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:
Oct. 28, 2025

Filed:

Oct. 01, 2021
Applicant:

Aurora Operations, Inc., Pittsburgh, PA (US);

Inventors:

Skanda Shridhar, Pittsburgh, PA (US);

Yuhang Ma, Pittsburgh, PA (US);

Tara Lynn Stentz, Pittsburgh, PA (US);

Zhengdi Shen, Pittsburgh, PA (US);

Galen Clark Haynes, Pittsburgh, PA (US);

Neil Traft, Burlington, VT (US);

Assignee:

Aurora Operations, Inc., Pittsburgh, PA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
B60W 60/00 (2020.01); B60W 40/08 (2012.01); B60W 40/10 (2012.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
B60W 60/001 (2020.02); B60W 40/08 (2013.01); B60W 40/10 (2013.01); G06N 20/00 (2019.01); B60W 2040/0881 (2013.01); B60W 2540/01 (2020.02); B60W 2554/4049 (2020.02);
Abstract

Systems and methods for generating performance metrics for autonomous vehicle systems are provided. The performance metrics include two complementary metrics that evaluate a machine-learning object prediction model relative to a number of potential trajectories of an autonomous vehicle. The performance metrics include an avoidance metric that quantifies a probability that a region occupied by a real-world or simulated object is reached by the autonomous vehicle, the region is not blocked by another object, and the region is not blocked by a prediction output by the machine-learning object prediction model. The performance metrics also include an availability metric that quantifies a probability that a simulated or real-world object is not located within a region, the region is not blocked by another simulated or real-world object, and the autonomous vehicle is unnecessarily blocked by the prediction output before the autonomous vehicle reaches the particular footprint.


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