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:
Jun. 04, 2019

Filed:

May. 31, 2017
Applicant:

Uber Technologies, Inc., San Francisco, CA (US);

Inventors:

Ankit Laddha, Pittsburgh, PA (US);

J. Andrew Bagnell, Pittsburgh, PA (US);

Varun Ramakrishna, Pittsburgh, PA (US);

Yimu Wang, Pittsburgh, PA (US);

Carlos Vallespi-Gonzalez, Pittsburgh, PA (US);

Assignee:

Uber Technologies, Inc., San Francisco, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G01S 17/89 (2006.01); G01S 17/93 (2006.01); G01S 17/66 (2006.01); G01S 7/48 (2006.01); G01S 17/50 (2006.01); G01S 13/93 (2006.01);
U.S. Cl.
CPC ...
G01S 17/89 (2013.01); G01S 7/4808 (2013.01); G01S 17/66 (2013.01); G01S 17/936 (2013.01); G06K 9/00201 (2013.01); G06K 9/00805 (2013.01); G01S 13/931 (2013.01); G01S 17/50 (2013.01);
Abstract

Systems and methods for detecting and classifying objects that are proximate to an autonomous vehicle can include receiving, by one or more computing devices, LIDAR data from one or more LIDAR sensors configured to transmit ranging signals relative to an autonomous vehicle, generating, by the one or more computing devices, a data matrix comprising a plurality of data channels based at least in part on the LIDAR data, and inputting the data matrix to a machine-learned model. A class prediction for each of one or more different portions of the data matrix and/or a properties estimation associated with each class prediction generated for the data matrix can be received as an output of the machine-learned model. One or more object segments can be generated based at least in part on the class predictions and properties estimations. The one or more object segments can be provided to an object classification and tracking application.


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