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 language | English |
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Title of host publication | Proceedings of the 15th International Conference on Network of the Future, NoF 2024 |
Editors | Toktam Mahmoodi, Raul Munoz, Prosper Chemouil, Sebastian Troia, Thi-Mai-Trang Nguyen |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 63-71 |
Number of pages | 9 |
ISBN (Electronic) | 9798350377767 |
DOIs | |
State | Published - 2024 |
Externally published | Yes |
Event | 15th International Conference on Network of the Future, NoF 2024 - Barcelona, Spain Duration: 2 Oct 2024 → 4 Oct 2024 |
Publication series
Name | Proceedings of the 15th International Conference on Network of the Future, NoF 2024 |
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Conference
Conference | 15th International Conference on Network of the Future, NoF 2024 |
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Country/Territory | Spain |
City | Barcelona |
Period | 2/10/24 → 4/10/24 |
Bibliographical note
Publisher Copyright:© 2024 IEEE.