A model for vessel trajectory prediction based on long short-term memory neural network

AbstractEach vessel has its own way of sailing in the port region. Any autonomous vessel navigating such a scene should be able to predict the trajectories of surrounding ships and adjust its behaviour to avoid a collision. In this paper, combined with the sequence prediction method, a Long Short-Term Memory (LSTM) model is proposed to predict the trajectories of the vessels. The ground-truth Automatic Identification System (AIS) data in the port of Tianjin, China are used to train and test the proposed model. The experimental results prove that our model can predict ship trajectories accurately, and it is applicable to the autonomous navigation system.KEYWORDS: Vessel Trajectory PredictionAutomatic Identification SystemLong Short-Term MemoryAutonomous Navigation AcknowledgementsThis research is supported by the project of Intelligent Ship Testing and Verification, 2018/473, China and the Maneouvering Simulation of Yunnan Inland Shipping Ships No. 851333 and the Fundamental Research Funds for the Central Universities No. 3132019011 and the Natural Science Foundation Guidance Project of Liaoning Province (no. 2019-ZD-0152) and the Educational Reform of Dalian Maritime University (2018Y08).Disclosure statementNo potential conflict of interest was reported by the authors.