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arXiv AI By Xiyan Su, Jianning Gao, Mahmoud Ashri, Frank Diermeyer

Mitigating Concept Drift in QoS Prediction for Teleoperation of Autonomous Vehicles Using Historic Data

Read the original on arXiv AI →

The paper proposes a prediction framework for two key network KPIs—uplink data‑rate and round‑trip latency—critical for teleoperation of autonomous vehicles. It introduces a method that incorporates historic data to mitigate concept drift in machine‑learning models, thereby improving prediction reliability on unseen data. Additionally, the authors present a metric for critical scenario detection to evaluate prediction performance specifically for teleoperation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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