Authors - Keesari Abhinav Reddy, Vanaparthi Sai Charan, Md. Sufiyan, Puvula Kiranmai, Madhuri. T, M. Venugopala Chari Abstract - The major challenge for road safety and traffic regulation continues to be categorized traffic offenses that include speeding, running of red lights, improper parking, and distracted driving. Recent innovations in artificial intelligence (AI) and machine learning (ML) have made it possible to develop automated systems that can detect and classify varied traffic violations in detail. This paper analyzes studies that have emerged recently, focusing on advanced technologies, including those such as YOLO-based object detection, OCR, integration with IoT, and real-time monitoring. The paper evaluates datasets, performance metrics, and methodologies covering violations including helmet use, lane changing, and the use of a mobile phone while driving. Significant challenges that have been touched upon in the review include issues of data privacy, high computational requirements, and environmental limitations. Some of the encouraging solution includes use of sophisticated deep learning models, big data analytics, sensor fusion, and edge computing as pathways to enhance scalability and reliability. Future effort will include improvement of real-time systems, reduction of false positives, and addressing socio-technical problems. Using approaches that merge existing advances, this paper has suggested some pathways for using AI-driven systems towards the improvement of road safety and adherence to traffic rules.
Open Zoom
