Explainable Failure Prediction and Prevention in Maritime
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The paper introduces a reliability‑aware short‑term roll prediction framework for unmanned surface vehicles (USVs) that combines a multi‑task learning architecture with an adaptive centralization strategy. The model uses a shared backbone to feed a regression head for precise roll prediction and a quantification head for confidence scoring, enabling accurate predictions alongside reliability estimates. Experiments on a real‑sea dataset show that the approach effectively quantifies prediction reliability and generalizes well across varying operational conditions.