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

arXiv: https://arxiv.org/abs/2608.08281 · HTML: https://mlsanity.com/paper/2608.08281/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 13 references that are in the map.
- First posted on arXiv 8 Aug 2026.

## Builds on (most cited first, 8 of 8)

- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [DriveLM: Driving with Graph Visual Question Answering](https://mlsanity.com/paper/2312.14150/) (2023)
- [DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Driving](https://mlsanity.com/paper/2312.09245/) (2023)
- [Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail](https://mlsanity.com/paper/2511.00088/) (2025)
- [WOMD-Reasoning: A Large-Scale Dataset for Interaction Reasoning in Driving](https://mlsanity.com/paper/2407.04281/) (2024)
- [Space-LLaVA: a Vision-Language Model Adapted to Extraterrestrial Applications](https://mlsanity.com/paper/2408.05924/) (2024)
- [LLM4AD: Large Language Models for Autonomous Driving -- Concept, Review, Benchmark, Experiments, and Future Trends](https://mlsanity.com/paper/2410.15281/) (2024)
- [Large Language Model-based Decision-making for COLREGs and the Control of Autonomous Surface Vehicles](https://mlsanity.com/paper/2411.16587/) (2024)

## Abstract (arXiv)

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.