公开(公告)号：US20190266418A1

公开(公告)日：2019-08-29

申请号：US16286329

申请日：2019-02-26

Applicant: NVIDIA Corporation

Inventor： Yifang Xu ,  Xin Liu ,  Chia-Chih Chen ,  Carolina Parada ,  Davide Onofrio ,  Minwoo Park ,  Mehdi Sajjadi Mohammadabadi ,  Vijay Chintalapudi ,  Ozan Tonkal ,  John Zedlewski ,  Pekka Janis ,  Jan Nikolaus Fritsch ,  Gordon Grigor ,  Zuoguan Wang ,  I-Kuei Chen ,  Miguel Sainz

IPC: G06K9/00 ,  G06K9/32 ,  G05D1/02 ,  G05D1/00 ,  G06N3/08 ,  G06T7/10

Abstract: In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.

公开(公告)号：US20240135173A1

公开(公告)日：2024-04-25

申请号：US18343291

申请日：2023-06-27

Applicant: NVIDIA Corporation

Inventor： Yilin Yang ,  Bala Siva Sashank Jujjavarapu ,  Pekka Janis ,  Zhaoting Ye ,  Sangmin Oh ,  Minwoo Park ,  Daniel Herrera Castro ,  Tommi Koivisto ,  David Nister

IPC: G06N3/08 ,  B60W30/14 ,  B60W60/00 ,  G06F18/214 ,  G06V10/762 ,  G06V20/56

CPC classification number: G06N3/08 ,  B60W30/14 ,  B60W60/0011 ,  G06F18/2155 ,  G06V10/763 ,  G06V20/56

Abstract: In various examples, a deep neural network (DNN) is trained to accurately predict, in deployment, distances to objects and obstacles using image data alone. The DNN may be trained with ground truth data that is generated and encoded using sensor data from any number of depth predicting sensors, such as, without limitation, RADAR sensors, LIDAR sensors, and/or SONAR sensors. Camera adaptation algorithms may be used in various embodiments to adapt the DNN for use with image data generated by cameras with varying parameters—such as varying fields of view. In some examples, a post-processing safety bounds operation may be executed on the predictions of the DNN to ensure that the predictions fall within a safety-permissible range.

公开(公告)号：US11854401B2

公开(公告)日：2023-12-26

申请号：US18067176

申请日：2022-12-16

Applicant: NVIDIA Corporation

Inventor： Yue Wu ,  Pekka Janis ,  Xin Tong ,  Cheng-Chieh Yang ,  Minwoo Park ,  David Nister

IPC: G08G1/16 ,  G06V10/82 ,  G06V20/58 ,  G06V20/10 ,  G06F18/214 ,  G05D1/00 ,  G05D1/02 ,  G06N3/04 ,  G06T7/20

CPC classification number: G08G1/166 ,  G05D1/0088 ,  G05D1/0289 ,  G06F18/214 ,  G06N3/0418 ,  G06T7/20 ,  G06V10/82 ,  G06V20/10 ,  G06V20/58 ,  G05D2201/0213

Abstract: In various examples, a sequential deep neural network (DNN) may be trained using ground truth data generated by correlating (e.g., by cross-sensor fusion) sensor data with image data representative of a sequences of images. In deployment, the sequential DNN may leverage the sensor correlation to compute various predictions using image data alone. The predictions may include velocities, in world space, of objects in fields of view of an ego-vehicle, current and future locations of the objects in image space, and/or a time-to-collision (TTC) between the objects and the ego-vehicle. These predictions may be used as part of a perception system for understanding and reacting to a current physical environment of the ego-vehicle.

公开(公告)号：US20230206394A1

公开(公告)日：2023-06-29

申请号：US17564703

申请日：2021-12-29

Applicant: Nvidia Corporation

Inventor： Pekka Janis

IPC: G06T3/40 ,  G06T7/207 ,  G06T3/00 ,  G06T11/00

CPC classification number: G06T3/4046 ,  G06T7/207 ,  G06T3/0093 ,  G06T3/4053 ,  G06T11/001 ,  G06T2207/10016 ,  G06T2207/20016 ,  G06T2207/20081

Abstract: Apparatuses, systems, and techniques are presented to reconstruct one or more images. In at least one embodiment, one or more anisotropic filters are used to determine one or more pixel blending weights.

公开(公告)号：US11676364B2

公开(公告)日：2023-06-13

申请号：US17222680

申请日：2021-04-05

Applicant: NVIDIA Corporation

Inventor： Yifang Xu ,  Xin Liu ,  Chia-Chih Chen ,  Carolina Parada ,  Davide Onofrio ,  Minwoo Park ,  Mehdi Sajjadi Mohammadabadi ,  Vijay Chintalapudi ,  Ozan Tonkal ,  John Zedlewski ,  Pekka Janis ,  Jan Nikolaus Fritsch ,  Gordon Grigor ,  Zuoguan Wang ,  I-Kuei Chen ,  Miguel Sainz

IPC: G06V10/44 ,  G06T7/10 ,  G05D1/00 ,  G06N3/084 ,  G05D1/02 ,  G06V20/56 ,  G06V10/46 ,  G06V20/40 ,  G06F18/2413 ,  G06V10/764 ,  G06V10/82

CPC classification number: G06V10/44 ,  G05D1/0088 ,  G05D1/0221 ,  G06F18/24143 ,  G06N3/084 ,  G06T7/10 ,  G06V10/457 ,  G06V10/46 ,  G06V10/764 ,  G06V10/82 ,  G06V20/41 ,  G06V20/588 ,  G06V10/471

Abstract: In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.

公开(公告)号：US20220114700A1

公开(公告)日：2022-04-14

申请号：US17066282

申请日：2020-10-08

Applicant: Nvidia Corporation

Inventor： Shiqiu Liu ,  Robert Pottorff ,  Guilin Liu ,  Karan Sapra ,  Jon Barker ,  David Tarjan ,  Pekka Janis ,  Edvard Fagerholm ,  Lei Yang ,  Kevin Shih ,  Marco Salvi ,  Timo Roman ,  Andrew Tao ,  Bryan Catanzaro

IPC: G06T3/40 ,  G06T5/50 ,  G06T5/20 ,  G06T5/00

Abstract: Apparatuses, systems, and techniques are presented to generate images. In at least one embodiment, one or more neural networks are used to generate one or more images using one or more pixel weights determined based, at least in part, on one or more sub-pixel offset values.

公开(公告)号：US20210224556A1

公开(公告)日：2021-07-22

申请号：US17222680

申请日：2021-04-05

Applicant: NVIDIA Corporation

Inventor： Yifang Xu ,  Xin Liu ,  Chia-Chih Chen ,  Carolina Parada ,  Davide Onofrio ,  Minwoo Park ,  Mehdi Sajjadi Mohammadabadi ,  Vijay Chintalapudi ,  Ozan Tonkal ,  John Zedlewski ,  Pekka Janis ,  Jan Nikolaus Fritsch ,  Gordon Grigor ,  Zuoguan Wang ,  I-Kuei Chen ,  Miguel Sainz

IPC: G06K9/00 ,  G06K9/32 ,  G06T7/10 ,  G05D1/00 ,  G06N3/08 ,  G05D1/02 ,  G06K9/46 ,  G06K9/48 ,  G06K9/62

Abstract: In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.

公开(公告)号：US10997433B2

公开(公告)日：2021-05-04

申请号：US16286329

申请日：2019-02-26

Applicant: NVIDIA Corporation

Inventor： Yifang Xu ,  Xin Liu ,  Chia-Chih Chen ,  Carolina Parada ,  Davide Onofrio ,  Minwoo Park ,  Mehdi Sajjadi Mohammadabadi ,  Vijay Chintalapudi ,  Ozan Tonkal ,  John Zedlewski ,  Pekka Janis ,  Jan Nikolaus Fritsch ,  Gordon Grigor ,  Zuoguan Wang ,  I-Kuei Chen ,  Miguel Sainz

IPC: G06K9/00 ,  G06K9/32 ,  G06T7/10 ,  G05D1/00 ,  G06N3/08 ,  G05D1/02 ,  G06K9/46 ,  G06K9/48 ,  G06K9/62

Abstract: In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.

公开(公告)号：US20200293064A1

公开(公告)日：2020-09-17

申请号：US16514404

申请日：2019-07-17

Applicant: NVIDIA Corporation

Inventor： Yue Wu ,  Pekka Janis ,  Xin Tong ,  Cheng-Chieh Yang ,  Minwoo Park ,  David Nister

IPC: G05D1/02 ,  G06N3/04 ,  G06K9/62 ,  G06K9/00 ,  G06T7/20 ,  G05D1/00

Abstract: In various examples, a sequential deep neural network (DNN) may be trained using ground truth data generated by correlating (e.g., by cross-sensor fusion) sensor data with image data representative of a sequences of images. In deployment, the sequential DNN may leverage the sensor correlation to compute various predictions using image data alone. The predictions may include velocities, in world space, of objects in fields of view of an ego-vehicle, current and future locations of the objects in image space, and/or a time-to-collision (TTC) between the objects and the ego-vehicle. These predictions may be used as part of a perception system for understanding and reacting to a current physical environment of the ego-vehicle.

公开(公告)号：US10284269B2

公开(公告)日：2019-05-07

申请号：US15005650

申请日：2016-01-25

Applicant: Nvidia Corporation

Inventor： Pekka Janis ,  Tommi Koivisto ,  Kari Hamalainen

IPC: H04B7/0456 ,  H04W16/28 ,  H04B7/06

Abstract: A communications system has a cellular structure including a base station that is located within a cell of the cellular structure and provides an elevation beamforming transmission based on a set of elevation precoding matrix indicator offsets in an elevation codebook. The communications system also includes user equipment that is located within the cell and coupled to the base station to receive the set of elevation precoding matrix indicator offsets and a set of reference signals to provide channel quality and inter-cell interference measurements, wherein a selected channel quality indicator is based on an increase in channel quality with respect to inter-cell interference at the user equipment and corresponds to one of the set of elevation precoding matrix indicator offsets. A method of operating a communications system having a cellular structure is also provided.