控制与信息技术Issue(5)：15-23,9.DOI:10.13889/j.issn.2096-5427.2025.05.500
基于异构图注意力网络的动态轨迹预测方法研究
Research on Dynamic Trajectory Prediction Based on Heterogeneous Graph Attention Network
摘要
Abstract
Trajectory prediction for heterogeneous multi-agent systems within local areas is essential for ensuring the safe and efficient operation of autonomous vehicles.However,complex urban traffic intersection scenarios present significant challenges due to diverse information exchanges,as well as the need for continuity and temporal consistency in behavioral intentions.Existing anchor-free query generation methods often suffer from mode collapse and training instability,and struggle to maintain intent continuity and temporal consistency at complex intersections.To address these challenges,this paper proposes a two-stage heterogeneous edge-augmented graph attention network(HEGANet)for dynamic trajectory prediction.The first stage performs spatiotemporal context encoding to extract dynamic agent features.These features are combined with heterogeneous graphs featuring directed edge attributes to model spatiotemporal interactions between agents.A heterogeneous graph attention network and a dynamic feature gating mechanism are introduced to optimize feature weighting,while a multi-scale dynamic interaction graph attention network is utilized to further model both inter-agent and intra-agent dynamic interactions.In the second stage,a multi-layer perceptron(MLP)decodes the predicted embeddings from the first stage,and iterative optimization is applied to refine multi-agent,multi-modal future trajectory predictions.Experimental results on the INTERACTION and Argoverse datasets demonstrate that the proposed model significantly outperforms existing methods in terms of trajectory prediction accuracy.关键词
动态轨迹预测/多尺度动态交互图注意力网络/图神经网络/异构交互Key words
dynamic trajectory prediction/multi-scale dynamic interaction graph attention network/graph neural network/heterogeneous interaction分类
航空航天引用本文复制引用
陈琳,宁念文,田诗涵,周毅..基于异构图注意力网络的动态轨迹预测方法研究[J].控制与信息技术,2025,(5):15-23,9.基金项目
国家重点研发计划政府间国际科技创新合作专项(2023YFE0112500) （2023YFE0112500）
国家自然科学基金项目(62176088) （62176088）
河南省高等学校重点科研项目(22A120001) （22A120001）
融合知识图谱的城市时空大模型关键技术与应用(ghfund202407028284) （ghfund202407028284）
东濮老区采出水高效智能化水质调控技术研发(231220038300001) （231220038300001）
