Level 1 — Operational Efficiency
Save Simulation Budget — for Any System, After Every Update
Find the most critical safety violations in a fraction of the simulation budget. Benchmarked against UNECE Regulation No. 157, OSCFuzzer achieves >90% reduction in simulation volume — cutting CPU, GPU, cloud compute, and simulator licensing costs without sacrificing coverage.
This applies to classical ADAS controllers, AV planners, and E2E neural systems alike. And because safety critical OTA update resets the certification clock — for any software-defined vehicle — OSCFuzzer’s efficiency gain compounds with every update cycle, not just at initial certification.
Classical ADAS & L3 Controllers
Rules-based and hybrid systems benefit immediately. Find failure scenarios in hours, not months. Critical OTA updates require re-verification — OSCFuzzer makes that continuous cycle economically viable.
E2E Neural Systems
Each retraining cycle changes the model’s behavioral boundaries. OSCFuzzer treats the trained model as a system to be discovered — re-mapping failure boundaries after each update, turning a compliance nightmare into a managed workflow.
Level 2 — Design-Space Illumination
Access Knowledge Your Engineers Cannot Reach Alone
Existing tools require engineers to define the question first — select two attributes, specify a grid, run simulations, read a frontier. The engineer’s intuition bounds what the tool can find. OSCFuzzer inverts this.
As it searches for critical failures, it simultaneously maps the high-dimensional behavioral landscape of your system — autonomously surfacing which variables interact, and under which precise combinations of conditions those interactions become safety-critical. Not a grid you define in advance. A structure you did not know existed.
SDVs and AV systems involve a genuinely new class of complexity: Ego vehicle dynamics, pedestrian behavior, traffic density, weather, time of day, V2X connectivity, and roadside infrastructure — all interacting simultaneously, in combinations that have no historical precedent. No engineer, however experienced, can pre-enumerate what matters here. The design space is too large, too new, and too non-linear for intuition to navigate.
OSCFuzzer does not wait for your engineers to ask the right question. It finds the structure of your system’s behavioral space — surfacing correlations between attribute X and attribute Y that only manifest when conditions Z, W, and K are simultaneously met. Insights that no predefined grid could surface, and that no team could reach by hand — regardless of how much time they had.
Design-Space Illumination for End-to-End Neural Systems
E2E systems introduce a fourth dimension: policy brittleness. In classical systems, you audit code. In E2E, there is no code — only a learned policy with catastrophic holes in specific feature combinations (low sun angle + specific road texture + cyclist, for example).
Illumination stress-tests the learned policy directly — mapping exactly where the neural network’s “intuition” fails. This is categorically different from attention maps, which show what the model perceives, not where its decision-making breaks down.
Continuous re-auditing after each retraining cycle turns Illumination from a one-time audit into a continuous service — providing OEMs with a Technical Passport that satisfies EU AI Act transparency requirements, ISO 21448 (SOTIF), and UN ECE WP.29 demands for principled understanding of failure boundaries.
Safety monitors only catch what they were designed to catch. Illumination works below the monitor layer — on the policy itself.
