Camara, F orcid.org/0000-0002-2655-1228, Cosar, S, Bellotto, N et al. (2 more authors) (2020) Continuous Game Theory Pedestrian Modelling Method for Autonomous Vehicles. In: Olaverri-Monreal, C, García-Fernández, F and Rossetti, RJF, (eds.) Human Factors in Intelligent Vehicles. River Publishers ISBN 978-8770222044
Abstract
Autonomous Vehicles (AVs) must interact with other road users. They must understand and adapt to complex pedestrian behaviour, especially during crossings where priority is not clearly defined. This includes feedback effects such as modelling a pedestrian’s likely behaviours resulting from changes in the AVs behaviour. For example, whether a pedestrian will yield if the AV accelerates, and vice versa. To enable such automated interactions, it is necessary for the AV to possess a statistical model of the pedestrian’s responses to its own actions. A previous work demonstrated a proof-of-concept method to fit parameters to a simplified model based on data from a highly artificial discrete laboratory task with human subjects. The method was based on lidar-based person tracking, game theory, and Gaussian process analysis. The present study extends this method to enable analysis of more realistic continuous human experimental data. It shows for the first time how game-theoretic predictive parameters can be fit into pedestrians natural and continuous motion during road-crossings, and how predictions can be made about their interactions with AV controllers in similar real-world settings.
Metadata
Item Type: | Book Section |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2020 River Publishers. This is an author produced version of a book chapter published in Human Factors in Intelligent Vehicles. Uploaded with permission from the publisher. |
Keywords: | Pedestrian Crossing Behaviour; Autonomous Vehicles; Interactions; Game Theory |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) |
Funding Information: | Funder Grant number EU - European Union 723395 |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 Jul 2020 11:26 |
Last Modified: | 29 Jul 2021 10:17 |
Status: | Published |
Publisher: | River Publishers |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:162717 |