Edge Intelligence Empowered Vehicular Metaverse: Key Design Aspects and Future Directions

Abstract

Emerging intelligent transportation system applications witnessed significantly different requirements and performance metrics (e.g., latency, reliability, and quality of experience). To meet the diverse requirements, one can use a convergence of the metaverse with vehicular networks at the network edge which offers proactive analysis and efficient real-time control for the management of vehicular network resources. Therefore, in this article, we present key design aspects of an edge intelligence-enabled vehicular metaverse. We also present a high-level architecture for an edge intelligence-based vehicular metaverse that has three main aspects: a metaverse engine, offline learning, and online real-time control. Moreover, we present two case studies: joint sampling and packet error rate minimization and object detection task at the network edge. Finally, we conclude the article.