• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2022, Vol. 58 ›› Issue (18): 31-55.doi: 10.3901/JME.2022.18.031

• 理论与综述 • 上一篇    下一篇

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智能汽车人机共享控制研究综述

杨俊儒1, 褚端峰1, 陆丽萍2, 王金湘3, 吴超仲1, 殷国栋3   

  1. 1. 武汉理工大学智能交通系统研究中心 武汉 430063;
    2. 武汉理工大学计算机与人工智能学院 武汉 430063;
    3. 东南大学机械工程学院 南京 211189
  • 收稿日期:2021-12-01 修回日期:2022-05-15 出版日期:2022-09-20 发布日期:2022-12-08
  • 通讯作者: 褚端峰(通信作者),男,1983年出生,博士,研究员,博士研究生导师。主要研究方向为自动驾驶、车路协同。E-mail:chudf@whut.edu.cn
  • 作者简介:杨俊儒,男,1994年出生,博士研究生。主要研究方向为车辆辅助驾驶与自动驾驶、车辆动力学控制;E-mail:yangjr@whut.edu.cn;陆丽萍,女,1977年出生,博士,副教授。主要研究方向为自动驾驶、车路协同;E-mail:luliping@whut.edu.cn ;王金湘,男,1979年出生,博士,副教授,博士研究生导师。主要研究方向为车辆动力学及控制、智能车辆路径规划与控制;E-mail:wangjx@seu.edu.cn;吴超仲,男,1972年出生,博士,教授,博士研究生导师。主要研究方向为交通安全、智能交通、车路协同、智能网联汽车;E-mail:wucz@whut.edu.cn;殷国栋,男,1976年出生,博士,教授,博士研究生导师。主要研究方向为车辆动力学与控制、智能网联汽车;E-mail:ygd@seu.edu.cn
  • 基金资助:
    国家自然科学基金(52172393)、湖北省重点研发计划(2020BAB096)和重庆市工程研究中心开放课题(21AKC44)资助项目。

Review on Human-machine Shared Control of Intelligent Vehicles

YANG Junru1, CHU Duanfeng1, LU Liping2, WANG Jinxiang3, WU Chaozhong1, YIN Guodong3   

  1. 1. Intelligent Transportation Systems Center, Wuhan University of Technology, Wuhan 430063;
    2. School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan 430063;
    3. School of Mechanical Engineering, Southeast University, Nanjing 211189
  • Received:2021-12-01 Revised:2022-05-15 Online:2022-09-20 Published:2022-12-08

摘要: 智能汽车人机共享控制由人类和机器共同完成驾驶任务,通过人机智能混合增强,保障行车安全,提升驾驶性能。对当前人机共享控制的研究现状及其概念进行梳理;从客观风险评估指标和考虑驾驶员因素论述人机共享控制权决策方法,分析直接式和间接式2种共享控制方式的特点和应用范围,讨论5种共享控制方法的优点和局限性,并总结人机共享控制性能评价指标;指出人机共享控制存在的问题和未来研究方向。分析表明,将机器学习与现有的基于模型的方法相结合,综合考虑驾驶员信息、车辆状态、动态环境对行车风险影响,是未来人机共享控制的研究方向。此外,建立健全自动驾驶预期功能安全测试标准和评价体系,通过预期功能安全认证,保障行车安全,是商业化应用的关键。

关键词: 智能汽车, 人机共享控制, 控制权决策, 共享控制方法, 性能评价

Abstract: A shared control of intelligent vehicles consists of a human driver and an automation to complete the driving task. Through human-machine intelligence hybrid enhancement, driving safety is guaranteed and driving performance is improved. According to the current research status of human-machine shared control, the concept is mentioned. The decision-making method of human-machine shared control can be divided into objective risk assessment indicators and driver factors. The characteristics and application scope of direct and indirect shared control frameworks are analyzed. Five shared control methods are reviewed, and the advantages and limitations of each control strategy are discussed. The evaluation indexes of human-machine shared control system are summarized. Finally, the existing problems and future research direction of human-machine shared control are pointed out. The analysis shows that the future research direction is to combine machine learning with existing model-based methods and comprehensively consider the impact of driver information, vehicle state and dynamic environment on driving risk. Simultaneously, the safety test standards and evaluation systems for the safety of the intended functionality of autonomous driving should be established. It is the key of commercial application to ensure driving safety through safety certification of the intended functionality.

Key words: intelligent vehicle, human-machine shared control, decision of control right, shared control method, performance evaluation

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