Managing Bandwidth: The Key to Cloud-Assisted Autonomous Driving

Fuente: arXiv
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Autori principali: Krentsel, Alexander, Schafhalter, Peter, Gonzalez, Joseph E., Ratnasamy, Sylvia, Shenker, Scott, Stoica, Ion
Natura: Preprint
Pubblicazione: 2024
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author Krentsel, Alexander
Schafhalter, Peter
Gonzalez, Joseph E.
Ratnasamy, Sylvia
Shenker, Scott
Stoica, Ion
author_facet Krentsel, Alexander
Schafhalter, Peter
Gonzalez, Joseph E.
Ratnasamy, Sylvia
Shenker, Scott
Stoica, Ion
contents Prevailing wisdom asserts that one cannot rely on the cloud for critical real-time control systems like self-driving cars. We argue that we can, and must. Following the trends of increasing model sizes, improvements in hardware, and evolving mobile networks, we identify an opportunity to offload parts of time-sensitive and latency-critical compute to the cloud. Doing so requires carefully allocating bandwidth to meet strict latency SLOs, while maximizing benefit to the car.
format Preprint
id arxiv_https___arxiv_org_abs_2410_16227
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Managing Bandwidth: The Key to Cloud-Assisted Autonomous Driving
Krentsel, Alexander
Schafhalter, Peter
Gonzalez, Joseph E.
Ratnasamy, Sylvia
Shenker, Scott
Stoica, Ion
Networking and Internet Architecture
Computer Vision and Pattern Recognition
Systems and Control
Prevailing wisdom asserts that one cannot rely on the cloud for critical real-time control systems like self-driving cars. We argue that we can, and must. Following the trends of increasing model sizes, improvements in hardware, and evolving mobile networks, we identify an opportunity to offload parts of time-sensitive and latency-critical compute to the cloud. Doing so requires carefully allocating bandwidth to meet strict latency SLOs, while maximizing benefit to the car.
title Managing Bandwidth: The Key to Cloud-Assisted Autonomous Driving
topic Networking and Internet Architecture
Computer Vision and Pattern Recognition
Systems and Control
url https://arxiv.org/abs/2410.16227