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Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships

Edited by
May 2023
262 pages
  • ISBN978-3-0365-7443-1 (Hardback)
  • ISBN978-3-0365-7442-4 (PDF)
https://doi.org/10.3390/books978-3-0365-7442-4
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This book is a reprint of the Special Issue Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships that was published in

Engineering
Environmental & Earth Sciences
Summary

Maritime traffic data (e.g., radar data, AIS data, and CCTV data) provide designers, officers on watch, and traffic operators with extensive information about the states of ships at present and in history, representing a treasure trove for behavior analysis. Additionally, navigation rules and regulations (i.e., knowledge) offer valuable prior knowledge about ship manners at sea. Combining multisource heterogeneous big data and artificial intelligence techniques inspires innovative and important means for the development of MASS. This reprint collects twelve contributions published in “Data-/Knowledge-Driven Behavior Analysis of Maritime Autonomous Surface Ships” Special Issue during 2021–2022, aiming to provide new views on data-/knowledge-driven analytical tools for maritime autonomous surface ships, including data-driven behavior modeling, knowledge-driven behavior modeling, multisource heterogeneous traffic data fusion, risk analysis and management of MASS, etc.

Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
unmanned surface vehicle; velocity obstacle; collision avoidance; obstacles classification; fuzzy rules; mixed waterborne traffic; ship behavior; ship autonomy; information perception; intelligent decision-making; execution; COLREGs; ship object; ship behavior; formal expression; complex waters; ship traffic flow; spatiotemporal dependence; gate recurrent unit; motion planning; unmanned surface vehicle (USV); effects of wind and current; regularization-trajectory cell; inland waterway transportation; AIS data; trajectory classification; clustering; deep convolutional neural network; ship intention identification; AIS; RANSAC; Bayesian framework; YOLO; intersection; maritime autonomous surface ships; hybrid causal logic; preliminary hazard analysis; risk assessment; hazard identification; autonomous ship; collision avoidance; ship manoeuvrability; velocity obstacle; deduction of the manoeuvring process; ship exhaust behavior; detection and tracking; multi-sensor; deep learning; morphological operation; collision alert system (CAS); available maneuvering margins (AMM); ship domain; ship stability; maritime safety; semantic modeling; ship behavior; cognitive space; multi-scale analysis; ontology; n/a; n/a