# VEPO-S2S: A VESsel Portrait Oriented Trajectory Prediction Model Based on S2S Framework
## 2 Related Works  
### Vessel Trajectory Prediction  
Traditional trajectory prediction methods have achieved favorable results in forecasting trajectories for vehicles, ships, and pedestrians. The authors of [19] proposed a dynamically assisted inertial navigation method for estimating observed values. The authors of [20] introduced a mathematical modeling-based Kalman filtering method for long-range surface tracking, enabling the direct prediction of the target position and heading without requiring coordinate system conversion. To improve ship motion prediction accuracy under environmental disturbances, ref. [21] proposed a ship motion recognition algorithm based on the least squares method. However, those methods exhibited limited predictive accuracy when faced with complex situations.  
In recent years, the development of deep learning methods for vessel trajectory prediction has progressed rapidly, and significant advancements have been made in this task. Most research adopts the RNN structure. Some research is based on an LSTM [22] or a GRU [6]. Moreover, to investigate ship prediction under varying trajectory densities, ref. [23] proposed a model based on an LSTM and the K-nearest neighbor (KNN). The authors of [24] introduced the MP-LSTM model, which integrates the strengths of TPNet and LSTM, addressing the shortcomings of existing methods in terms of both the accuracy and model complexity.  
Meanwhile, some scholars attempted to use Seq2 Seq architecture to address prediction problems. The authors of [25] introduced a neural network model based on an LSTM and Seq2 Seq, utilized to capture long-term dependencies in historical data within trajectories. The authors of [26] proposed the ST-Seq2 Sq model based on GRU architecture. The authors of [27] proposed a trajectory prediction model based on BiGRU and Seq2 Seq, which fully considers the interactions among ships. Furthermore, several other trajectory prediction models have been proposed. The authors of [13] introduced the METO-S2S model based on a multi-semantic decoder, considering the influence of various ship semantic information on trajectory prediction. They also used semantic vectors (SLV) to guide model predictions in PESO [28], achieving outstanding results on the open-source AIS dataset in the United States. In addition to the Seq2 Seq model, The authors of [29] applied the Transformer framework combined with LSTM to capture historical trajectories in time series and overcome issues related to distant information decay. To express the interdependence between ships, The authors of [30] proposed the spatiotemporal multi-graph convolutional neural network (STMGCN) model, which models both spatiotemporal data and ship types separately. The authors of [31] combined graph attention convolution (GAT) with an extended causal convolution structure and designed the GAGW model. The graph attention convolution network is responsible for extracting interaction information between different ships in space.  
The majority of the aforementioned studies primarily focus on the utilization of shallow-level trajectory information. These studies typically use speed, course, and position as model inputs. However, this is insufficient for guiding ship avoidance in an intricate situation. Acquiring richer and deeper characteristics, as well as their sensible application, is crucial for guiding ship avoidance and overall route planning. Therefore, current research on vessel trajectory prediction will pay more attention to excavating the abundant characteristics and understanding the dynamics of real-world environments.