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.

中文翻译：

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数字孪生和人工智能赋能的全景视频流：减少扩展现实辅助车辆元宇宙中的传输延迟

车辆元宇宙预计将提供广泛连接的虚拟车联网（IoV），其中扩展现实（XR）是关键基础设施之一。然而，XR和自动驾驶汽车（AV）网络的结合带来了一些重大挑战，例如低延迟的XR全景视频传输、高带宽和车辆的高移动性。本文介绍了 XR 辅助联网 AV 的数字孪生 (DT) 和人工智能 (AI) 支持的全景视频流，以减少传输延迟并智能响应用户需求。具体来说，我们提出了一个支持DT的分布式XR服务管理框架，以在车辆元宇宙中跨不同域提供低延迟和平滑的XR服务。此外，我们还提出了基于 XR 流的虚拟化资源分配的案例研究，并提出了一种基于深度强化学习（DRL）的新颖方法来最小化传输延迟。定量实验结果表明，DT 可以增强人工智能在互联 AV 网络中的积极作用。最后，讨论了 XR 辅助车辆虚拟宇宙的未决问题和潜在研究方向。