###### Abstract
In our research, we hypothesized that the early stages of driver drowsiness are accompanied by changes in driving performance, behavioral features, and physiological indices. This hypothesis was tested in our previous study [20]; we investigated the relationship between the drowsiness levels of driver, driving performance indices, behavioral indices, and physiological indices using a driving simulator (DS), driver monitoring system, and physiological measurement system. Additionally, to future validate the feasibility of the early detection of driver drowsiness, we attempted to distinguish between the alert state and slightly drowsy state of a driver with machine learning algorithms based on hybrid measures consisting of vehicle-based, behavioral, and physiological measures. General machine learning algorithms, namely logistic regression (LR), support vector machines (SVM), the k-nearest neighbor classifier (kNN), and random forest (RF), were used for classification in the previous study [20]. The LR is a widely used algorithm for classification. It is useful for solving linear classification problems and binary classification problems [21]. The SVM is also widely used for classification as a supervised learning method. It aims to maximize a value known as the margin, which is defined as the distance between the decision boundary and the closest training sample to the decision boundary. The SVM can efficiently perform not only linear classification, but also non-linear classification by utilizing a kernel trick [22]. The kNN is also commonly utilized method. It classifies the data samples based on a majority vote of their k-nearest neighbors [23]. In addition, decision tree (DT) classification is also widely used in data mining [24]. This makes a model that consists of a number of classification trees, which predict the value of a target variable based on several input variables. RF is an ensemble of the decision tree models. It has generalization properties and runs efficiently on large databases. Furthermore, it calculates the importance of features [25]. The results of the previous study showed that the RF algorithm can obtain approximately 80% accuracy when classifying the alert and slightly drowsy states. This demonstrated the feasibility of early detection of driver drowsiness; however, it was considered that improving the accuracy of drowsiness detection is necessary for its actual implementation into vehicles. In particular, the optimization of the machine learning algorithm and investigation of other algorithms was insufficient. Moreover, consideration for the difficulty of sensing each measure was also needed to facilitate the implementation of the algorithm. Therefore, in this paper we investigated the accuracy of drowsiness detection, along with the optimization of algorithms and utilization of ensemble machine learning. To discuss the implementation of the classification system, we also evaluated the performance of classification not only using full hybrid measures (vehicle-based, behavioral, and physiological measures) but also using hybrid measures without physiological measures, as these are considered more difficult to implement than other measures.