ABSTRACT：Drowsy driving is one of the main causes of traffic crashes, a serious threat to road traffic safety. The effective early detection of a drowsiness state can help provide a timely warning for drivers, but previous studies have seldom considered the cumulative effect of drowsiness over time. The purpose of this study is therefore to establish a model to detect a driver's drowsiness level by considering individual differences combined with the time cumulative effect (TCE) of drowsiness. Driving behavior and eye movement data from 27 drivers were collected by a driving simulator with an eye-tracking system, and the Karolinska Sleepiness Scale (KSS) was used to record drivers’ perceptions of their states of drowsiness. Since the degree of driver drowsiness was shown to increase with time, a mixed-effect ordered logit (MOL) model was established, and a non-decreasing function of time was applied to consider time accumulation. Results showed that with increasing drowsiness, the standard deviation of lateral position and percentage of driver eyelid closure (PERCLOS) increased significantly. Consideration of these variables can thus improve the accuracy of drowsy driving detection. The developed MOL-TCE model was compared with a non-TCE MOL and a TCE mixed generalized ordered response (MGOR) model. The drowsiness detection accuracy of the MOL-TCE model was 62.84%, higher than the 61.04% accuracy of the MGOR-TCE and appreciably higher than the 52.47% of the non-TCE MOL model.
Xuxin Zhang, Xuesong Wang*, Xiaohan Yang, Chuan Xu, Xiaohui Zhu, Jiaohua Wei. Driver Drowsiness Detection Using Mixed-effect Ordered Logit Model Considering Time Cumulative Effect, Analytic Methods in Accident Research, 2020.