Aircraft Trajectory Prediction With Enriched Intent Using Encoder-Decoder Architecture

Abstract

Aircraft trajectory prediction is a challenging problem in air traffic control, especially for conflict detection. Traditional trajectory predictors require a variety of inputs such as flight-plans, aircraft performance models, meteorological forecasts, etc. Many of these data are subjected to environmental uncertainties. Further, limited information about such inputs, especially the lack of aircraft tactical intent, makes trajectory prediction a challenging task. In this work, we propose a deep learning model that performs trajectory prediction by modeling and incorporating aircraft tactical intent. The proposed model adopts the encoder-decoder architecture and makes use of the convolutional layer as well as Gated Recurrent Units (GRUs). The proposed model does not require explicit information about aircraft performance and wind data. Results demonstrate that the provision of enriched aircraft intent, together with appropriate model design, could improve the prediction error up to 30% at a prediction horizon of 10 minutes (from 4.9 nautical miles to 3.4 nautical miles). The model also guarantees the mean error growth rate with increasing lookahead time to be lower than 0.2 nautical miles per minute. In addition, the model offers a very low variance in the prediction, which satisfies the variance-standard specified by EUROCONTROL (EU Organization for Safety and Navigation of Air Traffic) for trajectory predictors. The proposed model also outperforms the state-of-the-art trajectory prediction model, where the Root Mean Square Error (RMSE) is reduced from 0.0203 to 0.0018 for latitude prediction, and from 0.0482 to 0.0021 for longitude prediction in a single prediction step of 15 seconds look-ahead. We showed that the pre-trained model on ADS-B data maintains its high performance, in terms of cross-track and along-track errors, when being validated in the Bluesky Air Traffic Simulator. The proposed model would significantly improve the performance of conflict detection systems where such trajectory prediction models are needed.

A Supervised Learning Approach for 4D Air Traffic Conflict Prediction under Trajectory Uncertainty

“…The shifting distance is scaled by a factor of 4.5, based on the recommendations of Tran et al [9]. The flight path is then added to the conflict generation method described earlier.…”

Aircraft Trajectory Prediction Based on Bayesian Optimised Temporal Convolutional Network–Bidirectional Gated Recurrent Unit Hybrid Neural Network

“…However, the proposed model has yet to consider the spatial feature extraction of the data. Tran et al [19] provided a reliable model for a conflict detection system by combining encoder-decoder modelling with tactical intent, and verified that the prediction accuracy exceeded those of existing models using actual data. In 2020, Lv et al [20] proposed using a temporal convolutional network (TCN) for trajectory prediction, and compared it with a conventional model.…”

Data-Driven 4D Trajectory Prediction Model Using Attention-TCN-GRU

“…Fan Zhonghang et al [20] analysed the relationship between aircraft manoeuvring characteristics and trajectory prediction and, based on this, proposed a trajectory prediction method based on a residual recurrent neural network (RESRNN). Tran et al [21] proposed a deep-learning model that reasonably avoids the data effects of aircraft performance and meteorological elements, and achieves more accurate trajectory predictions through modelling and combining aircraft intent information. Wang et al [22] proposed a hybrid neural network for long-term prediction of trajectories: the TraNet model.…”