-
公开(公告)号：US20160218782A1
公开(公告)日：2016-07-28
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.
Abstract translation: 通信系统具有蜂窝结构，并且通信系统包括位于蜂窝结构的小区内的基站，并且使用用于波束成形的方位角和仰角预编码向量的克罗内克积。 此外，通信系统包括位于小区内并耦合到基站的用户设备，以接收使用参考预编码向量的参考信道状态信息过程，以用于非参考信道状态信息处理以导出补偿信道质量 指示。 还包括操作通信系统的方法。
-
公开(公告)号：US20250139934A1
公开(公告)日：2025-05-01
申请号：US19005672
申请日：2024-12-30
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 , G06F18/2413 , G06N3/084 , G06T7/10 , G06V10/46 , G06V10/764 , G06V10/82 , G06V20/40 , G06V20/56
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
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.
-
Patent Agency Ranking
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
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
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.
-
-
-
-
-
-
-
-
-
Search Results
41
records
(took 0.018 seconds)
