Hierarchical Parking Path Planning Based on Optimal Parking Positions

Automated valet parking (AVP) has attracted the attention of industry and academia in recent years. However, there are still many challenges to be solved, including shortest path search, optimal time efficiency, and applicability of algorithm in complex scenarios. In this paper, a hierarchical AVP path planner is proposed, which divides a complete AVP path planning into the guided layer and the planning layer from the perspective of global decision-making. The guided layer is mainly used to divide a complex AVP path planning into several simple path plannings, which makes the hybrid A* algorithm more applicable in a complex parking environment. The planning layer mainly adopts different optimization methods for driving and parking path planning. The proposed method is verified by a large number of simulations which include the verification of the optimal parking position, the performance of the planner for perpendicular parking, and the scalability of the planner for parallel parking and inclined parking. The simulation results reveal that the efficiency of the algorithm is increased by more than 20 times, and the average path length is also shortened by more than 20%. Furthermore, the planner overcomes the problem that the hybrid A* algorithm is not applicable in complex parking scenarios.