부산대학교 도서관

Safe driving is a serious and challenging concern that is dependent on driver behaviors including aggressive, distracted, fatigued, and drowsy driving. In a smart society, in-vehicle monitoring of drivers and detecting abnormal driving behavior as anomalies can reduce the rate of road crashes. Existing surveys cover the various schemes for detecting on-road driver behaviors through sensing data. We have identified a gap in the linkage of driver’s health or vehicle conditions to abnormal behaviors that are not yet covered. This work presents a taxonomy of schemes, and the analytical evaluation and identifies the open research challenges. The work specifically investigates the modeling of driver behavior and the detection of abnormal behavior, utilizing techniques such as AI-based image processing, signal processing, and traditional algorithmic approaches. This analysis encompasses methods and features based on both driver health and vehicle monitoring with the ultimate goal of ensuring safe driving. More specifically, existing approaches are classified in a coherent taxonomy by reviewing traditional mathematical, machine learning, and deep learning-based schemes. Moreover, a summary of schemes is presented to highlight the key points followed by a comprehensive analytical discussion. It aids in pinpointing research issues by leveraging the insights gained from the comparison. Finally, the work highlights the open research challenges for the researchers to provide innovative solutions.