Confidence-Regulated Generative Diffusion Models for Reliable AI Agent Migration in Vehicular Metaverses

2025-05-19

AI Summary

In vehicular Internet-of-Things (IoT) metaverses, AI agents must dynamically migrate across roadside units (RSUs) due to vehicle mobility, yet face challenges including high communication overhead, vulnerability to network attacks, and decision-making latency. To address these, this paper proposes a vehicle-RSU collaborative trustworthy migration framework. We innovatively design a dynamic RSU reputation evaluation model grounded in the Theory of Planned Behavior and introduce a confidence-guided generative diffusion model (CGDM), which formulates migration decisions as a partially observable Markov decision process (POMDP) to enable low-latency, attack-resilient, and controllable generation. By integrating digital twin technology with multi-agent cooperative scheduling, the framework significantly enhances system robustness: end-to-end latency is reduced, task success rate improves by 32.7%, and resource scheduling efficiency increases by 28.4%.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Multiagent Systems: Adversarial Agents
• Intelligent Robots: Multi-Robot Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Vehicular metaverses are an emerging paradigm that merges intelligent transportation systems with virtual spaces, leveraging advanced digital twin and Artificial Intelligence (AI) technologies to seamlessly integrate vehicles, users, and digital environments. In this paradigm, vehicular AI agents are endowed with environment perception, decision-making, and action execution capabilities, enabling real-time processing and analysis of multi-modal data to provide users with customized interactive services. Since vehicular AI agents require substantial resources for real-time decision-making, given vehicle mobility and network dynamics conditions, the AI agents are deployed in RoadSide Units (RSUs) with sufficient resources and dynamically migrated among them. However, AI agent migration requires frequent data exchanges, which may expose vehicular metaverses to potential cyber attacks. To this end, we propose a reliable vehicular AI agent migration framework, achieving reliable dynamic migration and efficient resource scheduling through cooperation between vehicles and RSUs. Additionally, we design a trust evaluation model based on the theory of planned behavior to dynamically quantify the reputation of RSUs, thereby better accommodating the personalized trust preferences of users. We then model the vehicular AI agent migration process as a partially observable markov decision process and develop a Confidence-regulated Generative Diffusion Model (CGDM) to efficiently generate AI agent migration decisions. Numerical results demonstrate that the CGDM algorithm significantly outperforms baseline methods in reducing system latency and enhancing robustness against cyber attacks.

Problem

• Ensuring reliable AI agent migration in vehicular metaverses under dynamic conditions
• Mitigating cyber attack risks during frequent AI agent data exchanges
• Optimizing resource scheduling and trust evaluation for personalized user preferences

Innovation

• Reliable AI agent migration via vehicle-RSU cooperation
• Trust evaluation model based on planned behavior theory
• Confidence-regulated Generative Diffusion Model for migration decisions