自动化学报2024，Vol.50Issue(7)：1315-1332,18.DOI:10.16383/j.aas.c210673
高速公路无人驾驶的分层抽样多动态窗口轨迹规划算法
Stratified Sampling Based Multi-dynamic Window Trajectory Planner for Autonomous Driving on Highway
摘要
Abstract
Autonomous driving trajectory planning on highways faces challenges of strong real-time performance and safety.This paper proposes a stratified sampling based multi-dynamic window trajectory planner(SMWTP)for unmanned vehicles on highway.Firstly,the search space of feasible trajectories is constructed with multi-dynamic windows.Then,the Bayesian network is used to derive the probability distribution model of trajectories.Secondly,the stratified sampling strategy where speed is sampled before path makes generated candidate trajectories meet the constraints in dynamic scenes.Finally,the uncertainty of traffic participant vehicles'speed estimation is embedded into responsibility sensitive safety(RSS)model to select the optimal trajectory.A large number of simulation exper-iments and real traffic scenario tests have verified the effectiveness of the algorithm.The comparative experimental results show that the performance of the proposed algorithm is significantly better than the optimal trajectory plan-ning algorithm based on artificial potential fields and multi-dynamic window simulated annealing-optimized traject-ory planning algorithm.关键词
无人驾驶/轨迹规划/运动规划/贝叶斯网络Key words
Autonomous driving/trajectory planning/motion planning/Bayesian network引用本文复制引用
张琳,薛建儒,马超,李庚欣,李勇强..高速公路无人驾驶的分层抽样多动态窗口轨迹规划算法[J].自动化学报,2024,50(7):1315-1332,18.基金项目
国家自然科学基金(62036008,61773311)资助Supported by National Natural Science Foundation of China(62036008,61773311) （62036008,61773311）
