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. 26, 2019

Filed:

Jan. 06, 2016
Applicant:

GM Global Technology Operations Llc, Detroit, MI (US);

Inventors:

Ido Zelman, Ra'anana, IL;

Upali P. Mudalige, Oakland Township, MI (US);

Thanura Ranmal Elvitigala, Hershey, PA (US);

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
B60W 40/09 (2012.01); G08G 1/16 (2006.01); B60W 50/00 (2006.01); B60W 30/18 (2012.01); G08G 1/0967 (2006.01);
U.S. Cl.
CPC ...
B60W 40/09 (2013.01); B60W 30/18145 (2013.01); B60W 30/18154 (2013.01); B60W 50/0097 (2013.01); G08G 1/096725 (2013.01); G08G 1/096758 (2013.01); G08G 1/096775 (2013.01); G08G 1/096783 (2013.01); G08G 1/096791 (2013.01); G08G 1/166 (2013.01); B60W 2050/0029 (2013.01); B60W 2050/0089 (2013.01); B60W 2420/42 (2013.01); B60W 2420/52 (2013.01); B60W 2520/06 (2013.01); B60W 2520/10 (2013.01); B60W 2520/105 (2013.01); B60W 2520/14 (2013.01); B60W 2540/18 (2013.01); B60W 2550/146 (2013.01); B60W 2550/22 (2013.01); B60W 2550/30 (2013.01); B60W 2550/402 (2013.01); B60W 2900/00 (2013.01);
Abstract

A system and method for predicting whether a driver of a host vehicle or a remote vehicle intends to make a left or right turn or travel straight through an intersection before the host vehicle or remote vehicle reaches the intersection that relies on a probability model that employs a dynamic Bayesian network. The method includes obtaining a plurality of environmental cues that identify external parameters at or around the intersection, where the environmental cues include position and velocity of the remote vehicle, and obtaining a plurality of host vehicle cues that define operation of the host vehicle. The method then predicts the turning intent of the host vehicle and/or remote vehicle at the intersection using the model based on both the external cues and the vehicle cues using the model. The model can use learned information about previous driver turns at the intersection.


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