Vessel trajectory prediction plays a pivotal role in numerous maritime applications and services. While the Automatic Identification System (AIS) offers a rich source of information to address this task, forecasting vessel trajectory using AIS data remains challenging, even for modern machine learning techniques, because of the inherent heterogeneous and multimodal nature of motion data. In this paper, we propose a novel approach to tackle these challenges. We introduce a discrete, high-dimensional representation of AIS data and a new loss function designed to explicitly address heterogeneity and multimodality. The proposed model-referred to as TrAISformer-is a modified transformer network that extracts long-term temporal patterns in AIS vessel trajectories in the proposed enriched space to forecast the positions of vessels several hours ahead. We report experimental results on real, publicly available AIS data. TrAISformer significantly outperforms state-of-the-art methods, with an average prediction performance below 10 nautical miles up to ~10 hours.

翻译：船舶轨迹预测在众多海事应用与服务中扮演着关键角色。尽管自动识别系统（AIS）为此任务提供了丰富的信息源，但由于运动数据固有的异质性与多模态特性，即便采用现代机器学习技术，基于AIS数据进行轨迹预测仍具挑战性。本文提出了一种应对这些挑战的新方法。我们引入了AIS数据的离散高维表示，并设计了一种针对异质性与多模态问题的新型损失函数。所提出的模型——名为TrAISformer——是一种改进的Transformer网络，能够在所构建的增强空间中提取AIS船舶轨迹的长期时间模式，从而预测船舶未来数小时的位置。我们在公开的真实AIS数据集上进行了实验验证。结果表明，TrAISformer显著优于现有最先进方法，在长达约10小时的预测范围内，其平均预测误差低于10海里。