A novel long sequence multi-step ship trajectory prediction method considering historical data

Abstract

Considering the importance of timeliness in ship risk assessment, a high-resolution and long sequence multi-step trajectory prediction method is proposed. Through the multi-step prediction and uncertainty analysis of the ship trajectory for a long period of time, it is possible to make timely ship navigation risk assessment. First, the ship trajectory with high resolution is obtained by cubic spline interpolation. Then, Dynamic Time Warping (DTW) is used as the metric of distance, and a method called Laplacian Eigenmaps Self-Organizing Map (LE-SOM) is used to extract the features from original high-dimensional data with unequal intervals, so as to select the historical trajectories that can be used as a reference. Finally, the trajectory is generated for multi-step prediction. The proposed method not only predicts the position of the ship and its uncertainty from statistical perspective, but also investigates the relationship between trajectory curvature and the prediction error. The case study on a ferry ship in the Jiangsu section of the Yangtze River indicates the validity of the method.