An End-to-End Platform for Safer, Smarter Autonomous Vehicles
Developing autonomous vehicles (AVs) requires rigorous training and testing to ensure safe and effective operation in real-world environments. NVIDIA offers a comprehensive infrastructure—spanning both hardware and software—to help you develop, train, and validate autonomous driving systems at scale.
At the core of NVIDIA’s three key AV computing platforms—NVIDIA DGX™ for AI model training, NVIDIA Omniverse™ and Cosmos™ for simulation and validation, and DRIVE AGX for in-vehicle compute—is NVIDIA Halos. As the foundation system for AV safety, Halos integrates the hardware, software, tools, and models to protect the entire AV stack, from the cloud to the car.
NVIDIA Launches Halos, a Full-Stack Comprehensive Safety System for Autonomous Vehicles
NVIDIA Halos is a full-stack comprehensive safety system that unifies vehicle architecture, AI models, chips, software, tools, and services to ensure the safe development of autonomous vehicles (AVs) from cloud to car.
Accelerated and Scalable Training for Autonomous Vehicles
It takes a powerful combination of AI hardware and software to develop safe, intelligent AVs. NVIDIA accelerates the process by delivering end-to-end solutions, from AI training to high-fidelity sensor simulation.
AI Training Infrastructure
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.
Accelerated and Scalable Training for Autonomous Vehicles
Training autonomous vehicles is one of the most challenging aspects of development. These vehicles have to perceive and respond to a wide range of scenarios—from busy urban intersections to quiet rural roads—while understanding the nuances of traffic laws, road conditions, and unpredictable human behavior.
Massive Data Processing
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.
Continuous Learning
AV systems need to improve over time, learning from new data and scenarios to refine their decision-making algorithms.
Replay at Scale
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.
Products
The Computers for Autonomous Vehicles
The NVIDIA three-computer solution powers every stage of autonomous vehicle development, from AI training to simulation and real-world deployment.
NVIDIA DGX Platform
These systems are purpose-built for AI and deep learning, providing unmatched computational power to train complex neural networks for AVs.
Build and enhance digital twins from real-world sensor data, model physics and behavior, and generate physically accurate and diverse sensor data with the NVIDIA Omniverse Blueprint for AV Simulation.
Streamline AV Development With the NVIDIA AI Enterprise Software Suite
NVIDIA AI Enterprise software gives you the essential tools you need for streamlining the development and deployment of AV software. This includes everything from data preparation and training to optimizing for inference and deploying at scale.
NVIDIA Halos: A State-of-the-Art System for AV Safety
Over 15,000 engineering years invested in AV safety have led to the NVIDIA Halos system for chip-to-deployment AV safety. It combines hardware, software, tools, models, and proven design principles to safeguard end-to-end AV stacks.
Unblock data bottlenecks with the NVIDIA Physical AI Dataset, an open-source dataset for autonomous vehicle, robot, and smart space development. The unified collection is composed of validated data used to build NVIDIA physical AI—now available to developers on Hugging Face.
Hyundai Motor Group will tap into NVIDIA’s data-center-level computing and infrastructure to efficiently manage the massive data volumes essential for training its advanced AI models and building a robust AV software stack.
Wayve’s partnership with NVIDIA enables seamless deployment of autonomous driving, AI training, and fleet learning. This collaboration accelerates scalable, high-performance adoption of AI-driven automotive systems.
Volvo Cars and its software subsidiary, Zenseact, are investing in NVIDIA DGX systems for model training in the cloud. This will help ensure that future fleets are equipped with the most advanced and well-tested AI-powered safety features.
Waabi trusts NVIDIA hardware to run complex simulations and trains its AI models. They'll also use NVIDIA Cosmos for data curation for software development and simulation
NIO, a smart electric vehicle manufacturer, is using the NVIDIA DGX AI platform to improve training efficiency and GPU utilization of perception models for autonomous vehicles.
The autonomous vehicle (AV) revolution is here — and NVIDIA is at its forefront, bringing more than two decades of automotive computing, software and safety expertise to power innovation from the cloud to the car. At NVIDIA GTC, a global AI conference taking place this week in San Jose, California, dozens of transportation leaders are
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Teaching autonomous robots and vehicles how to interact with the physical world requires vast amounts of high-quality data. To give researchers and developers a head start, NVIDIA is releasing a massive, open-source dataset for building the next generation of physical AI. Announced at NVIDIA GTC, a global AI conference taking place this week in San
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Physical AI is unlocking new possibilities at the intersection of autonomy and robotics — accelerating, in particular, the development of autonomous vehicles (AVs). The right technology and frameworks are crucial to ensuring the safety of drivers, passengers and pedestrians. That’s why NVIDIA today announced NVIDIA Halos — a comprehensive safety system bringing together NVIDIA’s lineup
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