Significance, Techniques, and Challenges in Driver Drowsiness Issue: A Guide Tour

Drowsiness is always a major concern that requires a high level of concentration in driving, piloting, controlling air traffic and industry labor. Yet, drowsiness drivers are the cause of many road accidents. State detection of driver drowsiness at runtime is a big challenge. Drowsiness not only decreases attention level but also slows down the retort time. A number of techniques have been suggested by researchers to detect driver drowsiness. Most of them are based on machine learning, deep learning, or processing the image of the face of the driver by extracting the facial expressions. This paper explores all the existing techniques used by researchers based on different features followed by the discussion over the result on driver drowsiness. The paper also focused on the comparative analysis of the techniques and suggested the best one. Almost all the techniques have common recommendation that the embedded software-based device is necessary to be mounted on the vehicle which can alerts the driver by early predicting the sleepiness to awake him. The aim of this paper is to bring together the current methods to resolve this issue. Moreover, the paper explored challenges that still exist in the current era. Finally, the paper is concluded the future aspects that can work in real-time scenario specially is night vision.