We present CloudEmu, a trace-driven, cloud-native cellular-emulation testbed for vehicle video uplink communication. Reliable, low-latency video uplink over cellular networks is essential for remote monitoring of autonomous vehicles. However, existing testbeds fall into two extremes. Physical-vehicle platforms provide realism but are costly and make validation under identical network conditions difficult, whereas simulations are inexpensive and reproducible but generally cannot replay field-measured end-to-end performance dynamics without substantial calibration or readily run production video-uplink stacks. A software-defined, cloud-native emulation approach can combine the fidelity of trace-driven replay with the agility and scalability that network softwarization principles offer. To this end, we propose CloudEmu that replays time-synchronized cellular and position traces, collected once from vehicles, on commodity Linux-based virtual vehicle and video-receiver nodes. A Linux-based emulation framework couples traffic replay with position replay, tying network dynamics to each point along the route and enabling repeatable, route-aware experiments without repeated on-road trials. Our demo deploys a production-grade video-uplink stack on CloudEmu, allowing attendees to experience low-cost, repeatable trials and controlled comparisons under identical replayed network conditions.

翻译：我们提出CloudEmu，一种基于轨迹驱动、面向车辆视频上行通信的云原生蜂窝网络仿真测试平台。在蜂窝网络上实现可靠、低延迟的视频上行传输对于自动驾驶车辆的远程监控至关重要。然而，现有测试平台存在两个极端：物理车辆平台虽能提供真实感，但成本高昂且难以在相同网络条件下进行验证；而仿真平台成本低、可复现，但通常无法在不进行大量校准的情况下回放实测端到端性能动态，也难以直接运行生产级视频上行堆栈。基于软件定义的云原生仿真方法能够结合轨迹驱动回放的保真度与网络软件化原则提供的敏捷性和可扩展性。为此，我们提出CloudEmu，该平台在基于Linux的商品化虚拟车辆和视频接收节点上，回放从车辆上一次性采集的时间同步蜂窝信号和位置轨迹。基于Linux的仿真框架将流量回放与位置回放相结合，将网络动态与路径上的每个点关联起来，从而无需重复进行道路试验即可实现可重复的路径感知实验。我们的演示在CloudEmu上部署了生产级视频上行堆栈，使参会者能够体验低成本的重复试验，并在相同的回放网络条件下进行受控比较。