湖北汽车工业学院学报2024，Vol.38Issue(1)：7-12,17,7.DOI:10.3969/j.issn.1008-5483.2024.01.002
基于预测风险场模型的智能车辆换道路径规划
Intelligent Vehicle Lane Changing Path Planning Based on Predictive Risk Field Model
摘要
Abstract
In view of complex driving environments and expression methods of intelligent vehicles,a predictive risk field model for intelligent vehicles incorporating long short term memory(LSTM)predic-tion model was proposed.Based on the traditional potential field model,the predictive information about dynamic target behaviors was considered,and the kinetic energy field of dynamic prediction was established.In addition,the kinetic energy field was superimposed with the risk field of other risk ele-ments in the road environment to construct a unified model,namely the predictive risk field.By design-ing the cost function of the risk field,the minimum cost evaluation of the planning trajectory cluster was completed,and the optimal path planning trajectory was obtained.In order to verify the effectiveness of the method,joint simulations and real-vehicle verification were conducted.The experimental results show that the predictive risk field model effectively expresses the traffic situation in the complex driving environment,and the selected optimal paths improve comprehensive safety.关键词
路径规划/轨迹预测/LSTM/预测风险场Key words
path planning/trajectory prediction/LSTM/predicted risk field分类
交通工程引用本文复制引用
杨正才,谷师锐,吴浩然,孙文..基于预测风险场模型的智能车辆换道路径规划[J].湖北汽车工业学院学报,2024,38(1):7-12,17,7.基金项目
中央引导地方科技发展专项(2022BGE248) （2022BGE248）
湖北汽车工业学院博士科研启动基金(BK202215) （BK202215）
