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NVIDIA is trying to become the technology backbone for robotaxis that Uber and vehicle partners plan to deploy—not launch a branded robotaxi fleet of its own. The strategy brings NVIDIA’s computing and autonomous-driving software together with Uber’s ride-hailing network and separate vehicle programs involving Stellantis, Lucid and Mercedes-Benz. The companies have announced development plans and rollout targets, not a functioning global network: Uber and NVIDIA’s March 2026 plan calls for launches in Los Angeles and the San Francisco Bay Area in the first half of 2027, followed by expansion to 28 cities by 2028.
What NVIDIA and Uber announced
On October 28, 2025, NVIDIA and Uber announced a collaboration to support autonomous mobility. NVIDIA’s pitch is broader than supplying an in-car processor: it is offering a computing and software platform, safety-related tools, sensor reference architecture, and data and simulation technologies. Uber is intended to connect autonomous vehicles with riders and help organize deployment through its mobility network.
The announcement described an Uber target to scale an autonomous fleet to 100,000 vehicles beginning in 2027. That is a company-announced ambition, not a report that 100,000 vehicles have been ordered, built or put into service. The initial announcement also described plans for a data-factory effort using NVIDIA Cosmos and more than 3 million hours of robotaxi-specific driving data for model training and validation. Treat that figure as a stated development plan, not a confirmed amount already collected. NVIDIA’s announcement and Uber’s announcement describe the program.
The March 2026 expansion plan
On March 16, 2026, Uber and NVIDIA set out a more specific roadmap: start with Los Angeles and the San Francisco Bay Area in the first half of 2027, then aim to expand NVIDIA DRIVE software-driven robotaxis across 28 cities on four continents by 2028. The plan includes a preliminary phase using data-collection vehicles to capture local driving conditions, and identifies NVIDIA DRIVE Hyperion and Alpamayo as parts of the program. These are planned milestones; the announcement does not establish that service is already operating in those cities. Uber’s March 2026 announcement sets out the timeline.
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- Rich Connectivity & AI-Ready Platform: Features 2×RJ45, SIM slot, 4×USB 3.2, HDMI 2.1, CAN, M.2 Key E/M, Mini-PCIe, and 4×CSI camera ports — supporting multi-camera vision, IoT, and robotics projects. Pre-installed with JetPack 6.2 and 128GB NVMe SSD, fully compatible with NVIDIA Isaac, ROS 1/2, and Hugging Face frameworks.
Who does what?
This is a hub-and-spoke ecosystem, not one conventional four- or five-company joint venture with identical obligations. NVIDIA and Uber are the central collaborators, while vehicle and technology partners have distinct programs.
| Company | Role in the announced programs |
|---|---|
| NVIDIA | Provides DRIVE computing and software platforms, safety-related technology, and data and simulation tools. It is a technology supplier, not the ride-hailing fleet operator. |
| Uber | Provides the ride-hailing marketplace and plans to coordinate deployment through its network; it is also collaborating with NVIDIA on data infrastructure. |
| Stellantis | Contributes vehicle engineering and manufacturing for planned L4-ready robotaxi platforms. Its announced collaboration also includes Foxconn. |
| Foxconn | Participates in the Stellantis collaboration, contributing electronics and systems-integration capabilities. |
| Lucid | Has a future consumer-vehicle autonomy roadmap using NVIDIA DRIVE. Separately, its Uber robotaxi vehicle program uses Nuro’s autonomous-driving software. |
| Nuro | Supplies the autonomous-driving software for the separate Lucid–Uber robotaxi program described in Lucid’s filings. |
| Mercedes-Benz | Is developing an S-Class-based robotaxi ecosystem with NVIDIA and Uber, combining its vehicle platform and MB.OS integration with NVIDIA technology and Uber access. |
The companies’ related Level 4 activity also involves other autonomous-vehicle firms, including Aurora, Volvo Autonomous Solutions, Waabi, Nuro, Pony.ai, Wayve, WeRide, May Mobility, Momenta and Avride. That broader ecosystem does not mean each participant has the same agreement or deployment plan. Stellantis’ announcement describes its collaboration with NVIDIA, Uber and Foxconn.
What NVIDIA’s technology stack is meant to do
DRIVE AGX Hyperion 10
NVIDIA describes Hyperion 10 as a reference compute-and-sensor architecture for vehicles designed for Level 4 development. It is a platform blueprint to help automakers and developers build compatible vehicles, not a finished universal kit that makes any car self-driving. Vehicle design, sensor integration, software validation, regulatory authorization and fleet operations remain essential.
DRIVE AV and DriveOS
DRIVE AV is NVIDIA’s autonomous-driving software layer. Uber’s announcement also described DriveOS as the safety-certified operating-system foundation for the platform. Those company descriptions explain the intended architecture; they do not by themselves establish that a particular vehicle has completed safety validation or is approved for driverless passenger service.
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- Robust Hardware Design: A compact, high-performance edge AI computer with NVIDIA Jetson Orin NX 8GB module in Super/MAXN mode, providing up to 117 TOPS of AI performance.
- Multiple Interfaces for robotics: Including dual RJ45, M.2 slots for 5G/Wi-Fi/BT modules, 6x USB 3.2, 2x CAN, GMSL2(additional purchase), I2C, and UART, functioning as a powerful robotic brain.
- Software Setup: Pre-installed with JetPack 6.2 and Linux BSP for seamless deployment.
- Application and Benefit: Ideal for rapid development of autonomous robots, accelerating time-to-market with ready-to-use interfaces and optimized AI frameworks.
- Wide Operating Range: Operates reliably across a temperature range of -20°C to 60°C at 25W mode and -20°C to 50°C at 40W mode
Halos, Cosmos and Alpamayo
NVIDIA’s Halos and Halos Certified Program are presented as tools for assessing physical-AI safety. Cosmos is part of the planned data-processing and curation effort with Uber. Alpamayo, included in the 2026 roadmap, is described by NVIDIA and Uber as a reasoning-based AI model intended to help with difficult, less common driving situations. These technologies are intended to support development and validation; their announcement is not proof that rare edge cases or safety challenges have been solved. NVIDIA’s autonomous-vehicle overview describes its broader platform offering.
How the vehicle programs differ
Stellantis: vehicle development and manufacturing scale
Stellantis announced a collaboration with NVIDIA, Uber and Foxconn on October 28, 2025, to explore joint development and future deployment of Level 4 driverless vehicles for robotaxi services. Stellantis contributes vehicle engineering and manufacturing; NVIDIA contributes AV software and AI computing; Foxconn brings electronics and systems integration; and Uber is the mobility-platform partner.
Uber said Stellantis would be among the first manufacturers expected to deliver at least 5,000 NVIDIA-DRIVE-powered L4 vehicles for Uber operations. This is a stated initial program target, not a confirmed delivery, production volume or current fleet count. Uber’s description of the target and Stellantis’ collaboration announcement set out the announced roles.
Lucid: distinguish consumer autonomy from Uber robotaxis
Lucid’s NVIDIA relationship concerns future consumer vehicles. The company says it intends to use NVIDIA DRIVE AV and integrate two DRIVE AGX Thor accelerated computers into its upcoming midsize lineup. Its stated sensor approach includes cameras, radar and lidar, and its roadmap begins with L2++ point-to-point driving before pursuing privately owned Level 4 capability. These are forward-looking plans, not a claim that a consumer L4 Lucid is currently available. Lucid’s announcement describes its intended roadmap.
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A separate Lucid–Uber robotaxi program uses Nuro’s autonomous-driving software, not the NVIDIA DRIVE AV consumer-vehicle stack described above. Lucid’s filings describe an agreement for at least 20,000 Gravity Plus vehicles over six years after a targeted late-2026 production start, followed by an agreement for at least 25,000 midsize-platform vehicles. Lucid described the aggregate Uber commitment as at least 35,000 vehicles. Those are commitments and plans in Lucid’s filings, not proof of delivered vehicles or current robotaxi service. They should not be added to Uber’s broader 100,000-vehicle ambition as though the figures represented equivalent, independent fleet totals. See Lucid’s first agreement, its second agreement and its filing describing the aggregate commitment.
Lucid has also said it plans to use NVIDIA Industrial AI, Omniverse and AI Enterprise technologies for manufacturing and digital-twin applications. That is a separate industrial use of NVIDIA’s tools, rather than evidence about robotaxi deployment.
Mercedes-Benz: an S-Class robotaxi development program
Mercedes-Benz, NVIDIA and Uber are developing an S-Class-based robotaxi ecosystem. Mercedes contributes the vehicle platform, MB.OS integration and automotive engineering; NVIDIA supplies DRIVE Hyperion and DRIVE AV; Uber is expected to make the vehicles available through its mobility platform. Mercedes said initial S-Class robotaxi test vehicles were planned for roads in Abu Dhabi in 2026. The announcement describes a development and testing plan, not an autonomous S-Class already offered to consumers or a confirmed commercial service. Mercedes-Benz’s robotaxi page describes the program.
Mercedes-Benz’s broader production-vehicle relationship with NVIDIA also includes L2++ driver-assistance systems. L2++ is not L4: the driver remains responsible in the former, whereas L4 refers to driverless operation within a defined operational design domain. A production-car assistance system should not be conflated with an S-Class robotaxi program.
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| Stage | Examples | What the announcement establishes |
|---|---|---|
| Platform described | Hyperion 10 and DRIVE AV | A reference architecture and software platform intended to support L4 development—not a ready-to-deploy vehicle. |
| Development plans | Stellantis platforms; Lucid midsize vehicles; Mercedes S-Class robotaxi architecture | Partners have described programs and intended integrations, not completed production and validation across all vehicles. |
| Testing plans | Mercedes’ planned Abu Dhabi test vehicles in 2026; data-collection vehicles in Uber and NVIDIA’s rollout plan | Testing or data collection is a step toward deployment, not commercial driverless service. |
| Planned commercial rollout | Los Angeles and the San Francisco Bay Area in the first half of 2027; 28 cities across four continents by 2028 | Uber and NVIDIA announced a roadmap, not completed launches in those locations. |
| Longer-term ambition | Uber’s 100,000-vehicle target beginning in 2027 | A stated scale target, not a verified fleet, purchase order or delivery count. |
Why NVIDIA wants to be the platform supplier
A single robotaxi operator is limited by its own vehicles, software and service areas. NVIDIA’s strategy instead aims to sell into multiple layers of an expanding ecosystem: in-vehicle computing, AV software, operating-system components, safety tools, sensor reference designs, data processing, simulation and industrial AI. If several manufacturers deploy compatible systems, NVIDIA could participate across vehicle programs rather than depending on one automaker or one city.
Uber offers a complementary advantage: an automaker can build a capable vehicle yet still need passenger demand, dispatch, pricing, rider support and fleet utilization. Uber’s network could connect vehicles from multiple suppliers to passengers and help manage service operations. The company has also described a role in gathering robotaxi-specific data, which can support training and validation when combined with appropriate testing and safety processes.
The partners span different positions in the market. Stellantis brings broad manufacturing capability, Lucid is pursuing premium EVs and a future consumer-autonomy roadmap, and Mercedes-Benz is developing a luxury S-Class robotaxi application. Multiple paths could broaden the opportunity, but they also make the ecosystem less uniform: the technology and software providers are not identical across every vehicle program.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What could slow or limit the plan?
Vehicle readiness is only one gate
“L4-ready” describes a design intention, not permission to operate without a human in every city. Hardware readiness, software validation, a safety case, regulator approval, driverless testing and commercial passenger service are separate milestones. Level 4 capability is also bounded by an operational design domain—the conditions and areas in which a system is designed to operate.
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Scaling requires more than software
A large fleet would require vehicle production and sensor supply, maintenance and charging capacity, remote-assistance operations, insurance and liability arrangements, city-by-city approvals, reliable unit economics and public acceptance. Delays or shortfalls in any of these areas could push back rollout dates or limit the number of vehicles that can operate profitably.
More data is useful, but not a safety guarantee
Driving data can help with model training and validation, but rare events and changing conditions remain hard: construction, emergency scenes, severe weather, sensor faults, cybersecurity threats and interactions with people outside the vehicle all demand careful handling. A larger dataset does not automatically prove that a system is safe across every location or scenario.
Automakers may resist platform dependence
Relying on one technology supplier can raise questions about long-term software costs, control of vehicle data, update responsibilities, cybersecurity and liability. Automakers may seek to differentiate their own systems, build more technology in-house or use competing suppliers. NVIDIA’s ability to become a common layer therefore depends not only on technical capability but also on partners accepting the commercial and governance terms of that role.
What the partnership does—and does not—mean
The strongest conclusion is that NVIDIA is working to standardize a technology layer for autonomous mobility while Uber provides a route to riders and deployment. The announcements show an ecosystem roadmap, partner programs and time-bound targets. They do not establish a global robotaxi network already in operation, universal regulatory approval, or one identical NVIDIA software stack in every partner vehicle. The clearest near-term test is whether the announced city launches, vehicle programs and testing plans advance on schedule and become approved, reliable passenger services.
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