Möbus, Claus and Eilers, Mark (2009) Further Steps towards Driver Modeling according to the Bayesian Programming Approach. In: Digital Human Modeling. Lecture Notes in Computer Science (LNCS 5620), 5620 . Springer Berlin Heidelberg, pp. 413-422. ISBN 978-3-642-02808-3


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The Human Centered Design (HCD) of Partial Autonomous Driver Assistance Systems (PADAS) requires Digital Human Models (DHMs) of human control strategies for simulating traffic scenarios. We describe first results to model lateral and longitudinal control behavior of drivers with simple dynamic Bayesian sensory-motor models according to the Bayesian Programming (BP) approach: Bayesian Autonomous Driver (BAD) models. BAD models are learnt from multivariate time series of driving episodes generated by single or groups of users. The variables of the time series describe phenomena and processes of perception, cognition, and action control of drivers. BAD models reconstruct the joint probability distribution (JPD) of those variables by a composition of conditional probability distributions (CPDs). The real-time control of virtual vehicles is achieved by inferring the appropriate actions under the evidence of sensory percepts with the help of the reconstructed JPD.

Item Type: Book Section
Uncontrolled Keywords: Partial Autonomous Driver Assistance Systems, lateral and longitudinal control behavior, simple dynamic Bayesian sensory-motor models,Bayesian Programming, Bayesian Autonomous Driver (BAD) models
Subjects: Generalities, computers, information > Computer science, internet
Philosophy and psychology > Psychology
Technology, medicine, applied sciences > Engineering and machine engineering
Divisions: School of Computing Science, Business Administration, Economics and Law > Department of Computing Science
Date Deposited: 11 Jul 2014 08:31
Last Modified: 11 Jul 2014 08:31
URI: https://oops.uni-oldenburg.de/id/eprint/1841
URN: urn:nbn:de:gbv:715-oops-19224
DOI: 10.1007/978-3-642-02809-0_44

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