Deep neural networks (DNNs) are increasingly used in safety-critical
autonomous systems as perception components processing high-dimensional image
data. Formal analysis of these systems is particularly challenging due to the
complexity of the perception DNNs, the sensors (cameras), and the environment
conditions. We present a case study applying formal probabilistic analysis
techniques to an experimental autonomous system that guides airplanes on
taxiways using a perception DNN. We address the above challenges by replacing
the camera and the network with a compact probabilistic abstraction built from
the confusion matrices computed for the DNN on a representative image data set.
We also show how to leverage local, DNN-specific analyses as run-time guards to
increase the safety of the overall system. Our findings are applicable to other
autonomous systems that use complex DNNs for perception.

本文应用正式的概率分析技术对一种通过感知深度神经网络（DNN）引导飞机滑行的实验自主系统进行了案例研究，通过使用代表性图像数据集计算 DNN 的混淆矩阵来替换摄像机和网络，构建紧凑的概率抽象，以解决感知 DNN、传感器和环境条件的复杂性所带来的挑战，并展示了如何利用本地、DNN 特定的分析作为运行时保护，以提高整个系统的安全性。