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：US20230004164A1

公开(公告)日：2023-01-05

申请号：US17940664

申请日：2022-09-08

Applicant: NVIDIA Corporation

Inventor： Davide Marco Onofrio ,  Hae-Jong Seo ,  David Nister ,  Minwoo Park ,  Neda Cvijetic

IPC: G05D1/02 ,  G06N3/08 ,  G06K9/62 ,  G05D1/00 ,  G06V20/56

Abstract: In various examples, a path perception ensemble is used to produce a more accurate and reliable understanding of a driving surface and/or a path there through. For example, an analysis of a plurality of path perception inputs provides testability and reliability for accurate and redundant lane mapping and/or path planning in real-time or near real-time. By incorporating a plurality of separate path perception computations, a means of metricizing path perception correctness, quality, and reliability is provided by analyzing whether and how much the individual path perception signals agree or disagree. By implementing this approach—where individual path perception inputs fail in almost independent ways—a system failure is less statistically likely. In addition, with diversity and redundancy in path perception, comfortable lane keeping on high curvature roads, under severe road conditions, and/or at complex intersections, as well as autonomous negotiation of turns at intersections, may be enabled.

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：US20250029357A1

公开(公告)日：2025-01-23

申请号：US18901977

申请日：2024-09-30

Applicant: NVIDIA Corporation

Inventor： Igor Tryndin ,  Abhishek Bajpayee ,  Yu Wang ,  Hae-Jong Seo

IPC: G06V10/60 ,  B60Q1/14 ,  G06V10/25 ,  G06V20/58 ,  H05B47/125

Abstract: In various examples, contrast values corresponding to pixels of one or more images generated using one or more sensors of a vehicle may be computed to detect and identify objects that trigger glare mitigating operations. Pixel luminance values are determined and used to compute a contrast value based on comparing the pixel luminance values to a reference luminance value that is based on a set of the pixels and the corresponding luminance values. A contrast threshold may be applied to the computed contrast values to identify glare in the image data to trigger glare mitigating operations so that the vehicle may modify the configuration of one or more illumination sources so as to reduce glare experienced by occupants and/or sensors of the vehicle.

公开(公告)号：US20240410705A1

公开(公告)日：2024-12-12

申请号：US18330145

申请日：2023-06-06

Applicant: NVIDIA Corporation

Inventor： Trung Pham ,  Minwoo Park ,  Ha Giang Truong ,  Atchuta Venkata Vijay Chintalapudi ,  Hae-Jong Seo

IPC: G01C21/34 ,  G06N20/00

Abstract: In various examples, path detection using machine learning models for autonomous or semi-autonomous systems and applications is described herein. Systems and methods are disclosed that use one or more machine learning models to determine a geometry associated with a path for a vehicle. To determine the geometry, the machine learning model(s) may process sensor data generated using the vehicle and, based at least on the processing, output points associated with the path. In some examples, the machine learning model(s) outputs a limited number of points, such as between five and twenty points. One or more algorithms, such as one or more Bezier algorithms, may then be used to generate the geometry based at least on the points. As such, in some examples, the geometry may correspond to a Bezier curve that represents the path.

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：US11508049B2

公开(公告)日：2022-11-22

申请号：US16570187

申请日：2019-09-13

Applicant: NVIDIA Corporation

Inventor： Hae-Jong Seo ,  Abhishek Bajpayee ,  David Nister ,  Minwoo Park ,  Neda Cvijetic

IPC: G06K9/00 ,  G06T7/00 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08 ,  G06V10/98

Abstract: In various examples, a deep neural network (DNN) is trained for sensor blindness detection using a region and context-based approach. Using sensor data, the DNN may compute locations of blindness or compromised visibility regions as well as associated blindness classifications and/or blindness attributes associated therewith. In addition, the DNN may predict a usability of each instance of the sensor data for performing one or more operations—such as operations associated with semi-autonomous or autonomous driving. The combination of the outputs of the DNN may be used to filter out instances of the sensor data—or to filter out portions of instances of the sensor data determined to be compromised—that may lead to inaccurate or ineffective results for the one or more operations of the system.

公开(公告)号：US20200249684A1

公开(公告)日：2020-08-06

申请号：US16781893

申请日：2020-02-04

Applicant: NVIDIA Corporation

Inventor： Davide Marco Onofrio ,  Hae-Jong Seo ,  David Nister ,  Minwoo Park ,  Neda Cvijetic

IPC: G05D1/02 ,  G06K9/00 ,  G06N3/08 ,  G05D1/00 ,  G06K9/62

Abstract: In various examples, a path perception ensemble is used to produce a more accurate and reliable understanding of a driving surface and/or a path there through. For example, an analysis of a plurality of path perception inputs provides testability and reliability for accurate and redundant lane mapping and/or path planning in real-time or near real-time. By incorporating a plurality of separate path perception computations, a means of metricizing path perception correctness, quality, and reliability is provided by analyzing whether and how much the individual path perception signals agree or disagree. By implementing this approach—where individual path perception inputs fail in almost independent ways—a system failure is less statistically likely. In addition, with diversity and redundancy in path perception, comfortable lane keeping on high curvature roads, under severe road conditions, and/or at complex intersections, as well as autonomous negotiation of turns at intersections, may be enabled.

公开(公告)号：US20200090322A1

公开(公告)日：2020-03-19

申请号：US16570187

申请日：2019-09-13

Applicant: NVIDIA Corporation

Inventor： Hae-Jong Seo ,  Abhishek Bajpayee ,  David Nister ,  Minwoo Park ,  Neda Cvijetic

IPC: G06T7/00 ,  G06N3/04 ,  G06N3/08 ,  G06K9/62 ,  G06K9/03

Abstract: In various examples, a deep neural network (DNN) is trained for sensor blindness detection using a region and context-based approach. Using sensor data, the DNN may compute locations of blindness or compromised visibility regions as well as associated blindness classifications and/or blindness attributes associated therewith. In addition, the DNN may predict a usability of each instance of the sensor data for performing one or more operations—such as operations associated with semi-autonomous or autonomous driving. The combination of the outputs of the DNN may be used to filter out instances of the sensor data—or to filter out portions of instances of the sensor data determined to be compromised—that may lead to inaccurate or ineffective results for the one or more operations of the system.

公开(公告)号：US12051332B2

公开(公告)日：2024-07-30

申请号：US17940664

申请日：2022-09-08

Applicant: NVIDIA Corporation

Inventor： Davide Marco Onofrio ,  Hae-Jong Seo ,  David Nister ,  Minwoo Park ,  Neda Cvijetic

IPC: G08G1/00 ,  G05D1/00 ,  G06F18/23 ,  G06N3/08 ,  G06V20/56 ,  G08G1/16

CPC classification number: G08G1/167 ,  G05D1/0088 ,  G05D1/0214 ,  G05D1/0219 ,  G05D1/0223 ,  G06F18/23 ,  G06N3/08 ,  G06V20/588

Abstract: In various examples, a path perception ensemble is used to produce a more accurate and reliable understanding of a driving surface and/or a path there through. For example, an analysis of a plurality of path perception inputs provides testability and reliability for accurate and redundant lane mapping and/or path planning in real-time or near real-time. By incorporating a plurality of separate path perception computations, a means of metricizing path perception correctness, quality, and reliability is provided by analyzing whether and how much the individual path perception signals agree or disagree. By implementing this approach—where individual path perception inputs fail in almost independent ways—a system failure is less statistically likely. In addition, with diversity and redundancy in path perception, comfortable lane keeping on high curvature roads, under severe road conditions, and/or at complex intersections.