Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships—2nd Edition
A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Ocean Engineering".
Deadline for manuscript submissions: closed (10 May 2024) | Viewed by 9865
Special Issue Editors
Interests: nautical traffic safety and simulation; artificial intelligence and its applications in maritime; maritime autonomous surface ships
Special Issues, Collections and Topics in MDPI journals
Interests: simulation; maritime systems; transportation; navigation
Special Issues, Collections and Topics in MDPI journals
Interests: safety and systems engineering; risk analysis; maritime safety; winter navigation; autonomous ships
Special Issues, Collections and Topics in MDPI journals
Interests: human–machine cooperation; autonomous ships; ship collision avoidance; maritime traffic management
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Understanding, modeling, and predicting ship behaviors are fundamental and essential issues for planning, controlling, and operating different levels of Maritime Autonomous Surface Ships (MASS). The maritime traffic data (e.g., radar data, AIS data, CCTV data, etc.) provide designers, officers on watch, and traffic operators with extensive information about the states of ships at present and in history, which are the treasure for behavior analysis. Additionally, navigation rules and regulations (i.e., knowledge) offer valuable prior knowledge about ship manners at sea. Combining multi-source heterogeneous big data and artificial intelligence techniques inspire innovative and important means for the development of MASS, leading to smart, safe, green, and efficient shipping.
With the successful Special Issue ”Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships” in 2021, this special issue is continuing to provide an excellent medium to present the latest developments on methods and tools suitable for relevant issues, including but not limited to:
- Data-driven behavior modeling and simulation
- Knowledge-driven behavior modeling and reasoning
- Multi-source heterogeneous traffic data fusion
- Semantic analysis of ship behaviors
- Quantifying COLREGs and seamanship for machine
- Inference engine and ontology reasoning for rule-compliant MASS
- Intention Inference based on behavior observations
- Maritime traffic situational awareness
- Multi-agent simulation
- Risk analysis and management of MASS
- Safety and Cyber Security of MASS
Prof. Dr. Yuanqiao Wen
Prof. Dr. Axel Hahn
Dr. Osiris Valdez Banda
Prof. Dr. Yamin Huang
Guest Editors
Manuscript Submission Information
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Keywords
- maritime autonomous surface ships (MASS)
- data-driven modeling
- knowledge-driven modeling
- behavior modeling
- knowledge graph
- navigation simulation
- multi-source heterogeneous data analysis
- nautical safety
- quantifying rules and regulations
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