Contact us
[email protected] | |
3275638434 | |
Paper Publishing WeChat |
Useful Links
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Article
Author(s)
Atsuo Murata and Yohei Uragami
Full-Text PDF XML 1052 Views
DOI:10.17265/2328-2142/2018.01.001
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.
Cite this paper
References