SEEV: Synthesis with Efficient Exact Verification for ReLU Neural
Barrier Functions
SEEV: Synthesis with Efficient Exact Verification for ReLU Neural
Barrier Functions
Neural Control Barrier Functions (NCBFs) have shown significant promise in enforcing safety constraints on nonlinear autonomous systems. State-of-the-art exact approaches to verifying safety of NCBF-based controllers exploit the piecewise-linear structure of ReLU neural networks, however, such approaches still rely on enumerating all of the activation regions of the network near …