  To help prevent motor vehicle accidents, there has been significant interest
in finding an automated method to recognize signs of driver distraction, such
as talking to passengers, fixing hair and makeup, eating and drinking, and
using a mobile phone. In this paper, we present an automated supervised
learning method called Drive-Net for driver distraction detection. Drive-Net
uses a combination of a convolutional neural network (CNN) and a random
decision forest for classifying images of a driver. We compare the performance
of our proposed Drive-Net to two other popular machine-learning approaches: a
recurrent neural network (RNN), and a multi-layer perceptron (MLP). We test the
methods on a publicly available database of images acquired under a controlled
environment containing about 22425 images manually annotated by an expert.
Results show that Drive-Net achieves a detection accuracy of 95%, which is 2%
more than the best results obtained on the same database using other methods
