Landing Trajectory Prediction for UAS Based on Generative Adversarial Network

• URL: http://arxiv.org/abs/2411.14403v1
• Date: Thu, 21 Nov 2024 18:34:33 GMT
• Title: Landing Trajectory Prediction for UAS Based on Generative Adversarial Network
• Authors: Jun Xiang, Drake Essick, Luiz Gonzalez Bautista, Junfei Xie, Jun Chen,
• Abstract summary: We propose a landing trajectory prediction model for Unmanned Aircraft systems based on Generative Adversarial Network (GAN) GAN is a prestigious neural network that has been developed for many years. 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.
• Score: 10.243874682724677
• License:
• 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.

Related papers

• VECTOR: Velocity-Enhanced GRU Neural Network for Real-Time 3D UAV Trajectory Prediction [2.1825723033513165]
  We propose a new trajectory prediction method using Gated Recurrent Units (GRUs) within sequence-based neural networks. We employ both synthetic and real-world 3D UAV trajectory data, capturing a wide range of flight patterns, speeds, and agility. The GRU-based models significantly outperform state-of-the-art RNN approaches, with a mean square error (MSE) as low as 2 x 10-8.
  arXiv  Detail & Related papers  (2024-10-24T07:16:42Z)
• JRDB-Traj: A Dataset and Benchmark for Trajectory Forecasting in Crowds [79.00975648564483]
  Trajectory forecasting models, employed in fields such as robotics, autonomous vehicles, and navigation, face challenges in real-world scenarios. This dataset provides comprehensive data, including the locations of all agents, scene images, and point clouds, all from the robot's perspective. The objective is to predict the future positions of agents relative to the robot using raw sensory input data.
  arXiv  Detail & Related papers  (2023-11-05T18:59:31Z)
• Context-Aware Generative Models for Prediction of Aircraft Ground Tracks [0.004807514276707785]
  Trajectory prediction plays an important role in supporting the decision-making of Air Traffic Controllers. Traditional TP methods are deterministic and physics-based, with parameters calibrated using aircraft surveillance data harvested across the world. This work proposes a generative method for lateral TP, using probabilistic machine learning to model the effect of the unknown effect of pilot behaviour and ATCO intentions.
  arXiv  Detail & Related papers  (2023-09-26T14:20:09Z)
• EquiDiff: A Conditional Equivariant Diffusion Model For Trajectory Prediction [11.960234424309265]
  We propose EquiDiff, a deep generative model for predicting future vehicle trajectories. EquiDiff is based on the conditional diffusion model, which generates future trajectories by incorporating historical information and random Gaussian noise. Our results demonstrate that EquiDiff outperforms other baseline models in short-term prediction, but has slightly higher errors for long-term prediction.
  arXiv  Detail & Related papers  (2023-08-12T13:17:09Z)
• Convolutional Neural Networks for the classification of glitches in gravitational-wave data streams [52.77024349608834]
  We classify transient noise signals (i.e.glitches) and gravitational waves in data from the Advanced LIGO detectors. We use models with a supervised learning approach, both trained from scratch using the Gravity Spy dataset. We also explore a self-supervised approach, pre-training models with automatically generated pseudo-labels.
  arXiv  Detail & Related papers  (2023-03-24T11:12:37Z)
• Inferring Traffic Models in Terminal Airspace from Flight Tracks and Procedures [52.25258289718559]
  We propose a probabilistic model that can learn the variability from procedural data and flight tracks collected from radar surveillance data. We show how a pairwise model can be used to generate traffic involving an arbitrary number of aircraft.
  arXiv  Detail & Related papers  (2023-03-17T13:58:06Z)
• Gesture Control of Micro-drone: A Lightweight-Net with Domain Randomization and Trajectory Generators [0.0]
  This study presents a computationally-efficient deep convolutional neural network that utilizes Gabor filters and spatial separable convolutions. The model aids a human operator in controlling a micro-drone via gestures. Using a low-cost DJI Tello drone for experiment verification, the computationally-efficient model shows promising results.
  arXiv  Detail & Related papers  (2023-01-29T15:38:15Z)
• Learning to Learn with Generative Models of Neural Network Checkpoints [71.06722933442956]
  We construct a dataset of neural network checkpoints and train a generative model on the parameters. We find that our approach successfully generates parameters for a wide range of loss prompts. We apply our method to different neural network architectures and tasks in supervised and reinforcement learning.
  arXiv  Detail & Related papers  (2022-09-26T17:59:58Z)
• 4D flight trajectory prediction using a hybrid Deep Learning prediction method based on ADS-B technology: a case study of Hartsfield-Jackson Atlanta International Airport(ATL) [2.2118683064997264]
  This paper proposes a novel hybrid deep learning model to extract the spatial and temporal features considering the uncertainty of the prediction model for Hartsfield-Jackson Atlanta International Airport(ATL) The results show that the proposed model has low error measurements compared to the other models (i.e., 3D CNN, CNN-GRU)
  arXiv  Detail & Related papers  (2021-10-14T23:48:44Z)
• DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation [68.8204255655161]
  We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE) It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase. Results show that the DAE achieves better results in both accuracy and speed of detection.
  arXiv  Detail & Related papers  (2021-09-08T14:07:55Z)
• MineGAN++: Mining Generative Models for Efficient Knowledge Transfer to Limited Data Domains [77.46963293257912]
  We propose a novel knowledge transfer method for generative models based on mining the knowledge that is most beneficial to a specific target domain. This is done using a miner network that identifies which part of the generative distribution of each pretrained GAN outputs samples closest to the target domain. We show that the proposed method, called MineGAN, effectively transfers knowledge to domains with few target images, outperforming existing methods.
  arXiv  Detail & Related papers  (2021-04-28T13:10:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.