Inside look at autonomous vehicle software

The DRIVE Labs video series takes an engineering-focused look at a range of self-driving challenges, from perceiving paths to handling intersections. These short clips illustrate how the NVIDIA DRIVE AV Software team is creating safe and robust self-driving systems.

Perceiving a New Dimension

Computing distance to objects using image data from a single camera can create challenges when it comes to hilly terrain. With the help of deep neural networks, autonomous vehicles can predict 3D distances from 2D images.

Surround Camera Vision

See how we use our six-camera setup to see 360 degrees around the car and track objects as they move in the surrounding environment.

Predicting the Future with RNNs

Autonomous vehicles must use computational methods and sensor data, such as a sequence of images, to figure out how an object is moving in time.

ClearSightNet Deep Neural Network

ClearSightNet DNN is trained to evaluate cameras’ ability to see clearly and determine causes of occlusions, blockages and reductions in visibility.

WaitNet Deep Neural Network

Learn how the WaitNet DNN is able to detect intersections without using a map.

Path Perception Ensemble

This trio of DNNs builds and evaluates confidence for center path and lane line predictions, as well as lane changes/splits/merges.

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