Research Paper#Maritime Autonomy, Vision-Language Models, Safety🔬 ResearchAnalyzed: Jan 3, 2026 09:27
Semantic Hazard Detection for Maritime Autonomy with Vision-Language Models
Published:Dec 30, 2025 21:20
•1 min read
•ArXiv
Analysis
This paper addresses a critical challenge in maritime autonomy: handling out-of-distribution situations that require semantic understanding. It proposes a novel approach using vision-language models (VLMs) to detect hazards and trigger safe fallback maneuvers, aligning with the requirements of the IMO MASS Code. The focus on a fast-slow anomaly pipeline and human-overridable fallback maneuvers is particularly important for ensuring safety during the alert-to-takeover gap. The paper's evaluation, including latency measurements, alignment with human consensus, and real-world field runs, provides strong evidence for the practicality and effectiveness of the proposed approach.
Key Takeaways
- •VLMs can provide semantic awareness for out-of-distribution situations in maritime autonomy.
- •A fast-slow anomaly pipeline with a short-horizon, human-overridable fallback maneuver is practical in the handover window.
- •The proposed "Semantic Lookout" approach demonstrates effectiveness in hazard detection and safe maneuver selection.
- •The approach aligns with the draft IMO MASS Code and operates within practical latency budgets.
Reference
“The paper introduces "Semantic Lookout", a camera-only, candidate-constrained vision-language model (VLM) fallback maneuver selector that selects one cautious action (or station-keeping) from water-valid, world-anchored trajectories under continuous human authority.”