Cognitive prediction of obstacle's movement for reinforcement learning pedestrian interacting model
Abstract: Recent studies in pedestrian simulation have been able to construct a highly realistic navigation behaviour in many circumstances. However, when replicating the close interactions between pedestrians, the replicated behaviour is often unnatural and lacks human likeness. One of the possible reasons is that the current models often ignore the cognitive factors in the human thinking process. Another reason is that many models try to approach the problem by optimising certain objectives. On the other hand, in real life, humans do not always take the most optimised decisions, particularly when interacting with other people. To improve the navigation behaviour in this circumstance, we proposed a pedestrian interacting model using reinforcement learning. Additionally, a novel cognitive prediction model, inspired by the predictive system of human cognition, is also incorporated. This helps the pedestrian agent in our model to learn to interact and predict the movement in a similar practice as humans. In our experimental results, when compared to other models, the path taken by our model’s agent is not the most optimised in certain aspects like path lengths, time taken and collisions. However, our model is able to demonstrate a more natural and human-like navigation behaviour, particularly in complex interaction settings.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Cognitive prediction of obstacle's movement for reinforcement learning pedestrian interacting model ; volume:31 ; number:1 ; year:2022 ; pages:127-147 ; extent:21
Journal of intelligent systems ; 31, Heft 1 (2022), 127-147 (gesamt 21)
- Creator
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Trinh, Thanh-Trung
Kimura, Masaomi
- DOI
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10.1515/jisys-2022-0002
- URN
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urn:nbn:de:101:1-2022071514064924930172
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:30 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Trinh, Thanh-Trung
- Kimura, Masaomi
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