Real-Time V2V Communication for Traffic Optimization and Collision Prevention Using Machine Learning.

Abstract

Vehicle-to-vehicle(V2V) communication represent a critical of next generation trans portation systems. This paper explores how to improve V2V systems through the integration of V2X, using machine learning models, and blockchain-based security tech niques, bringing greater road safety and traffic management optimization. Leveraging cellular Vehicle-to-Everything (C-V2X) technology, the proposed system offers enhanced capability in range, scalability, and low-latency communication, making it highly suitable for high-speed mobility scenarios. Furthermore, the study provides insights into the works of power transfer, providing discussions on how electric vehicles would be able to share power and data in real time. The paper also examines the role of machine learning algorithms, particularly Deep Reinforcement Learning (DRL) and transformer based models, in enhancing the efficiency, safety, and data security of V2V systems. Special emphasis is placed on the implications of these technologies for autonomous vehicle systems. By addressing key challenges and proposing innovative solutions, this research contributes to the advancement of intelligent and secure transportation networks.