Compositional Verification for Autonomous Systems with Deep Learning Components

TL;DR

This paper introduces a compositional verification method for autonomous systems with deep learning components, enabling scalable safety guarantees through assume-guarantee reasoning and formal contracts.

Contribution

It presents a novel compositional verification framework that incorporates deep neural networks using assume-guarantee contracts for autonomous systems.

Findings

Effective verification of DNN components in autonomous systems

Scalable approach demonstrated on autonomous vehicle example

Formal guarantees achieved through compositional reasoning

Abstract

As autonomy becomes prevalent in many applications, ranging from recommendation systems to fully autonomous vehicles, there is an increased need to provide safety guarantees for such systems. The problem is difficult, as these are large, complex systems which operate in uncertain environments, requiring data-driven machine-learning components. However, learning techniques such as Deep Neural Networks, widely used today, are inherently unpredictable and lack the theoretical foundations to provide strong assurance guarantees. We present a compositional approach for the scalable, formal verification of autonomous systems that contain Deep Neural Network components. The approach uses assume-guarantee reasoning whereby {\em contracts}, encoding the input-output behavior of individual components, allow the designer to model and incorporate the behavior of the learning-enabled components working side-by-side with the other components. We illustrate the approach on an example taken from the autonomous vehicles domain.