Probabilistic and Empirical Grounded Modeling of Agents in (Partial) Cooperative Traffic Scenarios

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5620)


The Human Centered Design (HCD) of Partial Autonomous Driver Assistance Systems (PADAS) requires Digital Human Models (DHMs) of human control strategies for simulations of traffic scenarios. The scenarios can be regarded as problem situations with one or more (partial) cooperative problem solvers. According to their roles models can be descriptive or normative. We present new model architectures and applications and discuss the suitability of dynamic Bayesian networks as control models of traffic agents: Bayesian Autonomous Driver (BAD) models. Descriptive BAD models can be used for simulating human agents in conventional traffic scenarios with Between-Vehicle-Cooperation (BVC) and in new scenarios with In-Vehicle-Cooperation (IVC). Normative BAD models representing error free behavior of ideal human drivers (e.g. driving instructors) may be used in these new IVC scenarios as a first Bayesian approximation or prototype of a PADAS.


digital human response models probabilistic driver models Bayesian autonomous driver models learning of human control strategies graphical modeling human behavior learning and transfer distributed cognition mixture-of-experts model visual attention allocation partial cooperative problem solvers partial autonomous assistance system Bayesian assistance system shared space probabilistic detection of anomalies driver assistance systems traffic agents dynamic Bayesian networks hidden Markov models between-vehicle-cooperation within-vehicle-cooperation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  1. 1.University of Oldenburg / OFFISGermany

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