Summary
SafeNav’s ambition is to develop and test a highly innovative digital collision prevention solution that will significantly reduce the probability of collisions, impact damage, grounding, and contribute to safer navigation by a) faster reliable real-time detection of a variety of obstacles (other vessels, fixed installations, submerged/semi-submerged objects, and marine mammals) in the marine environment, using data from state-of-the-art sensors and other relevant sources, and b) effective visual representation of the multi-source data to the navigators for quick COLREG-based decision-making support.
To this end, SafeNav unites 10 key partners from the maritime industry and academia, including renowned SMEs, R&D institutes and universities to address the ‘Navigational Accidents’ aspect of the work programme . We will design collision avoidance algorithms built on multi-sensory data input from propriety (LADARTM sensor suite) and off-the-shelf sensors already installed on vessels, extensive statistics of navigational accidents, and other sources (AIS and route exchange services) to create a holistic decision support system (DSS). Processed information from the automatic DSS will feed into SafeNav collision-avoidance algorithms and generate real-time COLREGs-compliant suggestions for the navigator when an obstacle is detected. This reduces pressure on navigators onboard, providing them with efficient decision-making aid and access to visual navigation data on a single graphical user-interface. Sensors will also be used for container tracking, and mathematical models will predict container drift trajectory, transmitting collected data to a SafeNav Navigational Hazard Database available to nearby vessels/stakeholders, facilitating the recovery of lost containers. Moreover, we propose to prevent vessel collisions with cetaceans with optimal-tuned pingers to alert them of an approaching vessels.
To this end, SafeNav unites 10 key partners from the maritime industry and academia, including renowned SMEs, R&D institutes and universities to address the ‘Navigational Accidents’ aspect of the work programme . We will design collision avoidance algorithms built on multi-sensory data input from propriety (LADARTM sensor suite) and off-the-shelf sensors already installed on vessels, extensive statistics of navigational accidents, and other sources (AIS and route exchange services) to create a holistic decision support system (DSS). Processed information from the automatic DSS will feed into SafeNav collision-avoidance algorithms and generate real-time COLREGs-compliant suggestions for the navigator when an obstacle is detected. This reduces pressure on navigators onboard, providing them with efficient decision-making aid and access to visual navigation data on a single graphical user-interface. Sensors will also be used for container tracking, and mathematical models will predict container drift trajectory, transmitting collected data to a SafeNav Navigational Hazard Database available to nearby vessels/stakeholders, facilitating the recovery of lost containers. Moreover, we propose to prevent vessel collisions with cetaceans with optimal-tuned pingers to alert them of an approaching vessels.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101077026 |
| Start date: | 01-09-2022 |
| End date: | 31-08-2025 |
| Total budget - Public funding: | 5 762 623,75 Euro - 4 424 871,00 Euro |
Cordis data
Original description
SafeNav’s ambition is to develop and test a highly innovative digital collision prevention solution that will significantly reduce the probability of collisions, impact damage, grounding, and contribute to safer navigation by a) faster reliable real-time detection of a variety of obstacles (other vessels, fixed installations, submerged/semi-submerged objects, and marine mammals) in the marine environment, using data from state-of-the-art sensors and other relevant sources, and b) effective visual representation of the multi-source data to the navigators for quick COLREG-based decision-making support.To this end, SafeNav unites 10 key partners from the maritime industry and academia, including renowned SMEs, R&D institutes and universities to address the ‘Navigational Accidents’ aspect of the work programme . We will design collision avoidance algorithms built on multi-sensory data input from propriety (LADARTM sensor suite) and off-the-shelf sensors already installed on vessels, extensive statistics of navigational accidents, and other sources (AIS and route exchange services) to create a holistic decision support system (DSS). Processed information from the automatic DSS will feed into SafeNav collision-avoidance algorithms and generate real-time COLREGs-compliant suggestions for the navigator when an obstacle is detected. This reduces pressure on navigators onboard, providing them with efficient decision-making aid and access to visual navigation data on a single graphical user-interface. Sensors will also be used for container tracking, and mathematical models will predict container drift trajectory, transmitting collected data to a SafeNav Navigational Hazard Database available to nearby vessels/stakeholders, facilitating the recovery of lost containers. Moreover, we propose to prevent vessel collisions with cetaceans with optimal-tuned pingers to alert them of an approaching vessels.
Status
SIGNEDCall topic
HORIZON-CL5-2022-D6-01-08Update Date
09-02-2023
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Organisations
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| O.M. OFFSHORE MONITORING LIMITED (Coördinator)
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| DANAOS SHIPPING COMPANY LIMITED
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| G.M.S. GLOBAL MARITIME SERVICES LIMITED
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| GREENOV-ITES
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| INTERUNIVERSITAIR MICRO-ELECTRONICA CENTRUM VZW
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| LADAR LIMITED
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| NAVAL GROUP (DCNS)
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| SAYFR AS
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| SOCIETE D'INGENIERIE DE RECHERCHESET D'ETUDES EN HYDRODYNAMIQUE NAVALE
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| SVEUCILISTE U RIJECI, POMORSKI FAKULTET
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| UNIVERSITA DEGLI STUDI DI GENOVA