Train AV 3.0 end-to-end models with massive, diverse datasets spanning real and synthetic driving. Leverage log replay, scenario libraries, and high-performance simulation to capture edge cases and safety-critical interactions, accelerating model robustness and generalization.

Real & Synthetic Data at Scale

Train AV 3.0 end-to-end models with massive datasets spanning both log replays and human-like synthetic scenarios. Capture rare, safety-critical edge cases that are impractical to source from road testing alone.

Photorealistic Data

Enhance model training with lifelike environments and sensor outputs. Using NVIDIA Omniverse™ to generate high-fidelity visual data from our scenario libraries, including accurate lighting, materials, and dynamic agent behavior powered by ITRA, we enable AV 3.0 models to learn in conditions that closely mimic real-world driving.

Robust Edge-Case Exposure

Systematically stress-test AV 3.0 policies against critical events such as red-light runners, obstacle avoidance, and adversarial behaviors. Improve generalization and safety by confronting models with worst-case conditions early in training.

Request the application note Creating Rare Safety-Critical Edge Cases for Next-Generation AV 3.0 Simulation to learn how Inverted AI is providing a unique solution for AV 3.0 developers by generating cost effective data for rare, safety-critical edge cases.

Learn More

Interested in the details? Fill in the form to get more information sent to your email.

All fields are required.