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NVIDIA DRIVE Full-Stack Autonomous Vehicle Software Rolls Out

Gaming News by Gaming News
June 12, 2025
in Gaming
Reading Time: 7 mins read
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Xinzhou Wu
2025-06-11 06:55:00
blogs.nvidia.com

NVIDIA is launching a comprehensive, industry-defining autonomous vehicle (AV) software platform to accelerate large-scale deployment of safe, intelligent transportation innovations for automakers, truck manufacturers, robotaxi companies and startups worldwide.

Announced today at NVIDIA GTC Paris at VivaTech, the full-stack NVIDIA DRIVE AV software platform is now in full production. Combined with NVIDIA accelerated compute, this provides the automotive industry with a robust foundation for AI-powered mobility — unlocking a multitrillion-dollar global opportunity in autonomous and highly automated vehicles. For consumers, this means safer journeys and enjoyable hands-free driving experiences.

Safety First: A Unified, Full-Stack Software Approach

NVIDIA DRIVE’s modular, flexible approach empowers customers to scale based on their specific needs — whether that means adopting the entire stack or a subset. NVIDIA’s robust, safety-certified AV software architecture supports real-time sensor fusion and continuous improvement through over-the-air updates.

Its scalability allows automakers to deploy a subset of advanced driver-assistance features — such as surround perception, automated lane changes, parking and active safety — for level 2++ and level 3 vehicles, with a seamless path to higher levels of automation as technologies and regulations evolve.

Augmenting NVIDIA’s full-stack, end-to-end AV software is NVIDIA’s three-computer solution, which spans the entire AV development pipeline and is designed to tackle the challenges associated with the safe deployment of autonomous vehicles at scale. The three computers include:

  • NVIDIA DGX systems and GPUs for training AI models and developing AV software.
  • The NVIDIA Omniverse and NVIDIA Cosmos platforms running on NVIDIA OVX systems for simulation and synthetic data generation, enabling the testing and validation of autonomous driving scenarios and optimization of smart factory operations.
  • The automotive-grade NVIDIA DRIVE AGX in-vehicle computer for processing real-time sensor data for safe, highly automated and autonomous driving capabilities.

Embracing Generative AI and an End-to-End Model Approach

Most traffic accidents are linked to human factors such as distraction or misjudgment, meaning there’s tremendous potential to make our roads safer. As such, the automotive industry is racing to develop AI-driven systems that improve road safety. But building an autonomous system that can safely navigate the complex physical world is extremely challenging.

AV software development has traditionally been based on a modular approach, with separate components for perception, prediction, planning and control. While there are benefits to this approach, it also opens up potential inefficiencies and errors that can hinder development at scale.

NVIDIA DRIVE AV software unifies these functions, using deep learning and foundation models trained on large datasets of human driving behavior to process sensor data and directly control vehicle actions, eliminating the need for predefined rules or traditional modular pipelines. As a result, vehicles can learn from vast amounts of real and synthetic driving behavior data to safely navigate complex environments and scenarios with human-like decision-making.

The NVIDIA Omniverse Blueprint for AV simulation can be used to further enhance the development pipeline, enabling physically accurate sensor simulation for AV training, testing and validation. By combining the blueprint with NVIDIA’s three-computer solution, developers can convert thousands of human-driven miles into billions of virtually driven miles, amplifying data quality and enabling efficient, scalable and continuously improving AV systems.

Bolstering End-to-End Safety With NVIDIA Halos

Safety is the most important component of all AVs. That’s why NVIDIA earlier this year launched NVIDIA Halos, a comprehensive end-to-end safety system integrating hardware, software, AI models and tools to ensure safe AV development and deployment from cloud to car. Halos provides guardrails for AV safety across simulation, training and deployment — and is backed by 15,000 engineering years of expertise.

A key part of this safety framework is the NVIDIA DriveOS safety-certified ASIL B/D operating system for autonomous driving, which provides a robust, reliable foundation for safe vehicle operation and meets stringent automotive safety standards.

The Future of Transportation, Here Today

With Halos and support for intelligent, adaptive sensors, NVIDIA’s AV stack delivers the tools, compute power and foundational AI models needed to accelerate safe, intelligent mobility — today.

NVIDIA has worked with the European auto industry for over a dozen years to drive automotive innovation, partnering with leading manufacturers, suppliers and mobility startups across the continent and the globe.

The work is transforming vehicle cockpits, along with automotive vehicle design, engineering and manufacturing, and enabling highly automated and self-driving vehicles with physical AI and accelerated computing.

At GTC Paris, NVIDIA also showcased how the transportation industry is using NVIDIA Omniverse and Cosmos for factory planning, vehicle design and simulation.

Plus, NVIDIA announced today at the Computer Vision and Pattern Recognition conference that it won the End-to-End Autonomous Driving Grand Challenge, recognized for creating technologies that allow the development of safer, smarter AVs using real-world and synthetic data — enabling these vehicles to handle even unexpected driving situations. This marks NVIDIA’s second consecutive year topping the leaderboard in the end-to-end category and its third straight Autonomous Grand Challenge award at CVPR.

Watch the NVIDIA GTC Paris keynote from NVIDIA founder and CEO Jensen Huang at VivaTech, and explore GTC Paris sessions.

See notice regarding software product information.

Source Link


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