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Title: Comparative Study to Detect Driver Drowsiness
Authors: Jaspreet Singh Bajaj
Naveen Kumar
Rajesh Kumar Kaushal
Keywords: Road accidents
Computational modeling
Machine learning
Feature extraction
Issue Date: 2021
Publisher: IEEE
Abstract: Driver Drowsiness is one of the major causes of road accidents which leads to fatal and non-fatal injuries, sudden deaths and substantial monetary losses. Recently, various approaches have been identified to detect the driver drowsiness by research community due to advancement in the area of Artificial Intelligence (AI) and Machine Learning (ML). This further assists with saving the precious human life and reduces the monetary losses. Many researchers have proposed different techniques and methods to detect the driver drowsiness. The most common methods are subjective measures, vehicle-based measures, physiological measures, behavioural measures and hybrid measures. The detailed review of these measures, working of the existing systems, limitations associated with these systems are discussed in the paper. This paper also highlights the comparative analysis of the hybrid measure and its effectiveness. Hybrid measure is state of the art and it is the combination of two or more measures to detect driver drowsiness with higher accuracy. We conclude that developing a driver drowsiness detection system by using hybrid measures would be more efficient and it is highly recommended.
URI: 10.1109/ICACITE51222.2021.9404761
Appears in Collections:Journals

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