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:
Jun. 23, 2020

Filed:

Feb. 27, 2015
Applicant:

Inrix Inc., Kirkland, WA (US);

Inventors:

Judith Rosalyn Elgie, Manchester, GB;

Dominic Jordan, Manchester, GB;

Assignee:

INRIX, Inc., Kirkland, WA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G08G 1/00 (2006.01); G01C 21/28 (2006.01); G08G 1/09 (2006.01); H04W 24/08 (2009.01); H04W 4/46 (2018.01); G08G 1/0967 (2006.01); H04W 4/50 (2018.01); G06N 20/00 (2019.01); G06F 16/29 (2019.01); H04W 4/024 (2018.01); H04W 4/029 (2018.01); G08G 1/01 (2006.01); B60W 40/08 (2012.01); B60W 40/09 (2012.01); G07B 15/06 (2011.01); G08G 1/0968 (2006.01); G08G 1/097 (2006.01); B60W 30/14 (2006.01); G05D 1/00 (2006.01); G07C 5/00 (2006.01); A61B 5/0205 (2006.01); A61B 5/0476 (2006.01); A61B 5/00 (2006.01); G05D 1/02 (2020.01); H04B 1/3822 (2015.01); H04L 29/08 (2006.01); B64C 39/02 (2006.01); H04B 7/185 (2006.01); G06Q 20/10 (2012.01); G06Q 30/02 (2012.01); H04W 12/08 (2009.01); H04M 15/00 (2006.01); G06Q 40/08 (2012.01); H04L 9/32 (2006.01); B60R 16/023 (2006.01); G07B 15/00 (2011.01); G08G 1/065 (2006.01); G01C 21/36 (2006.01); H04W 4/42 (2018.01); H04W 4/40 (2018.01); G01C 21/34 (2006.01); G08G 1/07 (2006.01); G08G 1/0962 (2006.01); G08G 1/0965 (2006.01); H04W 4/48 (2018.01); A61B 5/024 (2006.01); A61B 5/053 (2006.01); G06Q 50/30 (2012.01);
U.S. Cl.
CPC ...
G08G 1/096791 (2013.01); A61B 5/02055 (2013.01); A61B 5/0476 (2013.01); A61B 5/4845 (2013.01); B60R 16/0236 (2013.01); B60W 30/143 (2013.01); B60W 40/08 (2013.01); B60W 40/09 (2013.01); B64C 39/024 (2013.01); G01C 21/3415 (2013.01); G01C 21/3469 (2013.01); G01C 21/3617 (2013.01); G01C 21/3655 (2013.01); G01C 21/3667 (2013.01); G01C 21/3682 (2013.01); G05D 1/0011 (2013.01); G05D 1/0088 (2013.01); G05D 1/021 (2013.01); G06F 16/29 (2019.01); G06N 20/00 (2019.01); G06Q 20/102 (2013.01); G06Q 30/0283 (2013.01); G06Q 40/08 (2013.01); G07B 15/00 (2013.01); G07B 15/063 (2013.01); G07C 5/008 (2013.01); G08G 1/012 (2013.01); G08G 1/0112 (2013.01); G08G 1/0129 (2013.01); G08G 1/0141 (2013.01); G08G 1/0145 (2013.01); G08G 1/065 (2013.01); G08G 1/07 (2013.01); G08G 1/093 (2013.01); G08G 1/097 (2013.01); G08G 1/0962 (2013.01); G08G 1/0965 (2013.01); G08G 1/0967 (2013.01); G08G 1/096725 (2013.01); G08G 1/096741 (2013.01); G08G 1/096775 (2013.01); G08G 1/096811 (2013.01); G08G 1/096822 (2013.01); G08G 1/096838 (2013.01); H04B 1/3822 (2013.01); H04B 7/18504 (2013.01); H04L 9/3247 (2013.01); H04L 67/02 (2013.01); H04L 67/306 (2013.01); H04M 15/60 (2013.01); H04W 4/024 (2018.02); H04W 4/029 (2018.02); H04W 4/40 (2018.02); H04W 4/42 (2018.02); H04W 4/50 (2018.02); H04W 12/08 (2013.01); A61B 5/024 (2013.01); A61B 5/0531 (2013.01); B60W 2040/0809 (2013.01); B60W 2040/0872 (2013.01); B60W 2540/22 (2013.01); B60W 2552/00 (2020.02); B60W 2555/20 (2020.02); B60W 2710/1044 (2013.01); B60W 2710/18 (2013.01); B60W 2720/10 (2013.01); B64C 2201/123 (2013.01); G01C 21/3608 (2013.01); G06Q 50/30 (2013.01); G06Q 2240/00 (2013.01); H04W 4/48 (2018.02);
Abstract

One or more techniques and/or systems are provided for training and/or utilizing a traffic obstruction identification model for identifying traffic obstructions based upon vehicle location point data. For example, a training dataset, comprising sample vehicle location points (e.g., global positioning system location points of vehicles) and traffic obstruction identification labels (e.g., locations of known traffic obstructions such as stop signs, crosswalks, stop lights, etc.), may be evaluated to extract a set of training features indicative of traffic flow patterns. The set of training features and the traffic obstruction identification labels may be used to train a traffic obstruction identification model to create a trained traffic obstruction identification model. The trained traffic obstruction identification model may be used to determine whether a road segment has a traffic obstruction or not.


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