  The vehicular metaverse is envisioned as a blended immersive domain that
promises to bring revolutionary changes to the automotive industry. As a core
component of vehicular metaverses, Vehicle Twins (VTs) are digital twins that
cover the entire life cycle of vehicles, providing immersive virtual services
for Vehicular Metaverse Users (VMUs). Vehicles with limited resources offload
the computationally intensive tasks of constructing and updating VTs to edge
servers and migrate VTs between these servers, ensuring seamless and immersive
experiences for VMUs. However, the high mobility of vehicles, uneven deployment
of edge servers, and potential security threats pose challenges to achieving
efficient and reliable VT migrations. To address these issues, we propose a
secure and reliable VT migration framework in vehicular metaverses.
Specifically, we design a two-layer trust evaluation model to comprehensively
evaluate the reputation value of edge servers in the network communication and
interaction layers. Then, we model the VT migration problem as a partially
observable Markov decision process and design a hybrid-Generative Diffusion
Model (GDM) algorithm based on deep reinforcement learning to generate optimal
migration decisions by taking hybrid actions (i.e., continuous actions and
discrete actions). Numerical results demonstrate that the hybrid-GDM algorithm
outperforms the baseline algorithms, showing strong adaptability in various
settings and highlighting the potential of the hybrid-GDM algorithm for
addressing various optimization issues in vehicular metaverses.
