AD3S: Advanced Driver Drowsiness Detection System using Machine Learning

Abstract

Drivers drowsiness is one of the prime reasons for road accidents around the globe. Persistent monotonous driving for an extended period of time without rest leads to drowsiness and fatal mishaps. Automatic detection of driver's drowsiness can prevent a large number of road accidents and thus, can save valuable lives. In this work, an advanced system namely AD3S (Advanced Driver Drowsiness Detection System) using Android application has been developed. The system is capable of capturing real-time facial landmarks of the drivers. The facial landmarks are further utilized to compute several parameters namely Eye Aspect Ratio (EAR), Nose Length Ratio (NLR) and Mouth Opening Ratio (MOR) based on adaptive threshold which are capable of detecting driver's drowsiness. The highlighting feature of AD3S is that it is non-intrusive in nature and is cost effective. To test the efficacy of AD3S, machine learning and deep learning techniques have been applied over a data set of 1200 application users. The empirical results demonstrate that the proposed system is capable of detecting driver's drowsiness with an accuracy of approximately 98% with Bagging classifier.