Physics-Informed Mixed-Criticality Scheduling for F1Tenth Cars with Preemptable ROS 2 Executors

Autonomous systems are increasingly used in safety-critical domains, including industrial automation, autonomous vehicles, and the industrial Internet of Things. Verifying both the functional and temporal correctness of these systems is essential to ensure safety before deployment. However, end-to-end verification is challenging due to the interaction of continuous-time physical processes with discrete-time computational systems. Existing formal methods often assume simplified or static computational models, while traditional real-time systems focus on meeting timing constraints without explicitly linking them to physical safety. We address this gap by proposing a physics-informed mixed-criticality (MC) verification framework for cyber-physical systems, which allows the integration of computational and physical models for dynamic, fine-grained safety assurance. Our framework incorporates feedback from the local environment to guide criticality-based mode switching, ensuring adaptive responses to real-time physical states rather than relying on global worst-case assumptions. We demonstrate the feasibility of our approach with a prototype implementation on an autonomous F1 Tenth vehicle using preemptive EDF scheduling on ROS 2. Verification is conducted using UPPAAL to validate system behavior, mode transitions, and physical safety constraints. Results show that our framework effectively manages MC requirements, enhancing responsiveness and safety in dynamic environments.