Improving Robustness of Deep Neural Networks for Aerial Navigation by Incorporating Input Uncertainty
Abstract
Uncertainty quantification methods are required in autonomous systems that include deep learning (DL) components to assess the confidence of their estimations. However, to successfully deploy DL components in safety-critical autonomous systems, they should also handle uncertainty at the input rather than only at the output of the DL components. Considering a probability distribution in the input enables the propagation of uncertainty through different components to provide a representative measure of the overall system uncertainty. In this position paper, we propose a method to account for uncertainty at the input of Bayesian Deep Learning control policies for Aerial Navigation. Our early experiments show that the proposed method improves the robustness of the navigation policy in Out-of-Distribution (OoD) scenarios.
Keywords
Cite
@article{arxiv.2110.13729,
title = {Improving Robustness of Deep Neural Networks for Aerial Navigation by Incorporating Input Uncertainty},
author = {Fabio Arnez and Huascar Espinoza and Ansgar Radermacher and François Terrier},
journal= {arXiv preprint arXiv:2110.13729},
year = {2021}
}
Comments
Accepted at the Fourth International Workshop on Artificial Intelligence Safety Engineering, WAISE 2021