公开(公告)号：US20250036507A1

公开(公告)日：2025-01-30

申请号：US18359817

申请日：2023-07-26

Applicant: NVIDIA Corporation

Inventor： Vito MAGNANIMO ,  Daniel PERRIN ,  Joy JACOBS ,  Hanne BUUR

IPC: G06F11/07

Abstract: Embodiments of the present disclosure relate to a method of detecting an error corresponding to a monitored system. The method may additionally include determining whether the error corresponds to a safety module associated with the monitored system that may have one or more operations that are deemed to affect safety or whether the error corresponds to a safety process associated with the monitored system that may have one or more operations that are deemed to affect safety. In some embodiments, the method may additionally include determining whether to continue operations of the monitored system, where the determination may be based at least on whether the error may correspond to a safety module associated with the monitored system or whether the error corresponds to a safety process associated with the monitored system.

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.