Machine Learning Attempt to Conflict Detection for UAV with System Failure in U-Space: Recurrent Neural Network, RNNn

U-Space services will be used by millions of Unmanned Aerial Vehicles (UAVs) also called drones in the near future. To respond to growing demand, urban airspace needs more optimized utilization to prevent delay and collision. In this study, we have implemented a Machine Learning (ML) method to predict possible routes preventing collisions with a UAV which experiences system failure in the U-Space. Our ML implementation, by Recurrent Neural Network, provides information about the possible route to prevent unwanted events. Thus, Air Traffic Managements can reroute the necessary UAV in the U-Space environment.