Uber wants to turn its millions of drivers into a sensor grid for self-driving companies
Source: TechCrunch
Uber’s Sensor Grid Initiative
Uber’s long‑term ambition goes well beyond shuttling passengers: the company eventually wants to outfit its human drivers’ cars with sensors to collect real‑world data for autonomous‑vehicle (AV) companies—and potentially other firms training AI models on physical‑world scenarios.
Program Origin
Praveen Neppalli Naga, Uber’s chief technology officer, revealed the plan at TechCrunch’s StrictlyVC event in San Francisco. He described it as a natural extension of a nascent program announced in late January called AV Labs.
“That is the direction we want to go eventually,” Naga said of equipping human drivers’ 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 relies on a small, dedicated fleet of sensor‑equipped cars that Uber operates itself, separate from its driver network. The ambition, however, is much larger. Uber has millions of drivers globally, and even a fraction of those cars transformed into rolling data‑collection platforms would dwarf the data‑gathering capabilities of any individual AV company.
Data as the Bottleneck
The insight driving the program is that the limiting factor for AV development is no longer the underlying technology but data.
“The bottleneck is data,” Naga explained. “Companies like Waymo need to go around and collect the data, collect different scenarios. You may be able to say: in San Francisco, ‘At this school intersection, I want some data at this time of day so I can train my models.’ 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.”
Partnerships and AV Cloud
Uber currently partners with 25 AV companies—including Wayve, which operates in London—and is building what Naga calls an “AV cloud”: a library of labeled sensor data that partners can query to train their models. Partners, which Uber plans to more aggressively invest in directly, can also run their trained models in “shadow mode” against real Uber trips, simulating how an AV would have performed without actually putting one on the road.
“Our goal is not to make money out of this data,” Naga said. “We want to democratize it.”
Commercial Implications
Given the obvious commercial value of what Uber is building, that positioning may not last long. The company has already made equity investments in numerous AV players, and its ability to offer proprietary training data at scale could give it significant leverage over a sector that currently depends on Uber’s ride marketplace to reach customers.