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

Date
2016
Advisor
Instructor
Source Title
Proceedings of the 2016 American Control Conference, ACC 2016
Print ISSN
0743-1619
Electronic ISSN
Publisher
IEEE
Volume
Issue
Pages
1705 - 1710
Language
English
Type
Conference Paper
Journal Title
Journal ISSN
Volume Title
Abstract

This 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.

Course
Other identifiers
Book Title
Keywords
Automobiles, Cognition, Games, Learning (artificial intelligence), Markov processes, Decision making
Citation
Published Version (Please cite this version)