The upcoming metaverse will significantly promote the safety and efficiency of connected and automated vehicles (CAVs) as well as intelligent transportation systems (ITSs) with immersive information exchange between the parallel digital and physical worlds. To enable the virtual world to better reflect the physical world, a great deal of sensed information in types of text, pictures, voice, and videos should be fetched by metaverse applications. Edge caching has been considered to improve transmission quality and data protection by storing the needed contents near users rather than in the cloud. However, qualified edge caching for the metaverse of CAVs (meta-CAVs) and metaverse of ITSs (meta-ITSs) is challenged by ubiquitous mobilities, diversified requirements, dynamic content popularity, and heterogeneous infrastructure. In this article, we elaborate on the requirements and challenges of edge caching for meta-CAVs and meta-ITSs. We then discuss how artificial intelligence (AI) can be used in edge caching to improve the performance and security of meta-CAVs and meta-ITSs. To evaluate our idea, a case study with the Multi-Agent Federated Reinforcement Learning (MAFRL)-based intelligent edge caching is provided. Some perspective research directions are given to illuminate more ideas.

中文翻译：

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用于互联和自动化车辆元宇宙的人工智能辅助边缘缓存：建议、挑战和未来前景

即将到来的元宇宙将通过并行数字世界和物理世界之间的沉浸式信息交换，显着提高联网自动车辆（CAV）以及智能交通系统（ITS）的安全性和效率。为了使虚拟世界能够更好地反映物理世界，虚拟世界应用程序需要获取大量文本、图片、语音和视频类型的感知信息。边缘缓存被认为可以通过将所需内容存储在用户附近而不是存储在云中来提高传输质​​量和数据保护。然而，CAV 的元界 (meta-CAV) 和 ITS 的元界 (meta-ITS) 的合格边缘缓存面临着无处不在的移动性、多样化的需求、动态内容流行度和异构基础设施的挑战。在本文中，我们详细阐述了元 CAV 和元 ITS 的边缘缓存的要求和挑战。然后，我们讨论如何在边缘缓存中使用人工智能 (AI)，以提高元 CAV 和元 ITS 的性能和安全性。为了评估我们的想法，提供了基于多代理联合强化学习（MAFRL）的智能边缘缓存的案例研究。给出了一些前瞻性研究方向来阐明更多想法。