Robustness Assessment of a Runway Object Classifier for Safe Aircraft Taxiing
CoRR(2024)
摘要
As deep neural networks (DNNs) are becoming the prominent solution for many
computational problems, the aviation industry seeks to explore their potential
in alleviating pilot workload and in improving operational safety. However, the
use of DNNs in this type of safety-critical applications requires a thorough
certification process. This need can be addressed through formal verification,
which provides rigorous assurances – e.g., by proving the absence of certain
mispredictions. In this case-study paper, we demonstrate this process using an
image-classifier DNN currently under development at Airbus and intended for use
during the aircraft taxiing phase. We use formal methods to assess this DNN's
robustness to three common image perturbation types: noise, brightness and
contrast, and some of their combinations. This process entails multiple
invocations of the underlying verifier, which might be computationally
expensive; and we therefore propose a method that leverages the monotonicity of
these robustness properties, as well as the results of past verification
queries, in order to reduce the overall number of verification queries required
by nearly 60
achieved by the DNN classifier under study, and indicate that it is
considerably more vulnerable to noise than to brightness or contrast
perturbations.
更多查看译文
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要