As autonomous vehicles move from research labs to real-world deployment, the demand for high-fidelity simulations has never been greater. But over 50% of these simulations end up unusable because of bad input data – slowing teams and wasting millions in compute costs. This presentation explores how FiftyOne Physical AI Workbench solves the data pipeline bottleneck blocking physical AI at scale. Integrated with NVIDIA Omniverse NuRec and NVIDIA Cosmos, this data engine sits at the start of your simulation pipeline to ensure every reconstruction begins with validated, trustworthy data—cutting iteration cycles from weeks to hours.

In this session, you will learn:

• How to validate multimodal sensor data across 75+ critical checkpoints to catch alignment issues before they impact simulations
• How to transform raw sensor streams into structured, searchable datasets with automated enrichment
• How to trigger neural reconstructions with NVIDIA Omniverse NuRec for accurate 3D digital twins
• How to generate synthetic scene variations with NVIDIA Cosmos to expand test coverage

• What are the biggest data bottlenecks in current ML/AI data pipelines for ADAS and autonomous systems?
• As complexity increases, how do you maintain confidence that your datasets represent real-world conditions?
• What’s the biggest disconnect between your simulation/testing environments and what happens when systems are deployed in the field?
• Looking ahead, what do you think will be the defining factor that separates the leaders in autonomous vehicle development?
• What’s one assumption about AV development today that you think will prove wrong in the next few years?