V2X Communication Using Machine Learning and IoT Technology

Abstract

Vehicle-to-Everything (V2X) communication has emerged as a critical technology for enabling intelligent transportation systems, autonomous driving, and road safety improvements. The rapid growth of connected vehicles and smart infrastructure requires efficient communication mechanisms capable of managing high volumes of real-time data. Integrating Machine Learning (ML) and Internet of Things (IoT) technologies into V2X systems enhances decision-making capabilities, improves traffic management, and reduces accident risks. This research paper proposes a smart V2X communication framework that combines IoT sensors, vehicular communication modules, and machine learning algorithms to improve road safety and traffic efficiency. The proposed system collects real-time vehicular data such as speed, location, and traffic density through IoT devices and processes the data using machine learning models to predict traffic conditions and potential hazards. The system architecture enables vehicles to communicate with other vehicles, roadside infrastructure, pedestrians, and network servers in real time. Experimental evaluation demonstrates improvements in communication efficiency, reduced latency, and enhanced safety decision-making compared to traditional vehicular communication systems. The results indicate that integrating ML with IoT-based V2X networks can significantly improve intelligent transportation systems and support the development of autonomous vehicle ecosystems.