论文标题
FRA-LSTM：一种基于正向和反向子网络融合的血管轨迹预测方法
FRA-LSTM: A Vessel Trajectory Prediction Method Based on Fusion of the Forward and Reverse Sub-Network
论文作者
论文摘要
为了提高船舶的能力并确保海上交通安全，船只智能轨迹预测在船只的智能导航和智能碰撞系统中起着至关重要的作用。但是，目前的研究人员仅关注短期或长期血管轨迹预测，这导致轨迹预测的准确性不足，并且缺乏对综合历史轨迹数据的深入挖掘。本文提出了一个基于正向子网络和反向子网络（称为fra-lstm）的融合来预测容器轨迹的自动识别系统（AIS）数据驱动的长短期内存（LSTM）方法。我们方法中的正向子网络结合了LSTM和注意机制，以了解前向历史轨迹数据的特征。同时，反向子网络结合了双向LSTM（BILSTM）和注意机制，以挖掘向后历史轨迹数据的特征。最后，最终预测的轨迹是通过融合向前和反向子网络的输出功能来生成的。基于大量实验，我们证明，与Bilstm和Seq2Seq相比，我们提出的方法预测短期和中期轨迹的准确性平均增加了96.8％，平均增加了86.5％。此外，在预测长期轨迹时，我们方法的平均准确性比比较BilstM和Seq2Seq的平均准确性高90.1％。
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.
