aiqbench analysis
This research addresses concept drift in machine learning models used to predict network quality of service (QoS) for autonomous vehicle teleoperation, a critical safety fallback mechanism. By integrating historic data into the prediction pipeline, the authors propose a method to maintain model resilience against unseen environmental shifts that could degrade uplink rates or latency. The work is significant for AI practitioners focusing on robustness in safety-critical systems where network reliability cannot be guaranteed.
Mitigating Concept Drift in QoS Prediction for Teleoperation of Autonomous Vehicles Using Historic Data
arXiv:2610.08297v1 Announce Type: cross 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.
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.
Robotics (cs.RO); Artificial Intelligence (cs.AI)
arXiv:2610.08297 [cs.RO]
(or arXiv:2610.08297v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.08297 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
https://doi.org/10.1109/SMC58881.2025.11343100 Focus to learn more DOI(s) linking to related resources
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Full text under the publisher's open licence · 228 words
