## 4 Discussion  
In this study, we investigated the accuracy of drowsiness detection with the optimization of algorithms and utilization of ensemble machine learning to improve classification of the alert and drowsy states of drivers. We focused on early state detection by performing detection of the slightly drowsy state based on ensemble machine learning algorithms and hybrid measures applied to datasets containing 10-s segments of data. In the DS experiment, we validated that driver drowsiness level would increase in the scenario of driving on a monotonous highway. The RF algorithm was the best classifier in this study; it achieved 82.4% accuracy when classifying the alert and the slightly drowsy state. The accuracy rates of the RF algorithm were slightly improved compared with our previous results; this improvement is attributed to the optimization of the number of features and estimators [20]. The accuracy rates of the RF algorithm were higher than that of the MVC algorithm, excluding the conditions of full hybrid measures and alert vs. moderately drowsy. PERCLOS and RRI, indices based on behavioral and physiological measures, were selected as the most important features when using the RF algorithm. Features from EEG measures were also selected as an important feature, as listed in Table 5. This suggests that the behavioral and physiological response was more directly affected by changes in the drowsiness level of driver in the early stage than driving performance. This demonstrates the validity of behavioral and physiological measures for early detection of driver drowsiness. In their previous study, [PERSON] et al. [12] showed that 80.9% detection accuracy can be achieved using hybrid features consisting of EEG- and ECG-based data when classifying the alert and drowsy state, but not the slightly drowsy state. [PERSON] and [PERSON] [33] showed that 96.2% detection accuracy can be achieved using hybrid features consisting of EEG and head-movement. However, head-movement did not occur in the slightly drowsy state based on [PERSON]'s criteria (it could occur in the significantly drowsy state based on the same criteria). In the present study, as shown in Figure 7, up to 78.7% detection accuracy was obtained by utilizing the hybrid measures excluding physiological indices in the case of classifying the alert and the slightly drowsy states. Previous studies focused on EEG-based data that require direct contact with the driver for measurement. It was generally agreed that the implementation of a direct-contact physiological measurement system in the vehicle was more difficult than other measurement systems using non-contact sensors. Although the performance values in past studies were low for the experiments that did not use contact sensors compared to the ones that performed classification using full hybrid measures with contact sensors, this study demonstrates the feasibility of early detection of driver drowsiness using non-contact sensors. In the case of classifying the alert and moderately (or more than moderately) drowsy states, the accuracy rate was approximately 10% higher than that in the case of classifying the alert and the slightly drowsy. Detection accuracy of 89.8% was obtained by utilizing the hybrid measures excluding physiological indices, as shown in Figure 7. This indicates that a system could be implemented to