A New Approach to Detect Driver Distraction to Ensure Traffic Safety and Prevent Traffic Accidents: Image Processing and MCDM

Abstract

One of the factors that threaten traffic safety and cause various traffic problems is distracted drivers. Various studies have been carried out to ensure traffic safety and, accordingly, to reduce traffic accidents. This study aims to determine driver-distraction classes and detect driver violations with deep learning algorithms and decision-making methods. Different driver characteristics are included in the study by using a dataset created from five different countries. Weight classification in the range of 0-1 is used to determine the most important classes using the AHP method, and the most important 9 out of 23 classes are determined. The YOLOv8 algorithm is used to detect driver behaviors and distraction action classes. The YOLOv8 algorithm is examined according to performance-measurement criteria. According to mAP 0.5:0.95, an accuracy rate of 91.17% is obtained. In large datasets, it is seen that a successful result is obtained by using the AHP method, which is used to reduce transaction complexity, and the YOLOv8 algorithm, which is used to detect driver distraction. By detecting driver distraction, it is possible to partially avoid traffic accidents and the negative situations they create. While detecting and preventing driver distraction makes a significant contribution to traffic safety, it also provides a significant improvement in traffic accidents and traffic congestion, increasing transportation efficiency and the sustainability of cities. It also serves sustainable development goals such as energy efficiency and reducing carbon emissions.