基于强化邻接图卷积的交叉口车辆轨迹预测模型  

Vehicle Trajectory Prediction at Intersections Using Enhanced Adjacency Graph Convolution

作　　者：齐浩轩 曹弋 赵斌[1] QI Haoxuan;CAO Yi;ZHAO Bin(School of Transportation Engineering,Dalian Jiaotong University,Dalian 116028,China)

机构地区：[1]大连交通大学交通工程学院,大连116028

出　　处：《地球信息科学学报》2025年第3期623-635,共13页Journal of Geo-information Science

基　　金：辽宁省属本科高校基本科研业务费专项资金资助项目(LJ212410150048);2023年度辽宁省研究生教育教学改革研究项目(LNYJG2023138)。

摘　　要：【目的】为提高交叉口中车辆轨迹预测精度,解决交叉口多车交互场景中的轨迹预测难题。【方法】构建强化邻接图卷积(EAG-GCN-T)的车辆轨迹预测模型,采用公共数据集INTERACTION并对数据噪声进行平滑处理,开展模型对比验证试验,与不同基准模型比较误差评估指标,验证轨迹预测精度、对比交互能力、分析泛化能力与驾驶行为识别。在模型构造上,EAG-GCN-T模型将强化邻接图卷积网络(EAG-GCN)与Transformer模型结合,EAG-GCN模块通过考虑相对速度与距离,利用强化加权邻接矩阵精确建模车辆空间交互关系,Transformer模块捕捉时间依赖性并生成未来轨迹,提升了时空预测能力。【结果】在预测效能方面,单车轨迹长时段预测时,平均位移误差(ADE)和最终位移误差(FDE)较CV、ARIMA、CNN-LSTM分别降低69.4%、39.8%、33.3%和71.9%、32.5%、27.4%;多车交互预测中,与GRIP模型相比,FDE降低19.5%和20.6%;与3种交互机制对比,EAG-GCN-T在所有时域下总体误差最低,ADE/FDE分别为0.53和0.74,且行驶区域符合度(DCA)和轨迹点丢失率(MR)更合理,在匝道和环形交叉口适应性良好。在驾驶行为方面,模型能精准预判跟驰、变道、避让等行为及其对轨迹的影响,预测轨迹与真实轨迹高度一致。【结论】EAG-GCN-T模型有效解决了交叉口多车交互场景中的车辆轨迹预测问题,具有高精度、强交互性和良好的泛化能力,为智能交通系统的车辆轨迹预测提供了新的解决方案。[Objectives]The primary objective is to enhance the accuracy of vehicle trajectory prediction at intersections and address the challenges in predicting trajectories in multi-vehicle interaction scenarios.This is crucial for improving the safety and efficiency of autonomous driving and traffic management in complex urban intersections.[Methods]An Enhanced Adjacency Graph Convolutional Network-Transformer(EAG-GCN-T)vehicle trajectory prediction model is developed.The INTERACTION public dataset is employed,with data smoothing techniques applied to mitigate noise.Model comparison and validation experiments are conducted to assess performance.The model’s accuracy is evaluated by comparing error assessment indicators against different baseline models,analyzing interaction capabilities,generalization ability,and driving behavior recognition.The EAG-GCN-T model combines an Enhanced Adjacency Graph Convolutional Network(EAG-GCN)and a Transformer module.The EAG-GCN module accurately models spatial interactions between vehicles by considering relative speed and distance using an enhanced weighted adjacency matrix.The Transformer module captures temporal dependencies and generates future trajectories,improving spatiotemporal prediction ability.[Results]In long-term single-vehicle trajectory prediction,the Average Displacement Error(ADE)and Final Displacement Error(FDE)are reduced by 69.4%,39.8%,and 33.3%and 71.9%,32.5%,and 27.4%respectively,compared to CV,ARIMA,and CNN-LSTM models.In multi-vehicle interaction prediction,the FDE is reduced by 19.5%and 20.6%compared to the GRIP model.Compared with three interaction mechanisms,EAG-GCN-T achieves the lowest overall error across all time domains,with ADE/FDE values of 0.53 and 0.74,respectively.EAG-GCN-T achieves more reasonable Driving Area Compliance(DAC)and Trajectory Point Loss Rate(MR),demonstrating strong adaptability in ramps and roundabouts.The model accurately predicts driving behaviors such as following,lane-changing,evasion,and their impacts on trajectories,with pred

关 键 词：智能交通 车辆轨迹预测 交叉口 图卷积神经网络 TRANSFORMER 驾驶行为 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]