Generating Realistic, Protocol-Compliant Maritime Radio Dialogues using Self-Instruct and Low-Rank Adaptation
G\"ursel Akdeniz, Emin Cagatay Nakilcioglu

TL;DR
This paper presents a novel AI methodology for generating realistic, compliant maritime radio dialogues to improve safety, using self-instruct techniques, low-rank adaptation, and a multi-faceted verification pipeline.
Contribution
It introduces a compliance-aware self-instruct approach with a verification pipeline and LORA fine-tuning for generating realistic maritime dialogues, addressing data scarcity and regulatory constraints.
Findings
Produces diverse, compliant maritime dialogues
Enforces entity accuracy and linguistic diversity
Demonstrates effectiveness on public maritime datasets
Abstract
VHF radio miscommunication remains a major safety risk in maritime operations, with human factors accounting for over 58% of recorded incidents in Europe between 2014 and 2023. Despite decades of operational use, VHF radio communications are still prone to noise, interference, linguistic variability, and the absence of real-time transcription, making procedural errors both frequent and difficult to correct. Developing AI-assisted systems to support real-time communication and decision-making requires a considerable amount of high-quality maritime data, yet operational, regulatory, and privacy constraints render such datasets scarce. This study introduces a compliance aware Self-Instruct methodology for generating realistic maritime radio dialogues that conform to the IMO's SMCP. Our approach integrates a 26-filter verification pipeline directly into the iterative generation loop to…
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Taxonomy
TopicsMaritime Navigation and Safety · Wireless Signal Modulation Classification · Underwater Vehicles and Communication Systems
