发表机构
Institute of Automotive Technology, Technical University of Munich (TUM)（慕尼黑工业大学汽车技术研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对自动驾驶远程操作中网络QoS预测的概念漂移问题，提出融合历史数据的预测框架，预测上行速率与往返时延，并引入关键场景检测度量以提升预测可靠性。
AI 中文摘要
远程操作是自动驾驶的备用解决方案，但远程操作的可靠功能需要一定量的移动网络资源，而这些资源无法始终得到保证。因此，预测性服务质量（pQoS）被引入作为增强远程操作韧性的概念。本文基于数据测量活动，提出一个预测框架来预测远程操作的两个重要网络关键绩效指标（KPI）：上行数据速率和往返时延。此外，我们引入一种方法，通过将历史数据纳入预测流程，缓解基于机器学习的预测模型在未见数据上因概念漂移而导致的性能退化。另外，我们引入关键场景检测的度量标准，专门用于评估远程操作的预测性能。
英文摘要
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.
Comments2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
DOI：10.1109/SMC58881.2025.11343100
