Autonomous driving system progress has been driven by improvements in machine learning models, whose computational demands now exceed what edge devices alone can provide. The cloud offers abundant compute, but the network has long been treated as an unreliable bottleneck rather than a co-equal part of the autonomous vehicle control loop. We argue that this separation is no longer tenable: safety-critical autonomy requires co-design of control, models, and network resource allocation itself. We introduce TURBO, a cloud-augmented control framework that addresses this challenge, formulating bandwidth allocation and control pipeline configuration across both the car and cloud as a joint optimization problem. TURBO maximizes benefit to the car while guaranteeing safety in the face of highly variable network conditions. We implement TURBO and evaluate it in both simulation and real-world deployment, showing it can improve average accuracy by up to 15.6%pt over existing on-vehicle-only pipelines. Our code is made available at www.github.com/NetSys/turbo.

翻译：自动驾驶系统的进步一直由机器学习模型的改进所驱动，这些模型的计算需求现已超出边缘设备单独能够提供的范畴。云提供了丰富的计算资源，但网络长期以来被视为不可靠的瓶颈，而非自动驾驶车辆控制回路中同等重要的组成部分。我们认为这种分离已不再可行：安全关键的自主性要求对控制、模型及网络资源分配本身进行协同设计。我们提出了TURBO，一个应对这一挑战的云增强控制框架，它将车辆与云端之间的带宽分配和控制流水线配置表述为一个联合优化问题。TURBO在高度变化的网络条件下，最大化车辆收益的同时保证安全性。我们实现了TURBO，并在仿真和实际部署中对其进行了评估，结果表明相较于现有的纯车载流水线，其平均准确率最高可提升15.6个百分点。我们的代码已在www.github.com/NetSys/turbo公开。