新手驾驶人疲劳状态下的视觉特性研究  

Study on visual characteristics of novice drivers under fatigue state

作　　者：赵小平[1] 闵忠兵 薛运强 莫振龙[1] 张姝玮 龚俊 喻佳[1] ZHAO Xiaoping;MIN Zhongbing;XUE Yunqiang;MO Zhenlong;ZHANG Shuwei;GONG Jun;YU Jia(School of Transportation Engineering,East China Jiaotong University,Nanchang 330013,China)

机构地区：[1]华东交通大学交通运输工程学院,南昌330013

出　　处：《重庆理工大学学报（自然科学）》2023年第1期149-157,共9页Journal of Chongqing University of Technology：Natural Science

基　　金：国家自然科学基金项目(71961006)。

摘　　要：为探究新手驾驶人疲劳特性在视觉特征方面的表现,设计了基于驾驶模拟器和Dikablis Glass 3眼动仪的驾驶模拟实验,采集驾驶人视觉数据,结合视频专家法将驾驶人疲劳等级分为清醒、轻度疲劳、中度疲劳、重度疲劳。利用拉依达准则及卡尔曼滤波对数据进行清洗;依据单因素方差分析及事后多重比较结果,选取眨眼持续时长均值、扫视时长均值、扫视总时长、瞳孔面积均值、瞳孔变异系数均值、注视时间均值等视觉特征作为驾驶人的疲劳驾驶评价指标;构建基于新手驾驶人视觉特征的灰狼优化支持向量机(GWO-SVM)疲劳驾驶识别模型。研究结果表明:随着驾驶疲劳累积,新手驾驶人眨眼持续时长显著增加,扫视时长及扫视总时长显著降低,瞳孔面积缩小,瞳孔变异系数增大;SVM识别结果表明:新手驾驶人的疲劳状态可通过眼动指标进行有效识别,而GWO-SVM模型则进一步提升了识别精度,证明了眼动特征在新手驾驶人疲劳检测方面具有较好的适用性。In order to explore the performance of novice drivers’ fatigue characteristics in visual characteristics, this paper designs a driving simulation experiment based on driving simulators and Dikablis Glass 3 eye tracker to collect drivers’ visual data. Combined with the video expert method, drivers’ fatigue levels are divided into awakening, mild fatigue, moderate fatigue and severe fatigue. First of all, the data are cleaned by using Rayda criterion and Kalman filter. Secondly, according to the results of one-way ANOVA and post multiple comparison, mean value of blink duration, mean value of saccade duration, total saccade duration, mean value of pupil area, mean value of pupil variation coefficient, mean value of fixation time and other visual characteristics are selected as the evaluation indexes of drivers’ fatigue driving. Finally, a grey wolf optimized support vector machine(GWO-SVM) fatigue driving recognition model is constructed based on the visual features of novice drivers. The results show that, with the accumulation of driving fatigue of novice drivers, the duration of blinking increases significantly, the saccade duration and total saccade time decrease significantly, the pupil area decreases, and the pupil variation coefficient increases. The SVM identification results show that the fatigue state of novice drivers can be effectively identified by eye movement indicators, and the GWO-SVM model further improves the recognition accuracy, proving that eye movement features have a good applicability in the fatigue detection of novice drivers.

关 键 词：新手驾驶人 疲劳等级 视觉参数 GWO-SVM疲劳驾驶识别模型 

分 类 号：U491.254[交通运输工程—交通运输规划与管理]