Data-Driven Cellular Network Selector for Vehicle Teleoperations

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 hea...

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Bibliographic Details
Published in:arXiv.org 2024-10
Main Authors: Gahtan, Barak, Cohen, Reuven, Bronstein, Alex M, Shapira, Eli
Format: Article
Language:English
Subjects:
Algorithms
Cellular communication
Machine learning
Network latency
Real time
Remote control
Robot control
System effectiveness
Teleoperators
Video transmission
Online Access:Get full text
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Summary: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.
ISSN:2331-8422