Reachability Based Safety Analysis

Reachability-based safety analysis focuses on determining the set of states a system can reach, given its dynamics and constraints, to ensure safe operation. Current research emphasizes efficient algorithms for computing reachable sets, particularly for complex systems like those modeled by neural networks or hybrid systems, often employing techniques like Hamilton-Jacobi reachability, Monte Carlo tree search, and various optimization methods. This analysis is crucial for verifying the safety of autonomous systems (e.g., robots, autonomous vehicles) and improving the design of controllers and planning algorithms, ultimately leading to more reliable and trustworthy systems in various applications.