A Method for Autonomous Driving Trajectory Planning in Parking Environments

doi:10.16183/j.cnki.jsjtu.2021.443

Abstract

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

CLC Number:

TP242.6

LIN Chun, HE Yuesheng, FANG Xingqi, WANG Chunxiang. A Method for Autonomous Driving Trajectory Planning in Parking Environments[J]. Journal of Shanghai Jiao Tong University, 2023, 57(3): 345-353.

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参数	取值
位置离散分辨率/m	0.5
角度离散分辨率/(°)	7.5
前轮转角离散数	5.0
步长/m	0.8

算法	路径长度/m	拓展节点	档位切换	搜索时间/ms
文献[15]算法	13.5	1769	3	84
本文算法	13.7	478	3	18

参数	取值
离散状态点数	50
一阶差分权重	1
最大曲率	5
二阶差分权重	1000

实验组	平滑前的J_κ	本文方法的J_κ	文献[16]的J_κ
实验一	0.01707	0.01383	0.01372
实验二	0.02042	0.01434	0.01545

参数	取值
离散状态点数	24
最大速度/(m·s^-1)	1.0
最大加速度/(m·s^-2)	1.0
最大加加速度/(m·s^-3)	0.5
距离权重	10000
加速度权重	10
加加速度权重	10