Data-driven knowledge discovery and situational awareness analysis for maritime autonomous surface ships

Li, H ORCID: 0000-0002-4293-4763 (2025) Data-driven knowledge discovery and situational awareness analysis for maritime autonomous surface ships. Doctoral thesis, Liverpool John Moores University.

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Abstract

The advancement of Maritime Autonomous Surface Ships (MASS) relies heavily on data-driven knowledge discovery and situational awareness to ensure safe and efficient navigation. This PhD by Published Works comprises five peer-reviewed journal papers, contributing to the integration of Artificial Intelligence (AI), machine learning, deep learning, and Bayesian Network (BN) into key areas of maritime safety and security. The research aims to enhance risk assessment, piracy incident analysis, ship trajectory prediction, and autonomous route planning, forming a cohesive framework for intelligent decision-making in MASS operations. The first contribution focuses on data-driven risk analysis of global maritime accidents, employing a data-driven BN model to quantify accident risks, identify critical factors, and enhance safety management strategies. The second contribution addresses spatio-temporal piracy pattern analysis, utilising AI-driven methodologies to detect high-risk zones and extract patterns of piracy incidents, thereby improving security measures and navigational decision-making for MASS. The third and fourth contributions focus on ship trajectory prediction and route planning, which are crucial for autonomous navigation. A systematic review of machine learning and deep learning-based trajectory prediction methods highlights the effectiveness of data-driven models over traditional approaches. Further, an unsupervised route planning model integrates Automatic Identification System (AIS) data and machine learning techniques, enabling autonomous vessels to adapt dynamically to changing maritime conditions. The final contribution refines AIS-based trajectory prediction frameworks, leveraging machine learning methodologies to improve predictive accuracy and enhance collision avoidance capabilities. By synthesising insights from these studies, this research establishes a holistic framework for data-driven knowledge discovery and situational awareness in MASS. The integration of this research content and outcomes ensures that autonomous vessels can navigate safely, mitigate operational risks, and enhance adaptability to evolving maritime conditions. This study advances AI-driven autonomous navigation by bridging data-driven knowledge discovery with real-time maritime decision-making, providing a resilient and adaptive foundation for next-generation MASS technology.

Item Type:	Thesis (Doctoral)
Uncontrolled Keywords:	Data-driven knowledge discovery; Machine learning; Maritime Autonomous Surface Ships (MASS); Maritime safety; Situational awareness
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Engineering
Date of acceptance:	1 November 2025
Date of first compliant Open Access:	5 November 2025
Date Deposited:	05 Nov 2025 12:09
Last Modified:	05 Nov 2025 12:09
DOI or ID number:	10.24377/LJMU.t.00027474
Supervisors:	Yang, Z and Matthews, C
URI:	https://researchonline.ljmu.ac.uk/id/eprint/27474

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