TURBO让云端与车载系统协同优化带宽，提升自动驾驶安全性和准确性。
TURBO: Utility-Aware Bandwidth Allocation for Cloud-Augmented Autonomous Control
- 将车载与云端的控制与带宽分配联合优化，动态调整资源
- 在真实网络条件下，平均准确率提升15.6个百分点
- 适合关注云端协同控制的自动驾驶研究者与工程师
自动驾驶系统进展依赖于机器学习模型的提升，其计算需求已超出边缘设备能力。云端虽有充足算力，但网络长期被视为不可靠瓶颈，而非控制回路中的平等组成部分。我们认为这种分离已不再可行：安全关键的自主性需要对控制、模型与网络资源分配进行联合设计。本文提出TURBO，一种云增强控制框架，将车辆与云端的带宽分配和控制流水线配置统一为联合优化问题。TURBO在高度变化的网络条件下仍能最大化车辆收益并保障安全。我们在仿真与真实部署中实现并评估TURBO，结果显示其平均准确率相比仅在车载运行的现有方案最高提升15.6个百分点。代码已开源：www.github.com/NetSys/turbo。
原文摘要 · Abstract (English)
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.
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