  We study the problem of safety verification of direct perception neural
networks, where camera images are used as inputs to produce high-level features
for autonomous vehicles to make control decisions. Formal verification of
direct perception neural networks is extremely challenging, as it is difficult
to formulate the specification that requires characterizing input as
constraints, while the number of neurons in such a network can reach millions.
We approach the specification problem by learning an input property
characterizer which carefully extends a direct perception neural network at
close-to-output layers, and address the scalability problem by a novel
assume-guarantee based verification approach. The presented workflow is used to
understand a direct perception neural network (developed by Audi) which
computes the next waypoint and orientation for autonomous vehicles to follow.
