Wang, Y., Ramakrishnan Srinivasan, A., Jokinen, J.P.P. et al. (2 more authors) (2023) Modeling human road crossing decisions as reward maximization with visual perception limitations. In: Proceedings of 2023 IEEE Intelligent Vehicles Symposium (IV). 2023 IEEE Intelligent Vehicles Symposium (IV), 04-07 Jun 2023, Alaska, USA. IEEE ISBN 9798350346916
Abstract
Understanding the interaction between different road users is critical for road safety and automated vehicles (AVs). Existing mathematical models on this topic have been proposed based mostly on either cognitive or machine learning (ML) approaches. However, current cognitive models are incapable of simulating road user trajectories in general scenarios, and ML models lack a focus on the mechanisms generating the behavior and take a high-level perspective which can cause failures to capture important human-like behaviors. Here, we develop a model of human pedestrian crossing decisions based on computational rationality, an approach using deep reinforcement learning (RL) to learn boundedly optimal behavior policies given human constraints, in our case a model of the limited human visual system. We show that the proposed combined cognitive-RL model captures human-like patterns of gap acceptance and crossing initiation time. Interestingly, our model’s decisions are sensitive to not only the time gap, but also the speed of the approaching vehicle, something which has been described as a “bias” in human gap acceptance behavior. However, our results suggest that this is instead a rational adaption to human perceptual limitations. Moreover, we demonstrate an approach to accounting for individual differences in computational rationality models, by conditioning the RL policy on the parameters of the human constraints. Our results demonstrate the feasibility of generating more human-like road user behavior by combining RL with cognitive models.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This item is protected by copyright. This is an author produced version of a conference paper published in Proceedings of 2023 IEEE Intelligent Vehicles Symposium (IV), 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: | Human behavior; computational rationality; noisy perception; reinforcement learning |
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: | 11 Oct 2023 08:37 |
Last Modified: | 27 Mar 2024 19:19 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/iv55152.2023.10186617 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:204103 |