This paper describes a verification case study on an autonomous racing car with a neural
network (NN) controller. Although several verification approaches have been recently …

Recent advances in deep learning have enabled data-driven controller design for
autonomous systems. However, verifying safety of such controllers, which are often hard-to …

Deep Neural Networks (DNN) will emerge as a cornerstone in automotive software
engineering. However, developing systems with DNNs introduces novel challenges for …

In this paper, we consider the problem of formally verifying a Neural Network (NN) based
autonomous landing system. In such a system, a NN controller processes images from a …

Testing the full autonomy system in simulation is the safest and most scalable way to
evaluate autonomous vehicle performance before deployment. This requires simulating …

We demonstrate a unified approach to rigorous design of safety-critical autonomous systems
using the VerifAI toolkit for formal analysis of AI-based systems. VerifAI provides an …

Deep neural networks (DNNs) are increasingly being employed in safety-critical systems,
and there is an urgent need to guarantee their correctness. Consequently, the verification …

Urban driving simulators, such as CARLA, provide 3-D environments and useful tools to
easily simulate sensorimotor control systems in scenarios with complex multi-agent …

This paper proposes a new forward reachability analysis approach to verify safety of cyber-
physical systems (CPS) with reinforcement learning controllers. The foundation of our …

Deep reinforcement learning (DRL) has become a dominant deep-learning paradigm for
tasks where complex policies are learned within reactive systems. Unfortunately, these …