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

• A two-stage collision avoidance behavior extraction algorithm is constructed to obtain the collision avoidance scheme, which can reduce the length of the comparison motion parameters from the whole trajectory to some trajectory segments, so as to reduce the calculation burden and enhance the accuracy. • A novel path planning method is established by fusion the collision avoidance trajectory in similar scenarios, which can take into account good seamanship from officers’ experience and ordinary practice of seaman. 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, a 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.