基于触觉引导的L2级智能汽车人机共享控制技术综述

邓修金; 王艳阳; 黄秋实; 王珂; 廖凯凯

doi:10.3963/j.jssn.1674-4861.2022.05.003

基于触觉引导的L2级智能汽车人机共享控制技术综述

doi: 10.3963/j.jssn.1674-4861.2022.05.003

邓修金^{1, 2, 3,},
王艳阳^{1, 2, 3, ,},
黄秋实^{1, 2, 3},
王珂^{1, 2, 3},
廖凯凯^{1, 2, 3}

1.
西华大学汽车测控与安全四川省重点实验室成都 610039
2.
西华大学流体及动力机械教育部重点实验室成都 610039
3.
四川省新能源汽车智能控制与仿真测试技术工程研究中心成都 610039

基金项目:

国家自然科学基金项目 51605390

四川省自然科学基金面上项目 2022NSFSC0400

四川省重大科技专项 2018GZDZX0011

西华大学研究生创新基金资助项目 YCJJ2021089

详细信息

作者简介:
邓修金（1998—），硕士研究生. 研究方向：智能车、车辆系统动力学. E-mail：15555928010@163.com

通讯作者:
王艳阳（1979—），博士，副教授. 研究方向：智能车、车辆系统动力学. E-mail：yywang@mail.xhu.edu.cn

中图分类号: U461.6
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出版历程
- 收稿日期: 2021-12-31
- 网络出版日期: 2022-12-05

A Review of Progresses and Prospects of Human-machine Shared Control Technology for L2 Intelligent Driving Based on Haptic Guidance

DENG Xiujin^{1, 2, 3
,},
WANG Yanyang^{1, 2, 3
, ,},
HUANG Qiushi^{1, 2, 3},
WANG Ke^{1, 2, 3},
LIAO Kaikai^{1, 2, 3}

1.
Key Laboratory of Automobile Measurement and Control & Safety, Xihua University, Chengdu 610039, China
2.
Key Laboratory of Fluid and Power Machinery of Ministry of Education, Xihua University, Chengdu 610039, China
3.
Provincial Engineering Research Center for New Energy Vehicle Intelligent Control and Simulation Test Technology of Sichuan, Chengdu 610039, China

摘要

摘要: 因技术和法规等因素限制，智能汽车短时间内难以实现L1级到L5级的快速跨越，人机共驾将长期存在；基于触觉引导的人机共享控制技术为L2级智能汽车人机共驾提供了有效的人机共享控制途径。通过综述国内外车辆人机共享控制技术相关问题的研究现状，重点分析了基于触觉引导的车道保持、换道、避撞、倒车辅助等人机共享控制在路径规划、意图决策和权限分配转移等过程中，可能会造成人机冲突进而导致车辆稳定性降低、行车安全性变差和驾驶员操作舒适度与自由度恶化等关键问题。同时针对人机共享控制中固有的驾驶人风格及认知差异进行了探讨，以期进一步明确人机共享控制器的设计方法及人机冲突产生的机理。提出未来应在大量仿真或实测行车数据的基础上不断迭代优化智能系统，提高智能控制系统对行车环境和驾驶人状态识别的精准度，从而合理分配人机共享控制权重，有效解决人机冲突、车辆稳定性、行车安全性、驾驶员操作舒适性和自由度等问题。基于现有研究存在的问题，指出自适应性触觉引导共享控制器、权重分配共享控制器、基于神经肌肉反应共享控制器及基于高级辅助驾驶系统共享控制器等将是智能汽车人机共享控制的主要研究方向。
- 人机共驾 /
- 触觉引导 /
- 人机共享控制 /
- 车辆稳定性 /
- 人机冲突
Abstract: It is nearly impossible for intelligent automobiles to achieve a fast upgradation from L1 to L5 in a short time due to the limitations of technologies, regulations, and other factors. Thus, human-machine shared driving will be the case for a long run. The human-machine shared control (HMSC) technology based on haptic guidance provides an effective way for intelligent automobiles operating at the L2 level. Through reviewing the literature regarding the current progress of HMSC technology, this study focuses on studying conflicts created over the human-machine collaboration in the process of route planning, intention commitment, and control assignment related to the maneuvers like lane keeping, lane changing, collision avoidance and backing-up, which may result in reduced vehicle stability, poor driving safety, and deteriorated operating comfort and freedom. Meanwhile, the driving styles and cognition differences of drivers are studied to identify the design methods of human-machine shared controller and the mechanism of human-machine conflict. Therefore, it is proposed that the intelligent driving system should be iteratively optimized base on massive simulated or measured driving data in the future and the accuracy of the intelligent driving system in recognizing driving environment and driver's status should be improved. In this way, the control weights of human and machine can be assigned and the problems of control conflict, vehicle stability, driving safety, deteriorated operating comfort and degree of freedom can be solved. Based on the existing issues identified within the research to date, it is pointed out that shared controllers based on the adaptive haptic-guidance, assignment of control weight, neuromuscular response, and advanced assistance systems are major research directions of the HMSC.
- human-machine shared driving /
- haptic guidance /
- human-machine shared control /
- vehicle stability /
- conflicts in human-machine collaboration

HTML全文

图 1 H2-预览共享控制框架^[12]

Figure 1. H2-preview shared control framework^[12]

下载: 全尺寸图片幻灯片

图 2 人类驾驶人与驾驶人模型的比较

Figure 2. Comparison of human driver and driver models

下载: 全尺寸图片幻灯片

图 3 基于模型预测控制的车辆换道人机共享控制框架

Figure 3. Vehicle lane change sharing control framework based on model predictive control

下载: 全尺寸图片幻灯片

图 4 前向避撞共享控制系统框架

Figure 4. Forward collision avoidance sharing control system framework

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图 5 多个障碍物下行驶轨迹选择^[33]

Figure 5. Trajectory selection under multiple obstacles^[33]

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图 6 辅助期间和辅助后误差减少的关系

Figure 6. Relationship between error reduction during and after assistance

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图 7 基于风险预测的人机共享控制框图

Figure 7. The block diagram of human-machine shared control based on risk prediction

下载: 全尺寸图片幻灯片

表 1 交互转矩选取假设

Table 1. Interaction torque selection hypothesis

HSC调节的假设驱动程序任务	驾驶人肌肉神经任务
HSC调节的假设驱动程序任务	紧急任务（NH）最高匹配引导转矩	一般任务（FT）更高匹配引导转矩	放松任务（RT）基本匹配引导转矩
NH（低HSC转矩）	刚好匹配	HSC转矩太低	HSC转矩太低
FT（中等HSC转矩）	HSC转矩过高	刚好匹配	HSC转矩太低
RT（高HSC转矩）	HSC转矩过高	HSC转矩过高	刚好匹配

下载: 导出CSV

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