Editor��s note: This is the latest post in our NVIDIA DRIVE Labs series, which takes an engineering-focused look at individual autonomous vehicle challenges and how NVIDIA DRIVE addresses them. Catch up on all of our automotive posts, here. By Neda Cvijetic Lidar can give autonomous vehicles laser focus. By bouncing laser signals off the surrounding environment, these sensors can enable a��
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Editors note: Our annual GPU Technology Conference will be virtual. Stay tuned for more details. Autonomous vehicles are a complex AI challenge. Bringing them to market requires sharing knowledge and expertise in solutions from end-to-end. GTC is the place to experience what��s next for AI-powered transportation. And this year, NVIDIA DRIVE customers will get access to even more in-depth��
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By: Jordan Marr, Yu Sheng, Amir Akbarzadeh Editor��s note: This is the latest post in our NVIDIA DRIVE Labs series, which takes an engineering-focused look at individual autonomous vehicle challenges and how NVIDIA DRIVE addresses them. Catch up on all of our automotive posts, here. Localization is a critical capability for autonomous vehicles, making it possible to pinpoint their location��
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By: Xiaolin Lin, Yilin Yang Editor��s note: This is the latest post in our NVIDIA DRIVE Labs series, which takes an engineering-focused look at individual autonomous vehicle challenges and how NVIDIA DRIVE addresses them. Catch up on all of our automotive posts, here. Lane and road edge detection is critical for self-driving car development �� lane detection powers systems like lane��
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By: Bala Siva Sashank Jujjavarapu Editor��s note: This is the latest post in our NVIDIA DRIVE Labs series, which takes an engineering-focused look at individual autonomous vehicle challenges and how NVIDIA DRIVE addresses them. Catch up on all of our automotive posts, here. For autonomous driving technology to advance beyond automated assisted driving, it must have reliable��
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By JC Li Editor��s note: This is the latest post in our NVIDIA DRIVE Labs series, which takes an engineering-focused look at individual autonomous vehicle challenges and how NVIDIA DRIVE addresses them. Catch up on all automotive posts. AI can now make it easier for cars to see in the dark, while ensuring other vehicles won��t be blinded by the light. High beam lights can increase the��
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By: Mehmet Kocamaz, Senior Computer Vision and Machine Learning Engineer at NVIDIA Editor��s note: This is the latest post in our NVIDIA DRIVE Labs series, which takes an engineering-focused look at individual autonomous vehicle challenges and how NVIDIA DRIVE addresses them. Catch up on all of our automotive posts, here. Many cars on the road today equipped with advanced driver assistance��
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First announced at this year��s GTC, the latest DRIVE Software release includes enhanced perception and visualization capabilities, as well as many of the features in the recently introduced DRIVE AP2X Level 2+ automated driving platform. DRIVE Software provides developers with an open platform for autonomous vehicle development comprised of SDKs, powerful tools and AV applications.
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Developers at Wayve, a UK-based startup, recently taught a car to perform autonomous vehicle functions from scratch in under 20 minutes, resulting in the first application of reinforcement learning on a full-sized autonomous vehicle. ��In order to make autonomous driving a truly ubiquitous technology, we advocate for robotic systems which address the ability to drive and navigate in absence of��
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The NVIDIA DRIVE PX AI car computer enables OEMs, tier 1 suppliers, startups and research institutions to accelerate the self-driving car systems development. The NVIDIA DriveWorks companion Software Development Kit (SDK) for DRIVE PX includes a number of open-source reference samples, development tools and library modules targeting autonomous driving applications.
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As part of our autonomous driving research, NVIDIA has created a deep-learning based system, known as PilotNet, which learns to emulate the behavior of human drivers and can be deployed as a self-driving car controller. PilotNet is trained using road images paired with the steering angles generated by a human driving a data-collection car. It derives the necessary domain knowledge from data.
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In a new automotive application, we have used convolutional neural networks (CNNs) to map the raw pixels from a front-facing camera to the steering commands for a self-driving car. This powerful end-to-end approach means that with minimum training data from humans, the system learns to steer, with or without lane markings, on both local roads and highways. The system can also operate in areas with��
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In a recent interview with TIME, NVIDIA��s senior director of automotive Danny Shapiro shares how the company��s innovations in gaming graphics are well-suited to the needs of autonomous vehicles. Driverless cars, which take passengers from A to B with minimal human input, are already hitting American roads. A variety of automakers and technology firms are experimenting with driverless technology��
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