# Driver Distraction Identification with an Ensemble of Convolutional Neural Networks

## TL;DR

This paper introduces a new dataset and a deep learning ensemble approach for identifying driver distractions, achieving high accuracy and real-time performance to improve road safety.

## Contribution

It provides the first comprehensive dataset for driver distraction detection and proposes a genetically-weighted CNN ensemble that enhances classification accuracy.

## Key findings

- Achieved 90% accuracy with the ensemble system.
- Developed a real-time capable model with 84.64% accuracy.
- Studied the impact of visual cues like face and hand localization.

## Abstract

The World Health Organization (WHO) reported 1.25 million deaths yearly due to road traffic accidents worldwide and the number has been continuously increasing over the last few years. Nearly fifth of these accidents are caused by distracted drivers. Existing work of distracted driver detection is concerned with a small set of distractions (mostly, cell phone usage). Unreliable ad-hoc methods are often used.In this paper, we present the first publicly available dataset for driver distraction identification with more distraction postures than existing alternatives. In addition, we propose a reliable deep learning-based solution that achieves a 90% accuracy. The system consists of a genetically-weighted ensemble of convolutional neural networks, we show that a weighted ensemble of classifiers using a genetic algorithm yields in a better classification confidence. We also study the effect of different visual elements in distraction detection by means of face and hand localizations, and skin segmentation. Finally, we present a thinned version of our ensemble that could achieve 84.64% classification accuracy and operate in a real-time environment.