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.
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    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.

    Ride in NVIDIA's Self-Driving Car

    This special edition DRIVE Labs episode shows how NVIDIA DRIVE AV Software combines the essential building blocks of perception, localization, and planning/control to drive autonomously on public roads around our headquarters in Santa Clara, Calif.

    Classifying Traffic Signs and Traffic Lights with AI

    NVIDIA DRIVE AV software uses a combination of DNNs to classify traffic signs and lights. Watch how our LightNet DNN classifies traffic light shape (e.g. solid versus arrow) and state (i.e. color), while the SignNet DNN identifies traffic sign type.

    Eliminating Collisions with Safety Force Field

    Our Safety Force Field (SFF) collision avoidance software acts as an independent supervisor on the actions of the vehicle’s primary planning and control system. SFF double-checks controls that were chosen by the primary system, and if it deems them to be unsafe, it will veto and correct the primary system’s decision.

    High-Precision Lane Detection

    Deep neural network (DNN) processing has emerged as an important AI-based technique for lane detection. Our LaneNet DNN increases lane detection range, lane edge recall, and lane detection robustness with pixel-level precision.

    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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