Uber Plans to Turn Its Driver Network Into a Real-World Data Grid for Autonomous Vehicle Companies

Uber is exploring a plan to equip its millions of human drivers with sensor kits, transforming their vehicles into rolling data-collection platforms for autonomous vehicle companies and AI developers training models on real-world scenarios.

Praveen Neppalli Naga, Uber's chief technology officer, outlined the ambition in an interview at TechCrunch's StrictlyVC event in San Francisco, describing it as a natural extension of a program called AV Labs that the company announced in late January.

"That is the direction we want to go eventually," Naga said of outfitting driver-owned vehicles. "But first we need to get the understanding of the sensor kits and how they all work. There are some regulations — we have to make sure every state has [clarity on] what sensors mean, and what sharing it means."

For now, AV Labs operates a small, dedicated fleet of sensor-equipped cars that Uber manages directly, separate from its broader driver network. The longer-term vision, however, is considerably wider in scope.

At the core of the initiative is a diagnosis of where autonomous vehicle development currently stalls. "The bottleneck is data," Naga said. "Companies need to go around and collect the data, collect different scenarios. The problem for all these companies is access to that data, because they don't have the capital to deploy the cars and go collect all this information."

Uber already has partnerships with 25 AV companies, including Wayve, which operates in London. The company is building what Naga described as an "AV cloud" — a library of labeled sensor data that partner companies can query and use to train their models.

Partners can also use the system to run trained models in "shadow mode" against live Uber trips, simulating how an autonomous vehicle would have performed without placing one on public roads.

"Our goal is not to make money out of this data," Naga said. "We want to democratize it."

That framing positions Uber as a neutral infrastructure provider rather than a commercial competitor to the AV companies that depend on its ride marketplace to reach customers — a distinction that may prove difficult to maintain as the platform scales.

The strategy reflects Uber's repositioning since it exited the self-driving car business years ago. Without its own autonomous vehicles, the company has faced questions about its long-term relevance as AV deployments expand globally. Building a proprietary data layer for the broader AV ecosystem offers one answer to those questions, reinforcing Uber's role as an essential intermediary even as the technology evolves.

Uber has also indicated plans to invest more aggressively and directly in its AV partners, which would give the company equity stakes in businesses that could become its primary customers for sensor data.

Whether regulators in all 50 states will accommodate the kind of sensor-equipped civilian vehicle network Uber envisions remains an open question — one that Naga himself acknowledged as a prerequisite before the program can expand beyond its current limited footprint.