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.

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Thermal factors not only affect the thermal comfort sensation of occupants, but also affect their arousal level, productivity, and health. Therefore, it is necessary to control thermal factors appropriately. In this study, we aim to design a thermal environment that improves both the arousal level and thermal comfort of the occupants. To this end, we investigated the relationships between the physiological indices, subjective evaluation values, and task performance under several conditions of changes in the indoor ambient temperature. In particular, we asked subjects to perform a mathematical task and subjective evaluation related to their thermal comfort sensation and drowsiness levels. Simultaneously, we measured their physiological parameters, such as skin temperature, respiration rate, electroencephalography, and electrocardiography, continuously. We investigated the relationship between the comfort sensation and drowsiness level of occupants, and the physiological indices. From the results, it was confirmed that changes in the indoor ambient temperature can improve both the thermal comfort and the arousal levels of occupants. Moreover, we proposed the evaluation indices of the thermal comfort and the drowsiness level of occupants using physiological indices.

Driving comfort and driving safety are essential factors for drivers. As the environment of vehicle affects the comfort sensation and the arousal level of the drivers, it is necessary to contemplate the way to design of environmental factors inside vehicle. In this study, we focus on the thermal factors inside vehicle, and we aim to design a thermal environment which can improve both the thermal comfort and the arousal level of drivers. In our previous research, we showed that the changes in indoor temperature have possibility to improve both the comfort sensation and the arousal level of driver by analyzing the subjective parameters. To clarify the design requirements, it is needed to evaluate the thermal comfort and the arousal level of drivers continuously, quantitatively and separately. So, we focused on the physiological parameters which can be measured continuously, we investigated the relationship between the thermal comfort sensation, the arousal level of drivers based on facial expression and the physiological parameters, such as Electroencephalogram (EEG) and Electrocardiogram (ECG) when the indoor temperature changed. As a result, we showed that it is possible to evaluate the thermal comfort sensation and the arousal level of drivers quantitatively, continuously and separately by using those physiological parameters.

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Narrow tilting vehicles have been proposed to address transportation issues such as traffic congestion, lack of parking space. The investigation of the effects of narrow tilting vehicles on user is insufficient though many methods for improving the stability of those were proposed. The purpose of the present study is to investigate the effects of tilting mechanism of narrow vehicles on psychophysiological states of driver as a fundamental study. Focused on user satisfaction among the components of usability, the hypotheses that a tilting mechanism affects the user's psychological state, and that the physiological indices such as a frontal alpha asymmetry, beta wave per alpha wave power based on brain activity are valid to evaluate the state were tested. The subjective evaluation of emotional states based on Russell's circumplex model and the measurement of electroencephalography (EEG) were performed in the experiment using the proposed vehicle with the tilting mechanism. As a result, both the subject evaluation and the physiological indices based on EEG showed a significantly higher value of arousal and valence in the case of the tilting vehicle compared to the control vehicle. These results suggest that both arousal and valence levels of narrow vehicle users can be improved by a tilting mechanism.

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