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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.
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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
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.
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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：US12093824B2
公开(公告)日：2024-09-17
申请号：US18343291
申请日：2023-06-28
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 , B60W30/14 , B60W60/00 , G06F18/214 , G06N3/08 , 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.
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Patent Agency Ranking
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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公开(公告)号：US11579629B2
公开(公告)日：2023-02-14
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
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.
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公开(公告)号：US20220253706A1
公开(公告)日：2022-08-11
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
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.
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公开(公告)号：US20220108465A1
公开(公告)日：2022-04-07
申请号：US17522624
申请日：2021-11-09
Applicant: NVIDIA Corporation
Inventor： Yilin Yang , Bala Siva Jujjavarapu , Pekka Janis , Zhaoting Ye , Sangmin Oh , Minwoo Park , Daniel Herrera Castro , Tommi Koivisto , David Nister
Abstract: In various examples, a deep neural network (DNN) is trained—using image data alone—to accurately predict distances to objects, obstacles, and/or a detected free-space boundary. The DNN may be trained with ground truth data that is generated using sensor data representative of motion of an ego-vehicle and/or sensor data from any number of depth predicting sensors—such as, without limitation, RADAR sensors, LIDAR sensors, and/or SONAR sensors. The DNN may be trained using two or more loss functions each corresponding to a particular portion of the environment that depth is predicted for, such that—in deployment—more accurate depth estimates for objects, obstacles, and/or the detected free-space boundary are computed by the DNN. In some embodiments, a sampling algorithm may be used to sample depth values corresponding to an input resolution of the DNN from a predicted depth map of the DNN at an output resolution of the DNN.
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公开(公告)号：US20210272304A1
公开(公告)日：2021-09-02
申请号：US16728598
申请日：2019-12-27
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
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.
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