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

• Autres auteurs: Wen, Yuanqiao (Éditeur intellectuel), Hahn, Axel (Éditeur intellectuel), Valdez Banda, Osiris (Éditeur intellectuel), Huang, Yamin (Éditeur intellectuel)
• Format: Électronique Chapitre de livre
• Langue: anglais
• Publié: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
• Sujets: Technology: general issues; History of engineering & technology; Transport technology & trades; 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; 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; ship manoeuvrability; 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; cognitive space; multi-scale analysis; ontology; n/a
• Accès en ligne: DOAB: download the publication; DOAB: description of the publication

Résumé

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.

Description matérielle

1 electronic resource (262 p.)

ISBN

books978-3-0365-7442-4
9783036574431
9783036574424

Accès

Open Access