Jian Cen, Jiaxi Li, Xi Liu, Jiahao Chen, Haisheng Li, Weisheng Huang, Linzhe Zeng, Jun-Xi Kang, Silin Ke
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引用次数: 0
摘要
随着全球航运量的增加和海上运输系统的复杂化,船舶轨迹预测成为提高海上安全的重要工具。然而,现有的船舶轨迹预测方法大多只关注单一特征,无法融合高维特征。为了解决这些问题,本文基于自动识别系统(AIS)数据,提出了具有混合注意机制(AM)的 CNN-GRU 模型。首先提出了卷积神经网络(CNN)来提取轨迹数据的时空信息。然后设计一个门控递归单元(GRU)来提取轨迹的时间关系。最后,引入 AM 来学习深层特征并预测船只轨迹。为了验证模型的有效性,我们在三个真实的 AIS 数据集上进行了实验。与其他模型相比,该方法具有较高的轨迹预测精度。
A hybrid prediction model of vessel trajectory based on attention mechanism and CNN-GRU
With the increase in global shipping volumes and the complexity of maritime transport systems, vessel trajectory prediction serves an important tool in improving maritime safety. However, most existing vessel trajectory prediction methods focus on a single feature and unable fuse high-dimensional features. To solve these problems, CNN-GRU model with a hybrid attention mechanism (AM) is proposed based on Automatic Identification System (AIS) data. First convolutional neural network (CNN) is proposed to extract the spatio-temporal information of the trajectory data. Then a gated recurrent unit (GRU) is designed to extract the temporal relationship of the trajectories. Finally, AM is introduced to learn the deep-level features and predict the vessel trajectories. To validate the effectiveness of the model, experiments are conducted on three real AIS datasets. In comparison with other models, the method has a high trajectory prediction accuracy.
期刊介绍:
The Journal of Engineering for the Maritime Environment is concerned with the design, production and operation of engineering artefacts for the maritime environment. The journal straddles the traditional boundaries of naval architecture, marine engineering, offshore/ocean engineering, coastal engineering and port engineering.