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We demonstrate a unified approach to rigorous design of safety-criticalautonomous systems using the VerifAI toolkit for formal analysis of AI-basedsystems. VerifAI provides an integrated toolchain for tasks spanning the designprocess, including modeling, falsification, debugging, and ML componentretraining. We evaluate all of these applications in an industrial case studyon an experimental autonomous aircraft taxiing system developed by Boeing,which uses a neural network to track the centerline of a runway. We definerunway scenarios using the Scenic probabilistic programming language, and usethem to drive tests in the X-Plane flight simulator. We first performfalsification, automatically finding environment conditions causing the systemto violate its specification by deviating significantly from the centerline (oreven leaving the runway entirely). Next, we use counterexample analysis toidentify distinct failure cases, and confirm their root causes with specializedtesting. Finally, we use the results of falsification and debugging to retrainthe network, eliminating several failure cases and improving the overallperformance of the closed-loop system.

Formal Analysis and Redesign of a Neural Network-Based Aircraft Taxiing System with VerifAI