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Drowsy driving is one of the main causes of traffic accidents. To reduce such accidents, early detection of drowsy driving is needed. In previous studies, it was shown that driver drowsiness affected driving performance, behavioral indices, and physiological indices. The purpose of this study is to investigate the feasibility of classification of the alert states of drivers, particularly the slightly drowsy state, based on hybrid sensing of vehicle-based, behavioral, and physiological indicators with consideration for the implementation of these identifications into a detection system. First, we measured the drowsiness level, driving performance, physiological signals (from electroencephalogram and electrocardiogram results), and behavioral indices of a driver using a driving simulator and driver monitoring system. Next, driver alert and drowsy states were identified by machine learning algorithms, and a dataset was constructed from the extracted indices over a period of 10 s. Finally, ensemble algorithms were used for classification. The results showed that the ensemble algorithm can obtain 82.4% classification accuracy using hybrid methods to identify the alert and slightly drowsy states, and 95.4% accuracy classifying the alert and moderately drowsy states. Additionally, the results show that the random forest algorithm can obtain 78.7% accuracy when classifying the alert vs. slightly drowsy states if physiological indicators are excluded and can obtain 89.8% accuracy when classifying the alert vs. moderately drowsy states. These results represent the feasibility of highly accurate early detection of driver drowsiness and the feasibility of implementing a driver drowsiness detection system based on hybrid sensing using non-contact sensors.

Gaze estimation is a key technology to understand a person's interests and intents, and it is becoming more popular in daily situations such as driving scenarios. Wearable gaze estimation devices are use for long periods of time, therefore non-active sources are not desirable from a safety point of view.Gaze estimation that does not rely on active source, is performed by locating iris position. To estimate the iris position accurately, most studies use ellipse fitting in which the ellipse is defined by 5 parameters(position (x,y) , rotation angle, semi-major axis and semi-minor axis). We claim that, for iris position estimation, 5 parameters are redundant because they might be influenced by non-iris edges. Therefore, we propose to use 2 parameters(position) introducing a 3D eye model(the transformation between eye and camera coordinate and eyeball/iris size). Given 3D eye model, projected ellipse that represents iris shape can be specified only by position under weak-perspective approximation. We quantitatively evaluate our method on both iris position and gaze estimation. Our results show that our method outperforms other state-of-the-art's iris estimation and is competitive to commercial product that use infrared ray with respect to both accuracy and robustness.

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