面向人机共驾车辆的驾驶人风险感知研究综述

冯忠祥; 李靖宇; 张卫华; 尤志栋

doi:10.3963/j.jssn.1674-4861.2022.02.001

面向人机共驾车辆的驾驶人风险感知研究综述

doi: 10.3963/j.jssn.1674-4861.2022.02.001

冯忠祥^1,,
李靖宇^2, ,,
张卫华^{2, 3},
尤志栋⁴

1.
东南大学交通学院南京 210096
2.
合肥工业大学土木与水利工程学院合肥 230009
3.
合肥工业大学汽车与交通工程学院合肥 230009
4.
公安部交通管理科学研究所江苏无锡 214000

基金项目:

国家自然科学基金项目 71971073

中国博士后科学基金面上项目 2019M661694

公安部道路交通安全重点实验室开放基金项目 2021ZDSYSKFKT01

认知智能国家重点实验室开放基金计划项目 CIOS-2020SC04

详细信息

作者简介:
冯忠祥(1982—)，博士，教授. 研究方向：交通安全. E-mail：fzx@hfut.edu.cn

通讯作者:
李靖宇(1996—)，博士研究生. 研究方向：交通安全. E-mail：2018170753@mail.hfut.edu.cn

中图分类号: X951
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出版历程
- 收稿日期: 2021-10-20
- 网络出版日期: 2022-05-18

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

1.
School of Transportation, Southeast University, Nanjing 210096, China
2.
College of Civil Engineering, Hefei University of Technology, Hefei 230009, China
3.
School of Automobile and Traffic Engineering, Hefei University of Technology, Hefei 230009, China
4.
Traffic Management Research Institute of the Ministry of Public Security, Wuxi 214000, Jiangsu, China

摘要

摘要: 面向人机共驾车辆的驾驶人风险感知是接管时正确应激反应和操作的前提，是交通安全领域的研究重点。分析了人机共驾车辆驾驶人风险感知概念及其特性；从驾驶人特性、自动驾驶系统、驾驶情景这3个方面分析了人机共驾车辆驾驶人风险感知的影响因素；从驾驶行为表现、接管绩效和主观评价这3个方面对人机共驾车辆驾驶人风险感知衡量方法进行归纳总结；梳理归纳了基于驾驶人培训、辅助设备调节的风险感知能力提升方法。结果表明：相比于手动驾驶，人机共驾车辆驾驶人风险感知能力较低，且是多因素耦合作用下的结果；现有风险感知能力评价方法各有弊端，缺少可广泛应用的普适性量化方法；对驾驶人状态进行动态监测和调节是保障人机共驾车辆安全应用的前提。基于现有研究中存在的问题，指出了人机共驾车辆驾驶人风险感知未来研究方向，主要包括多因素耦合情况下的风险感知研究、风险感知能力量化模型构建、风险感知能力安全阈值研究、风险感知能力动态监测与稳态保持方法研究。
- 交通安全 /
- 自动驾驶 /
- 人机共驾 /
- 风险感知 /
- 驾驶行为
Abstract: 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.
- Traffic safety /
- autonomous driving /
- human-computer shared control /
- risk perception /
- driving behavior

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表 1 人机共驾车辆驾驶人风险感知能力衡量方法

Table 1. Evaluation methods of the driver's risk perception for human-computer shared control vehicles

衡量方法	指标	描述	优缺点
自动驾驶期间行为	生理特性心理特性眼动特性	通过生理、心理等数据来探查推断驾驶人的风险感知水平	生理、心理指标只能用于检验用户是否感知到了情境信息，而无法推断驾驶人对于情境的理解与预测
自动驾驶期间行为	风险发现	通过风险发现数及碰撞事故数来反映风险感知水平	较为客观准确，但实验中风险点多由人为制造，实际行车中数据不易获取
接管绩效	接管反应车辆操作	通过观测驾驶人在接管中的驾驶行为表现，推测其风险感知水平	方便、客观，高水平的风险感知有助于驾驶人获得良好绩效，但绩效结果还会受到其他因素的影响。因此这种方式不一定能准确反映驾驶人的风险感知水平
主观评价	量表	通过量表和回忆式访谈等方式对自己的风险意识水平进行评价	较为直观的形式获取驾驶人的风险感知水平，但由于评价主要来源于其主观判断，数据的客观性较低

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参考文献(50)

[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 60^th 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 4^th 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 12^th 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 57^th 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 12^th 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 35^th 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 9^th 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 9^th 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.