公开(公告)号：US20210133947A1

公开(公告)日：2021-05-06

申请号：US16670575

申请日：2019-10-31

申请人： Dalong Li ,  Stephen Horton ,  Neil R Garbacik

发明人： Dalong Li ,  Stephen Horton ,  Neil R Garbacik

IPC分类号： G06T7/00 ,  G06K9/00 ,  G05D1/00 ,  G01S17/89 ,  G01S17/87 ,  G01S17/93 ,  G05D1/02

摘要： An autonomous driving technique comprises determining an image quality metric for each image frame of a series of image frames of a scene outside of a vehicle captured by a camera system and determining an image quality threshold based on the image quality metrics for the series of image frames. The technique then determines whether the image quality metric for a current image frame satisfies the image quality threshold. When the image quality metric for the current image frame satisfies the image quality threshold, object detection is performed by at least utilizing a first deep neural network (DNN) with the current image frame. When the image quality metric for the current image frame fails to satisfy the image quality threshold, object detection is performed by utilizing a second, different DNN with the information captured by another sensor system and without utilizing the first DNN or the current image frame.

公开(公告)号：US11594040B2

公开(公告)日：2023-02-28

申请号：US16985460

申请日：2020-08-05

申请人： Dalong Li ,  Stephen Horton ,  Neil R Garbacik

发明人： Dalong Li ,  Stephen Horton ,  Neil R Garbacik

IPC分类号： G06V20/58 ,  G06K9/62 ,  G06N3/08 ,  G06N20/20 ,  G06T7/00 ,  G06T7/20 ,  G01S17/89 ,  G06N3/04

摘要： Techniques for training multiple resolution deep neural networks (DNNs) for vehicle autonomous driving comprise obtaining a training dataset for training a plurality of DNNs for an autonomous driving feature of the vehicle, sub-sampling the training dataset to obtain a plurality of training datasets comprising the training dataset and one or more sub-sampled datasets each having a different resolution than a remainder of the plurality of training datasets, training the plurality of DNNs using the plurality of training datasets, respectively, determining a plurality of outputs for the autonomous driving feature using the plurality of trained DNNs and the input data, receiving input data for the autonomous driving feature captured by a sensor device, and determining a best output for the autonomous driving feature using the plurality of outputs.