公开(公告)号：US20150327325A1

公开(公告)日：2015-11-12

申请号：US14707203

申请日：2015-05-08

Applicant: Nvidia Corporation

Inventor： Tommi Koivisto ,  Tero Kuosmanen ,  Timo Roman

IPC: H04W76/04 ,  H04W74/00

CPC classification number: H04L5/0073 ,  H04L5/00 ,  H04L5/005 ,  H04L5/0082 ,  H04L5/0092 ,  H04W74/006 ,  H04W76/27 ,  H04W76/28 ,  H04W84/045

Abstract: Methods for operating a small cell in a discontinued reception (DRX) mode include maintaining the small cell in a discontinuous transmission (DTX) mode during a first time period having a plurality of first time slots. The methods include transmitting common reference signals in a predetermined number of second time slots prior to the first time slots and in a predetermined number of third time slots following commencement of the first time slots. The methods include discontinuing transmission of the common reference signals and common channel signals if mobile devices are in a discontinuous reception mode during the first time period. The methods include discontinuing transmission of the common reference signals during a predetermined number of fourth time slots following commencement of the first time period if there is no dedicated common transmission to the mobile devices.

Abstract translation: 在不连续接收（DRX）模式下操作小小区的方法包括在具有多个第一时隙的第一时间段期间将小小区维持在不连续传输（DTX）模式。 所述方法包括在第一时隙之前的预定数量的第二时隙中传输公共参考信号，以及在第一时隙开始之后的预定数量的第三时隙中。 如果移动设备在第一时间段期间处于不连续接收模式，则该方法包括中止公共参考信号和公共信道信号的传输。 所述方法包括在第一时间段开始之后的预定数量的第四时隙期间停止发送公共参考信号，如果没有向移动设备的专用公共传输。

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：US11474519B2

公开(公告)日：2022-10-18

申请号：US16286330

申请日：2019-02-26

Applicant: NVIDIA Corporation

Inventor： Gary Hicok ,  Michael Cox ,  Miguel Sainz ,  Martin Hempel ,  Ratin Kumar ,  Timo Roman ,  Gordon Grigor ,  David Nister ,  Justin Ebert ,  Chin Shih ,  Tony Tam ,  Ruchi Bhargava

IPC: G05D1/00 ,  G06Q50/30 ,  G05B13/02 ,  G05D1/02 ,  G06Q10/02

Abstract: A system and method for an on-demand shuttle, bus, or taxi service able to operate on private and public roads provides situational awareness and confidence displays. The shuttle may include ISO 26262 Level 4 or Level 5 functionality and can vary the route dynamically on-demand, and/or follow a predefined route or virtual rail. The shuttle is able to stop at any predetermined station along the route. The system allows passengers to request rides and interact with the system via a variety of interfaces, including without limitation a mobile device, desktop computer, or kiosks. Each shuttle preferably includes an in-vehicle controller, which preferably is an AI Supercomputer designed and optimized for autonomous vehicle functionality, with computer vision, deep learning, and real time ray tracing accelerators. An AI Dispatcher performs AI simulations to optimize system performance according to operator-specified system parameters.

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.

公开(公告)号：US20190258878A1

公开(公告)日：2019-08-22

申请号：US16277895

申请日：2019-02-15

Applicant: NVIDIA Corporation

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

IPC: G06K9/00 ,  G06K9/46 ,  G06K9/62

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.