公开(公告)号：US11941819B2

公开(公告)日：2024-03-26

申请号：US17457825

申请日：2021-12-06

Applicant: NVIDIA Corporation

Inventor： Dongwoo Lee ,  Junghyun Kwon ,  Sangmin Oh ,  Wenchao Zheng ,  Hae-Jong Seo ,  David Nister ,  Berta Rodriguez Hervas

IPC: G06T7/13 ,  G06T7/40 ,  G06T17/30 ,  G06V10/44 ,  G06V10/75 ,  G06V10/772 ,  G06V10/82 ,  G06V20/58

CPC classification number: G06T7/13 ,  G06T7/40 ,  G06T17/30 ,  G06V10/454 ,  G06V10/751 ,  G06V10/772 ,  G06V10/82 ,  G06V20/586 ,  G06T2207/10021 ,  G06T2207/20084 ,  G06T2207/30264

Abstract: A neural network may be used to determine corner points of a skewed polygon (e.g., as displacement values to anchor box corner points) that accurately delineate a region in an image that defines a parking space. Further, the neural network may output confidence values predicting likelihoods that corner points of an anchor box correspond to an entrance to the parking spot. The confidence values may be used to select a subset of the corner points of the anchor box and/or skewed polygon in order to define the entrance to the parking spot. A minimum aggregate distance between corner points of a skewed polygon predicted using the CNN(s) and ground truth corner points of a parking spot may be used simplify a determination as to whether an anchor box should be used as a positive sample for training.

公开(公告)号：US11897471B2

公开(公告)日：2024-02-13

申请号：US18162576

申请日：2023-01-31

Applicant: NVIDIA Corporation

Inventor： Sayed Mehdi Sajjadi Mohammadabadi ,  Berta Rodriguez Hervas ,  Hang Dou ,  Igor Tryndin ,  David Nister ,  Minwoo Park ,  Neda Cvijetic ,  Junghyun Kwon ,  Trung Pham

IPC: B60W30/18 ,  G06N3/08 ,  G08G1/01 ,  B60W30/095 ,  B60W60/00 ,  B60W30/09 ,  G06V20/56 ,  G06V10/25 ,  G06V10/764 ,  G06V10/80 ,  G06V10/82 ,  G06V20/70 ,  G06V10/75

CPC classification number: B60W30/18154 ,  B60W30/09 ,  B60W30/095 ,  B60W60/0011 ,  G06N3/08 ,  G06V10/25 ,  G06V10/751 ,  G06V10/764 ,  G06V10/803 ,  G06V10/82 ,  G06V20/56 ,  G06V20/588 ,  G06V20/70 ,  G08G1/0125

Abstract: In various examples, live perception from sensors of a vehicle may be leveraged to detect and classify intersections in an environment of a vehicle in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute various outputs—such as bounding box coordinates for intersections, intersection coverage maps corresponding to the bounding boxes, intersection attributes, distances to intersections, and/or distance coverage maps associated with the intersections. The outputs may be decoded and/or post-processed to determine final locations of, distances to, and/or attributes of the detected intersections.

公开(公告)号：US20220318559A1

公开(公告)日：2022-10-06

申请号：US17356224

申请日：2021-06-23

Applicant: NVIDIA Corporation

Inventor： Yichun Shen ,  Wanli Jiang ,  Junghyun Kwon ,  Siyi Li ,  Sangmin Oh ,  Minwoo Park

IPC: G06K9/00 ,  G06T7/194 ,  G06N3/04 ,  G06N20/00 ,  G06K9/66

Abstract: Apparatuses, systems, and techniques to identify bounding boxes of objects with in an image. In at least one embodiment, bounding boxes are determined in an image using an intersection over union threshold that is based at least in part on a size of an object.

公开(公告)号：US20220222480A1

公开(公告)日：2022-07-14

申请号：US17160271

申请日：2021-01-27

Applicant: NVIDIA Corporation

Inventor： Wanli Jiang ,  Yichun Shen ,  Junghyun Kwon ,  Siyi Li ,  Sangmin Oh ,  Minwoo Park

IPC: G06K9/20 ,  G06K9/00 ,  G06N3/08

Abstract: Apparatuses, systems, and techniques to generate bounding box information. In at least one embodiment, for example, bounding box information is generated based, at least in part, on a plurality of candidate bounding box information.

公开(公告)号：US20210406560A1

公开(公告)日：2021-12-30

申请号：US17353231

申请日：2021-06-21

Applicant: NVIDIA Corporation

Inventor： Minwoo Park ,  Junghyun Kwon ,  Mehmet K. Kocamaz ,  Hae-Jong Seo ,  Berta Rodriguez Hervas ,  Tae Eun Choe

IPC: G06K9/00 ,  B60W60/00 ,  G06T7/292

Abstract: In various examples, a multi-sensor fusion machine learning model—such as a deep neural network (DNN)—may be deployed to fuse data from a plurality of individual machine learning models. As such, the multi-sensor fusion network may use outputs from a plurality of machine learning models as input to generate a fused output that represents data from fields of view or sensory fields of each of the sensors supplying the machine learning models, while accounting for learned associations between boundary or overlap regions of the various fields of view of the source sensors. In this way, the fused output may be less likely to include duplicate, inaccurate, or noisy data with respect to objects or features in the environment, as the fusion network may be trained to account for multiple instances of a same object appearing in different input representations.

公开(公告)号：US12175739B2

公开(公告)日：2024-12-24

申请号：US17149437

申请日：2021-01-14

Applicant: NVIDIA Corporation

Inventor： Yichun Shen ,  Wanli Jiang ,  Junghyun Kwon ,  Siyi Li ,  Minwoo Park ,  Sangmin Oh

IPC: G06V10/94 ,  G06F18/21 ,  G06V10/22 ,  G06V10/40 ,  G06V10/46

Abstract: Apparatuses, systems, and techniques to perform non-maximum suppression (NMS) in parallel to remove redundant bounding boxes. In at least one embodiment, two or more parallel circuits to perform two or more portions of a NMS algorithm in parallel to remove one or more redundant bounding boxes corresponding to one or more objects within one or more digital images.

公开(公告)号：US20240265555A1

公开(公告)日：2024-08-08

申请号：US18614160

申请日：2024-03-22

Applicant: NVIDIA Corporation

Inventor： Dong Zhang ,  Sangmin Oh ,  Junghyun Kwon ,  Baris Evrim Demiroz ,  Tae Eun Choe ,  Minwoo Park ,  Chethan Ningaraju ,  Hao Tsui ,  Eric Viscito ,  Jagadeesh Sankaran ,  Yongqing Liang

IPC: G06T7/246 ,  B60W60/00 ,  G06F18/214 ,  G06N3/08 ,  G06V10/25 ,  G06V10/75 ,  G06V20/56 ,  G06V20/58

CPC classification number: G06T7/246 ,  B60W60/001 ,  G06F18/2148 ,  G06N3/08 ,  G06V10/25 ,  G06V10/751 ,  G06V20/58 ,  G06V20/56

Abstract: Systems and methods are disclosed that use a geometric approach to detect objects on a road surface. A set of points within a region of interest between a first frame and a second frame are captured and tracked to determine a difference in location between the set of points in two frames. The first frame may be aligned with the second frame and the first pixel values of the first frame may be compared with the second pixel values of the second frame to generate a disparity image including third pixels. Subsets of the third pixels that have an disparity image value about a first threshold may be combined, and the third pixels may be scored and associated with disparity values for each pixel of the one or more subsets of the third pixels. A bounding shape may be generated based on the scoring that corresponds to the object.

公开(公告)号：US20230334317A1

公开(公告)日：2023-10-19

申请号：US18337854

申请日：2023-06-20

Applicant: NVIDIA Corporation

Inventor： Junghyun Kwon ,  Yilin Yang ,  Bala Siva Sashank Jujjavarapu ,  Zhaoting Ye ,  Sangmin Oh ,  Minwoo Park ,  David Nister

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

CPC classification number: G06N3/08 ,  B60W30/14 ,  B60W60/0011 ,  G06V20/56 ,  G06F18/2155 ,  G06V10/763

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.

公开(公告)号：US20230213945A1

公开(公告)日：2023-07-06

申请号：US17565837

申请日：2021-12-30

Applicant: NVIDIA Corporation

Inventor： Neeraj Sajjan ,  Mehmet K. Kocamaz ,  Junghyun Kwon ,  Sangmin Oh ,  Minwoo Park ,  David Nister

IPC: G05D1/02 ,  G06N3/08 ,  G05D1/00

CPC classification number: G05D1/0248 ,  G05D1/0257 ,  G06N3/08 ,  G05D1/0221 ,  G05D1/0219 ,  G05D1/0088 ,  G05D1/0251 ,  G05D1/0255 ,  G05D2201/0213

Abstract: In various examples, one or more output channels of a deep neural network (DNN) may be used to determine assignments of obstacles to paths. To increase the accuracy of the DNN, the input to the DNN may include an input image, one or more representations of path locations, and/or one or more representations of obstacle locations. The system may thus repurpose previously computed information—e.g., obstacle locations, path locations, etc.—from other operations of the system, and use them to generate more detailed inputs for the DNN to increase accuracy of the obstacle to path assignments. Once the output channels are computed using the DNN, computed bounding shapes for the objects may be compared to the outputs to determine the path assignments for each object.

公开(公告)号：US20220222477A1

公开(公告)日：2022-07-14

申请号：US17149437

申请日：2021-01-14

Applicant: NVIDIA Corporation

Inventor： Yichun Shen ,  Wanli Jiang ,  Junghyun Kwon ,  Siyi Li ,  Minwoo Park ,  Sangmin Oh

IPC: G06K9/00 ,  G06K9/62 ,  G06K9/20 ,  G06K9/46

Abstract: Apparatuses, systems, and techniques to perform non-maximum suppression (NMS) in parallel to remove redundant bounding boxes. In at least one embodiment, two or more parallel circuits to perform two or more portions of a NMS algorithm in parallel to remove one or more redundant bounding boxes corresponding to one or more objects within one or more digital images.