Emerging safety-critical Vehicle-to-Everything (V2X) applications require networks to proactively adapt to rapid environmental changes rather than merely reacting to them. While Network Digital Twins (NDTs) offer a pathway to such predictive capabilities, existing solutions typically struggle to reconcile high-fidelity physical modeling with strict real-time constraints. This paper presents a novel, end-to-end real-time V2X Digital Twin framework that integrates live mobility tracking with deterministic channel simulation. By coupling the Tokyo Mobility Digital Twin-which provides live sensing and trajectory forecasting-with VaN3Twin-a full-stack simulator with ray tracing-we enable the prediction of network performance before physical events occur. We validate this approach through an experimental proof-of-concept deployed in Tokyo, Japan, featuring connected vehicles operating on 60 GHz links. Our results demonstrate the system's ability to predict Received Signal Strength (RSSI) with a maximum average error of 1.01 dB and reliably forecast Line-of-Sight (LoS) transitions within a maximum average end-to-end system latency of 250 ms, depending on the ray tracing level of detail. Furthermore, we quantify the fundamental trade-offs between digital model fidelity, computational latency, and trajectory prediction horizons, proving that high-fidelity and predictive digital twins are feasible in real-world urban environments.

翻译：新兴的安全关键型车联网（V2X）应用要求网络能够主动适应快速的环境变化，而不仅仅是事后响应。虽然网络数字孪生为实现此类预测能力提供了途径，但现有解决方案通常难以在高度逼真的物理建模与严格的实时约束之间取得平衡。本文提出了一种新颖的端到端实时V2X数字孪生框架，该框架将实时移动性跟踪与确定性信道仿真相结合。通过将提供实时感知与轨迹预测的“东京移动数字孪生”与具备光线追踪功能的全栈模拟器“VaN3Twin”相耦合，我们能够在物理事件发生之前预测网络性能。我们在日本东京部署了一个实验性概念验证系统，该系统采用工作在60 GHz链路上的联网车辆，以此验证了所提方法。我们的结果表明，该系统能够预测接收信号强度，其最大平均误差为1.01 dB，并且能够在最大平均端到端系统延迟250毫秒内（具体取决于光线追踪的细节级别）可靠地预测视距链路的切换。此外，我们量化了数字模型保真度、计算延迟与轨迹预测范围之间的基本权衡，证明了高保真度与预测性数字孪生在现实城市环境中是可行的。