Authors: Jun Xiang, Drake Essick, Luiz Gonzalez Bautista, Junfei Xie, Jun Chen
Abstract: Models for trajectory prediction are an essential component of many advanced
air mobility studies. These models help aircraft detect conflict and plan
avoidance maneuvers, which is especially important in Unmanned Aircraft systems
(UAS) landing management due to the congested airspace near vertiports. In this
paper, we propose a landing trajectory prediction model for UAS based on
Generative Adversarial Network (GAN). The GAN is a prestigious neural network
that has been developed for many years. In previous research, GAN has achieved
many state-of-the-art results in many generation tasks. The GAN consists of one
neural network generator and a neural network discriminator. Because of the
learning capacity of the neural networks, the generator is capable to
understand the features of the sample trajectory. The generator takes the
previous trajectory as input and outputs some random status of a flight.
According to the results of the experiences, the proposed model can output more
accurate predictions than the baseline method(GMR) in various datasets. To
evaluate the proposed model, we also create a real UAV landing dataset that
includes more than 2600 trajectories of drone control manually by real pilots.
Source: http://arxiv.org/abs/2411.14403v1
