Ship regulatory method for maritime mixed traffic scenarios based on key risk ship identification

With the development of intelligent ships, ships with multiple driving modes will exist in maritime traffic scenarios. The traditional methods of ship regulation, which rely on factors such as ship size, speed, and heading to identify navigation risks, are not able to accurately assess the navigation risks between ships of different autonomy levels in the context of mixed maritime traffic. The impact of a ship's autonomy level on navigation risks should also be taken into consideration. Therefore, this paper proposes a key risk ship identification method that considers the autonomy level of ships to assist in ship regulation. The method utilizes the OPTICS algorithm to cluster areas with high ship density and employs an improved Potential Risk Ship Domain (PRSD) approach to determine the Potential Collision Risk (PCR) for different autonomy-level ships, thereby constructing a directed complex network. The centrality characteristics of the network are used to identify key risk ships. Through empirical validation, the proposed method is shown to effectively determine the importance ranking of different autonomy-level ships in maritime mixed traffic scenarios. This method can provide support for regulatory agencies in the supervision and scheduling of ships in mixed scenarios.