ISSN: 2222-6990
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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.
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In-Text Citation: (Jamil et al., 2020)
To Cite this Article: Jamil, A. H.-F. S. A., Khidir, M. L. M., & Mokhtar, M. F. M. (2020). Brain Signal Based Driver Drowsiness Prediction. International Journal of Academic Research in Business and Social Sciences, 10(11), 1438–1443.
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