Machine Learning for Safer Landings

The Alexandra Institute is involved in developing an optical sensor for measuring friction on airport runways in winter conditions. Machine learning is used to relate the optical features with the runway state.

If you feel nervous when driving a car on a slippery road, then think about what it would be like to maneuver a 300 tonne airplane with 300 passengers at 200 kph on a slippery runway. Thankfully, airports operating in winter conditions put in considerable effort to keep runways clean from ice, snow, water and other contaminants that could reduce the grip. An important part of this effort is to regularly measure the friction and register unwanted contaminants, both for reporting it to the pilots and for planning the cleaning work.

To make life easier for the field staff, we’re currently trying to develop an optical sensor that can replace the existing mechanical one, which is expensive and cumbersome to use. The sensor can be mounted on a car, and it has lasers of different wavelengths directed towards the runway and optical instruments to measure five different features of the reflected light. The task now is to find the relationship between these features and the state of the runway. And here is where machine learning comes into play.

PCA plot of optical measurements for five runway contamination classes.

The above figure shows some data collected from a runway in summer time with contaminants like water, rubber and white paint (winter is coming to Denmark just now and I’m expecting to receive some real winter data soon). Each cross corresponds to a measurement from the sensor, projected from five features to two using a method called principal component analysis (PCA), so they can be plotted in a two-dimensional chart. One can clearly see that the wet measurements end up in another region than the dry ones, and indeed a classifier can differ between these two classes with 99.5% accuracy. However, when differing between all five classes, the accuracy is down to 88.1%. The problem can also be seen in the graph: Dry rubber is mixed up with both Paint and Dry. I will get back to what we can do about this problem and increase the accuracy in a later post.

We also tried to relate the optical features to the friction number, which was measured simultaneously with a mechanical device. Now we’re not doing classification any more, but regression, where the objective is to predict a number instead of a class. My colleague Christian trained a small neural network with our optical and friction data, and the result can be seen below.

Estimated friction from neural network and actual measurement from a mechanical device along two runways, joined at the middle of the chart.

As you can see, the estimate (green) follows the actual measurement (blue) quite well. We even have reasons to believe that wherever the difference is large, for example at 3450m, it might be the mechanical device that’s wrong and our neural network that’s right: After a wet spot, which gives low friction, the mechanics will stay wet for a little while and report a too low friction number.


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