Data-driven Probabilistic Trajectory Learning with High Temporal Resolution in Terminal Airspace
Not on the map. Why?
Abstract (arXiv)
Predicting flight trajectories is a research area that holds significant merit. In this paper, we propose a data-driven learning framework, that leverages the predictive and feature extraction capabilities of the mixture models and seq2seq-based neural networks while addressing prevalent challenges caused by error propagation and dimensionality reduction. After training with this framework, the learned model can improve long-step prediction accuracy significantly given the past trajectories and the context information. The accuracy and effectiveness of the approach are evaluated by comparing the predicted trajectories with the ground truth. The results indicate that the proposed method has outperformed the state-of-the-art predicting methods on a terminal airspace flight trajectory dataset. The trajectories generated by the proposed method have a higher temporal resolution(1 timestep per second vs 0.1 timestep per second) and are closer to the ground truth.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 29 of its 67 references that are in the map. First posted on arXiv 25 Sep 2024.
Builds on
29 of 67 references.
References · 29
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
- 2017Graph Attention Networkscited by 8,854
- 2014Going Deeper with Convolutionscited by 4,231
- 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,447
- 2019An Introduction to Variational Autoencoderscited by 997
- 2018Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networkscited by 687
- 2021Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Datasetcited by 532
- 2020VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representationcited by 528
- 2014Semi-Supervised Learning with Deep Generative Modelscited by 494
17 more
- 2019Selective Kernel Networkscited by 401
- 2020TNT: Target-driveN Trajectory Predictioncited by 345
- 2022Motion Transformer with Global Intention Localization and Local Movement Refinementcited by 267
- 2021MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Predictioncited by 246
- 2020Transformer Networks for Trajectory Forecastingcited by 180
- 2020Attentional Feature Fusioncited by 115
- 2023VMT-Adapter: Parameter-Efficient Transfer Learning for Multi-Task Dense Scene Understandingcited by 62
- 2020Multimodal Deep Generative Models for Trajectory Prediction: A Conditional Variational Autoencoder Approachcited by 47
- 2024Time Series Modeling for Heart Rate Prediction: From ARIMA to Transformerscited by 35
- 2019Uncertainty-Aware Driver Trajectory Prediction at Urban Intersectionscited by 26
- 2023Bridging the Domain Gap: Self-Supervised 3D Scene Understanding with Foundation Modelscited by 26
- 2021Multimodal Semi-Supervised Learning for 3D Objectscited by 24
- 2018Learning Probabilistic Trajectory Models of Aircraft in Terminal Airspace from Position Datacited by 8
- 2018Predicting Aircraft Trajectories: A Deep Generative Convolutional Recurrent Neural Networks Approachcited by 8
- 2024Online Learning of Multiple Tasks and Their Relationships : Testing on Spam Email Data and EEG Signals Recorded in Construction Fieldscited by 8
- 2024Self-Adaptive Robust Motion Planning for High DoF Robot Manipulator using Deep MPCcited by 7
- 2021Predicting Like A Pilot: Dataset and Method to Predict Socially-Aware Aircraft Trajectories in Non-Towered Terminal Airspacecited by 5
Built on by
Citing papers per year: 2024: 1; 2025: 1.
2 papers.