吉林大学学报(工学版) ›› 2021, Vol. 51 ›› Issue (3): 799-809.doi: 10.13229/j.cnki.jdxbgxb20200092

• 车辆工程·机械工程 • 上一篇    下一篇

基于驾驶人风险响应机制的人机共驾模型

何仁1(),赵晓聪1,杨奕彬2,王建强2()   

  1. 1.江苏大学 汽车与交通工程学院，江苏 镇江 212013
    2.清华大学 汽车安全与节能国家重点实验室，北京 100084
  • 收稿日期:2020-02-20 出版日期:2021-05-01 发布日期:2021-05-07
  • 通讯作者: 王建强 E-mail:heren@mail.ujs.edu.cn;wjqlws@tsinghua.edu.cn
  • 作者简介:何仁(1962-)，男，教授，博士生导师. 研究方向：汽车综合节能与环保技术. E-mail:heren@mail.ujs.edu.cn
  • 基金资助:
    国家杰出青年科学基金项目(51625503);国家自然科学基金重点项目(61790561);江苏省研究生科研与实践创新计划项目(SJKY19_2537)

Man⁃machine shared driving model using risk⁃response mechanism of human driver

Ren HE1(),Xiao-cong ZHAO1,Yi-bin YANG2,Jian-qiang WANG2()   

  1. 1.School of Automotive and Traffic Engineering，Jiangsu University，Zhenjiang 212013，China
    2.State Key Laboratory of Automotive Safety and Energy，Tsinghua University，Beijing 100084，China
  • Received:2020-02-20 Online:2021-05-01 Published:2021-05-07
  • Contact: Jian-qiang WANG E-mail:heren@mail.ujs.edu.cn;wjqlws@tsinghua.edu.cn

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摘要：

提出了一种根据驾驶人对环境风险的实时响应进行控制权切换的智能汽车人机共驾模型。首先，从基于真实道路信息的highD数据集中提取出跟车和并道两类典型驾驶片段。接着，应用行车风险场理论对驾驶片段中的环境风险进行统一量化。然后，通过拟合环境风险作用与驾驶人的行驶加速度，得到安全风险响应策略曲面，并提出了基于策略偏差的人机共驾控制权柔性切换模型（FCTM）。最后，以纵向控制模型（LCM）作为辅助控制模型，在前车紧急制动和旁车切入两类危险场景中进行了人机共驾仿真实验。结果表明：本文FCTM模型可以通过平稳的人机控制权切换，修正驾驶人在危险场景中的驾驶操作，提高行驶安全性。

关键词: 车辆工程, 人机共驾, 行车风险场, 控制权柔性切换, 风险响应策略

Abstract:

A man-machine shared driving model for the intelligent vehicle was proposed， employing human drivers' real-time response to the environmental risk. Firstly， typical driving segments， including car-following and cut-in segments， were extracted from a real-traffic-based dataset， the highD dataset. Then the driving risk field theory was employed to quantify the environmental risk in extracted driving segments. By fitting the environmental risk effect and driving acceleration， a safe risk-response strategy was obtained， following which the Flexible Control-Transition Model （FCTM） based on strategy deviation was proposed. Finally， the Longitudinal Control Model （LCM） was applied as the auxiliary control model， and the man-machine shared driving simulation was carried out in two dangerous driving scenes， namely front-vehicle emergency braking and adjacent-car cut-in. The results show that the proposed FCTM can modify driving behavior of the human driver in dangerous scenarios through smooth man-machine control transition and improve driving safety.

Key words: vehicle engineering, man-machine shared driving, driving risk field, flexible control-transition model （FCTM）, risk-response strategy

中图分类号: 

  • U491.25

图1

控制权柔性切换模型设计架构"

图2

highD数据记录场景"

图3

行车风险场模型"

图4

跟车场景风险响应策略"

图5

左向换道风险响应策略"

图6

右向换道风险响应策略"

图7

基于策略偏差的控制权分配"

图8

前车紧急制动下的人机共驾"

图9

前车紧急制动下的最小车头间距"

图10

旁车切入情况下的人机共驾"

图11

旁车切入情况下的最小车头间距"

1 Walch M, Colley M, Weber M. Driving-task-related human-machine interaction in automated driving: towards a bigger picture[C]∥11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications: Adjunct Proceedings—AutomotiveUI'19, Utrecht, Netherlands, 2019:427-433.
2 Blaschke C, Breyer F, Färber B, et al. Driver distraction based lane-keeping assistance[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2009, 12(4):288-299.
3 Chen L, Yang C. Control authority determination of a vehicle lane keeping assist controller[C]∥2009 IEEE Intelligent Vehicles Symposium, Xi'an, China, 2009:1383-1388.
4 刘瑞, 朱西产, 刘霖, 等. 基于非合作模型预测控制的人机共驾策略[J]. 同济大学学报:自然科学版, 2019, 47(7):1037-1045.
Liu Rui, Zhu Xi-chan, Liu Lin, et al. Cooperative driving strategy based on non-cooperative model predictive control[J]. Journal of Tongji University: Natural Science, 2019, 47(7):1037-1045.
5 Li R, Li Y, Li S E, et al. Driver-automation indirect shared control of highly automated vehicles with intention-aware authority transition[C]∥2017 IEEE Intelligent Vehicles Symposium, Los Angeles, CA, USA, 2017:26-32.
6 Li R, Li S, Gao H, et al. Effects of human adaptation and trust on shared control for driver-automation cooperative driving[C]∥SAE Technical Paper, 2017-01-1987.
7 Treat J R, Tumbas N S, McDonald S T, et al. Tri-level study of the causes of traffic accidents: final report[R]. Washington DC, USA: National Highway Traffic Safety Administration, 1979.
8 Wilde G J S. The theory of risk homeostasis: Implications for safety and health[J]. Risk Analysis, 1982, 2(4):209-225.
9 把余韬. 基于行为和生理指标的驾驶风险分析与模式识别研究[D]. 北京:清华大学机械工程学院, 2015.
Ba Yu-tao. Risk factors identification during driving based on behavioral and physiological measures[D]. Beijing:Department of Industrial Engineering, Tsinghua University, 2015.
10 Krajewski R, Bock J, Kloeker L, et al. HighD dataset[EB/OL]. [2020-05-10].
11 Krajewski R, Bock J, Kloeker L, et al. The highD dataset: a drone dataset of naturalistic vehicle trajectories on German highways for validation of highly automated driving systems[C]∥IEEE International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA, 2018:2118-2125.
12 季学武, 费聪, 何祥坤, 等. 基于LSTM网络的驾驶意图识别及车辆轨迹预测[J]. 中国公路学报, 2019, 32(6):34-42.
Ji Xue-wu, Fei Cong, He Xiang-kun, et al. Intention recognition and trajectory prediction for vehicles using LSTM network[J]. China Journal of Highway and Transport, 2019, 32(6):34-42.
13 王建强, 吴剑, 李洋. 基于人-车-路协同的行车风险场概念、原理及建模[J]. 中国公路学报, 2016, 29(1):105-114.
Wang Jian-qiang, Wu Jian, Li Yang. Concept, principle and modeling of driving risk field based on driver-vehicle-road interaction[J]. China Journal of Highway and Transport, 2016, 29(1):105-114.
14 Wang J, Wu J, Li Y. The driving safety field based on driver-vehicle-road interactions[J]. IEEE Transactions on Intelligent Transportation Systems, 2015,16(4):2203-2214.
15 Wang J, Wu J, Zheng X, et al. Driving safety field theory modeling and its application in pre-collision warning system[J]. Transportation Research Part C:Emerging Technologies,2016,72:306-324.
16 Zheng X, Zhang D, Gao H, et al. A novel framework for road traffic risk assessment with HMM-based prediction model[J]. Sensors,2018,18(12):4313.
17 傅贵. 安全管理学:事故预防的行为控制方法[M]. 北京:科学出版社, 2013.
18 Macadam C C. Understanding and modeling the human driver[J]. Vehicle System Dynamics,2003,40(1-3):101-134.
19 许洪国,张慧永,宗芳. 交通事故致因分析的贝叶斯网络建模[J]. 吉林大学学报:工学版,2011,41():89-94.
Xu Hong-guo, Zhang Hui-yong, Zong Fang. Bayesian network modeling for causation analysis of traffic accident[J]. Journal of Jilin University (Engineering and Technology Edition), 2011, 41(Sup.1):89-94.
20 Wetton M A, Hill A, Horswill M S. The development and validation of a hazard perception test for use in driver licensing[J]. Accident Analysis & Prevention, 2011, 43(5):1759-1770.
21 Driving in Germany—travel guide at Wikivoyage[EB/OL]. [2020-05-10].
22 Ni D, Leonard J D, Jia C, et al. Vehicle longitudinal control and traffic stream modeling[J]. Transportation Science, 2016, 50(3):1016-1031.
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