Fatigue negatively affects the safety and performance of drivers on the road. In fact, drowsiness and fatigue are the cause of a substantial number of motor vehicle accidents. Drowsiness among the drivers can be detected using variety of modalities, including electroencephalogram (EEG), eye movement, and vehicle driving dynamics. Among these EEG is highly accurate but very intrusive and cumbersome. On the other hand, vehicle driving dynamics are very easy to acquire but accuracy is not very high. Eye movement based approach is very attractive in terms of balance between these two extremes. However, eye movement based techniques normally require an eye tracking device which consists of high speed camera with sophisticated algorithm to extract eye movement related parameters such as blinking, eye closure, saccades, fixation etc. This makes eye tracking based drowsiness detection difficult to implement as a practical system, especially on an embedded platform.

In this paper, authors propose to use eye images from camera directly without the need for expensive eye-tracking system. Here, eye related movements are captured by Recurrent Neural Network (RNN) to detect the drowsiness. Long Short Term Memory (LSTM) is a class of RNN which has several advantages over vanilla RNNs. In this work an array of LSTM cells are utilized to model the eye movements. Two types of LSTMs were employed: 1-D LSTM (R-LSTM) which is used as baseline and the convolutional LSTM (C-LSTM) which facilitates using 2-D images directly. Patches of size 48 × 48 around each eye were extracted from 38 subjects, participating in a simulated driving experiment. The state of vigilance among the subjects were independently assessed by power spectral analysis of multichannel electroencephalogram (EEG) signals, recorded simultaneously, and binary labels of alert and drowsy (baseline) were generated.

Results show high efficacy of the proposed system. R-LSTM based approach resulted in accuracy around 82 % and C-LSTM based approach resulted in accuracy in the range of 95%–97%. Comparison is also provided with a recently published eye-tracking based approach, showing the proposed LSTM technique outperform with a wide margin.

疲劳会对道路上的驾驶员的安全和性能产生负面影响。实际上，嗜睡和疲劳是大量机动车事故的原因。可以使用多种模式来检测驾驶员的睡意，包括脑电图（EEG），眼球运动和车辆行驶动态。在这些脑电图中，其准确性很高，但非常麻烦且累赘。另一方面，车辆行驶动力学非常容易获得，但准确性不是很高。就这两个极端之间的平衡而言，基于眼动的方法非常有吸引力。但是，基于眼动的技术通常需要一个眼动追踪设备，该设备由具有复杂算法的高速相机组成，以提取与眼动有关的参数，例如眨眼，闭眼，扫视，注视等。

在本文中，作者建议直接使用来自摄像机的眼睛图像，而不需要昂贵的眼睛跟踪系统。在这里，与眼睛有关的运动由递归神经网络（RNN）捕获以检测睡意。长期记忆（LSTM）是一类RNN，它具有优于普通RNN的优势。在这项工作中，使用LSTM细胞阵列来模拟眼睛运动。使用两种类型的LSTM：用作基线的1-D LSTM（R-LSTM）和有助于直接使用2-D图像的卷积LSTM（C-LSTM）。从38位受试者中提取了每只眼睛周围大小为48×48的斑块，参加了模拟驾驶实验。通过多通道脑电图（EEG）信号的功率谱分析对受试者之间的警觉状态进行独立评估，并同时进行记录，并生成警觉和昏昏欲睡（基线）的二进制标签。

结果表明该系统具有很高的功效。基于R-LSTM的方法的准确度约为82％，基于C-LSTM的方法的准确度在95％–97％范围内。比较还与最近发布的基于眼动追踪的方法进行了比较，显示了所提议的LSTM技术在很大范围内的表现优于同类产品。