Deep Learning for Lane Discipline: AI-Powered Traffic Enforcement in Smart Cities

Connected vehicles are poised to revolutionize future transportation systems by enabling seamless communication and real-time data exchange, enhancing safety, and optimizing traffic flow. This study proposes a novel data-driven AI framework for real-time lane enforcement in connected vehicle ecosystems, aiming to ensure compliance with lane restrictions and traffic rules while minimizing manual intervention. The proposed algorithm integrates advanced AI techniques, stochastic modelling, and real-time monitoring to create a robust and scalable model. By combining probabilistic reasoning, adaptive learning, and optimization strategies, the framework achieves higher efficiency and accuracy than existing methods. The experimental results demonstrate the superior performance of the proposed methodology in reducing violations, improving response times, and enhancing overall traffic safety, making it a promising solution for modern intelligent transportation systems. Keywords: Connected vehicles, AI-driven framework, real-time lane enforcement, stochastic modelling, traffic safety, probabilistic reasoning, adaptive learning, intelligent transportation systems.