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.

基于A*算法，结合车辆运动学模型、启发式函数优化、双向搜索和贝塞尔曲线优化的改进型自动停车算法，提高了算法的计算速度和实时能力，并利用数值优化方法生成最终停车轨迹，确保了停车路径的安全性。与传统算法相比，该方法在更具挑战性的碰撞风险测试案例中计算时间减少，并在舒适性指标上的性能提升。

基于视觉的BEV自动停车规划