交通信息与安全2025，Vol.43Issue(1)：42-51,10.DOI:10.3963/j.jssn.1674-4861.2025.01.004
基于障碍车辆轨迹预测的驾驶碰撞风险模型
Driving Collision Risk Modeling with Trajectory Prediction of Obstacle Vehicles
摘要
Abstract
In order to address critical challenges in intelligent driving systems,including insufficient dynamic inter-action modeling,limited accuracy in multimodal trajectory prediction,and over-reliance on single physical metrics for collision risk quantification.A proactive collision risk assessment framework is proposed by integrating probabi-listic quantification with multimodal trajectory prediction.For trajectory prediction,a hierarchical graph attention network is developed to capture dynamic environmental features through adaptive fusion of high-definition maps,lane geometries,and vehicle motion history.A sliding window-optimized decoder is introduced within the conven-tional two-stage prediction architecture to refine trajectory outputs.For risk assessment,a probabilistic collision quantification method is designed to calculate collision likelihood between ego and surrounding vehicles based on predicted trajectories.Results on the Argoverse dataset demonstrate state-of-the-art performance with minimum fi-nal displacement error(=0.785),average displacement error(=1.157),and miss rate(=0.126),achieving 1%and 15.1%error reduction in endpoint prediction compared to HiVT and LaneGCN respectively.simulation of urban mo-bility,SUMO simulations reveal 5%deviation between predicted and actual risks,with risk fluctuation amplitude re-duced by 33.3%and 18.75%against time to collision(TTC)and dynamic safety index(DSI)methods.The proposed model shows enhanced stability in continuous driving scenarios(risk fluctuation=0.3)and demonstrates superior ac-curacy in forecasting potential collision risks through systematic integration of trajectory prediction and probabilistic analysis.These findings validate the framework's effectiveness in proactive safety warning for intelligent vehicles.关键词
交通安全/预见性碰撞风险评估/车辆轨迹预测/图神经网络/风险概率量化Key words
traffic safety/predictive crash risk assessment/vehicle trajectory prediction/graph neural networks/Risk probability quantification分类
交通工程引用本文复制引用
杨厚新,陆丽萍,秦恒,杨奥,褚端峰..基于障碍车辆轨迹预测的驾驶碰撞风险模型[J].交通信息与安全,2025,43(1):42-51,10.基金项目
国家自然科学基金面上项目(52172392)、湖北省交通运输厅科技项目(2023-121-3-2)资助 （52172392）
