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1. US20180292825 - Performing autonomous path navigation using deep neural networks

Office United States of America
Application Number 15939116
Application Date 28.03.2018
Publication Number 20180292825
Publication Date 11.10.2018
Grant Number 10705525
Grant Date 07.07.2020
Publication Kind B2
IPC
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
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
B62D 6/00
BPERFORMING OPERATIONS; TRANSPORTING
62LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
DMOTOR VEHICLES; TRAILERS
6Arrangements for automatically controlling steering depending on driving conditions sensed and responded to, e.g. control circuits
G06N 3/08
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
08Learning methods
G06N 7/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
7Computer systems based on specific mathematical models
B62D 15/02
BPERFORMING OPERATIONS; TRANSPORTING
62LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
DMOTOR VEHICLES; TRAILERS
15Steering not otherwise provided for
02Steering position indicators
CPC
B62D 6/001
BPERFORMING OPERATIONS; TRANSPORTING
62LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
DMOTOR VEHICLES; TRAILERS
6Arrangements for automatically controlling steering depending on driving conditions sensed and responded to, e.g. control circuits
001the torque NOT being among the input parameters
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
B62D 15/025
BPERFORMING OPERATIONS; TRANSPORTING
62LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
DMOTOR VEHICLES; TRAILERS
15Steering not otherwise provided for
02Steering position indicators ; ; Steering position determination; Steering aids
025Active steering aids, e.g. helping the driver by actively influencing the steering system after environment evaluation
G05D 1/024
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
0231using optical position detecting means
0238using obstacle or wall sensors
024in combination with a laser
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 1/0242
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
0231using optical position detecting means
0242using non-visible light signals, e.g. IR or UV signals
Applicants NVIDIA Corporation
Inventors Nikolai Smolyanskiy
Alexey Kamenev
Jeffrey David Smith
Stanley Thomas Birchfield
Agents Davis Wright Tremaine LLP
Title
(EN) Performing autonomous path navigation using deep neural networks
Abstract
(EN)

A method, computer readable medium, and system are disclosed for performing autonomous path navigation using deep neural networks. The method includes the steps of receiving image data at a deep neural network (DNN), determining, by the DNN, both an orientation of a vehicle with respect to a path and a lateral position of the vehicle with respect to the path, utilizing the image data, and controlling a location of the vehicle, utilizing the orientation of the vehicle with respect to the path and the lateral position of the vehicle with respect to the path.