Still Another Airport Near Crash – Can AI Help?

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Still Another Airport Near Crash – Can AI Help? Southwest Plane Just Missed Cessna at San Diego

Close Call Between A Southwest Plane And A Cessna

WASHINGTON, D.C. (August 13, 2023) – On Saturday a Southwest Airlines (NYSE:LUV) Boeing 737 came within 100 feet of crashing into a Cessna Citation 560X business jet in San Diego; the 7th near-death runway incursion already this year and a very alarming trend since things seem to be getting worse rather than better despite some remedial efforts.

As a result of apparently snowballing number of runway incursions, the FAA has awarded more than $100 million to 12 airports across the country to reduce runway incursions by reconfiguring taxiways that may cause confusion, install airfield lighting or construct new taxiways to provide more flexibility on the airfield.

Unfortunately, these and many of the proposed remedies – e.g., hiring and training more controllers and pilots, employing next generation radars, more and different lights and markers at airports, etc. – are expensive, and would take a considerable amount of time to fully implement; time during which another runway incursion could easily occur and cost hundreds of lives.

Moreover, current incursion-avoidance systems – e.g., Airport Surface Detection Equipment-Model X, or ASDE-X – even at airports where it has been installed, is not reliable, and many major airports don’t even have it. Indeed, half of the recent close calls occurred at important airports without this protective system. These include Santa Barbara, Austin, Sarasota, and Burbank. See, e.g.:

As Runway Near-Misses Surge, Radar That Keeps Planes Apart Is Aging and Unreliable : “A crucial safety system that’s relied on to avoid potentially fatal collisions at major US airports is aging and plagued by outages that have left travelers unprotected for months at a time. At some airports, it hasn’t ever been installed.

The technology — which tracks vehicles on or near runways to alert controllers before impending crashes — often uses decades-old radar equipment for which spare parts are difficult to find, according to government data and the president of the union representing air-traffic controllers. . . some of the most serious incidents happened at airports without the technology.”

But one idea worth at least considering and then conducting a simple test, and which could be implemented within months at very low cost, comes from a professor at George Washington University who is a safety expert with a degree from MIT and several patents to his name, and experience regarding computers and data processing.

His idea in a nutshell is to use existing AI software to monitor airport radio transmissions, and to then warn controllers of possible runway incursions; eventually also possibly providing the AI program with input from other existing technologies including ground-based radar, digital cameras and complex target-analytics software already in use and tested in airports such as Miami’s.

Using AI To Help With Airport Traffic

We’ve all now learned that existing inexpensive AI programs can already understand speech potentially involving more than a million possible words (in English) and an almost infinite variety of subjects, and analyze it using its vast database to do incredible things such as writing a thesis and even passing difficult professional exams.

In contrast, radio traffic at airports uses only a much smaller number of words and covers only a tiny number of well known topics, so existing AI programs can easily understand what is being said and analyze it to help anticipate possible incursions, all in real time, argues Professor John Banzhaf.

To make such analysis even easier and quicker, a very detailed map of the airport showing the locations, lengths, markings, etc. of each runway and roadway at the airport, as well as a constantly updated schedule of aircraft landings and takeoffs, would likewise be entered into its memory.

Then, after only several months month of operation, even a simple AI program should be able to learn how long each type of aircraft needs to taxi on each runway and roadway, how long it takes to become airborne once each type of aircraft begins its takeoff, the time it will take each type of aircraft to land from each approach and from a variety of altitudes and distances from the runway, and a myriad of other bits of valuable information which can help it calculate if any time-and-distance aircraft separation requirements are likely to be violated, and/or if for any reason an incursion seems likely.

If it seems, based upon the vast amount of input and information it can process in milliseconds, that the probability of a runway incursion exceeds any pre-programmed danger-limit parameters, the AI program can immediately warn the controller(s) handling the flights.

In this way any decisions about whether or not to issue orders to pilots (e.g., to abort takeoff, climb and go around, etc.) would not be made by a computer, but rather by human controllers who could if necessary override a warning from an AI program if appropriate.

Once such an AI runway incursion warning program has been tested and has proven its value, aviation experts can consider adding additional input from – for example – ground-based radar and digital cameras mounted so as to cover every inch of the airport.

We know that inexpensive video cameras linked to simple inexpensive on-board vehicle computers are now to the point where they can almost drive a truck on an interstate highway, or even a car on city streets, which is much more complicated.

Keeping track of airplanes and their movements is obviously orders of magnitude simpler because airplanes are bigger and much easier to see and detect than cars (or children who might run into the street in front of a car), they generally move quite slowly while taxing, can only move along a small number of clearly defined paths at a airport’s map stored in a computer’s memory, and are supposed to coordinate their movement with orders from controllers which are also simultaneously being analyzed by the AI program.

Since the FAA is often slow to move and embrace new ideas and technology, skilled computer enthusiasts – including even a professor teaching computer science at a local university and his eager students, or a white-hat hacker collective – could pick up radio traffic from a nearby airport, feed it into their own computer using AI software, and keep track of how often it was able to predict possible runway incursions – even without add-ons such as ground-based radar and digital cameras, suggests Banzhaf.

In summary, the professor asks whether a simple test of using AI to warn about possible runway incursions isn’t warranted, especially now that so many life-threatening near crashes have occurred already just this year.