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.

Date of Patent:
Nov. 03, 2020

Filed:

Jan. 09, 2020
Applicant:

Stradvision, Inc., Gyeongsangbuk-do, KR;

Inventors:

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;

Assignee:

StradVision, Inc., Gyeongsangbuk-do, KR;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 20/10 (2019.01); G06N 3/08 (2006.01); G06N 3/04 (2006.01); G06N 20/20 (2019.01); G06K 9/62 (2006.01); G05D 1/00 (2006.01); B60W 60/00 (2020.01); B60W 50/00 (2006.01);
U.S. Cl.
CPC ...
G06N 3/084 (2013.01); B60W 50/00 (2013.01); B60W 60/00 (2020.02); G05D 1/0088 (2013.01); G06K 9/6262 (2013.01); G06K 9/6289 (2013.01); G06N 3/0454 (2013.01); G06N 20/10 (2019.01); G06N 20/20 (2019.01); B60W 2050/0004 (2013.01); B60W 2420/42 (2013.01);
Abstract

A learning method for supporting a safer autonomous driving through a fusion of information acquired from images and communications is provided. And the method includes steps of: (a) a learning device instructing a first neural network and a second neural network to generate an image-based feature map and a communication-based feature map by using a circumstance image and circumstance communication information; (b) the learning device instructing a third neural network to apply a third neural network operation to the image-based feature map and the communication-based feature map to generate an integrated feature map; (c) the learning device instructing a fourth neural network to apply a fourth neural network operation to the integrated feature map to generate estimated surrounding motion information; and (d) the learning device instructing a first loss layer to train parameters of the first to the fourth neural networks.


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