计算机工程与应用2024，Vol.60Issue(14)：114-122,9.DOI:10.3778/j.issn.1002-8331.2304-0208
面向高密度交通场景的自动驾驶运动规划
Motion Planning for Autonomous Driving in Dense Traffic Scenarios
摘要
Abstract
Aiming at the problem that the existing motion planning methods for autonomous driving ignore the interaction of surrounding vehicles when extracting state information and the bad planning effect in dense traffic scenarios,a motion planning model combined with graph neural network and deep reinforcement learning is proposed.Firstly,based on the graph neural network,an interactive feature representation method of self-driving vehicles is proposed to extract spatial interaction features of multiple traffic participants.In this case,a learning strategy for motion planning is designed based on twin delayed deep deterministic policy gradient(TD3),and the next action is predicted from the interactive features so as to realize motion planning.The proposed method is compared with the current motion planning model LSTM+TD3,TD3 and deep deterministic policy gradient(DDPG)for autonomous driving,in dense traffic scenarios,the experimental results of training and testing in the PGDrive driving simulator increased by 36％,43％,23％and 13,19,53 percentage points compared with the comparison method,which means the proposed method can effectively solve the problem of interactive information perception of surrounding vehicles for better motion planning of autonomous driving.关键词
自动驾驶/运动规划/交互式特征/图神经网络/强化学习Key words
autonomous driving/motion planning/interactive feature/graph neural network/reinforcement learning分类
信息技术与安全科学引用本文复制引用
肖雨微,姚溪子,胡学敏,罗显志..面向高密度交通场景的自动驾驶运动规划[J].计算机工程与应用,2024,60(14):114-122,9.基金项目
国家自然科学基金面上项目(62273135) （62273135）
湖北省自然科学基金(2021CFB460) （2021CFB460）
湖北省大学生创新创业训练计划基金(S202210512030,S202110512065) （S202210512030,S202110512065）
湖北大学大学生创新创业训练计划基金(X202110512086). （X202110512086）
