Uber's Ambitious Plan to Turn Its Drivers into a Sensor Grid for Self-Driving Companies

# Introduction

Uber has unveiled an ambitious plan to transform its millions of drivers into a sensor grid for self-driving companies. The company aims to equip its human drivers' cars with sensors to collect real-world data for autonomous vehicle (AV) companies and other organizations training AI models on physical-world scenarios.

Background

Uber's chief technology officer, Praveen Neppalli Naga, revealed the plan at TechCrunch's StrictlyVC event in San Francisco. The initiative is an extension of the company's nascent program, AV Labs, which was announced in late January. AV Labs currently relies on a small, dedicated fleet of sensor-equipped cars operated by Uber itself.

The Vision

The long-term goal is to utilize Uber's vast network of drivers to collect data at a scale that would be impossible for individual AV companies to achieve on their own. With millions of drivers globally, even a fraction of those cars could provide an enormous amount of data. This would enable Uber to become the data layer for the entire AV ecosystem.

The Bottleneck

According to Naga, the limiting factor for AV development is no longer the underlying technology, but rather the availability of data. Companies like Waymo need to collect data on various scenarios, which can be a time-consuming and costly process. Uber's plan would provide these companies with access to a vast amount of data, which they can use to train their models.

Partnerships and Investments

Uber has already partnered with 25 AV companies, including Wayve, and is building an "AV cloud" – a library of labeled sensor data that partner companies can query and use to train their models. The company plans to invest in these partners directly, allowing them to use the system to run their trained models in "shadow mode" against real Uber trips.

Democratizing Data

Naga stated that Uber's goal is not to make money from this data, but rather to democratize it. However, given the commercial value of what Uber is building, this 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 the sector.

Conclusion

Uber's plan to turn its drivers into a sensor grid for self-driving companies is a smart play, particularly considering the company's abandonment of its own self-driving car ambitions. As the AV industry continues to grow, Uber's ability to provide access to a vast amount of data could make it an essential partner for companies looking to develop and improve their autonomous vehicles.