Physical AI ：https://www.nvidia.com/en-us/glossary/generative-physical-ai/ is moving rapidly from research to large-scale deployment. By 2035, ABI Research ：https://my.abiresearch.com/research/15976/ projects an installed base of 49 million level 3-5 autonomous vehicles (AVs) , while Omdia ：https://omdia.tech.informa.com/om146251/robotics-hardware-market-forecast--2026 estimates that roughly 60 million industrial robots will be deployed between 2026 and 2035 . As these machines enter roads, factories, warehouses and other environments shared with people, safety must scale with them.
Physical AI safety means proving that AI-driven machines — AVs ：https://www.nvidia.com/en-us/glossary/autonomous-vehicles/ , humanoid robots ：https://www.nvidia.com/en-us/glossary/humanoid-robot/ , industrial robots and more — behave safely when their decisions turn into physical action. That requires safety across the hardware, software, AI, operating environment and deployment lifecycle — not a one-time check before deployment.
After years of testing and benchmarking, AVs ：https://www.nvidia.com/en-us/solutions/autonomous-vehicles/ continue to expand commercially. That progress has required developers to demonstrate how automated systems address potential hardware and software failures, limitations in intended functionality and AI-specific risks.
Robotics is approaching a similar inflection point as autonomous machines move into factories, warehouses and other environments shared with people.
Across physical AI, manufacturers, regulators, insurers and workplace safety teams need evidence that hardware, software, AI behavior and operating environments can work together safely without human intervention.
Four shifts define new safety standards:
Together, these shifts require safety to be operationalized across design, deployment and validation, from the underlying hardware to AI behavior and the operating environment.
Physical AI safety requires specialized engineering, data, processes and validation that few companies can reproduce alone. NVIDIA’s safety foundation draws on more than a decade of development in AV safety, building expertise in functional safety, sensor fusion, AI behavior assurance, vision AI, simulation and real-world validation.
NVIDIA Halos ：https://www.nvidia.com/en-us/ai-trust-center/halos/autonomous-vehicles/?deeplink=use-case-tabs--2 is the first and only full-stack safety system for physical AI, helping developers engineer safety across every layer of design, validation and deployment. The principles are shared across AVs and robotics, while the platforms, standards and evidence remain specific to each domain.
Together, these elements connect cloud-based AI development and simulation with in-vehicle deployment so safety evidence can remain traceable across the vehicle lifecycle.
Across both AV and robotics, the NVIDIA Halos AI Systems Inspection Lab ：https://www.nvidia.com/en-us/ai-trust-center/physical-ai/safety-certification/ turns safety, cybersecurity and AI safety requirements into repeatable inspections and helps prepare Halos integrations for final system-level certification by third-party agencies.
NVIDIA Halos connects the companies that build, integrate, assess and deploy physical AI solutions, including product developers, software and embedded-system providers, sensor and silicon companies, safety solution developers and certification bodies.
In autonomous vehicles, Geely, Isuzu , Nissan (powered by Wayve software) and Einride are building level 4-ready vehicles on NVIDIA Hyperion, supported by Halos OS.
Uber, Grab, Lyft and other mobility providers are also using Hyperion to scale robotaxi development and deployment. Members of the NVIDIA Halos AI Systems Inspection Lab ：https://www.nvidia.com/en-us/ai-trust-center/physical-ai/safety-certification/?_gl=1*qks3dj*_gcl_aw*R0NMLjE3ODc3NTcxODguQ2owS0NRanduYnJVQmhET0FSSXNBS0toUHBleEl2bnlBMGloYWR2bUEyTlIxVjlycFVSaVViZnZuYnFySWZOaUZaUGp3bnlnU3RfNTIxVWFBdTI1RUFMd193Y0I.*_gcl_au*MTM2NDIwNjE5Mi4xNzg4Mzk0NDg1Li0uLS4xNzg4Mzk0NTQ0LjEyNjE4OTg4MzkuMTc4OTE0OTcwMy4xNzg5MTczNzgz include AUMOVIO, Bosch , Gatik , Hesai, Lucid , MIRA, onsemi , PlusAI, Sony, Valeo and Wayve, spanning autonomous-driving development, ADAS, sensors, silicon, systems integration, validation and safety assurance.
In robotics, acontis and QNX provide the embedded software needed to run safety functions predictably, while Advantech ：https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.advantech.com%2Fen%2Fresources%2Fnews%2Fadvantech-mic-735-brings-functional-safety-to-physical-ai-systems&data=05%7C02%7Cpfox%40nvidia.com%7C0223fcf382c746a60e5d08df1201f825%7C43083d15727340c1b7db39efd9ccc17a%7C0%7C0%7C639249471820682901%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=jQ3sOLBihRlPDCJXaYi34Po9HVvyhUAnAByBPhB0TTQ%3D&reserved=0 and NexCOBOT build safety-designed NVIDIA IGX systems. Infineon , NXP , STMicroelectronics and Texas Instruments contribute sensor, safety-microcontroller and other semiconductor technologies. KION Group is developing functional safety agents for autonomous forklifts. Agilit y is integrating NVIDIA IGX Thor and Halos Core into the safety system for its Digit 5 humanoid ：https://www.agilityrobotics.com/content/agility-unveils-digit-5-humanoid-robot-built-for-cooperatively-safe-work-at-scale .
For AVs, TÜV SÜD certified NVIDIA’s Automotive Product Lifecycle software process and DriveOS 6.0 to ISO 26262 ASIL D, as well as NVIDIA’s automotive engineering processes to ISO/SAE 21434. TÜV Rheinland also performed an independent UNECE safety assessment of NVIDIA DRIVE AV.
For robotics, TÜV Rheinland is inspecting NVIDIA IGX Thor, Halos OS and Holoscan Sensor Bridge for functional-safety certification readiness, building on TÜV SÜD’s inspection of the Thor SoC and Halos Core for ISO 26262.
Across physical AI, ANAB has accredited the NVIDIA Halos AI Systems Inspection Lab as an ISO/IEC 17020 inspection body. The lab inspects scoped Halos integrations and helps companies prepare for final certification by independent third-party bodies.
The companies that scale physical AI will not simply build the most capable systems. They will build systems that can be assessed, certified, deployed and trusted in the real world. Designing functional safety from the start is what separates a prototype from a scalable solution.
Learn more about NVIDIA Halos for AVs ：http://nvidia.com/en-us/ai-trust-center/halos/autonomous-vehicles/ and robotics ：https://www.nvidia.com/en-us/ai-trust-center/halos/robotics/ , and explore the full-stack safety architecture for physical AI.
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