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:
Aug. 30, 2022

Filed:

Mar. 31, 2020
Applicants:

Toyota Research Institute, Inc., Los Altos, CA (US);

Massachusetts Institute of Technology, Cambridge, MA (US);

Inventors:

Guy Rosman, Newton, MA (US);

Igor Gilitschenski, Cambridge, MA (US);

Arjun Gupta, Cambridge, MA (US);

Sertac Karaman, Cambridge, MA (US);

Daniela Rus, Weston, MA (US);

Assignees:
Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); B60W 50/02 (2012.01); G01R 31/317 (2006.01); G06N 3/08 (2006.01); B60W 50/06 (2006.01); G07C 5/08 (2006.01); B60W 60/00 (2020.01); B60R 16/023 (2006.01); G06K 9/62 (2022.01); G06N 3/04 (2006.01); G06V 20/20 (2022.01); G06V 20/56 (2022.01); G05D 1/00 (2006.01);
U.S. Cl.
CPC ...
B60W 50/0205 (2013.01); B60R 16/0231 (2013.01); B60W 50/06 (2013.01); B60W 60/001 (2020.02); B60W 60/0027 (2020.02); G01R 31/3172 (2013.01); G01R 31/31707 (2013.01); G06K 9/6257 (2013.01); G06N 3/0445 (2013.01); G06N 3/0454 (2013.01); G06N 3/082 (2013.01); G06N 3/088 (2013.01); G06V 20/20 (2022.01); G06V 20/588 (2022.01); G07C 5/0808 (2013.01); G05D 1/0088 (2013.01); G05D 2201/0213 (2013.01);
Abstract

Systems and methods for predicting the trajectory of an object are disclosed herein. One embodiment receives sensor data that includes a location of the object in an environment of the object; accesses a location-specific latent map, the location-specific latent map having been learned together with a neural-network-based trajectory predictor during a training phase, wherein the neural-network-based trajectory predictor is deployed in a robot; inputs, to the neural-network-based trajectory predictor, the location of the object and the location-specific latent map, the location-specific latent map providing, to the neural-network-based trajectory predictor, a set of location-specific biases regarding the environment of the object; and outputs, from the neural-network-based trajectory predictor, a predicted trajectory of the object.


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