A Multiple-Output Hybrid Ship Trajectory Predictor With Consideration for Future Command Assumption

Abstract

Onboard sensors contribute to data-driven understanding of complex and nonlinear ship dynamics in real time. By using sensors, precise ship trajectory prediction plays a key role in intelligent collision avoidance. A hybrid predictor makes prediction based on a mathematical model of which error is compensated by a black-box model. A Multiple-output Hybrid Predictor (MHP), which makes a long-horizon prediction at a time based on onboard sensor data, was developed in the previous study. However, it can not handle a time series of future command assumption. This limitation hinders an MHP from being applied to the evaluation of future command assumption in the predictive decision making. A novel architecture of MHP presented in this study converts a long time series of future command assumption into a fixed-length model-based-predicted vessel state; then, it is included in inputs of a black-box error compensator. This idea is robust to multidimensionality of commands and long control horizon. Assuming a low-fidelity vessel model and a limited data are available, simulation experiments are conducted. The effect of environmental disturbances and maneuverings on the prediction performance is examined comprehensively for the first time. The present study successfully incorporates a long time series of future command assumption in an MHP. It reduces the mean error by 81.8% compared to a model-based predictor and by 45.6% compared to a data-driven predictor under Beaufort wind force scale 4 wave, wind, and ocean current. Present study expands the application of MHP to the predictive decision making of future autonomous ships.

Artificial Intelligence in Ship Trajectory Prediction

“…Additionally, it assisted in producing the best traffic orders for Yangtze River traffic management. In order to achieve ship trajectory smoothing and prediction, Zhao et al [53] proposed an ensemble machine learning framework to predict the tendency of ship trajectory fluctuation and eliminate outliers from raw AIS data (see Figure 9a). To suppress the AIS data outliers, the ensemble framework used the empirical mode decomposition (EMD) model to split the raw AIS data into low-and high-frequency components.…”

Knowledge and data in cooperative modeling: Case studies on ship trajectory prediction

“…As explained in Section 2, in the field of ship dynamics, previous studies have presented different types of cooperative models combining physics-based and data-driven models. In this study, we employ a geometry-based cooperative model, that makes a data-driven compensation for multiple-step-ahead position errors made by the physicsbased model, based on the idea presented in Skulstad et al (2021a) and Kanazawa et al (2021).…”

Research into Ship Trajectory Prediction Based on An Improved LSTM Network

“…An attention-based aggregation layer is used to connect the encoder and decoder networks, which more effectively captures spatiotemporal dependencies in the data, resulting in improved prediction accuracy. Kanazawa M. et al [12] proposed a ship trajectory prediction method based on a multi-output hybrid predictor (MHP). The method utilizes onboard sensor data and combines black box error compensation with an LSTM neural network to form a multi-output hybrid prediction method, which can predict ship positions 30 s after.…”