ISSN: 2222-6990
Open access
Drowsiness has been one of the leading causes of injuries or fatalities in vehicle accidents. Therefore, in this research work, it is proposed to develop an adaptive heavy vehicle driver fatigue and alertness model based on EEG frequency bands by combining signal processing algorithms and soft computing techniques such as Neuro-fuzzy algorithm to estimate the driver cognitive state while driving a vehicle in a virtual reality (VR)-based dynamic simulator under monotonous driving environment. Thus in this research, it is proposed to minimize the number of features using soft computing techniques and classification using non-linear supervised classification algorithms. The proposed adaptive model identifies the
discrimination between the driver's level of fatigue by recognizing whether the driver is fatigue due to task-induced factors or attitude/behaviour using the brain responses, then the level of fatigue is related with sleepiness (i.e. level of alertness towards driving). Further, the adaptive model can be utilized to alert drivers and regulators in optimizing the properties of the interface systems in identifying potential catastrophe. The proposed system alerts the driver during fatigue/drowsiness according to the recognition of cognitive state and produce the fatigue index and level of alertness. The proposed system also helps the driver to be more attentive and intuitive to prevent from fatal road accidents.
Al-Sultan, S., Al-Bayatti, A. H., & Zedan, H. (2013). Context-Aware Driver Behavior Detection System in Intelligent Transportation Systems. IEEE Transactions on Vehicular Technology, 62(9), 4264–4275. https://doi.org/10.1109/TVT.2013.2263400
Charbonnier, S., Roy, R. N., Bonnet, S., & Campagne, A. (2016). EEG index for control operators’ mental fatigue monitoring using interactions between brain regions. Expert Systems with Applications, 52, 91–98. https://doi.org/10.1016/j.eswa.2016.01.013
Fai, L. C. (2015). Miros statistics say human error causes 80% of traffic accidents. The Sun Daily. Retrieved from https://www.thesundaily.my/archive/1333889-KRARCH296470
Johnson, R. R., Popovic, D. P., Olmstead, R. E., Stikic, M., Levendowski, D. J., & Berka, C. (2011). Drowsiness/alertness algorithm development and validation using synchronized EEG and cognitive performance to individualize a generalized model. Biological Psychology, 87(2), 241–250. https://doi.org/10.1016/j.biopsycho.2011.03.003
Jonathan, L. (2015). Road deaths in Malaysia above world average – report. Retrieved from https://paultan.org/2015/10/13/road-deaths-in-malaysia-above-world-average-report/
Lal, S. K. L., Craig, A., Boord, P., Kirkup, L., & Nguyen, H. (2003). Development of an algorithm for an EEG-based driver fatigue countermeasure. Journal of Safety Research, 34(3), 321–328. https://doi.org/10.1016/S0022-4375(03)00027-6
Lee, B.-G., & Chung, W.-Y. (2012). Driver Alertness Monitoring Using Fusion of Facial Features and Bio-Signals. IEEE Sensors Journal, 12(7), 2416–2422. https://doi.org/10.1109/JSEN.2012.2190505
Liang, S. F., Lin, C. T., Wu, R. C., Chen, Y. C., Huang, T. Y., & Jung, T. P. (2005). Monitoring Driver’s Alertness Based on the Driving Performance Estimation and the EEG Power Spectrum Analysis. 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, 5738–5741. https://doi.org/10.1109/IEMBS.2005.1615791
Mustafa, M. N. (2015). Overview Of Current Road Safety Situation In Malaysia. First International Visual Informatics Conference.
Stern, J. A., Boyer, D., & Schroeder, D. (1994). Blink Rate: A Possible Measure of Fatigue. Human Factors: The Journal of the Human Factors and Ergonomics Society, 36(2), 285–297. https://doi.org/10.1177/001872089403600209
Touryan, J., Lance, B. J., Kerick, S. E., Ries, A. J., & McDowell, K. (2016). Common EEG features for behavioral estimation in disparate, real-world tasks. Biological Psychology, 114, 93–107. https://doi.org/10.1016/j.biopsycho.2015.12.009
Wang, W., & Wets, G. (Eds.). (2013). Computational Intelligence for Traffic and Mobility. https://doi.org/10.2991/978-94-91216-80-0
Yang, G., Lin, Y., & Bhattacharya, P. (2010). A driver fatigue recognition model based on information fusion and dynamic Bayesian network. Information Sciences, 180(10), 1942–1954. https://doi.org/10.1016/j.ins.2010.01.011
In-Text Citation: (Jamil et al., 2021)
To Cite this Article: Jamil, A. H. S. A., Khidir, M. L. M., & Mokhtar, M. F. M. (2021). Brain Signal Based Driver Drowsiness Prediction. International Journal of Academic Research in Business and Social Sciences, 11(2), 75–80.
Copyright: © 2021 The Author(s)
Published by HRMARS (www.hrmars.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode