重庆理工大学学报2026，Vol.40Issue(9)：36-42,7.DOI:10.3969/j.issn.1674-8425(z).2026.05.005
轨迹拓扑引导的多智能体轨迹预测模型
Multi-agent trajectory prediction guided by trajectory topology
摘要
Abstract
Existing trajectory prediction methods usually struggle to accurately comprehend interactions and complex scene information.To address the issue,this paper proposes a multi-agent trajectory prediction model TTG(trajectory topology-guided).A spatiotemporal feature encoding method is employed to encode scene information.A topology fusion module and a topology decoder are employed to extract encoded features and compute interaction probability.A feature fusion module imposes scene road constraints on the future predicted trajectories.Experiments conducted on the Argoverse motion forecasting benchmark demonstrate TTGNet achieves a probability-weighted minimum final displacement error(b-mFDE6)of 1.73,down by 4.42％and a minimum average displacement Error(mADE6)of 0.76,down by 3.8％compared to those of the baseline model SIMPL.关键词
轨迹预测/多智能体/特征编码/轨迹拓扑Key words
trajectory prediction/multi-agent/feature encoding/trajectory topology分类
信息技术与安全科学引用本文复制引用
邓召学,王金权,王戡,李兴泉..轨迹拓扑引导的多智能体轨迹预测模型[J].重庆理工大学学报,2026,40(9):36-42,7.基金项目
国家自然科学基金项目(52072054) （52072054）
