CP-NCBF: A Conformal Prediction-based Approach to Synthesize Verified Neural Control Barrier Functions
This addresses the problem of ensuring safety in control systems for domains like autonomous vehicles, though it is incremental by building on existing neural CBF methods with a new verification approach.
The paper tackles the challenge of constructing verified neural control barrier functions (NCBFs) for safety-critical control by proposing CP-NCBF, a framework using split-conformal prediction to provide probabilistic guarantees, resulting in larger and less conservative safe sets in case studies like autonomous driving and aerial vehicle geo-fencing.
Control Barrier Functions (CBFs) are a practical approach for designing safety-critical controllers, but constructing them for arbitrary nonlinear dynamical systems remains a challenge. Recent efforts have explored learning-based methods, such as neural CBFs (NCBFs), to address this issue. However, ensuring the validity of NCBFs is difficult due to potential learning errors. In this letter, we propose a novel framework that leverages split-conformal prediction to generate formally verified neural CBFs with probabilistic guarantees based on a user-defined error rate, referred to as CP-NCBF. Unlike existing methods that impose Lipschitz constraints on neural CBF-leading to scalability limitations and overly conservative safe sets--our approach is sample-efficient, scalable, and results in less restrictive safety regions. We validate our framework through case studies on obstacle avoidance in autonomous driving and geo-fencing of aerial vehicles, demonstrating its ability to generate larger and less conservative safe sets compared to conventional techniques.