A game theoretical model of traffic with multiple interacting drivers for use in autonomous vehicle development

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dc.citation.epage1710en_US
dc.citation.spage1705en_US
dc.contributor.authorOyler, D. W.en_US
dc.contributor.authorYıldız, Yıldırayen_US
dc.contributor.authorGirard, A. R.en_US
dc.contributor.authorLi, N. I.en_US
dc.contributor.authorKolmanovsky, İ. V.en_US
dc.coverage.spatialBoston, MA, USAen_US
dc.date.accessioned2018-04-12T11:41:33Z
dc.date.available2018-04-12T11:41:33Z
dc.date.issued2016en_US
dc.departmentDepartment of Mechanical Engineeringen_US
dc.descriptionDate of Conference: 6-8 July 2016en_US
dc.descriptionConference Name: 2016 American Control Conference, ACC 2016en_US
dc.description.abstractThis paper describes a game theoretical model of traffic where multiple drivers interact with each other. The model is developed using hierarchical reasoning, a game theoretical model of human behavior, and reinforcement learning. It is assumed that the drivers can observe only a partial state of the traffic they are in and therefore although the environment satisfies the Markov property, it appears as non-Markovian to the drivers. Hence, each driver implicitly has to find a policy, i.e. a mapping from observations to actions, for a Partially Observable Markov Decision Process. In this paper, a computationally tractable solution to this problem is provided by employing hierarchical reasoning together with a suitable reinforcement learning algorithm. Simulation results are reported, which demonstrate that the resulting driver models provide reasonable behavior for the given traffic scenarios.en_US
dc.identifier.doi10.1109/ACC.2016.7525162en_US
dc.identifier.issn0743-1619en_US
dc.identifier.urihttp://hdl.handle.net/11693/37487
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/ACC.2016.7525162en_US
dc.source.titleProceedings of the 2016 American Control Conference, ACC 2016en_US
dc.subjectAutomobilesen_US
dc.subjectCognitionen_US
dc.subjectGamesen_US
dc.subjectLearning (artificial intelligence)en_US
dc.subjectMarkov processesen_US
dc.subjectDecision makingen_US
dc.titleA game theoretical model of traffic with multiple interacting drivers for use in autonomous vehicle developmenten_US
dc.typeConference Paperen_US

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