DNN input
Technical Walkthrough 1

Detecting Obstacles and Drivable Free Space with RadarNet

Detecting drivable free space is a critical component of advanced driver assistance systems (ADAS) and autonomous vehicle (AV) perception. Obstacle detection is... 6 MIN READ
A gif showing cars on a street with potential collision
Technical Walkthrough 8

Generating AI-Based Potential Accident Scenarios for Autonomous Vehicles

Autonomous vehicles (AVs) must be able to safely handle any type of traffic scenario that could be encountered in the real world. This includes hazardous... 4 MIN READ
News 10

Validating Active Sensors in NVIDIA DRIVE Sim

Autonomous vehicle development is all about scale. Engineers must collect and label massive amounts of data to train self-driving neural networks.  This... 2 MIN READ
Technical Walkthrough 0

Explainer: What’s the Difference Between Level 2 and Level 5 Autonomy?

To define the path to fully realized autonomy, the Society of Automotive Engineers (better known as SAE International) detailed six categories of... < 1
Graphic featuring car
News 0

Updated Course: Integrating Sensors with NVIDIA DRIVE

Learn how to integrate your sensor of choice for NVIDIA DRIVE. This updated self-paced course now uses DriveWorks 5.8 and includes lidar examples. < 1
Technical Walkthrough 0

Explainer: What Is Edge Computing?

Edge computing is the practice of processing data physically closer to its source. < 1