上海交通大学学报

上海交通大学学报 >

2023 , Vol. 57 >Issue 3: 345 - 353

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2021.443

电子信息与电气工程

一种面向泊车场景智能车辆轨迹规划方法

展开
  • 上海交通大学 自动化系,上海 200240
林 淳(1997-),硕士生,主要研究方向为机器人.

收稿日期: 2021-11-03

  录用日期: 2022-01-11

  网络出版日期: 2023-03-30

基金资助

国家自然科学基金(61873165);国家自然科学基金(U1764264)

收起

A Method for Autonomous Driving Trajectory Planning in Parking Environments

Expand
  • Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China

Received date: 2021-11-03

  Accepted date: 2022-01-11

  Online published: 2023-03-30

Fold

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

本文引用格式

林淳, 贺越生, 方兴其, 王春香 . 一种面向泊车场景智能车辆轨迹规划方法[J]. 上海交通大学学报, 2023 , 57(3) : 345 -353 . DOI: 10.16183/j.cnki.jsjtu.2021.443

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

参考文献

[1] 胡兵, 杨明, 郭林栋, 等. 基于地面快速鲁棒特征的智能车全局定位方法[J]. 上海交通大学学报, 2019, 53(2): 203-208.
[1] HU Bing, YANG Ming, GUO Lindong, et al. Global localization for intelligent vehicles using ground SURF[J]. Journal of Shanghai Jiao Tong University, 2019, 53(2): 203-208.
[2] WERLING M, ZIEGLER J, KAMMEL S, et al. Optimal trajectory generation for dynamic street scenarios in a Frenét Frame[C]// 2010 IEEE International Conference on Robotics and Automation. Anchorage, AK, USA: IEEE, 2010: 987-993.
[3] CHEN S T, JIAN Z Q, HUANG Y H, et al. Autonomous driving: Cognitive construction and situation understanding[J]. Science China Information Sciences, 2019, 62(8): 1-27.
[4] MA L, YANG J, ZHANG M. A two-level path planning method for on-road autonomous driving[C]// 2012 Second International Conference on Intelligent System Design and Engineering Application. Sanya, China: IEEE, 2012: 661-664.
[5] MAEKAWA T, NODA T, TAMURA S, et al. Curvature continuous path generation for autonomous vehicle using B-spline curves[J]. Computer-Aided Design, 2010, 42(4): 350-359.
[6] PIVTORAIKO M, KNEPPER R A, KELLY A. Differentially constrained mobile robot motion planning in state lattices[J]. Journal of Field Robotics, 2009, 26(3): 308-333.
[7] DOLGOV D, THRUN S, MONTEMERLO M, et al. Path planning for autonomous vehicles in unknown semi-structured environments[J]. The International Journal of Robotics Research, 2010, 29(5): 485-501.
[8] KUWATA Y, FIORE G A, TEO J, et al. Motion planning for urban driving using RRT[C]// 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems. Nice, France: IEEE, 2008: 1681-1686.
[9] BANZHAF H, NIENHüSER D, KNOOP S, et al. The future of parking: A survey on automated valet parking with an outlook on high density parking[C]// 2017 IEEE Intelligent Vehicles Symposium. Los Angeles, CA, USA: IEEE, 2017: 1827-1834.
[10] 王旭东, 王春香, 杨明, 等. 一种基于环视相机的自动泊车方法[J]. 上海交通大学学报, 2013, 47(7): 1077-1081.
[10] WANG Xudong, WANG Chunxiang, YANG Ming, et al. Automatic parking based on bird’s eye view cameras[J]. Journal of Shanghai Jiao Tong University, 2013, 47(7): 1077-1081.
[11] LIN L T, ZHU J J. Path planning for autonomous car parking[C]// Proceedings of ASME 2018 Dynamic Systems and Control Conference. Atlanta, Georgia, USA: ASME, 2018: 1-10.
[12] MOON J, BAE I, CHA J G, et al. A trajectory planning method based on forward path generation and backward tracking algorithm for Automatic Parking Systems[C]// 17th International IEEE Conference on Intelligent Transportation Systems. Qingdao, China: IEEE, 2014: 719-724.
[13] 齐尧, 徐友春, 李华, 等. 一种基于改进混合A*的智能车路径规划算法[J]. 军事交通学院学报, 2018, 20(8): 85-90.
[13] QI Yao, XU Youchun, LI Hua, et al. Hybrid A* algorithm-based path planning for intelligent vehicle[J]. Journal of Military Transportation University, 2018, 20(8): 85-90.
[14] 陈鑫鹏, 徐彪, 胡满江, 等. 一种基于等步长分层拓展的混合A*路径规划方法[J]. 控制与信息技术, 2021(1): 17-22.
[14] CHEN Xinpeng, XU Biao, HU Manjiang, et al. A hybrid-A* path planning method based on equal step hierarchical expansion[J]. Control and Information Technology, 2021(1): 17-22.
[15] ZHANG S Y, CHEN Y, CHEN S T, et al. Hybrid A*-based curvature continuous path planning in complex dynamic environments[C]// 2019 IEEE Intelligent Transportation Systems Conference. Auckland, New Zealand: IEEE, 2019: 1468-1474.
[16] MIZUNO N, OHNO K, HAMADA R, et al. Enhanced path smoothing based on conjugate gradient descent for firefighting robots in petrochemical complexes[J]. Advanced Robotics, 2019, 33(14): 687-698.
[17] ZHANG X J, LINIGER A, SAKAI A, et al. Autonomous parking using optimization-based collision avoidance[C]// 2018 IEEE Conference on Decision and Control. Miami, FL, USA: IEEE, 2018: 4327-4332.
Options
文章导航

/

〈 〉