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1. US20210048826 - DEEP LEARNING-BASED AUTONOMOUS VEHICLE CONTROL DEVICE, SYSTEM INCLUDING THE SAME, AND METHOD THEREOF

Office
United States of America
Application Number 17073808
Application Date 19.10.2020
Publication Number 20210048826
Publication Date 18.02.2021
Publication Kind A1
IPC
G05D 1/02
GPHYSICS
05CONTROLLING; REGULATING
DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
1Control of position, course, altitude, or attitude of land, water, air, or space vehicles, e.g. automatic pilot
02Control of position or course in two dimensions
G05B 23/02
GPHYSICS
05CONTROLLING; REGULATING
BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
23Testing or monitoring of control systems or parts thereof
02Electric testing or monitoring
G06N 3/04
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architecture, e.g. interconnection topology
CPC
G05B 23/0294
GPHYSICS
05CONTROLLING; REGULATING
BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
23Testing or monitoring of control systems or parts thereof
02Electric testing or monitoring
0205by means of a monitoring system capable of detecting and responding to faults
0259characterized by the response to fault detection
0286Modifications to the monitored process, e.g. stopping operation or adapting control
0294Optimizing process, e.g. process efficiency, product quality
G05D 1/0221
GPHYSICS
05CONTROLLING; REGULATING
DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
1Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
02Control of position or course in two dimensions
021specially adapted to land vehicles
0212with means for defining a desired trajectory
0221involving a learning process
G05D 2201/0213
GPHYSICS
05CONTROLLING; REGULATING
DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
2201Application
02Control of position of land vehicles
0213Road vehicle, e.g. car or truck
G06N 3/0454
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architectures, e.g. interconnection topology
0454using a combination of multiple neural nets
G06N 3/0427
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architectures, e.g. interconnection topology
0427in combination with an expert system
G05B 23/0229
GPHYSICS
05CONTROLLING; REGULATING
BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
23Testing or monitoring of control systems or parts thereof
02Electric testing or monitoring
0205by means of a monitoring system capable of detecting and responding to faults
0218characterised by the fault detection method dealing with either existing or incipient faults
0224Process history based detection method, e.g. whereby history implies the availability of large amounts of data
0227Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions
0229knowledge based, e.g. expert systems; genetic algorithms
Applicants HYUNDAI MOTOR COMPANY
KIA MOTORS CORPORATION
Inventors Byung Yong YOU
Title
(EN) DEEP LEARNING-BASED AUTONOMOUS VEHICLE CONTROL DEVICE, SYSTEM INCLUDING THE SAME, AND METHOD THEREOF
Abstract
(EN)

A deep learning-based autonomous vehicle control system includes: a processor determining an autonomous driving control based on deep learning, correcting an error in determination of the deep learning-based autonomous driving control based on determination of an autonomous driving control based on a predetermined expert rule, and controlling an autonomous vehicle; and a non-transitory computer-readable storage medium storing data for the determination of the deep learning-based autonomous driving control, data for the determination of the expert rule-based autonomous driving control, and information about the error in the determination of the deep learning-based autonomous driving control.