Study of Driver's Fatigue Level Grading Experiment Based on His Physiological Signal

doi:10.3969/j.issn.1674-0696.2017.01.14

Journal of Chongqing Jiaotong University(Natural Science) ›› 2017, Vol. 36 ›› Issue (1): 77-81.DOI: 10.3969/j.issn.1674-0696.2017.01.14

• Traffic & Transportation Engineering • Previous Articles Next Articles

Study of Driver's Fatigue Level Grading Experiment Based on His Physiological Signal

LU Zhangping1，YIN Chuanbin1，LI Rui1，HE Ren2

（1. School of Mechanical Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, P.R.China；2.School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, P.R.China）

Received:2015-09-28 Revised:2015-12-11 Online:2017-01-20 Published:2017-02-08
Contact: 尹传斌（1990—），男，山东临沂人，硕士研究生，主要从事道路交通安全方面的研究。E-mail: yinchuanbin1990@126.com。

基于生理信号的驾驶疲劳分级检测研究

卢章平1，尹传斌1，李瑞1，何仁2

（1.江苏大学机械工程学院，江苏镇江212013；2.江苏大学汽车与交通工程学院，江苏镇江212013）

作者简介:第一作者：卢章平（1958—），男，江苏扬州人，教授，博士，主要从事计算机辅助设计、人机交互理论及应用方面的研究。E-mail: lzping@ujs.edu.cn。
基金资助:
高等学校博士学科点专项科研基金联合资助课题项目（20113227110007）；“江苏大学”博士创新计划项目（KYLX15_1050）

Abstract

Abstract: With the help of Electroencephalogram （EEG） and Electrocardiogram （ECG）, a real traffic driving experiment which combining physiology test and subjective fatigue survey was conducted to study the law of fatigue level variation of a diver who was driving during 09：00—12：00AM，12：00—14：00PM and 21：00—23：00PM. By the principal component analysis （PCA）, this study was able to establish the relationship between EEG and ECG signal, and to set up a comprehensive indicator to determine driver fatigue. The results show that the above-mentioned comprehensive indicator can recognize different levels of driver fatigue and the fusion of different indicators thas can improve the accuracy in detecting driver′s different fatigue levels.

Key words: traffic engineering, physiological signal, driver′s fatigue level, principal component analysis（PCA）, driving experience

摘要： 采用实验生理学测试与主观疲劳调查的方法，通过实车驾驶实验，以脑电信号和心电信号为基本指标，研究不同驾驶经验驾驶员在09：00—12：00，12：00—14：00，21：00—23：00这3个驾驶过程中疲劳等级的变化。通过主成分分析法，建立脑电信号与心电信号之间的关系，确定驾驶疲劳综合评价指标。结果显示：上述疲劳综合指标在在不同疲劳等级状态下存在显著性差异，通过对不同指标的融合提高了对驾驶员不同疲劳等级的识别准确率。

关键词: 交通工程, 生理信号, 疲劳驾驶分级, 主成分分析法, 驾驶经验

CLC Number:

U491

LU Zhangping1，YIN Chuanbin1，LI Rui1，HE Ren2. Study of Driver's Fatigue Level Grading Experiment Based on His Physiological Signal[J]. Journal of Chongqing Jiaotong University(Natural Science), 2017, 36(1): 77-81.

卢章平1，尹传斌1，李瑞1，何仁2. 基于生理信号的驾驶疲劳分级检测研究[J]. 重庆交通大学学报（自然科学版）, 2017, 36(1): 77-81.

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Study of Driver's Fatigue Level Grading Experiment Based on His Physiological Signal

基于生理信号的驾驶疲劳分级检测研究

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