Autonomous Trajectory Planning Based on Two-Stage Sampling and Multiple Constraints for Mobile Vehicle

How to guarantee the effective planning trajectory of autonomous driving for mobile vehicles is the main challenge. This study provides a multi-constraint trajectory planning technique to construct safe, smooth, and dynamically viable trajectories in complicated situations. Firstly, a two-stage sampling path generation algorithm is proposed to obtain a cluster of candidate paths, considering road geometry, vehicle kinematics, and static obstacle avoidance constraints. Secondly, a cost function is designed to select the optimal path based on smoothness, consistency with the reference path, and distance to obstacles. Finally, a speed planning model is developed using convex optimization to allocate speed profiles for each path point with dynamic constraints, including time efficiency, boundary conditions, and dynamic obstacle avoidance. Experimental results on mobile vehicles demonstrate the effectiveness and stability of the proposed trajectory planning algorithm in real-world environments and its ability to handle various typical driving scenarios. The success rate of trajectory planning in the experiments was 94%, with an average planning time of 37.3ms. Note to Practitioners—This paper is inspired by limited access to the effective planning trajectory of mobile vehicle under a complex situations. Current target detection methods are mostly operated by computer vision, which is representing less responsiveness on practical vehicle system. Enhancing the optimal planning performance by increasing the accuracy with fast operation of mobile vehicle has fundamental importance.