arXiv：cs.AI· Xiyan Su, Jianning Gao, Mahmoud Ashri, Frank Diermeyer·· 6 小时前AI 评分30
利用历史数据缓解自动驾驶远程操控 QoS 预测中的概念漂移
Mitigating Concept Drift in QoS Prediction for Teleoperation of Autonomous Vehicles Using Historic Data
AI 导读
针对自动驾驶远程操控中预测服务质量（pQoS）模型在未见数据上因概念漂移而性能下降的问题，研究者提出将历史数据纳入预测流程的方法，并基于实测数据构建了预测上行数据速率与往返时延两项网络 KPI 的框架。该研究同时引入关键场景检测指标，用于专门评估远程操控场景下的预测性能。
正文
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.
| Comments: | 2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC) |
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.08297 [cs.RO] |
| (or arXiv:2610.08297v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08297 arXiv-issued DOI via DataCite (pending registration) |
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| Related DOI: | https://doi.org/10.1109/SMC58881.2025.11343100
DOI(s) linking to related resources |
Submission history
From: Xiyan Su [view email]
[v1]
Tue, 6 Oct 2026 13:01:36 UTC (3,708 KB)
来源：arXiv：cs.AI · arxiv.org