Data Driven Aircraft Trajectory Prediction with Deep Imitation Learning

• URL: http://arxiv.org/abs/2005.07960v1
• Date: Sat, 16 May 2020 11:53:19 GMT
• Title: Data Driven Aircraft Trajectory Prediction with Deep Imitation Learning
• Authors: Alevizos Bastas, Theocharis Kravaris and George A. Vouros
• Abstract summary: The current Air Traffic Management system worldwide has reached its limits in terms of predictability, efficiency and cost effectiveness. We present a comprehensive framework comprising the Generative Adrialversa Learning state of the art method, in a pipeline with trajectory clustering and classification methods.
• Score: 0.8508198765617195
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The current Air Traffic Management (ATM) system worldwide has reached its limits in terms of predictability, efficiency and cost effectiveness. Different initiatives worldwide propose trajectory-oriented transformations that require high fidelity aircraft trajectory planning and prediction capabilities, supporting the trajectory life cycle at all stages efficiently. Recently proposed data-driven trajectory prediction approaches provide promising results. In this paper we approach the data-driven trajectory prediction problem as an imitation learning task, where we aim to imitate experts "shaping" the trajectory. Towards this goal we present a comprehensive framework comprising the Generative Adversarial Imitation Learning state of the art method, in a pipeline with trajectory clustering and classification methods. This approach, compared to other approaches, can provide accurate predictions for the whole trajectory (i.e. with a prediction horizon until reaching the destination) both at the pre-tactical (i.e. starting at the departure airport at a specific time instant) and at the tactical (i.e. from any state while flying) stages, compared to state of the art approaches.

Related papers

• Pedestrian Trajectory Prediction with Missing Data: Datasets, Imputation, and Benchmarking [7.9449756510822915]
  TrajImpute is a pedestrian trajectory prediction dataset that simulates missing coordinates in the observed trajectory. In this work, we examine several imputation methods to reconstruct the missing coordinates and benchmark them for imputing pedestrian trajectories. Our dataset provides a foundational resource for future research on imputation-aware pedestrian trajectory prediction.
  arXiv  Detail & Related papers  (2024-10-31T19:42:42Z)
• Data-driven Probabilistic Trajectory Learning with High Temporal Resolution in Terminal Airspace [9.688760969026305]
  We propose a data-driven learning framework, that leverages the predictive and feature extraction capabilities of the mixture models and seq2seq-based neural networks. After training with this framework, the learned model can improve long-step prediction accuracy significantly. The accuracy and effectiveness of the approach are evaluated by comparing the predicted trajectories with the ground truth.
  arXiv  Detail & Related papers  (2024-09-25T21:08:25Z)
• Building Real-time Awareness of Out-of-distribution in Trajectory Prediction for Autonomous Vehicles [8.398221841050349]
  Trajectory prediction describes the motions of surrounding moving obstacles for an autonomous vehicle. In this paper, we aim to establish real-time awareness of out-of-distribution in trajectory prediction for autonomous vehicles. Our solutions are lightweight and can handle the occurrence of out-of-distribution at any time during trajectory prediction inference.
  arXiv  Detail & Related papers  (2024-09-25T18:43:58Z)
• Progressive Pretext Task Learning for Human Trajectory Prediction [44.07301075351432]
  We introduce a novel Progressive Pretext Task learning (PPT) framework, which progressively enhances the model's capacity of capturing short-term dynamics and long-term dependencies. We design a Transformer-based trajectory predictor, which is able to achieve highly efficient two-step reasoning.
  arXiv  Detail & Related papers  (2024-07-16T10:48:18Z)
• Certified Human Trajectory Prediction [66.1736456453465]
  Tray prediction plays an essential role in autonomous vehicles. We propose a certification approach tailored for the task of trajectory prediction. We address the inherent challenges associated with trajectory prediction, including unbounded outputs, and mutli-modality.
  arXiv  Detail & Related papers  (2024-03-20T17:41:35Z)
• Pre-training on Synthetic Driving Data for Trajectory Prediction [61.520225216107306]
  We propose a pipeline-level solution to mitigate the issue of data scarcity in trajectory forecasting. We adopt HD map augmentation and trajectory synthesis for generating driving data, and then we learn representations by pre-training on them. We conduct extensive experiments to demonstrate the effectiveness of our data expansion and pre-training strategies.
  arXiv  Detail & Related papers  (2023-09-18T19:49:22Z)
• Trajectory Forecasting from Detection with Uncertainty-Aware Motion Encoding [121.66374635092097]
  Trajectories obtained from object detection and tracking are inevitably noisy. We propose a trajectory predictor directly based on detection results without relying on explicitly formed trajectories.
  arXiv  Detail & Related papers  (2022-02-03T09:09:56Z)
• Human Trajectory Prediction via Counterfactual Analysis [87.67252000158601]
  Forecasting human trajectories in complex dynamic environments plays a critical role in autonomous vehicles and intelligent robots. Most existing methods learn to predict future trajectories by behavior clues from history trajectories and interaction clues from environments. We propose a counterfactual analysis method for human trajectory prediction to investigate the causality between the predicted trajectories and input clues.
  arXiv  Detail & Related papers  (2021-07-29T17:41:34Z)
• The Unsupervised Method of Vessel Movement Trajectory Prediction [1.2617078020344619]
  This article presents an unsupervised method of ship movement trajectory prediction. It represents the data in a three-dimensional space which consists of time difference between points, the scaled error distance between the tested and its predicted forward and backward locations, and the space-time angle. Unlike most statistical learning or deep learning methods, the proposed clustering-based trajectory reconstruction method does not require computationally expensive model training.
  arXiv  Detail & Related papers  (2020-07-27T17:45:21Z)
• Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors [124.30562402952319]
  The ability to predict and plan into the future is fundamental for agents acting in the world. Current learning approaches for visual prediction and planning fail on long-horizon tasks. We propose a framework for visual prediction and planning that is able to overcome both of these limitations.
  arXiv  Detail & Related papers  (2020-06-23T17:58:56Z)
• The Importance of Prior Knowledge in Precise Multimodal Prediction [71.74884391209955]
  Roads have well defined geometries, topologies, and traffic rules. In this paper we propose to incorporate structured priors as a loss function. We demonstrate the effectiveness of our approach on real-world self-driving datasets.
  arXiv  Detail & Related papers  (2020-06-04T03:56:11Z)

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.