Flight Trajectory Prediction

Flight trajectory prediction aims to accurately forecast the future paths of aircraft, drones, and other airborne vehicles, crucial for air traffic management and autonomous navigation. Current research emphasizes improving prediction accuracy and efficiency using deep learning models, such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), graph attention networks (GATs), and transformer-based architectures, often incorporating multi-modal data and addressing challenges like error accumulation and real-time constraints. These advancements have significant implications for enhancing air traffic safety, optimizing airspace utilization, and enabling more sophisticated autonomous flight systems. The development of more robust and efficient prediction models is a key focus, particularly in complex scenarios involving multiple vehicles or unexpected events.