Paper Title
Advancements in Distracted Driver Detection and Alert Systems: A Critical Review2021-2023

Abstract
Distracted driving poses a significant threat to road safety, resulting in numerous accidents and fatalities worldwide. This review paper presents a comprehensive analysis of the latest developments in distracted driver detection and alert systems. With the proliferation of in-car technology and mobile devices, the need for effective safety measures has never been more critical. We explore various approaches and technologies employed to detect driver distraction, including computer vision, machine learning, and sensor-based systems. Furthermore, we delve into the diverse methodologies for alerting distracted drivers, such as auditory, visual, and haptic cues, while considering their effectiveness and user acceptance. The review also discusses the challenges and limitations associated with these systems, including false positives, privacy concerns, and real-world feasibility. The paper concludes by highlighting emerging trends, research gaps, and potential future directions in the field, emphasizing the importance of continued innovation to enhance road safety and mitigate the risks associated with distracted driving. Keywords - Distracted Driver Detection and Alert (DDDA), CNN, Machine Learning, Deep Learning, RESNET, ImageNet