公开(公告)号：US20240410981A1

公开(公告)日：2024-12-12

申请号：US18810728

申请日：2024-08-21

Applicant: NVIDIA CORPORATION

Inventor： Nikolai Smolyanskiy ,  Ryan Oldja ,  Ke Chen ,  Alexander Popov ,  Joachim Pehserl ,  Ibrahim Eden ,  Tilman Wekel ,  David Wehr ,  Ruchi Bhargava ,  David Nister

IPC: G01S7/48 ,  B60W60/00 ,  G01S17/89 ,  G01S17/931 ,  G05D1/81 ,  G06N3/045 ,  G06T19/00 ,  G06V10/10 ,  G06V10/25 ,  G06V10/26 ,  G06V10/44 ,  G06V10/764 ,  G06V10/774 ,  G06V10/80 ,  G06V10/82 ,  G06V20/56 ,  G06V20/58

Abstract: A deep neural network(s) (DNN) may be used to detect objects from sensor data of a three dimensional (3D) environment. For example, a multi-view perception DNN may include multiple constituent DNNs or stages chained together that sequentially process different views of the 3D environment. An example DNN may include a first stage that performs class segmentation in a first view (e.g., perspective view) and a second stage that performs class segmentation and/or regresses instance geometry in a second view (e.g., top-down). The DNN outputs may be processed to generate 2D and/or 3D bounding boxes and class labels for detected objects in the 3D environment. As such, the techniques described herein may be used to detect and classify animate objects and/or parts of an environment, and these detections and classifications may be provided to an autonomous vehicle drive stack to enable safe planning and control of the autonomous vehicle.

公开(公告)号：US20220197284A1

公开(公告)日：2022-06-23

申请号：US17692706

申请日：2022-03-11

Applicant: NVIDIA Corporation

Inventor： Nikolai Smolyanskiy ,  Alexey Kamenev ,  Jeffrey David Smith ,  Stanley Thomas Birchfield

IPC: G05D1/00 ,  G05D1/02 ,  B62D6/00 ,  G06N3/08 ,  G06N7/00 ,  B62D15/02 ,  G06K9/62 ,  G05D1/10 ,  G06K9/00 ,  G06N3/04 ,  G06V10/94 ,  G06V20/56

Abstract: A method, computer readable medium, and system are disclosed for performing autonomous path navigation using deep neural networks. The method includes the steps of receiving image data at a deep neural network (DNN), determining, by the DNN, both an orientation of a vehicle with respect to a path and a lateral position of the vehicle with respect to the path, utilizing the image data, and controlling a location of the vehicle, utilizing the orientation of the vehicle with respect to the path and the lateral position of the vehicle with respect to the path.

公开(公告)号：US20210326678A1

公开(公告)日：2021-10-21

申请号：US17356140

申请日：2021-06-23

Applicant: NVIDIA Corporation

Inventor： Nikolai Smolyanskiy ,  Alexey Kamenev ,  Stan Birchfield

IPC: G06N3/04 ,  G06T7/593 ,  G06N3/08 ,  G06T1/20 ,  G06K9/62 ,  G06N3/063 ,  G01S17/86 ,  G01S17/89

Abstract: Various examples of the present disclosure include a stereoscopic deep neural network (DNN) that produces accurate and reliable results in real-time. Both LIDAR data (supervised training) and photometric error (unsupervised training) may be used to train the DNN in a semi-supervised manner. The stereoscopic DNN may use an exponential linear unit (ELU) activation function to increase processing speeds, as well as a machine learned argmax function that may include a plurality of convolutional layers having trainable parameters to account for context. The stereoscopic DNN may further include layers having an encoder/decoder architecture, where the encoder portion of the layers may include a combination of three-dimensional convolutional layers followed by two-dimensional convolutional layers.

公开(公告)号：US20210156963A1

公开(公告)日：2021-05-27

申请号：US16836618

申请日：2020-03-31

Applicant: NVIDIA Corporation

Inventor： Alexander Popov ,  Nikolai Smolyanskiy ,  Ryan Oldja ,  Shane Murray ,  Tilman Wekel ,  David Nister ,  Joachim Pehserl ,  Ruchi Bhargava ,  Sangmin Oh

IPC: G01S7/41 ,  G01S13/86 ,  G01S13/89 ,  G06N3/08 ,  G06N3/04

Abstract: In various examples, a deep neural network(s) (e.g., a convolutional neural network) may be trained to detect moving and stationary obstacles from RADAR data of a three dimensional (3D) space. In some embodiments, ground truth training data for the neural network(s) may be generated from LIDAR data. More specifically, a scene may be observed with RADAR and LIDAR sensors to collect RADAR data and LIDAR data for a particular time slice. The RADAR data may be used for input training data, and the LIDAR data associated with the same or closest time slice as the RADAR data may be annotated with ground truth labels identifying objects to be detected. The LIDAR labels may be propagated to the RADAR data, and LIDAR labels containing less than some threshold number of RADAR detections may be omitted. The (remaining) LIDAR labels may be used to generate ground truth data.

公开(公告)号：US20210150230A1

公开(公告)日：2021-05-20

申请号：US16915346

申请日：2020-06-29

Applicant: NVIDIA Corporation

Inventor： Nikolai Smolyanskiy ,  Ryan Oldja ,  Ke Chen ,  Alexander Popov ,  Joachim Pehserl ,  Ibrahim Eden ,  Tilman Wekel ,  David Wehr ,  Ruchi Bhargava ,  David Nister

IPC: G06K9/00 ,  B60W60/00 ,  G05D1/00 ,  G06N3/04 ,  G06T19/00 ,  G01S17/89 ,  G01S17/931

Abstract: A deep neural network(s) (DNN) may be used to detect objects from sensor data of a three dimensional (3D) environment. For example, a multi-view perception DNN may include multiple constituent DNNs or stages chained together that sequentially process different views of the 3D environment. An example DNN may include a first stage that performs class segmentation in a first view (e.g., perspective view) and a second stage that performs class segmentation and/or regresses instance geometry in a second view (e.g., top-down). The DNN outputs may be processed to generate 2D and/or 3D bounding boxes and class labels for detected objects in the 3D environment. As such, the techniques described herein may be used to detect and classify animate objects and/or parts of an environment, and these detections and classifications may be provided to an autonomous vehicle drive stack to enable safe planning and control of the autonomous vehicle.

公开(公告)号：US12050285B2

公开(公告)日：2024-07-30

申请号：US17976581

申请日：2022-10-28

Applicant: NVIDIA Corporation

Inventor： Alexander Popov ,  Nikolai Smolyanskiy ,  Ryan Oldja ,  Shane Murray ,  Tilman Wekel ,  David Nister ,  Joachim Pehserl ,  Ruchi Bhargava ,  Sangmin Oh

IPC: G01S7/41 ,  G01S13/86 ,  G01S13/89 ,  G06N3/04 ,  G06N3/08

CPC classification number: G01S7/417 ,  G01S13/865 ,  G01S13/89 ,  G06N3/04 ,  G06N3/08

Abstract: In various examples, a deep neural network(s) (e.g., a convolutional neural network) may be trained to detect moving and stationary obstacles from RADAR data of a three dimensional (3D) space. In some embodiments, ground truth training data for the neural network(s) may be generated from LIDAR data. More specifically, a scene may be observed with RADAR and LIDAR sensors to collect RADAR data and LIDAR data for a particular time slice. The RADAR data may be used for input training data, and the LIDAR data associated with the same or closest time slice as the RADAR data may be annotated with ground truth labels identifying objects to be detected. The LIDAR labels may be propagated to the RADAR data, and LIDAR labels containing less than some threshold number of RADAR detections may be omitted. The (remaining) LIDAR labels may be used to generate ground truth data.

公开(公告)号：US20230013338A1

公开(公告)日：2023-01-19

申请号：US17855233

申请日：2022-06-30

Applicant: NVIDIA Corporation

Inventor： Nikolai Smolyanskiy ,  Alexey Kamenev ,  Jeffrey David Smith ,  Stanley Thomas Birchfield

IPC: G05D1/00 ,  G05D1/02 ,  G06K9/00 ,  G06K9/62 ,  G06V20/56 ,  B62D15/02 ,  G06V10/94 ,  G06N3/04 ,  G06N7/00 ,  G05D1/10 ,  G06N3/08 ,  B62D6/00

Abstract: A method, computer readable medium, and system are disclosed for performing autonomous path navigation using deep neural networks. The method includes the steps of receiving image data at a deep neural network (DNN), determining, by the DNN, both an orientation of a vehicle with respect to a path and a lateral position of the vehicle with respect to the path, utilizing the image data, and controlling a location of the vehicle, utilizing the orientation of the vehicle with respect to the path and the lateral position of the vehicle with respect to the path.

公开(公告)号：US11531088B2

公开(公告)日：2022-12-20

申请号：US16836618

申请日：2020-03-31

Applicant: NVIDIA Corporation

Inventor： Alexander Popov ,  Nikolai Smolyanskiy ,  Ryan Oldja ,  Shane Murray ,  Tilman Wekel ,  David Nister ,  Joachim Pehserl ,  Ruchi Bhargava ,  Sangmin Oh

IPC: G01S7/41 ,  G01S13/86 ,  G06N3/04 ,  G06N3/08 ,  G01S13/89

Abstract: In various examples, a deep neural network(s) (e.g., a convolutional neural network) may be trained to detect moving and stationary obstacles from RADAR data of a three dimensional (3D) space. In some embodiments, ground truth training data for the neural network(s) may be generated from LIDAR data. More specifically, a scene may be observed with RADAR and LIDAR sensors to collect RADAR data and LIDAR data for a particular time slice. The RADAR data may be used for input training data, and the LIDAR data associated with the same or closest time slice as the RADAR data may be annotated with ground truth labels identifying objects to be detected. The LIDAR labels may be propagated to the RADAR data, and LIDAR labels containing less than some threshold number of RADAR detections may be omitted. The (remaining) LIDAR labels may be used to generate ground truth data.

公开(公告)号：US20210342608A1

公开(公告)日：2021-11-04

申请号：US17377053

申请日：2021-07-15

Applicant: NVIDIA Corporation

Inventor： Nikolai Smolyanskiy ,  Ryan Oldja ,  Ke Chen ,  Alexander Popov ,  Joachim Pehserl ,  Ibrahim Eden ,  Tilman Wekel ,  David Wehr ,  Ruchi Bhargava ,  David Nister

IPC: G06K9/00 ,  G01S17/931 ,  B60W60/00 ,  G01S17/89 ,  G05D1/00 ,  G06N3/04 ,  G06T19/00

Abstract: A deep neural network(s) (DNN) may be used to detect objects from sensor data of a three dimensional (3D) environment. For example, a multi-view perception DNN may include multiple constituent DNNs or stages chained together that sequentially process different views of the 3D environment. An example DNN may include a first stage that performs class segmentation in a first view (e.g., perspective view) and a second stage that performs class segmentation and/or regresses instance geometry in a second view (e.g., top-down). The DNN outputs may be processed to generate 2D and/or 3D bounding boxes and class labels for detected objects in the 3D environment. As such, the techniques described herein may be used to detect and classify animate objects and/or parts of an environment, and these detections and classifications may be provided to an autonomous vehicle drive stack to enable safe planning and control of the autonomous vehicle.

公开(公告)号：US20210295171A1

公开(公告)日：2021-09-23

申请号：US16824199

申请日：2020-03-19

Applicant: NVIDIA Corporation

Inventor： Alexey Kamenev ,  Nikolai Smolyanskiy ,  Ishwar Kulkarni ,  Ollin Boer Bohan ,  Fangkai Yang ,  Alperen Degirmenci ,  Ruchi Bhargava ,  Urs Muller ,  David Nister ,  Rotem Aviv

IPC: G06N3/08 ,  G06N3/04

Abstract: In various examples, past location information corresponding to actors in an environment and map information may be applied to a deep neural network (DNN)—such as a recurrent neural network (RNN)—trained to compute information corresponding to future trajectories of the actors. The output of the DNN may include, for each future time slice the DNN is trained to predict, a confidence map representing a confidence for each pixel that an actor is present and a vector field representing locations of actors in confidence maps for prior time slices. The vector fields may thus be used to track an object through confidence maps for each future time slice to generate a predicted future trajectory for each actor. The predicted future trajectories, in addition to tracked past trajectories, may be used to generate full trajectories for the actors that may aid an ego-vehicle in navigating the environment.