Enhancing Road Safety: Real-Time Detection of Driver Distraction through Convolutional Neural Networks

TL;DR

This paper explores the use of CNN models, specifically VGG16 and VGG19, for real-time detection of driver distraction to improve vehicle safety systems and reduce traffic accidents.

Contribution

It compares various CNN architectures to identify the most effective model for detecting driver distraction in real-time conditions.

Findings

VGG16 and VGG19 outperform other CNNs in accuracy

The models demonstrate robustness under different environmental conditions

Potential integration into vehicle safety systems is feasible

Abstract

As we navigate our daily commutes, the threat posed by a distracted driver is at a large, resulting in a troubling rise in traffic accidents. Addressing this safety concern, our project harnesses the analytical power of Convolutional Neural Networks (CNNs), with a particular emphasis on the well-established models VGG16 and VGG19. These models are acclaimed for their precision in image recognition and are meticulously tested for their ability to detect nuances in driver behavior under varying environmental conditions. Through a comparative analysis against an array of CNN architectures, this study seeks to identify the most efficient model for real-time detection of driver distractions. The ultimate aim is to incorporate the findings into vehicle safety systems, significantly boosting their capability to prevent accidents triggered by inattention. This research not only enhances our understanding of automotive safety technologies but also marks a pivotal step towards creating vehicles that are intuitively aligned with driver behaviors, ensuring safer roads for all.