School of Transportation Engineering, Tongji University, Shanghai 201804, China; Road and Traffic Key Laboratory, Ministry of Education, Shanghai 201804, China.
School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China; National United Engineering Laboratory of Integrated and Intelligent Transportation, Chengdu 610031, China.
Accid Anal Prev. 2016 Oct;95(Pt B):350-357. doi: 10.1016/j.aap.2015.09.002. Epub 2015 Oct 1.
Drowsy driving is a serious highway safety problem. If drivers could be warned before they became too drowsy to drive safely, some drowsiness-related crashes could be prevented. The presentation of timely warnings, however, depends on reliable detection. To date, the effectiveness of drowsiness detection methods has been limited by their failure to consider individual differences. The present study sought to develop a drowsiness detection model that accommodates the varying individual effects of drowsiness on driving performance.
Nineteen driving behavior variables and four eye feature variables were measured as participants drove a fixed road course in a high fidelity motion-based driving simulator after having worked an 8-h night shift. During the test, participants were asked to report their drowsiness level using the Karolinska Sleepiness Scale at the midpoint of each of the six rounds through the road course. A multilevel ordered logit (MOL) model, an ordered logit model, and an artificial neural network model were used to determine drowsiness.
The MOL had the highest drowsiness detection accuracy, which shows that consideration of individual differences improves the models' ability to detect drowsiness. According to the results, percentage of eyelid closure, average pupil diameter, standard deviation of lateral position and steering wheel reversals was the most important of the 23 variables.
The consideration of individual differences on a drowsiness detection model would increase the accuracy of the model's detection accuracy.
昏昏欲睡的驾驶是一个严重的道路安全问题。如果司机在变得太困而无法安全驾驶之前能够得到警告，一些与困倦相关的撞车事故可能会被预防。然而，及时发出警告取决于可靠的检测。迄今为止，困倦检测方法的有效性一直受到其未能考虑个体差异的限制。本研究旨在开发一种困倦检测模型，该模型可以适应困倦对驾驶性能的不同个体影响。
19 个驾驶行为变量和 4 个眼睛特征变量在参与者在高保真度基于运动的驾驶模拟器中驾驶固定道路路线后，经过 8 小时的夜班工作后进行测量。在测试过程中，参与者被要求在通过道路路线的每轮的中点使用 Karolinska 嗜睡量表报告他们的困倦水平。多级有序逻辑（MOL）模型、有序逻辑模型和人工神经网络模型用于确定困倦。
MOL 具有最高的困倦检测准确性，这表明考虑个体差异可以提高模型检测困倦的能力。根据结果，闭眼百分比、平均瞳孔直径、横向位置标准差和方向盘反转是 23 个变量中最重要的。
在困倦检测模型中考虑个体差异将提高模型检测准确性。
