PESO: A Seq2 Seq-Based Vessel Trajectory Prediction Method with Parallel Encoders and Ship-Oriented Decoder  
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trajectories in which the prediction process is always considered a time-series regression task. Some of them use a single recurrent neural network that takes the spatiotemporal feature information of the track points as inputs and outputs the position information of the predicted trajectories. In contrast, other studies adopt a Seq2 Seq structure consisting of an encoder and a decoder. The input feature information is embedded by the encoder to generate high-dimensional representations and transmitted to the decoder for prediction. These methods obtain more semantic level information than traditional methods, enabling more accurate predictions. However, due to the complexity of historical information dependencies and the strong influence of spatial correlations, accurate prediction remains a challenge. Specifically, the prediction of the track point locations during the following parts of a voyage often relies on historical trajectory information and is also influenced by the historical spatial position of the ship. As a result, it is crucial to make the network capture richer features from historical information and better represent the spatial correlation of historical track points. More recently, relying on the technology support from trajectory prediction methods, most commercial systems can detect collisions and provide navigation services. A prediction with significant deviation will influence the efficiency and safety of the vessels. Several state-of-the-art methods have been proposed to address the problem. For example, ref. [1] applies uncertainty quantification in their works, and [2] proposes a network optimized by a genetic algorithm. Unlike these methods, we propose a novel deep learning model, PESO, for vessel trajectory prediction, consisting of the Parallel Encoders and the Ship-Oriented Decoder. The Parallel Encoders are designed to integrate more feature information into deep representation by using different encoders to obtain multiple features, which include the Location Encoder and the Sailing Status Encoder. Processing different types of features with the same encodery will produce more noise and affect the accuracy of prediction. The Location Encoder embeds the longitude and latitude into representations, while the Sailing Status Encoder encodes the course, speed, and sailing distance. The Parallel Encoders embed different types of features simultaneously and enrich feature representation, avoiding the noise inside the features. The Ship-Oriented Decoder is targeted to utilize the Semantic Location Vector (SLV) of each ship to guide the prediction process, which better represents the spatial correlation of historical track points. The SLV is the semantic representation of each vessel, which contains spatial information related to the historical track points of the vessel. After dividing the map into several grids and obtaining the semantic vector of each grid by the algorithm of continuous bag-of-words (CBOW) [3], SLV is generated  
Figure 1: An example of vessel trajectory prediction. The black ships denote the historical AIS trajectories. The grey ships denote the prediction results, while the blue ships denote the actual trajectories. by the mean of the semantic vectors of all track points of each ship. The Ship-Oriented Decoder uses the combination of feature representations from the Parallel Encoders and SLV as input and outputs the predicted longitude and latitude. To sum up, the main contributions of this paper can be summarized as follows:  
* We propose a novel deep learning model, PESO, based on a Seq2 Seq network for vessel trajectory prediction, which aims to capture richer features from previous information and better represent the spatial correlation of historical trajectory points.