Trajectory Planning for Autonomous Valet Parking in Narrow Environments With Enhanced Hybrid A Search and Nonlinear Optimization

• Published in: IEEE transactions on intelligent vehicles 2023-06, Vol.8 (6), p.1-12
• Main Authors: Lian, Jing, Ren, Weiwei, Yang, Dongfang, Li, Linhui, Yu, Fengning
• Format: Article
• Language: English
• Subjects: Aerospace electronics; Algorithms; Autonomous parking; Collision avoidance; Kinematics; numerical optimization; Optimal control; Optimization; Parking; Robustness (mathematics); Task analysis; Trajectory; Trajectory analysis; Trajectory optimization; Trajectory planning
• ISSN: 2379-8858; 2379-8904
• DOI: 10.1109/TIV.2023.3268088

This paper focuses on the problem of autonomous valet parking trajectory planning in complex environments. The task normally can be well described by an optimal control problem (OCP) for the rapid, accurate, and optimal trajectory generation. Appropriate initial guesses obtained by sampling or searching-based methods are important for the numerical optimization procedure. Still, in highly complex environments with narrow passages, it may incur high computational costs or fail to find the proper initial guess. To address this challenge, an enhanced hybrid A* (EHA) algorithm is proposed to address the issue. The EHA includes four steps. The first step is to quickly obtain the global coarse trajectory using a traditional A* search. The second step is constructing a series of driving corridors along the rough trajectory, then evaluating and extracting nodes from each wide or narrow passage based on the length of the box's side. The third step is to extract each passage's boundary points. The final step is connecting boundary points by hybrid A* and generating a feasible initial guess for OCP. To reduce safety risks, vehicles in particular areas (Fig. 1b) should travel as slowly as possible. The global and local speed restrictions are distinct, and local restrictions are only activated when the vehicle enters a particular area. This "if-else" structure makes the optimization problem difficult. A novel approximation formulation for these local state constraints is introduced to overcome this issue. The experimental results demonstrate that the proposed method for trajectory planning is effective and robust.