SIAT: Pedestrian trajectory prediction via social interaction-aware transformer
Abstract Pedestrian trajectory prediction is crucial for mitigating collision risks in intelligent transportation and surveillance systems. Despite recent advances, accurately capturing and modeling complex social interactions among pedestrians remains a challenge. This paper introduces the Social I...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-06-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-025-01944-3 |
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| _version_ | 1849388675842965504 |
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| author | Chengdong Wang Jianming Wang Wenbo Gao Lei Guo |
| author_facet | Chengdong Wang Jianming Wang Wenbo Gao Lei Guo |
| author_sort | Chengdong Wang |
| collection | DOAJ |
| description | Abstract Pedestrian trajectory prediction is crucial for mitigating collision risks in intelligent transportation and surveillance systems. Despite recent advances, accurately capturing and modeling complex social interactions among pedestrians remains a challenge. This paper introduces the Social Interaction-Aware Transformer (SIAT), a novel approach that leverages a Transformer encoder to process pedestrian embedding features and a Graph Convolutional Network (GCN) to construct a social graph for extracting spatial interaction features. The future pedestrian trajectory is predicted using a Transformer decoder that integrates both pedestrian embeddings and social graph features. Extensive experiments on the ETH/UCY and Stanford Drone datasets demonstrate that SIAT significantly outperforms state-of-the-art methods in terms of accuracy and robustness, particularly in densely populated environments. SIAT’s contributions include improved precision through temporal and spatial processing, deep contextual understanding of pedestrian dynamics, and robustness across various settings. The novel model framework establishes a new benchmark for mixed models in trajectory prediction. |
| format | Article |
| id | doaj-art-b6a80c6664604d1cbd209a3c0bb9a9f4 |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-b6a80c6664604d1cbd209a3c0bb9a9f42025-08-20T03:42:11ZengSpringerComplex & Intelligent Systems2199-45362198-60532025-06-0111811410.1007/s40747-025-01944-3SIAT: Pedestrian trajectory prediction via social interaction-aware transformerChengdong Wang0Jianming Wang1Wenbo Gao2Lei Guo3School of Computer and Software Engineering, Anhui Institute of Information TechnologySchool of Computer and Software Engineering, Anhui Institute of Information TechnologySchool of Computer and Software Engineering, Anhui Institute of Information TechnologyYunnan Key Laboratory of Computer Technology Applications, Faculty of Information Engineering and Automation, Kunming University of Science and TechnologyAbstract Pedestrian trajectory prediction is crucial for mitigating collision risks in intelligent transportation and surveillance systems. Despite recent advances, accurately capturing and modeling complex social interactions among pedestrians remains a challenge. This paper introduces the Social Interaction-Aware Transformer (SIAT), a novel approach that leverages a Transformer encoder to process pedestrian embedding features and a Graph Convolutional Network (GCN) to construct a social graph for extracting spatial interaction features. The future pedestrian trajectory is predicted using a Transformer decoder that integrates both pedestrian embeddings and social graph features. Extensive experiments on the ETH/UCY and Stanford Drone datasets demonstrate that SIAT significantly outperforms state-of-the-art methods in terms of accuracy and robustness, particularly in densely populated environments. SIAT’s contributions include improved precision through temporal and spatial processing, deep contextual understanding of pedestrian dynamics, and robustness across various settings. The novel model framework establishes a new benchmark for mixed models in trajectory prediction.https://doi.org/10.1007/s40747-025-01944-3Pedestrian trajectory predictionSocial InteractionTransformerGCN |
| spellingShingle | Chengdong Wang Jianming Wang Wenbo Gao Lei Guo SIAT: Pedestrian trajectory prediction via social interaction-aware transformer Complex & Intelligent Systems Pedestrian trajectory prediction Social Interaction Transformer GCN |
| title | SIAT: Pedestrian trajectory prediction via social interaction-aware transformer |
| title_full | SIAT: Pedestrian trajectory prediction via social interaction-aware transformer |
| title_fullStr | SIAT: Pedestrian trajectory prediction via social interaction-aware transformer |
| title_full_unstemmed | SIAT: Pedestrian trajectory prediction via social interaction-aware transformer |
| title_short | SIAT: Pedestrian trajectory prediction via social interaction-aware transformer |
| title_sort | siat pedestrian trajectory prediction via social interaction aware transformer |
| topic | Pedestrian trajectory prediction Social Interaction Transformer GCN |
| url | https://doi.org/10.1007/s40747-025-01944-3 |
| work_keys_str_mv | AT chengdongwang siatpedestriantrajectorypredictionviasocialinteractionawaretransformer AT jianmingwang siatpedestriantrajectorypredictionviasocialinteractionawaretransformer AT wenbogao siatpedestriantrajectorypredictionviasocialinteractionawaretransformer AT leiguo siatpedestriantrajectorypredictionviasocialinteractionawaretransformer |