Abstract
Accidents occurring at night account for a significant percentage when compared to those that happen during the daytime. One of the leading causes of such accidents is driver drowsiness, which could result from inadequate sleep or exhaustion. Continuous monitoring of the facial expressions of the driver can detect signs of drowsiness, and an alarm system can alert the driver and prevent potential accidents. Various research studies have been conducted in this area. However, the low lighting conditions at night make it challenging to obtain detailed images, making it difficult to apply the proposed models effectively. This paper presents an effective model for detecting driver drowsiness using deep learning techniques. The study utilizes AlexNet as a classifier and attains an impressive accuracy of 98.3% after hyperparameter tuning.
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