Paper Info
Title
Further Steps towards Driver Modeling According to the Bayesian Programming Approach
Abstract
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.
Year
DOI
Venue
2009
10.1007/978-3-642-02809-0_44
HCI (11)
Keywords
Field
DocType
bayesian autonomous driver,bad model,simple dynamic bayesian sensory-motor,digital human models,action control,human centered design,human control strategy,bayesian programming,real-time control,longitudinal control behavior,real time control,conditional probability,psychology,time series,human behavior,probability distribution,graphical model
Joint probability distribution,Conditional probability,Multivariate statistics,Computer science,Advanced driver assistance systems,Bayesian programming,Artificial intelligence,Perception,Machine learning,Bayesian probability,User-centered design
Conference
Volume
ISSN
Citations 
5620
0302-9743
6
PageRank 
References 
Authors
0.96
4
2
Name
Order
Citations
PageRank
Claus Möbus15815.18
Mark Eilers2203.70