Abstract
According to the statistics of maritime accidents, most collision accidents have been caused by human factors. In an encounter situation, the prediction of ship’s trajectory is a good way to notice the intention of the other ship. This paper proposes a methodology for predicting the ship’s trajectory that can be used for an intelligent collision avoidance algorithm at sea. To improve the prediction performance, the density-based spatial clustering of applications with noise (DBSCAN) has been used to recognize the pattern of the ship trajectory. Since the DBSCAN is a clustering algorithm based on the density of data points, it has limitations in clustering the trajectories with nonlinear curves. Thus, we applied the spectral clustering method that can reflect a similarity between individual trajectories. The similarity measured by the longest common subsequence (LCSS) distance. Based on the clustering results, the prediction model of ship trajectory was developed using the bidirectional long short-term memory (Bi-LSTM). Moreover, the performance of the proposed model was compared with that of the long short-term memory (LSTM) model and the gated recurrent unit (GRU) model. The input data was obtained by preprocessing techniques such as filtering, grouping, and interpolation of the automatic identification system (AIS) data. As a result of the experiment, the prediction accuracy of Bi-LSTM was found to be the highest compared to that of LSTM and GRU.
Highlights
According to statistics compiled by Korea Maritime Safety Tribunal (KMST), a total of 13,687 marine accidents occurred over the last five years (2016–2020), and among them, collision accidents between ships account for 45% of the total, which account for the largest proportion [1]
This situation is due to the erroneous judgment of the risk of collision despite the provision of the navigation information from the radio detection and ranging (RADAR), electric chart display and information system (ECDIS), and the automatic identification system (AIS)
The purpose of this study is to propose the methodology of ship trajectory prediction that can be used for the intelligent ship collision avoidance algorithm
Summary
According to statistics compiled by Korea Maritime Safety Tribunal (KMST), a total of 13,687 marine accidents occurred over the last five years (2016–2020), and among them, collision accidents between ships account for 45% of the total, which account for the largest proportion [1]. The trajectory prediction based on such a neural network can derive relatively accurate results through the learning process of the observed data of parameters of the ship navigation without applying the hydrodynamic model of the ship or collecting the accurate disturbance data at sea. Since the trajectory of a ship is a nonlinear curve composed of several position data, the clustering algorithm based on the density of the data point has limitations in clustering the ship trajectory To solve this problem, it is necessary to apply the spectral clustering that can reflect the similarity between individual trajectories on the result. The remainder of this article is organized as follows: Section 2 proposes the method for predicting the ship trajectory using the spectral clustering and the extended RNNs. Section 3 shows the results of the experiment using AIS data.
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