Product · Safety bound generator for ML decision modules
Exact, regulator-ready safety bounds
for machine-learning decision modules.
1.0 · Description
Type-approval of learned components in safety-critical control stacks demands bounds that hold by construction, not by sampling. Zonotropic generates closed-form activation envelopes around any ML decision module, paired with a regulator-ready evidence pack on every run. The envelope is verifiable offline and runs alongside the model in production at sub-frame latency.
1.1 · Reference application
Worked example: Tier 1 Autonomous Emergency Braking activation module. 97-test regression suite — pass-rate baseline available on request.
Fig. 1 · Certified envelope across the AEB operational design domain
Each gray dot is the worst-case certified braking magnitude (|u_min|) for one of 1,505 closing-case input boxes (v_rel > 0). Solid orange traces the upper envelope (max |u_min| per 1 m/s bin); dashed orange traces the lower envelope (min |u_max| per bin). Bounds are computed analytically over each input box of radius eps = [0.5, 0.5, 0.2]. All 1,505 cases passed certification at a_max = 7.5 m/s².
1.2 · Evidence pack
Every run produces a regulator-ready submission bundle. Reproducible from artifacts and traceable to source data via content hashes.