Sixth Generation (6G)-enabled Internet of Vehicles (IoV) facilitates efficient data synchronization through ultra-fast bandwidth and high-density connectivity, enabling the emergence of Vehicle Twins (VTs). As highly accurate replicas of vehicles, VTs can support intelligent vehicular applications for occupants in 6G-enabled IoV. Thanks to the full coverage capability of 6G, resource-constrained vehicles can offload VTs to edge servers, such as roadside units, unmanned aerial vehicles, and satellites, utilizing their computing and storage resources for VT construction and updates. However, communication between vehicles and edge servers with limited coverage is prone to interruptions due to the dynamic mobility of vehicles. Consequently, VTs must be migrated among edge servers to maintain uninterrupted and high-quality services for users. In this paper, we introduce a VT migration framework in 6G-enabled IoV. Specifically, we first propose a Long Short-Term Memory (LSTM)-based Transformer model to accurately predict long-term workloads of edge servers for migration decision-making. Then, we propose a Dynamic Mask Multi-Agent Proximal Policy Optimization (DM-MAPPO) algorithm to identify optimal migration routes in the highly complex environment of 6G-enabled IoV. Finally, we develop a practical platform to validate the effectiveness of the proposed scheme using real datasets. Simulation results demonstrate that the proposed DM-MAPPO algorithm significantly reduces migration latency by 20.82% and packet loss by 75.07% compared with traditional deep reinforcement learning algorithms.

翻译：第六代移动通信技术（6G）赋能的车辆互联网通过超高速带宽和高密度连接促进高效数据同步，从而催生了车辆数字孪生体。作为车辆的高精度数字副本，车辆数字孪生体可为6G车联网中的乘员提供智能车载应用服务。得益于6G的全覆盖能力，资源受限的车辆可将车辆数字孪生体卸载至边缘服务器（如路侧单元、无人机和卫星），利用其计算和存储资源进行孪生体构建与更新。然而，由于车辆动态移动的特性，车辆与覆盖范围有限的边缘服务器之间的通信易发生中断。因此，必须在边缘服务器之间迁移车辆数字孪生体，以维持用户服务的不间断性与高质量。本文提出一种6G车联网中的车辆数字孪生体迁移框架。具体而言，我们首先提出一种基于长短期记忆网络的Transformer模型，以精准预测边缘服务器的长期工作负载，为迁移决策提供依据。随后，针对6G车联网高度复杂的环境，我们提出一种动态掩码多智能体近端策略优化算法，以确定最优迁移路径。最后，我们构建实际平台，利用真实数据集验证所提方案的有效性。仿真结果表明，与传统深度强化学习算法相比，所提动态掩码多智能体近端策略优化算法能显著降低迁移延迟20.82%，并将丢包率减少75.07%。