An Autonomous Valet Parking Algorithm for Path Planning and Tracking

Autonomous valet parking (AVP) is a popular application scenario for autonomous driving in the future. For AVP path planning, the original hybrid A-star ($\mathrm{A}^{*}$) algorithm has problems of large search costs, searching towards wrong directions and generating unreasonable parking paths. To solve these problems and generate a better path, a path planning method is proposed for typical AVP scenarios. The method divides path planning into global part and local part. The global path is planned based on graph search and state lattice algorithm. Then the hybrid $\mathrm{A}^{*}$ algorithm and Reeds-Shepp curve are modified to complete the local path planning, and finally a complete path that can be executed by the vehicle is generated. Then, a controller for path tracking based on model predictive control (MPC) is designed to overcome the shortcomings of traditional proportional integral derivative (PID) control such as overshoot and difficulty in precise control. Finally, the feasibility of the path planning and tracking method is verified by simulation using MATLAB and the vehicle simulation software CarSim. The results show that the planning efficiency and rationality are improved by implementing the proposed method, and the parking process can be done well with a small tracking error.