A Comprehensive Examination of Machine Learning and Deep Learning Techniques for Driver Distraction Recognition

Abstract

As the number of vehicles owned worldwide has rapidly increased, traffic accidents caused by distracted driving have become a serious public safety concern. Despite advancements in driver monitoring systems, it is still challenging to accurately identify distracted behavior. This paper thoroughly analyzes recent developments in driver distraction detection, with a focus on Deep Learning (DL) and conventional Machine Learning (ML) methods. It begins by analyzing the benefits of conventional ML methods, such as Support Vector Machines (SVM), Decision Trees (DT), and Logistic Regression (LR), with regard to interpretability, computational effectiveness, and practical use. The DL methods, such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Video Transformer Networks (VTN), are then thoroughly examined. Because of their strong feature extraction capabilities and capacity to represent intricate temporal and spatial dependencies present in driver behaviors; these DL techniques provide better performance. This review also discusses important issues that DL-based systems must deal with, like interpretability, applicability, and privacy. The paper also concludes by discussing potential avenues for future research, highlighting the significance of cause-aware intervention mechanisms, multimodal data fusion, and human-in-the-loop frameworks. These strategies aim to improve overall safety, user trust, and detection accuracy. In addition to summarizing contemporary approaches, this thorough review offers practitioners and researchers insightful information that will help them reduce the frequency of traffic accidents and enhance technologies for detecting driving behavior.