ASCENT: Transformer-Based Aircraft Trajectory Prediction in Non-Towered Terminal Airspace

• URL: http://arxiv.org/abs/2603.16550v1
• Date: Tue, 17 Mar 2026 14:16:10 GMT
• Title: ASCENT: Transformer-Based Aircraft Trajectory Prediction in Non-Towered Terminal Airspace
• Abstract summary: ASCENT is a lightweight transformer-based model for multi-modal 3D aircraft trajectory forecasting.<n>It integrates domain-aware 3D coordinate normalization and parameterized predictions.
• Score: 54.94692733670454
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate trajectory prediction can improve General Aviation safety in non-towered terminal airspace, where high traffic density increases accident risk. We present ASCENT, a lightweight transformer-based model for multi-modal 3D aircraft trajectory forecasting, which integrates domain-aware 3D coordinate normalization and parameterized predictions. ASCENT employs a transformer-based motion encoder and a query-based decoder, enabling the generation of diverse maneuver hypotheses with low latency. Experiments on the TrajAir and TartanAviation datasets demonstrate that our model outperforms prior baselines, as the encoder effectively captures motion dynamics and the decoder aligns with structured aircraft traffic patterns. Furthermore, ablation studies confirm the contributions of the decoder design, coordinate-frame modeling, and parameterized outputs. These results establish ASCENT as an effective approach for real-time aircraft trajectory prediction in non-towered terminal airspace.

Related papers

• DESCENT: Directed Edge Scene Encoding for Airport Surface Movement Prediction [54.94692733670454]
  DESCENT is a transformer-based architecture designed to handle heterogeneous dynamics and strict topological constraints.<n>Our approach features a Potential Reachable Set (PRS) context sampling mechanism that adaptively collects airfield environment context.<n>Our evaluations on the Amelia-10 benchmark demonstrate significant performance improvements over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-08-26T16:49:50Z)
• A prior information informed learning architecture for flying trajectory prediction [15.723257387969712]
  Trajectory prediction for flying objects is critical in domains ranging from sports analytics to aerospace.<n>This paper introduces a novel, hardware-efficient trajectory prediction framework that integrates environmental priors with a Dual-Transformer- Cascaded architecture.<n>We demonstrate this approach by predicting the landing points of tennis balls in real-world outdoor courts.
  arXiv  Detail & Related papers  (2026-03-06T20:33:07Z)
• Synthetic Aircraft Trajectory Generation Using Time-Based VQ-VAE [0.18416014644193066]
  We propose a novel method for trajectory synthesis by adapting the Time-Based Vector Quantized Variational Autoencoder (TimeVQVAE)<n>We evaluate the adapted TimeVQVAE using an extensive suite of quality, statistical, and distributional metrics, as well as a flyability assessment conducted in an open-source air traffic simulator.<n>Results indicate that TimeVQVAE outperforms a temporal convolution VAalE baseline, generating synthetic trajectories that mirror real flight data in terms of accuracy, temporal consistency, and statistical properties.
  arXiv  Detail & Related papers  (2025-04-12T06:46:51Z)
• OPUS: Occupancy Prediction Using a Sparse Set [64.60854562502523]
  We present a framework to simultaneously predict occupied locations and classes using a set of learnable queries. OPUS incorporates a suite of non-trivial strategies to enhance model performance. Our lightest model achieves superior RayIoU on the Occ3D-nuScenes dataset at near 2x FPS, while our heaviest model surpasses previous best results by 6.1 RayIoU.
  arXiv  Detail & Related papers  (2024-09-14T07:44:22Z)
• 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)
• A Non-autoregressive Multi-Horizon Flight Trajectory Prediction Framework with Gray Code Representation [11.29608316753389]
  A novel framework, called FlightBERT++, is proposed to i) forecast multi-horizon flight trajectories directly in a non-autoregressive way. The proposed framework is implemented by a generalized encoder-decoder architecture. The experimental results demonstrate that the proposed framework outperformed the competitive baselines in both FTP performance and computational efficiency.
  arXiv  Detail & Related papers  (2023-05-02T04:11:23Z)
• Inferring Traffic Models in Terminal Airspace from Flight Tracks and Procedures [39.89295870460643]
  We propose a simple probabilistic model that can learn variability from procedural data and flight tracks collected from radar surveillance data.<n>We generate synthetic trajectories by sampling a series of deviations from the Gaussian mixture model and reconstructing the aircraft trajectory.<n>We demonstrate the proposed models on the arrival tracks and procedures of the John F. Kennedy International Airport.
  arXiv  Detail & Related papers  (2023-03-17T13:58:06Z)
• Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution [57.71199089609161]
  Long-term time-series forecasting (LTTF) has become a pressing demand in many applications, such as wind power supply planning. Transformer models have been adopted to deliver high prediction capacity because of the high computational self-attention mechanism. We propose an efficient Transformerbased model, named Conformer, which differentiates itself from existing methods for LTTF in three aspects.
  arXiv  Detail & Related papers  (2023-01-05T13:59:29Z)
• Wireless-Enabled Asynchronous Federated Fourier Neural Network for Turbulence Prediction in Urban Air Mobility (UAM) [101.80862265018033]
  Urban air mobility (UAM) has been proposed in which vertical takeoff and landing (VTOL) aircraft are used to provide a ride-hailing service. In UAM, aircraft can operate in designated air spaces known as corridors, that link the aerodromes. A reliable communication network between GBSs and aircraft enables UAM to adequately utilize the airspace.
  arXiv  Detail & Related papers  (2021-12-26T14:41:52Z)
• 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)

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.