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NVIDIA Helps Transportation Industry With AI Technology

NVIDIA is helping the transportation industry by giving it access to its deep neural networks (DNNs) for autonomous vehicles.

NVIDIA is helping the transportation industry by giving it access to its deep neural networks (DNNs) for autonomous vehicles.

NVIDIA is providing access to its AI (artificial intelligence) model and introducing advanced training tools. This helps the company to strengthen its end-to-end platform for autonomous vehicle development and, eventually, deployment.

Image via NVIDIA

Automakers and other companies that develop autonomous vehicles (AVs) on the NVIDIA GPU Cloud container registry will get access. NVIDIA DRIVE is pretty much the standard for the development of autonomous vehicles. It is used by automakers, truck manufacturers, and robotaxi companies along with related software companies and universities. 

NVIDIA is giving access to its pre-trained AI models and training codes to AV developers. This suite of tools will enable the ecosystem to freely extend and customize the models to increase the robustness and the 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 what powers these self-driving vehicles. AI allows them to see and react in real-time to their surroundings. At its cores are dozens of DNNs. This helps with 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

Along with providing access to DNNs NVIDIA has spent years developing and training, the company has announced the availability of its suite of advanced tools developers can use to customize and enhance the DNNs using their own datasets and target feature set. This will allow for more training of DNNs while using active learning, federated learning, and transfer learning. Active learning improves model accuracy while reducing data collection costs. This comes from automating data selection using AI.

Federated learning helps companies use datasets across countries and with other companies without breaching data privacy. This protects each company’s intellectual property. Transfer learning gives DRIVE customers a way to speed up the development of their perception software by leveraging NVIDIA’s investment in AV development. They can then further develop these networks for their own applications and target capability.

 
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Johnna owns less than one share of $TSLA currently and supports Tesla's mission. She also gardens, collects interesting minerals and can be found on TikTok

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