Modeling human road crossing decisions as reward maximization with visual perception limitations
Wang, Y., Srinivasan, A. R., Jokinen, J., Oulasvirta, A., & Markkula, G. (2023). Modeling human road crossing decisions as reward maximization with visual perception limitations. In 2023 IEEE Intelligent Vehicles Symposium (IV). IEEE. IEEE Intelligent Vehicles Symposium. https://doi.org/10.1109/IV55152.2023.10186617
Julkaistu sarjassa
IEEE Intelligent Vehicles SymposiumTekijät
Päivämäärä
2023Tekijänoikeudet
© Authors 2023
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.
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Julkaisija
IEEEEmojulkaisun ISBN
979-8-3503-4692-3Konferenssi
Kuuluu julkaisuun
2023 IEEE Intelligent Vehicles Symposium (IV)ISSN Hae Julkaisufoorumista
1931-0587Asiasanat
human behavior computational rationality noisy perception reinforcement learning adaptation models pedestrians intelligent vehicles computational modeling mathematical models road safety tieliikenne matemaattiset mallit koneoppiminen mallintaminen käyttäytyminen havaitseminen kognitiiviset prosessit jalankulkijat liikenneturvallisuus liikenne
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/184697721
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Lisätietoja rahoituksesta
This project has received funding from UK Engineering and Physical Sciences Research Council under fellowship named COMMOTIONS - Computational Models of Traffic Interactions for Testing of Automated Vehicles - EP/S005056/1.Lisenssi
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