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NVIDIA DGX is a purpose-built AI supercomputer for training deep learning models used in AV perception, mapping, and decision-making.

Accelerate multimodal data processing with NVIDIA Cosmos NeMo Curator, optimize model training with CUDA-X? AI and GPU-optimized kernels in NGC containers, and enhance inference with NVIDIA TensorRT? and Triton?. They all come together to help streamline end-to-end AV development workflows for maximum efficiency and performance.

The NVIDIA Omniverse Blueprint for AV Simulation is a reference workflow to create rich 3D worlds for training, testing, and validation. The blueprint lets you replay driving data, generate new ground-truth data, and perform closed-loop testing.

Autonomous vehicles generate terabytes of data daily from sensors like cameras, lidar, and radar. This data must be processed, labeled, and used for training AI models.

AV systems need to improve over time, learning from new data and scenarios to refine their decision-making algorithms.

Sensor data, system logs, and other data can be replayed to recreate the conditions under which a problem occurred and help identify the root cause.