AIS Data-Based Hybrid Predictor for Short-Term Ship Trajectory Prediction Considering Uncertainties

To decrease the risk of collisions and ensure safe navigation, an Automatic Identification System (AIS) was developed to broadcast real-time ship states information, such as ship position and sailing speed. Due to the real-time characteristic of AIS data, it, hence, has been widely used to construct a data-driven model for ship trajectory prediction, which can provide onboard decision support for navigators. However, data provided by AIS is sometimes partially missing or simply wrong. Poor quality of AIS data can degrade the performance of data-driven models and lead to large uncertainty in the predicted ship positions. Therefore, this paper proposes a hybrid model to reduce uncertainty by integrating the multi-output Gaussian process (MOGP) model predictions and historical trajectory information. Historical trajectories provide useful prior knowledge to calibrate the performance of MOGP model predictions. This model is built through three steps: 1) extracting historical ship trajectory information from the route that a ship is following; 2) predicting ship positions with a data-driven predictor built by an MOGP; and 3) obtaining a trajectory by knowledge fusion of historical information and predicted results. The experiment shows the proposed hybrid model outperforms other data-driven models and has small errors and uncertainty quantification of the ship position prediction. Especially for the 6-minute position prediction, its RMSE is over 100 meters smaller than other methods.