  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.
