The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/2311d80d-fb88-420d-bd66-4019207fdb5d

Song, Rongxin (2025): 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. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/2311d80d-fb88-420d-bd66-4019207fdb5d.v1

Categories

• Water Transport
• Transport

Keywords

Collision avoidance Human preferences Human trust Situational awareness

Licence

CC BY-NC-SA 4.0

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by Rongxin Song

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.ResearchData

Format

.png; .py; .jpg

Associated peer-reviewed publication

Integrating situation-aware knowledge maps and dynamic window approach for safe path planning by maritime autonomous surface ships

References

• https://doi.org/10.1016/j.oceaneng.2024.118882

Funding

• China Scholarship Council

Organizations

TU Delft, Faculty of Technology, Policy and Management, Department of Values, Technology and Innovation