公开(公告)号：US11281221B2

公开(公告)日：2022-03-22

申请号：US16921796

申请日：2020-07-06

Applicant: NVIDIA Corporation

Inventor： Nikolai Smolyanskiy ,  Alexey Kamenev ,  Jeffrey David Smith ,  Stanley Thomas Birchfield

IPC: G05D1/00 ,  G05D1/02 ,  B62D6/00 ,  G06N3/08 ,  G06N7/00 ,  B62D15/02 ,  G06K9/00 ,  G06K9/62 ,  G05D1/10 ,  G06N3/04

Abstract: A method, computer readable medium, and system are disclosed for performing autonomous path navigation using deep neural networks. The method includes the steps of receiving image data at a deep neural network (DNN), determining, by the DNN, both an orientation of a vehicle with respect to a path and a lateral position of the vehicle with respect to the path, utilizing the image data, and controlling a location of the vehicle, utilizing the orientation of the vehicle with respect to the path and the lateral position of the vehicle with respect to the path.

公开(公告)号：US20210342609A1

公开(公告)日：2021-11-04

申请号：US17377064

申请日：2021-07-15

Applicant: NVIDIA Corporation

Inventor： Nikolai Smolyanskiy ,  Ryan Oldja ,  Ke Chen ,  Alexander Popov ,  Joachim Pehserl ,  Ibrahim Eden ,  Tilman Wekel ,  David Wehr ,  Ruchi Bhargava ,  David Nister

IPC: G06K9/00 ,  G01S17/931 ,  B60W60/00 ,  G01S17/89 ,  G05D1/00 ,  G06N3/04 ,  G06T19/00

Abstract: A deep neural network(s) (DNN) may be used to detect objects from sensor data of a three dimensional (3D) environment. For example, a multi-view perception DNN may include multiple constituent DNNs or stages chained together that sequentially process different views of the 3D environment. An example DNN may include a first stage that performs class segmentation in a first view (e.g., perspective view) and a second stage that performs class segmentation and/or regresses instance geometry in a second view (e.g., top-down). The DNN outputs may be processed to generate 2D and/or 3D bounding boxes and class labels for detected objects in the 3D environment. As such, the techniques described herein may be used to detect and classify animate objects and/or parts of an environment, and these detections and classifications may be provided to an autonomous vehicle drive stack to enable safe planning and control of the autonomous vehicle.

公开(公告)号：US20200341469A1

公开(公告)日：2020-10-29

申请号：US16921796

申请日：2020-07-06

Applicant: NVIDIA Corporation

Inventor： Nikolai Smolyanskiy ,  Alexey Kamenev ,  Jeffrey David Smith ,  Stanley Thomas Birchfield

IPC: G05D1/00 ,  G05D1/02 ,  B62D6/00 ,  G06N3/08 ,  G06N7/00 ,  B62D15/02 ,  G06K9/00 ,  G06K9/62 ,  G05D1/10 ,  G06N3/04

Abstract: A method, computer readable medium, and system are disclosed for performing autonomous path navigation using deep neural networks. The method includes the steps of receiving image data at a deep neural network (DNN), determining, by the DNN, both an orientation of a vehicle with respect to a path and a lateral position of the vehicle with respect to the path, utilizing the image data, and controlling a location of the vehicle, utilizing the orientation of the vehicle with respect to the path and the lateral position of the vehicle with respect to the path.