Abstract

International audience; Closest Point of Approach (CPA) is one of the main problems in aircraft Conflict Detection (CD). It aims to find out the minimum distance and the associated time between two aircraft. Conventional CPA calculation algorithm generally assumes that the speed and heading of aircraft are constant. But the uncertainties in real operational scenarios lead to inaccuracy of CPA calculation. This project presents a novel data-driven CD framework with Machine Learning (ML) algorithms. The proposed framework provides a promising solution for improving the CPA prediction accuracy with the help of real trajectory data. It contributes to not only reduce the number of fault Short-mid term conflict alert for air traffic controllers, but also support the implementation of future free flight concept, so as to reduce fuel consumption and emission.