Classification of Human Driver Distraction Using 3D Convolutional Neural Networks

Abstract

Distracted driving is a dangerous driving behavior that causes numerous accidents on US roads each year. It is critical to identify distracted drivers in order to prevent such accidents. Previous studies attempted to detect distracted driving using heuristics and machine learning; however, none of these methods could capture the problem's spatiotemporal features. As a result, the purpose of this study was to use a 3D convolutional neural network (CNN) that can capture both spatial and temporal information to classify distracted drivers based on facial features and behavioral cues. We used the Database to Enable Facial Analysis for Driving Studies (DEFADS), an open-source dataset containing 77 human subjects performing scripted driving-related activities, to achieve this goal. The PyTorch video library was used to train the model. The 3D CNN achieved an overall recall and precision of 97.6 and 98.1, respectively, indicating its efficacy in detecting distracted drivers in the real world.