高速公路无人驾驶的分层抽样多动态窗口轨迹规划算法

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 experiments 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 planning algorithm based on artificial potential fields and multi-dynamic window simulated annealing-optimized trajectory planning algorithm.