公开(公告)号：US20230214654A1

公开(公告)日：2023-07-06

申请号：US18174856

申请日：2023-02-27

Applicant: c/o NVIDIA Corporation

Inventor： Minwoo Park ,  Yilin Yang ,  Xiaolin Lin ,  Abhishek Bajpayee ,  Hae-Jong Seo ,  Eric Jonathan Yuan ,  Xudong Chen

IPC: G06V20/58 ,  G06V20/56 ,  B60W60/00

CPC classification number: G06V20/58 ,  G06V20/588 ,  B60W60/001 ,  B60W2420/42

Abstract: 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.

公开(公告)号：US11651215B2

公开(公告)日：2023-05-16

申请号：US17109421

申请日：2020-12-02

Applicant: NVIDIA Corporation

Inventor： Minwoo Park ,  Yilin Yang ,  Xiaolin Lin ,  Abhishek Bajpayee ,  Hae-Jong Seo ,  Eric Jonathan Yuan ,  Xudong Chen

IPC: G06N3/08 ,  G06V20/58 ,  G06V20/56 ,  G06F18/23 ,  G06F18/214 ,  G06V10/762 ,  G06V10/764 ,  G06V10/82 ,  G06V10/44 ,  G06V10/26 ,  G06V10/46 ,  G05D1/00 ,  G06N3/045 ,  G06V10/75 ,  G06V10/774 ,  G06V10/94

CPC classification number: G06N3/08 ,  G05D1/0088 ,  G06F18/214 ,  G06F18/23 ,  G06N3/045 ,  G06V10/26 ,  G06V10/454 ,  G06V10/46 ,  G06V10/757 ,  G06V10/763 ,  G06V10/764 ,  G06V10/774 ,  G06V10/82 ,  G06V10/955 ,  G06V20/582 ,  G06V20/588 ,  G05D2201/0213 ,  G06V10/471

Abstract: 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.

公开(公告)号：US20250138530A1

公开(公告)日：2025-05-01

申请号：US19005734

申请日：2024-12-30

Applicant: NVIDIA Corporation

Inventor： Minwoo Park ,  Xiaolin Lin ,  Hae-Jong Seo ,  David Nister ,  Neda Cvijetic

IPC: G05D1/00 ,  G05D1/228 ,  G06F18/214 ,  G06F18/23 ,  G06F18/2411 ,  G06N3/04 ,  G06V10/44 ,  G06V10/48 ,  G06V10/75 ,  G06V10/764 ,  G06V10/776 ,  G06V10/82 ,  G06V10/94 ,  G06V20/56

Abstract: 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.

公开(公告)号：US11801861B2

公开(公告)日：2023-10-31

申请号：US17150954

申请日：2021-01-15

Applicant: NVIDIA Corporation

Inventor： Tae Eun Choe ,  Pengfei Hao ,  Xiaolin Lin ,  Minwoo Park

IPC: B60W60/00 ,  G06N3/04 ,  G06N3/08 ,  B60W50/06

CPC classification number: B60W60/001 ,  B60W50/06 ,  G06N3/04 ,  G06N3/08 ,  B60W2420/42 ,  B60W2554/4029 ,  B60W2554/80

Abstract: 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—e.g., navigating around the road debris, driving over the road debris, or coming to a complete stop—in a variety of autonomous machine applications.

公开(公告)号：US20230152801A1

公开(公告)日：2023-05-18

申请号：US18151012

申请日：2023-01-06

Applicant: NVIDIA Corporation

Inventor： Minwoo Park ,  Xiaolin Lin ,  Hae-Jong Seo ,  David Nister ,  Neda Cvijetic

IPC: G05D1/00 ,  G06N3/04 ,  G06V20/56 ,  G06F18/214 ,  G06F18/23 ,  G06F18/2411 ,  G06V10/764 ,  G06V10/776 ,  G06V10/82 ,  G06V10/44 ,  G06V10/48 ,  G06V10/94

CPC classification number: G05D1/0077 ,  G05D1/0088 ,  G06F18/23 ,  G06F18/2155 ,  G06F18/2411 ,  G06N3/0418 ,  G06V10/48 ,  G06V10/82 ,  G06V10/457 ,  G06V10/764 ,  G06V10/776 ,  G06V10/955 ,  G06V20/588 ,  G05D2201/0213

Abstract: 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.

公开(公告)号：US12248319B2

公开(公告)日：2025-03-11

申请号：US18340255

申请日：2023-06-23

Applicant: NVIDIA Corporation

Inventor： Minwoo Park ,  Xiaolin Lin ,  Hae-Jong Seo ,  David Nister ,  Neda Cvijetic

IPC: G05D1/00 ,  G05D1/228 ,  G06F18/214 ,  G06F18/23 ,  G06F18/2411 ,  G06N3/04 ,  G06N3/045 ,  G06N3/08 ,  G06V10/14 ,  G06V10/44 ,  G06V10/48 ,  G06V10/75 ,  G06V10/764 ,  G06V10/766 ,  G06V10/776 ,  G06V10/82 ,  G06V10/94 ,  G06V20/56

Abstract: 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.

公开(公告)号：US11921502B2

公开(公告)日：2024-03-05

申请号：US18151012

申请日：2023-01-06

Applicant: NVIDIA Corporation

Inventor： Minwoo Park ,  Xiaolin Lin ,  Hae-Jong Seo ,  David Nister ,  Neda Cvijetic

IPC: G05D1/00 ,  G05D1/02 ,  G06F18/214 ,  G06F18/23 ,  G06F18/2411 ,  G06N3/04 ,  G06N3/08 ,  G06V10/44 ,  G06V10/48 ,  G06V10/75 ,  G06V10/764 ,  G06V10/766 ,  G06V10/776 ,  G06V10/82 ,  G06V10/94 ,  G06V20/56

CPC classification number: G05D1/0077 ,  G05D1/0088 ,  G06F18/2155 ,  G06F18/23 ,  G06F18/2411 ,  G06N3/0418 ,  G06V10/457 ,  G06V10/48 ,  G06V10/751 ,  G06V10/764 ,  G06V10/776 ,  G06V10/82 ,  G06V10/955 ,  G06V20/588 ,  G05D2201/0213

Abstract: 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.

公开(公告)号：US20210309248A1

公开(公告)日：2021-10-07

申请号：US17150954

申请日：2021-01-15

Applicant: NVIDIA Corporation

Inventor： Tae Eun Choe ,  Pengfei Hao ,  Xiaolin Lin ,  Minwoo Park

IPC: B60W60/00 ,  G06N3/04 ,  G06N3/08 ,  B60W50/06

Abstract: 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—e.g., navigating around the road debris, driving over the road debris, or coming to a complete stop—in a variety of autonomous machine applications.

公开(公告)号：US20240001957A1

公开(公告)日：2024-01-04

申请号：US18467123

申请日：2023-09-14

Applicant: NVIDIA Corporation

Inventor： Tae Eun Choe ,  Pengfei Hao ,  Xiaolin Lin ,  Minwoo Park

IPC: B60W60/00 ,  G06N3/04 ,  G06N3/08 ,  B60W50/06

CPC classification number: B60W60/001 ,  G06N3/04 ,  G06N3/08 ,  B60W50/06 ,  B60W2554/80 ,  B60W2420/42 ,  B60W2554/4029

Abstract: 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—e.g., navigating around the road debris, driving over the road debris, or coming to a complete stop—in a variety of autonomous machine applications.

公开(公告)号：US11604944B2

公开(公告)日：2023-03-14

申请号：US16514230

申请日：2019-07-17

Applicant: NVIDIA Corporation

Inventor： Minwoo Park ,  Xiaolin Lin ,  Hae-Jong Seo ,  David Nister ,  Neda Cvijetic

IPC: G06K9/62 ,  G06V10/75 ,  G06V20/56 ,  G06V10/44 ,  G06V10/764 ,  G06V10/776 ,  G06V10/82 ,  G06V10/94 ,  G05D1/00 ,  G06N3/04 ,  G06N3/08

Abstract: 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.