公开(公告)号：US11921502B2

公开(公告)日：2024-03-05

申请号：US18151012

申请日：2023-01-06

申请人： NVIDIA Corporation

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

IPC分类号： 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分类号： 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

摘要： 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

申请人： NVIDIA Corporation

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

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

摘要： 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

申请人： NVIDIA Corporation

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

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

摘要： 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

申请人： NVIDIA Corporation

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

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

摘要： 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：US11651215B2

公开(公告)日：2023-05-16

申请号：US17109421

申请日：2020-12-02

申请人： NVIDIA Corporation

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

IPC分类号： 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分类号： 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

摘要： 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

申请人： NVIDIA Corporation

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

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

摘要： 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：US12051332B2

公开(公告)日：2024-07-30

申请号：US17940664

申请日：2022-09-08

申请人： NVIDIA Corporation

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

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

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

摘要： 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.

公开(公告)号：US20240169549A1

公开(公告)日：2024-05-23

申请号：US18424219

申请日：2024-01-26

申请人： NVIDIA Corporation

发明人： Dongwoo Lee ,  Junghyun Kwon ,  Sangmin Oh ,  Wenchao Zheng ,  Hae-Jong Seo ,  David Nister ,  Berta Rodriguez Hervas

IPC分类号： G06T7/13 ,  G06T7/40 ,  G06T17/30 ,  G06V10/44 ,  G06V10/75 ,  G06V10/772 ,  G06V10/82 ,  G06V20/58

CPC分类号： G06T7/13 ,  G06T7/40 ,  G06T17/30 ,  G06V10/454 ,  G06V10/751 ,  G06V10/772 ,  G06V10/82 ,  G06V20/586 ,  G06T2207/10021 ,  G06T2207/20084 ,  G06T2207/30264

摘要： A neural network may be used to determine corner points of a skewed polygon (e.g., as displacement values to anchor box corner points) that accurately delineate a region in an image that defines a parking space. Further, the neural network may output confidence values predicting likelihoods that corner points of an anchor box correspond to an entrance to the parking spot. The confidence values may be used to select a subset of the corner points of the anchor box and/or skewed polygon in order to define the entrance to the parking spot. A minimum aggregate distance between corner points of a skewed polygon predicted using the CNN(s) and ground truth corner points of a parking spot may be used simplify a determination as to whether an anchor box should be used as a positive sample for training.

公开(公告)号：US20230214654A1

公开(公告)日：2023-07-06

申请号：US18174856

申请日：2023-02-27

申请人： c/o NVIDIA Corporation

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

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

CPC分类号： G06V20/58 ,  G06V20/588 ,  B60W60/001 ,  B60W2420/42

摘要： 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

申请人： NVIDIA Corporation

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

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

CPC分类号： G06V10/60 ,  B60Q1/1423 ,  G06V10/25 ,  G06V20/58 ,  H05B47/125

摘要： 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.