基于驾驶员转向操作特性的疲劳驾驶检测  

Drowsy Driving Detection Based on Driver's Steering Operation Characteristics

作　　者：屈肖蕾[1] 成波[1] 林庆峰[2] 李升波[1] 

机构地区：[1]清华大学,汽车安全与节能国家重点实验室,北京100084 [2]北京航空航天大学交通科学与工程学院,北京100191

出　　处：《汽车工程》2013年第9期803-807,831,共6页Automotive Engineering

基　　金：国家863计划项目(2009AA11Z214);清华大学自主课题(20101081763)资助

摘　　要：本文旨在利用驾驶模拟器开展疲劳驾驶试验,研究疲劳驾驶的检测方法。首先采用面部视频的专家评分方法,建立驾驶员3级疲劳(清醒、疲劳和非常疲劳)的样本数据库;然后定量提取描述疲劳操作特性的特征指标,采用序列浮动前向选择算法筛选最优的特征指标组合,最终建立了一种基于SVM的驾驶员3级疲劳的在线检测算法。测试结果表明,驾驶模拟器工况下,本文算法识别3级疲劳的准确率达到87.7%,具有较高的鲁棒性和实用性。This paper aims to conduct drowsy driving tests on driving simulator for studying the method of drowsy driving detection. Firstly, a sample database of driver＇s three levels of fatigue （ alert, drowsy and very drow- sy） is built based on the rating on driver＇s facial video images by experts. Then, the feature indicators describing the steering operation characteristics of driver at different fatigue levels are quantitatively extracted, and the best combination of feature indicators is screened out by using sequential floating forward selection algorithm. Finally a driver fatigue online detection algorithm with three drowsiness levels is established based on support vector machine technique. The test results show that with driving simulator the accuracy rate of the algorithm in three-level drowsi- ness identification reaches 87.7 % with high robustness and practicability.

关 键 词：疲劳驾驶 转向操作 车辆状态 支持向量机 

分 类 号：U492.8[交通运输工程—交通运输规划与管理]