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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
Tracking and Collision Avoidance of Autonomous Vehicle Based on Adaptive Preview Path
WEI Lingtao1, WANG Xiangyu1, QIU Bin1,2, 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
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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。
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魏凌涛
王翔宇
邱彬
李亮
周道林
林进贵
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 wordspath planning    particle swarm optimization    adaptive control    polynomial fitting    vehicle dynamics
收稿日期: 2021-07-04      出版日期: 2022-05-19
ZTFLH:  U495  
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。
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
引用本文:   
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. Journal of Mechanical Engineering, 2022, 58(6): 184-193.
链接本文:  
http://qikan.cmes.org/jxgcxb/CN/10.3901/JME.2022.06.184      或      http://qikan.cmes.org/jxgcxb/CN/Y2022/V58/I6/184
[1] HU C,WANG R,YAN F,et al.Output constraint control on path following of four-wheel independently actuated autonomous ground vehicles[J]. IEEE Transactions on Vehicular Technology,2015,65(6):4033-4043.
[2] RAFFO G V,GOMES G K,NORMEY-RICO J E,et al. A predictive controller for autonomous vehicle path tracking[J]. IEEE Transactions on Intelligent Transportation Systems,2009,10(1):92-102.
[3] 徐俊艳,张培仁,程剑锋. 基于Backstepping时变反馈和PID控制的移动机器人实时轨迹跟踪控制[J]. 电机与控制学报,2004,8(1):35-38. XU Junyan,ZHANG Peiren,CHENG Jianfeng. Real-time
trajectory tracking control of mobile robot based on Backstepping time-varying state feedback and PID control method[J]. Electric Machines and Control,2004,8(1):35-38.
[4] YU R,GUO H,SUN Z,et al. MPC-based regional path tracking controller design for autonomous ground vehicles[C]// 2015 IEEE International Conference on Systems,Man,and Cybernetics,2015:2510-2515.
[5] JI J,KHAJEPOUR A,MELEK W W,et al. Path planning and tracking for vehicle collision avoidance based on model predictive control with multiconstraints[J]. IEEE Transactions on Vehicular Technology,2016,66(2):952-964.
[6] 武星,楼佩煌,唐敦兵. 自动导引车路径跟踪和伺服控制的混合运动控制[J]. 机械工程学报,2011,47(3):43-48. WU Xing,LOU Peihuang,TANG Dunbing. Integrated motion control of path tracking and servo control for an automated guided vehicle[J]. Journal of Mechanical Engineering,2011,47 (3):43-48.
[7] THOMMYPPILLAI M,EVANGELOU S,SHARP R S. Car driving at the limit by adaptive linear optimal preview control[J]. Vehicle System Dynamics,2009,47 (12):1535-1550.
[8] XU S,PENG H. Design,analysis,and experiments of preview path tracking control for autonomous vehicles[J]. IEEE Transactions on Intelligent Transportation Systems,2019,21(1):48-58.
[9] LAURENSE V A,GERDES J C. Speed control for robust path-tracking for automated vehicles at the tire-road friction limit[C/CD]//14th International Symposium on Advanced Vehicle Control (AVEC). Beijing,China,2018.
[10] JI X,HE X,LV C,et al. Adaptive-neural-network-based robust lateral motion control for autonomous vehicle at driving limits[J]. Control Engineering Practice,2018,76:41-53.
[11] PENG H,WANG W,AN Q,et al. Path tracking and direct yaw moment coordinated control based on robust MPC with the finite time horizon for autonomous independent-drive vehicles[J]. IEEE Transactions on Vehicular Technology,2020,69(6):6053-6066.
[12] GUO J,LUO Y,LI K,et al. Coordinated path-following and direct yaw-moment control of autonomous electric vehicles with sideslip angle estimation[J]. Mechanical Systems and Signal Processing,2018,105:183-199.
[13] 赵治国,周良杰,朱强. 无人驾驶车辆路径跟踪控制预瞄距离自适应优化[J]. 机械工程学报,2018,54(24):166-173. ZHAO Zhiguo,ZHOU Liangjie,ZHU Qiang. Preview distance adaptive optimization for the path tracking control of unmanned vehicle[J]. Journal of Mechanical Engineering,2018,54(24):166-173.
[14] LI S,CHEN H,LI R,et al. Predictive lateral control to stabilise highly automated vehicles at tire-road friction limits[J]. Vehicle System Dynamics,2020,58 (5):768-786.
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