• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2020, Vol. 56 ›› Issue (10): 127-143.doi: 10.3901/JME.2020.10.127

• 运载工程 • 上一篇    下一篇



熊璐1,2, 杨兴1,2, 卓桂荣1,2, 冷搏1,2, 章仁夑1,2   

  1. 1. 同济大学汽车学院 上海 201804;
    2. 同济大学新能源汽车工程中心 上海 201804
  • 收稿日期:2019-04-27 修回日期:2019-10-12 出版日期:2020-05-20 发布日期:2020-06-11
  • 通讯作者: 卓桂荣(通信作者),女,1968年出生,博士,副教授。主要研究方向为车辆系统动力学与控制。E-mail:zhuoguirong@tongji.edu.cn
  • 作者简介:熊璐,男,1978年出生,博士,教授,博士研究生导师。主要研究方向为车辆系统动力学与控制。E-mail:xiong_lu@tongji.edu.cn;杨兴,男,1995年出生,博士研究生。主要研究方向为车辆系统动力学与控制。E-mail:yang_xing@tongji.edu.cn;冷搏,男,1991年出生,博士研究生。主要研究方向为车辆系统动力学与控制。E-mail:harrisonleng@gmail.com;章仁夑,男,1989年出生,博士研究生。主要研究方向为车辆系统动力学与控制。E-mail:zhangrenxie@126.com
  • 基金资助:

Review on Motion Control of Autonomous Vehicles

XIONG Lu1,2, YANG Xing1,2, ZHUO Guirong1,2, LENG Bo1,2, ZHANG Renxie1,2   

  1. 1. School of Automotive Studies, Tongji University, Shanghai 201804;
    2. Clean Energy Automotive Engineering Center, Tongji University, Shanghai 201804
  • Received:2019-04-27 Revised:2019-10-12 Online:2020-05-20 Published:2020-06-11

摘要: 回顾无人驾驶车辆的运动控制问题。从系统模型、控制方法以及控制结构等角度切入,分别在纵向运动控制、路径跟踪控制和轨迹跟踪控制三个层面对国内外的研究进展进行综述,并提出对无人驾驶车辆运动控制技术的发展展望。当前运动控制研究多集中于常规工况,为实现无人驾驶车辆在处理人类驾驶员认为具有挑战性或缺乏操纵能力的复杂动态场景下的潜力,运动控制研究须从常规工况向极限工况拓展,但是极限工况下车辆的非线性和多维运动耦合特征显著增强,对系统建模以及算法的自适应性和鲁棒性的要求进一步提高。同时,为应对复杂场景下的多目标协调优化问题,考虑环境不确定性的运动规划与控制集成设计需要深入研究。增加执行器手段可以提升极限工况下车辆的侧向响应速度和控制裕度,但是冗余异构执行器的控制分配研究仍有待突破。运动控制的实现依赖于路面附着系数、质心侧偏角等信息输入,因此基于多源传感信息融合的关键状态与参数估计问题亟需解决。此外,将机器学习应用到车辆运动控制领域也是一个重要的发展方向。

关键词: 无人驾驶车辆, 运动控制, 纵向控制, 路径跟踪, 轨迹跟踪

Abstract: The motion control problem of autonomous vehicles is reviewed. From the perspective of model, algorithm, and control structure, the domestic and foreign research progress is reviewed at three levels of longitudinal motion control, path following and trajectory tracking control, and the development prospect of motion control technology for autonomous vehicles is proposed. The current motion control research mainly focuses on normal conditions. In order to realize the potential of autonomous vehicles in handling critical scenarios that human drivers find challenging or lack the ability to navigate, it is necessary to extend the research to extreme working conditions. However, the properties of non-linearity and multi-dimensional coupled dynamics are significantly enhanced in extreme working conditions. The requirements of system modeling and adaptability and robustness of motion control algorithm are further increased. At the same time, in order to deal with the multi-objective coordination in complex scenarios, the integration of motion planning and control considering environmental uncertainty needs to be studied in depth. Adding actuators can increase the lateral response speed and control margin, but the research of control allocation of redundant and heterogeneous actuators is still to be broken through. The realization of motion control depends on road adhesion coefficient, sideslip angle, etc. Therefore, it is urgent to solve the problem of key state and parameter estimation under multi-source sensor information fusion. In addition, the application of machine learning to the field of vehicle motion control is also an important development direction.

Key words: autonomous vehicles, motion control, longitudinal control, path following, trajectory tracking