The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Nov. 12, 2019

Filed:

Jan. 26, 2016
Applicant:

Ford Global Technologies, Llc, Dearborn, MI (US);

Inventors:

Ashley Elizabeth Micks, Mountain View, CA (US);

Jinesh J Jain, San Mateo, CA (US);

Harpreetsingh Banvait, Sunnyvale, CA (US);

Kyu Jeong Han, Palo Alto, CA (US);

Assignee:

FORD GLOBAL TECHNOLOGIES, LLC, Dearborn, MI (US);

Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 20/00 (2019.01); B60W 30/09 (2012.01); G06F 17/50 (2006.01); G06K 9/00 (2006.01); G06K 9/66 (2006.01); G06N 3/08 (2006.01); H04R 29/00 (2006.01);
U.S. Cl.
CPC ...
G06N 20/00 (2019.01); B60W 30/09 (2013.01); G06F 17/5009 (2013.01); G06K 9/00805 (2013.01); G06K 9/00825 (2013.01); G06K 9/66 (2013.01); G06N 3/088 (2013.01); H04R 29/00 (2013.01); H04R 2499/13 (2013.01);
Abstract

A machine learning model is trained by defining a scenario including models of vehicles and a typical driving environment. A model of a subject vehicle is added to the scenario and sensor locations are defined on the subject vehicle. A perception of the scenario by sensors at the sensor locations is simulated. The scenario further includes a model of a lane-splitting vehicle. The location of the lane-splitting vehicle and the simulated outputs of the sensors perceiving the scenario are input to a machine learning algorithm that trains a model to detect the location of a lane-splitting vehicle based on the sensor outputs. A vehicle controller then incorporates the machine learning model and estimates the presence and/or location of a lane-splitting vehicle based on actual sensor outputs input to the machine learning model.


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