基于周边车辆轨迹预测的智能汽车路径规划  

Intelligent vehicle path planning method based on peripheral vehicle trajectory prediction

作　　者：黄晨[1,2] 贾丁鹏 孙晓强 许庆[2] HUANG Chen;JIA Dingpeng;SUN Xiaoqiang;XU Qing(Automotive Engineering Research Institute,Jiangsu University,Zhenjiang 210031,China;State Key Laboratory of Intelligent Green Vehicle and Mobility(Formerly State Key Laboratory of Automotive Safety and Energy),Tsinghua University,Beijing 100084,China)

机构地区：[1]江苏大学汽车工程研究院,镇江212013 [2]智能绿色车辆与交通全国重点实验室(原汽车安全与节能国家重点实验室),清华大学,北京100084

出　　处：《汽车安全与节能学报》2024年第5期753-762,共10页Journal of Automotive Safety and Energy

基　　金：汽车安全与节能国家重点实验室开放基金课题(KFY2207)。

摘　　要：为提高智能汽车在动态行车环境下的行驶安全和通行效率,研究了基于周边车辆轨迹预测的路径规划方法,并进行了仿真。提出了一种基于时空图卷积网络(STGCN)的周边车辆轨迹预测方法,通过STGCN对车辆历史轨迹进行编码,提取交通图的时空特征,并结合长短时记忆网络实现周边车辆的轨迹预测。在此基础上,提出了一种基于改进人工势场(APF)的路径规划方法;建立了基于APF的行车危险评价模块;利用Frenet坐标描述驾驶危险度,通过目标障碍物和道路边界的势能分布及梯度下降法完成路径规划。结果表明:本算法的短时预测精度提高了3%,长时预测精度提高了1%;所得路径曲线的前轮转角不超过0.12 rad,曲率不超过0.1;因此,在确保有效避撞的前提下,保证了车辆行驶的舒适性和高效性。A path planning method was investigated based on the peripheral vehicle trajectory prediction with doing digital simulations to improve the driving safety and access efficiency of intelligent vehicles in dynamic driving environments.The peripheral vehicle trajectory prediction method was proposed based on the Spatio-Temporal Graph Convolutional Network(STGCN),which encoded the historical vehicle trajectories through STGCN,extracted the spatio-temporal features of traffic maps and combined with long and short-term memory networks to achieve the trajectory prediction of peripheral vehicles.On this basis,a path planning method was proposed based on an Improved Artificial Potential Field(APF),and an APF-based driving hazard evaluation module was established,which described the driving hazard by using the Frenet coordinates,and completed the path planning through the potential distribution of target obstacles and road boundaries and the gradient descent method.The results show that the proposed algorithm improves prediction accuracy by about 3%in the short-time prediction and by 1%in the long-time prediction with a path curve of the front wheel angle not exceeding 0.12 rad,and a curvature not exceeding 0.1,ensuring comfort and high efficiency during vehicle travel while effectively avoiding collisions.

关 键 词：智能汽车 路径规划 轨迹预测 时空图卷积网络(STGCN) 人工势场(APF) 

分 类 号：U495[交通运输工程—交通运输规划与管理]