Pedestrian trajectory prediction based on dynamic topology reasoning

Zhao Shuai
Li Lin
School of Optical-Electrical & Computer Engineering, University of Shanghai for Science & Technology, Shanghai 200093, China

Abstract

To address the inability of existing pedestrian trajectory prediction methods to represent high-order group interactions and their lack of feedback correction mechanisms, this study developed a new prediction method integrating dynamic topology generation and iterative closed-loop optimization. A dynamic topology generation module used an attention mechanism to construct simplicial complexes and mine multi-order group interaction features. A generalized simplicial attention network achieved cross-dimensional hierarchical information aggregation. Additionally, an iterative feedback loop based on Hodge decomposition parsed prediction uncertainty and fed back correction signals to realize dynamic closed-loop optimization. Experiments on the ETH and UCY datasets show that the method reduces the Average Displacement Error (ADE) and Final Displacement Error (FDE) by 27.8% and 25.4%, respectively, compared with the STG-DTBTA model. The approach significantly improves prediction accuracy and adaptability in complex scenarios.

Foundation Support

国家自然科学基金资助项目(61673277)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.01.0010
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 9

Publish History

[2026-06-02] Accepted Paper

Cite This Article

赵帅, 李琳. 基于动态拓扑推理的行人轨迹预测算法 [J]. 计算机应用研究, 2026, 43 (9). (2026-06-02). https://doi.org/10.19734/j.issn.1001-3695.2026.01.0010. (Zhao Shuai, Li Lin. Pedestrian trajectory prediction based on dynamic topology reasoning [J]. Application Research of Computers, 2026, 43 (9). (2026-06-02). https://doi.org/10.19734/j.issn.1001-3695.2026.01.0010. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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