计算机应用研究2026，Vol.43Issue(9)：2639-2647,9.DOI:10.19734/j.issn.1001-3695.2026.01.0011
基于几何约束稀疏图的行人多模态轨迹预测方法
Multi-modal pedestrian trajectory prediction method based on geometric constrained sparse graph
摘要
Abstract
Aiming at the problems of inaccurate interaction modeling and insufficient representation of multimodal uncertainty in pedestrian trajectory prediction for complex dynamic scenes,this study proposed a pedestrian multimodal trajectory predic-tion method named GCSG-Traj based on geometric constrained sparse graphs.This method constructed a sparse interaction graph under three constraints including viewing scope,distance threshold and motion approaching property,extracted spatio-temporal features by integrating graph convolutional networks and Transformers,and realized multimodal trajectory prediction through two-dimensional Gaussian distribution parameter prediction and Latin hypercube sampling.Experimental results on the ETH/UCY datasets show that GCSG-Traj achieves an average displacement error of 0.31 m and a final displacement error of 0.51 m.It outperforms most comparison methods and maintains an excellent balance between prediction accuracy and model compactness.The results verify that this method effectively improves the accuracy and diversity of pedestrian trajectory predic-tion in complex dynamic scenes.关键词
行人轨迹预测/几何约束/稀疏交互图/拉丁超立方采样/多模态轨迹预测Key words
pedestrian trajectory prediction/geometric constraints/sparse interaction graph/Latin hypercube sampling/multimodal trajectory prediction分类
信息技术与安全科学引用本文复制引用
刘希斌,陈豪..基于几何约束稀疏图的行人多模态轨迹预测方法[J].计算机应用研究,2026,43(9):2639-2647,9.基金项目
国家自然科学基金资助项目(42205150) （42205150）
中国博士后科学基金资助项目(2024M761470) （2024M761470）
2024年江苏省农业科技自主创新资金项目(CX(24)1022) （CX(24）
