On January 6, 2026, Uber Technologies and TomTom formalized a multi-year renewal of their mapping and location services agreement, extending integrations of TomTom’s global maps, Maps APIs and live traffic feeds across Uber’s ride-hail and delivery platforms. Under this deal, TomTom will process and serve billions of data points daily—sourced from thousands of contributors—to sharpen routing algorithms that inform fare estimates, pick-up/drop-off accuracy and dynamic ETAs in dense transport hubs like airports and stadiums. Uber will feed anonymized trip telemetry back to TomTom, creating a closed-loop system designed to update turn restrictions and road closures in near real time. Executives at both companies project that the reinforced collaboration will cut average rider wait times by up to 10% in major metros, enhance driver utilization rates and support Uber’s target to improve overall platform efficiency by mid-2027.

At CES 2026, Uber unveiled progress on its $300 million strategic investment into Lucid Motors and commitment to procure 20,000 Lucid Gravity electric SUVs—each fitted with Nuro’s autonomous driving stack—for a planned commercial robotaxi service in the San Francisco Bay Area later this year. Testing on public roads has commenced under human supervision; the production-intent vehicle features a roof-mounted lidar and camera “halo,” integrated radar units, and Nvidia Drive AGX Thor compute to power perception and planning. A dual-screen in-vehicle UI will display real-time trip progress, climate controls and support functions. Uber’s autonomous mobility head has indicated that this fleet will act as a flagship before scaling to additional U.S. cities, with six-seat configurations and expanded luggage capacity set to differentiate the service from four-passenger competitors.

During NVIDIA’s CES keynote, the company launched its Alpamayo suite of open-source vision-language-action models and simulation tools aimed at accelerating Level 4 AV development. Uber’s global head of Autonomous Mobility and Delivery publicly endorsed Alpamayo’s chain-of-thought reasoning capabilities—citing the need to address rare or complex driving scenarios—to bolster safety and transparency in its autonomous fleet roadmap. By leveraging over 1,700 hours of real-world driving data from NVIDIA’s Physical AI Open Datasets and fine-tuning Alpamayo weights, Uber plans to integrate humanlike decision-making into its Nuro-powered vehicles, potentially reducing disengagement rates by up to 15% compared to current end-to-end learning architectures.