimised CVT-Hybrid powertrain of the Technische Universit¨at M¨unchen. First of all, the basic saving potentials of hybrid cars by downsizing, the usage of a continuous variable transmission (CVT), cyclic operation of the combustion engine and regeneration of breaking energy is stated and the results are compared to each other. Hence possible effects to the whole energy consumption in Germany will be derived. Furthermore, methods for rating the electrical components of the hybrid powertrain will be shown. Therefore, aspects about dimensioning of each component separately as well as a systematic integrated rating regarding to drive cycles by using offline optimisation is explained. The focus of the thesis lies in methods for the real-time operation of hybrid cars. The proposed prioritised torque split reduces the number of input parameters. The problem of the operation of the hybrid car is therefore reduced to determine the variable gear ratio of the CVT and the gear position, and to pilot the energy storage device. By derivating an inverse model out of the model equations of the powertrain, an ideal reference value for the state of charge of the energy storage device is calculated. An operating strategy based on heuristic rules as well as a model based loss minimization is shown for the operation of the hybrid powertrain. Both methods use the ideal reference value for the state of charge for an accurate energy management of the energy storage device. In addition, a method for logging previous driving sequences by neural networks and by segmentation is shown. Future driving behaviour is predicted by this data and the prediction information is used to calculate the ideal reference value for the state of charge by the inverted model individually for each driving situation, so that the energy management can be improved. Additionally, a method for using the prediction information to calculate an opti- mal operating trajectory for the predicted driving sequence in real time is shown. Consequently, a linear model with additional switching states is derived from the nonlinear model of the longitudinal dynamic of the powertrain. By using the algo- rithm of Mixed Integer Linear Programming an optimal solution can be calculated for the simplified model. The previous calculated trajectories show a very good mat- ching with the simulation and measurement results. The outcome of this is, that the optimal solution for the linear problem including switching states, is also a very good solution for the nonlinear model of the powertrain. For explanation and verification of the proposed methods various simulation re- sults are shown. The measurement results from the test stands as well as from the prototyping car show the practical applicability of the methods.},
author = {{Andreas J{\"{o}}rg}},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/02{\_}Dissertation/Diss{\_}2009{\_}Optimale Auslegung und Betriebsfuehrung von Hybridfahrzeugen{\_}Adreas Joerg{\_}TUM{\_}EAL.pdf:pdf},
title = {{Optimale Auslegung und Betriebsf{\"{u}}hrung von Hybridfahrzeugen}},
year = {2008}
}
@book{Bourier2011,
address = {Wiesbaden},
author = {Bourier, G{\"{u}}nther},
doi = {10.1007/978-3-8349-6555-4},
edition = {7},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2011{\_}Wahrscheinlichkeitsrechnung und schlie{\ss}ende Statistik{\_}Bourier.pdf:pdf},
isbn = {978-3-8349-2762-0},
publisher = {Springer Fachmedien Wiesbaden GmbH},
title = {{Wahrscheinlichkeitsrechnung und schlie{\ss}ende Statistik}},
url = {http://link.springer.com/10.1007/978-3-8349-6555-4},
year = {2011}
}
@misc{FilevDimitarPetrovNoviMich.US;KolmanovskylIyaVladimirNoviMich.US;GusikhinOlegYurievitchWestBloomfieldMich.US;SzwabowskiStevenJosephNorthvilleMich.US;MacNeillePerryRobinsonLathrupVillageMich.US;TeslakCh2012,
author = {{Filev, Dimitar Petrov, Novi, Mich., US; Kolmanovsky, lIya Vladimir, Novi, Mich., US; Gusikhin, Oleg Yurievitch, West Bloomfield, Mich., US; Szwabowski, Steven Joseph, Northville, Mich., US; MacNeille, Perry Robinson, Lathrup Village, Mich., US; Teslak, Ch}, US},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/04{\_}Patente/22571 Y2 - DE102012209518A1 20236492.pdf:pdf},
title = {{FAHRZEUGFAHRER-BERATUNGSSYSTEM}},
year = {2012}
}
@book{Butz,
author = {Butz, Torsten},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/02{\_}Dissertation/Diss{\_}2004{\_}Optimaltheoretische Modellierung und Identifizierung von Fahrereigenschaften{\_}Butz Torsten.pdf:pdf},
isbn = {3185080084},
number = {1},
title = {{Optimaltheoretische Modellierung und Identifizierung von Fahrereigenschaften}}
}
@book{Cramer2008b,
address = {Berlin, Heidelberg},
author = {Cramer, Erhard and Kamps, Udo},
doi = {10.1007/978-3-540-77761-8},
file = {:C$\backslash$:/Users/ziegmann/Desktop/Dissertation/03{\_}DissPromotion/00{\_}Literatur/01{\_}Books/Book{\_}2008{\_}Grundlagen der Wahrscheinlichkeitsrechnung und Statistik{\_}Cramer{\_}Springer.pdf:pdf},
isbn = {978-3-540-77760-1},
publisher = {Springer Berlin Heidelberg},
series = {Springer-Lehrbuch},
title = {{Grundlagen der Wahrscheinlichkeitsrechnung und Statistik}},
url = {http://link.springer.com/10.1007/978-3-540-77761-8},
year = {2008}
}