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:
Nov. 10, 2020
Filed:
Dec. 23, 2019
Stradvision, Inc., Gyeongsangbuk-do, KR;
Kye-Hyeon Kim, Seoul, KR;
Yongjoong Kim, Gyeongsangbuk-do, KR;
Hak-Kyoung Kim, Gyeongsangbuk-do, KR;
Woonhyun Nam, Gyeongsangbuk-do, KR;
SukHoon Boo, Gyeonggi-do, KR;
Myungchul Sung, Gyeongsangbuk-do, KR;
Dongsoo Shin, Gyeonggi-do, KR;
Donghun Yeo, Gyeongsangbuk-do, KR;
Wooju Ryu, Gyeongsangbuk-do, KR;
Myeong-Chun Lee, Gyeongsangbuk-do, KR;
Hyungsoo Lee, Seoul, KR;
Taewoong Jang, Seoul, KR;
Kyungjoong Jeong, Gyeongsangbuk-do, KR;
Hongmo Je, Gyeongsangbuk-do, KR;
Hojin Cho, Gyeongsangbuk-do, KR;
StradVision, Inc., Gyeongsangbuk-do, KR;
Abstract
A learning method for providing a functional safety by warning a driver about a potential dangerous situation by using an explainable AI which verifies detection processes of a neural network for an autonomous driving is provided. And the learning method includes steps of: (a) a learning device for verification, if at least one training image for verification is acquired, instructing a property extraction module to apply extraction operation to the training image for verification to extract property information on characteristics of the training image for verification to thereby generate a quality vector; (b) the learning device for verification instructing the neural network for verification to apply first neural network operations to the quality vector, to thereby generate predicted safety information; and (c) the learning device for verification instructing a loss module to generate a loss, and perform a backpropagation by using the loss, to thereby learn parameters included in the neural network for verification.