Nvidia Corporation

Eric Jonathan Yuan

Minwoo Park

Hae-Jong Seo

Yilin Yang

Xiaolin Lin

Abhishek Bajpayee

Xudong Chen

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, 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.

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Menlo Park, CA, United States of America

Eric Jonathan Yuan