Schumann, J.F., Srinivasan, A.R. orcid.org/0000-0001-9280-7837, Kober, J. et al. (2 more authors) (2023) Using Models Based on Cognitive Theory to Predict Human Behavior in Traffic: A Case Study. In: Proceedings of 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC). 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), 24-28 Sep 2023, Bilbao, Spain. IEEE , pp. 5870-5875. ISBN 979-8-3503-9947-9
Abstract
The development of automated vehicles has the potential to revolutionize transportation, but they are currently unable to ensure a safe and time-efficient driving style. Reliable models predicting human behavior are essential for overcoming this issue. While data-driven models are commonly used to this end, they can be vulnerable in safety-critical edge cases. This has led to an interest in models incorporating cognitive theory, but as such models are commonly developed for explanatory purposes, this approach's effectiveness in behavior prediction has remained largely untested so far. In this article, we investigate the usefulness of the Commotions model - a novel cognitively plausible model incorporating the latest theories of human perception, decision-making, and motor control - for predicting human behavior in gap acceptance scenarios, which entail many important traffic interactions such as lane changes and intersections. We show that this model can compete with or even outperform well-established data-driven prediction models across several naturalistic datasets. These results demonstrate the promise of incorporating cognitive theory in behavior prediction models for automated vehicles.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This is an author produced version of a conference paper published in Proceedings of 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), made available under the terms of the Creative Commons Attribution License (CC-BY), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | autonomous vehicles, gap acceptance, behavior prediction, cognitive 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) > ITS: Safety and Technology (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/S005056/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 21 Jun 2024 10:21 |
Last Modified: | 23 Jun 2024 01:29 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ITSC57777.2023.10421837 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:213717 |