The vehicular metaverse is expected to provide a widely connected virtual Internet of Vehicles (IoV), where extended reality (XR) is one of the critical infrastructures. However, the combination of XR and automated vehicle (AV) networks brings several significant challenges, e.g., low-latency XR panoramic video transmission, high bandwidth, and the high mobility of vehicles. This article introduces digital twin (DT) and artificial intelligence (AI)-empowered panoramic video streaming for XR-assisted connected AVs to reduce transmission latency and intelligently respond to user requirements. Specifically, we propose a DT-enabled distributed XR service management framework to provide low-latency and smooth XR services across different domains in the vehicular metaverse. In addition, we present a case study on XR streaming-based virtualized resource allocation and propose a novel deep reinforcement learning (DRL)-based method to minimize transmission latency. Quantitative experimental results demonstrate that the positive role of AI in connected AV networks can be enhanced by DTs. Finally, open issues and potential research directions for the XR-assisted vehicular metaverse are discussed.
