Advanced Driver Assistance System for Drivers using Machine Learning and Artificial Intelligence Techniques

Abstract

Machine learning techniques have been used in order to predict the condition and emotion of a driver to provide information that will improve safety on the road. It is an application of artificial intelligence. The face, an important part of the body, conveys a lot of information. When a driver is in a state of fatigue, the facial expressions, e.g., the frequency of blinking and yawning, are different from those in the normal state. In this paper, we propose a system called “Advanced Driver Assistant System”, which detects the drivers fatigue status, such as yawning, blinking, and duration of eye closure, using video images, without equipping their bodies with devices. Artificial Intelligence is a method by which systems can automatically learn as well as improve without being explicitly programmed. A driver’s condition can be estimated by bio-indicators, behavior while driving as well as the expressions on the face of a driver. In this paper we present an all-inclusive survey of recent works related to driver drowsiness detection and alert system. We also present the various machine learning techniques such as CNN algorithm, HAAR based cascade classifier, OpenCV which are used in order to determine the driver’s condition. Finally, we identify the challenges faced by the current systems and present the corresponding research opportunities. Keywords: Convolutional neural network, fatigue detection, feature location, face tracking, Artificial Intelligence, Autonomous Vehicle Technology, Drowsiness Detection, Machine Learning.