Mitigating Concept Drift in QoS Prediction for Teleoperation of Autonomous Vehicles Using Historic Data
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 incorpora...
| Authors | Xiyan Su, Jianning Gao, Mahmoud Ashri et al. |
|---|---|
| Primary category | cs.RO |
| Published | 6 October 2026 |
| Last updated at source | 6 October 2026 |
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