Hey HN, we’re Eric and Kristian of Enhanced Radar. We’re working on making air travel safer by augmenting control services in our stressed airspace system.

Recent weeks have put aviation safety on everyone’s mind, but we’ve been thinking about this problem for years. Both of us are pilots — we have 2,500 hours of flight time between us. Eric flew professionally and holds a Gulfstream 280 type rating and both FAA and EASA certificates. Kristian flies recreationally, and before this worked on edge computer vision for satellites.

We know from our flying experience that air traffic management is imperfect (every pilot can tell stories of that one time…), so this felt like an obvious problem to work on.

Most accidents are the result of an overdetermined “accident chain” (https://code7700.com/accident_investigation.htm). The popular analogy here is the swiss cheese model, where holes in every slice line up perfectly to cause an accident. Often, at least one link in that chain is human error.

We’ll avoid dissecting this year’s tragedies and take a close call from last April at DCA as an example:

The tower cleared JetBlue 1554 to depart on Runway 04, but simultaneously a ground controller on a different frequency cleared a Southwest jet to cross that same runway, putting them on a collision course. Controllers noticed the conflict unfolding and jumped in to yell at both aircraft to stop, avoiding a collision with about 8 seconds to spare (https://www.youtube.com/watch?v=yooJmu30DxY).

Importantly, the error that caused this incident occurred approximately 23 seconds before the conflict became obvious. In this scenario, a good solution would be a system that understands when an aircraft has been cleared to depart from a runway, and then makes sure no aircraft are cleared to cross (or are in fact crossing) that runway until the departing aircraft is wheels-up. And so on.

To do this, we’ve developed Yeager, an ensemble of models including state of the art speech-to-text that can understand ATC audio. It’s trained on a large amount of our own labeled ATC audio collected from our VHF receivers located at airports around the US. We improve performance by injecting context such as airport layout details, nearby/relevant navaids, and information on all relevant aircraft captured via ADS-B.

Our product piggy-backs on the raw signal in the air (VHF radio from towers to pilots) by having our own antennas, radios, and software installed at the airport. This system is completely parallel to existing infrastructure, requires zero permission, and zero integration. It’s an extra safety net over existing systems (no replacement required). All the data we need is open-source and unencrypted.

Building models for processing ATC speech is our first step toward building a safety net that detects human error (by both pilots and ATC). The latest system transcribes the VHF control audio at about ~1.1% WER (Word Error Rate), down from a previous record of ~9%. We’re using these transcripts with NLP and ADS-B (the system that tracks aircraft positions in real time) for readback detection (ensuring pilots correctly repeat ATC instructions) and command compliance.

There are different views about the future of ATC. Our product is naturally based on our own convictions and experience in the field. For example, it’s sometimes said that voice comms are going away — we think they aren’t (https://www.ericbutton.co/p/speech). People also point out that airplanes are going to fly themselves — in fact they already do. But passenger airlines, for example, will keep a pilot onboard (or on the ground) with ultimate control, for a long time from now; the economics and politics and mind-boggling safet...