公开(公告)号：US12162418B2

公开(公告)日：2024-12-10

申请号：US18481603

申请日：2023-10-05

Applicant: NVIDIA Corporation

Inventor： Atousa Torabi ,  Sakthivel Sivaraman ,  Niranjan Avadhanam ,  Shagan Sah

IPC: B60R21/017 ,  B60R21/013 ,  B60W50/14 ,  B60W60/00 ,  G06N3/02 ,  B60R21/01 ,  B60W50/00

Abstract: In various examples, systems and methods are disclosed that accurately identify driver and passenger in-cabin activities that may indicate a biomechanical distraction that prevents a driver from being fully engaged in driving a vehicle. In particular, image data representative of an image of an occupant of a vehicle may be applied to one or more deep neural networks (DNNs). Using the DNNs, data indicative of key point locations corresponding to the occupant may be computed, a shape and/or a volume corresponding to the occupant may be reconstructed, a position and size of the occupant may be estimated, hand gesture activities may be classified, and/or body postures or poses may be classified. These determinations may be used to determine operations or settings for the vehicle to increase not only the safety of the occupants, but also of surrounding motorists, bicyclists, and pedestrians.

公开(公告)号：US20240034260A1

公开(公告)日：2024-02-01

申请号：US18481603

申请日：2023-10-05

Applicant: NVIDIA Corporation

Inventor： Atousa Torabi ,  Sakthivel Sivaraman ,  Niranjan Avadhanam ,  Shagan Sah

IPC: B60R21/017 ,  B60R21/013 ,  B60W60/00 ,  G06N3/02 ,  B60W50/14

CPC classification number: B60R21/017 ,  B60R21/013 ,  B60W60/005 ,  G06N3/02 ,  B60W50/14 ,  B60W2050/0062

Abstract: In various examples, systems and methods are disclosed that accurately identify driver and passenger in-cabin activities that may indicate a biomechanical distraction that prevents a driver from being fully engaged in driving a vehicle. In particular, image data representative of an image of an occupant of a vehicle may be applied to one or more deep neural networks (DNNs). Using the DNNs, data indicative of key point locations corresponding to the occupant may be computed, a shape and/or a volume corresponding to the occupant may be reconstructed, a position and size of the occupant may be estimated, hand gesture activities may be classified, and/or body postures or poses may be classified. These determinations may be used to determine operations or settings for the vehicle to increase not only the safety of the occupants, but also of surrounding motorists, bicyclists, and pedestrians.

公开(公告)号：US11851014B2

公开(公告)日：2023-12-26

申请号：US17939613

申请日：2022-09-07

Applicant: NVIDIA Corporation

Inventor： Atousa Torabi ,  Sakthivel Sivaraman ,  Niranjan Avadhanam ,  Shagan Sah

IPC: B60R21/017 ,  B60R21/013 ,  B60W60/00 ,  G06N3/02 ,  B60W50/14 ,  B60W50/00 ,  B60R21/01

CPC classification number: B60R21/017 ,  B60R21/013 ,  B60W50/14 ,  B60W60/005 ,  G06N3/02 ,  B60R2021/01211 ,  B60R2021/01286 ,  B60W2050/0062

Abstract: In various examples, systems and methods are disclosed that accurately identify driver and passenger in-cabin activities that may indicate a biomechanical distraction that prevents a driver from being fully engaged in driving a vehicle. In particular, image data representative of an image of an occupant of a vehicle may be applied to one or more deep neural networks (DNNs). Using the DNNs, data indicative of key point locations corresponding to the occupant may be computed, a shape and/or a volume corresponding to the occupant may be reconstructed, a position and size of the occupant may be estimated, hand gesture activities may be classified, and/or body postures or poses may be classified. These determinations may be used to determine operations or settings for the vehicle to increase not only the safety of the occupants, but also of surrounding motorists, bicyclists, and pedestrians.

公开(公告)号：US20250050831A1

公开(公告)日：2025-02-13

申请号：US18931478

申请日：2024-10-30

Applicant: NVIDIA Corporation

Inventor： Atousa Torabi ,  Sakthivel Sivaraman ,  Niranjan Avadhanam ,  Shagan Sah

IPC: B60R21/017 ,  B60R21/01 ,  B60R21/013 ,  B60W50/00 ,  B60W50/14 ,  B60W60/00 ,  G06N3/02

Abstract: In various examples, systems and methods are disclosed that accurately identify driver and passenger in-cabin activities that may indicate a biomechanical distraction that prevents a driver from being fully engaged in driving a vehicle. In particular, image data representative of an image of an occupant of a vehicle may be applied to one or more deep neural networks (DNNs). Using the DNNs, data indicative of key point locations corresponding to the occupant may be computed, a shape and/or a volume corresponding to the occupant may be reconstructed, a position and size of the occupant may be estimated, hand gesture activities may be classified, and/or body postures or poses may be classified. These determinations may be used to determine operations or settings for the vehicle to increase not only the safety of the occupants, but also of surrounding motorists, bicyclists, and pedestrians.

公开(公告)号：US20230410650A1

公开(公告)日：2023-12-21

申请号：US18462287

申请日：2023-09-06

Applicant: NVIDIA Corporation

Inventor： Ambrish Dantrey ,  Atousa Torabi ,  Anshul Jain ,  Ram Ganapathi ,  Abhijit Patait ,  Revanth Reddy Nalla ,  Niranjan Avadhanam

IPC: G08G1/0965 ,  H04R3/00 ,  G08G1/0967 ,  G06N3/048

CPC classification number: G08G1/0965 ,  H04R3/005 ,  G08G1/096708 ,  G06N3/048 ,  H04R2430/20

Abstract: In various examples, audio alerts of emergency response vehicles may be detected and classified using audio captured by microphones of an autonomous or semi-autonomous machine in order to identify travel directions, locations, and/or types of emergency response vehicles in the environment. For example, a plurality of microphone arrays may be disposed on an autonomous or semi-autonomous machine and used to generate audio signals corresponding to sounds in the environment. These audio signals may be processed to determine a location and/or direction of travel of an emergency response vehicle (e.g., using triangulation). Additionally, to identify siren types—and thus emergency response vehicle types corresponding thereto—the audio signals may be used to generate representations of a frequency spectrum that may be processed using a deep neural network (DNN) that outputs probabilities of alert types being represented by the audio data.

公开(公告)号：US11816987B2

公开(公告)日：2023-11-14

申请号：US16951224

申请日：2020-11-18

Applicant: NVIDIA Corporation

Inventor： Ambrish Dantrey ,  Atousa Torabi ,  Anshul Jain ,  Ram Ganapathi ,  Abhijit Patait ,  Revanth Reddy Nalla ,  Niranjan Avadhanam

IPC: G08G1/0965 ,  H04R3/00 ,  G08G1/0967 ,  G06N3/048

CPC classification number: G08G1/0965 ,  G06N3/048 ,  G08G1/096708 ,  H04R3/005 ,  H04R2430/20

Abstract: In various examples, audio alerts of emergency response vehicles may be detected and classified using audio captured by microphones of an autonomous or semi-autonomous machine in order to identify travel directions, locations, and/or types of emergency response vehicles in the environment. For example, a plurality of microphone arrays may be disposed on an autonomous or semi-autonomous machine and used to generate audio signals corresponding to sounds in the environment. These audio signals may be processed to determine a location and/or direction of travel of an emergency response vehicle (e.g., using triangulation). Additionally, to identify siren types—and thus emergency response vehicle types corresponding thereto—the audio signals may be used to generate representations of a frequency spectrum that may be processed using a deep neural network (DNN) that outputs probabilities of alert types being represented by the audio data. The locations, direction of travel, and/or siren type may allow an ego-vehicle or ego-machine to identify an emergency response vehicle and to make planning and/or control decisions in response.

公开(公告)号：US11485308B2

公开(公告)日：2022-11-01

申请号：US16915577

申请日：2020-06-29

Applicant: NVIDIA Corporation

Inventor： Atousa Torabi ,  Sakthivel Sivaraman ,  Niranjan Avadhanam ,  Shagan Sah

IPC: B60R21/017 ,  B60R21/013 ,  B60W60/00 ,  G06N3/02 ,  B60W50/14 ,  B60W50/00 ,  B60R21/01

Abstract: In various examples, systems and methods are disclosed that accurately identify driver and passenger in-cabin activities that may indicate a biomechanical distraction that prevents a driver from being fully engaged in driving a vehicle. In particular, image data representative of an image of an occupant of a vehicle may be applied to one or more deep neural networks (DNNs). Using the DNNs, data indicative of key point locations corresponding to the occupant may be computed, a shape and/or a volume corresponding to the occupant may be reconstructed, a position and size of the occupant may be estimated, hand gesture activities may be classified, and/or body postures or poses may be classified. These determinations may be used to determine operations or settings for the vehicle to increase not only the safety of the occupants, but also of surrounding motorists, bicyclists, and pedestrians.

公开(公告)号：US20220157165A1

公开(公告)日：2022-05-19

申请号：US16951224

申请日：2020-11-18

Applicant: NVIDIA Corporation

Inventor： Ambrish Dantrey ,  Atousa Torabi ,  Anshul Jain ,  Ram Ganapathi ,  Abhijit Patait ,  Revanth Reddy Nalla ,  Niranjan Avadhanam

IPC: G08G1/0965 ,  H04R3/00 ,  G08G1/0967 ,  G06N3/04

Abstract: In various examples, audio alerts of emergency response vehicles may be detected and classified using audio captured by microphones of an autonomous or semi-autonomous machine in order to identify travel directions, locations, and/or types of emergency response vehicles in the environment. For example, a plurality of microphone arrays may be disposed on an autonomous or semi-autonomous machine and used to generate audio signals corresponding to sounds in the environment. These audio signals may be processed to determine a location and/or direction of travel of an emergency response vehicle (e.g., using triangulation). Additionally, to identify siren types—and thus emergency response vehicle types corresponding thereto—the audio signals may be used to generate representations of a frequency spectrum that may be processed using a deep neural network (DNN) that outputs probabilities of alert types being represented by the audio data. The locations, direction of travel, and/or siren type may allow an ego-vehicle or ego-machine to identify an emergency response vehicle and to make planning and/or control decisions in response.

公开(公告)号：US20210402942A1

公开(公告)日：2021-12-30

申请号：US16915577

申请日：2020-06-29

Applicant: NVIDIA Corporation

Inventor： Atousa Torabi ,  Sakthivel Sivaraman ,  Niranjan Avadhanam ,  Shagan Sah

IPC: B60R21/017 ,  B60R21/013 ,  B60W60/00 ,  B60W50/14 ,  G06N3/02

Abstract: In various examples, systems and methods are disclosed that accurately identify driver and passenger in-cabin activities that may indicate a biomechanical distraction that prevents a driver from being fully engaged in driving a vehicle. In particular, image data representative of an image of an occupant of a vehicle may be applied to one or more deep neural networks (DNNs). Using the DNNs, data indicative of key point locations corresponding to the occupant may be computed, a shape and/or a volume corresponding to the occupant may be reconstructed, a position and size of the occupant may be estimated, hand gesture activities may be classified, and/or body postures or poses may be classified. These determinations may be used to determine operations or settings for the vehicle to increase not only the safety of the occupants, but also of surrounding motorists, bicyclists, and pedestrians.