Social-Passing: A New Approach to Trajectory Prediction in Multi-Vessel Scenarios for Sensing Intrinsic Social Information of Vessels

Currently existing vessel trajectory predictions are predicted for individual vessels. However, due to the complexity of the maritime environment, it’s not feasible to capture the mutual information among vessels. In order to better capture the dynamic interrelationships between vessels at sea, inspired by social pooling and message passing mechanism, we design a Social-Passing mechanism to dynamically update the node information of vessels, and combined with LSTM network, we propose a SP-LSTM model. In our model, we first utilize the LSTM network to extract features from the historical trajectory data of a single vessel. Subsequently, we perform the Social-Passing analysis of the vessels at the current time node, based on the distance range between vessels, to update the information of all current nodes. Meanwhile, since multi-vessel datasets are difficult to collect, we propose an AIS Multi-Vessel Data processing algorithm (AMVD) to process the datasets. Finally, we compare several existing models, and all of our SP-LSTM achieves better performance results.