CRHT：用于船舶轨迹预测的连续回归混合Transformer，结合在线聚类采样
CRHT: A Continuous Regression Hybrid Transformer for Vessel Trajectory Prediction with Online Cluster Sampling
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中文总结 AI 辅助
针对船舶轨迹预测的地理偏差与真实性问题，提出CRHT框架，结合在线K-means聚类采样与混合架构，在短期预测中表现最优，平衡了精度与机动跟踪能力。
中文摘要 AI 辅助
准确的船舶轨迹预测对海上安全和异常检测至关重要，但现有模型常受地理偏差和航海真实性问题困扰。我们提出连续回归混合Transformer（CRHT），这一深度学习框架利用自动识别系统（AIS）数据预测船舶运动。为缓解空间数据不平衡问题，我们引入在线K-means聚类采样策略，确保训练期间充分接触罕见机动动作。我们的混合架构整合1D卷积层以提取局部运动学特征，结合多头注意力机制捕捉全局时间上下文。CRHT在短期预测中表现优异，在1小时预测 horizon 下误差最低。结果表明，离散模型在长期预测中提供较高航海稳定性，而CRHT在精度与机动动作跟踪间实现最优平衡，适用于实时海上监视。
英文摘要
Accurate vessel trajectory prediction is critical for maritime safety and anomaly detection, yet existing models often struggle with geographic bias and navigational realism. We propose the Continuous Regression Hybrid Transformer (CRHT), a deep learning framework designed to forecast vessel motion using Automatic Identification System (AIS) data. To mitigate spatial data imbalance, we introduce an online K-means cluster sampling strategy that ensures diverse exposure to rare maneuvers during training. Our hybrid architecture integrates 1D convolutional layers for local kinematic feature extraction with a multi-head attention mechanism for global temporal context. CRHT demonstrates superior performance in short-term forecasting, achieving the lowest errors at the 1-hour horizon. The results demonstrate that while discrete models provide high navigational stability over long horizons, CRHT offers an optimal balance of precision and maneuver tracking for real-time maritime surveillance.
