# When Cyber-Physical Systems Meet AI: A Benchmark, an Evaluation, and a Way Forward

arXiv: https://arxiv.org/abs/2111.04324 · HTML: https://mlsanity.com/paper/2111.04324/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2021.

## Built on by (most cited first, 5 of 5)

- [Distributionally Robust Statistical Verification with Imprecise Neural Networks](https://mlsanity.com/paper/2308.14815/) (2023)
- [Tolerance of Reinforcement Learning Controllers against Deviations in Cyber Physical Systems](https://mlsanity.com/paper/2406.17066/) (2024)
- [Towards Counterfactual Explanation and Assertion Inference for CPS Debugging](https://mlsanity.com/paper/2604.07679/) (2026)
- [Architectural Transformations and Emerging Verification Demands in AI-Enabled Cyber-Physical Systems](https://mlsanity.com/paper/2510.00519/) (2025)
- [An Empirical Study of Architectural Shift from Traditional to AI-Enabled Simulink Controllers](https://mlsanity.com/paper/2609.38504/) (2026)

## Abstract (arXiv)

Cyber-physical systems (CPS) have been broadly deployed in safety-critical domains, such as automotive systems, avionics, medical devices, etc. In recent years, Artificial Intelligence (AI) has been increasingly adopted to control CPS. Despite the popularity of AI-enabled CPS, few benchmarks are publicly available. There is also a lack of deep understanding on the performance and reliability of AI-enabled CPS across different industrial domains. To bridge this gap, we initiate to create a public benchmark of industry-level CPS in seven domains and build AI controllers for them via state-of-the-art deep reinforcement learning (DRL) methods. Based on that, we further perform a systematic evaluation of these AI-enabled systems with their traditional counterparts to identify the current challenges and explore future opportunities. Our key findings include (1) AI controllers do not always outperform traditional controllers, (2) existing CPS testing techniques (falsification, specifically) fall short of analyzing AI-enabled CPS, and (3) building a hybrid system that strategically combines and switches between AI controllers and traditional controllers can achieve better performance across different domains. Our results highlight the need for new testing techniques for AI-enabled CPS and the need for more investigations into hybrid CPS systems to achieve optimal performance and reliability.