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. 05, 2023

Filed:

Jan. 15, 2021
Applicant:

Uatc, Llc, San Francisco, CA (US);

Inventors:

Raquel Urtasun, Toronto, CA;

Abbas Sadat, Toronto, CA;

Sergio Casas, Toronto, CA;

Mengye Ren, Toronto, CA;

Assignee:

UATC, LCC, Mountain View, CA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
B60W 60/00 (2020.01); G06N 20/00 (2019.01); G06V 20/56 (2022.01); G06F 18/213 (2023.01); G06V 10/764 (2022.01); G06V 10/77 (2022.01); G06V 10/80 (2022.01);
U.S. Cl.
CPC ...
B60W 60/0011 (2020.02); G06F 18/213 (2023.01); G06N 20/00 (2019.01); G06V 10/764 (2022.01); G06V 10/7715 (2022.01); G06V 10/806 (2022.01); G06V 20/56 (2022.01); B60W 2420/52 (2013.01); B60W 2540/01 (2020.02); B60W 2554/4049 (2020.02);
Abstract

Systems and methods for generating semantic occupancy maps are provided. In particular, a computing system can obtain map data for a geographic area and sensor data obtained by the autonomous vehicle. The computer system can identify feature data included in the map data and sensor data. The computer system can, for a respective semantic object type from a plurality of semantic object types, determine, by the computing system and using feature data as input to a respective machine-learned model from a plurality of machine-learned models, one or more occupancy maps for one or more timesteps in the future, and wherein the respective machine-learned model is trained to determine occupancy for the respective semantic object type. The computer system can select a trajectory for the autonomous vehicle based on a plurality of occupancy maps associated with the plurality of semantic object types.


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