AI Meets Maritime Training: Precision Analytics for Enhanced Safety and Performance

2025-07-01

AI Summary

Traditional maritime simulation training relies on subjective instructor assessments, suffering from poor objectivity, difficulty in quantifying critical behavioral features, and cognitive biases. To address these limitations, this study proposes the first multimodal AI evaluation framework specifically designed for maritime scenarios. It integrates eye-tracking (including pupillary dilation analysis), vessel-domain–adapted speech recognition, large language model–driven verification of communication compliance, and pitch-frequency–based acoustic stress detection to simultaneously quantify operational attention, communication quality, and physiological stress levels. In simulated navigation tasks, the framework achieves 92%, 91%, and 90% accuracy in visual behavior detection, speech recognition, and stress identification, respectively—significantly outperforming baseline methods. By overcoming the constraints of subjective assessment, the framework enables real-time, personalized feedback and training efficacy optimization. It establishes a novel, interpretable, and verifiable paradigm for intelligent evaluation in high-risk maritime training.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Traditional simulator-based training for maritime professionals is critical for ensuring safety at sea but often depends on subjective trainer assessments of technical skills, behavioral focus, communication, and body language, posing challenges such as subjectivity, difficulty in measuring key features, and cognitive limitations. Addressing these issues, this study develops an AI-driven framework to enhance maritime training by objectively assessing trainee performance through visual focus tracking, speech recognition, and stress detection, improving readiness for high-risk scenarios. The system integrates AI techniques, including visual focus determination using eye tracking, pupil dilation analysis, and computer vision; communication analysis through a maritime-specific speech-to-text model and natural language processing; communication correctness using large language models; and mental stress detection via vocal pitch. Models were evaluated on data from simulated maritime scenarios with seafarers exposed to controlled high-stress events. The AI algorithms achieved high accuracy, with ~92% for visual detection, ~91% for maritime speech recognition, and ~90% for stress detection, surpassing existing benchmarks. The system provides insights into visual attention, adherence to communication checklists, and stress levels under demanding conditions. This study demonstrates how AI can transform maritime training by delivering objective performance analytics, enabling personalized feedback, and improving preparedness for real-world operational challenges.

Problem

• Objective assessment of maritime trainee performance
• Overcoming subjectivity in traditional training evaluations
• Enhancing safety through AI-driven analytics

Innovation

• AI-driven visual focus and pupil tracking
• Maritime-specific speech recognition and NLP
• Vocal pitch analysis for stress detection