Data-Driven Cellular Network Selector for Vehicle Teleoperations

Barak Gahtan, Reuven Cohen, Alex M. Bronstein, Eli Shapira

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Remote control of robotic systems, also known as teleoperation, is crucial for the development of autonomous vehicle (AV) technology. It allows a remote operator to view live video from AVs and, in some cases, to make real-Time decisions. The effectiveness of video-based teleoperation systems is heavily influenced by the quality of the cellular network and, in particular, its packet loss rate and latency. To optimize these parameters, an AV can be connected to multiple cellular networks and determine in real time over which cellular network each video packet will be transmitted. We present an algorithm, called Active Network Selector (ANS), which uses a time series machine learning approach for solving this problem. We compare ANS to a baseline non-learning algorithm, which is used today in commercial systems, and show that ANS performs much better, with respect to both packet loss and packet latency.

Original languageEnglish
Title of host publicationProceedings of the 15th International Conference on Network of the Future, NoF 2024
EditorsToktam Mahmoodi, Raul Munoz, Prosper Chemouil, Sebastian Troia, Thi-Mai-Trang Nguyen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages63-71
Number of pages9
ISBN (Electronic)9798350377767
DOIs
StatePublished - 2024
Externally publishedYes
Event15th International Conference on Network of the Future, NoF 2024 - Barcelona, Spain
Duration: 2 Oct 20244 Oct 2024

Publication series

NameProceedings of the 15th International Conference on Network of the Future, NoF 2024

Conference

Conference15th International Conference on Network of the Future, NoF 2024
Country/TerritorySpain
CityBarcelona
Period2/10/244/10/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

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