App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 11

Pedestrian trajectory prediction method based on risk-aware interaction and heteroscedastic uncertainty modeling

Tian Hongpeng
Zhao Yifan
College of Artificial Intelligence & Computer Science, Xi'an University of Science & Technology, Xi'an 710600, China
DOI: 10.19734/j.issn.1001-3695.2026.03.0061

Abstract

To address the insufficient identification of key interacting agents, sensitivity to input noise, and inadequate representation of prediction reliability in pedestrian trajectory prediction under complex urban traffic environments, this study proposes a pedestrian trajectory prediction method based on risk-aware interaction and heteroscedastic uncertainty modeling. Pedestrian-specific prediction samples are constructed from the Argoverse 2 dataset. Historical trajectories and vectorized maps are jointly used to represent scene information, and a dual-branch network consisting of a social encoder and a map encoder is established. A risk-aware mechanism is designed based on time to closest approach, distance at closest approach, and the rate of bearing change to adaptively select potential conflict agents. Heteroscedastic uncertainty modeling is introduced into the prediction module, and a negative log-likelihood loss is used to jointly optimize trajectory positions and predictive distributions. Experimental results show that the proposed method achieves average displacement errors and final displacement errors of 0.453 m and 0.914 m on the validation split, and 0.463 m and 0.943 m on the test split, respectively, outperforming the compared methods. The proposed method enhances the accuracy and stability of pedestrian trajectory prediction in complex interaction scenarios.

Foundation Support

国家自然科学基金项目(62401460)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.03.0061
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 11

Publish History

[2026-07-30] Accepted Paper

Cite This Article

田红鹏, 赵一帆. 基于风险感知交互与异方差不确定性建模的行人轨迹预测方法 [J]. 计算机应用研究, 2026, 43 (11). (2026-07-31). https://doi.org/10.19734/j.issn.1001-3695.2026.03.0061. (Tian Hongpeng, Zhao Yifan. Pedestrian trajectory prediction method based on risk-aware interaction and heteroscedastic uncertainty modeling [J]. Application Research of Computers, 2026, 43 (11). (2026-07-31). https://doi.org/10.19734/j.issn.1001-3695.2026.03.0061. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

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.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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