Accurate motion planning enables lower risk in advance for autonomous vehicles, thus promising the safety and smoothness of automated driving. Due to the influence of dynamic objects and traffic rules, most of the existing methods on motion planning when applying to the dynamic urban environment show limited performance. To tackle this challenge, we propose a new motion planning method for autonomous vehicles by integrating the spatio-temporal map into motion planning. The proposed method contains three stages: spatio-temporal map, path planning, and trajectory optimization. In the first stage, a spatio-temporal map is constructed based on the static environments and predicted trajectories of dynamic objects. In the second stage, an improved hybrid A star algorithm integrating the obstacle avoidance, vehicle kinematics, and traffic rules is developed to efficiently search collision-free feasible paths. In the last state, a B-spline curve combined with vehicle dynamics is proposed to further optimize the trajectory, thus the autonomous vehicle can move more safely and smoothly. Extensive simulation experiments are also conducted to evaluate the performance of the proposed method in comparison with other existing methods. The results show that the proposed motion planning method achieves promising performance in a dynamic urban environment for autonomous driving.
 

Motion Planning;Spatio-Temporal;trajectory optimization