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

TL;DR

This paper systematically evaluates the feasibility of teleoperating autonomous vehicles over commercial 5G networks, focusing on uplink sensor data latency and the impact of network factors on real-time data delivery.

Contribution

It provides a comprehensive analysis of 5G network effects on AV teleoperation, highlighting current limitations and informing future network and application co-design.

Findings

5G channel conditions significantly affect latency

Handover processes introduce delays in data transmission

Existing streaming protocols face challenges in low-latency delivery

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.