基于驾驶员指标的驾驶员风险感知水平评价

doi:10.16265/j.cnki.issn1003-3033.2018.12.023

中国安全科学学报 ›› 2018, Vol. 28 ›› Issue (12): 144-149.doi: 10.16265/j.cnki.issn1003-3033.2018.12.023

基于驾驶员指标的驾驶员风险感知水平评价

艾倩楠

江苏城乡建设职业学院公用事业学院,江苏常州 213147

收稿日期:2018-09-15 修回日期:2018-11-23 发布日期:2020-11-25
作者简介:艾倩楠 (1993—),女,河北唐山人,硕士,助教,主要从事城市道路交通安全方面的研究。E-mail:2455866753@qq.com。

Driver risk perception level evaluation based on driver's index

AI Qiannan

Department of Public Utilities, Jiangsu Urban and Rural Construction College, Changzhou Jiangsu 213147, China

Received:2018-09-15 Revised:2018-11-23 Published:2020-11-25

摘要/Abstract

摘要： 为准确评价驾驶员风险感知水平,获取不同交通流状态、不同交叉口几何特性、不同干扰情况下驾驶员反应数据、心电生理数据、眼动数据以及行驶过程中车辆性能数据、车辆行为数据、与其他车辆交互情况等,从驾驶员生理、物理指标2个方面分析风险评价指标,在风险感知量化、情景提取和行为分析基础上建立评价指标体系;运用隐马尔可夫模型(HMM)预测驾驶员风险感知量化值,并分析不同风险感知量化值下的驾驶行为参数,得到风险感知量化值的观察序列,进而验证模型有效性。结果表明:用该模型预测驾驶员风险感知量化值能达到85%以上的准确率。

关键词: 交通工程, 风险感知, 生理行为, 车辆行为, 隐马尔科夫模型(HMM)

Abstract: To accurately evaluate the level of driver's risk perception during driving, data on driving behavior, electrophysiological status and eye movement of driver's response to different traffic flow conditions, intersection geometry and sudden events were collected, together with the data on vehicle performance, vehicle behavior and the interaction with other vehicles. The risk assessment indicators were analyzed from the perspectives of driver's physiological and physical indicators. Based on the risk perception quantification, scenario extraction and behavior analysis, an evaluation index system was established. Then, by using the HMM, quantitative values of risk perception were predicted, driving behavior parameters under different risk perception quantitative values were analyzed, and an observation sequence of risk perception quantization values was finally obtained. The validity of the model was verified. The results show that the HMM model can predict the quantified value of driver's risk perception with an accuracy over 85%.

Key words: traffic engineering, risk perception, physiological behavior, vehicle behavior, hidden Markov model(HMM)

中图分类号:

X913.4

艾倩楠. 基于驾驶员指标的驾驶员风险感知水平评价[J]. 中国安全科学学报, 2018, 28(12): 144-149.

AI Qiannan. Driver risk perception level evaluation based on driver's index[J]. China Safety Science Journal, 2018, 28(12): 144-149.

参考文献

[1] 马国忠, 李燕, 郭孜政,等. 驾驶行为险态辨识方法[J]. 公路交通科技, 2013, 30(7): 113-118.
MA Guozhong, LI Yan, GUO Zizheng, et al. A method for identifying risk status of driving behavior[J]. Journal of Highway and Transportation Research and Development, 2013, 30(7): 113-118.
[2] 郭孜政, 陈崇双, 闫伟,等. 驾驶威胁感知评估方法[J]. 吉林大学学报:工学版, 2012, 42(1): 46-50.
GUO Zizheng, CHEN Chongshuang, YAN Wei, et al. Assessment method for driving threat perception[J]. Journal of Jilin University: Engineering and Technology Edition, 2012, 42(1): 46-50.
[3] 严利鑫, 黄珍, 朱敦尧,等. 基于马尔科夫链和隐朴素贝叶斯的驾驶行为险态辨识[J]. 吉林大学学报:工学版, 2016, 46(6): 1 851-1 857.
YAN Lixin, HUANG Zhen, ZHU Dunyao, et al. Driving risk status identification based on Markov blanket hidden nave Bayes[J]. Journal of Jilin University: Engineering and Technology Edition, 2016, 46(6): 1 851-1 857.
[4] STEM R A, ABULARACH L M, SEICHEPINE D R, et al. Office-based assessment of at-risk driving in older adults with and without cognitive impairment.[J]. Journal of Geriatric Psychiatry & Neurology, 2016, 29(6): 352-360.
[5] PARK S W, CHOI E S, LIM M H, et al. Association between unsafe driving performance and cognitive-perceptual dysfunction in older drivers[J]. PM& R: the Journal of Injury, Function, and Rehabilitation, 2011, 3(3): 198-203.
[6] 王燕青, 刘媛媛, 徐晶. 基于生理—心理的机场安检人员疲劳风险评估[J]. 综合运输, 2016, 38(7): 49-55.
WANG Yanqing, LIU Yuanyuan, XU Jing. Fatigue risk assessment of airport security personnel based on the physiological and psychological analysis[J]. China Transportation Review, 2016, 38(7): 49-55.
[7] 李平凡, 王殿海, 刘东波,等. 驾驶中拨打手机对驾驶人脑力负荷及驾驶行为的影响分析[J]. 交通信息与安全, 2010, 28(4): 103-107.
LI Pingfan, WANG Dianhai, LIU Dongbo, et al. Effect of using cell phone while driving on mental workload and driving behavior[J]. Traffic Information and Safety, 2010, 28(4): 103-107.
[8] WIBERG H, NILSSON E, LINDEN P, et al. Physiological responses related to moderate mental load during car driving in field conditions[J]. Biological Psychology, 2015, 108: 115-125.
[9] 杨梦英. 基于模拟驾驶的天气环境对驾驶员危险感知特性影响[J]. 北京汽车, 2017, 4(2): 11-13.
YANG Mengying. Influence of weather environment on driver's hazard perception based on simulated driving[J]. Beijing Automobile, 2017, 4(2): 11-13.
[10] 马勇, 付锐, 王畅,等. 视觉分心时驾驶人注视行为特性分析[J]. 中国安全科学学报, 2013, 23(5): 10-14.
MA Yong, FU Rui, WANG Chang, et al. Analysis of drivers' gazing behavior during visual distraction[J]. China Safety Science Journal, 2013, 23(5): 10-14.

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基于驾驶员指标的驾驶员风险感知水平评价

Driver risk perception level evaluation based on driver's index

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