An interpretable knowledge-based decision support method for ship collision avoidance using AIS data

Jinfen Zhang, Jiongjiong Liu*, Spyros Hirdaris, Mingyang Zhang, Wuliu Tian

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

2 Sitaatiot (Scopus)


AIS data include ship spatial-temporal and motion parameters which can be used to excavate the deep-seated information. In this article, an interpretable knowledge-based decision support method is established to guide the ship to make collision avoidance decisions with good seamanship and ordinary practice of seamen using AIS data. First, AIS data is preprocessed and trajectory reconstructed to restore the ship historical navigation state, and a ship encounter identification model is constructed according to the encounter characteristics; Second, two-stage collision avoidance behavior extraction algorithm is formed to build a behavior knowledge base, and the scenario similarity model is constructed to measure and match similar scenarios based on ship position, motion tendency and collision risk. Then, the Delaunay Triangulation Network is used to fuse ship trajectories of similar scenario to form the collision avoidance path. Finally, a case study is performed using the real AIS data outside Ningbo-Zhoushan Port waters, China, and the effectiveness of the planned path is verified by setting the head-on and crossing situations and comparison between the planned and real paths. Results indicate that the proposed model can extract the ship collision avoidance behavior accurately, and the planned path can ensure navigation safety.

JulkaisuReliability Engineering and System Safety
DOI - pysyväislinkit
TilaJulkaistu - helmik. 2023
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu


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