  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.
