While massive datasets like Waymo or nuScenes capture extensive real-world behavior, raw data alone isn't a validation asset. Using Nvidia's Physical AI Autonomous Vehicles Dataset as a case study, this presentation introduces a transferable workflow for converting proprietary fleet data into structured, reusable scenarios. The presentation will demonstrate a full pipeline: extracting trajectories, classifying events and mapping data to OSC2 scenario primitives. By quantifying coverage against a defined operational design domain (ODD) and performing gap analysis, Foretellix will show how to transform raw recordings into actionable insights that feed directly into verification plans and support critical ADAS/AD release decisions.

• Transform raw data into assets: Learn to convert massive volumes of raw fleet recordings into structured, reusable verification and validation assets
• Standardized scenario mapping: Master the workflow for extracting vehicle trajectories and mapping events to OpenSCENARIO 2.0 (OSC2) primitives
• ODD coverage quantification: Discover how to quantify data coverage against a defined operational design domain to identify critical testing gaps
• Transferable data workflows: Apply a proven case study approach to your proprietary data for consistent event recognition and classification
• Data-driven release decisions: See how coverage gap analysis feeds directly into validation plans to support confident ADAS/AD release decisions