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:
Dec. 21, 2021

Filed:

Jun. 02, 2021
Applicant:

Motional Ad Llc, Boston, MA (US);

Inventors:

Oscar Olof Beijbom, Santa Monica, CA (US);

Bassam Helou, Santa Monica, CA (US);

Radboud Duintjer Tebbens, Winchester, MA (US);

Calin Belta, Sherborn, MA (US);

Anne Collin, Cambridge, MA (US);

Tichakorn Wongpiromsarn, Ames, IA (US);

Assignee:

Motional AD LLC, Boston, MA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/62 (2006.01); B60W 60/00 (2020.01); G06N 3/08 (2006.01); G05D 1/00 (2006.01); G06K 9/00 (2006.01); G01C 21/36 (2006.01); G01C 21/34 (2006.01); G05D 1/02 (2020.01);
U.S. Cl.
CPC ...
B60W 60/0011 (2020.02); G01C 21/3407 (2013.01); G01C 21/3691 (2013.01); G05D 1/0088 (2013.01); G05D 1/0212 (2013.01); G06K 9/00718 (2013.01); G06K 9/00791 (2013.01); G06K 9/6257 (2013.01); G06K 9/6262 (2013.01); G06K 9/6277 (2013.01); G06N 3/08 (2013.01); B60W 2552/00 (2020.02);
Abstract

Enclosed are embodiments for scoring one or more trajectories of a vehicle through a given traffic scenario using a machine learning model that predicts reasonableness scores for the trajectories. In an embodiment, human annotators, referred to as a 'reasonable crowd,' are presented with renderings of two or more vehicle trajectories traversing through the same or different traffic scenarios. The annotators are asked to indicate their preference for one trajectory over the other(s). Inputs collected from the human annotators are used to train the machine learning model to predict reasonableness scores for one or more trajectories for a given traffic scenario. These predicted trajectories can be used to rank trajectories generated by a route planner based on their scores, compare AV software stacks, or used by any other application that could benefit from a machine learning model that scores vehicle trajectories.


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