Real-Time Lane Detection and Vehicle Speed Prediction for Intelligent Transportation Systems

This research study presents an insight on an advanced intelligent transportation system framework using YOLOv8, regarded as one of the leading-edge deep learning models, which promises in real-time to address critical problems associated with lane detection, vehicle tracking, and speed estimation. The proposed system would accurately extract the lane boundaries and calculate the speed at which the vehicles are traveling through the identification of the position coordinates, thus providing accurate monitoring of the traffic dynamics across various road conditions. Thorough testing on various traffic scenarios demonstrates the excellent performance of YOLOv8 in both lane detection and speed estimation over traditional methods, achieving precision and responsiveness. This includes refined detection of sudden movements and lane changes by using a real-time vehicle tracking mechanism on multiple frames as well as potential traffic violations. It ensures minimal latency since it uses the YOLOv8 with high-speed object detection ability to be easily and seamlessly integrated into existing traffic infrastructures. Additionally, the system predicts patterns of future traffic by using both real-time inputs and historical data, which enables authorities to take proactive measures and try to alleviate congestion to optimize flow. By incorporating cautionary alerts, drivers get instant feedback, and they get to behave in time when it comes to adapting driving behaviour to avoid an accident or traffic violation. The system also allows for the gathering of detailed vehicle behaviour data that would prove useful in law enforcement and enhancements of algorithms for autonomous vehicles. It is designed to adapt to various traffic conditions, such as low-speed residential areas and high-speed highways, enhancing its applicability in various urban and suburban environments.