Improving automatic detection of driver fatigue and distraction using machine learning

TL;DR

This paper presents vision-based machine learning techniques, including facial feature analysis and CNNs, to improve the detection of driver fatigue and distraction, enhancing vehicle safety systems.

Contribution

It introduces new datasets and combines facial alignment with CNNs for more accurate and efficient detection of driver fatigue and distraction behaviors.

Findings

Improved accuracy over previous methods

Reduced computation time

Effective detection on public and custom datasets

Abstract

Changes and advances in information technology have played an important role in the development of intelligent vehicle systems in recent years. Driver fatigue and distracted driving are important factors in traffic accidents. Thus, onboard monitoring of driving behavior has become a crucial component of advanced driver assistance systems for intelligent vehicles. In this article, we present techniques for simultaneously detecting fatigue and distracted driving behaviors using vision-based and machine learning-based approaches. In driving fatigue detection, we use facial alignment networks to identify facial feature points in the images, and calculate the distance of the facial feature points to detect the opening and closing of the eyes and mouth. Furthermore, we use a convolutional neural network (CNN) based on the MobileNet architecture to identify various distracted driving behaviors. Experiments are performed on a PC based setup with a webcam and results are demonstrated using public datasets as well as custom datasets created for training and testing. Compared to previous approaches, we build our own datasets and provide better results in terms of accuracy and computation time.