Project description
Innovative collision prevention system for safe navigation
Ship collisions are responsible for human casualties, environmental pollution, financial losses, and infrastructure damage. The EU-funded SafeNav project will develop and test a highly innovative digital collision prevention solution to reduce the probability of collisions, impact damage, and grounding, and increase safe navigation. The project will use data from advanced sensors and other sources to deliver faster, reliable real-time detection of other vessels, fixed installations, submerged/semi-submerged objects, and marine mammals, and an effective visual representation of the multi-source data. SafeNav will create a holistic decision support system (DSS) by designing collision avoidance algorithms built on multi-sensory data input from propriety (LADARTM sensor suite) and off-the-shelf sensors already installed on vessels.
Objective
SafeNavs 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.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- natural sciencescomputer and information sciencesdatabases
- natural sciencesbiological scienceszoologymammalogycetology
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
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Programme(s)
Funding Scheme
HORIZON-IA - HORIZON Innovation ActionsCoordinator
3071 Limassol
Cyprus
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.