Buchumschlag

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

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 regula...

Ausführliche Beschreibung

Gespeichert in:
Format: Online
Sprache:Englisch
Veröffentlicht: MDPI - Multidisciplinary Digital Publishing Institute 2023
Schlagworte:
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
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TR Transport technology and trades
Online-Zugang:ONIX_20230623_9783036574431_21
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Zusammenfassung: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.

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