English(EN) Mitigating Concept Drift in QoS Prediction for Teleoperation of Autonomous Vehicles Using Historic Data

AI研究解决自动驾驶汽车远程操作服务质量预测中的概念漂移问题

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-07 04:00

本文介绍了一个用于预测自动驾驶汽车远程操作服务质量（QoS）的框架，重点关注上行链路数据速率和往返延迟。所提出的方法旨在通过将历史数据整合到预测流程中，来缓解由概念漂移引起的机器学习模型性能下降。此外，还提出了一个用于关键场景检测的指标，以专门评估远程操作预测性能。 AI

影响这项研究可以提高自动驾驶汽车后备系统的可靠性，有可能在自动驾驶故障期间提高安全性。

排序理由关于特定AI/ML技术在小众应用中使用的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.AI TIER_1 English(EN) · Xiyan Su, Jianning Gao, Mahmoud Ashri, Frank Diermeyer · 2026-10-07 04:00

利用历史数据缓解自动驾驶汽车远程操作中服务质量预测的概念漂移

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 q…