Data-Driven Cellular Network Selector for Vehicle Teleoperations
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866913564670820352 |
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| author | Gahtan, Barak Cohen, Reuven Bronstein, Alex M. Shapira, Eli |
| author_facet | Gahtan, Barak Cohen, Reuven Bronstein, Alex M. Shapira, Eli |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19791 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data-Driven Cellular Network Selector for Vehicle Teleoperations Gahtan, Barak Cohen, Reuven Bronstein, Alex M. Shapira, Eli Signal Processing Computer Vision and Pattern Recognition Machine Learning Networking and Internet Architecture 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. |
| title | Data-Driven Cellular Network Selector for Vehicle Teleoperations |
| topic | Signal Processing Computer Vision and Pattern Recognition Machine Learning Networking and Internet Architecture |
| url | https://arxiv.org/abs/2410.19791 |