Solutions that Redefines Maritime Navigation with Digitalization of COLREGs and AI-Powered Technologies
Built for Navigators, By Navigators, Powered by Innovation
Our Key Technologies
Decision Support System (DSS)
- Digital Co-Pilot
The DSS reduces the risk of collision, and reduces mental load for navigators.
With COLREG-compliant suggestions on how to avoid collisions, the SafeNav Co-Pilot (Decision Support System) is a critical component of the SafeNav system, responsible for generating alternative routes to avoid collisions with obstacles identified by the situational awareness system.
The DSS module consists of three main components: a COLREG (Collision Regulations / Convention on the International Regulations for Preventing Collisions at Sea) Classification Module, a Mathematical Obstacle Avoidance Model, and a Path Planning Module. Currently we are going for 34 out of 40 COLREGs, and fully compliant with all regulations.
Ocean Observations Database (OOD) & Map
- “Google Maps of the Sea”
Data collection and sharing of all kinds of hazards across systems, both universal like metocean data (wind, waves, currents, depth) and extreme weather avoidance, singular events that are logged from other ships, and in situ weather from cameras local observation.
SafeNav runs all of this through the DSS Co-Pilot algorithms in order to give navigators well informed and data-backed suggestions. All visualised via the Map View.
Risk Module & Assessment
Currently taking into consideration historical accidents data, and in future also all external and internal factors like speed, rate of turn, current, wind, wave height, depth, traffic etc.
The risk assessment has a measured risk number for probability, the impact/outcome if that risk happens, the residual risk, and then advice on how to mitigate the risk level. Manning level can also be predicted with this module.
Data Fusion Module
Combines all incoming sensor data together (Lidar/Ladar, visual/thermal cameras, and ship radar), enabling the real-time display of full situational awareness of the maritime environment, on the bridge in a unified, clear and intuitive manner, all in one point of access.
The module uses advanced algorithms, including deep learning and probabilistic reasoning, to process data from the Lidar/Ladar and camera sensors. For the ship radar, decision-level fusion is used. The module is trained using VR (Virtual Reality) simulations and data augmentation, and its performance is assessed through rigorous testing.
A bridge simulator environment is used to gather early user feedback.
Route/Voyage Optimization
Contributes to Decarbonization
With current functionality to upload optimised routes, view other ship routes via Route Exchange, an API integration for new fuels coming soon, and the IMO’s future goals in mind.
The International Maritime Organization's (IMO) 2030 and 2050 goals are to reduce greenhouse gas (GHG) emissions from international shipping by 20% and 70% respectively, relative to 2008 levels. The IMO also aims to reach net-zero GHG emissions by 2050.
We aim to partner with Voyage/Route Optimization providers in order to integrate even more route optimization capabilities straight into SafeNav.
Additional Solutions we have worked on include:
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Currently in our Map View and used for situational awareness, this feature has a bright future ahead, with contributing to route optimization and more. Currently all SafeNav systems can request to share routes, and preview all WPs (waypoints) on the Map View next to your own vessel’s route.
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The alerts menu in the Graphical User Interface (GUI), where you get real-time alerts on everything, risk level, weather, DSS Co-Pilot, Ocean Observations Database etc.
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Risk assessment model to forecast container loss risk along a containership's planned route. We calculate this risk based on the lashing load percentage, roll angle, speed, ocean conditions, weather etc. In collaboration and integration with the DNV Anti-Roll App.
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For lost containers, we have worked on a predicted drift trajectory. This is based on algorithms which take into consideration the details of the loss incidents and conditions of the ocean. Research and development into attachable devices for container for recovery purposes. This can be used for valuable cargo.
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Addressing the critical issue of collision avoidance with marine mammals, led by our project partner GREENOV. We developed a user-friendly web application for reporting marine mammal detections, integrated with SafeNav’s Map View and accessible to smaller vessels separately. Featuring probability heat map, single sightings, and regulated zones, for collision avoidance with mammals. The data collected is then sent to the Decision Support and Data Fusion Module for collision avoidance and integrated into the SafeNav Ocean Observations Database. Additionally, we performed research and development of solutions that utilize acoustic and electromagnetic (EM) pinger instruments to detect, alert, and ward off marine mammals from approaching vessels.
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Another good feature integrated into our Map View and data sent to the Ocean Observations Database. These areas are indicated as red-zones to avoid, usually restricted by governmental bodies all over the world.
Interface & Testing
