EA-VTP: Environment-Aware Long-Term Vessel Trajectory Prediction

This paper investigates the long-term vessel trajectory prediction problem. A challenge in long-term prediction is modeling the navigation intention efficiently. We propose a novel model called Environment-Aware Vessel Trajectory Prediction Network (EA-VTP), which introduces the environment feature from the vessel density map. The vessel density map records the number of vessels of each position and therefore indicates the conventional tracks. A convolutional neural network is applied to the vessel density map to extract the information, which is then utilized by the recurrent module for the prediction. In addition, we introduce higher-order differentials and the residual prediction, which exploits the continuity of trajectory and balances the gradient of all steps. Besides, EA-VTP also provides the confidence range of position distribution, thus increases the reliability. The experiment shows that our EA-VTP outperforms baselines, and the environment feature effectively improves both short-term and long-term accuracy.