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.

翻译：车载元宇宙旨在通过互联车辆与路侧基础设施（如路侧单元RSU）间的沉浸式安全体验，推动现代汽车产业发展。为实现与虚拟空间的无缝同步，车辆数字孪生被构建为物理实体的数字化表征。然而，资源密集型的数字孪生更新任务与车辆的高移动性对计算、通信及存储资源提出了极高要求，尤其在覆盖范围有限的RSU间进行迁移时更为突出。为解决这些问题，我们提出一种属性感知的拍卖机制，通过综合考虑价格与非货币属性（如地理位置与信誉度）来优化数字孪生迁移过程中的资源分配。该机制为多属性资源市场中的车载用户与元宇宙服务提供商设计了两阶段匹配方案：首先，资源属性匹配算法实现资源属性的完全匹配，使得买卖双方能够参与双重荷兰式拍卖；随后，我们采用基于生成式预训练变换器的深度强化学习算法训练拍卖控制器，以在拍卖过程中动态优化拍卖时钟。通过在不同参数设置下与前沿基线方法对比社会福利与拍卖信息交换成本，仿真结果表明我们提出的基于GPT的深度强化学习拍卖方案具有更优越的性能。