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.
Patent No.:
Date of Patent:
Oct. 21, 2025
Filed:
Oct. 01, 2024
Aurora Operations, Inc., Pittsburgh, PA (US);
J. Andrew Bagnell, Pittsburgh, PA (US);
Michael William Bode, Pittsburgh, PA (US);
Micol Marchetti-Bowick, Pittsburgh, PA (US);
Sanjiban Choudry, Ithaca, NY (US);
Pengju Jin, San Francisco, CA (US);
Sumit Kumar, Sunnyvale, CA (US);
Yuhang Ma, Pittsburgh, PA (US);
Venkatraman Narayanan, Mountain View, CA (US);
Arun Venkatraman, Mountain View, CA (US);
Carl Wellington, Pittsburgh, PA (US);
AURORA OPERATIONS, INC., Pittsburgh, PA (US);
Abstract
The present disclosure provides an example method that includes: (a) obtaining context data descriptive of an environment surrounding an autonomous vehicle, the context data based on map data and perception data; (b) generating, by a proposer and based on the context data: (i) a plurality of candidate trajectories, and (ii) a plurality of actor forecasts for a plurality of actors in the environment; (c) generating, by a ranker and based on the context data, the plurality of candidate trajectories, and the plurality of actor forecasts, a ranking of the plurality of candidate trajectories; and (d) controlling a motion of the autonomous vehicle based on a candidate trajectory selected based on the ranking of the plurality of candidate trajectories, wherein the proposer comprises a first machine-learned model and the ranker comprises a second machine-learned model, and wherein the first machine-learned model and the second machine-learned model use a common backbone architecture.