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.