Driver drowsiness detection with eyelid related parameters by Support Vector Machine
Driver drowsiness detection with eyelid related parameters by Support Vector Machine
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DOI：
10.1016/j.eswa.2008.09.030
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发表时间：
2009-05-01
影响因子：
8.5
通讯作者：
Zheng Gangtie
中科院分区：
文献类型：
--
作者：
Hu Shuyan;Zheng Gangtie
各种调查表明，司机的困倦是交通事故的主要原因之一。因此，目前在许多领域中需要对策装置来防止与困倦相关的事故。本文拟采用支持向量机（SVM）与眼睑相关参数提取的EOG数据中收集的驾驶模拟器提供的欧盟项目SENSATION的困倦预测。首先将数据集划分为三个递增的困倦水平，然后进行配对t检验以确定参数与驾驶员困倦状况的关联。利用所有特征，构建了支持向量机睡意检测模型。验证结果表明，困倦检测的准确性是相当高的，特别是当受试者非常困倦。(C)2008爱思唯尔有限公司版权所有。
Various investigations show that drivers' drowsiness is one of the main causes of traffic accidents. Thus, countermeasure device is Currently required in many fields for sleepiness related accident prevention. This paper intends to perform the drowsiness prediction by employing Support Vector Machine (SVM) with eyelid related parameters extracted from EOG data collected in a driving simulator provided by EU Project SENSATION. The dataset is firstly divided into three incremental drowsiness levels, and then a paired t-test is done to identify how the parameters are associated with drivers' sleepy condition. With all the features, a SVM drowsiness detection model is constructed. The validation results show that the drowsiness detection accuracy is quite high especially when the subjects are very sleepy. (C) 2008 Elsevier Ltd. All rights reserved.