## 4 Experiments
### Ablation Study  
In order to explore each component of PESO, we performed ablation studies on the proposed method. We conducted five groups of different experiments, which are introduced in the following part:  
1. _Without SOG, COG, and DIS_. Delete the speed, course, and sailing distance on the input of the Sailing Status Encoder of the Parallel Encoders;
2. _Without SOG_. Delete the speed on the input of the Sailing Status Encoder of the Parallel Encoders;
3. _Without COG_. Delete the course on the input of the Sailing Status Encoder of the Parallel Encoders;  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model Name** & **First** & **Second** & **Third** & **Fourth** & **Fifth** \\ \hline LSTM & 0.000319 & 0.000511 & 0.000767 & 0.001026 & 0.001276 \\ BiLSTM & 0.000380 & 0.000577 & 0.000823 & 0.001075 & 0.001322 \\ GRU & 0.000324 & 0.000513 & 0.000760 & 0.001013 & 0.001261 \\ BiGRU & 0.000341 & 0.000519 & 0.000763 & 0.001017 & 0.001266 \\ LSTMLSTM-LSTM & 0.000299 & 0.000458 & 0.000693 & 0.000971 & 0.001279 \\ GRU-GRU & _0.00025_7 & 0.000549 & 0.000937 & 0.001354 & 0.001795 \\ BiGRU-GRU & _0.000579_ & 0.000701 & 0.000874 & 0.001211 & 0.001738 \\ BiLSTM-LSTM & 0.000458 & 0.000482 & 0.000589 & 0.000709 & 0.000877 \\ PESO & 0.000259 & _0.000278_ & _0.000314_ & _0.000362_ & _0.000426_ \\ \hline \hline \end{tabular}
\end{table}
Table 8: Quantitative results on each track point under the metric of MAE.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model Name** & **First** & **Second** & **Third** & **Fourth** & **Fifth** \\ \hline LSTM & 0.000493 & 0.000809 & 0.001225 & 0.001646 & 0.002052 \\ BiLSTM & 0.000611 & 0.000927 & 0.001330 & 0.001740 & 0.002140 \\ GRU & 0.000495 & 0.000805 & 0.001216 & 0.001634 & 0.002040 \\ BiGRU & 0.000528 & 0.000825 & 0.001228 & 0.001643 & 0.002046 \\ LSTM-LSTM & 0.000462 & 0.000711 & 0.001071 & 0.001494 & 0.001955 \\ GRU-GRU & _0.000399_ & 0.000899 & 0.001561 & 0.002299 & 0.003087 \\ BiGRU-GRU & 0.000914 & 0.001077 & 0.001296 & 0.001737 & 0.002469 \\ BiLSTM-LSTM & 0.000740 & 0.000766 & 0.000947 & 0.001147 & 0.001436 \\ PESO & 0.000412 & _0.000446_ & _0.000501_ & _0.000578_ & _0.000681_ \\ \hline \hline \end{tabular}
\end{table}
Table 9: Quantitative results on each track point under the metric of FDE.  
4. _Without \(D1S\)_. Delete the sailing distance on the input of the Sailing Status Encoder of the Parallel Encoders;
5. _Without \(SLV\)_. Delete the Semantic Location Vector on the input of the Ship-Oriented Decoder.  
Table 10 shows the ablation results. We evaluated the performance under the metrics of RMSE, MAE, ADE, and FDE. It is obvious that the prediction accuracy will drop sharply without the sailing status information of a ship. The course of the vessel determines the direction of the following sailing status to a great extent, and the prediction accuracy will have a negative effect without the nearest course information. Sailing distance and speed denote the endurance and oil storage of a ship. Without these, the sailing status of a ship is not fully expressed, and the prediction performance of the model will be affected. We can also see from the table that the Semantic Location Vector plays a positive role in guiding the prediction process. The Semantic Location Vector in our model better represents the spatial correlation between a ship's historical trajectory points and forecast ones. Naturally, without the Semantic Location Vector as the guidance information of the decoder, the model will have poor performance. To sum up, the current feature selection of the model is conducive to improving the prediction effect.