Optimization Method for Semantic Communication and Decision Making in Telematics Based on Priority-Driven Transmission and Federated Learning

With the development of Intelligent Transportation Systems (ITS), Telematics plays a key role in improving traffic management efficiency and road safety. However, the traditional communication model of Telematics faces many challenges such as high bandwidth consumption, high transmission delay, and data privacy protection. To address these issues, this paper proposes a semantic communication and decision optimization method for Telematics based on priority-driven transmission and federated learning. A graph neural network is used to model the complex relationship between vehicles and environmental elements to achieve efficient semantic information extraction. Adaptive semantic compression mechanism is implemented using variational self-encoder combined with deep reinforcement learning, and the compression rate is dynamically adjusted to optimize the bandwidth utilization. In order to further improve the real-time and reliability of transmission, a priority-driven transmission strategy is designed to dynamically assign transmission priorities according to the importance and urgency of semantic information to ensure the timely delivery of critical data. Through the federated learning framework, a collaborative decision-making model training mechanism between multiple vehicles is constructed to achieve efficient optimization of the global model.