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by holycrapwhodat
1798 days ago
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As both a software developer and a pilot... We're (much?) closer to Fully-Self-Flying planes than FSD cars because the problem space is - perhaps counterintuitively - MUCH smaller to tackle. And we have a lot more experience tackling it. Additionally there could easily be remote pilots as backup in case of catastrophe (See remote piloted military and border patrol UAVs) And pulling a parachute at 1000'+ altitude actually has quite a bit of precedent (See https://en.wikipedia.org/wiki/Cirrus_Airframe_Parachute_Syst...) Now... There's probably a lot of cultural and regulatory reasons why the "string of automated glider ports" idea will never come to fruition. But... As far as technical hurdles go, there's not much new technology that would need to be invented here. |
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However, certification requirements and safety assurance needs will drive both cost and time into realizing fully autonomous aircraft. They will be here, but we are 10-15 years away.
The problem is in how to certify machine learning code. Today, you can't. Existing AMCs (accepted means of compliance) are incompatible with the nature of ML. (The breakdown is specifically with assurance architectures focused on code traceability and coverage.) A new architecture for demonstrating safety assurance with AI/ML is needed, and is being built, but is still 1.5-2 years away from being released, and then it will take another year or two before a CAA (civil aviation authority, like the FAA or EASA) will certify a component with ML code--and that will not be an autonomous pilot. That will come in time, but the industry is conservative--especially on safety-critical matters--and it will take years to develop trust in both technology, human factors, and methodology to work up to autonomously flown passenger aircraft.
From the regulator perspective, EASA has taken poll position in thought leadership. Google their AI Roadmap or their Concept Paper for Level 1 Machine Learning Applications.