Generative Diffusion-Based Contract Design for Efficient AI Twin Migration in Vehicular Embodied AI Networks

作者：Yue Zhong, Jiawen Kang, Jinbo Wen, Dongdong Ye, Jiangtian Nie, Dusit Niyato, Xiaozheng Gao, Shengli Xie · 发表于：IEEE Transactions on Mobile Computing · 年份：2025 · DOI：10.1109/tmc.2025.3526230 · 被引用次数：16 · 研究领域：Transportation and Mobility Innovations、Blockchain Technology Applications and Security

Embodied Artificial Intelligence (AI) bridges the cyberspace and the physical space, driving advancements in autonomous systems like theVehicularEmbodiedAINETwork (VEANET). VEANET integrates advanced AI capabilities into vehicular systems to enhance autonomous operations and decision-making. Embodied agents, such as Autonomous Vehicles (AVs), are autonomous entities that can perceive their environment and take actions to achieve specific goals, actively interacting with the physical world. Embodied Agent Twins (EATs) are digital models of these embodied agents, with various Embodied Agent AI Twins (EAATs) for intelligent applications in cyberspace. In VEANETs, EAATs act as in-vehicle AI assistants to perform diverse tasks supporting autonomous driving using generative AI models. Due to limited onboard computational resources, AVs offload EAATs to nearby RoadSide Units (RSUs). However, the mobility of AVs and limited RSU coverage necessitates dynamic migrations of EAATs, posing challenges in selecting suitable RSUs under information asymmetry. To address this, we construct a multi-dimensional contract theoretical model between AVs and alternative RSUs. Considering that AVs may exhibit irrational behavior, we utilize prospect theory instead of expected utility theory to model the actual utilities of AVs. Finally, we employ a Generative Diffusion Model (GDM)-based algorithm to identify the optimal contract designs, thus enhancing the efficiency of EAAT migrations. Numerical resu...