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
Main Authors: Gahtan, Barak, Cohen, Reuven, Bronstein, Alex M., Shapira, Eli
Format: Conference Proceeding
Language:English
Subjects:
Cellular networks
Machine learning algorithms
Packet loss
Prediction algorithms
Real-time systems
Remote control
Robots
Streaming media
Testing
Time series analysis
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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:2833-0072
DOI:10.1109/NoF62948.2024.10741500