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公开(公告)号：US20250123605A1
公开(公告)日：2025-04-17
申请号：US18989849
申请日：2024-12-20
Applicant: NVIDIA Corporation
Inventor： Hans Jonas Nilsson , Michael Cox , Sangmin Oh , Joachim Pehserl , Aidin Ehsanibenafati
Abstract: In various examples, systems and methods are disclosed that perform sensor fusion using rule-based and learned processing methods to take advantage of the accuracy of learned approaches and the decomposition benefits of rule-based approaches for satisfying higher levels of safety requirements. For example, in-parallel and/or in-serial combinations of early rule-based sensor fusion, late rule-based sensor fusion, early learned sensor fusion, or late learned sensor fusion may be used to solve various safety goals associated with various required safety levels at a high level of accuracy and precision. In embodiments, learned sensor fusion may be used to make more conservative decisions than the rule-based sensor fusion (as determined using, e.g., severity (S), exposure (E), and controllability (C) (SEC) associated with a current safety goal), but the rule-based sensor fusion may be relied upon where the learned sensor fusion decision may be less conservative than the corresponding rule-based sensor fusion.
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公开(公告)号：US20240296068A1
公开(公告)日：2024-09-05
申请号：US18657540
申请日：2024-05-07
Applicant: NVIDIA Corporation
Inventor： Ashutosh Tadkase , Ian Tramble , Akash Bellubbi , Suraj Das , Ranvijay Singh , Linda Xiong , John Lore , Albert Davies , Ian Howson , Peter Boonstoppel , Sai Gurrappadi , Pulkit Desai , Sever Topan , Sharat Janapareddy , Ashkan Vafaee , Michael Cox
CPC classification number: G06F9/4881 , G06F9/30087 , G06F9/3836 , G06F9/485 , G06F9/5055 , G06F9/5083 , G06F9/544 , G06F11/0721 , G06F11/0757 , G06F21/52 , G06F2221/2151
Abstract: One or more embodiments of the present disclosure relate to switching between execution schedules related to execution of tasks, or runnables, by multiple compute engines. The execution schedules includes respective sets of commands that dictate timing and order of execution, by the compute engines, of tasks, or runnables, corresponding to computing applications.
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公开(公告)号：US20230096502A1
公开(公告)日：2023-03-30
申请号：US17929674
申请日：2022-09-02
Applicant: NVIDIA Corporation
Inventor： Ashutosh Tadkase , Akash Bellubbi , Ian Tramble , Peter Boonstoppel , Suraj Das , Ranvijay Singh , Sever Topan , Albert Davies , Linda Xiong , Sharat Janapareddy , Ashkan Vafaee , Sai Gurrappadi , Bruce Holmer , Vishanth Iyer , John Lore , Ian Howson , Pulkit Desai , Michael Cox
Abstract: One or more embodiments of the present disclosure relate to executing, by a plurality of compute engines, a plurality of runnables of a computing application based at least on an execution schedule and a set of commands associated with the execution schedule. The execution schedule may be generated using a compiling system to include the set of commands. The set of commands may include one or more individual commands corresponding to one or more timing fences dictating a timing and order of execution of one or more individual runnables of the plurality of runnables.
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公开(公告)号：US20190303759A1
公开(公告)日：2019-10-03
申请号：US16366875
申请日：2019-03-27
Applicant: NVIDIA Corporation
Inventor： Clement Farabet , John Zedlewski , Zachary Taylor , Greg Heinrich , Claire Delaunay , Mark Daly , Matthew Campbell , Curtis Beeson , Gary Hicok , Michael Cox , Rev Lebaredian , Tony Tamasi , David Auld
Abstract: In various examples, physical sensor data may be generated by a vehicle in a real-world environment. The physical sensor data may be used to train deep neural networks (DNNs). The DNNs may then be tested in a simulated environment—in some examples using hardware configured for installation in a vehicle to execute an autonomous driving software stack—to control a virtual vehicle in the simulated environment or to otherwise test, verify, or validate the outputs of the DNNs. Prior to use by the DNNs, virtual sensor data generated by virtual sensors within the simulated environment may be encoded to a format consistent with the format of the physical sensor data generated by the vehicle.
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公开(公告)号：US20240272943A1
公开(公告)日：2024-08-15
申请号：US18637207
申请日：2024-04-16
Applicant: NVIDIA Corporation
Inventor： Ashutosh Tadkase , Akash Bellubbi , Ian Tramble , Peter Boonstoppel , Suraj Das , Ranvijay Singh , Sever Topan , Albert Davies , Linda Xiong , Sharat Janapareddy , Ashkan Vafaee , Sai Gurrappadi , Pulkit Desai , John Lore , Michael Cox , Ian Howson
CPC classification number: G06F9/4881 , G06F9/30087 , G06F9/3836 , G06F9/485 , G06F9/5055 , G06F9/5083 , G06F9/544 , G06F11/0721 , G06F11/0757 , G06F21/52 , G06F2221/2151
Abstract: One or more embodiments of the present disclosure relate to executing, by a plurality of compute engines, a plurality of runnables of a computing application based at least on an execution schedule and a set of commands associated with the execution schedule. The execution schedule may be generated using a compiling system to include the set of commands. The set of commands may include one or more individual commands corresponding to one or more timing fences dictating a timing and order of execution of one or more individual runnables of the plurality of runnables.
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Patent Agency Ranking
6.发明公开公开(公告)号：US20240085914A1
公开(公告)日：2024-03-14
申请号：US17942551
申请日：2022-09-12
Applicant: NVIDIA Corporation
Inventor： Sever Ioan Topan , Karen Yan Ming Leung , Yuxiao Chen , Pritish Tupekar , Edward Fu Schmerling , Hans Jonas Nilsson , Michael Cox , Marco Pavone
CPC classification number: G05D1/0214 , G05D1/0253 , G06V20/58 , G06V2201/07
Abstract: In various examples, techniques for determining perception zones for object detection are described. For instance, a system may use a dynamic model associated with an ego-machine, a dynamic model associated with an object, and one or more possible interactions between the ego-machine and the object to determine a perception zone. The system may then perform one or more processes using the perception zone. For instance, if the system is validating a perception system of the ego-machine, the system may determine whether a detection error associated with the object is a safety-critical error based on whether the object is located within the perception zone. Additionally, if the system is executing within the ego-machine, the system may determine whether the object is a safety-critical object based on whether the object is located within the perception zone.
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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：US20230102089A1
公开(公告)日：2023-03-30
申请号：US17929669
申请日：2022-09-02
Applicant: NVIDIA Corporation
Inventor： Akash Bellubbi , Albert Davies , Ashutosh Tadkase , Sharat Janapareddy , Suraj Das , Ranvijay Singh , Ashkan Vafaee , Pulkit Desai , Michael Cox , Peter Boonstoppel
Abstract: One or more embodiments of the present disclosure relate to monitoring execution of runnables that may be executed by a computing system, the executing begin based at least on a schedule. The monitoring may include one or more of: monitoring timing of execution of the runnables, monitoring one or more sequences of execution of the runnables, or monitoring health of at least a portion of the computing system executing the runnables. Additionally or alternatively, one or more embodiments may relate to determining compliance with respect to one or more execution constraints based at least in part on the monitoring.
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公开(公告)号：US11474519B2
公开(公告)日：2022-10-18
申请号：US16286330
申请日：2019-02-26
Applicant: NVIDIA Corporation
Inventor： Gary Hicok , Michael Cox , Miguel Sainz , Martin Hempel , Ratin Kumar , Timo Roman , Gordon Grigor , David Nister , Justin Ebert , Chin Shih , Tony Tam , Ruchi Bhargava
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：US20220297706A1
公开(公告)日：2022-09-22
申请号：US17698695
申请日：2022-03-18
Applicant: NVIDIA Corporation
Inventor： Hans Jonas Nilsson , Michael Cox , Sangmin Oh , Joachim Pehserl , Aidin Ehsanibenafati
Abstract: In various examples, systems and methods are disclosed that perform sensor fusion using rule-based and learned processing methods to take advantage of the accuracy of learned approaches and the decomposition benefits of rule-based approaches for satisfying higher levels of safety requirements. For example, in-parallel and/or in-serial combinations of early rule-based sensor fusion, late rule-based sensor fusion, early learned sensor fusion, or late learned sensor fusion may be used to solve various safety goals associated with various required safety levels at a high level of accuracy and precision. In embodiments, learned sensor fusion may be used to make more conservative decisions than the rule-based sensor fusion (as determined using, e.g., severity (S), exposure (E), and controllability (C) (SEC) associated with a current safety goal), but the rule-based sensor fusion may be relied upon where the learned sensor fusion decision may be less conservative than the corresponding rule-based sensor fusion.
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