Towards Safer Roads: A Deep Learning Approach to Driver Distraction Detection in Four-wheeler Cars

Abstract

The fact that there are more than 50 million cars sold annually and more than 1.3 million fatal motor vehicle accidents each year indicates the urgent need for stronger road safety regulations. Driver behaviour needs to be addressed, especially in emerging nations like India, which is responsible for 11% of all road fatalities worldwide. Diversion has been identified as the leading cause of 78% of accidents involving drivers. Distractions can take many different forms, from using a phone to interacting with others, and they greatly hinder road safety. This work aims to address this important problem by creating a highly effective machine learning (ML) model that employs computer vision techniques to classify various driver distractions in real-time. Utilising cutting-edge models, such as an ensemble of CNNs and convolutional neural networks (CNNs) like ResNet50. Our goal is to effectively identify and categorise distractions through the use of deep learning and picture recognition, allowing for proactive intervention to avert mishaps. Beyond classification accuracy, this study evaluates the model’s overall speed and scalability, which are critical for deployment on edge devices. We assess the practical practicality and wide-spread adoption of our approach by analysing performance parameters like inference time and resource utilisation