Ship trajectory prediction method and system based on automatic encoder and bidirectional LSTM

Abstract

The invention provides a ship trajectory prediction method and system based on an LSTM automatic encoder and a bidirectional LSTM, and the method comprises the steps: carrying out the preprocessing ofship AIS trajectory data, and carrying out the feature extraction of the trajectory data through an automatic encoder; then, combining the extracted features with trajectory longitude and latitude data to represent the current navigation state of the ship; and taking the extracted features with trajectory longitude and latitude data as model input, learning a ship motion law implied in the trajectory data through a bidirectional LSTM neural network model containing an attention mechanism, and predicting the position of the ship at the next moment by using the ship motion law learned by the model. According to the method, the ship track prediction is carried out by adopting a scheme of the LSTM automatic encoder, the attention mechanism and the bidirectional LSTM neural network, and the bidirectional LSTM model can better mine the space-time association relationship of the track data on the premise of reserving enough effective information of the ship track data. According to the method, the trajectory prediction precision can be effectively improved, the real-time prediction is realized, and the requirement of a scene with relatively high trajectory prediction timeliness and accuracy is met.

Description

technical field [0001] The invention belongs to the technical field of track intelligent prediction, and in particular relates to a ship track prediction method and system based on an LSTM autoencoder and a bidirectional LSTM. Background technique [0002] In recent years, the number of ships has continued to increase, the traffic density in the surrounding waters has continued to increase, the waterways are crowded, ship collisions occur frequently, and the safety of maritime navigation has become more prominent. Predicting ship trajectories can effectively predict navigation trends, analyze ship behavior patterns, and then guide navigation, forecast navigation risks, reduce ship collision accidents, and achieve orderly management of ships, which is of great significance to maritime traffic safety. [0003] The characteristics of ship navigation and vehicle driving are different, there is no obvious road network constraint, the track is more random, and the prediction is mo...

AI Technical Summary

Problems solved by technology

At present, the prediction effect based on the Attention-LSTM model is better. The attention mechanism and LSTM model are used to highlight the key features and the time series characteristics of trajectory data are used. However, this method lacks the application of high-dimensional data processing of trajectory and the temporal and spatial correlation of trajectory data.

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