Collision Avoidance Situation Matching with Vessel Maneuvering Actions Identification from Vessel Trajectories

Abstract

Vessel trajectories implied in AIS data are crucial to obtain a good understanding of the maritime traffic situation for shipping safety. Starting from raw AIS data, a trajectory database is created for vessels within surveillance area after parsing, noise reduction, and DBSCAN clustering. With mmsi as the key index, the trajectory for each vessel is extracted ordering by timestamp. To remove the time interval difference between points in trajectories, interpolation and cleaning are carried out on each vessel trajectory to get trajectories with equal time intervals. Through implied motion pattern computation between adjacent points in each trajectory, maneuvering actions can be identified. Then, sailing segments with continuous same maneuvering actions are merged. With sailing segments partition results, critical points are extracted for already known different collision avoidance situations. Trajectory similarity computation for different vessels are computed with our new multi-scale and multi-resolution trajectory matching method. Experiments for the recognition of collision avoidance situations show that the adoption of the matching algorithm with multi-scale and multi-resolution trajectories for different vessel pairs to complete collision avoidance situations analysis is effective and achieves good performance.

CNN-Based Vessel Meeting Knowledge Discovery From AIS Vessel Trajectories

“…Here, a represents the number of test tracks correctly assigned to category c, b represents the number of test tracks incorrectly assigned to category c, and j represents the number of test tracks incorrectly assigned to category. The comparison result between the LCS model in Chen et al (2019), HSSVM in Chen et al (2018) and our method is shown in Figure 29. Clearly, the performance of our Here, it is assumed that the calculation amount of each layer is concentrated in conv2d, M and N are the number of input and output channels respectively, K is the size of convolution kernel, and H and W are the space size of output feature map respectively.…”

“…Based on exploring maritime trajectory data for anomalous behavior detection, Lei (2016) proposed a so-called MT-MAD framework for maritime trajectory modeling and anomaly detection. For vessel motion pattern recognition, Chen et al (2019) put forward a method to classify collision avoidance situations, and Sun et al (2018) tried to mine spatial-temporal motion patterns for vessel recognition. There are also similar studies in Le et al (2016), Yan et al (2020), andFu et al (2017).…”