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:
Mar. 02, 2021

Filed:

Feb. 03, 2017
Applicant:

Volvo Car Corporation, Gothenburg, SE;

Inventors:

Erik Israelsson, Gothenburg, SE;

Nasser Mohammadiha, Gothenburg, SE;

Ghazaleh Panahandeh, Gothenburg, SE;

Assignee:

VOLVO CAR CORPORATION, Gothenburg, SE;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 3/04 (2006.01); G08G 1/01 (2006.01); H04Q 9/00 (2006.01); G01N 19/02 (2006.01); G01N 33/42 (2006.01); G06N 3/08 (2006.01); G08G 1/00 (2006.01);
U.S. Cl.
CPC ...
G06N 3/0427 (2013.01); G01N 19/02 (2013.01); G01N 33/42 (2013.01); G06N 3/08 (2013.01); G08G 1/0112 (2013.01); G08G 1/0129 (2013.01); G08G 1/0141 (2013.01); G08G 1/205 (2013.01); H04Q 9/00 (2013.01); H04Q 2209/50 (2013.01); H04Q 2209/826 (2013.01);
Abstract

A system and method are described for predicting road friction using data from a fleet of connected vehicles. Each fleet vehicle includes a communication arrangement for reporting, to a back end system, floating car data sets, including a position of the vehicle, time data and data regarding determined road friction influencing parameters and a determined road friction associated with that position. The data sets are collected and aggregated in a central database, over a predetermined time period. A neural network computer is trained and validated, using aggregated data sets, to create a model to predict future road friction for a specific road network position or segment associated with that specific position. Once trained and validated, the neural network computer, upon receiving the same type of new, up to date, input data, uses the model to predict future road friction for that specific position or segment of the road network.


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