吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (3): 642-647.doi: 10.13229/j.cnki.jdxbgxb201403010

• 论文 • 上一篇    下一篇

不同道路线形下驾驶人认知分散状态监测

金立生1,牛清宁1,刘景华2,秦彦光1,吕欢欢1   

  1. 1. 吉林大学 交通学院,长春 130022;
    2. 郑州宇通客车股份有限公司,郑州 450000
  • 收稿日期:2013-04-08 出版日期:2014-03-01 发布日期:2014-03-01
  • 作者简介:金立生(1975),男,教授,博士生导师.研究方向:汽车安全与智能车辆导航技术.E-mail:jinls@jlu.edu.cn
  • 基金资助:
    教育部新世纪优秀人才基金项目(NCET-10-0435);高等学校博士学科点专项科研基金项目(20110061110036);吉林省人才开发基金项目(801121100417);吉林省科技厅重大项目 (20116017).

Driver cognitive distraction detection in different road lines

JIN Li-sheng1,NIU Qing-ning1,LIU Jing-hua2,QIN Yan-guang1,YU Huan-huan1   

  1. 1.College of Transportation, Jilin University, Changchun 130022, China;
    2.Zhengzhou Yutong Bus Co, Ltd, Zhengzhou 450000, China
  • Received:2013-04-08 Online:2014-03-01 Published:2014-03-01

RICH HTML

0   

PDF (PC)

531

摘要： 通过驾驶模拟实验采集了不同驾驶人在不同道路线形下的驾驶行为参数,通过对参数的统计分析,确立了表征正常驾驶和认知分散状态下驾驶的特征参数组。利用提取的特征参数组作为支持向量机模型输入,建立了不同驾驶人在不同道路线形下的认知分散状态监测模型。实验结果表明,在不同道路线形下分别进行监测的准确度(直道88.58%,弯道81.25%)高于采用同一模型不区分道路线形直接进行监测的准确度74.17%。研究同时表明个人驾驶习惯对驾驶人意识监测结果有重要影响。

关键词: 交通运输安全工程, 认知分散, 驾驶行为, 支持向量机

Abstract: Through driving simulator experiments, the original performance data of different drivers on different road lines were collected, from which the characteristic parameters were extracted using statistical analysis. Then cognitive distraction detection models were developed based on the support vector machine in view of different road lines. The characteristic parameters were used as the input of the models. The experiment results show that, using the proposed models, the detection accuracy is 88.58% for straight road, and 81.25% for curve road. The performance of the proposed models is better than that of the universal model, using which the detection accuracy is only 74.17%. Experiment results also show that the cognitive distraction detection is also influenced by the driving styles of the individual drivers.

Key words: transportation safety engineering, cognitive distraction, driving performance, support vector machine

中图分类号: 

  • U492.8
[1] Bergasa L, Nuevo J, Sotelo M, et al. Real-time system for monitoring driver vigilance[J]. IEEE Transactions on Intelligent Transportation Systems,2006,7(1):63-77.
[2] Wollmer M, Blaschke C, Schindl T, et al. Online driver distraction detection using long short-term memory[J]. IEEE Transactions on Intelligent Transportation Systems,2011,12(2):574-582.
[3] Jin L S, Niu Q N, Hou H J, et al. Driver cognitive distraction detection using driving performance Measures[J]. Discrete Dynamics in Nature and Society, 2012,2012:1-12.
[4] Kircher K, Ahlstrom C, Kircher A. Comparison of two eye-gaze based real-time driver distraction detection algorithms in a small-scale field operational test[C]∥Proc 5th Int Driving Symp Human Factors Driver Assessment, Train Veh Des, 2009:16-23.
[5] Strayer D L, Drews F A, Johnston W A. Cell phone-induced failures of visual attention during simulated driving[J]. Journal of Experimental Psychology: Applied, 2003, 9(1):23-32.
[6] Blanco M, Biever W J, Gallagher J P, et al. The impact of secondary task cognitive processing demand on driving performance[J]. Accident Analysis and Prevention, 2006,38: 895-906.
[7] Haigney D, Westerman S J. Mobile (cellular) phone use and driving: A critical review of research methodology[J]. Ergonom,2001,44(2): 132-143.
[8] Liang Yu-lan. Detecting driver distraction[D]. Lowa: College of Industrial Engineering, University of Lowa,2009.
[9] Recarte M A, Nunes L M. Mental workload while driving: Effects on visual search, discrimination, and decision making[J]. Journal of Experimental Psychology: Applied, 2003, 9(2):119-137.
[10] Reyes M L, Lee J D. Effects of cognitive load presence and duration on driver eye movements and event detection performance[J]. Transportation Research Part F: Traffic Psychology and Behavior, 2008, 11(6):391-402.
[11] 李伟,何其昌,范秀敏. 基于汽车操纵信号的驾驶员疲劳状态检测[J].上海交通大学学报, 2010,44(2):292-296.
Li Wei, He Qi-chang, Fan Xiu-min. Detection of driver's fatigue based on vehicle performance output[J]. Journal of Shanghai Jiaotong University, 2010, 44(2):292-296.
[12] Liu Yung-ching, Wu tsun-ju. Fatigued driver's driving behavior and cognitive task performance: Effects of road environments and road environment changes[J]. Safety Science, 2009, 47: 1083-1089.
[13] Horberry Tim, Anderson Janet, Regan Michael A, et al. Driver distraction: The effects of concurrent in-vehicle tasks, road environment complexity and age on driving performance[J]. Accident Analysis and Prevention, 2006,38:185-191.
[1] 代存杰,李引珍,马昌喜,柴获,牟海波. 不确定条件下危险品配送路线多准则优化[J]. 吉林大学学报(工学版), 2018, 48(6): 1694-1702.
[2] 隗海林, 包翠竹, 李洪雪, 李明达. 基于最小二乘支持向量机的怠速时间预测[J]. 吉林大学学报(工学版), 2018, 48(5): 1360-1365.
[3] 耿庆田, 于繁华, 王宇婷, 高琦坤. 基于特征融合的车型检测新算法[J]. 吉林大学学报(工学版), 2018, 48(3): 929-935.
[4] 蔡振闹, 吕信恩, 陈慧灵. 基于反向细菌优化支持向量机的躯体化障碍预测模型[J]. 吉林大学学报(工学版), 2018, 48(3): 936-942.
[5] 王芳荣, 郭柏苍, 金立生, 高琳琳, 岳欣羽. 次任务驾驶安全评价指标筛选及其权值计算[J]. 吉林大学学报(工学版), 2017, 47(6): 1710-1715.
[6] 谭立东, 刘丹, 李文军. 基于蝇复眼的交通事故现场全景图像阵列仿生设计[J]. 吉林大学学报(工学版), 2017, 47(6): 1738-1744.
[7] 万平, 吴超仲, 林英姿, 马晓凤. 基于驾驶行为多元时间序列特征的愤怒驾驶状态检测[J]. 吉林大学学报(工学版), 2017, 47(5): 1426-1435.
[8] 李显生, 孟祥雨, 郑雪莲, 程竹青, 任圆圆. 非满载罐体内液体冲击动力学特性[J]. 吉林大学学报(工学版), 2017, 47(3): 737-743.
[9] 王占中, 赵利英, 曹宁博. 基于多层编码遗传算法的危险品运输调度模型[J]. 吉林大学学报(工学版), 2017, 47(3): 751-755.
[10] 王海玮, 温惠英, 刘敏. 夜间环境驾驶员精神负荷的生理特性评估与实验[J]. 吉林大学学报(工学版), 2017, 47(2): 420-428.
[11] 徐进, 陈薇, 周佳, 罗骁, 邵毅明. 汽车转向盘操作与驾驶负荷的相关性[J]. 吉林大学学报(工学版), 2017, 47(2): 438-445.
[12] 袁哲明, 张弘杨, 陈渊. 基于特征选择和支持向量机的HIV-1型蛋白酶剪切位点预测[J]. 吉林大学学报(工学版), 2017, 47(2): 639-646.
[13] 梁士利, 魏莹, 潘迪, 张玲, 许廷发, 王双维. 基于语谱图行投影的特定人二字汉语词汇识别[J]. 吉林大学学报(工学版), 2017, 47(1): 294-300.
[14] 商强, 杨兆升, 张伟, 邴其春, 周熙阳. 基于奇异谱分析和CKF-LSSVM的短时交通流量预测[J]. 吉林大学学报(工学版), 2016, 46(6): 1792-1798.
[15] 郭应时, 付锐, 赵凯, 马勇, 袁伟. 驾驶人换道意图实时识别模型评价及测试[J]. 吉林大学学报(工学版), 2016, 46(6): 1836-1844.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 《吉林大学学报(工学版)》编辑部
地址：长春市人民大街5988号　电话：+86-431-85095297　传真：+86-431-85094074　E-mail: xbgxb@jlu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发