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1. US20180299841 - AUTONOMOUS VEHICLE NEURAL NETWORK OPTIMIZATION

Office
United States of America
Application Number 15489142
Application Date 17.04.2017
Publication Number 20180299841
Publication Date 18.10.2018
Publication Kind A1
IPC
G05B 13/02
GPHYSICS
05CONTROLLING; REGULATING
BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
13Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
02electric
G05D 1/00
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
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
G06N 3/02
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
G06N 5/04
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
5Computer systems using knowledge-based models
04Inference methods or devices
G06N 99/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
99Subject matter not provided for in other groups of this subclass
CPC
G05B 13/027
GPHYSICS
05CONTROLLING; REGULATING
BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
13Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
02electric
0265the criterion being a learning criterion
027using neural networks only
G05B 13/0285
GPHYSICS
05CONTROLLING; REGULATING
BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
13Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
02electric
0265the criterion being a learning criterion
0285using neural networks and fuzzy logic
G05D 1/0088
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
0088characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours
G06N 3/02
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
G06N 3/049
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
049Temporal neural nets, e.g. delay elements, oscillating neurons, pulsed inputs
G06N 3/08
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
08Learning methods
Applicants Intel Corporation
Inventors Abhishek R. Appu
Altug Koker
Linda L. Hurd
Dukhwan Kim
Mike B. MacPherson
John C. Weast
Justin E. Gottschlich
Jingyi Jin
Barath Lakshmanan
Chandrasekaran Sakthivel
Michael S. Strickland
Joydeep Ray
Kamal Sinha
Prasoonkumar Surti
Balaji Vembu
Ping T. Tang
Anbang Yao
Tatiana Shpeisman
Xiaoming Chen
Vasanth Ranganathan
Sanjeev S. Jahagirdar
Title
(EN) AUTONOMOUS VEHICLE NEURAL NETWORK OPTIMIZATION
Abstract
(EN)

Methods and apparatus relating to autonomous vehicle neural network optimization techniques are described. In an embodiment, the difference between a first training dataset to be used for a neural network and a second training dataset to be used for the neural network is detected. The second training dataset is authenticated in response to the detection of the difference. The neural network is used to assist in an autonomous vehicle/driving. Other embodiments are also disclosed and claimed.