US 10,768,629 B2 · Generative adversarial network enriched driving simulation

Abstract

A computer-implemented method and a system for training a computer-based autonomous driving model used for an autonomous driving operation by an autonomous vehicle are described. The method includes: creating time-dependent three-dimensional (3D) traffic environment data using at least one of real traffic element data and simulated traffic element data; creating simulated time-dependent 3D traffic environmental data by applying a time-dependent 3D generic adversarial network (GAN) model to the created time-dependent 3D traffic environment data; and training a computer-based autonomous driving model using the simulated time-dependent 3D traffic environmental data.

Claims18

Dependent claims

A computer-implemented method comprising:

creating time-dependent three-dimensional (3D) traffic environment data using at least one of real traffic element data and simulated traffic element data;
creating simulated time-dependent 3D traffic environmental data by applying a time-dependent 3D generative adversarial network (GAN) model to the created time-dependent 3D traffic environment data, wherein the simulated time-dependent 3D traffic environmental data includes object movement data indicating an irregular movement of one or more objects around a road;
and
training a computer-based autonomous driving model using the simulated time-dependent 3D traffic environmental data.

1 more element

A system for an autonomous-driving vehicle, comprising:

one or more processors;
and
memory storing instructions that, when executed by the one or more processors, cause the one or more processors to:
create time-dependent three-dimensional (3D) traffic environment data using at least one of real traffic element data and simulated traffic element data;
create simulated time-dependent 3D traffic environmental data by applying a time-dependent 3D generative adversarial network (GAN) model to the created time-dependent 3D traffic environment data, wherein the simulated time-dependent 3D traffic environmental data includes object movement data indicating an irregular movement of one or more objects around a road;
and
train a computer-based autonomous driving model using the simulated time-dependent 3D traffic environmental data.

4 more elements

Dependencies are inferred from the claim text.

Facts

Application

16/043,706Filed 24 Jul 2018

Published

US 2020/0033866 A130 Jan 2020

Granted

8 Sep 2020

Term ends

Not estimatedThe source records don’t support an estimate.

Classification15 codes

G06N 3/08
Neural networks: learning methods
B60W 50/06
Details of control systems for road vehicle drive control not related to the control of a particular sub-unit , e.g. process diagnostic or…
G05D 1/00
Control of position, course, altitude or attitude of land, water, air or
G06N 3/045
Architecture, e.g. interconnection topology: combinations of networks

Generative adversarial network enriched driving simulation

Life of this patent

Generative adversarial network enriched driving simulation

Life of this patent

Abstract

Claims18