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：US20230177839A1

公开(公告)日：2023-06-08

申请号：US17540654

申请日：2021-12-02

Applicant: NVIDIA Corporation

Inventor： Abhishek Bajpayee ,  Arjun Gupta ,  Dylan Doblar ,  Hae-Jong Seo ,  George Tang ,  Keerthi Raj Nagaraja

IPC: G06V20/56 ,  G06K9/62 ,  G06T3/40 ,  G06V10/764 ,  H04N5/232 ,  G06V10/82 ,  G06N3/04 ,  B60R16/023

CPC classification number: G06V20/56 ,  G06K9/6232 ,  G06T3/4046 ,  G06V10/764 ,  H04N5/23229 ,  G06V10/82 ,  G06N3/04 ,  B60R16/0231

Abstract: In various examples, methods and systems are provided for determining, using a machine learning model, one or more of the following operational domain conditions related to an autonomous and/or semi-autonomous machine: amount of camera blindness, blindness classification, illumination level, path surface condition, visibility distance, scene type classification, and distance to a scene. Once one or more of these conditions are determined, an operational level of the machine may be determined, and the machine may be controlled according to the operational level.

公开(公告)号：US20230012645A1

公开(公告)日：2023-01-19

申请号：US17952881

申请日：2022-09-26

Applicant: NVIDIA Corporation

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

IPC: 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：US12136249B2

公开(公告)日：2024-11-05

申请号：US17549418

申请日：2021-12-13

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：US20230110027A1

公开(公告)日：2023-04-13

申请号：US17449306

申请日：2021-09-29

Applicant: NVIDIA Corporation

Inventor： Abhishek Bajpayee ,  Arjun Gupta ,  George Tang ,  Hae-Jong Seo

IPC: B60W30/095 ,  B60W30/09 ,  B60W40/02 ,  G06N3/08 ,  G06K9/62

Abstract: In various examples, systems and methods are disclosed that use one or more machine learning models (MLMs) - such as deep neural networks (DNNs) - to compute outputs indicative of an estimated visibility distance corresponding to sensor data generated using one or more sensors of an autonomous or semi-autonomous machine. Once the visibility distance is computed using the one or more MLMs, a determination of the usability of the sensor data for one or more downstream tasks of the machine may be evaluated. As such, where an estimated visibility distance is low, the corresponding sensor data may be relied upon for less tasks than when the visibility distance is high.

公开(公告)号：US20210166052A1

公开(公告)日：2021-06-03

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: G06K9/46 ,  G05D1/00 ,  G06K9/62 ,  G06K9/00 ,  G06N3/08 ,  G06N3/04

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：US12187187B1

公开(公告)日：2025-01-07

申请号：US18450989

申请日：2023-08-16

Applicant: NVIDIA Corporation

Inventor： Keerthi Raj Nagaraja ,  Arjun Gupta ,  Abhishek Bajpayee ,  Kivanc Temel ,  Dylan Doblar ,  Sai Krishnan Chandrasekar ,  Yu Wang

IPC: B60Q1/14 ,  G06V10/60 ,  G06V10/82 ,  G06V20/56

Abstract: The present disclosure relates to determining a first illumination level corresponding to an area based at least on a first illumination detection obtained using a first illumination detector corresponding to a machine. A second illumination level corresponding to the area may be determined based at least on a second illumination detection obtained using a second illumination detector corresponding to the machine. Based at least on the first illumination level and the second illumination level, a scene illumination state of the area may be determined. Based at least on the scene illumination state, one or more lights of the machine may be controlled.

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：US20230186593A1

公开(公告)日：2023-06-15

申请号：US17549418

申请日：2021-12-13

Applicant: NVIDIA Corporation

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

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

CPC classification number: G06V10/60 ,  B60Q1/1423 ,  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：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.