``` The era of widespread "autonomous driving technology" has arrived? Nvidia is making a full push, Uber and Lyft both surge. ```

``` The era of widespread "autonomous driving technology" has arrived? Nvidia is making a full push, Uber and Lyft both surge. ```

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Nvidia is accelerating its layout in the autonomous driving sector, pushing the commercialization of driverless taxis (Robotaxi) by expanding cooperation with ride-hailing giants such as Uber and Lyft.

At the recent GTC conference, Nvidia announced plans to expand cooperation with companies including Uber and Lyft. Boosted by this news, Uber's stock soared more than 5% in recent trading, while Lyft's stock rose about 3%. Nvidia's stock rose 1.6% on Monday and then slightly fell about 0.4% in recent trading.

Nvidia and Uber plan to launch autonomous vehicles equipped with Nvidia software in Los Angeles and San Francisco in the first half of 2027, and to expand the service to dozens of cities by the end of 2028.

In addition, Nvidia announced new or expanded collaborations with automakers including Hyundai, BYD, Geely, Isuzu, and Nissan. The stocks of these companies have also seen increases on their home exchanges.

Nvidia’s “ChatGPT moment” for physical AI

Nvidia’s core weapon in the autonomous driving field is its latest open-source inference VLA (Vision-Language-Action) model, Alpamayo 1. The model aims to enable vehicles to “think” solutions in unexpected situations.

Company CEO Jensen Huang stated:

“The ChatGPT moment for physical AI has arrived—machines are beginning to understand, reason, and act in the real world. Driverless taxis are among the first beneficiaries. Alpamayo brings reasoning capabilities to autonomous vehicles, allowing them to think through rare scenarios, drive safely in complex environments, and explain their driving decisions—this is the foundation for safe, scalable autonomous driving.”

The first vehicles equipped with Nvidia technology are expected to hit the roads in the US in the first quarter of this year, in Europe in the second quarter, and in Asia in the second half of the year.

Solving the “long tail” problem in autonomous driving

Unlike traditional models that map vision input directly to actions, the inference VLA model can break down complex driving tasks into manageable sub-problems and display its reasoning process in an interpretable form.

For example, when approaching an intersection, the system will think similarly to a human: “I see a stop sign, there are vehicles coming from the left, and pedestrians crossing the street. I should slow down, come to a full stop, wait for the pedestrians to cross, and proceed once it's safe.” This ability is vital for handling rare and unpredictable “long tail” scenarios in autonomous driving.

Sarfraz Maredia, Head of Global Autonomous Mobility and Delivery at Uber, commented: “Handling long tail and unpredictable driving scenarios is one of the defining challenges in autonomous driving. Alpamayo creates exciting new opportunities for the industry.”

Beyond autonomous driving, Nvidia is also deploying a broader physical AI strategy. The company has released several open-source models and tools, including the Nemotron family for agent AI, the Cosmos platform for physical AI, Isaac GR00T for robotics, and Clara for biomedicine.

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