基于自适应预瞄路径的自动驾驶车辆寻迹和避障控制

doi:10.3901/JME.2022.06.184

机械工程学报 ›› 2022, Vol. 58 ›› Issue (6): 184-193.doi: 10.3901/JME.2022.06.184

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基于自适应预瞄路径的自动驾驶车辆寻迹和避障控制

魏凌涛¹, 王翔宇¹, 邱彬^1,2, 李亮¹, 周道林¹, 林进贵³

1. 清华大学汽车节能与安全国家重点实验室北京 100084;
2. 工业和信息化部装备工业发展中心北京 100846;
3. 天津所托瑞安汽车科技有限公司天津 300450

收稿日期:2021-07-04 修回日期:2021-12-24 出版日期:2022-03-20 发布日期:2022-05-19
通讯作者: 王翔宇,男,1993年出生,博士。主要研究方向为混合动力技术、车辆线控技术。E-mail:wangxy_15@163.com
作者简介:魏凌涛,男,1997年出生,博士研究生。主要研究方向车辆动力学、车辆稳定性控制系统。E-mail:wltmec@163.com
基金资助:
博士后创新人才支持计划(BX20200184)和安徽省新能源汽车暨智能网联汽车产业技术创新工程(JAC2019022505)资助项目。

Tracking and Collision Avoidance of Autonomous Vehicle Based on Adaptive Preview Path

WEI Lingtao¹, WANG Xiangyu¹, QIU Bin^1,2, LI Liang¹, ZHOU Daolin¹, LIN Jingui³

1. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084;
2. Equipment Industry Development Center, Ministry of Industry and Information Technology, Beijing 100846;
3. Tianjin Soterea Company, Tianjin 300450

Received:2021-07-04 Revised:2021-12-24 Online:2022-03-20 Published:2022-05-19

摘要/Abstract

摘要： 寻迹控制作为自动驾驶车辆横向控制中最基本环节，其稳定性和跟踪精度通常与车速、转弯曲率等相关，直接影响车辆在复杂行驶工况中的安全性。为提高自动驾驶车辆在复杂工况下的稳定性和跟踪精度，结合路径规划、寻迹控制并考虑车辆稳定性提出基于自适应预瞄路径的自动驾驶车辆寻迹和避障控制方法。首先，基于车辆二自由度模型设计出预瞄距离自适应算法，其根据车辆动力学状态和路面附着调节预瞄距离；其次，通过三次多项式拟合方法给出给定预瞄距离下的预瞄路径；最后，基于避障能力、跟踪精度、车辆稳定性指标设计出粒子群优化算法(PSO)，实现了算法参数的寻优。通过硬件在环试验和实车试验验证了算法在寻迹、换道和避障工况下效果，结果表明算法以小运算量实现了跟踪时的预瞄路径自适应调节，兼顾跟踪精度和车辆稳定性。

关键词: 路径规划, 粒子群优化, 自适应控制, 多项式拟合, 车辆动力学

Abstract: Path Tracking plays important role in the lateral control of autonomous vehicles. The stability and tracking accuracy are usually related to vehicle speed, road curvature, etc., which directly affect the safety in complex driving conditions. To improve the stability and tracking accuracy under complex conditions, path planning, tracking control and stability control are combined together to design a tracking control method based on adaptive preview paths. First, based on the vehicle's two-degree-of-freedom model, a preview distance adaptive algorithm is designed, which adjusts the preview distance according to the vehicle dynamics state and road adhesion. Secondly, the preview path at the desired preview distance is given by a cubic polynomial fitting method. Finally, based on performance of obstacle avoidance, tracking accuracy, and vehicle stability, a particle swarm optimization algorithm(PSO) is designed to optimize the algorithm parameters. The performances in path tracking, lane changing and obstacle avoidance conditions are verified in the hardware-in-the-loop tests and vehicle tests. The results show that the algorithm can adaptively adjust the preview path during tracking with low computation burden, and achieve the balance of tracking accuracy and vehicle stability.

Key words: path planning, particle swarm optimization, adaptive control, polynomial fitting, vehicle dynamics

中图分类号:

U495

魏凌涛, 王翔宇, 邱彬, 李亮, 周道林, 林进贵. 基于自适应预瞄路径的自动驾驶车辆寻迹和避障控制[J]. 机械工程学报, 2022, 58(6): 184-193.

WEI Lingtao, WANG Xiangyu, QIU Bin, LI Liang, ZHOU Daolin, LIN Jingui. Tracking and Collision Avoidance of Autonomous Vehicle Based on Adaptive Preview Path[J]. Journal of Mechanical Engineering, 2022, 58(6): 184-193.

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基于自适应预瞄路径的自动驾驶车辆寻迹和避障控制

Tracking and Collision Avoidance of Autonomous Vehicle Based on Adaptive Preview Path

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