Feasibility Study on Amalgamation of Multiple Measures to Detect Driver Drowsiness

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. Due to advancements in technologies like Artificial Intelligence (AI), various approaches have been carried out to detect driver drowsiness at the early stage. The existing measures comprises certain issues like intrusiveness, variation in results, and tested in simulated environment only. A hybrid solution is the need for early detection of drowsiness of driver by amalgamation of multiple effective measures. Many researchers have concluded that developing a driver drowsiness detection system by using hybrid measures would be more efficient and highly recommended. The main contribution of this paper is to evaluate and identify the effective measures to detect driver drowsiness and choose the best measures to combine. It will help in early detection of the driver drowsiness in a more efficient manner and avoid crashes on the roads.