公开(公告)号：US20240320986A1

公开(公告)日：2024-09-26

申请号：US18734354

申请日：2024-06-05

申请人： NVIDIA Corporation

发明人： Mehmet Kocamaz ,  Neeraj Sajjan ,  Sangmin Oh ,  David Nister ,  Junghyun Kwon ,  Minwoo Park

IPC分类号： G06V20/58 ,  G06N3/08 ,  G06V10/20 ,  G06V10/94 ,  G06V20/56 ,  G06V20/64

CPC分类号： G06V20/58 ,  G06N3/08 ,  G06V10/255 ,  G06V10/95 ,  G06V20/588 ,  G06V20/64

摘要： In various examples, live perception from sensors of an ego-machine may be leveraged to detect objects and assign the objects to bounded regions (e.g., lanes or a roadway) in an environment of the ego-machine in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute outputs—such as output segmentation masks—that may correspond to a combination of object classification and lane identifiers. The output masks may be post-processed to determine object to lane assignments that assign detected objects to lanes in order to aid an autonomous or semi-autonomous machine in a surrounding environment.

公开(公告)号：US20230213945A1

公开(公告)日：2023-07-06

申请号：US17565837

申请日：2021-12-30

申请人： NVIDIA Corporation

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

IPC分类号： G05D1/02 ,  G06N3/08 ,  G05D1/00

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

摘要： 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：US20230099494A1

公开(公告)日：2023-03-30

申请号：US17489346

申请日：2021-09-29

申请人： NVIDIA Corporation

发明人： Mehmet Kocamaz ,  Neeraj Sajjan ,  Sangmin Oh ,  David Nister ,  Junghyun Kwon ,  Minwoo Park

IPC分类号： G06K9/00 ,  G06K9/32 ,  G06N3/08

摘要： In various examples, live perception from sensors of an ego-machine may be leveraged to detect objects and assign the objects to bounded regions (e.g., lanes or a roadway) in an environment of the ego-machine in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute outputs—such as output segmentation masks—that may correspond to a combination of object classification and lane identifiers. The output masks may be post-processed to determine object to lane assignments that assign detected objects to lanes in order to aid an autonomous or semi-autonomous machine in a surrounding environment.

公开(公告)号：US12026955B2

公开(公告)日：2024-07-02

申请号：US17489346

申请日：2021-09-29

申请人： NVIDIA Corporation

发明人： Mehmet Kocamaz ,  Neeraj Sajjan ,  Sangmin Oh ,  David Nister ,  Junghyun Kwon ,  Minwoo Park

IPC分类号： G06V20/58 ,  G06N3/08 ,  G06V10/20 ,  G06V10/94 ,  G06V20/56 ,  G06V20/64

CPC分类号： G06V20/58 ,  G06N3/08 ,  G06V10/255 ,  G06V10/95 ,  G06V20/588 ,  G06V20/64

摘要： In various examples, live perception from sensors of an ego-machine may be leveraged to detect objects and assign the objects to bounded regions (e.g., lanes or a roadway) in an environment of the ego-machine in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute outputs—such as output segmentation masks—that may correspond to a combination of object classification and lane identifiers. The output masks may be post-processed to determine object to lane assignments that assign detected objects to lanes in order to aid an autonomous or semi-autonomous machine in a surrounding environment.

公开(公告)号：US20240211748A1

公开(公告)日：2024-06-27

申请号：US18146671

申请日：2022-12-27

申请人： NVIDIA Corporation

发明人： Neeraj Sajjan ,  Mehmet Kocamaz ,  Parthiv Parikh

IPC分类号： G06N3/08

CPC分类号： G06N3/08

摘要： In various examples, systems and methods are disclosed relating to determining associations between objects represented in sensor data and predicted states of the objects in multi-sensor systems such as autonomous or semi-autonomous vehicle perception systems. Systems and methods are disclosed that employ neural network models, such as multi-layer perceptron (MLP) models or other deep neural network (DNN) models, in learning association costs between sensor measurements and predicted states of objects. During training, the systems and methods can generate data for updating parameters of the neural network models such that, during deployment, the neural network models can receive sensor data and predicted states, and provide corresponding association costs.