TY - GEN
T1 - Probabilistic aircraft trajectory prediction with weather uncertainties using approximate bayesian variational inference to neural networks
AU - Pang, Yutian
AU - Wang, Yuhao
AU - Liu, Yongming
N1 - Funding Information:
The work was performed at the Prognostic Analysis and Reliability Assessment Lab at Arizona State University. The research reported in this paper was supported by funds from NASA University Leadership Initiative program (Contract No. NNX17AJ86A, Project Officer: Dr. Anupa Bajwa, Principal Investigator: Dr. Yongming Liu). The support is gratefully acknowledged.
Publisher Copyright:
© 2020, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.
PY - 2020
Y1 - 2020
N2 - A key consideration in Trajectory Prediction (TP) tools is the confidence that can be placed on the prediction. We purpose a non-deterministic TP neural network using tractable approximate Bayesian variational inference for the model parameters considering weather effects. This work adopts the state-of-art in Bayesian Deep Learning research and builds a neural network model with stochastic convolutional, recurrent, and fully-connect layers. The purposed stochastic variational method outperforms the dropout approximate to Variational Inference and performs reliable uncertainty estimates. It can be easily applied to most neural net architectures and also provides a simple pruning heuristic that can drastically reduce the number of model parameters compares to ensemble methods. The experiment is conducted with the Atlanta Air Route Traffic Control Center (ZTL) flight data and the corridor integrated weather system (CIWS) weather data from Sherlock Data Warehouse (SDW) on June 24th, 2019. The experimental results show better variance reduction than dropout-based methods. The uncertainty estimates are more reliable thanks to the Kullback–Leibler divergence (KL-divergence) term within the optimization objective.
AB - A key consideration in Trajectory Prediction (TP) tools is the confidence that can be placed on the prediction. We purpose a non-deterministic TP neural network using tractable approximate Bayesian variational inference for the model parameters considering weather effects. This work adopts the state-of-art in Bayesian Deep Learning research and builds a neural network model with stochastic convolutional, recurrent, and fully-connect layers. The purposed stochastic variational method outperforms the dropout approximate to Variational Inference and performs reliable uncertainty estimates. It can be easily applied to most neural net architectures and also provides a simple pruning heuristic that can drastically reduce the number of model parameters compares to ensemble methods. The experiment is conducted with the Atlanta Air Route Traffic Control Center (ZTL) flight data and the corridor integrated weather system (CIWS) weather data from Sherlock Data Warehouse (SDW) on June 24th, 2019. The experimental results show better variance reduction than dropout-based methods. The uncertainty estimates are more reliable thanks to the Kullback–Leibler divergence (KL-divergence) term within the optimization objective.
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U2 - 10.2514/6.2020-2897
DO - 10.2514/6.2020-2897
M3 - Conference contribution
AN - SCOPUS:85092781991
SN - 9781624105982
T3 - AIAA AVIATION 2020 FORUM
BT - AIAA AVIATION 2020 FORUM
PB - American Institute of Aeronautics and Astronautics Inc, AIAA
T2 - AIAA AVIATION 2020 FORUM
Y2 - 15 June 2020 through 19 June 2020
ER -