Data and code underlying chapters 3-5 of the PhD thesis: Human-MASS Interaction in Decision-Making for Safety and Efficiency in Mixed Waterborne Transport Systems
DOI: 10.4121/2311d80d-fb88-420d-bd66-4019207fdb5d
Datacite citation style
Dataset
This dataset supports the doctoral research of Rongxin Song, M.Sc., at Delft University of Technology (2021–2025), focusing on enhancing maritime situational awareness, collision avoidance, and human-MASS (Maritime Autonomous Surface Ships) interaction. It includes AIS (Automatic Identification System) data from the Rotterdam area (spanning 51.897°–51.913° N, 4.411°–4.425° E and 51.833°–52.167° N, 3.167°–4° E) collected between 1 and 15 October 2023. The dataset also contains Python scripts for DWA-based path planning and trajectory prediction, MATLAB scripts for modelling and visualizing trust dynamics, and supporting files in .csv, .png, .py, and .m formats. The research integrates ontology-driven knowledge maps, machine learning for preference-aware ship navigation, and trust behaviour analysis to address challenges in mixed waterborne transport system. This dataset provides a structured resource for replicating experiments in dynamic maritime environments, with a README file included for usage guidance.
History
- 2025-01-15 first online, published, posted
Publisher
4TU.ResearchDataFormat
.png; .py; .jpgAssociated peer-reviewed publication
Integrating situation-aware knowledge maps and dynamic window approach for safe path planning by maritime autonomous surface shipsFunding
- China Scholarship Council
Organizations
TU Delft, Faculty of Technology, Policy and Management, Department of Values, Technology and InnovationDATA
Files (1)
- 6,777,476 bytesMD5:
b9cc4b2296d40c02a6210499bc368377
thesis-data-code.zip