Objective To address the complex problem of cooperative collision avoidance among multiple intelligent ships in intersection waters, this paper proposes a decision-making method for multi-ship cooperative collision avoidance driven by enhanced large language models (LLMs).

Method By analyzing the navigational characteristics of intersection waters, this study formulates the multi-ship cooperative collision-avoidance problem as a partially observable Markov decision process (POMDP), thereby providing a formal mathematical foundation for decision-making. The cooperative process is decomposed into four causally linked modules—state perception, intent sharing, conflict coordination, and avoidance decision—to structure both information flow and reasoning. Guided by the embodied practices of human seafarers, a novel central-distributed dual-layer architecture is proposed. At the central layer, an LLM-based coordinator aggregates multi-ship situational data, applicable navigation rules, and conflict severity metrics to infer passage-priority sequences. At the distributed layer, individual ship agents leverage LLMs in combination with chain-of-thought prompt engineering to perform progressive, stepwise reasoning. These agents synthesize structured scene descriptions, coordination directives and retrieved navigation experience to generate executable avoidance maneuvers along with accompanying semantic explanations. To address known limitations of LLMs in precise numerical computation and continual learning, and to reduce potential hallucinations, the architecture incorporates two complementary augmentation mechanisms. A lightweight mathematical engine is employed to update kinematic states and compute deterministic conflict metrics, providing rigorous quantitative inputs to the reasoning pipeline. A retrieval-augmented generation (RAG) navigation knowledge base integrates a static corpus of navigation rules with a dynamic repository of historical scene−decision−evaluation tuples, enabling case-based grounding and continuous learning from past interactions. By embedding formal computation and evidence-based verification into the LLM reasoning loop, the proposed framework preserves the interpretive and interactive strengths of large models while ensuring verifiable, rule-compliant, and practically executable collision-avoidance decisions in complex intersection waters.

Results Simulation experiments demonstrate that the proposed enhanced LLM-driven method, implemented in DeepSeek-v3, achieves safe and efficient cooperative collision avoidance in typical intersection scenarios involving two, three, and four ships. The system maintains a minimum maneuvering speed exceeding 3 knots throughout the simulations and ensures a safety margin greater than twice the ship length.

Conclusion This method advances the engineering application of LLMs in maritime decision-making and provides a new pathway for realizing highly autonomous shipboard artificial intelligence in complex operational environments.