# VEPO-S2S: A VESsel Portrait Oriented Trajectory Prediction Model Based on S2S Framework
## 5 Experiments
### Qualitative Analysis
#### 5.4.3 Qualitative Ablation Results  
We conducted several detailed studies to better illustrate the changes in the experimental results before and after ablation. Seven figures display the results of the experiments on the Shallow-level Attributes, the Sailing Location Preference, the Voyage Time Preference, the Anchoring Time Preference, the Portrait Selection Component, the Feature Fusion Component, and the Multi-head Decoder Component for a tugboat. More precisely, we present four trajectories in each image, including the input track points, labels, and the predictive results before and after ablation. For each figure, it is evident that the green curve closely aligns with the actual trajectory. We observe that if a vessel lacks any one of the Shallow-level Attributes, the Sailing Location Preference, the Voyage Time Preference, or the Anchoring Time Preference, there are significant biases in both the direction and distance in the predicted results. Figure 14 illustrates the results with and without Shallow-level Attributes. In the absence of Shallow-level Attributes, VEPO-S2S predictions deviate from the actual trajectory at the fourth and fifth points. This is because Shallow-level Attributes determine the  
Figure 12: The predictions of cargo ships and container ships under various models, with the difficulty of predictions increasing from (**a**) to (**b**). Our model performs the best in both scenarios. In (**a**), which involves straight-line navigation of cargo ships, all models except GRU achieve decent prediction results. In (**b**), which involves container ship turning. Additionally, the METO-S2S model is also able to accomplish the prediction tasks to some extent. However, other models struggle to achieve satisfactory prediction performance. Figure 13: The trajectory predictions of the cargo ship and oil tanker using different structures of the VEPO-S2S model, with the predictive difficulty increasing gradually from (**a**) to (**b**). As depicted in the graph, VEPO-GRU-GRU achieves the best predictive performance. vessel's inertia and turning capabilities. Typically, larger vessels have more difficulty in altering their current motion states. When there is a lack of Shallow-level Attributes, the model struggles to accurately assess these abilities of the vessel. Therefore, in long-term predictions, the model fails to provide effective guidance and leads to deviations from the correct trajectory in later stages. Figure 15 demonstrates the results with and without the Sailing Location Preference. The Sailing Location Preference helps the model recognize a vessel's adaptability to terrain. Vessels with varying degrees of adaptability to terrains choose different collision avoidance routes. Without the Sailing Location Preference, the model struggles to capture details of routes, which makes it difficult to generate corresponding choices and leads to oscillations in the predicted trajectories. Figure 16 displays the comparison results with and without the Voyage Time Preference. The Voyage Time Preference reflects the habits of the crews and vessel sailing at various times. For instance, the collision avoidance maneuvers are different during high and low vessel traffic periods. Without guidance from the Voyage Time Preference, the predicted results exhibit considerable fluctuations. Figure 14: The predicted trajectories of a tugboat, where the green and red lines are the prediction results using VEPO-S2S with and without Shallow-level Attributes, respectively. The Shallow-level Attributes are associated with the vessel’s inertia and turning capabilities. The model without Shallow-level Attributes is not able to grasp this ability well, which may cause errors. Figure 15: The predicted trajectories of a tugboat, where the green and red lines represent the prediction results of VEPO-S2S with and without considering the Sailing Location Preference, respectively. The Sailing Location Preference helps the model identify the adaptability of the vessel to the geographical environment.