A Federated Learning Approach for Self-Parking in V2X Environment by Enhancing Intelligence in Vehicle System

In this survey paper, the integration of cutting-edge technologies in urban mobility is examined, with a specific focus on improving intelligent transportation systems (ITS) and self-parking systems. These technologies include the Internet of Things (IoT), the Internet of Vehicles (IOV), and Vehicle-to-Everything (V2X) communication. The survey intends to overcome the shortcomings of conventional parking management, optimize parking space allocation, and enhance the overall urban driving experience through dynamic parking management and cutting-edge vehicle navigation systems by utilizing these integrated technologies. Federated Learning (FL) provides decentralization, and when paired with IoT, IOV, and V2X's real-time data interchange capabilities, it creates a strong framework for creating efficient, scalable, resilient, and privacy-preserving ITS. In order to provide a thorough understanding of how various technologies might work together to shorten parking search times and support dynamic parking management, this paper examines recent literature as well as case experiences. The results point to the possibility of major advancements in automobile navigation and self-parking systems, which could result in smarter, safer, and more sustainable urban environments. With this poll, we add to the continuing conversation about how technology is revolutionizing urban mobility and offer predictions for the development of self-parking and intelligent transportation. Keywords: Vehicle navigation systems, dynamic parking management, self-parking systems, and intelligent transportation systems (ITS).