Daedalean
The Future of Air Transport
This article first appeared in the Future Transport-News magazine, Issue 1 2023.
The future of air transport is almost here. Situational Intelligence™ will soon herald its arrival.
Beyond just situational awareness, Situational Intelligence is the ability of a machine to understand and make sense of the current flight environment and then to anticipate and react to threats to flight safety. Put another way, it’s a computer able to sense, analyse and respond to environmental situations – just like a human pilot does. But better.
In the near term, Situational Intelligence will provide indispensable pilot assistance in the fundamentals of flying, conveying to the pilot answers to three essential questions:
Where am I?
Where can I fly?
Where can I land?
One day soon, Situational Intelligence will act upon the answers to these questions enabling aircraft to fly autonomously, always with a human in control, but, quite possibly, without the need for human intervention.
Situational Intelligence will usher in a new era enabling all air transport to reach safety levels now possible only for the heavy infrastructure-burdened airlines and airports. The dream of air taxis and flying cars is now emerging from imagination as the new reality where distance and division are easily overcome. At scale, movement about the planet will be more accessible, providing more opportunity and equity to all.
The Technology for Autonomy
Coined by Daedalean, the leading software and hardware developer of pilotless flight, the term Situational Intelligence is made possible by a new level of flight control algorithms resulting from recent developments in artificial intelligence, or, more accurately, machine learning.
“Machine learning is central to our work in providing intelligent cockpit systems,” says Luuk van Dijk, who founded Daedalean in 2016 along with Anna Chernova. “It’s a more specific term than the much bandied about ‘AI’. Machine learning is, basically, training systems to learn to do a task without being explicitly programmed to do that task. We train a machine in the lab, for example, to recognise a runway so that it can recognise runways out in the world in real life.”
Out in the world, a live stream of video footage from cameras mounted on the exterior of an aircraft is fed to an onboard computer, which interprets and categorises in real time the various objects it detects: mountains, clouds, lakes, rivers, aircraft (and even specific aircraft types), masts, cranes, wires hanging between utility poles and, of course, runways, and for a rotorcraft – flat, clear areas that could serve as emergency landing options.
