A novel decision support methodology for oceangoing vessel collision avoidance

Mizythras, Panagiotis; Pollalis, Christos; Boulougouris, Evangelos; Theotokatos, Gerasimos

doi:10.1016/j.oceaneng.2021.109004

Cited by 16 publications

(2 citation statements)

References 83 publications

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“…The VesselAI components capitalize the amount of data that are generated from the maritime vessels (such as vessel sensor data, autonomous vessels data, AIS data) and combine them with data originating from weather & meteorological databases, satellite images, oceanographic data, and port data to support the machine and deep learning models training (Mouzakitis et al, 2023 ). The data management and data fusion mechanisms of VesselAI (Herodotou et al, 2020 ) support the inference functionalities in a wide range of use cases under different perspectives like the vessel traffic management (van Westrenen, 2014 ), vessel manoeuvering (Gil et al, 2020 ), fuel consumption optimization (Yan et al, 2021 ), collision avoidance (Mizythras et al, 2021 ) and other plethora of scenarios that will be extended under front-end applications utilized by maritime stakeholders. In general, VesselAI is an AI-oriented solution that tackles data processing and computational problems by leveraging state-of-the art HPC technologies, real time analytics and cloud technologies for creating data driven digital twins that offer (a) ship modeling for global vessel traffic monitoring and management, (b) optimal design of ship energy systems, (c) autonomous ships management in sea transport, and (d) weather routing and fleet intelligence.…”

Section: Vesselai Concept and Architecturementioning

confidence: 99%

Leveraging extreme scale analytics, AI and digital twins for maritime digitalization: the VesselAI architecture

Ilias¹,

Tsapelas²,

Kapsalis³

et al. 2023

Front. Big Data

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The modern maritime industry is producing data at an unprecedented rate. The capturing and processing of such data is integral to create added value for maritime companies and other maritime stakeholders, but their true potential can only be unlocked by innovative technologies such as extreme-scale analytics, AI, and digital twins, given that existing systems and traditional approaches are unable to effectively collect, store, and process big data. Such innovative systems are not only projected to effectively deal with maritime big data but to also create various tools that can assist maritime companies, in an evolving and complex environment that requires maritime vessels to increase their overall safety and performance and reduce their consumption and emissions. An integral challenge for developing these next-generation maritime applications lies in effectively combining and incorporating the aforementioned innovative technologies in an integrated system. Under this context, the current paper presents the architecture of VesselAI, an EU-funded project that aims to develop, validate, and demonstrate a novel holistic framework based on a combination of the state-of-the-art HPC, Big Data and AI technologies, capable of performing extreme-scale and distributed analytics for fuelling the next-generation digital twins in maritime applications and beyond.

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Section: Vesselai Concept and Architecturementioning

confidence: 99%

Leveraging extreme scale analytics, AI and digital twins for maritime digitalization: the VesselAI architecture

Ilias¹,

Tsapelas²,

Kapsalis³

et al. 2023

Front. Big Data

View full text Add to dashboard Cite

show abstract

“…However, a bulk of currently available AIS data are only used for the normal navigation without explicitly revealing more complex navigation details including the encountering situations. Currently, there are lots of publications on the collision avoidance based on various methods such as velocity obstacle (Yuan et al, 2021), decision-making (Zheng et al;Mizythras et al, 2021;Gao and Shi, 2020). These methods are mainly on the global situational awareness and they do not contain any two ship's encounter situation strategy.…”

Section: Introductionmentioning

confidence: 99%