  Vehicular Metaverses are developed to enhance the modern automotive industry
with an immersive and safe experience among connected vehicles and roadside
infrastructures, e.g., RoadSide Units (RSUs). For seamless synchronization with
virtual spaces, Vehicle Twins (VTs) are constructed as digital representations
of physical entities. However, resource-intensive VTs updating and high
mobility of vehicles require intensive computation, communication, and storage
resources, especially for their migration among RSUs with limited coverages. To
address these issues, we propose an attribute-aware auction-based mechanism to
optimize resource allocation during VTs migration by considering both price and
non-monetary attributes, e.g., location and reputation. In this mechanism, we
propose a two-stage matching for vehicular users and Metaverse service
providers in multi-attribute resource markets. First, the resource attributes
matching algorithm obtains the resource attributes perfect matching, namely,
buyers and sellers can participate in a double Dutch auction (DDA). Then, we
train a DDA auctioneer using a generative pre-trained transformer (GPT)-based
deep reinforcement learning (DRL) algorithm to adjust the auction clocks
efficiently during the auction process. We compare the performance of social
welfare and auction information exchange costs with state-of-the-art baselines
under different settings. Simulation results show that our proposed GPT-based
DRL auction schemes have better performance than others.
