Compositional Verification for Autonomous Systems with Deep Learning Components
This addresses safety assurance for autonomous systems, which is critical for applications like autonomous vehicles, but the approach appears incremental as it builds on existing compositional methods.
The paper tackles the challenge of providing safety guarantees for autonomous systems with deep learning components by introducing a compositional verification approach using assume-guarantee reasoning and contracts, and demonstrates it on an autonomous vehicle example.
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.