Improving automatic detection of driver fatigue and distraction using
  machine learning
Dongjiang Wu
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.