Title:
EDGE-DEEP-LEARNING-BASED VEHICLE SAFE DRIVING SYSTEM USING VEHICLE DRIVING STATE INFORMATION
Document Type and Number:
WIPO Patent Application WO/2022/211140
Kind Code:
A1
Abstract:
The present invention relates to an edge-deep-learning-based vehicle safe driving system which forms a neural network model through deep learning training using driver state information, vehicle environment state information, and vehicle driving state information including vehicle-traveling-state information in an edge deep learning structure, and which identifies safe driving threat factors through the neural network model, so as to influence a driver during vehicle traveling, and thus can remove the causes of a situation in which safe driving of the vehicle is threatened and increase a vehicle safe driving effect. According to the present invention, causes triggering a vehicle safe driving threat situation can fundamentally be removed by influencing a driver. In addition, neural network model training and application for vehicle driving situations can be effectively achieved at low cost through a deep learning structure.
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Inventors:
KIM JIN BOK (KR)
LEE IN SUB (KR)
LEE YUN HEE (KR)
HONG HYUN CHUL (KR)
KANG JI MIN (KR)
LEE IN SUB (KR)
LEE YUN HEE (KR)
HONG HYUN CHUL (KR)
KANG JI MIN (KR)
Application Number:
PCT/KR2021/003886
Publication Date:
October 06, 2022
Filing Date:
March 30, 2021
Export Citation:
Assignee:
LITBIG INC (KR)
International Classes:
G06N3/04; B60W40/02; B60W40/08; B60W50/00; B60W50/14; G06N3/08
Foreign References:
| KR20190095199A | 2019-08-14 | |||
| KR20190110068A | 2019-09-27 | |||
| JP2018027776A | 2018-02-22 | |||
| KR102061320B1 | 2019-12-31 | |||
| KR20200127381A | 2020-11-11 |
Attorney, Agent or Firm:
KIM, Do Hyoung (KR)
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