News, 29 January 2026
Uber has unveiled AV Labs, a new internal division focused on collecting and preparing high‑quality driving data to support autonomous vehicle (AV) and robotaxi developers, according to multiple reports from major tech outlets including TechCrunch, TechBuzz and TrendHunter. This marks a strategic shift for the ride‑hailing giant: rather than building its own self‑driving robotaxi fleet, Uber will leverage its massive footprint to help AV partners improve their next‑gen systems by feeding them rich, real‑world data from everyday road environments.
AV Labs will deploy Uber‑equipped vehicles outfitted with sensors such as cameras, radar and lidar to gather diverse driving scenarios and edge cases that are hard for autonomous systems to learn in simulations alone. Instead of simply handing over raw sensor logs, Uber plans to process, refine and tailor this data to fit the specific needs of partners like Waymo, Waabi and Lucid Motors and others that are still training or optimizing their robotaxi software. This curated approach aims to help AV developers tackle rare but critical traffic situations, ultimately improving safety and real‑world reliability.
According to Uber’s engineering leadership, the division is starting small — initially operating a test vehicle equipped with advanced sensor arrays — but intends to scale rapidly, potentially expanding to hundreds of vehicles and employees within the next year. Uber also plans to run partner AV software in “shadow mode” on its fleet, comparing autonomous decisions against human driving behavior to highlight discrepancies and uncover learning opportunities.
Crucially, Uber is not re‑entering the robotaxi manufacturing space it exited after selling its previous autonomous program in 2020; rather, AV Labs positions Uber as a data infrastructure provider for the broader autonomous mobility ecosystem. With real‑world driving data becoming a key ingredient in training next‑generation AV systems, this initiative could accelerate progress across multiple partners and help push robotaxi deployments closer to scalability and safety benchmarks.
Compiled using AI
