Researchers propose a runtime enforcement framework using Hybrid Automata to actively prevent safety violations in autonomous and cyber-physical systems by monitoring and modifying unsafe behaviors in real time. The approach combines discrete-event editing with continuous monitoring and is validated through an Adaptive Cruise Control case study, demonstrating effective safety compliance with minimal computational overhead.
This research addresses a critical gap in autonomous system safety by moving beyond passive runtime verification to active enforcement mechanisms. Traditional approaches observe system behavior and report violations after they occur, leaving a dangerous window where unsafe actions persist. The proposed Hybrid Automata framework intervenes proactively by synthesizing corrective actions—suppressing, delaying, or inserting events—before violations manifest, fundamentally changing how safety-critical systems can be protected in unpredictable environments.
The work builds on decades of formal methods research but introduces practical innovations for cyber-physical systems operating with both discrete-event logic and continuous dynamics. Previous enforcement frameworks struggled with such hybrid systems, limiting their applicability to modern autonomous vehicles, industrial control systems, and medical devices. By combining reachability analysis with online enforcement algorithms, the framework enables real-time safety assurance without prohibitive computational costs.
For the autonomous systems industry, this represents a significant advancement in runtime safety assurance complementing design-time verification approaches. Organizations developing self-driving vehicles, robotic systems, and automated industrial equipment gain a deployable tool for runtime safety guarantees, reducing liability exposure and accelerating certification processes. The ACC case study validates practical applicability in a domain where safety failures have direct consequences.
Future development will likely focus on scaling the approach to more complex systems, integrating it with existing middleware architectures, and establishing formal certification paths for autonomous systems. The framework's minimal computational overhead suggests viability for embedded deployment, potentially enabling a new category of runtime safety solutions in AI-driven autonomous platforms.
- →Runtime enforcement framework actively prevents safety violations in autonomous systems rather than passively detecting them after occurrence
- →Hybrid Automata modeling enables enforcement across systems with both discrete-event logic and continuous dynamics
- →Online algorithm demonstrates minimal computational overhead while maintaining real-time safety compliance
- →Adaptive Cruise Control case study validates practical applicability in automotive autonomous systems
- →Framework synthesizes corrective actions including event suppression, delay, and insertion at arbitrary time instants