摘要
精确预测邻近车辆的未来轨迹对自动驾驶汽车的决策和运动规划至关重要,现有研究倾向于利用递归神经网络(RNN)对车辆的时间交互进行建模,但其对车辆交互建模的可解释性差,忽略了实际的车道结构,在捕捉车辆与其环境的交互方面存在不足。为解决这一问题,本文提出了一种基于图卷积交互网络的考虑车道拓扑约束的车辆轨迹预测模型。其中车辆交互关系提取模块在构建车辆的空间关系时增加了边缘权重,以考虑车辆的邻近交互,使交互更具可解释性;行驶场景表征模块旨在通过从高精地图中提取车道拓扑来提高车辆轨迹预测的准确性;轨迹预测模块将上述两个模块的输出进行集成,并输出预测的未来轨迹。这种集成允许对道路结构和车辆行驶轨迹之间的相互作用进行更精确的建模。实验结果表明,与主流方法相比,该模型在Argoverse数据集上取得了良好的性能,提高了复杂道路结构下车辆轨迹规划的准确性和合理性。
Accurate prediction of the future trajectory of surrounding vehicles is crucial to the decisionmaking and motion planning of autonomous vehicle.Existing research tends to use Recurrent Neural Networks(RNN)to model the time interaction of vehicles,but its interpretability of vehicle interaction modeling is poor,ignoring the actual lane structure,and there are deficiencies in capturing the interaction between vehicles and the environment.To address this problem,in this paper,a vehicle trajectory prediction model based on graph convolutional interactive networks that considers lane topology constraints is proposed.The vehicle interaction relationship extraction module adds edge weights when constructing the spatial relationship of vehicles to consider their neighboring interaction,making the interaction more interpretable.The driving scene representation module aims to improve the accuracy of vehicle trajectory prediction by extracting lane topology from high-precision maps.The trajectory prediction module integrates the output of the above two modules and outputs the predicted future trajectory.This integration allows for more precise modeling of the interaction between road structures and vehicle driving trajectories.The experimental results show that compared with mainstream methods,this model has achieved good performance on the Argoverse dataset,improving the accuracy and rationality of vehicle trajectory planning under complex road structures.
作者
王梦茜
蔡英凤
王海
饶中钰
陈龙
李祎承
Wang Mengxi;Cai Yingfeng;Wang Hai;Rao Zhongyu;Chen Long;Li Yicheng(Automotive Engineering Research Institute,Jiangsu University,Zhenjiang 212013;School of Automotive and Traffic Engineering,Jiangsu University,Zhenjiang 212013)
出处
《汽车工程》
EI
CSCD
北大核心
2024年第10期1863-1872,共10页
Automotive Engineering
基金
国家自然科学基金(52225212,U20A20333)资助。
关键词
自动驾驶汽车
轨迹预测
图卷积网络
交互建模
autonomous vehicle
trajectory prediction
graph convolution network
interaction behavior
