公开(公告)号：US20180370540A1

公开(公告)日：2018-12-27

申请号：US15881426

申请日：2018-01-26

Applicant: NVIDIA Corporation

Inventor： Mohammed Abdulla YOUSUF ,  T. Y. CHAN ,  Ram GANAPATHI ,  Ashok SRINIVASAN ,  Mike TRUOG

IPC: B60W50/04 ,  G06F11/20 ,  B60W10/04 ,  B60W10/18 ,  B60W10/20 ,  G05D1/00

Abstract: In a self-driving autonomous vehicle, a controller architecture includes multiple processors within the same box. Each processor monitors the others and takes appropriate safe action when needed. Some processors may run dormant or low priority redundant functions that become active when another processor is detected to have failed. The processors are independently powered and independently execute redundant algorithms from sensor data processing to actuation commands using different hardware capabilities (GPUs, processing cores, different input signals, etc.). Intentional hardware and software diversity improves fault tolerance. The resulting fault-tolerant/fail-operational system meets ISO26262 ASIL D specifications based on a single electronic controller unit platform that can be used for self-driving vehicles.

公开(公告)号：US20240045426A1

公开(公告)日：2024-02-08

申请号：US18143360

申请日：2023-05-04

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

CPC classification number: G05D1/0088 ,  G05D1/0248 ,  G05D1/0274 ,  G06F15/7807 ,  G06N3/063 ,  G06V20/58 ,  G06V20/588 ,  G05D2201/0213 ,  G06N3/045

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：US20230176577A1

公开(公告)日：2023-06-08

申请号：US18076543

申请日：2022-12-07

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

CPC classification number: G05D1/0088 ,  G05D1/0248 ,  G05D1/0274 ,  G06F15/7807 ,  G06N3/063 ,  G06V20/58 ,  G06V20/588 ,  G05D2201/0213 ,  G06N3/045

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：US20220080992A1

公开(公告)日：2022-03-17

申请号：US17532283

申请日：2021-11-22

Applicant: NVIDIA Corporation

Inventor： Mohammed Abdulla YOUSUF ,  T. Y. CHAN ,  Ram GANAPATHI ,  Ashok SRINIVASAN ,  Mike TRUOG

IPC: B60W50/04 ,  G06F11/20 ,  B60W10/04 ,  B60W10/18 ,  G05D1/00 ,  B60W10/20 ,  G06F11/18 ,  G06F11/16 ,  G06F11/14

Abstract: In a self-driving autonomous vehicle, a controller architecture includes multiple processors within the same box. Each processor monitors the others and takes appropriate safe action when needed, Some processors may run dormant or low priority redundant functions that become active when another processor is detected to have failed. The processors are independently powered and independently execute redundant algorithms from sensor data processing to actuation commands using different hardware capabilities (GPUs, processing cores, different input signals, etc.). Intentional hardware and software diversity improves fault tolerance. The resulting fault-tolerant/fail-operational system meets ISO26262 ASIL-D specifications based on a single electronic controller unit platform that can be used for self-driving vehicles.

公开(公告)号：US20190258251A1

公开(公告)日：2019-08-22

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: G05D1/00 ,  G05D1/02 ,  G06K9/00

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.