上海交通大学学报 ›› 2023, Vol. 57 ›› Issue (3): 345-353.doi: 10.16183/j.cnki.jsjtu.2021.443

所属专题: 《上海交通大学学报》2023年“电子信息与电气工程”专题

• 电子信息与电气工程 • 上一篇    下一篇

一种面向泊车场景智能车辆轨迹规划方法

林淳, 贺越生(), 方兴其, 王春香   

  1. 上海交通大学 自动化系,上海 200240
  • 收稿日期:2021-11-03 接受日期:2022-01-11 出版日期:2023-03-28 发布日期:2023-03-30
  • 通讯作者: 贺越生,助理研究员;E-mail:heyuesh@sjtu.edu.cn.
  • 作者简介:林 淳(1997-),硕士生,主要研究方向为机器人.
  • 基金资助:
    国家自然科学基金(61873165);国家自然科学基金(U1764264)

A Method for Autonomous Driving Trajectory Planning in Parking Environments

LIN Chun, HE Yuesheng(), FANG Xingqi, WANG Chunxiang   

  1. Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
  • Received:2021-11-03 Accepted:2022-01-11 Online:2023-03-28 Published:2023-03-30

摘要:

局部轨迹规划是自主代客泊车系统的关键技术之一,在该场景下,现有智能车辆的局部轨迹规划方法存在规划耗时长、曲率不连续、安全性不足等问题.针对该类问题,提出一种面向泊车场景的智能车辆轨迹规划方法.该方法通过改进混合A*算法的解析扩展以及引入风险函数,提升了初始路径搜索的实时性和安全性.进一步,结合初始路径以及二次规划方法实现路径平滑和速度规划,最终完成轨迹生成.仿真实验表明,所提方法能够提升智能车辆轨迹规划实时性、平滑性以及安全性,并且在实车试验上验证所提方法在实际泊车场景的可行性.

关键词: 智能车辆, 泊车场景, 轨迹规划, 混合A*算法, 二次规划

Abstract:

Local trajectory planning is one of the key technologies of the autonomous valet parking system. In this scenario, there exist problems such as long planning time, discontinuous curvature, and insufficient safety in local trajectory planning methods for intelligent vehicles. Aimed at these problems, this paper proposes a trajectory planning method for intelligent vehicles in parking scenarios. This method improves the real-time performance and security of the initial path search by improving the analytic expansions of the hybrid A* algorithm and introducing the risk function. Further, according to the initial path, the quadratic programming method is used to realize path smoothing and speed planning. Finally, the trajectory generation is completed. Simulation experiments show that the method can improve the real-time, smoothness, and safety of intelligent vehicle trajectory planning. In addition, in actual parking environment, the feasibility of the method is verified in real-world vehicle experiments.

Key words: autonomous vehicles, parking environments, trajectory planning, hybrid A* algorithm, quadratic programming

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