Driver distraction detection and early prediction and avoidance of accidents using convolutional neural networks

Abstract

In this fast-moving world, accidents in four wheeled vehicles occur due to the break failure or because of the carelessness or the fatigue of the driver. The driver pattern of the driver plays a major role in providing road safety as well as in fuel consumption. The distraction of drivers is found by installing various sensors which is used for gathering real time data. The behaviour of drivers under stress condition and their behavioural patterns for early detection and avoidance of accidents are found using convolutional neural networks. Convolutional Neural Networks are efficient classifiers in handling image processing and computer vision problem. The input dataset is a collection of driving behaviour of 10 different drivers collected from Kaggle. The behaviour of drivers under 7 distracted situations like texting, talking through phone, playing music, drinking, eating, doing make up and talking to passenger are considered. The batch normalization is used at the right of the input layer in order to avoid skewing of data at a direction. It is shown, the convolutional neural networks at 4 epochs have shown 99% accuracy.