Mitigating network changes improves teleoperation quality for autonomous vehicles
Mitigating Concept Drift in QoS Prediction for Teleoperation of Autonomous Vehicles Using Historic Data
RoboticsArtificial Intelligence
Summary
Controlling driverless cars remotely relies on good and stable mobile network connections, which can change unexpectedly. The authors created a method to predict two important network factors—how fast data moves uplink and the delay in communication—and keep predictions accurate even when network conditions shift. They use past network data to help the prediction model adjust to new situations. They also developed a way to spot critical moments when network performance might threaten teleoperation safety.
What this means in practice
- •For autonomous vehicle operators: Improve remote driving systems by predicting network issues to maintain control quality despite changing mobile networks.
- •For mobile network planners: Use historic data-based prediction models to detect and anticipate critical network scenarios affecting teleoperation.
Authors
Xiyan Su, Jianning Gao, Mahmoud Ashri, Frank Diermeyer
Abstract
Teleoperation serves as the fallback solution to autonomous driving but reliable functions of the teleoperation require a certain amount of mobile network resources, which cannot be guaranteed at all times. Therefore, predictive quality of service (pQoS) is introduced as a concept to increase the resilience of the teleoperation. In this paper, based on a data measurement campaign, we propose a prediction framework to prediction two important network KPIs of teleoperation: uplink data-rate and round-trip latency. Furthermore, we introduce a method to alleviate the performance degradation of machine-learning-based prediction models on previously unseen data due to concept drift by incorporating historic data into the prediction pipeline. Additionally, we introduce the metric of critical scenario detection to evaluate the prediction performance specifically for teleoperation.