UPDATE: Zenodo migration postponed to Oct 13 from 06:00-08:00 UTC. Read the announcement.

Conference paper Open Access

Combined AI Capabilities for Enhancing Maritime Safety in a Common Information Sharing Environment

Paladin Zdravko; Kapidani Nexhat; Lukšić Žarko; Mihailović Andrej; Scrima Piero; Jacobé de Naurois Charlotte; Laudy Claire; Rizogiannis Constantinos; Astyakopoulos Alkiviadis; Blum Alexis

The complexity of maritime traffic operations indicates an unprecedented necessity for joint introduction and exploitation of artificial intelligence (AI) technologies, that take advantage of the vast amount of vessels’ data, offered by disparate surveillance systems to face challenges at sea. This paper reviews the recent Big Data and AI technology implementations for enhancing the maritime safety level in the common information sharing environment (CISE) of the maritime agencies, including vessel behavior and anomaly monitoring, and ship collision risk assessment. Specifically, the trajectory fusion implemented with InSyTo module for soft information fusion and management toolbox, and the Early Notification module for Vessel Collision are presented within EFFECTOR Project. The focus is to elaborate technical architecture features of these modules and combined AI capabilities for achieving the desired interoperability and complementarity between maritime systems, aiming to provide better decision support and proper information to be distributed among CISE maritime safety stakeholders.

Files (634.3 kB)
Name Size
Paladin et al_Bled Conference Paper_Combining AI for mar. safety in CISE_v3.1.pdf
md5:b584a93062d9cab2dbeea56477dff7b2
634.3 kB Download
30
32
views
downloads
Views 30
Downloads 32
Data volume 20.3 MB
Unique views 22
Unique downloads 25

Share

Cite as