驾驶疲劳状态波动性特征的识别方法  

Method of Recognizing the Variability Characteristics of Driver's Fatigue State

作　　者：唐优华[1] 郭孜政[1] 牛林博 杨露 

机构地区：[1]西南交通大学交通运输与物流学院综合交通运输智能化国家地方联合工程实验室,成都610031 [2]四川省交通投资集团有限责任公司,成都610031

出　　处：《北京工业大学学报》2015年第8期1225-1229,共5页Journal of Beijing University of Technology

基　　金：国家自然科学基金资助项目(51108390);中国博士后基金资助项目(2012M510051);国家自然科学基金委铁道联合基金资助项目(U1234206)

摘　　要：针对疲劳状态变化的波动性特征,基于心率变异性指标构建了一种驾驶疲劳状态识别方法.以驾驶行为绩效为疲劳客观测评指标,给出了适应疲劳波动性特征的驾驶疲劳分级方法.以心率变异性的3项时域指标、5项频域指标为特征因子构建驾驶疲劳识别特征向量,结合支持向量机提出了一种适应小样本的驾驶疲劳状态识别模型.采用10名驾驶员连续4 h的驾驶行为绩效与心电数据,对模型方法予以了测试.测试结果表明：10名驾驶员1级、2级疲劳状态的正确识别率介于70%~82%,平均正确识别率为75%.According to the variability characteristics of fatigue, based onindexes of heart rate variability （ HRV） , a method of recognizing driver’s mental fatigue was proposed. The method proposed hierarchy partition of driving mental fatigue through using the driver’s behavior performance as objective evaluationindexes,which could be the response on the variability of fatigue state. Meanwhile, according to the 3 time-domain indexes and 5 frequency-domain indexes of HRV as recognizing fatigue characterized factors and which combined with support vector machine （ SVM） ,the model was established to recogniz the state of driver’s mental fatigue. Finally, combining with the examples, the mental fatigue was dividedinto two classifications. 4 hours of continual driving behavior performance and ECG data from 10 drivers were collected to test the model. The result showed that the average recognition accuracy rate was between 70%-82%, and the average accuracy rate was 75%.

关 键 词：驾驶疲劳 波动性 识别方法 心率变异性 

分 类 号：U491.3[交通运输工程—交通运输规划与管理]