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Learning Individual Driver’s Mental Models Using POMDPs and BToM
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0002-3052-9277
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0003-2949-4123
2020 (English)In: DHM2020: Proceedings of the 6th International Digital Human Modeling Symposium, August 31 – September 2, 2020 / [ed] Lars Hanson, Dan Högberg, Erik Brolin, Amsterdam: IOS Press, 2020, p. 51-60Conference paper, Published paper (Refereed)
Abstract [en]

Advanced driver assistant systems are supposed to assist the driver and ensure their safety while at the same time providing a fulfilling driving experience that suits their individual driving styles. What a driver will do in any given traffic situation depends on the driver’s mental model which describes how the driver perceives the observable aspects of the environment, interprets these aspects, and on the driver’s goals and beliefs of applicable actions for the current situation. Understanding the driver’s mental model has hence received great attention from researchers, where defining the driver’s beliefs and goals is one of the greatest challenges. In this paper we present an approach to establish individual drivers’ temporal-spatial mental models by considering driving to be a continuous Partially Observable Markov Decision Process (POMDP) wherein the driver’s mental model can be represented as a graph structure following the Bayesian Theory of Mind (BToM). The individual’s mental model can then be automatically obtained through deep reinforcement learning. Using the driving simulator CARLA and deep Q-learning, we demonstrate our approach through the scenario of keeping the optimal time gap between the own vehicle and the vehicle in front.

Place, publisher, year, edition, pages
Amsterdam: IOS Press, 2020. p. 51-60
Series
Advances in Transdisciplinary Engineering, ISSN 2352-751X, E-ISSN 2352-7528 ; 11
Keywords [en]
Driver Mental Model, Partially Observable Markov Decision Processes (POMDPs), Bayesian Theory of Mind (BToM), Reinforcement Learning
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
URN: urn:nbn:se:his:diva-19122DOI: 10.3233/ATDE200009ISI: 000680825700006Scopus ID: 2-s2.0-85091228001ISBN: 978-1-64368-104-7 (print)ISBN: 978-1-64368-105-4 (electronic)OAI: oai:DiVA.org:his-19122DiVA, id: diva2:1471458
Conference
6th International Digital Human Modeling Symposium, August 31 – September 2, 2020, Skövde, Sweden
Projects
Intention Recognition for Real-time Automotive 3D situation awareness (IRRA)
Funder
Vinnova, 2018-05012
Note

CC BY-NC 4.0 This work has been supported by VINNOVA, the Swedish Government Agency for Innovation Systems, proj. “Intention Recognition for Real-time Automotive 3D situation awareness (IRRA)”, in the funding program FFI: Strategic Vehicle Research and Innovation (DNR 2018-05012)

Available from: 2020-09-29 Created: 2020-09-29 Last updated: 2023-10-04Bibliographically approved

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Darwish, AmenaSteinhauer, H. Joe

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