公开(公告)号：US11644834B2

公开(公告)日：2023-05-09

申请号：US16186473

申请日：2018-11-09

Applicant: NVIDIA Corporation

Inventor： Michael Alan Ditty ,  Gary Hicok ,  Jonathan Sweedler ,  Clement Farabet ,  Mohammed Abdulla Yousuf ,  Tai-Yuen Chan ,  Ram Ganapathi ,  Ashok Srinivasan ,  Michael Rod Truog ,  Karl Greb ,  John George Mathieson ,  David Nister ,  Kevin Flory ,  Daniel Perrin ,  Dan Hettena

IPC: G05D1/02 ,  G06V20/58 ,  G06V20/56 ,  G05D1/00 ,  G06F15/78 ,  G06N3/063 ,  G06N3/04

CPC classification number: G05D1/0088 ,  G05D1/0248 ,  G05D1/0274 ,  G06F15/7807 ,  G06N3/063 ,  G06V20/58 ,  G06V20/588 ,  G05D2201/0213 ,  G06N3/0454

Abstract: Autonomous driving is one of the world's most challenging computational problems. Very large amounts of data from cameras, RADARs, LIDARs, and HD-Maps must be processed to generate commands to control the car safely and comfortably in real-time. This challenging task requires a dedicated supercomputer that is energy-efficient and low-power, complex high-performance software, and breakthroughs in deep learning AI algorithms. To meet this task, the present technology provides advanced systems and methods that facilitate autonomous driving functionality, including a platform for autonomous driving Levels 3, 4, and/or 5. In preferred embodiments, the technology provides an end-to-end platform with a flexible architecture, including an architecture for autonomous vehicles that leverages computer vision and known ADAS techniques, providing diversity and redundancy, and meeting functional safety standards. The technology provides for a faster, more reliable, safer, energy-efficient and space-efficient System-on-a-Chip, which may be integrated into a flexible, expandable platform that enables a wide-range of autonomous vehicles, including cars, taxis, trucks, and buses, as well as watercraft and aircraft.

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.

公开(公告)号：US20220180125A1

公开(公告)日：2022-06-09

申请号：US17130966

申请日：2020-12-22

Applicant: NVIDIA Corporation

Inventor： Yichun Shen ,  Siyi Li ,  Yuhong Wen ,  Clement Farabet

IPC: G06K9/62 ,  G06N3/08 ,  G06N3/04 ,  G06F9/48 ,  G16H30/40 ,  G06K9/00

Abstract: Apparatuses, systems, and techniques to train a machine-learned model. In at least one embodiment, a plurality of training clients each obtain an exclusive right to update a model in turn, and each client trains said model with training data not accessible to other training clients.