Objective To address the challenge of ineffective communication of avoidance intentions between autonomous ships and traditional manned ships in a heterogeneous traffic environment, this study proposes an interactive collision avoidance decision-making method based on Stackelberg game and Chain of Thought（COT）, aiming to enhance the interactive collision avoidance decision-making capabilities of ships in heterogeneous traffic environments.

Methods First, the ship collision avoidance scenarios in mixed environments are defined, and research hypotheses are proposed. Autonomous ships and traditional manned ships are modeled as a leader-follower Stackelberg game model, with strategy spaces and payoff functions designed from a navigational practice perspective. Subsequently, considering the interaction process between ships, a COT-based interactive collision avoidance decision algorithm （COT-GCA）is designed, comprising four sub-modules: state perception, intention sharing, strategy negotiation, and collision avoidance decision. Finally, the effectiveness of the proposed method is verified through experiments involving three-ship and four-ship encounter situations.

Results The experimental results demonstrate that ships in both groups can efficiently understand the avoidance intentions of other ships and safely avoid collisions. The response, steering range, and resumption of collision avoidance behavior exhibit earliness, sharpness, and stability. The average output efficiency evaluation values before and after decision-making, calculated using the decision unit evaluation method, are 1 and 0.993, respectively, indicating the high efficiency of the game model in solving ship interaction collision avoidance problems.

Conclusions The proposed model and algorithm effectively enhance the interactive collision avoidance decision-making capabilities of ships in heterogeneous traffic environments, providing theoretical significance for future practical applications.