Modelling trajectory in general, and vessel trajectory in particular, is a difficult task because of the multimodal and complex nature of motion data. In this paper, we present TrAISformer-a novel deep learning architecture that can forecast vessel positions using AIS (Automatic Identification System) observations. We address the multimodality by introducing a discrete representation of AIS data and re-frame the prediction, which is originally a regression problem, as a classification problem. The model encodes complex movement patterns in AIS data in high-dimensional vectors, then applies a transformer to extract useful long-term correlations from sequences of those embeddings to sample future vessel positions. Experimental results on real, public AIS data demonstrate that TrAISformer significantly outperforms state-of-the-art methods.
翻译:由于运动数据的多式和复杂性质,模拟轨迹,特别是船舶轨迹是一项艰巨的任务。在本文件中,我们介绍了TrAISexex这一新的深层次学习结构,它能够利用自动识别系统观测来预测船只位置。我们通过采用独立表示自动识别系统数据的方式处理多式联运问题,并将最初是一个回归问题的预测重新设计为一个分类问题。该模型将高维矢量的AIS数据复杂的移动模式编码成,然后用变压器从这些嵌入的序列中提取有用的长期关联性,以便在未来的船舶位置取样。关于实际的公开AIS数据的实验结果表明,TrAISxexer明显超出最新方法。