Ship trajectory prediction method based on conditional recurrent neural network

By introducing conditional recurrent neural networks and artificial potential field models in ship trajectory prediction, a priori knowledge is used to improve prediction accuracy, the problem of insufficient data during medium and long-term prediction is solved, and the prediction efficiency in complex environments is improved.

CN119312838BActive Publication Date: 2025-03-18HEFEI UNIV OF TECH +1
2 Cites 0 Cited by

Patent Information

Application Number
CN202411877645.3
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-12-19
Publication Date
2025-03-18
Estimated Expiration
2044-12-19

AI Technical Summary

Technical Problem

The prior art may reach limits or lead to unsatisfactory results in medium- and long-term ship trajectory prediction, especially in the case of insufficient training data and complex marine environments.

Method used

Using a conditional recurrent neural network method, the combined force size and joint force point coordinates with prior knowledge are obtained by introducing an artificial potential field model, and embedded them as non-time sequence feature conditions into the conditional recurrent neural network model, forming mixed information with the time sequence features to obtain the best predicted trajectory sequence of the ship.

Benefits of technology

It improves the accuracy and efficiency of ship trajectory prediction in the face of complex marine environments, solves the problem of insufficient training data, and effectively integrates prior knowledge into the prediction process.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The present invention provides a ship trajectory prediction method, system, storage medium and electronic device based on conditional recurrent neural network, and relates to the field of trajectory prediction technology. In the present invention, firstly, according to the information of the attraction point and repulsion point of the current trajectory, an artificial potential field model is introduced to obtain the magnitude of the resultant force and the coordinates of the resultant force point with prior knowledge; then the magnitude of the resultant force and the coordinates of the resultant force point are correspondingly used as the first non-time series characteristic condition and the second non-time series characteristic condition, and the conditional recurrent neural network model and the time series characteristic are embedded to form mixed information to obtain the best predicted trajectory sequence of the ship. On the one hand, it solves the problem of insufficient training data in the actual track prediction application scenario; on the other hand, the important prior knowledge for track prediction is added to the time series prediction process, giving full play to the role of time series characteristics and non-time series characteristic conditions with prior knowledge in ship track prediction, and improving the accuracy and efficiency of ship track prediction in the face of complex marine environments.
Need to check novelty before this filing date? Find Prior Art