Traffic Predictions Supporting General Aviation

General Aviation (GA) pilots are responsible to stay well-clear of other traffic and avoid conflicts. This paper outlines a research that is aimed to enlarge the time horizon over which a GA pilot can solve a conflict. This is done by moving beyond state-based predictions to intent-based predictions of flight paths. The idea is to use a stochastic filter with a dynamical model that embeds a notion of flight intent. This flight intent can either be estimated by comparison to a statistic of recurring GA flight patterns or by using intents that are shared prior to flight. A preliminary assessment of the prediction concept showed promising results: the algorithm is capable of producing realistic longer-term predictions, also in the presence of turns and sudden changes of the pilot’s flight intent.