Vehicular metaverses are an emerging paradigm that integrates extended reality technologies and real-time sensing data to bridge the physical space and digital spaces for intelligent transportation, providing immersive experiences for Vehicular Metaverse Users (VMUs). VMUs access the vehicular metaverse by continuously updating Vehicular Twins (VTs) deployed on nearby RoadSide Units (RSUs). Due to the limited RSU coverage, VTs need to be continuously online migrated between RSUs to ensure seamless immersion and interactions for VMUs with the nature of mobility. However, the VT migration process requires sufficient bandwidth resources from RSUs to enable online and fast migration, leading to a resource trading problem between RSUs and VMUs. To this end, we propose a learning-based incentive mechanism for migration task freshness-aware VT migration in vehicular metaverses. To quantify the freshness of the VT migration task, we first propose a new metric named Age of Twin Migration (AoTM), which measures the time elapsed of completing the VT migration task. Then, we propose an AoTM-based Stackelberg model, where RSUs act as the leader and VMUs act as followers. Due to incomplete information between RSUs and VMUs caused by privacy and security concerns, we utilize deep reinforcement learning to learn the equilibrium of the Stackelberg game. Numerical results demonstrate the effectiveness of our proposed learning-based incentive mechanism for vehicular metaverses.

翻译：车载元宇宙是一种新兴范式，它整合扩展现实技术与实时传感数据，为智能交通系统架起物理空间与数字空间的桥梁，从而为车载元宇宙用户（VMUs）提供沉浸式体验。VMUs通过持续更新部署在附近路侧单元（RSUs）上的车载孪生体（VTs）来接入车载元宇宙。受限于RSU的覆盖范围，VTs需要在RSU间持续在线迁移，以确保移动特性下VMUs的沉浸式交互体验无缝衔接。然而，VT迁移过程需要RSU提供充足的带宽资源以实现快速在线迁移，这引发了RSU与VMUs之间的资源交易问题。为此，我们提出了一种基于学习的激励方法，用于车载元宇宙中感知迁移任务新鲜度的VT迁移。为量化VT迁移任务的新鲜度，我们首先提出名为孪生迁移年龄（AoTM）的新指标，该指标衡量完成VT迁移任务所经过的时间。继而，我们构建基于AoTM的斯塔克尔伯格模型，其中RSU作为领导者，VMUs作为追随者。由于隐私与安全考量导致RSU与VMUs之间存在信息不完整问题，我们采用深度强化学习来学习斯塔克尔伯格博弈的均衡态。数值结果表明，我们提出的基于学习方法对车载元宇宙具有有效性。