Prediction of Point in Time with High Crash Risk by Integration of Bayesian Estimation of Drowsiness, Tracking Error, and Subjective Drowsiness

Abstract

Abstract:The aim of this study was to predict drivers' drowsy states with high risk of encountering a crash and prevent drivers from continuing to drive under such drowsy states with high risk of crash. While the participants were required to carry out a simulated driving task, EEG (Electroencephalography) (EEG-MPF and EEG-α/β), ECG (Electrocradiogram) (RRV3), tracking error, and subjective rating on drowsiness were measured. On the basis of such measurements, an attempt was made to predict the point in time with high crash risk using Bayesian estimation of posterior probability of drowsiness, tracking error, and subjective drowsiness. As a result of applying the proposed method to the data of each participant, it was verified that the proposed method could predict the point in time with high crash risk before the point in time of crash.