Driver Drowsiness Detection Using Mixed-effect Ordered Logit Model Considering Time Cumulative Effect

Publication date: Available online 25 January 2020Source: Analytic Methods in Accident ResearchAuthor(s): Xuxin Zhang, Xuesong Wang, Xiaohan Yang, Chuan Xu, Xiaohui Zhu, Jiaohua WeiAbstractDrowsy 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) mode...
Source: Analytic Methods in Accident Research - Category: Accident Prevention Source Type: research