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TURBO: Utility-Aware Bandwidth Allocation for Cloud-Augmented Autonomous Control

Authors Peter Schafhalter , Alexander Krentsel , Hongbo Wei , Joseph E. Gonzalez , Sylvia Ratnasamy , Scott Shenker , Ion Stoica

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Subject Classification

ACM Subject Classification
  • Networks → Network resources allocation
  • Computer systems organization → Real-time systems
  • Computer systems organization → Embedded and cyber-physical systems
Keywords
  • autonomous vehicles
  • bandwidth allocation
  • cloud computing
  • edge computing
  • machine learning

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Abstract

Autonomous driving system progress has been driven by improvements in machine learning (ML) 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.

Cite As Get BibTex

Peter Schafhalter, Alexander Krentsel, Hongbo Wei, Joseph E. Gonzalez, Sylvia Ratnasamy, Scott Shenker, and Ion Stoica. TURBO: Utility-Aware Bandwidth Allocation for Cloud-Augmented Autonomous Control. In 1st New Ideas in Networked Systems (NINeS 2026). Open Access Series in Informatics (OASIcs), Volume 139, pp. 18:1-18:34, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/OASIcs.NINeS.2026.18

Author Details

Peter Schafhalter
  • University of California, Berkeley, CA, USA
Alexander Krentsel
  • University of California, Berkeley, CA, USA
Hongbo Wei
  • University of California, Berkeley, CA, USA
Joseph E. Gonzalez
  • University of California, Berkeley, CA, USA
Sylvia Ratnasamy
  • University of California, Berkeley, CA, USA
Scott Shenker
  • University of California, Berkeley, CA, USA
Ion Stoica
  • University of California, Berkeley, CA, USA

Supplementary Materials

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