# Reliability-aware short-term roll prediction for unmanned surface vehicles via multi-task learning and adaptive centralization

arXiv: https://arxiv.org/abs/2610.00996 · HTML: https://mlsanity.com/paper/2610.00996/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 21 references that are in the map.
- First posted on arXiv 1 Oct 2026.

## Builds on (most cited first, 1 of 1)

- [A Data Driven Method for Multi-step Prediction of Ship Roll Motion in High Sea States](https://mlsanity.com/paper/2207.12673/) (2022)

## Abstract (arXiv)

Reliable roll prediction of unmanned surface vehicles (USVs) is essential for ensuring navi?gational safety and enhancing autonomous decision-making. While existing studies primarily focus on improving prediction accuracy, the quantification of prediction reliability remains insufficiently addressed. To bridge this gap, this paper proposes a reliability-aware prediction paradigm that integrates confidence assessment into the predictive pipeline. The architecture utilizes a multi-task learning structure where a shared feature extraction backbone feeds into dual heads: a regression head for precise roll prediction and a quantification head for confidence scoring. This configuration provides accurate prediction and corresponding confidence for risk?sensitive downstream tasks. In addition, an adaptive centralization strategy tailored for short?term real-time roll prediction is introduced to improve model generalization under varying operational conditions. Experiments conducted on a real-sea dataset demonstrate that the proposed method effectively quantifies the reliability of prediction results and maintains superior generalization under varying conditions, offering significant potential for practical engineering applications.