← 研踪 PaperTrailarXiv:2603.25670 · v5
Uncertainty-Guided Label Rebalancing for CPS Safety Monitoring
首次提交 2026-03-27 01:26
版本更新 2026-11-12 19:23
雷达观测 2026-11-13 14:15
摘要
Safety monitoring is essential for Cyber-Physical Systems (CPSs) such as Unmanned Aerial Vehicles (UAVs). However, unsafe events are rare in CPS operations, creating class imbalance that degrades data-driven safety predictors. Behavioral uncertainty, reflected in erratic control signals or rapid heading changes, is often correlated with safety outcomes: uncertain behaviors occur more often during unsafe operation. Yet safety monitoring rarely exploits it. We propose U-Balance, a supervised approach that leverages behavioral uncertainty to rebalance datasets before training a safety predictor. U-Balance first trains a GatedMLP-based uncertainty predictor that represents telemetry windows using distributional kinematic features and outputs an uncertainty score. Its uncertainty-guided label rebalancing (uLNR) mechanism probabilistically relabels $\textit{safe}$ windows with unusually high uncertainty as $\textit{unsafe}$, enriching the minority class without synthesizing data. A safety predictor is then trained on the rebalanced dataset. We evaluate U-Balance on a UAV benchmark and ForEnt, a quadruped-robot entrapment dataset. On UAV, U-Balance achieves F1/AUROC/AUPRC of $0.801/0.972/0.776$, outperforming the strongest safety-prediction baselines by 14.3/2.2/6.1 percentage points ($\textit{pp}$), respectively. While the strongest baseline varies across metrics (MLP for F1 and AUPRC; CNN for AUROC), U-Balance leads on all three. On ForEnt, it achieves $0.522/0.806/0.323$, outperforming the strongest baselines by 4.4/9.2/1.4 $\textit{pp}$. Using the same Bi-LSTM safety predictor, uLNR outperforms the strongest alternative rebalancing methods by 17.0/4.1/8.2 $\textit{pp}$ on UAV and 2.3/6.5/1.9 on ForEnt. uLNR also outperforms Early and Late Fusion on both datasets. To our knowledge, this work is the first to use behavioral uncertainty for dataset rebalancing in CPS safety monitoring.
作者备注：14 pages (main content), 3 pages references, 6 figures, 6 tables
观测到的版本
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v5 · 2026-11-12 19:23 · 最新观测版本
Uncertainty-Guided Label Rebalancing for CPS Safety Monitoring
Safety monitoring is essential for Cyber-Physical Systems (CPSs) such as Unmanned Aerial Vehicles (UAVs). However, unsafe events are rare in CPS operations, creating class imbalance that degrades data-driven safety predictors. Behavioral uncertainty, reflected in erratic control signals or rapid heading changes, is often correlated with safety outcomes: uncertain behaviors occur more often during unsafe operation. Yet safety monitoring rarely exploits it. We propose U-Balance, a supervised approach that leverages behavioral uncertainty to rebalance datasets before training a safety predictor. U-Balance first trains a GatedMLP-based uncertainty predictor that represents telemetry windows using distributional kinematic features and outputs an uncertainty score. Its uncertainty-guided label rebalancing (uLNR) mechanism probabilistically relabels $\textit{safe}$ windows with unusually high uncertainty as $\textit{unsafe}$, enriching the minority class without synthesizing data. A safety predictor is then trained on the rebalanced dataset. We evaluate U-Balance on a UAV benchmark and ForEnt, a quadruped-robot entrapment dataset. On UAV, U-Balance achieves F1/AUROC/AUPRC of $0.801/0.972/0.776$, outperforming the strongest safety-prediction baselines by 14.3/2.2/6.1 percentage points ($\textit{pp}$), respectively. While the strongest baseline varies across metrics (MLP for F1 and AUPRC; CNN for AUROC), U-Balance leads on all three. On ForEnt, it achieves $0.522/0.806/0.323$, outperforming the strongest baselines by 4.4/9.2/1.4 $\textit{pp}$. Using the same Bi-LSTM safety predictor, uLNR outperforms the strongest alternative rebalancing methods by 17.0/4.1/8.2 $\textit{pp}$ on UAV and 2.3/6.5/1.9 on ForEnt. uLNR also outperforms Early and Late Fusion on both datasets. To our knowledge, this work is the first to use behavioral uncertainty for dataset rebalancing in CPS safety monitoring.
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