In order to improve the vessel's capacity and ensure maritime traffic safety, vessel intelligent trajectory prediction plays an essential role in the vessel's smart navigation and intelligent collision avoidance system. However, current researchers only focus on short-term or long-term vessel trajectory prediction, which leads to insufficient accuracy of trajectory prediction and lack of in-depth mining of comprehensive historical trajectory data. This paper proposes an Automatic Identification System (AIS) data-driven long short-term memory (LSTM) method based on the fusion of the forward sub-network and the reverse sub-network (termed as FRA-LSTM) to predict the vessel trajectory. The forward sub-network in our method combines LSTM and attention mechanism to mine features of forward historical trajectory data. Simultaneously, the reverse sub-network combines bi-directional LSTM (BiLSTM) and attention mechanism to mine features of backward historical trajectory data. Finally, the final predicted trajectory is generated by fusing output features of the forward and reverse sub-network. Based on plenty of experiments, we prove that the accuracy of our proposed method in predicting short-term and mid-term trajectories has increased by 96.8% and 86.5% on average compared with the BiLSTM and Seq2seq. Furthermore, the average accuracy of our method is 90.1% higher than that of compared the BiLSTM and Seq2seq in predicting long-term trajectories.

翻译：为提升船舶通航能力并保障海上交通安全，船舶智能轨迹预测在船舶智能导航与智能避碰系统中具有关键作用。然而，当前研究仅聚焦于船舶短期或长期轨迹预测，导致轨迹预测精度不足，且缺乏对完整历史轨迹数据的深度挖掘。本文提出一种基于自动识别系统（AIS）数据驱动的长短期记忆网络（LSTM）方法，通过融合前向子网络与反向子网络（称为FRA-LSTM）实现船舶轨迹预测。该方法中，前向子网络结合LSTM与注意力机制挖掘前向历史轨迹数据特征，同步地，反向子网络结合双向LSTM（BiLSTM）与注意力机制挖掘后向历史轨迹数据特征。最后，通过融合前向与反向子网络的输出特征生成最终预测轨迹。基于大量实验证明，与BiLSTM和Seq2seq相比，本方法在预测短期与中期轨迹时精度平均提升96.8%和86.5%；在预测长期轨迹时，本方法平均精度较对比的BiLSTM和Seq2seq提升90.1%。