Uber wants to turn its millions of drivers into a sensor network for self-driving companies

Uber has a long-term ambition that goes beyond just transporting passengers: The company wants to eventually outfit human drivers’ cars with sensors to ingest real-world data for autonomous vehicle (AV) companies — and perhaps other companies that train AI models on physical-world scenarios.

Praveen Neppalli Naga, Uber’s chief technology officer, revealed the plan in an interview at TechCrunch’s StrictlyVC event in San Francisco on Thursday night, calling it a natural extension of a startup program the company announced in late January called AV Labs.

“This is the direction we ultimately want to go in,” Naga said of outfitting vehicles for human drivers. “But first we need to understand the sensor suites and how they all work. There are some regulations — we have to make sure every state has them [clarity on] What sensors mean, and what it means to share them.

Currently, AV Labs relies on a small, dedicated fleet of sensor-equipped cars that Uber operates itself, separate from its network of drivers. But it is clear that the ambition is much greater than that. Uber has millions of drivers globally, and if even a fraction of those cars can be turned into data collection platforms, the scale of what Uber could do for the AV industry would dwarf what any individual AV company could collect on its own.

The idea driving the program is that the limiting factor for autonomous vehicle development is no longer the core technology, Naga said. “The bottleneck is data,” he said. “[Companies like Waymo] Need to walk around and collect data, collect different scenarios. In San Francisco you might be able to say, “At this school intersection, I want some data at this time of day so I can train my models.” The problem for all of these companies is access to that data, because they don’t have the capital to deploy the cars and collect all that information.

Becoming the data layer for the entire autonomous vehicle ecosystem is a very smart play, especially considering that Uber years ago abandoned its own ambitions to build self-driving cars (a move that co-founder Travis Kalanick publicly called a big mistake). In fact, many industry observers have wondered whether Uber, in the absence of its self-driving cars, might one day become irrelevant as self-driving vehicles increasingly emerge around the world.

The company currently has partnerships with 25 autonomous vehicle companies — including Wayve, which operates in London — and is building what Naga describes as an “AV cloud”: a library of labeled sensor data that partner companies can query and use to train their models. Partners, which Uber plans to invest in more aggressively, could also use the system to run their trained models in “shadow mode” against real Uber trips, simulating how a self-driving car would perform without actually putting it on the road.

“Our goal is not to make money off this data,” Naga said. “We want to democratize it.”

Given the clear business value of what Uber is building, this positioning may not last long. The company has already made equity investments in several autonomous vehicle operators, and its ability to offer private training data at scale could give it significant leverage over a sector that now relies on the Uber ride market to reach customers.