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：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：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.