Mitigating Concept Drift in QoS Prediction for Teleoperation of Autonomous Vehicles Using Historic Data
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.
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Key points
- 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.
- 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.
Sources (1)
- [1]Mitigating Concept Drift in QoS Prediction for Teleoperation of Autonomous Vehicles Using Historic DataarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 01:01 PM
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.
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.
Extractive summary: sentences quoted from the sources.