ABSTRACT
The aim of this study was to predict in advance drivers’ drowsy states with a high risk of encountering a traffic accident and prevent drivers from continuing to drive under drowsy states. While the participants were required to carry out a simulated driving task, electroencephalography (EEG) (EEG-MPF and EEG-α/β), electrocardiography (ECG) (RRV3), tracking error, and subjective rating of drowsiness were measured. On the basis of such measurements, we made an attempt to predict in advance the point in time with a high risk of a crash (a state of a remarkably decreased arousal level with a high risk of a crash) using Bayesian estimation. As a result of applying the proposed method to the data points of each participant, it was verified that the proposed method could predict in advance the point in time with a high risk of a virtual crash before the point in time of a virtual accident when the participant would surely have encountered a serious accident with a high probability.
