1. [Topics](/topics/) 2. Automotive # Autonomous Vehicle Developer Resources Get started with NVIDIA DRIVE® open models, SDKs, and tools, and DRIVE AGX™ developer kits for autonomous vehicle development. Explore toolkits, SDKs, and more: [Downloads Center](/drive/downloads) Quick Links: - [DRIVE SDK Docs](/drive/documentation) - [Alpamayo Recipes](https://github.com/NVlabs/alpamayo-recipes) - [NGC Automotive Catalog](https://catalog.ngc.nvidia.com/search?filters=industry%7CAutomotive+%2F+Transportation%7Cindus_automotive_transportation&orderBy=weightPopularDESC) - [Omniverse NuRec Docs](/omniverse/nurec) - [Cosmos Cookbook](https://nvidia-cosmos.github.io/cosmos-cookbook/) ## Explore Autonomous Vehicle Solutions 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. ### Model Development Transform raw sensor data into high-quality training datasets with the NVIDIA [Physical AI Data Factory Blueprint](https://nvidianews.nvidia.com/news/nvidia-announces-open-physical-ai-data-factory-blueprint-to-accelerate-robotics-vision-ai-agents-and-autonomous-vehicle-development). NVIDIA [Cosmos™ Curator](https://github.com/NVIDIA/cosmos-curator) and [Cosmos Dataset Search](https://github.com/NVIDIA-Omniverse-blueprints/cosmos-dataset-search) automate data curation and rare scenario retrieval at scale. The data fine-tunes NVIDIA [Alpamayo](https://github.com/NVlabs/alpamayo-recipes) open VLA reasoning models and [AlpaGym](https://github.com/NVlabs/alpagym) runs closed-loop RL post-training at GPU scale. [Learn More](/drive/infrastructure) ### Simulation and Validation Accelerate AV development with high-fidelity, scalable simulation workflows. Reconstruct real-world driving logs into interactive simulation with NVIDIA [Omniverse NuRec](/omniverse/nurec), generate photorealistic synthetic scenarios with [Cosmos-Dreams](https://github.com/nv-tlabs/omni-dreams), and run closed-loop policy evaluation at GPU scale with [AlpaSim](https://github.com/NVlabs/alpasim). [Learn More](/drive/simulation) ### DRIVE AGX DRIVE AGX™ is an automotive-grade compute platform delivering industry-leading performance. High-performance DRIVE AGX compute, paired with trusted NVIDIA [DRIVE AGX Orin](/drive/ecosystem-orin)™ and [DRIVE AGX Thor](/drive/ecosystem-thor)™ ecosystem partners, accelerates production deployment. DRIVE AGX is powered by the [DriveOS](/drive/os)™ SDK featuring NVIDIA [DriveWorks](/drive/driveworks), CUDA®, TensorRT™, NvMedia, and NvStreams. [Learn More](/drive/agx) ## Get Started with Autonomous Vehicle Use Cases DRIVE AGX Development Training Data Preparation Synthetic Data Generation Model Training Simulation and Validation ### Develop and Test on Production-Equivalent Compute The NVIDIA DRIVE AGX developer kit is the reference compute platform for the DRIVE Hyperion™ architecture, providing production-equivalent in-vehicle hardware for integrating and testing AV software stacks before vehicle-level deployment. - [DRIVE AGX™ Developer Kits](/drive/agx) - [DRIVE AGX™ SDK Developer Program](/drive-agx-program) ### Run AV Software on a Safety-Certified Operating System 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. - [DriveOS™ SDK](/drive/os) ### Process Raw Sensor Data Into Training Datasets 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. - [Physical AI Autonomous Vehicle Dataset](https://huggingface.co/datasets/nvidia/PhysicalAI-Autonomous-Vehicles) - [Cosmos Curator Developer Docs](https://github.com/NVIDIA/cosmos-curator) - [Cosmos Curator on GitHub](https://github.com/NVIDIA/cosmos-curator) ### Retrieve Rare Scenarios at Scale 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. - [Cosmos Dataset Search Developer Docs](https://github.com/NVIDIA-Omniverse-blueprints/cosmos-dataset-search) - [Cosmos Dataset Search on GitHub](https://github.com/NVIDIA-Omniverse-blueprints/cosmos-dataset-search) ### Automate Reasoning Label Generation 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. - [CoC Autolabeling Pipeline on GitHub](https://github.com/NVlabs/alpamayo-coc-autolabeler) ### Reconstruct Real-World Scenes for Simulation 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. - [NuRec Developer Docs](/omniverse/nurec) - [NuRec on NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/nre/containers/nre-ga/-) - [InstantNuRec on GitHub](https://github.com/NVIDIA/instant-nurec) ### Generate Synthetic Driving Data Across Conditions NVIDIA Cosmos Transfer is a world foundation model that generates photorealistic synthetic driving data from structured inputs, including HD Maps, lidar depth, and text prompts. Transfer produces multi-view consistent video across weather, lighting, and environmental variations for AV perception and planning model training. - [Cosmos Transfer Developer Docs](https://docs.nvidia.com/cosmos/latest/transfer2.5/index.html) - [Cosmos Transfer on GitHub](https://github.com/nvidia-cosmos/cosmos-transfer2.5) - [Cosmos Transfer on Hugging Face](https://huggingface.co/nvidia/Cosmos-Transfer2.5-2B) ### Insert and Harmonize Assets Across Lighting Conditions 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. - [Fixer on GitHub](https://github.com/nv-tlabs/Fixer) - [Harmonizer on GitHub](https://github.com/NVIDIA/harmonizer/) - [Asset Harvester on GitHub](https://github.com/NVIDIA/asset-harvester) ### Fine-Tune a Reasoning Foundation Model on Fleet Data 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. - [Alpamayo Recipes](https://github.com/NVlabs/alpamayo-recipes) - [Alpamayo 2 Super on Hugging Face](https://huggingface.co/nvidia/Alpamayo2-Super) - [Alpamayo 2 Super on GitHub](https://github.com/NVlabs/alpamayo2) ### Post-Train AV Policies With Reinforcement Learning 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. - [Post-Train with AlpaGym Tech Blog](/blog/how-to-post-train-autonomous-vehicle-models-in-closed-loop-with-nvidia-alpamayo/) - [AlpaGym on GitHub](https://github.com/NVlabs/alpagym) ### Run Closed-Loop Simulations 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. - [AlpaSim on GitHub](https://github.com/NVlabs/alpasim) - [AlpaSim E2E Closed-Loop Challenge](https://huggingface.co/spaces/nvidia/AlpasimE2EClosedLoopChallenge2026) ### Replay and Modify Real-World Driving Scenarios 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. - [NuRec Developer Docs](/omniverse/nurec) - [NuRec on NGC](https://catalog.ngc.nvidia.com) ### Validate Policy Behavior in Novel Environments NVIDIA Cosmos-Dreams renders photorealistic camera frames conditioned on live policy actions for continuous novel scenario generation in closed-loop simulation, at up to 54 fps on 1x GB300 and 30 fps on RTX 6000. - [Cosmos-Dreams Research Blog](https://research.nvidia.com/labs/sil/projects/omnidreams-blog/) - [Cosmos-Dreams on GitHub](https://github.com/nv-tlabs/omni-dreams) - [Cosmos-Dreams on Hugging Face](https://huggingface.co/nvidia/omni-dreams-models) ## Autonomous Vehicle Learning Resources | title | featured | x_formats | document_url | technologies | document_date | short_summary | document_title | learning_level | x_content_types | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Accelerating AV Simulation with Neural Reconstruction and World Foundation Models | false | blog | https://developer.nvidia.com/blog/accelerating-av-simulation-with-neural-reconstruction-and-world-foundation-models/ | Cosmos, Omniverse NuRec | 2025-11-13T00:00:00.000Z | Implement NuRec and Cosmos WFMs in CARLA to build photorealistic AV simulation pipelines. | Accelerating AV Simulation with Neural Reconstruction and World Foundation Models | Technical - Intermediate | Explainer | | How to Build In-Vehicle AI Agents: From Cloud to Car | true | blog | https://developer.nvidia.com/blog/how-to-build-in-vehicle-ai-agents-with-nvidia-from-cloud-to-car/ | DRIVE AGX, Nemotron | 2026-05-14T00:00:00.000Z | Design and deploy agentic AI cockpit systems using DRIVE AGX Thor and Nemotron VLMs. | How to Build In-Vehicle AI Agents: From Cloud to Car | Technical - Intermediate | How-to | | Inside the Alpamayo Ecosystem: Partners, Open Research, and What's Next | false | video | https://www.nvidia.com/en-us/on-demand/session/alpamayo26-alpa06/ | Alpamayo | 2026-06-03T00:00:00.000Z | Survey the Alpamayo open ecosystem, active partners, and upcoming research directions. | Inside the Alpamayo Ecosystem: Partners, Open Research, and What's Next | Technical - Beginner | Overview | | Safety in the Loop: Scaling AV Safety Through Industry Standards | false | video | https://www.nvidia.com/en-us/on-demand/session/alpamayo26-s1/ | DRIVE AGX, DRIVE AV | 2026-06-03T00:00:00.000Z | Evaluate how DriveOS and Hyperion meet safety standards for scalable L4 AV deployment. | Safety in the Loop: Scaling AV Safety Through Industry Standards | Technical - Intermediate | Explainer | | From Research to Production: How Alpamayo Accelerates AV Development | false | video | https://www.nvidia.com/en-us/on-demand/session/gtc26-s81779/ | Alpamayo | 2026-03-17T00:00:00.000Z | Discover how Alpamayo advances end-to-end AV stacks toward scalable Level 4 deployment. | From Research to Production: How Alpamayo Accelerates AV Development | Technical - Intermediate | Overview | | How Open World Models Are Powering Breakthroughs in Physical AI | false | video | https://www.nvidia.com/en-us/on-demand/session/gtc26-s81667/ | Cosmos | 2026-03-17T00:00:00.000Z | Examine how open world foundation models generate photorealistic data for physical AI reasoning. | How Open World Models Are Powering Breakthroughs in Physical AI | Technical - Intermediate | Overview | | Advancing Autonomous Vehicles With World Models | false | video | https://www.nvidia.com/en-us/on-demand/session/gtc26-s82446/ | Alpamayo, Cosmos | 2026-03-17T00:00:00.000Z | Evaluate how world foundation models accelerate AV policy simulation and validation. | Advancing Autonomous Vehicles With World Models | Technical - Intermediate | Explainer | | An Introduction to NVIDIA Cosmos for Physical AI | false | hands-on | https://www.nvidia.com/en-us/on-demand/session/gtcdc25-dct51187/ | Cosmos | 2025-10-29T00:00:00.000Z | Explore Cosmos world foundation models, tokenizers, and data pipelines in a hands-on introductory course. | An Introduction to NVIDIA Cosmos for Physical AI | Technical - Beginner | Tutorial | | Safety in the Loop: Open Datasets and Models for Safe AVs | false | video | https://www.youtube.com/watch?v=-5jfeuv2Lkw | Alpamayo | 2026-01-05T00:00:00.000Z | Discover how open datasets and models support safe AV development and validation. | Safety in the Loop: Open Datasets and Models for Safe AVs | Technical - Intermediate | Explainer | | Autonomous Driving With Reasoning Models | false | video | https://www.nvidia.com/en-us/on-demand/session/alpamayo26-alpa03/ | Alpamayo | 2026-06-03T00:00:00.000Z | Apply reasoning model concepts to end-to-end autonomous vehicle policy development. | Autonomous Driving With Reasoning Models | Technical - Intermediate | Explainer | | Inside Alpamayo 1: Making Autonomous Vehicles Reason | false | video | https://www.youtube.com/watch?v=V9E4GX5vaC8 | Alpamayo | 2026-02-11T00:00:00.000Z | Examine the architecture and capabilities of the Alpamayo 1 reasoning model. | Inside Alpamayo 1: Making Autonomous Vehicles Reason | Technical - Beginner | Explainer | | Simplify End-to-End AV Development with Cosmos World Foundation Models | false | blog | https://developer.nvidia.com/blog/simplify-end-to-end-autonomous-vehicle-development-with-new-nvidia-cosmos-world-foundation-models/ | Cosmos | 2025-06-11T00:00:00.000Z | Integrate Cosmos Predict, Transfer, and Reason WFMs to accelerate AV synthetic data pipelines. | Simplify End-to-End AV Development with Cosmos World Foundation Models | Technical - Intermediate | Explainer | | Build Physical AI with Edge-First LLMs for Autonomous Vehicles | false | blog | https://developer.nvidia.com/blog/build-next-gen-physical-ai-with-edge%e2%80%91first-llms-for-autonomous-vehicles-and-robotics/ | Alpamayo, DRIVE AGX | 2026-03-12T00:00:00.000Z | Deploy Alpamayo with TensorRT Edge-LLM for on-vehicle inference in production AV stacks. | Build Physical AI with Edge-First LLMs for Autonomous Vehicles | Technical - Advanced | Tutorial | | How to Enhance 3D Gaussian Reconstruction Quality for Simulation | false | blog | https://developer.nvidia.com/blog/how-to-enhance-3d-gaussian-reconstruction-quality-for-simulation/ | Cosmos, Omniverse NuRec | 2025-12-11T00:00:00.000Z | Apply NuRec Fixer to remove artifacts and restore fidelity in 3DGS simulation environments. | How to Enhance 3D Gaussian Reconstruction Quality for Simulation | Technical - Intermediate | How-to | | How to Post-Train AV Models in Closed-Loop with Alpamayo | true | blog | https://developer.nvidia.com/blog/how-to-post-train-autonomous-vehicle-models-in-closed-loop-with-nvidia-alpamayo/ | Alpamayo | 2026-06-03T00:00:00.000Z | Apply AlpaGym closed-loop RL to post-train AV driving policies with compounding-error awareness. | How to Post-Train AV Models in Closed-Loop with Alpamayo | Technical - Advanced | Tutorial | | Generate Synthetic Data for Physical AI with Cosmos World Foundation Models | false | hands-on | https://www.nvidia.com/en-us/on-demand/session/gtc26-dlit81644/ | Cosmos | 2026-03-17T00:00:00.000Z | Build synthetic data generation pipelines for physical AI using Cosmos WFMs in a hands-on lab. | Generate Synthetic Data for Physical AI with Cosmos World Foundation Models | Technical - Intermediate | Tutorial | | Closing the Development Loop: Simulation and Training With AlpaSim and AlpaGym | false | video | https://www.nvidia.com/en-us/on-demand/session/alpamayo26-alpa04/ | Alpamayo | 2026-06-03T00:00:00.000Z | Apply AlpaSim and AlpaGym to build closed-loop reinforcement learning pipelines for AVs. | Closing the Development Loop: Simulation and Training With AlpaSim and AlpaGym | Technical - Advanced | Tutorial | | Building Autonomous Vehicles That Reason with Alpamayo | false | blog | https://developer.nvidia.com/blog/building-autonomous-vehicles-that-reason-with-nvidia-alpamayo/ | Alpamayo | 2026-05-01T00:00:00.000Z | Build and evaluate a reasoning-based AV pipeline using Alpamayo 1 and AlpaSim. | Building Autonomous Vehicles That Reason with Alpamayo | Technical - Beginner | Tutorial | | Advancing Level 4 Autonomy: Scalable, Safe AVs and Robotaxis | false | video | https://www.youtube.com/watch?v=vUBytUceIjM | DRIVE AV | 2026-03-16T00:00:00.000Z | Analyze the technical path to scalable, safe Level 4 autonomous vehicle deployment. | Advancing Level 4 Autonomy: Scalable, Safe AVs and Robotaxis | Technical - Intermediate | Overview | | How AI Helps Autonomous Vehicles See Outside the Box | false | video | https://www.youtube.com/watch?v=HS1wV9NMLr8 | DRIVE AV | 2019-10-23T00:00:00.000Z | Explore DRIVE Labs perception techniques that extend AV sensor coverage and understanding. | How AI Helps Autonomous Vehicles See Outside the Box | Technical - Beginner | Explainer | | Alpamayo 2 Super: The Open Reasoning Model for Robotaxis | true | video | https://www.youtube.com/watch?v=kJRVwaYwvt0 | Alpamayo | 2026-06-16T00:00:00.000Z | Explore Alpamayo 2 Super, a 32B reasoning VLA model for Level 4 robotaxi development. | Alpamayo 2 Super: The Open Reasoning Model for Robotaxis | Technical - Beginner | Overview | | Accelerate AV Development with the DRIVE AGX Thor Developer Kit | false | blog | https://developer.nvidia.com/blog/accelerate-autonomous-vehicle-development-with-the-nvidia-drive-agx-thor-developer-kit/ | DRIVE AGX | 2026-01-07T00:00:00.000Z | Set up and deploy the DRIVE AGX Thor Developer Kit for production-level AV development. | Accelerate AV Development with the DRIVE AGX Thor Developer Kit | Technical - Intermediate | How-to | | The ChatGPT Moment for Autonomous Driving | false | video | https://www.nvidia.com/en-us/on-demand/session/alpamayo26-alpa01/ | Alpamayo | 2026-06-03T00:00:00.000Z | Understand how reasoning-based VLA models are transforming autonomous vehicle development. | The ChatGPT Moment for Autonomous Driving | Technical - Beginner | Overview | | Train and Test End-to-End Autonomous Vehicles with Alpamayo | true | video | https://www.nvidia.com/en-us/on-demand/session/gtc26-dlit82311/ | Alpamayo | 2026-03-17T00:00:00.000Z | Learn to train and evaluate end-to-end AV models using Alpamayo and AlpaSim. | Train and Test End-to-End Autonomous Vehicles with Alpamayo | Technical - Intermediate | Tutorial | | How Simulation Enables Safer Autonomous Vehicles | false | video | https://www.nvidia.com/en-us/on-demand/session/ces25-foretellix/ | DRIVE AV | 2026-01-06T00:00:00.000Z | Learn how the Foretellix toolchain uses simulation to test and validate AV safety at scale. | How Simulation Enables Safer Autonomous Vehicles | Technical - Beginner | Explainer | | Training at Scale: The Physical AI Dataset | false | video | https://www.nvidia.com/en-us/on-demand/session/alpamayo26-alpa02/ | Alpamayo | 2026-06-03T00:00:00.000Z | Discover how the Physical AI dataset enables large-scale AV model training across 25 countries. | Training at Scale: The Physical AI Dataset | Technical - Intermediate | Explainer | | The Generative AI In-Vehicle Experience Powered by NVIDIA DRIVE | false | video | https://www.youtube.com/watch?v=t-UPlPlrYgQ | DRIVE AGX, Nemotron | 2024-03-18T00:00:00.000Z | Examine generative AI capabilities for personalized in-cabin experiences on DRIVE hardware. | The Generative AI In-Vehicle Experience Powered by NVIDIA DRIVE | Technical - Beginner | Demo | [Download the raw results data (JSON)](https://developer.nvidia.com/search-data/automotive.json)