Volume 40 Issue 2
Apr.  2022
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FENG Zhongxiang, LI Jingyu, ZHANG Weihuang, YOU Zhidong. A Reviewon Driver's Perception of Risk Associated with Autonomous Driving Under Human-computer Shared Control[J]. Journal of Transport Information and Safety, 2022, 40(2): 1-10. doi: 10.3963/j.jssn.1674-4861.2022.02.001
Citation: FENG Zhongxiang, LI Jingyu, ZHANG Weihuang, YOU Zhidong. A Reviewon Driver's Perception of Risk Associated with Autonomous Driving Under Human-computer Shared Control[J]. Journal of Transport Information and Safety, 2022, 40(2): 1-10. doi: 10.3963/j.jssn.1674-4861.2022.02.001

A Reviewon Driver's Perception of Risk Associated with Autonomous Driving Under Human-computer Shared Control

doi: 10.3963/j.jssn.1674-4861.2022.02.001
  • Received Date: 2021-10-20
    Available Online: 2022-05-18
  • Timely perception to risk associated with autonomous driving under human-computer shared control is the premise of the correct stress response and operation of drivers, and it is the focus of road safety research. The characteristics of risk perception for drivers of human-computer shared control are analyzed. Influencing factors are analyzed from three aspects: driver's characteristics, automatic driving system, and driving scenario. Besides, evaluation methods are analyzed and summarized from the following three aspects: driving behavior, take-over performance, and subjective evaluation. Moreover, improvement methods for increasing the ability of risk perception through driver training and auxiliary equipment are summarized. Study results show that compared with manual driving vehicles, the capability of drivers' risk perception to human-computer interaction during the operation of autonomous vehicles is lower, which results from the interactions of multiple factors. The existing methods for evaluating the capability of driver's risk perception have their own advantages and disadvantages, and there is no universally applicable method that can be widely used. Dynamic monitoring and adjustment of driver's state is the safety prerequisite of autonomous driving under human-computer shared control. Based on the issues identified from the existing studies, it can be concluded that future studies should address the following: risk perception under the interaction of multiple factors, quantitative modeling of the capability of driver's risk perception, dynamic monitoring, and steady-state maintenance methods for driver's risk perception.

     

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  • [1]
    吴超仲, 吴浩然, 吕能超. 人机共驾智能汽车的控制权切换与安全性综述[J]. 交通运输工程学报, 2018, 18(6): 131-141. doi: 10.3969/j.issn.1671-1637.2018.06.014

    WU C Z, WU H R, LYU N C. Review of control switch and safety of human-computer driving intelligent vehicle[J]. Journal of Traffic and Transportation Engineering, 2018, 18(6): 131-141. (in Chinese) doi: 10.3969/j.issn.1671-1637.2018.06.014
    [2]
    李青, 景云超, 朱彤, 等. 基于LightGBM的驾驶人风险感知能力判别方法[J]. 交通信息与安全, 2021, 39(4): 16-25. doi: 10.3963/j.jssn.1674-4861.2021.04.003

    LI Q, JING Y C, ZHU T, et al. A method for identifying drivers' risk perception based on LightGBM[J]. Journal of Transport Information and Safety, 2021, 39(4): 16-25. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2021.04.003
    [3]
    ENDSLEY M R. Toward a theory of situation awareness in dynamic systems[J]. Human Factors, 1995, 37(1): 32-64. doi: 10.1518/001872095779049543
    [4]
    HORSWILL, MARK S. Hazard perception in driving[J]. Current Directions in Psychological Science, 2016, 25(6): 425-430. doi: 10.1177/0963721416663186
    [5]
    CASNER S M, HUTCHINS E L, NORMAN D. The challenges of partially automated driving[J]. Communications of the Acm, 2016, 59(5): 70-77. doi: 10.1145/2830565
    [6]
    KOERBER M, GOLD C, LECHNER D, et al. The influence of age on the take-over of vehicle control in highly automated driving[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2016(39): 19-32.
    [7]
    WRIGHT T J, SAMUEL S, BOROWSKY A, et al. Experienced drivers are quicker to achieve situation awareness than inexperienced drivers in situations of transfer of control within a Level 3 autonomous environment[C]. The 60th Human Factors and Ergonomics Society Annual Meeting, Los Angeles, USA: Sage Publications, 2016.
    [8]
    KOERBER M, BASELER E, BENGLER K. Introduction mat- 8ters: manipulating trust in automation and reliance in automated driving[J]. Applied Ergonomics, 2018(66): 18-31.
    [9]
    VOGELPOHL T, KUEHN M, HUMMEL T, et al. Asleep at the automated wheel: Sleepiness and fatigue during highly automated driving[J]. Accident Analysis & Prevention, 2019 (126): 70-84.
    [10]
    JAROSCH O, KUHNT M, BENGLER K, et al. It's out of our hands now! Effects of non-driving related tasks during highly automated driving on drivers' fatigue[C]. International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, Iowa, USA: University of Iowa, 2017.
    [11]
    WÖRLE J, METZ B, OTHERSEN I, et al. Sleep in highly automated driving: Takeover performance after waking up[J]. Accident Analysis & Prevention, 2020(144): 105617.
    [12]
    WINTER J C F D, HAPPEE R, MARTENS M H, et al. Effects of adaptive cruise control and highly automated driving on workload and situation awareness: A review of the empirical evidence[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2014(27): 196-217.
    [13]
    NAUJOKS F, HOFLING S, PURUCKER C, et al. From partial and high automation to manual driving: relationship between non-driving related tasks, drowsiness and take-over performance[J]. Accident Analysis & Prevention, 2018 (121): 28-42.
    [14]
    ONNASCH L, WICKENS C D, LI H, et al. Human performance consequences of stages and levels of automation an integrated meta-analysis[J]. Human Factors, 2014, 56(3): 476-488. doi: 10.1177/0018720813501549
    [15]
    刘永涛, 华珺, 赵俊玮, 等. 场景风险引导下驾驶人应激反应能力研究[J]. 交通信息与安全, 2019, 37(3): 35+41, 50. doi: 10.3963/j.issn.1674-4861.2019.03.005

    LIU Y T, HUA J, ZHAO J W, et al. Emergency response ability of drivers under risk guidance situations[J]. Journal of Transport Information and Safety, 2019, 37(3): 35+41, 50. (in Chinese) doi: 10.3963/j.issn.1674-4861.2019.03.005
    [16]
    DOGAN E, RAHAL M C, DEBORNE R, et al. Transition of control in a partially automated vehicle: Effects of anticipation and non-driving-related task involvement[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2017(46): 205-215.
    [17]
    PARK K, IM Y. Ergonomic guidelines of head-up display user interface during semi-automated driving[J]. Electronics, 2020, 9(4): 611-626. doi: 10.3390/electronics9040611
    [18]
    SO J, PARK S, KIM J, et al. Investigating the impacts of road traffic conditions and driver's characteristics on automated vehicle takeover time and quality using a driving simulator[J]. Journal ofAdvanced Transportation, 2021(2021): 8859553.
    [19]
    LOUW T, KOUNTOURIOTIS G, CARSTEN O, et al. Driver inattention during vehicle automation: How does driver engagement affect resumption of control?[C]. The 4th International Conference on Driver Distraction and Inattention, Sydney, Australia: ARRB Group, 2015.
    [20]
    DU N A, PULVER E, ROBERT L P, et al. Evaluating effects of cognitive load, takeover request lead time, and traffic density on drivers' takeover performance in conditionally automated driving[C]. The 12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Washington, D.C., USA: Association for Computing Machinery, 2020.
    [21]
    RUSCIO D, BOS A J, CICERI M R. Distraction or cognitive overload? Using modulations of the autonomic nervous system to discriminate the possible negative effects of advanced assistance system[J]. Accident Analysis & Prevention, 2017 (103): 105-111.
    [22]
    ARAKAWA T, HIBI R, FUJISHIRO T A. Psychophysical assessment of a driver's mental state in autonomous vehicles[J]. Transportation Research Part A: Policy and Practice, 2019(124): 587-610.
    [23]
    NACPIL E J C, WANG Z, NAKANO K. Application of physiological sensors for personalization in semi-autonomous driving: A review[J]. IEEE Sensors Journal, 2021(21): 76-91.
    [24]
    CARSTEN O, LAI F C H, BARNARD Y, et al. Control task substitution in semiautomated driving: Does it matter what aspects are automated?[J]. Human Factors: The Journal of the Human Factors and Ergonomics Society, 2012, 54(5): 747-761. doi: 10.1177/0018720812460246
    [25]
    LOUW T, MERAT N. Are you in the loop? Using gaze dispersion to understand driver visual attention during vehicle automation[J]. Transportation Research Part C: Emerging Technologies, 2017(76): 35-50.
    [26]
    MERAT N, JAMSON A H, LAI F C H, et al. Highly automated driving, secondary task performance, and driver state[J]. Human Factors: The Journal of the Human Factors and Ergonomics Society, 2012, 54(5): 762-771. doi: 10.1177/0018720812442087
    [27]
    BARNARD Y, LAI F C H. Spotting sheep in Yorkshire: Using eye-tracking for studying situation awareness in a driving simulator[M]. Maastricht, The Netherlands: Shaker Publishing B V, 2010.
    [28]
    ALSEN J. Integrated human modelling and simulation to support human error risk analysis of partially autonomous driver assistance systems: The ISI-PADAS project[R]. Brussels: Transport Research Arena Europe, 2010.
    [29]
    VLAKVELD W, NICOLE V N, JONATHAN D B, et al. Situation awareness increases when drivers have more time to take over the wheel in a Level 3 automated car: A simulator study[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2018(58): 917-929.
    [30]
    GOLD C, DAMBOCK D, LORENZ L, et al. "Take over!" How long does it take to get the driver back into the loop?[C]. The 57th Human Factors and Ergonomics Society Annual Meeting, San Diego, USA: HFES, 2013.
    [31]
    林庆峰, 王兆杰, 鲁光泉. 城市道路环境下自动驾驶车辆接管绩效分析[J]. 中国公路学报, 2019, 32(6): 240-247. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201906025.htm

    LIN Q F, WANG Z J, LU G Q. Analysis of take-over performance for automated vehicles in urban road environments[J]. China Journal of Highway and Transport, 2019, 32(6): 240-247. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201906025.htm
    [32]
    RUDIN-BROWN C M, PARKER H A. Behavioural adaptation to adaptive cruise control(ACC): Implications for preventive strategies[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2004(7): 59-76.
    [33]
    MERAT N, JAMSON A H, LAI F C H, et al. Transition to manual: Driver behaviour when resuming control from a highly automated vehicle[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2014(27): 274-282.
    [34]
    KLEIJ R V D, HUETING T, SCHRAAGEN J M. Change detection support for supervisory controllers of highly automated systems: Effects on performance, mental workload, and recovery of situation awareness following interruptions[J]. International Journal of Industrial Ergonomics, 2018(66): 75-84.
    [35]
    STAPEL J, MULLAKKAL-BABU F A, HAPPEE R. Automated driving reduces perceived workload, but monitoring causes higher cognitive load than manual driving[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2019(60): 590-605.
    [36]
    KRAMPELL M, SOLIS-MARCOS I, HJLMDAHL M. Driving automation state-of-mind: Using training to instigate rapid mental model development[J]. Applied Ergonomics, 2020 (83): 102986.
    [37]
    YAN S Y, TENG Y X, SMITH J S, et al. Driver behavior recognition based on deep convolutional neural networks[C]. The 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, Changsha, China: IEEE, 2016.
    [38]
    肖赛, 雷叶维. 驾驶疲劳致因及监测研究进展[J]. 交通科技与经济, 2017, 19(4): 14-19. https://www.cnki.com.cn/Article/CJFDTOTAL-KJJJ201704003.htm

    XIAO S, LEI Y W. Research on the causes for driver fatigue and the monitoring technology progress[J]. Technology and Economy in Areas of Communications, 2017, 19(4): 14-19. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-KJJJ201704003.htm
    [39]
    葛慧敏, 郑明强, 吕能超, 等. 驾驶分心综述[J]. 交通运输工程学报, 2021, 21(2): 38-55. https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC202102007.htm

    GE H M, ZHENG M Q, LYU N C, et al. Review on driving distraction[J]. Journal of Traffic and Transportation Engineering, 2021, 21(2): 38-55. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC202102007.htm
    [40]
    TANG X X, ZHOU P F, WANG P. Real-time image-based driver fatigue detection and monitoring system for monitoring driver vigilance[C]. The 35th Chinese Control Conference, Chengdu, China: IEEE, 2016.
    [41]
    朱冰, 李伟男, 赵健, 等. 考虑驾驶人驾驶习性的自适应车道偏离预警策略[J]. 同济大学学报(自然科学版), 2019, 47(增刊1): 171-177. https://www.cnki.com.cn/Article/CJFDTOTAL-TJDZ2019S1032.htm

    ZHU B, LI W N, ZHAO J, et al. Adaptive lane departure warning strategy considering driver's driving style[J]. Journal of Tongji University(Natural Science), 2019, 47(S1): 171-177. https://www.cnki.com.cn/Article/CJFDTOTAL-TJDZ2019S1032.htm
    [42]
    KUMMETHA V C, KONDYLI A, DEVOS H. Evaluating driver comprehension of the roadway environment to retain accountability of safety during driving automation[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2021(81): 457-471.
    [43]
    PAYRE W, CESTAC J, DANG N T, et al. Impact of training and in-vehicle task performance on manual control recovery in an automated car[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2017(46): 216-227.
    [44]
    EBNALI M, HULME K, EBNALI-HEIDARI A, et al. How does training effect users' attitudes and skills needed for highly automated driving?[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2019(66): 184-195.
    [45]
    SPORTILLO D, PALJIC A, OJEDA L. Get ready for automated driving using virtual reality[J]. Accident Analysis & Prevention, 2018(118): 102-113.
    [46]
    ROUSE W B, CANNON-BOWERS J A, SALAS E. The role of mental models in team performance in complex systems[J]. IEEE Transactions on Systems Man and Cybernetics, 1992, 22(6): 1296-1308. doi: 10.1109/21.199457
    [47]
    KRAFT A K, NAUJOKS F, WORLE J, ET AL. The impact of an in-vehicle display on glance distribution in partially automated driving in an on-road experiment[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2018 (52): 40-50.
    [48]
    WANG C, STEEGHS S, CHAKRABORTY D, et al. Designing for enhancing situational awareness of semi-autonomous driving vehicles[C]. The 9th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, Oldenburg, Germany: Association for Computing Machinery, 2017.
    [49]
    VAN VEEN T, KARJANTO J, TERKEN J, et al. Situation awareness in automated vehicles through proximal peripheral light signals[C]. The 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, New York, USA: ACM, 2017.
    [50]
    KARJANTO J, YUSOF N M, WANG C, et al. The effect of peripheral visual feed forward system in enhancing situation awareness and mitigating motion sickness in fully automated driving[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2018(58): 678-692.
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