Exploring LLM Capabilities for Situational Understanding and COLREG compliance on real-world maritime navigation scenarios
Julius Wirbel, P. Nicholas Hansen, Line K. H. Clemmensen, Roberto Galeazzi
cs.AI, cs.RO
Submitted: 2026-08-08
Updated: 2026-08-11
Comments: Submitted and accepted to the IFAC WC 2026 as an invited session paper for track 7.2 Transportation and Vehicle Systems - Marine Systems
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
The gist: 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.
Terminology
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.
Sources
- Large Language Model-based Decision-making for COLREGs and the Control of Autonomous Surface Vehicles
- LLM4AD: Large Language Models for Autonomous Driving -- Concept, Review, Benchmark, Experiments, and Future Trends
- Space-LLaVA: a Vision-Language Model Adapted to Extraterrestrial Applications
- WOMD-Reasoning: A Large-Scale Dataset for Interaction Reasoning in Driving
- Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail
- DriveLM: Driving with Graph Visual Question Answering
- DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Driving
- HuggingFace's Transformers: State-of-the-art Natural Language Processing
Related papers
- MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
- Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
- Agents' Last Exam
- The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
- MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
- Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems