FRA-LSTM: A Vessel Trajectory Prediction Method Based on Fusion of the Forward and Reverse Sub-Network

TL;DR

This paper introduces FRA-LSTM, a novel vessel trajectory prediction method that fuses forward and reverse sub-networks with attention mechanisms, significantly improving prediction accuracy for maritime navigation.

Contribution

The paper proposes a fusion-based LSTM approach combining forward and reverse sub-networks with attention mechanisms for enhanced vessel trajectory prediction.

Findings

Prediction accuracy increased by 96.8% for short-term trajectories.

Prediction accuracy increased by 86.5% for mid-term trajectories.

Long-term trajectory prediction accuracy improved by 90.1% over existing methods.

Abstract

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.