认知分心情境下驾驶人应激反应行为研究

doi:10.3969/j.issn.1674-0696.2024.03.07

重庆交通大学学报（自然科学版） ›› 2024, Vol. 43 ›› Issue (3): 54-63.DOI: 10.3969/j.issn.1674-0696.2024.03.07

• 交通+大数据人工智能 • 上一篇

认知分心情境下驾驶人应激反应行为研究

杨艳群1,2，黄永健1,2，罗秀玲1,2，Said M.EASA2,3，郑新夷2,4

(1. 福州大学土木工程学院，福建福州 350116； 2. 福州大学交通心理与行为国际联合实验室，福建福州 350116； 3. 多伦多大都会大学土木工程系，安大略多伦多，加拿大； 4. 福州大学人文社会科学院，福建福州 350116)

收稿日期:2023-01-04 修回日期:2024-02-02 发布日期:2024-03-21
作者简介:杨艳群（1969—），男，河北任丘人，教授，主要从事交通规划、交通心理和交通安全等研究。E-mail:yangyanqun@fzu.edu.cn 通信作者：郑新夷（1971—），女，福建福州人，副教授，博士，主要从事交通安全心理方面的研究。E-mail:zhengxinyi@fzu.edu.cn
基金资助:
河北省交通运输厅科技项目（SH2018-5）；福建省教育科学规划课题（FJJKBK21-173）

Drivers Stress Response Behavior in Cognitive Distraction Condition

YANG Yanqun1, 2, HUANG Yongjian1, 2, LUO Xiuling1, 2, Said M. EASA2, 3 , ZHENG Xinyi2, 4

(1. College of Civil Engineering, Fuzhou University,Fuzhou 350116, Fujian, China; 2. Joint International Research Laboratory on Traffic Psychology & Behaviors, Fuzhou University, Fuzhou 350116, Fujian, China; 3. Department of Civil Engineering, Toronto Metropolitan University, Toronto Ontario, Canada; 4. College of Humanities and Social Sciences, Fuzhou University, Fuzhou 350116, Fujian, China)

Received:2023-01-04 Revised:2024-02-02 Published:2024-03-21

摘要/Abstract

摘要： 选取40名被试，采用2-back任务诱导认知分心，利用驾驶模拟系统构建行人-机动车、非机动车-机动车和机动车-机动车等3种应激场景，开展了不同应激场景正常驾驶及认知分心驾驶2种工况驾驶人的应激反应实验研究；根据采集的被试的眼动、脑电和驾驶行为等数据，分析了认知分心驾驶工况驾驶人的应激反应行为特性；对实验结果进行集对分析（SPA），完成了不同应激场景下驾驶人应激反应行为的安全性评估。结果表明：驾驶人应激反应行为的安全性因受认知分心的影响而降低，其中在行人应激场景下的影响最为显著。

关键词: 交通工程；认知分心；应激反应；驾驶模拟；集对分析

Abstract: 40 participants were selected and the 2-back task to induce cognitive distraction was employed. Three stress scenarios, including pedestrian-motorized vehicle, non-motorized vehicle-motorized vehicle and motorized vehicle-motorized vehicle, were established by use of driving simulation system. The experimental studies on the stress response of drivers under normal driving condition and cognitive distraction driving condition in various stress scenarios were carried out. By collecting eye-tracking, EEG, and driving behavior data throughout the experiments, the stress response behavior characteristics of drivers in cognitive distraction condition were analyzed. A set pair analysis (SPA) was conducted on the experimental results to evaluate the safety of driver stress response behavior in different stress scenarios. The results show that the safety of drivers stress response behaviors is reduced by cognitive distraction, and the impact is the most significant in pedestrian stress scenario.

Key words: traffic engineering; cognitive distraction; stress response; driving simulation; set pair analysis (SPA)

中图分类号:

U491.254

杨艳群1,2，黄永健1,2，罗秀玲1,2，Said M.EASA2,3，郑新夷2,4. 认知分心情境下驾驶人应激反应行为研究[J]. 重庆交通大学学报（自然科学版）, 2024, 43(3): 54-63.

YANG Yanqun1, 2, HUANG Yongjian1, 2, LUO Xiuling1, 2, Said M. EASA2, 3 , ZHENG Xinyi2, 4. Drivers Stress Response Behavior in Cognitive Distraction Condition[J]. Journal of Chongqing Jiaotong University(Natural Science), 2024, 43(3): 54-63.

参考文献

［1］ LIANG Yulan, LEE J D. A hybrid Bayesian network approach to detect driver cognitive distraction ［J］. Transportation Research Part C: Emerging Technologies, 2014, 38: 146-155.
［2］ REGAN M A, LEE J D, YOUNG K L.Driver Distraction: Theory, Effects and Mitigation ［M］. Boca Raton, Florida, USA: CRC Press, 2008.
［3］ KLAUER S G, DINGUS T A, NEALE T V, et al. The Impact of Driver Inattention on Near-Crash/Crash Risk: An Analysis Using the 100-Car Naturalistic Driving Study Data ［R］. Springfield, Virginia, USA: Department of Transportation, National Highway Traffic Safety Administration, 2006.
［4］吕爱红, 马勇, 李腾飞, 等. 认知分心时驾驶人应激反应特征分析［J］. 中国安全科学学报, 2018, 28(7): 7-12.
LYU Aihong, MA Yong, LI Tengfei, et al. Analysis of stress response characteristics of driver with cognitive distraction ［J］. China Safety Science Journal, 2018, 28(7): 7-12.
［5］ HORREY W J, WICKENS C D, CONSALUS K P. Modeling drivers visual attention allocation while interacting with in-vehicle technologies ［J］. Journal of Experimental Psychology Applied, 2006, 12(2): 67-78.
［6］ RECARTE M A, PREZ E, CONCHILLO A, et al. Mental workload and visual impairment: Differences between pupil, blink, and subjective rating ［J］. The Spanish Journal of Psychology, 2008, 11(2): 374-385.
［7］韩西阳. 基于驾驶绩效的驾驶员认知分心检测方法研究［D］. 长沙: 湖南大学, 2019.
HAN Xiyang.A Study on Driver Cognitive Distraction Detection Based on the Driving Performance Measures ［D］. Changsha: Hunan University, 2019.
［8］ MUHRER E, VOLLRATH M. The effect of visual and cognitive distraction on drivers anticipation in a simulated car following scenario ［J］. Transportation Research Part F: Traffic Psychology and Behaviour, 2011, 14(6): 555-566.
［9］ DADDARIO P, DONMEZ B. The effect of cognitive distraction on perception-response time to unexpected abrupt and gradually onset roadway hazards ［J］. Accident Analysis & Prevention, 2019, 127: 177-185.
［10］ RASTGOO M N, NAKISA B, RAKOTONIRAINY A, et al. A critical review of proactive detection of driver stress levels based on multimodal measurements ［J］. ACM Computing Surveys, 2019, 51(5): 88.
［11］李娜. 驾驶员应激研究综述［J］. 社会心理科学, 2013, 28(10): 40-42.
LI Na. A review of drivers stress ［J］. Science of Social Psychology, 2013, 28(10): 40-42.
［12］ FAURE V, LOBJOIS R, BENGUIGUI N. The effects of driving environment complexity and dual tasking on drivers mental workload and eye blink behavior ［J］. Transportation Research Part F: Traffic Psychology and Behaviour, 2016, 40: 78-90.
［13］ ALREFAIE M T, SUMMERSKILL S, JACKON T W. In a heart beat: Using drivers physiological changes to determine the quality of a takeover in highly automated vehicles ［J］. Accident Analysis & Prevention, 2019, 131: 180-190.
［14］ KOUNTOURIOTIS G K, MERAT N. Leading to distraction: Driver distraction, lead car, and road environment［J］. Accident Analysis & Prevention, 2016, 89: 22-30.
［15］ DOZZA M. What factors influence drivers response time for evasive maneuvers in real traffic? ［J］. Accident Analysis & Prevention, 2013, 58: 299-308.
［16］ SONNLEITNER A, TREDER M S, SIMON M, et al. EEG alpha spindles and prolonged brake reaction times during auditory distraction in an on-road driving study ［J］. Accident Analysis & Prevention, 2014, 62: 110-118.
［17］ GAO Cuicui, YUAN Xiaohui, YU Pengcheng. Drivers stress reaction times features when driving on urban road ［C］∥Proceedings of the 2015 International Conference on Education Technology, Management and Humanities Science, March 21-22, 2015. Shaanxi, China. Paris, France: Atlantis Press, 2015: 10.2991/etmhs-15.2015.205.
［18］吴心平, 赵清华. 汽车制动性评价及制动距离分析［J］. 河南科技, 2011(19): 66-67.
WU Xinping, ZHAO Qinghua. Automobile braking evaluation and braking distance analysis ［J］. Henan Science and Technology, 2011(19): 66-67.
［19］ SINGH R R, CONJETI S, BANERJEE R. A comparative evaluation of neural network classifiers for stress level analysis of automotive drivers using physiological signals ［J］. Biomedical Signal Processing and Control, 2013, 8(6): 740-754.
［20］ PESONEN M, HMLINEN H, KRAUSE C M. Brain oscillatory 4-30 Hz responses during a visual n-back memory task with varying memory load ［J］. Brain Research, 2007, 1138: 171-177.
［21］ ALMAHASNEH H, CHOOI W T, KAMEL N, et al. Deep in thought while driving: An EEG study on drivers cognitive distraction ［J］. Transportation Research Part F: Traffic Psychology and Behaviour, 2014, 26: 218-226.
［22］ YANG Yanqun, LIU Xianhui, EASA S M, et al. Effect of distance infor-mation and number of warning signs on driving safety of young adults near road work zones in China ［J］. Accident Analysis & Preven-tion, 2023, 192: 107230.
［23］ MIYAJI M, KAWANAKA H, OGURI K. Drivers cognitive distraction detection using physiological features by the adaboost ［C］∥2009 12th International IEEE Conference on Intelligent Transportation Systems. St. Louis, MO, USA. IEEE, 2009: 1-6.
［24］ CAUSSE M, LEPRON E, MANDRICK K, et al. Facing successfully high mental workload and stressors:An FMRI study ［J］. Human Brain Mapping, 2022, 43(3): 1011-1031.
［25］王俊翔. 城市道路环境下驾驶员应激响应视觉特性［D］. 西安: 长安大学, 2015.
WANG Junxiang.Research on the Visual Characters of Drivers Stress Response on City Road ［D］. Xian: Changan University, 2015.
［26］贾丙硕. 基于驾驶模拟器的城市道路驾驶人应激反应特性试验研究［D］. 西安: 长安大学, 2019.
［26］ JIA Bingshuo.Experimental Study on Stress Response Characteristics of Urban Road Drivers Based on Driving Simulator ［D］. Xian: Changan University, 2019.
［27］ RANNEY T A, HARBLUK J L, NOY Y I. Effects of voice technology on test track driving performance: Implications for driver distraction ［J］. Human Factors, 2005, 47(2): 439-454.
［28］ VINCENT P. A comparative study on suitability of AHP and TOPSIS for identifying optimal conceptual design of bearing puller［J］. Journal of Engineering Research and Reports, 2023 , 25(12):194-205.
［29］刘秀梅, 赵克勤. 基于五元联系数的纯模糊语言多属性决策［J］. 模糊系统与数学, 2019, 33(5): 73-79.
LIU Xiumei, ZHAO Keqin. Pure fuzzy language multi-attribute decision making based on five-element connection number ［J］. Fuzzy Systems and Mathematics, 2019, 33(5): 73-79.
［30］刘小艳, 刘欣宇, 王梅. 隶属函数的确定及应用［J］. 电脑知识与技术, 2010, 6(31): 8831-8832.
LIU Xiaoyan, LIU Xinyu, WANG Mei. The determination of member-ship function and use ［J］. Computer Knowledge and Technology, 2010, 6(31): 8831-8832.
［31］ VOGEL K, KIRCHER A, ALM H, et al. Traffic sense—Which factors influence the skill to predict the development of traffic scenes? ［J］. Accident Analysis & Prevention, 2003, 35(5): 749-762.

[1]	潘义勇，丁袁，陈璐. 景区与城市道路环境下驾驶人眼动特性差异性分析[J]. 重庆交通大学学报（自然科学版）, 2019, 38(06): 84-89.
[2]	吴付威1，2，黎莉1，付锐1，2，王畅1，2，马勇1. 城市道路干扰跟车对驾驶人注意力分配的影响[J]. 重庆交通大学学报（自然科学版）, 2018, 37(06): 87-91.
[3]	陈雨人，王瑞云，董永杰，贺思虹. 应用CatMull-Rom样条曲线描述道路透视图中心线特征的方法研究[J]. 重庆交通大学学报（自然科学版）, 2015, 34(4): 45-51.
[4]	赵炜华,刘浩学,毕仁忠,晏远春. 驾驶员对夜间红绿障碍物空间距离判识差异[J]. 重庆交通大学学报（自然科学版）, 2009, 28(6): 1079-1082.
[5]	韩万里1，吕云飞2，赵建有2. 高海拔高速路动物穿行警示标志设置研究[J]. 重庆交通大学学报（自然科学版）, 2021, 40(12): 47-53.
[6]	黄平文1，夏号杰2，余彪1，刘星良3，刘唐志3. 基于自然驾驶试验的小半径弯坡路段大型载货汽车驾驶员视觉特性研究[J]. 重庆交通大学学报（自然科学版）, 2023, 42(2): 121-127.

认知分心情境下驾驶人应激反应行为研究

Drivers Stress Response Behavior in Cognitive Distraction Condition

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 6

编辑推荐

Metrics