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Article
Affiliation(s)

Department of Intelligent Mechanical Systems, Graduate School of Natural Science and Technology, Okayama University, Okayama, 700-8530, Japan

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.

KEYWORDS

Bayesian estimation, drowsy driving, simulated driving task, tracking error, physiological measure, crash risk.

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