Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (3): 799-809.doi: 10.13229/j.cnki.jdxbgxb20200092

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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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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

CLC Number: 

  • U491.25

Fig.1

Design structure of the flexible control-transition model"

Fig.2

Data collection scene of highD"

Fig.3

Driving risk field model"

Fig.4

Risk-response strategy in thecar-following scenarios"

Fig.5

Risk-response strategy in left lane changes"

Fig.6

Risk-response strategy in right lane changes"

Fig.7

Control authority allocation based on strategy deviation"

Fig.8

Man-machine shared driving process infront-car emergency braking scenarios"

Fig.9

Minimum headway in front-caremergency braking scenarios"

Fig.10

Man-machine shared driving process inadjacent-car cut-in scenarios"

Fig.11

Minimum headway in the adjacent-carcut-in scenarios"

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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