Published Paper Details:

Operating an automobile is a multifaceted endeavor, demanding unwavering focus and attention from the driver. Distracted driving, encompassing any behavior that diverts the driver's concentration away from the road, poses a grave threat to road safety. Alarming statistics reveal that approximately 1.35 million lives are tragically lost each year due to road traffic accidents, inflicting significant economic ramifications, with road traffic crashes costing most nations an estimated 3% of their gross domestic product. In light of this sobering reality, the primary objective of our project is to implement a comprehensive system for the discernment of safe driving practices and the identification of potentially hazardous actions by drivers. Leveraging a diverse array of Machine Learning Models, we endeavor to accurately categorize the provided images into distinct classes corresponding to different forms of driver distraction. Additionally, our research extends beyond mere classification, aiming to conduct a comparative evaluation of various Machine Learning Models to ascertain their performance and accuracy within the context of cognitive driver action recognition. This holistic approach underscores our commitment to enhancing road safety and minimizing the risk of accidents and harm to fellow road users.