基于视觉BEV方法的自动泊车规划
Automated Parking Planning with Vision-Based BEV Approach
AI总结：
针对BEV场景下自动泊车规划的实时性与安全性需求，提出融合运动学模型、启发式优化等的改进A*泊车算法，经CARLA-ROS仿真验证，相较传统算法计算更快、舒适性与复杂场景安全性更优。
摘要：
自动代客泊车（Automated Valet Parking，AVP）是高级自动驾驶系统的关键组成部分，聚焦“人车交互”流程中的终点任务，以解决“最后一公里”的挑战。自动泊车算法的感知模块已从使用超声波雷达的局部感知+全局场景高精地图匹配定位，发展为无高精地图的高级鸟瞰图（Birds Eye View，BEV）感知方案。BEV场景对自动泊车规划任务的实时性与安全性提出了更高要求。本文提出一种基于A*算法的改进自动泊车算法，融合车辆运动学模型、启发式函数优化、双向搜索与贝塞尔曲线优化，提升规划算法的计算速度与实时能力。采用数值优化方法生成最终泊车轨迹，保障泊车路径的安全性。所提方法在工业界常用的CARLA-ROS联合仿真环境中进行了实验验证。与传统算法相比，该方法在更具挑战性的碰撞风险测试用例中计算时间更短，且舒适性指标表现更优。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
