AI-Assisted Edge Caching for Metaverse of Connected and Automated Vehicles: Proposal, Challenges, and Future Perspectives

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.