Systematic Testing of Convolutional Neural Networks for Autonomous Driving

Understand

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.