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:
Sep. 07, 2021

Filed:

Oct. 24, 2017
Applicants:

Nissan North America, Inc., Franklin, TN (US);

Renault S.a.s., Boulogne-Billancourt, FR;

Inventors:

Samer Nashed, Amherst, MA (US);

David Ilstrup, Santa Cruz, CA (US);

Assignees:

Nissan North America, Inc., Franklin, TN (US);

Renault S.A.S., Boulogne-Billancourt, FR;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G01C 21/34 (2006.01); H04W 4/029 (2018.01); G06K 9/00 (2006.01); G06K 9/62 (2006.01);
U.S. Cl.
CPC ...
G01C 21/3461 (2013.01); G01C 21/3492 (2013.01); G06K 9/00798 (2013.01); G06K 9/6297 (2013.01); H04W 4/029 (2018.02);
Abstract

Traversing a vehicle transportation network, by a vehicle, may include determining vehicle operational information, determining a metric location estimate for the vehicle using the vehicle operational information, determining operational environment information of a portion of the vehicle transportation network, determining a topological location estimate for the vehicle within the vehicle transportation network using the metric location estimate and the operational environment information, and traversing the vehicle transportation network based on the topological location estimate for the vehicle. The operational environment information can include sensor data of a portion of the vehicle transportation network that is observable to the vehicle. To determine the metric location estimate, a non-linear loss function with a Kalman filter may mitigate effects of un-modeled sensor error(s). Techniques using Hidden Markov Models and the Earth Mover's Distance to determine the topological location estimate are also described.


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