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

Yue Zhong, Jiawen Kang*, Jinbo Wen, Dongdong Ye, Jiangtian Nie, Dusit Niyato, Xiaozheng Gao, Shengli Xie

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Embodied Artificial Intelligence (AI) bridges the cyberspace and the physical space, driving advancements in autonomous systems like the Vehicular Embodied AI NETwork (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 results demonstrate the superior efficiency of the proposed GDM-based scheme in facilitating EAAT migrations compared with traditional deep reinforcement learning methods.

Original languageEnglish
JournalIEEE Transactions on Mobile Computing
DOIs
Publication statusAccepted/In press - 2025

Keywords

  • Generative diffusion model
  • multi-dimensional contract theory
  • prospect theory
  • vehicular embodied AI

Fingerprint

Dive into the research topics of 'Generative Diffusion-Based Contract Design for Efficient AI Twin Migration in Vehicular Embodied AI Networks'. Together they form a unique fingerprint.

Cite this

Zhong, Y., Kang, J., Wen, J., Ye, D., Nie, J., Niyato, D., Gao, X., & Xie, S. (Accepted/In press). Generative Diffusion-Based Contract Design for Efficient AI Twin Migration in Vehicular Embodied AI Networks. IEEE Transactions on Mobile Computing. https://doi.org/10.1109/TMC.2025.3526230