Overview
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.
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.
SafeNavwill 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.