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公开(公告)号：US11874663B2
公开(公告)日：2024-01-16
申请号：US17896825
申请日：2022-08-26
Applicant: NVIDIA Corporation
Inventor： Gary Hicok , Michael Cox , Miguel Sainz , Martin Hempel , Ratin Kumar , Timo Roman , Gordon Grigor , David Nister , Justin Ebert , Chin-Hsien Shih , Tony Tam , Ruchi Bhargava
CPC classification number: G05D1/0088 , G05B13/027 , G05D1/0055 , G05D1/0242 , G05D1/0246 , G05D1/0257 , G06Q10/02 , G06Q50/30 , G05D2201/0213
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.
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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
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.
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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
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.
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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
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。
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公开(公告)号：US20220114702A1
公开(公告)日：2022-04-14
申请号：US17406902
申请日：2021-08-19
Applicant: Nvidia Corporation
Inventor： Shiqiu Liu , Robert Pottorff , Guilin Liu , Karan Sapra , Jon Barker , David Tarjan , Pekka Janis , Edvard Fagerholm , Lei Yang , Kevin Jonathan Shih , Marco Salvi , Timo Roman , Andrew Tao , Bryan Catanzaro
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.
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Patent Agency Ranking
公开(公告)号：US20220114701A1
公开(公告)日：2022-04-14
申请号：US17172330
申请日：2021-02-10
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
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.
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公开(公告)号：US20220101635A1
公开(公告)日：2022-03-31
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: G06V20/58 , G06V10/25 , G06V10/774 , G06V10/77 , G06N20/00
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.
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公开(公告)号：US11210537B2
公开(公告)日：2021-12-28
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 , B60W50/00 , G06N3/04 , G01S7/41 , G05D1/02 , G06N3/08 , G06K9/32 , G06K9/48 , G01S13/86 , G01S7/48 , G01S17/931 , G01S13/931
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.
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公开(公告)号：US09831999B2
公开(公告)日：2017-11-28
申请号：US14707203
申请日：2015-05-08
Applicant: Nvidia Corporation
Inventor： Tommi Koivisto , Tero Kuosmanen , Timo Roman
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.
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公开(公告)号：US20240192320A1
公开(公告)日：2024-06-13
申请号：US18582358
申请日：2024-02-20
Applicant: NVIDIA Corporation
Inventor： Tommi Koivisto , Pekka Janis , Tero Kuosmanen , Timo Roman , Sriya Sarathy , William Zhang , Nizar Assaf , Colin Tracey
IPC: G01S7/41 , B60W50/00 , G01S7/48 , G01S13/86 , G01S13/931 , G01S17/931 , G06F16/35 , G06F18/21 , G06F18/214 , G06F18/23 , G06F18/2413 , G06N3/044 , G06N3/045 , G06N3/047 , G06N3/048 , G06N3/084 , G06N20/00 , G06V10/20 , G06V10/44 , G06V10/46 , G06V10/762 , G06V10/764 , G06V10/77 , G06V10/774 , G06V20/58
CPC classification number: G01S7/417 , B60W50/00 , 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.
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