NVIDIA will provide the transportation industry with access to its NVIDIA DRIVE deep neural networks (DNNs) for autonomous vehicle development on the NVIDIA GPU Cloud (NGC) container registry.
NVIDIA DRIVE has become a de facto standard for AV development, used broadly by automakers, truck manufacturers, robotaxi companies, software companies and universities. Now, NVIDIA is providing access of its pre-trained AI models and training code to AV developers. Using a suite of NVIDIA AI tools, the ecosystem can freely extend and customize the models to increase the robustness and capabilities of their self-driving systems.
The AI autonomous vehicle is a software-defined vehicle required to operate around the world on a wide variety of datasets. By providing AV developers access to our DNNs and the advanced learning tools to optimize them for multiple datasets, we’re enabling shared learning across companies and countries, while maintaining data ownership and privacy. Ultimately, we are accelerating the reality of global autonomous vehicles.—Jensen Huang, founder and CEO of NVIDIA
AI is central to the development of safe, self-driving cars—allowing them to perceive and react in real time to their surroundings for intelligent operation. At its core are dozens of DNNs that tackle redundant and diverse tasks, ensuring accurate perception, localization and path planning.
NVIDIA leads the world in developing the deepest and broadest suite of DNNs and AI tools for the transportation industry. Making these algorithms available to others, along with the tools and workflow infrastructure to customize them, will help enable the deployment of safe autonomous transportation.—Luca De Ambroggi, senior research director of Artificial Intelligence at IHS Markit
NVIDIA has spent years developing and training DNNs that run on the NVIDIA DRIVE AGX platform, turning raw sensor data into a deep understanding of the world. These DNNs cover such tasks as traffic-light and sign detection, object detection (for vehicles, pedestrians, bicycles) and path perception, as well as gaze detection and gesture recognition inside the vehicle.
In addition to providing access to the DNNs, NVIDIA announced the availability of a suite of advanced tools so developers can customize and enhance NVIDIA’s DNNs using their own datasets and target feature set. These tools allow the training of DNNs using active learning, federated learning and transfer learning:
Active learning improves model accuracy and reduces data collection costs by automating data selection using AI, rather than manual curation.
Federated learning enables companies to utilize datasets across countries and with other companies while maintaining data privacy and protecting their intellectual property.
Transfer learning gives DRIVE customers the ability to speed development of their perception software by leveraging NVIDIA’s significant investment in AV development, then further developing these networks for their own applications and target capability.
By providing access to its AI models on NGC and introducing advanced training tools, NVIDIA strengthens its end-to-end platform for AV development and deployment.