  Automated Valet Parking (AVP) is a crucial component of advanced autonomous
driving systems, focusing on the endpoint task within the "human-vehicle
interaction" process to tackle the challenges of the "last mile".The perception
module of the automated parking algorithm has evolved from local perception
using ultrasonic radar and global scenario precise map matching for
localization to a high-level map-free Birds Eye View (BEV) perception
solution.The BEV scene places higher demands on the real-time performance and
safety of automated parking planning tasks. This paper proposes an improved
automated parking algorithm based on the A* algorithm, integrating vehicle
kinematic models, heuristic function optimization, bidirectional search, and
Bezier curve optimization to enhance the computational speed and real-time
capabilities of the planning algorithm.Numerical optimization methods are
employed to generate the final parking trajectory, ensuring the safety of the
parking path. The proposed approach is experimentally validated in the commonly
used industrial CARLA-ROS joint simulation environment. Compared to traditional
algorithms, this approach demonstrates reduced computation time with more
challenging collision-risk test cases and improved performance in comfort
metrics.
