Physical AI is moving from research labs into the real world, powering intelligent robots and autonomous vehicles (AVs) — such as robotaxis — that must reliably sense, reason and act...
Physical AI models — which power robots, autonomous vehicles and other intelligent machines — must be safe, generalized for dynamic scenarios and capable of perceiving, reasoning and operating in real...
Simulated driving environments enable engineers to safely and efficiently train, test and validate autonomous vehicles (AVs) across countless real-world and edge-case scenarios without the risks and costs of physical testing....
Autonomous vehicle (AV) stacks are evolving from many distinct models to a unified, end-to-end architecture that executes driving actions directly from sensor data. This transition to using larger models is...
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...
Editor’s note: This blog has been updated to reflect that as of July 24, 2025, NVIDIA Omniverse on cloud is being offered as a part of NVIDIA DGX Cloud. Generative...
The path to safe, widespread autonomous vehicles is going digital. MITRE — a government-sponsored nonprofit research organization — today announced its partnership with Mcity at the University of Michigan to...
To make transportation safer, autonomous vehicles (AVs) must have processes and underlying systems that meet the highest standards. NVIDIA DRIVE OS is the operating system for in-vehicle accelerated computing powered...
As the autonomous vehicle industry enters the next year, it will start navigating into even greater technology frontiers. Next-generation vehicles won’t just be defined by autonomous driving capabilities. Everything from...
Training, testing and validating autonomous vehicles requires a continuous pipeline — or data factory — to introduce new scenarios and refine deep neural networks. A key component of this process...
