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. 10, 2020

Filed:

Dec. 23, 2019
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:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2006.01); G06N 3/04 (2006.01); H04W 4/40 (2018.01); G08G 1/01 (2006.01);
U.S. Cl.
CPC ...
G06N 3/084 (2013.01); G06N 3/0445 (2013.01); G06N 3/0454 (2013.01); G06N 3/088 (2013.01); G08G 1/0133 (2013.01); H04W 4/40 (2018.02);
Abstract

A learning method for detecting driving events occurring during driving, to thereby detect driving scenarios including at least part of the driving events is provided. The method includes: (a) in response to a specific enumerated event vector including each of pieces of information on each of specific driving events as its specific components in a specific order being acquired, a learning device instructing a recurrent neural network (RNN) to apply RNN operations to the specific components of the specific enumerated event vector, to thereby detect a specific predicted driving scenario including the specific driving events; (b) the learning device instructing a loss module to generate an RNN loss by referring to the specific predicted driving scenario and a specific ground-truth (GT) driving scenario and to perform a backpropagation through time (BPTT) by using the RNN loss, to thereby learn at least part of the parameters of the RNN.


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