Avatars, as promising digital assistants in Vehicular Metaverses, can enable drivers and passengers to immerse in 3D virtual spaces, serving as a practical emerging example of Artificial Intelligence of Things (AIoT) in intelligent vehicular environments. The immersive experience is achieved through seamless human-avatar interaction, e.g., augmented reality navigation, which requires intensive resources that are inefficient and impractical to process on intelligent vehicles locally. Fortunately, offloading avatar tasks to RoadSide Units (RSUs) or cloud servers for remote execution can effectively reduce resource consumption. However, the high mobility of vehicles, the dynamic workload of RSUs, and the heterogeneity of RSUs pose novel challenges to making avatar migration decisions. To address these challenges, in this paper, we propose a dynamic migration framework for avatar tasks based on real-time trajectory prediction and Multi-Agent Deep Reinforcement Learning (MADRL). Specifically, we propose a model to predict the future trajectories of intelligent vehicles based on their historical data, indicating the future workloads of RSUs.Based on the expected workloads of RSUs, we formulate the avatar task migration problem as a long-term mixed integer programming problem. To tackle this problem efficiently, the problem is transformed into a Partially Observable Markov Decision Process (POMDP) and solved by multiple DRL agents with hybrid continuous and discrete actions in decentralized. Numerical results demonstrate that our proposed algorithm can effectively reduce the latency of executing avatar tasks by around 25% without prediction and 30% with prediction and enhance user immersive experiences in the AIoT-enabled Vehicular Metaverse (AeVeM).

翻译：数字分身作为车辆元宇宙中有前景的数字助手，能够使驾驶员和乘客沉浸于三维虚拟空间，成为智能车辆环境中人工智能物联网（AIoT）的一个实用新兴范例。这种沉浸式体验通过无缝的人-数字分身交互（例如增强现实导航）得以实现，这需要大量资源，在智能车辆本地处理既低效又不切实际。幸运的是，将数字分身任务卸载至路侧单元（RSU）或云服务器进行远程执行，可以有效降低资源消耗。然而，车辆的高移动性、RSU的动态工作负载以及RSU的异构性，给数字分身迁移决策带来了新的挑战。为应对这些挑战，本文提出了一种基于实时轨迹预测和多智能体深度强化学习（MADRL）的数字分身任务动态迁移框架。具体而言，我们提出了一种基于历史数据预测智能车辆未来轨迹的模型，该模型可指示RSU的未来工作负载。基于RSU的预期工作负载，我们将数字分身任务迁移问题表述为长期混合整数规划问题。为高效解决该问题，将其转化为部分可观测马尔可夫决策过程（POMDP），并由多个具有混合连续和离散动作的DRL智能体以分布式方式求解。数值结果表明，所提算法在不使用预测时可将数字分身任务执行延迟降低约25%，在使用预测时降低约30%，从而增强用户在AIoT赋能的车辆元宇宙（AeVeM）中的沉浸式体验。