Formal Verification of Neural Networks: a Case Study about Adaptive Cruise Control

Abstract

Formal verification of neural networks is a promising technique to improve their dependability for safety critical applications. Autonomous driving is one such application where the controllers supervising different functions in a car should undergo a rigorous certification process. In this paper we present an example about learning and verification of an adaptive cruise control function on an autonomous car. We detail the learning process as well as the attempts to verify various safety properties using the tool NeVer2 a new framework that integrates learning and verification in a single easy-to-use package intended for practictioners rather than experts in formal methods and/or machine learning.