The vehicular ad hoc network (VANET) constitutes a key technology for realizing intelligent transportation services. However, VANET is characterized by diverse message types, complex security attributes of communication nodes, and rapid network topology changes. In this case, how to ensure safe, efficient, convenient, and comfortable message services for users has become a challenge that should not be ignored. To improve the flexibility of routing matching multiple message types in VANET, this paper proposes a secure intelligent message forwarding strategy based on deep reinforcement learning (DRL). The key supporting elements of the model in the strategy are reasonably designed in combination with the scenario, and sufficient training of the model is carried out by deep Q networks (DQN). In the strategy, the state space is composed of the distance between candidate and destination nodes, the security attribute of candidate nodes and the type of message to be sent. The node can adaptively select the routing scheme according to the complex state space. Simulation and analysis show that the proposed strategy has the advantages of fast convergence, well generalization ability, high transmission security, and low network delay. The strategy has flexible and rich service patterns and provides flexible security for VANET message services.

With the rapid evolution of 5G communications, artificial intelligence, and new energy technologies, intelligent driving has become a pivotal component of modern transportation infrastructure. However, the openness and inherent complexity of the Internet of Vehicles (IoV) pose significant challenges to data privacy and security. While Federated Learning (FL) facilitates collaborative training of data models without requiring raw data to leave local devices, its centralized aggregation architecture struggles to address privacy concerns in cross-trust domain collaborations and fails to effectively handle the challenges posed by vehicle node mobility. In this paper, we investigate cross-trust domain collaborative federated learning within the IoV and construct a privacy-preserving Cloud-Edge-End architecture based on Fully Homomorphic Encryption (FHE). To address the dynamic nature of vehicle nodes, we propose a hybrid privacy-preserving scheme that integrates Threshold Fully Homomorphic Encryption (ThFHE) and Multi-Key Fully Homomorphic Encryption (MKFHE). The experimental results indicate that the proposed collaborative architecture is theoretically reasonable. In addition, the scheme effectively achieves collaborative model training while maintaining security at the privacy protection level and supporting seamless vehicle mobility.