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.

困倦驾驶是交通事故的主要原因之一，严重威胁着道路交通安全。有效地及早发现睡意状态可以帮助驾驶员及时预警，但是以前的研究很少考虑睡意随时间的累积影响。因此，本研究的目的是建立一个模型，通过考虑个体差异和嗜睡的时间累积效应（TCE）来检测驾驶员的嗜睡程度。通过具有眼动追踪系统的驾驶模拟器收集了来自27位驾驶员的驾驶行为和眼动数据，并使用了Karolinska睡眠量表（KSS）来记录驾驶员对睡意状态的感知。由于驾驶员困倦程度随时间增加，因此建立了混合效应有序logit（MOL）模型，并使用时间的非递减函数来考虑时间累积。结果表明，随着睡意的增加，横向位置的标准偏差和驾驶员眼睑闭合百分比（PERCLOS）显着增加。因此，考虑这些变量可以提高睡意驾驶检测的准确性。将开发的MOL-TCE模型与非TCE MOL和TCE混合广义有序响应（MGOR）模型进行了比较。MOL-TCE模型的睡意检测精度为62.84％，高于MGOR-TCE的61.04％精度，并且明显高于非TCE MOL模型的52.47％。横向位置的标准偏差和驾驶员眼睑闭合百分比（PERCLOS）显着增加。因此，考虑这些变量可以提高睡意驾驶检测的准确性。将开发的MOL-TCE模型与非TCE MOL和TCE混合广义有序响应（MGOR）模型进行了比较。MOL-TCE模型的睡意检测精度为62.84％，高于MGOR-TCE的61.04％精度，并且明显高于非TCE MOL模型的52.47％。横向位置的标准偏差和驾驶员眼睑闭合百分比（PERCLOS）显着增加。因此，考虑这些变量可以提高睡意驾驶检测的准确性。将开发的MOL-TCE模型与非TCE MOL和TCE混合广义有序响应（MGOR）模型进行了比较。MOL-TCE模型的睡意检测精度为62.84％，高于MGOR-TCE的61.04％精度，并且明显高于非TCE MOL模型的52.47％。