The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Aug. 10, 2021
Filed:
Dec. 20, 2019
Stradvision, Inc., Pohang-si, KR;
Kye-Hyeon Kim, Seoul, KR;
Yongjoong Kim, Pohang-si, KR;
Hak-Kyoung Kim, Pohang-si, KR;
Woonhyun Nam, Pohang-si, KR;
SukHoon Boo, Anyang-si, KR;
Myungchul Sung, Pohang-si, KR;
Dongsoo Shin, Suwon-si, KR;
Donghun Yeo, Pohang-si, KR;
Wooju Ryu, Pohang-si, KR;
Myeong-Chun Lee, Pohang-si, KR;
Hyungsoo Lee, Seoul, KR;
Taewoong Jang, Seoul, KR;
Kyungjoong Jeong, Pohang-si, KR;
Hongmo Je, Pohang-si, KR;
Hojin Cho, Pohang-si, KR;
Stradvision, Inc., Pohang-si, KR;
Abstract
A method for learning a recurrent neural network to check an autonomous driving safety to be used for switching a driving mode of an autonomous vehicle is provided. The method includes steps of: a learning device (a) if training images corresponding to a front and a rear cameras of the autonomous vehicle are acquired, inputting each pair of the training images into corresponding CNNs, to concatenate the training images and generate feature maps for training, (b) inputting the feature maps for training into long short-term memory models corresponding to sequences of a forward RNN, and into those corresponding to the sequences of a backward RNN, to generate updated feature maps for training and inputting feature vectors for training into an attention layer, to generate an autonomous-driving mode value for training, and (c) allowing a loss layer to calculate losses and to learn the long short-term memory models.