公开(公告)号：US09887751B2

公开(公告)日：2018-02-06

申请号：US15005602

申请日：2016-01-25

Applicant: Nvidia Corporation

Inventor： Pekka Janis ,  Tommi Koivisto ,  Kari Hamalainen

IPC: H04B7/02 ,  H04B7/04 ,  H04W16/28 ,  H04B7/06

CPC classification number: H04B7/0469 ,  H04B7/0478 ,  H04B7/0639 ,  H04B7/065 ,  H04W16/28

Abstract: A communications system has a cellular structure and the communications system includes a base station that is located within a cell of the cellular structure and employs a Kronecker product of azimuth and elevation precoding vectors for beamforming. Additionally, the communications system includes user equipment that is located within the cell and coupled to the base station to receive a reference channel state information process employing a reference precoding vector for use in a non-reference channel state information process to derive a compensated channel quality indication. A method of operating a communications system is also included.

公开(公告)号：US09602230B2

公开(公告)日：2017-03-21

申请号：US14656810

申请日：2015-03-13

Applicant: Nvidia Corporation

Inventor： Timo Roman ,  Tommi Koivisto ,  Tero Kuosmanen ,  Pekka Janis

IPC: G01R31/08 ,  H04J11/00 ,  H04B7/06 ,  H04W24/08 ,  H04B7/04

CPC classification number: H04J11/005 ,  H04B7/0417 ,  H04B7/0626 ,  H04W24/08

Abstract: Disclosed is a method of providing channel state information for a desired downlink channel of a wireless communication system. In a configuration phase, the method comprises receiving on a signaling channel configuration information comprising an identifier of an interference source and an association which associates the identifier with at least one resource element not used for transmission on the desired downlink channel. In an estimation phase, the method comprises estimating channel state information for an expected transmission on the desired downlink channel accounting for an incoming interference transmission from the identified interference source as observed from the at least one resource element. In a reporting phase, the method comprises reporting the channel state information.

公开(公告)号：US20150270917A1

公开(公告)日：2015-09-24

申请号：US14656810

申请日：2015-03-13

Applicant: Nvidia Corporation

Inventor： Timo Roman ,  Tommi Koivisto ,  Tero Kuosmanen ,  Pekka Janis

IPC: H04J11/00 ,  H04B7/06 ,  H04W24/08 ,  H04W72/04

CPC classification number: H04J11/005 ,  H04B7/0417 ,  H04B7/0626 ,  H04W24/08

Abstract: Disclosed is a method of providing channel state information for a desired downlink channel of a wireless communication system. In a configuration phase, the method comprises receiving on a signaling channel configuration information comprising an identifier of an interference source and an association which associates the identifier with at least one resource element not used for transmission on the desired downlink channel. In an estimation phase, the method comprises estimating channel state information for an expected transmission on the desired downlink channel accounting for an incoming interference transmission from the identified interference source as observed from the at least one resource element. In a reporting phase, the method comprises reporting the channel state information.

Abstract translation: 公开了一种为无线通信系统的期望的下行链路信道提供信道状态信息的方法。 在配置阶段，该方法包括在信令信道配置信息上接收包括干扰源的标识符和将该标识符与至少一个不用于在期望的下行链路信道上进行传输的资源元素相关联的信息。 在估计阶段中，所述方法包括：从所述至少一个资源元素观察到，估计所述期望下行链路信道上的期望传输的信道状态信息，以对来自所识别的干扰源的进入干扰传输进行核算。 在报告阶段，该方法包括报告信道状态信息。

公开(公告)号：US20250104186A1

公开(公告)日：2025-03-27

申请号：US17514448

申请日：2021-10-29

Applicant: Nvidia Corporation

Inventor： Loudon Cohen ,  Gregory Massal ,  Pekka Janis

IPC: G06T3/40 ,  G06N3/04 ,  G06T5/00 ,  G06T5/50

Abstract: Apparatuses, systems, and techniques are presented to reconstruct one or more images. In at least one embodiment, one or more neural networks are used to upsample one or more images based, at least in part, on one or more brightness values.

公开(公告)号：US20240403640A1

公开(公告)日：2024-12-05

申请号：US18799229

申请日：2024-08-09

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

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：US12072442B2

公开(公告)日：2024-08-27

申请号：US17456045

申请日：2021-11-22

Applicant: NVIDIA Corporation

Inventor： Tommi Koivisto ,  Pekka Janis ,  Tero Kuosmanen ,  Timo Roman ,  Sriya Sarathy ,  William Zhang ,  Nizar Assaf ,  Colin Tracey

IPC: G06V10/46 ,  B60W50/00 ,  G01S7/41 ,  G05D1/00 ,  G06F16/35 ,  G06F18/21 ,  G06F18/214 ,  G06F18/23 ,  G06F18/2413 ,  G06N3/044 ,  G06N3/045 ,  G06N3/084 ,  G06N20/00 ,  G06V10/20 ,  G06V10/44 ,  G06V10/762 ,  G06V10/764 ,  G06V10/77 ,  G06V10/774 ,  G06V20/58 ,  G01S7/48 ,  G01S13/86 ,  G01S13/931 ,  G01S17/931 ,  G06N3/047 ,  G06N3/048

CPC classification number: G01S7/417 ,  B60W50/00 ,  G05D1/0246 ,  G06F16/35 ,  G06F18/214 ,  G06F18/217 ,  G06F18/23 ,  G06F18/2414 ,  G06N3/044 ,  G06N3/045 ,  G06N3/084 ,  G06N20/00 ,  G06V10/255 ,  G06V10/454 ,  G06V10/46 ,  G06V10/762 ,  G06V10/764 ,  G06V10/7715 ,  G06V10/774 ,  G06V20/58 ,  G06V20/584 ,  G01S7/412 ,  G01S7/4802 ,  G01S13/867 ,  G01S2013/9318 ,  G01S2013/9323 ,  G01S17/931 ,  G06N3/047 ,  G06N3/048

Abstract: In various examples, detected object data representative of locations of detected objects in a field of view may be determined. One or more clusters of the detected objects may be generated based at least in part on the locations and features of the cluster may be determined for use as inputs to a machine learning model(s). A confidence score, computed by the machine learning model(s) based at least in part on the inputs, may be received, where the confidence score may be representative of a probability that the cluster corresponds to an object depicted at least partially in the field of view. Further examples provide approaches for determining ground truth data for training object detectors, such as for determining coverage values for ground truth objects using associated shapes, and for determining soft coverage values for ground truth objects.

公开(公告)号：US11790230B2

公开(公告)日：2023-10-17

申请号：US17723195

申请日：2022-04-18

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: G06K9/00 ,  G06N3/08 ,  B60W30/14 ,  B60W60/00 ,  G06V20/56 ,  G06F18/214 ,  G06V10/762

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：US20230267701A1

公开(公告)日：2023-08-24

申请号：US18309882

申请日：2023-05-01

Applicant: NVIDIA Corporation

Inventor： Yifang Xu ,  Xin Liu ,  Chia-Chin 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 ,  G06T7/10 ,  G05D1/0088 ,  G06N3/084 ,  G05D1/0221 ,  G06V20/588 ,  G06V10/46 ,  G06V10/457 ,  G06V20/41 ,  G06F18/24143 ,  G06V10/764 ,  G06V10/82 ,  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：US20230122119A1

公开(公告)日：2023-04-20

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: G05D1/02 ,  G06N3/04 ,  G06T7/20 ,  G05D1/00 ,  G06V20/10 ,  G06V20/58 ,  G06F18/214

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：US20220222778A1

公开(公告)日：2022-07-14

申请号：US17710643

申请日：2022-03-31

Applicant: NVIDIA Corporation

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

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