[1]牛清宁,周志强,金立生,等.基于眼动特征的疲劳驾驶检测方法[J].哈尔滨工程大学学报,2015,(03):394-398.[doi:10.3969/j.issn.1006-7043.201311044]
 NIU Qingning,ZHOU Zhiqiang,JIN Lisheng,et al.Detection of driver fatigue based on eye movements[J].hebgcdxxb,2015,(03):394-398.[doi:10.3969/j.issn.1006-7043.201311044]
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基于眼动特征的疲劳驾驶检测方法
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《哈尔滨工程大学学报》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2015年03期
页码:
394-398
栏目:
出版日期:
2015-03-25

文章信息/Info

Title:
Detection of driver fatigue based on eye movements
作者:
牛清宁1 周志强1 金立生2 刘文超1 于鹏程1
1. 公安部道路交通安全研究中心, 北京 100062;
2. 吉林大学 交通学院, 吉林 长春 130022
Author(s):
NIU Qingning1 ZHOU Zhiqiang1 JIN Lisheng2 LIU Wenchao1 YU Pengcheng1
1. Road Traffic Safety Research Center of Ministry of Public Security, Beijing 100062, China;
2. College of Transportation, Jilin University, Changchun 130022, China
关键词:
疲劳驾驶眼动特征支持向量机滑移时窗时窗检测模型
分类号:
U461.91
DOI:
10.3969/j.issn.1006-7043.201311044
文献标志码:
A
摘要:
为了提高疲劳驾驶检测模型准确率和实时性,基于驾驶模拟实验,利用SmartEye系统提取了驾驶人不同驾驶状态下眼动数据.基于眼动参数协议,提出了眨眼频率、PERCLOS、注视方向和注视时间4个特征参数的计算方法.分析了各特征参数的最优时窗,针对不同特征参数最优时窗差异,提出了滑移时窗的数据融合方法.基于支持向量机,搭建了疲劳驾驶检测模型.实验结果表明,该模型可以有效地进行疲劳状态检测,准确率能够达到83.84%.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2013-11-13;改回日期:。
基金项目:教育部新世纪优秀人才基金资助项目(NCET-10-0435);高校博士学科点专项科研基金资助项目(20110061110036);吉林省人才开发基金资助项目(801121100417); 吉林省科技厅国际合作资助项目(20130413056GH).
作者简介:牛清宁(1987-),男,助理研究员,博士;金立生(1975-),男,教授,博士生导师.
通讯作者:金立生,E-mail:jinls@jlu.edu.cn.
更新日期/Last Update: 2015-06-16