Eilers, Mark and Möbus, Claus (2011) Learning the Relevant Percepts of Modular Hierarchical Bayesian Driver Models Using a Bayesian Information Criterion. In: Digital Human Modeling. LNCS (6777). Springer, Heidelberg, Berlin, pp. 463-472. ISBN 978-3-642-21799-9


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Modeling drivers’ behavior is essential for the rapid prototyping of error-compensating assistance systems. Various authors proposed control- theoretic and production-system models. Based on psychological studies various percepts and measures (angles, distances, time-to-x-measures) have been proposed for such models. These proposals are partly contradictory and depend on special experimental settings. A general computational vision theory of driving behavior is still pending. We propose the selection of drivers’ percepts according to their statistical relevance. In this paper we present a new machine-learning method based on a variant of the Bayesian Information Criterion (BIC) using a parent-child-monitor to obtain minimal sets of percepts which are relevant for drivers’ actions in arbitrary scenarios or maneuvers.

Item Type: Book Section
Uncontrolled Keywords: Percepts, Learning, Driver Model, Bayesian Driver Model, Structure Learning, Bayesian Information Criterion, BIC
Subjects: Generalities, computers, information > Computer science, internet
Philosophy and psychology > Psychology
Technology, medicine, applied sciences > Engineering and machine engineering
Technology, medicine, applied sciences > Electrical engineering, electronics
Divisions: School of Computing Science, Business Administration, Economics and Law > Department of Computing Science
Date Deposited: 21 Feb 2014 09:28
Last Modified: 21 Feb 2014 09:28
URI: https://oops.uni-oldenburg.de/id/eprint/1781
URN: urn:nbn:de:gbv:715-oops-18628
DOI: 10.1007/978-3-642-21799-9_52

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