Nvidia Corporation

Xiaolin Lin

Minwoo Park

Hae-Jong Seo

David Nister

Neda Cvijetic

Yilin Yang

Abhishek Bajpayee

Xudong Chen

Eric Jonathan Yuan

Tae Eun Choe

Pengfei Hao

In various examples, one or more deep neural networks (DNNs) are executed to regress on control points of a curve, and the control points may be used to perform a curve fitting operation—e.g., Bezier curve fitting—to identify landmark locations and geometries in an environment. The outputs of the DNN(s) may thus indicate the two-dimensional (2D) image-space and/or three-dimensional (3D) world-space control point locations, and post-processing techniques—such as clustering and temporal smoothing—may be executed to determine landmark locations and poses with precision and in real-time. As a result, reconstructed curves corresponding to the landmarks—e.g., lane line, road boundary line, crosswalk, pole, text, etc.—may be used by a vehicle to perform one or more operations for navigating an environment.

Landmark detection using curve fitting for autonomous driving applications

In various examples, systems and methods are disclosed that preserve rich spatial information from an input resolution of a machine learning model to regress on lines in an input image. The machine learning model may be trained to predict, in deployment, distances for each pixel of the input image at an input resolution to a line pixel determined to correspond to a line in the input image. The machine learning model may further be trained to predict angles and label classes of the line. An embedding algorithm may be used to train the machine learning model to predict clusters of line pixels that each correspond to a respective line in the input image. In deployment, the predictions of the machine learning model may be used as an aid for understanding the surrounding environment—e.g., for updating a world model—in a variety of autonomous machine applications.

Regression-based line detection for autonomous driving machines

In various examples, systems and methods are disclosed that preserve rich, detail-centric information from a real-world image by augmenting the real-world image with simulated objects to train a machine learning model to detect objects in an input image. The machine learning model may be trained, in deployment, to detect objects and determine bounding shapes to encapsulate detected objects. The machine learning model may further be trained to determine the type of road object encountered, calculate hazard ratings, and calculate confidence percentages. In deployment, detection of a road object, determination of a corresponding bounding shape, identification of road object type, and/or calculation of a hazard rating by the machine learning model may be used as an aid for determining next steps regarding the surrounding environment&#x2014;e.g., navigating around the road debris, driving over the road debris, or coming to a complete stop&#x2014;in a variety of autonomous machine applications.

Using image augmentation with simulated objects for training machine learning models in autonomous driving applications

In various examples, one or more deep neural networks (DNNs) are executed to regress on control points of a curve, and the control points may be used to perform a curve fitting operation&#x2014;e.g., Bezier curve fitting&#x2014;to identify landmark locations and geometries in an environment. The outputs of the DNN(s) may thus indicate the two-dimensional (2D) image-space and/or three-dimensional (3D) world-space control point locations, and post-processing techniques&#x2014;such as clustering and temporal smoothing&#x2014;may be executed to determine landmark locations and poses with precision and in real-time. As a result, reconstructed curves corresponding to the landmarks&#x2014;e.g., lane line, road boundary line, crosswalk, pole, text, etc.&#x2014;may be used by a vehicle to perform one or more operations for navigating an environment.

In various examples, systems and methods are disclosed that preserve rich spatial information from an input resolution of a machine learning model to regress on lines in an input image. The machine learning model may be trained to predict, in deployment, distances for each pixel of the input image at an input resolution to a line pixel determined to correspond to a line in the input image. The machine learning model may further be trained to predict angles and label classes of the line. An embedding algorithm may be used to train the machine learning model to predict clusters of line pixels that each correspond to a respective line in the input image. In deployment, the predictions of the machine learning model may be used as an aid for understanding the surrounding environment&#x2014;e.g., for updating a world model&#x2014;in a variety of autonomous machine applications.

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Sunnyvale, CA, United States of America

Xiaolin Lin