Expand an ODD to include elements not included in existing training datasets. Utilize synthetic data to address edge cases by targeting data sparsity issues or class imbalances.

Train ML models on synthetic data to improve performance on long tail cases by up to 3x.

Augment real data with synthetic datasets to reduce the cycle time and cost for collecting and labeling new training datasets when failures occur in testing and production.

Physically accurate synthetic data generated with validated, hardware-specific sensor models.

Error-free ground truth labels in industry-standard formats.

Mitigate the simulation-to-real domain gap with parameters to tune sensor and material behavior and domain adaptations to remove synthetic-specific features.

Procedurally generated parking structures with thousands of variations in parking spot markings, materials, vehicles, VRUs, and other ODD elements.

Directly define distributions over all scene components such as environments, weather, and lighting to maximize diversity and target specific edge cases.

Regions include the U.S., Canada, Europe, Japan, China, South Korea, and more.