Enhancing V2X Communication with Edge Computing: (A Path to Low-Latency and Intelligent Transportation)

The highly established transport infrastructure, effective, precise, and timely vehicle-to-vehicle communication is a necessary process. Edge computing is transforming V2X communication by virtue of near-source real-time processing of data and thus accelerating decision-making and the system in turn. In this paper, an improved framework of Reinforcement Learning and Random Forest algorithms for improving data filtering, bandwidth utilization, and system scalability is presented. Compared to the classical cloud-based systems, the emerging edge computing paradigm allows for faster response times in mission-critical applications such as collision avoidance, intelligent traffic signaling, and real-time navigation. The system makes use of Reinforcement Learning for real-time decision-making tasks and Random Forest algorithms for optimal data clustering with VB.NET. The approach also minimizes unnecessary data transfer to the cloud, thereby minimizing bandwidth usage and maximizing performance. Experimental results corroborate that the target model attains 20% reduced latency, 15% increased bandwidth efficiency, and 10% improved prediction accuracy over conventional V2X systems. In addition, network convergence with the 5G network infrastructure facilitates improved communication speed and quality and facilitates seamless end-to-end connectivity among and to connected and autonomous vehicles. While the system has positive impacts, current global issues such as hardware limitation and energy consumption are still present. Future research will further extend the hardware support, accelerate data processing mechanisms, and improve scalability to large-scale V2X environments. In conclusion, this paper presents a realistic and effective solution to improving V2X communication and making transportation systems safer, smarter, and more efficient.