云南民族大学学报（自然科学版）2026，Vol.35Issue(2)：234-241,277,9.DOI:10.3969/j.issn.1672-8513.2026.02.010
一种基于时空注意力机制的行人轨迹预测模型
A pedestrian trajectory prediction network based on spatio-temporal attention mechanism
摘要
Abstract
With the rapid progress of urbanization,the ways in which people interact with cities are constantly changing.Precisely predicting the future trajectory of pedestrians can provide a decision-making basis and technical support for areas such as transportation and urban planning.To better establish a social model for predicting pedestrian trajectories,most researchers have introduced attention mechanisms to model the spatial relationships between pedestrians in the model,but less attention has been paid to the temporal correlation of mutual influence between pedestrians.In response to the above problems,this paper proposes a pedestrian trajectory prediction model based on Spatio-Temporal Multi-head Self-attention(STMS).STMS constructs an improved Graph Attention structure(N-GAT)to efficiently capture interactions and spatial interactions between pedestrians.Secondly,it innovatively introduces the S-GRU and TC-GRU modules to model the temporal dependencies of pedestrian spatial interactions.This approach not only extracts key features in the temporal dimension but also captures the relationships between interactions at different time steps.Finally,the model adopts an encoder-decoder(Seq2seq)framework to integrate the modeled pedestrian information and employs D-GRU as the decoder to predict trajectories.Through comparative experiments and ablation experiments on two public datasets ETH and UCY,we demonstrate that STMS exhibits superior modeling capabilities in capturing the interactive relationships among pedestrians,significantly enhancing the overall accuracy of pedestrian trajectory prediction.关键词
时空多头自注意力机制/图注意力网络/时间相关性/Seq2seq框架Key words
spatio-temporal multi-head self-attention/graph attention network/temporal correlation/Seq2seq分类
信息技术与安全科学引用本文复制引用
姚杰,操星,平红,杨欣..一种基于时空注意力机制的行人轨迹预测模型[J].云南民族大学学报（自然科学版）,2026,35(2):234-241,277,9.基金项目
国家自然科学基金(61573182、62073164) （61573182、62073164）
中央高校基本科研业务费基金(NS2020025). （NS2020025）
