In this modern era where ‘being fast’ and ‘being accelerated’ is the buzz word, the interest in equipping vehicle with an intelligent system for driver fatigue detection is very much obvious. The report [16] by 2013 World Health Organization that states that a massive figure of 1.6 million deaths due to road accidents per year, and approximately 6% of these accidents are caused by the vehicles driven by fatigued drivers in a drowsy state. In response to this mounting problem, intensive research studies have been carried out in developing an efficient, accurate and fast fatigue detection system, especially in the field of automotive.


Driver fatigue detection system is an intelligent and self-learning system which uses advanced deep learning networks, that alerts the driver about impending fatigue, by analyzing the combination of parameters like eye blink rates, yawn rates, PERCLOS (percentage of eye closure beyond 80).


A Multilayered Convolutional Neural network is used to extract the above-mentioned parameters and the output is then forwarded to an artificial neural network for further classification of driver’s fatigue.


This whitepaper gives a detailed framework for driver fatigue detection in vehicles using deep learning. Download now!