Vehicle Twins (VTs) as digital representations of vehicles can provide users with immersive experiences in vehicular metaverse applications, e.g., Augmented Reality (AR) navigation and embodied intelligence. VT migration is an effective way that migrates the VT when the locations of physical entities keep changing to maintain seamless immersive VT services. However, an efficient VT migration is challenging due to the rapid movement of vehicles, dynamic workloads of Roadside Units (RSUs), and heterogeneous resources of the RSUs. To achieve efficient migration decisions and a minimum latency for the VT migration, we propose a multi-agent split Deep Reinforcement Learning (DRL) framework combined with spatio-temporal trajectory generation. In this framework, multiple split DRL agents utilize split architecture to efficiently determine VT migration decisions. Furthermore, we propose a spatio-temporal trajectory generation algorithm based on trajectory datasets and road network data to simulate vehicle trajectories, enhancing the generalization of the proposed scheme for managing VT migration in dynamic network environments. Finally, experimental results demonstrate that the proposed scheme not only enhances the Quality of Experience (QoE) by 29% but also reduces the computational parameter count by approximately 25% while maintaining similar performances, enhancing users' immersive experiences in vehicular metaverses.

翻译：车辆孪生体（VTs）作为车辆的数字化表征，可为用户提供车载元宇宙应用（如增强现实导航与具身智能）中的沉浸式体验。VT迁移是一种在物理实体位置持续变化时迁移VT以维持无缝沉浸式VT服务的有效手段。然而，由于车辆的高速移动、路侧单元（RSUs）的动态工作负载以及RSUs的异构资源特性，实现高效的VT迁移面临挑战。为实现最优迁移决策与最小化VT迁移延迟，本文提出一种结合时空轨迹生成的多智能体分割深度强化学习（DRL）框架。该框架中，多个分割DRL智能体利用分割架构高效确定VT迁移决策。此外，我们提出一种基于轨迹数据集与路网数据的时空轨迹生成算法，用于模拟车辆轨迹，从而增强所提方案在动态网络环境中管理VT迁移的泛化能力。最终实验结果表明，所提方案在保持相近性能的同时，不仅将体验质量（QoE）提升29%，还将计算参数量降低约25%，显著增强了用户在车载元宇宙中的沉浸式体验。