NVIDIA provides an end-to-end stack for autonomous vehicle development—from sensor data curation and synthetic data generation to model training, closed-loop simulation, and production-grade in-vehicle compute.
Transform raw sensor data into high-quality training datasets with the NVIDIA Physical AI Data Factory Blueprint. NVIDIA Cosmos™ Curator and Cosmos Dataset Search automate data curation and rare scenario retrieval at scale. The data fine-tunes NVIDIA Alpamayo open VLA reasoning models and AlpaGym runs closed-loop RL post-training at GPU scale.
Accelerate AV development with high-fidelity, scalable simulation workflows. Reconstruct real-world driving logs into interactive simulation with NVIDIA Omniverse NuRec, generate photorealistic synthetic scenarios with NVIDIA Cosmos, and run closed-loop policy evaluation at GPU scale with AlpaSim.
DRIVE AGX™ is an automotive-grade compute platform delivering industry-leading performance. High-performance DRIVE AGX compute, paired with trusted NVIDIA DRIVE AGX Orin™ and DRIVE AGX Thor™ ecosystem partners, accelerates production deployment. DRIVE AGX is powered by the DriveOS™ SDK featuring NVIDIA DriveWorks, CUDA®, TensorRT™, NvMedia, and NvStreams.
The NVIDIA DRIVE AGX developer kit is the reference compute platform for the NVIDIA Hyperion architecture, providing production-equivalent in-vehicle hardware for integrating and testing AV software stacks before vehicle-level deployment.
NVIDIA DriveOS is the safety-certified automotive operating system for DRIVE AGX, providing sensor abstraction, compute scheduling, and middleware integration for automotive-grade AV development.
NVIDIA Cosmos Curator filters, annotates, and deduplicates large amounts of sensor data necessary for autonomous vehicle development. It taps into NVIDIA Cosmos Reason VLM for multimodal reasoning and shortens data processing pipelines from months to days.
NVIDIA Cosmos Dataset Search instantly searches and retrieves targeted scenarios from massive training datasets, powered by Cosmos-Embed NIM for semantic search across billions of clips in seconds.
NVIDIA CoC Auto-Labeling Pipeline automatically generates Chain of Causation reasoning labels for driving clips, removing manual annotation and accelerating dataset preparation for model training.
The NVIDIA Cosmos Data Factory’s Autonomous Vehicle Dataset Generation agent skill transforms driving video and vehicle signals into curated, searchable, augmented, evaluated datasets. The workflow reduces manual orchestration and expands training coverage without additional road collection.
NVIDIA Omniverse NuRec uses Gaussian-based methods to reconstruct and render interactive simulation from real-world driving data. Its 3DGUT core combines the speed of Gaussian splatting with the photorealism of ray-tracing for physically accurate AV simulation. NVIDIA InstantNuRec accelerates reconstruction by generating a 3D Gaussian splat in a single forward pass, reducing NuRec training iterations by up to 25% on a single GPU.
Generate synchronized camera and lidar data, targeting with up to 11 camera views, including fisheye. Vary road structure, traffic, lighting, and weather, and post-train the model on your own driving data to expand coverage of rare scenarios and reduce dependence on collecting every condition on roads.
NVIDIA Omniverse NuRec generative models improve reconstruction quality and scene diversity. Fixer removes flickering artifacts from rendered novel views. NVIDIA Harmonizer normalizes lighting across weather and time-of-day variations. NVIDIA Asset Harvester extracts 3D Gaussian objects from partial views.
NVIDIA Alpamayo 2 Super is an open 34B VLA reasoning foundation model with RL post-training, flexible multi-camera support, and navigation guidance. Post-training scripts for SFT and RL fine-tuning on proprietary fleet data are available on GitHub under Apache 2.0.
NVIDIA AlpaGym is the first modular RL framework for training AV policy models at GPU scale, running models through continuous decision and observation cycles to expose compounding errors that static datasets miss.
NVIDIA Alpamayo 1.5 Nano is a 10B-parameter reasoning vision-language-action model for autonomous driving research, generating trajectories and causal explanations to evaluate and adapt driving policies.
NVIDIA Alpamayo 2 Edge is a 2B-parameter in-vehicle reasoning vision-language-action model using adaptive reasoning to generate trajectories and causal explanations for fine-tuning and efficient deployment.
NVIDIA AlpaSim is an open-source closed-loop AV simulation framework. Its microservice architecture assigns rendering, physics, traffic behavior, and policy execution to separate GPU resources, with support for NVIDIA Omniverse NuRec and NVIDIA Cosmos-Dreams as rendering backends.
NVIDIA Omniverse NuRec reconstructs captured driving scenes into interactive 3D Gaussian splat environments for regression testing and safety evaluation, with sub-25 ms photoreal frame playback.
NVIDIA Cosmos™ 3 is the frontier open world foundation model for physical AI and the first full omnimodel to understand and generate text, image, video, ambient sound, and action. It unifies vision reasoning, world and action generation to accelerate vision analytics, synthetic data, simulation, and policy improvement.
NVIDIA Cosmos 3 simulation models are designed to expand training and testing for autonomous driving through synchronized multi camera and lidar generation across diverse driving scenarios.