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

[https://finance.yahoo.com/sectors/technology/articles/uber-wants-to-turn-its-millions-of-drivers-into-a-sensor-grid-for-self-driving-companies-063628164.html](https://finance.yahoo.com/sectors/technology/articles/uber-wants-to-turn-its-millions-of-drivers-into-a-sensor-grid-for-self-driving-companies-063628164.html) Uber has a long-term ambition that goes well beyond shuttling passengers: the company eventually wants to outfit its human drivers’ cars with sensors to soak up real-world data for autonomous vehicle (AV) companies and potentially other companies training 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, describing it as a natural extension of a nascent program the company 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. But the ambition is clearly much larger. Uber has millions of drivers globally, and if even a fraction of those cars could be transformed into rolling data-collection platforms, the scale of what Uber could offer the AV industry would dwarf what any individual AV company could assemble on its own. The insight driving the program, Naga said, is that the limiting factor for AV development is no longer the underlying technology. “The bottleneck is data,” he said. “\[Companies like Waymo\] 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.”