Volkswagen Group Builds Physical AI Data Flywheel for Autonomous Vehicle Development with Integrations from Voxel51, NVIDIA, and Microsoft

New reference architecture for AV connects real-world fleet data to simulation, training, and validation. The collaboration is expected to reduce repeated physical test drives by up to 90% and shorten AI regression test cycles by up to 85%.

BERLIN, Oct. 21, 2026 /PRNewswire/ -- At NVIDIA GTC Berlin, Porsche, Volkswagen Group brands, and CARIAD announced that they are jointly building a shared AV data flywheel, with Porsche leading the initial implementation and ecosystem integration together with NVIDIA, Microsoft, and Voxel51. With a fleet generating data equivalent to 11,000 average drivers per day, the Volkswagen Group is leading the development of a physical AI stack that connects fleet data to simulation, training, and evaluation.

• Volkswagen is building the physical AI stack. It brings its connected fleet, real-world driving data, vehicle platform, and system requirements to build a faster development loop for advanced driving systems.
• Voxel51 provides the data layer for the flywheel. Its multimodal data platform turns raw fleet data into high-quality training and simulation inputs, then connects model failures back to the scenarios and data needed for the next iteration.
• NVIDIA provides data retrieval, generation and simulation technologies including NVIDIA Cosmos Data Factory for preparing, augmenting, and evaluating training data, NVIDIA Omniverse NuRec libraries for reconstructing real-world drives, and open models and tools for testing driving systems in simulation.
• Azure provides the infrastructure. It supplies the cloud and compute needed to run these workflows securely at fleet scale on infrastructure Volkswagen already uses.

The collaboration brings together specialized systems across the AV workflow to create a reference architecture for automakers building the next generation of advanced driving systems.

The data layer turns raw driving logs into better physical AI models

Automotive is one of the clearest proving grounds for physical AI. Advanced driving systems increasingly depend on models that can perceive, reason about, and act in the physical world, using data collected from real vehicles operating in complex, changing environments.

In physical AI, that makes real-world data one of an automaker's most valuable assets. Models improve by learning from the environments and behaviors they encounter, which means vehicles on the road can contribute to a growing body of experience that is difficult to reproduce anywhere else. For automakers with large connected fleets, that data advantage compounds over time.

Voxel51 powers the following workflows in Volkswagen Group's AV data flywheel: multimodal search and retrieval, automated labeling, scenario-based curation, dataset balancing, training data selection, and model validation.

"The key to really industry-leading performance is to harness the data that your company has access to. You need to own the development and iteration loop, even if you don't build every layer of the stack yourself," said Voxel51 CEO Brian Moore.

Virtual validation: testing driving-software releases on reconstructed drives

For Porsche and the Volkswagen Group, virtual validation of driving-software releases is one of the first applications of the data flywheel. The team renders reconstructed camera inputs from real drives and compares simulated outputs with real-world measurements. The aim is to make testing repeatable, shorten feedback cycles, and reduce dependence on physical test drives.

Reconstruction is unforgiving. A camera calibration that's off by a few degrees or a LiDAR clock offset won't stop a reconstruction job. It produces a scene that scores fine and looks wrong, hours of GPU time later — with every downstream simulation inheriting that error.

So before a drive goes anywhere near a GPU, Voxel51 audits the raw recording across roughly 80 checks covering sensor integrity, timing, calibration, trajectories, actors, and coordinate frames. The audit reads MCAP files directly, and the fastest checks complete in a fraction of a second per drive, making it practical to validate data across an entire fleet.

Each check records whether it passed, failed, or could not run, so missing inputs are not mistaken for valid data. Those results determine whether a recording is suitable for downstream reconstruction and simulation, where gaps in calibration, synchronization, projection, or metadata can compromise scene fidelity.

"Simulation is transforming how autonomous vehicles are developed, turning real-world driving experience into a continuous cycle of learning and improvement," said Rishi Dhall, vice president of automotive at NVIDIA. "With Voxel51's data platform and NVIDIA Omniverse NuRec and Cosmos, Volkswagen Group can turn fleet data into high-fidelity simulation scenarios to accelerate the development and validation of next-generation driving systems."

From Voxel51, engineers can launch and track NVIDIA Omniverse NuRec reconstructions and data generated from NVIDIA Cosmos runs. Each synthetic output is automatically checked for quality and remains linked to the source clip, generation recipe, and model that produced it. Completed reconstructions return with the original camera view, the rendered output, and the reconstructed 3D scene ready for the team's simulator.

Voxel51 evaluates each reconstruction across multiple dimensions rather than collapsing quality into a single score. Image quality is measured by camera and object class, with separate checks for vehicles and pedestrians so localized failures are not hidden by aggregate metrics. Each run then enters a review queue, where engineers can approve it for simulation, route it back for retraining, or reject it. 

Each stage runs as a job on Microsoft Azure, where the Volkswagen Group manages its fleet data. Azure provides the storage and compute needed to scale data processing, reconstruction, and simulation while keeping those workloads inside the Volkswagen Group's existing security, identity, and regional governance model.

More details are available in the technical deep dive from Daniel Gural, Voxel51's Head of Technical Partnerships.

Porsche is playing a key role in the development of a shared AV data flywheel across Volkswagen Group brands, using Cosmos Data Factory through Voxel51 on Microsoft Azure. This collaboration is expected to reduce repeated physical test drives by up to 90% and shorten AI regression test cycles by up to 85%.

Why fleet data is the advantage in AV

The Volkswagen Group recognized early that the data and software development loop would become a core source of competitive advantage.

A common data model lets driving data and scenarios created in one project carry forward across vehicle programs, software releases, and brands. Over time, every new drive expands that scenario base, while reconstructed scenes can be reused across future development and validation cycles. The result is a compounding data flywheel.

For automakers, the opportunity is increasingly defined not only by how much proprietary data they have, but by how quickly they can turn what happens on the road into the next useful training, simulation, and validation workload.

Try Voxel51 at app.voxel51.com.

About Voxel51

Voxel51 is an end-to-end data platform for physical AI, helping teams turn complex multimodal datasets into better AI models. The platform unifies search, visualization, curation, annotation, and evaluation, with AI-powered workflows that automate data-intensive tasks, while giving developers control over their models and infrastructure. Voxel51 is trusted by teams at Berkshire Grey, Porsche, Volkswagen Group, Ford and Precision Planting to develop AI systems that operate in the physical world.

SOURCE Voxel51