An interpretable data-driven method for ship collision avoidance decision-making research

Marine officers’ experience with ship handling is crucial for ensuring safe navigation. The data-driven methods can learn from the experience contained in historical automatic identification system data, thereby providing avoidance decision-making for ships in real-time scenarios. This paper proposes a novel data-driven method for ship collision avoidance decision-making research that includes three interpretable models. First, this method develops a model to match the real-time scenario with similar historical ones, thereby facilitating learning from the experience in such scenarios. Second, an avoidance path generation model is constructed to generate multiple avoidance paths for the real-time scenario. Third, an avoidance path optimization model is established to generate an optimal avoidance path that meets safety requirements and conforms to ship maneuverability. Subsequently, the waterway at the entrance of the Yangtze River is selected as the experimental area, and a real-time scenario within this waterway is used to verify the feasibility of the proposed method. The results demonstrate that the method can effectively generate an optimal avoidance path for the real-time scenario. This research can provide valuable insights for the development of intelligent decision-making systems for smart ships.