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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.20438 |
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| _version_ | 1866913962113630208 |
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| author | Fezeu, Rostand A. K. Carpenter, Jason Zende, Rushikesh Divakarla, Sree Ganesh Lalitaditya Varyani, Nitin Bilal, Faaiq Sleder, Steven Naik, Nanditha Joly, Duncan Ramadan, Eman Gurumadaiah, Ajay Kumar Zhang, Zhi-Li |
| author_facet | Fezeu, Rostand A. K. Carpenter, Jason Zende, Rushikesh Divakarla, Sree Ganesh Lalitaditya Varyani, Nitin Bilal, Faaiq Sleder, Steven Naik, Nanditha Joly, Duncan Ramadan, Eman Gurumadaiah, Ajay Kumar Zhang, Zhi-Li |
| contents | Remote driving, or teleoperating Autonomous Vehicles (AVs), is a key application that emerging 5G networks aim to support. In this paper, we conduct a systematic feasibility study of AV teleoperation over commercial 5G networks from both cross-layer and end-to-end (E2E) perspectives. Given the critical importance of timely delivery of sensor data, such as camera and LiDAR data, for AV teleoperation, we focus in particular on the performance of uplink sensor data delivery. We analyze the impacts of Physical Layer (PHY layer) 5G radio network factors, including channel conditions, radio resource allocation, and Handovers (HOs), on E2E latency performance. We also examine the impacts of 5G networks on the performance of upper-layer protocols and E2E application Quality-of-Experience (QoE) adaptation mechanisms used for real-time sensor data delivery, such as Real-Time Streaming Protocol (RTSP) and Web Real Time Communication (WebRTC). Our study reveals the challenges posed by today's 5G networks and the limitations of existing sensor data streaming mechanisms. The insights gained will help inform the co-design of future-generation wireless networks, edge cloud systems, and applications to overcome the low-latency barriers in AV teleoperation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_20438 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Teleoperating Autonomous Vehicles over Commercial 5G Networks: Are We There Yet? Fezeu, Rostand A. K. Carpenter, Jason Zende, Rushikesh Divakarla, Sree Ganesh Lalitaditya Varyani, Nitin Bilal, Faaiq Sleder, Steven Naik, Nanditha Joly, Duncan Ramadan, Eman Gurumadaiah, Ajay Kumar Zhang, Zhi-Li Networking and Internet Architecture Other Computer Science C.2.0 Remote driving, or teleoperating Autonomous Vehicles (AVs), is a key application that emerging 5G networks aim to support. In this paper, we conduct a systematic feasibility study of AV teleoperation over commercial 5G networks from both cross-layer and end-to-end (E2E) perspectives. Given the critical importance of timely delivery of sensor data, such as camera and LiDAR data, for AV teleoperation, we focus in particular on the performance of uplink sensor data delivery. We analyze the impacts of Physical Layer (PHY layer) 5G radio network factors, including channel conditions, radio resource allocation, and Handovers (HOs), on E2E latency performance. We also examine the impacts of 5G networks on the performance of upper-layer protocols and E2E application Quality-of-Experience (QoE) adaptation mechanisms used for real-time sensor data delivery, such as Real-Time Streaming Protocol (RTSP) and Web Real Time Communication (WebRTC). Our study reveals the challenges posed by today's 5G networks and the limitations of existing sensor data streaming mechanisms. The insights gained will help inform the co-design of future-generation wireless networks, edge cloud systems, and applications to overcome the low-latency barriers in AV teleoperation. |
| title | Teleoperating Autonomous Vehicles over Commercial 5G Networks: Are We There Yet? |
| topic | Networking and Internet Architecture Other Computer Science C.2.0 |
| url | https://arxiv.org/abs/2507.20438 |