Date of Publication :17th February 2024

Abstract: Road safety is a paramount concern worldwide. Enhancing road safety remains a critical societal concern, prompting the need for innovative solutions to reduce accidents and enhance transportation security. This research aims to propose a system that utilizes an affordable and accessible multi-factored detection approach, diverging from prevalent single-factor detection methods. By examining the intricate web of factors contributing to road mishaps and harnessing advanced technologies, this study aims to tackle the diverse factors contributing to accidents. Employing a deductive and confirmatory research approach, this study systematically investigates crucial factors that intensify the risks of accidents, encompassing over speeding, rash driving, DUI, driver fatigue, and distractions while driving. This study combines a range of advanced technologies to reinforce its methodology, incorporating OpenCV-based object detection, machine learning models specifically designed for distraction recognition, facial and pose detection systems, sensor-based technologies, and GPS. These technological facets collectively work to identify, classify, and mitigate multiple aspects, including over speeding, inattention, physical fatigue, and rash driving, forming the bedrock of the research approach. The proposed system, marked by ease of use, budget-friendliness, and multifactor detection capabilities, stands as a potential frontier in road safety technology. The implications of this study underscore the feasibility and effectiveness of integrating these technologies to not only enhance road safety but also significantly reduce accidents, thereby saving lives and fostering a more secure transportation environment. This comprehensive approach paves the way for a paradigm shift in mitigating road accidents, highlighting the significance of technology-driven solutions for ensuring a secure transportation landscape.j

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