Think of this as a standardized obstacle course and scorecard for a self‑driving car’s “eyes and brain.” It systematically throws different road hazards at the car’s perception system (cameras, lidar, radar, and the AI that interprets them) to see what it notices, what it misses, and how often it makes dangerous mistakes.
Autonomous vehicles rely on complex perception systems, but today there is no universally trusted, comprehensive way to measure how safe and reliable those systems are across a wide range of real‑world hazards (e.g., pedestrians, debris, unusual vehicles, bad weather). This work defines a rigorous evaluation framework so OEMs, suppliers, and regulators can objectively benchmark perception performance and identify safety gaps before deployment on public roads.
If operationalized as a product or service, the moat would come from: (1) a large and diverse curated corpus of hazardous scenarios and edge cases; (2) validated test metrics and methodologies that gain acceptance from regulators and industry; and (3) integration into OEMs’ simulation, test, and safety‑case workflows, creating switching costs.
Unknown
Unknown
High (Custom Models/Infra)
Generation and maintenance of a sufficiently rich, labeled, and up‑to‑date hazard scenario dataset (including rare edge cases and varied environments), plus the computational cost of running large‑scale perception evaluations across many scenarios and model versions.
Early Adopters
Focuses specifically on comprehensive, hazard‑oriented evaluation of perception systems—rather than end‑to‑end self‑driving performance—enabling fine‑grained measurement of what the perception stack sees, misclassifies, or misses under a wide variety of dangerous or unusual conditions.