Trust Model-Based Consensus Optimization for Vehicle Platooning Networks: A Novel Deep Reinforcement Learning Approach With GenAI

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Abstract
Vehicle platooning has emerged as a promising solution for efficient traffic management. Multiple platoons traveling in a cooperative way can alleviate congestion and enhance driving safety by information sharing and consensus. To address the data security and privacy concerns, blockchain could be applied to enable secure data sharing and consensus across multiple platoons. However, existing performance of blockchain is insufficient to ensure reliable and efficient data consensus among multiple platoons. First, the hierarchical structure of platoons with different roles of vehicles complicates the trust establishment between platoons, making it challenging to evaluate their trustworthiness and ensure consensus reliability. Additionally, data sharing in vehicle platooning networks demands timely information and efficient consensus-building. To tackle above challenges, we design a role-adaptive trust model for trust evaluation of platoons in consideration of different roles of vehicles within a platoon. Based on the proposed model, we formulate a blockchain consensus optimization problem to facilitate both reliability and efficiency of data consensus among multiple platoons. Leveraging Generative Artificial Intelligence (GenAI) techniques, we then propose the Diffusion Enhanced Soft Actor-Critic (DESAC) by integrating the diffusion model and SAC, to further improve the performance of blockchain consensus. Experiment results demonstrate the effectiveness and efficiency of the proposed consensus optimization approach.
Funding Information
  • Natural Science Foundation of China (62272053, 62272195)
  • Beijing Nova Program (20230484364)

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