Möbus, Claus and Eilers, Mark (2011) Mixture of Behaviors and Levels-of-Expertise in a Bayesian Autonomous Driver Model. In: Advances in Applied Digital Human Modeling. CRC Press, Taylor & Francis Group, Boca Raton, USA, pp. 425-435. ISBN 978-1-4398-3511-1

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Abstract

Traffic scenario simulations and risk-based design require Digital Human Models (DHMs) of human control strategies. Furthermore, it is tempting to prototype assistance systems on the basis of a human driver model cloning an expert driver. We present the model architecture for embedding probabilistic models of human driver expertise with sharing of behaviors in different driving maneuvers. These models implement the sensory-motor system of human drivers in a mixture- of-behaviors (MoB) architecture with autonomous and goal-based attention allocation processes. A Bayesian MoB model is able to decompose complex skills (maneuvers) into basic skills (behaviors) and vice versa. The Bayesian-MoB-Model defines a probability distribution over driver-vehicle trajectories so that it has the ability to predict agent’s behavior, to abduct hazardous situations, to generate anticipatory plans and control, and to plan counteractive measures by simulating counterfactual behaviors or actions preventing hazardous situations.

Item Type:	Book Section
Uncontrolled Keywords:	Bayesian Modeling, Bayesian Autonomous Driver Model, Probabilistic Driver Model
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:	28 Jan 2014 10:42
Last Modified:	28 Jan 2014 10:42
URI:	https://oops.uni-oldenburg.de/id/eprint/1756
URN:	urn:nbn:de:gbv:715-oops-18379
DOI:
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