Vehicular Network

Vehicular networks aim to improve transportation efficiency and safety by enabling communication and data sharing among vehicles and infrastructure. Current research heavily focuses on developing efficient and secure data transmission and processing methods, often employing reinforcement learning (RL), federated learning (FL), and deep learning (DL) models, including vision transformers and graph neural networks, to address challenges like dynamic environments, data heterogeneity, and security threats. These advancements have significant implications for autonomous driving, intelligent transportation systems, and the development of secure and reliable communication protocols for connected vehicles.