Exploring LLM Capabilities for Situational Understanding and COLREG compliance on real-world maritime navigation scenarios

• URL: http://arxiv.org/abs/2608.08281v1
• Date: Sat, 08 Aug 2026 18:23:26 GMT
• Title: Exploring LLM Capabilities for Situational Understanding and COLREG compliance on real-world maritime navigation scenarios
• Abstract summary: Large Language Models (LLMs) have shown considerable capability for situational understanding, reasoning, and decision making.<n>We construct a dataset consisting of 50 diverse, real-world navigation scenarios from AIS data, label scenarios with applicable COLREG rules, recommended actions, and the reasoning for the action.<n>The results indicate that the maritime navigation task remains difficult to solve without fine-tuning, even for larger online models.
• Score: 0.4199844472131921
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recently, Large Language Models (LLMs) have shown considerable capability for situational understanding, reasoning, and decision making in different domains, most notable in the automotive sector. Therefore, we explore current state-of-the-art LLMs as a tool for maritime navigation, which includes both codified rules in the Collision Regulations (COLREGs) and uncodified best practices summarized in the concept of ``Good Seamanship''. We construct a dataset consisting of 50 diverse, real-world navigation scenarios from AIS data, label scenarios with applicable COLREG rules, recommended actions, and the reasoning for the action. We explore a variety of different LLM architectures and sizes to determine their understanding of maritime navigation tasks as well as evaluate their reasoning capabilities in this domain. The results obtained indicate that the maritime navigation task remains difficult to solve without fine-tuning, even for larger online models.

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