Ship interactive game collision avoidance decision-making in a mixed traffic environment

To address the challenge of ineffective communication regarding avoidance intentions between autonomous ships （AS） and traditional manned ships (hereafter referred to as TS) in a mixed traffic environment, this study proposes an interactive collision avoidance decision-making method based on Stackelberg game (S-G) theory and Chain of Thought (COT). The aim is to enhance the interactive collision avoidance decision-making capabilities of ships operating in mixed environments.

First, ship collision avoidance scenarios in mixed environments are defined, and relevant research hypotheses are proposed. AS and TS ships are modeled using a leader-follower S-G game framework, with strategy spaces and payoff functions designed from a navigational practice perspective. Next, considering the interaction process between ships, a COT-game collision avoidance (COT-GCA) algorithm is developed, consisting of four sub-modules: state perception, intention sharing, strategy negotiation, and collision avoidance decision-making. Finally, the effectiveness of the proposed method is verified through experiments involving three-ship and four-ship encounter situations.

The experimental results demonstrate that ships in both groups can efficiently understand the avoidance intentions of other ships and successfully avoid collisions. The response time, steering range, and resumption of collision avoidance behavior exhibit timeliness, precision, 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 S-G model in solving ship interaction collision avoidance problems.

The proposed model and algorithm effectively enhance the interactive collision avoidance decision-making capabilities of ships in mixed environments, providing significant theoretical insights for future practical applications.