Enhancing Road Safety: Real-Time Detection of Driver Distraction through Convolutional Neural Networks (2405.17788v1)

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.