Page 23: of Maritime Reporter Magazine (December 2024)

ARTIFICIAL INTELLIGENCE

Oslo School of Architecture and Design, in partnership with a wide range of other companies including Kongsberg, Brunvoll and Vard. Together, they have developed a collection of tools and approaches to improve the design of bridges based on mod- ern user interface technology and human-centered design prin- ciples. The aim is to avoid the fragmentation that comes with many different user interfaces on a bridge, increasing the need for training and also increasing the chances of human error.

Over 1,000 companies have now registered to access the guidelines, and the success of OpenBridge has led to the

OpenAR project which is expanding the guidance to AR func- tionality. Most of the project’s technology demonstrations so far have focused on situational awareness support through points-of-interest display systems showing vessels and other

Image courtesy ShipIn Systems Inc. information over the real world, says Professor Kjetil Nor- dby of the Oslo School of Architecture and Design. “These

Shipin Systems uses an AI-based are now been made for video in remote operation centers, camera system placed in core window-projected interfaces, on-ship screen-based situational operational areas throughout a vessel. awareness systems and head-up displays similar to car sys- tems. We have not seen any partner make head mounted sys- tems yet, but we expect that is also on the horizon.”

His focus on workplace design is extending to engine rooms, and most recently, with the OpenZero project, he is encompassing decarbonization technologies that boost energy ef? ciency and reduce fuel consumption. Partners for this proj- ect include ABB, GE Marine and DNV.

All these projects are designed to support decision-making by crews, but the systems being developed are also the build- ing blocks for the safe navigation and management of autono- mous ships. For this, it’s the decision-making of machines that needs to be augmented.

“Predicting pedestrians and other vehicles or vessels is one of the most funded research areas in autonomous navigation in land, air or maritime systems,” says Professor Lokukaluge

Prasad Perera from the Arctic University of Norway. Perera is testing models for predicting ship behavior at long and close range using neural networks that can learn from extensive da- tabases, such as those generated on training simulators, as well as from onboard sensor and AIS data. The aim is to enable safe decision-making on autonomous ships and to help crews under- stand the behavior of autonomous ships if they encounter them.

Perera’s team is working on a large-scale predictor that com- bines neural network learning with AIS data to predict up to 20 minutes of a ship’s trajectory. A local predictor is also be- ing developed that combines ship kinematic models and neural network learning from onboard ship performance data to ac- curately predict the immediate 20 seconds of a ship’s trajectory. “The local predictor is important for many close ship encoun- ter situations to evaluate the possible collision risk. Therefore, both local and global scale predictors can help autonomous ships to detect possible collision situations and then take appropriate action at an early stage,” says Perera. “When systems are mak- ing decisions, these early predictions are extremely important.” www.marinelink.com 23

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