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公开(公告)号：US20240273919A1
公开(公告)日：2024-08-15
申请号：US18647415
申请日：2024-04-26
Applicant: NVIDIA CORPORATION
Inventor： Nikolai Smolyanskiy , Ryan Oldja , Ke Chen , Alexander Popov , Joachim Pehserl , Ibrahim Eden , Tilman Wekel , David Wehr , Ruchi Bhargava , David Nister
CPC classification number: G06V20/584 , B60W60/0011 , B60W60/0016 , B60W60/0027 , G01S17/89 , G01S17/931 , G06N3/045 , G06T19/006 , G06V20/58 , B60W2420/403 , G06T2207/10028 , G06T2207/20081 , G06T2207/20084 , G06T2207/30261
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.
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22.发明授权公开(公告)号：US11960026B2
公开(公告)日：2024-04-16
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
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.
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公开(公告)号：US11915493B2
公开(公告)日：2024-02-27
申请号：US17895940
申请日：2022-08-25
Applicant: NVIDIA Corporation
Inventor： Nikolai Smolyanskiy , Ryan Oldja , Ke Chen , Alexander Popov , Joachim Pehserl , Ibrahim Eden , Tilman Wekel , David Wehr , Ruchi Bhargava , David Nister
CPC classification number: G06V20/584 , B60W60/0011 , B60W60/0016 , B60W60/0027 , G01S17/89 , G01S17/931 , G05D1/0088 , G06N3/045 , G06T19/006 , G06V20/58 , B60W2420/403 , G06T2207/10028 , G06T2207/20081 , G06T2207/20084 , G06T2207/30261
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.
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公开(公告)号：US11874663B2
公开(公告)日：2024-01-16
申请号：US17896825
申请日：2022-08-26
Applicant: NVIDIA Corporation
Inventor： Gary Hicok , Michael Cox , Miguel Sainz , Martin Hempel , Ratin Kumar , Timo Roman , Gordon Grigor , David Nister , Justin Ebert , Chin-Hsien Shih , Tony Tam , Ruchi Bhargava
CPC classification number: G05D1/0088 , G05B13/027 , G05D1/0055 , G05D1/0242 , G05D1/0246 , G05D1/0257 , G06Q10/02 , G06Q50/30 , G05D2201/0213
Abstract: A system and method for an on-demand shuttle, bus, or taxi service able to operate on private and public roads provides situational awareness and confidence displays. The shuttle may include ISO 26262 Level 4 or Level 5 functionality and can vary the route dynamically on-demand, and/or follow a predefined route or virtual rail. The shuttle is able to stop at any predetermined station along the route. The system allows passengers to request rides and interact with the system via a variety of interfaces, including without limitation a mobile device, desktop computer, or kiosks. Each shuttle preferably includes an in-vehicle controller, which preferably is an AI Supercomputer designed and optimized for autonomous vehicle functionality, with computer vision, deep learning, and real time ray tracing accelerators. An AI Dispatcher performs AI simulations to optimize system performance according to operator-specified system parameters.
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公开(公告)号：US20230357076A1
公开(公告)日：2023-11-09
申请号：US18311172
申请日：2023-05-02
Applicant: NVIDIA Corporation
Inventor： Michael Kroepfl , Amir Akbarzadeh , Ruchi Bhargava , Viabhav Thukral , Neda Cvijetic , Vadim Cugunovs , David Nister , Birgit Henke , Ibrahim Eden , Youding Zhu , Michael Grabner , Ivana Stojanovic , Yu Sheng , Jeffrey Liu , Enliang Zheng , Jordan Marr , Andrew Carley
IPC: C03C17/36
CPC classification number: C03C17/3607 , C03C17/3639 , C03C17/3644 , C03C17/366 , C03C17/3626 , C03C17/3668 , C03C17/3642 , C03C17/3681 , C03C2217/70 , C03C2217/216 , C03C2217/228 , C03C2217/24 , C03C2217/256 , C03C2217/281 , C03C2217/22 , C03C2217/23 , C03C2218/156
Abstract: An end-to-end system for data generation, map creation using the generated data, and localization to the created map is disclosed. Mapstreams—or streams of sensor data, perception outputs from deep neural networks (DNNs), and/or relative trajectory data—corresponding to any number of drives by any number of vehicles may be generated and uploaded to the cloud. The mapstreams may be used to generate map data—and ultimately a fused high definition (HD) map—that represents data generated over a plurality of drives. When localizing to the fused HD map, individual localization results may be generated based on comparisons of real-time data from a sensor modality to map data corresponding to the same sensor modality. This process may be repeated for any number of sensor modalities and the results may be fused together to determine a final fused localization result.
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Patent Agency Ranking
公开(公告)号：US20230204383A1
公开(公告)日：2023-06-29
申请号：US18175713
申请日：2023-02-28
Applicant: NVIDIA Corporation
Inventor： Amir Akbarzadeh , David Nister , Ruchi Bhargava , Birgit Henke , Ivana Stojanovic , Yu Sheng
CPC classification number: G01C21/3841 , G01C21/1652 , G01C21/3811 , G01C21/3867 , G01C21/3878 , G01C21/3896 , G06N3/02
Abstract: An end-to-end system for data generation, map creation using the generated data, and localization to the created map is disclosed. Mapstreams – or streams of sensor data, perception outputs from deep neural networks (DNNs), and/or relative trajectory data – corresponding to any number of drives by any number of vehicles may be generated and uploaded to the cloud. The mapstreams may be used to generate map data – and ultimately a fused high definition (HD) map – that represents data generated over a plurality of drives. When localizing to the fused HD map, individual localization results may be generated based on comparisons of real-time data from a sensor modality to map data corresponding to the same sensor modality. This process may be repeated for any number of sensor modalities and the results may be fused together to determine a final fused localization result.
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公开(公告)号：US20210063200A1
公开(公告)日：2021-03-04
申请号：US17007873
申请日：2020-08-31
Applicant: NVIDIA Corporation
Inventor： Michael Kroepfl , Amir Akbarzadeh , Ruchi Bhargava , Vaibhav Thukral , Neda Cvijetic , Vadim Cugunovs , David Nister , Birgit Henke , Ibrahim Eden , Youding Zhu , Michael Grabner , Ivana Stojanovic , Yu Sheng , Jeffrey Liu , Enliang Zheng , Jordan Marr , Andrew Carley
Abstract: An end-to-end system for data generation, map creation using the generated data, and localization to the created map is disclosed. Mapstreams—or streams of sensor data, perception outputs from deep neural networks (DNNs), and/or relative trajectory data—corresponding to any number of drives by any number of vehicles may be generated and uploaded to the cloud. The mapstreams may be used to generate map data—and ultimately a fused high definition (HD) map—that represents data generated over a plurality of drives. When localizing to the fused HD map, individual localization results may be generated based on comparisons of real-time data from a sensor modality to map data corresponding to the same sensor modality. This process may be repeated for any number of sensor modalities and the results may be fused together to determine a final fused localization result.
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公开(公告)号：US20210026355A1
公开(公告)日：2021-01-28
申请号：US16938706
申请日：2020-07-24
Applicant: NVIDIA Corporation
Inventor： Ke Chen , Nikolai Smolyanskiy , Alexey Kamenev , Ryan Oldja , Tilman Wekel , David Nister , Joachim Pehserl , Ibrahim Eden , Sangmin Oh , Ruchi Bhargava
Abstract: A deep neural network(s) (DNN) may be used to perform panoptic segmentation by performing pixel-level class and instance segmentation of a scene using a single pass of the DNN. Generally, one or more images and/or other sensor data may be stitched together, stacked, and/or combined, and fed into a DNN that includes a common trunk and several heads that predict different outputs. The DNN may include a class confidence head that predicts a confidence map representing pixels that belong to particular classes, an instance regression head that predicts object instance data for detected objects, an instance clustering head that predicts a confidence map of pixels that belong to particular instances, and/or a depth head that predicts range values. These outputs may be decoded to identify bounding shapes, class labels, instance labels, and/or range values for detected objects, and used to enable safe path planning and control of an autonomous vehicle.
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29.发明授权公开(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
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.
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公开(公告)号：US11698272B2
公开(公告)日：2023-07-11
申请号：US17007873
申请日：2020-08-31
Applicant: NVIDIA Corporation
Inventor： Michael Kroepfl , Amir Akbarzadeh , Ruchi Bhargava , Vaibhav Thukral , Neda Cvijetic , Vadim Cugunovs , David Nister , Birgit Henke , Ibrahim Eden , Youding Zhu , Michael Grabner , Ivana Stojanovic , Yu Sheng , Jeffrey Liu , Enliang Zheng , Jordan Marr , Andrew Carley
CPC classification number: G01C21/3841 , G01C21/1652 , G01C21/3811 , G01C21/3867 , G01C21/3878 , G01C21/3896 , G06N3/02
Abstract: An end-to-end system for data generation, map creation using the generated data, and localization to the created map is disclosed. Mapstreams—or streams of sensor data, perception outputs from deep neural networks (DNNs), and/or relative trajectory data—corresponding to any number of drives by any number of vehicles may be generated and uploaded to the cloud. The mapstreams may be used to generate map data—and ultimately a fused high definition (HD) map—that represents data generated over a plurality of drives. When localizing to the fused HD map, individual localization results may be generated based on comparisons of real-time data from a sensor modality to map data corresponding to the same sensor modality. This process may be repeated for any number of sensor modalities and the results may be fused together to determine a final fused localization result.
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