# Reasoning about Safety of Learning-Enabled Components in Autonomous Cyber-physical Systems

arXiv: https://arxiv.org/abs/1804.03973 · HTML: https://mlsanity.com/paper/1804.03973/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2018.

## Built on by (most cited first, 3 of 3)

- [Systems Challenges for Trustworthy Embodied Systems](https://mlsanity.com/paper/2201.03413/) (2022)
- [Identifying the Hazard Boundary of ML-enabled Autonomous Systems Using Cooperative Co-Evolutionary Search](https://mlsanity.com/paper/2301.13807/) (2023)
- [Causal Repair of Learning-enabled Cyber-physical Systems](https://mlsanity.com/paper/2304.02813/) (2023)

## Abstract (arXiv)

We present a simulation-based approach for generating barrier certificate functions for safety verification of cyber-physical systems (CPS) that contain neural network-based controllers. A linear programming solver is utilized to find a candidate generator function from a set of simulation traces obtained by randomly selecting initial states for the CPS model. A level set of the generator function is then selected to act as a barrier certificate for the system, meaning it demonstrates that no unsafe system states are reachable from a given set of initial states. The barrier certificate properties are verified with an SMT solver. This approach is demonstrated on a case study in which a Dubins car model of an autonomous vehicle is controlled by a neural network to follow a given path.