System Modeling and Performance Evaluation of Predictive QoS for Future Tele-Operated Driving

TL;DR

This paper models and evaluates predictive QoS methods for future tele-operated driving, focusing on 5G networks, to ensure reliable data rates and low latency essential for safe remote vehicle control.

Contribution

It introduces a novel REM-based prediction method with layered optimization to improve end-to-end data rate forecasting for tele-operated driving applications.

Findings

Enhanced prediction accuracy with layered REM approach

Feasibility demonstrated through scaled vehicular platform tests

Analysis of 5G NR capabilities for teleoperation

Abstract

Future Tele-operated Driving (ToD) applications place challenging Quality of Service (QoS) demands on existing mobile communication networks that are of highly important to comply with for safe operation. New remote control and platooning services will emerge and pose high data rate and latency requirements. One key enabler for these applications is the newly available 5G New Radio (NR) promising higher bandwidth and lower latency than its predecessors. In addition to that, public 5G networks do not consistently deliver and do not guarantee the required data rates and latency of ToD. In this paper, we discuss the communication-related requirements of tele-operated driving. ToD is regarded as a complex system consisting of multiple research areas. One key aspect of ToD is the provision and maintenance of the required data rate for teleoperation by the mobile network. An in-advance prediction method of the end-to-end data rate based on so-called Radio Environmental Maps (REMs) is discussed. Furthermore, a novel approach improving the prediction accuracy is introduced and it features individually optimized REM layers. Finally, we analyze the implementation of tele-operated driving applications on a scaled vehicular platform combined with a cyber-physical test environment consisting of real and virtual objects. This approach enables large-scale testing of remote operation and autonomous applications.