  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.
