发表机构
Shanghai University; Shanghai Artificial Intelligence Laboratory（上海大学; 上海人工智能实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对无人水面艇横摇预测可靠性不足的问题，提出多任务学习与自适应中心化方法，实现准确预测与置信度量化，并在真实数据上验证了泛化能力。
AI 中文摘要
可靠的无人水面艇（USV）横摇预测对于确保航行安全和增强自主决策至关重要。现有研究主要侧重于提高预测精度，而对预测可靠性的量化仍关注不足。为弥补这一空白，本文提出了一种可靠性感知的预测范式，将置信度评估集成到预测流程中。该架构采用多任务学习结构，共享特征提取骨干网络连接两个头部：一个用于精确横摇预测的回归头和一个用于置信度评分的量化头。这种配置为风险敏感的后续任务提供了准确的预测及相应的置信度。此外，针对短期实时横摇预测，引入了一种自适应中心化策略，以提高模型在不同运行条件下的泛化能力。在真实海域数据集上进行的实验表明，所提方法能够有效量化预测结果的可靠性，并在不同条件下保持优异的泛化性能，为实际工程应用提供了重要潜力。
英文摘要
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.
