Teleoperating Autonomous Vehicles over Commercial 5G Networks: Are We There Yet?

2025-07-27

AI Summary

This study systematically evaluates the feasibility of commercial 5G networks for autonomous vehicle teleoperation, focusing on end-to-end ultra-low-latency transmission of uplink sensor data (e.g., camera and LiDAR streams). Addressing key bottlenecks—including high latency variability, frequent handovers, and inflexible resource scheduling under mobility—we conduct empirical measurements and performance attribution from a cross-layer, end-to-end perspective. Our methodology integrates 5G physical-layer analysis, real-time channel monitoring, handover modeling, and QoE-aware adaptation mechanisms for RTSP and WebRTC. Results identify handover procedures and uplink resource contention as the dominant causes of latency violations; existing streaming protocols fail to simultaneously guarantee reliability and sub-100 ms end-to-end latency. This work provides critical empirical evidence and concrete optimization pathways for 5G-Advanced/6G network design, edge-cloud co-processing architectures, and next-generation real-time transport protocols tailored to remote driving applications.

Technology Category

• Application Domains: Mobility, Driving & Flight
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Agent Communication

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• Feasibility of AV teleoperation over commercial 5G networks
• Impact of 5G radio factors on end-to-end latency
• Challenges in real-time sensor data delivery for AVs

Innovation

• Cross-layer and E2E 5G network analysis
• Uplink sensor data delivery optimization
• RTSP and WebRTC QoE adaptation mechanisms