Machine Learning Algorithms for Decluttering Aircraft Cockpit Traffic Displays

Project Goal: Make the multifunctional displays, or MFDs, in a pilot's cockpit easier to read using course predictive software.

Commercial aircrafts are equipped with multifunctional displays, or MFDs, that use sensory equipment to collect the position of surrounding aircrafts and display them on a screen. The MFDs that pilots currently use are often cluttered with irrelevant aircraft information, making it difficult for the pilot to extract necessary traffic data. The team was tasked with developing machine learning algorithms to remove irrelevant traffic data, thereby improving pilots’ situational awareness.

The team developed three Predictive Aircraft Navigation, or PAN, algorithms, each of which includes a unique method for predicting aircraft flight paths: Convolutional Neural Network Long Short-Term Memory, Naive, and Encoder Decoder models.

Each of these models combine current and past flight data to predict future flight paths. The team then incorporated a comparison algorithm to rate the relevance of surrounding aircrafts according to their relation to the pilot. The results show a decluttered MFD so a pilot can extract critical information and make vital decisions in dense airspace.