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.

通过比较分析卷积神经网络结构，本研究旨在识别出最有效的实时检测驾驶员分心的模型，以提升车辆安全系统的能力，从而预防因不注意引发的事故。

通过卷积神经网络实时检测驾驶员注意力分散以增强道路安全

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

In this paper, we present a new dataset for "distracted driver" posture
estimation. In addition, we propose a novel system that achieves 95.98% driving
posture estimation classification accuracy. The system consists of a
genetically-weighted ensemble of Convolutional Neural Networks (CNNs). We show
that a weighted ensemble of classifiers using a genetic algorithm yields in
better classification confidence. We also study the effect of different visual
elements (i.e. hands and face) in distraction detection and classification by
means of face and hand localizations. Finally, we present a thinned version of
our ensemble that could achieve a 94.29% classification accuracy and operate in
a realtime environment.

本文介绍一种新的基于卷积神经网络集成学习和遗传算法优化的 “分心驾驶” 姿势估计系统，能够高效检测和识别驾驶人员的姿态，同时降低交通事故风险。