###### Abstract  
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. driver drowsiness; hybrid sensing; machine learning; physiological signal +
Footnote †: journal: Applied Soft  
physiological states of drivers. An electroencephalogram (EEG) is utilized to investigate the brain activity related to arousal level [7; 8]; therefore, EEG-based methods to detect driver drowsiness have been proposed [9; 10]. Since an electrocardiogram (ECG) is easier to measure than EEG and measures autonomic nervous system activity, ECG-based methods have been proposed [11], along with hybrid methods of both EEG and ECG measures [12]. In addition, previous studies showed that drowsiness level affects driving performance [13; 14; 15]. The movement of the steering wheel is mainly utilized to evaluate driving performance and detect driver drowsiness [13]. As the drowsiness level of the driver increases, performance related to lane keeping decreases. As an example, the standard deviation of lateral position (SDLP), which is widely utilized as the evaluation index of steering control, increases [14]. Performance related to preceding-car following such as Time Headway (THW), which is defined as the time between successive vehicles that pass a certain point in the path of traffic flow, decreases [15]. To determine the drowsiness level of a driver based on these known indices, machine learning algorithms have been widely used. In previous studies, machine learning algorithms were tested to classify the drowsy and alert states of drivers based on datasets containing behavioral measures [16], physiological measures [17; 18], and more [19]. As mentioned above, the drowsiness level of a driver has a relationship with the driver's behavioral features, physiological responses, and driving performance. Many methods for driver drowsiness detection utilizing these features have been proposed. However, the proposals of previous studies were limited in their ability to perform early detection of driver drowsiness, because the slightly drowsy state was not focused on and methods for optimal accuracy, such as data measured over a short period of time and hybrid measures (vehicle-based measures, behavioral measures, and physiological measures), were not utilized.