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-class Autonomous Emergency Braking activation module. Reproducible regression suite — pass-rate baseline available on request.
Fig. 1 · Certified envelope across the AEB operational design domain
Solid orange: the certified worst-case |u| envelope across the AEB operational design domain, computed analytically at each closing-velocity slice over the full (d, v_ego) input space. Dashed line: the physical safe envelope. Blue dots: 5,055 closing-case input boxes (v_rel > 0) from the Zonotropic AEB regression suite — a subset of the 10,000-case evidence-pack run, all of which passed certification. Every regression scenario sits at or under the analytical envelope by construction.
1.2 · Evidence pack
Every run produces a regulator-ready submission bundle. Reproducible from artifacts and traceable to source data via content hashes.