Real-time detection of distracted driving based on deep learning

Abstract

Driver distraction is a leading factor in car crashes. With a goal to reduce traffic accidents and improve transportation safety, this study proposes a driver distraction detection system which identifies various types of distractions through a camera observing the driver. An assisted driving testbed is developed for the purpose of creating realistic driving experiences and validating the distraction detection algorithms. The authors collected a dataset which consists of images of the drivers in both normal and distracted driving postures. Four deep convolutional neural networks including VGG-16, AlexNet, GoogleNet, and residual network are implemented and evaluated on an embedded graphic processing unit platform. In addition, they developed a conversational warning system that alerts the driver in real-time when he/she does not focus on the driving task. Experimental results show that the proposed approach outperforms the baseline one which has only 256 neurons in the fully-connected layers. Furthermore, the results indicate that the GoogleNet is the best model out of the four for distraction detection in the driving simulator testbed.