TURBO: Utility-Aware Bandwidth Allocation for Cloud-Augmented Autonomous Control
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arXiv
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| Format: | Preprint |
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2025
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| author | Schafhalter, Peter Krentsel, Alexander Wei, Hongbo Gonzalez, Joseph E. Ratnasamy, Sylvia Shenker, Scott Stoica, Ion |
| author_facet | Schafhalter, Peter Krentsel, Alexander Wei, Hongbo Gonzalez, Joseph E. Ratnasamy, Sylvia Shenker, Scott Stoica, Ion |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_20127 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TURBO: Utility-Aware Bandwidth Allocation for Cloud-Augmented Autonomous Control Schafhalter, Peter Krentsel, Alexander Wei, Hongbo Gonzalez, Joseph E. Ratnasamy, Sylvia Shenker, Scott Stoica, Ion Robotics Networking and Internet Architecture 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. |
| title | TURBO: Utility-Aware Bandwidth Allocation for Cloud-Augmented Autonomous Control |
| topic | Robotics Networking and Internet Architecture |
| url | https://arxiv.org/abs/2503.20127 |