  We present a framework to systematically analyze convolutional neural
networks (CNNs) used in classification of cars in autonomous vehicles. Our
analysis procedure comprises an image generator that produces synthetic
pictures by sampling in a lower dimension image modification subspace and a
suite of visualization tools. The image generator produces images which can be
used to test the CNN and hence expose its vulnerabilities. The presented
framework can be used to extract insights of the CNN classifier, compare across
classification models, or generate training and validation datasets.
