Daedalean’s newest white paper outlines how the AI-based flight control instruments will develop from basic pilot assistance to advanced pilot assistance (human/machine collaboration) and then to monitored autonomy.
Amid a cockpit full of instruments that demand attention, pilots today risk an information overload. Daedalean thinks that next-generation instruments should de-clutter the cockpit, not add to the problem. The AI will have to take over situational awareness and risk management tasks, like a trustworthy electronic copilot.
After that stage, the ultimate goal is monitored autonomy. Once the output of the system is fed directly to the flight control systems, the role of the crew changes to the one of oversight.
Xwing and Daedalean AG have announced a formal collaboration to share data, knowledge, and processes in the field of machine learning and AI.
Daedalean engineer, Giovanni Balduzzi, recently earned his pilots license and chronicled his findings along the way.
Daedalean has been awarded a grant by Innosuisse to develop its BRAVEN (Bimodal Radar for Aviation Environments) Solution.
Daedalean believes one of the three key skills of a human pilot is to fly according to visual flight rules (VFR) and in visual conditions.
At Daedalean, the team has always focused on developing products for aviation with an eye toward future certification.
Daedalean recently looked into accidents related to navigation, flight planning, geographic disorientation & navigation error.
Daedalean’s Visual Awareness System (VXS) relies on the input from several (one to four) cameras installed on an aircraft.
Daedalean leverages the power of artificial intelligence to provide superhuman situational awareness systems to aerospace.
Daedalean’s data-collection aircraft photoshoot in the Swiss Alps illustrates the beauty of nature and technology.
Daedalean hires Dr. Yemaya Bordain to lead its new office in Phoenix, AZ, as the company officially opens for business in the United States.
Daedalean has concluded a joint research project with the FAA on Neural Network-Based Runway Landing Guidance for General Aviation.
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