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Sökning: id:"swepub:oai:DiVA.org:mdh-66496" > Modeling interpreta...

Modeling interpretable social interactions for pedestrian trajectory

Liu, Q. (författare)
Department of Civil Engineering, McGill University, Montreal, Quebec, H3A 0C3, Canada. Department of Traffic Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
Shi, Xiaodan (författare)
Mälardalens universitet,Framtidens energi,Center for Spatial Information Science, The University of Tokyo, Kashiwa, Japan
Jiang, R. (författare)
Center for Spatial Information Science, The University of Tokyo, Kashiwa, Japan. Information Technology Center, The University of Tokyo, Kashiwa, Japan
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Zhang, H. (författare)
School of Urban Planning and Design, Peking University, Shenzhen, Guangdong, 518055, China
Lu, L. (författare)
Department of Traffic Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
Shibasaki, R. (författare)
Center for Spatial Information Science, The University of Tokyo, Kashiwa, Japan
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Department of Civil Engineering, McGill University, Montreal, Quebec, H3A 0C3, Canada Department of Traffic Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China Framtidens energi (creator_code:org_t)
Elsevier Ltd, 2024
2024
Engelska.
Ingår i: Transportation Research Part C. - : Elsevier Ltd. - 0968-090X .- 1879-2359. ; 162
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • The abilities to understand pedestrian social interaction behaviors and to predict their future trajectories are critical for road safety, traffic management and more broadly autonomous vehicles and robots. Social interactions are intuitively heterogeneous and dynamic over time and circumstances, making them hard to explain. In this paper, we creatively investigate modeling interpretable social interactions for pedestrian trajectory, which is not considered by the existing trajectory prediction research. Moreover, we propose a two-stage methodology for interaction modeling - “mode extraction” and “mode aggregation”, and develop a long short-term memory (LSTM)-based model for long-term trajectory prediction, which naturally takes into account multi-types of social interactions. Different from previous models that do not explain how pedestrians interact socially, we extract latent modes that represent social interaction types which scales to an arbitrary number of neighbors. Extensive experiments over two public datasets have been conducted. The quantitative and qualitative results demonstrate that our method is able to capture the multi-modality of human motion and achieve better performance under specific conditions. Its performance is also verified by the interpretation of predicted modes, of which the results are in accordance with common sense. Besides, we have performed sensitivity analysis on the crucial hyperparameters in our model. Code is available at: https://github.com/xiaoluban/Modeling-Interpretable-Social-Interactions-for-Pedestrian-Trajectory.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences (hsv//eng)

Nyckelord

Deep learning
Explainability and comprehensibility of AI
Interpretable social interactions
Long short-term memory (LSTM)
Multi-modality
Trajectory prediction
Brain
Forecasting
Motor transportation
Pedestrian safety
Sensitivity analysis
Trajectories
Interaction behavior
Interpretable social interaction
Long short-term memory
Pedestrian trajectories
Performance
Social interactions

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Av författaren/redakt...
Liu, Q.
Shi, Xiaodan
Jiang, R.
Zhang, H.
Lu, L.
Shibasaki, R.
Om ämnet
NATURVETENSKAP
NATURVETENSKAP
och Data och informa ...
Artiklar i publikationen
Transportation R ...
Av lärosätet
Mälardalens universitet

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