公开(公告)号：US20240078363A1

公开(公告)日：2024-03-07

申请号：US18505672

申请日：2023-11-09

Applicant: NVIDIA Corporation

Inventor： Ahmed Nassar ,  Justyna Zander ,  David Auld

IPC: G06F30/27 ,  B60W50/00 ,  G05D1/00

CPC classification number: G06F30/27 ,  B60W50/00 ,  G05D1/0088 ,  B60W60/001

Abstract: In various examples, scenarios may be defined using a declarative description—e.g., defining a behavior of interest—that the present system may convert into a procedural description for generating one or more instances and/or variations of a scenario for testing an autonomous or semi-autonomous machine in a virtual environment. The system may execute observers or evaluators for testing the performance and accuracy of the machine and may compute coverage of various elements based on the generated virtual scenarios, and may feed the results back to the system to generate additional instances and/or variations where the coverage or accuracy is below a desired level. As a result, the system may include an end-to-end framework for generating scenarios in virtual environments, testing and validating the scenarios themselves, and/or testing and validating the underlying autonomous or semi-autonomous systems of the machine—all based on a declarative description.

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

IPC: G06N3/08 ,  G06F9/455 ,  G06N20/00

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.

公开(公告)号：US11436484B2

公开(公告)日：2022-09-06

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

IPC: G06N3/08 ,  G06N3/04 ,  G06N20/00 ,  G06F9/455 ,  G06N3/063 ,  G06K9/62 ,  G06V10/44 ,  G06V20/56

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.

公开(公告)号：US20210294944A1

公开(公告)日：2021-09-23

申请号：US16824202

申请日：2020-03-19

Applicant: NVIDIA Corporation

Inventor： Ahmed Nassar ,  Justyna Zander ,  David Auld

IPC: G06F30/27 ,  G05D1/00 ,  B60W50/00

Abstract: In various examples, scenarios may be defined using a declarative description—e.g., defining a behavior of interest—that the present system may convert into a procedural description for generating one or more instances and/or variations of a scenario for testing an autonomous or semi-autonomous machine in a virtual environment. The system may execute observers or evaluators for testing the performance and accuracy of the machine and may compute coverage of various elements based on the generated virtual scenarios, and may feed the results back to the system to generate additional instances and/or variations where the coverage or accuracy is below a desired level. As a result, the system may include an end-to-end framework for generating scenarios in virtual environments, testing and validating the scenarios themselves, and/or testing and validating the underlying autonomous or semi-autonomous systems of the machine—all based on a declarative description.

公开(公告)号：US20250111216A1

公开(公告)日：2025-04-03

申请号：US18980252

申请日：2024-12-13

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

IPC: G06N3/063 ,  G06F9/455 ,  G06F18/2413 ,  G06N3/045 ,  G06N3/08 ,  G06N20/00 ,  G06V10/44 ,  G06V10/764 ,  G06V10/82 ,  G06V20/56

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.

公开(公告)号：US12182694B2

公开(公告)日：2024-12-31

申请号：US17898887

申请日：2022-08-30

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

IPC: G06F9/455 ,  G06F18/2413 ,  G06N3/045 ,  G06N3/063 ,  G06N3/08 ,  G06N20/00 ,  G06V10/44 ,  G06V10/764 ,  G06V10/82 ,  G06V20/56

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.

公开(公告)号：US20230004801A1

公开(公告)日：2023-01-05

申请号：US17898887

申请日：2022-08-30

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

IPC: G06N3/08 ,  G06N20/00 ,  G06F9/455 ,  G06N3/063 ,  G06K9/62 ,  G06N3/04 ,  G06V10/44 ,  G06V20/56

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.