Detection of Distracted Driving using Deep Learning Algorithm

A.S. Hasan, D. Patel, M. Jalayer
Rowan University,
United States

Keywords: distracted driving, artificial intelligence, YOLO, deep learning, New Jersey


Thousands of people die every year in the United States due to distracted driving crashes, with distracted driving accounting for 25% of all fatal traffic crashes in New Jersey. The transportation safety community implemented various AI techniques to detect distracted driving events inside the vehicle. However, most of those approaches are overt, where the subjects are aware about being recorded. To close this gap, this study collected video data on distracted driving events from outside the vehicle in the state of New Jersey. The method involved a data collection crew continuously driving through the selected corridors to track driver distraction events by video recording using high-resolution cameras. The recorded videos were preprocessed, and more than 26,000 unique images were annotated for training and testing YOLO-V5, an artificial intelligence (AI) model to detect the driver’s distraction. The suggested model performed reasonably well in predicting distracted driving events, with an accuracy of 90.9%. It is expected that the results obtained from this study will further assist state and local agencies in strengthening law enforcement in New Jersey by better detecting distracted driving behaviors.