Drowsiness or fatigue is one of the major causes of road accidents. Numerous deadly mishaps can be forestalled if the drowsy drivers are cautioned in time. A variety of drowsiness detection techniques exist that monitor the state of the driver while driving and a warning alert is triggered if they do not concentrate on driving. In order to determine the state of the driver, various relevant features from facial expressions can be extracted such as yawning, eye closure, and head movements. This paper aims to study the existing techniques in order to enhance them or create a hybrid of them for a better result. The study highlights existing behavioural drowsiness detection techniques. Firstly, in this paper, we classify the existing techniques into three categories: behavioural, vehicular, and physiological parameters-based techniques. Our main focus is on the behavioural parameters. Secondly, implementation techniques for behavioural parameters used for drowsiness detection are reviewed in detail. In the end, the accuracy of each technique implemented is represented in a tabular format. The challenges faced along with the conclusion of the study may help researchers for finding further work in the relevant field.

Driver drowsiness, fatigue detection, supervised learning, classification, support vector machine (SVM), yawning, eye closure

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