Automated Parking Planning with Vision-Based BEV Approach

TL;DR

This paper presents an improved vision-based BEV parking planning algorithm that enhances real-time performance and safety using A* search, vehicle kinematic models, and trajectory optimization, validated in simulation.

Contribution

It introduces a novel BEV perception and planning approach integrating heuristic optimization and Bezier curves for faster, safer automated parking.

Findings

Reduced computation time compared to traditional methods

Improved safety in collision-risk scenarios

Enhanced parking comfort metrics

Abstract

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.