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:
Feb. 03, 2026

Filed:

Feb. 12, 2024
Applicant:

Nec Laboratories America, Inc., Princeton, NJ (US);

Inventors:

Biplob Debnath, Princeton, NJ (US);

Christoph Reich, Hessen, DE;

Deep Patel, Franklin Park, NJ (US);

Srimat Chakradhar, Manalapan, NJ (US);

Assignee:

NEC Corporation, Tokyo, JP;

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
H04N 19/146 (2014.01); G06N 20/00 (2019.01); G06V 20/40 (2022.01); G06V 20/58 (2022.01); H04N 7/18 (2006.01); H04N 19/119 (2014.01); H04N 19/124 (2014.01); H04N 19/14 (2014.01); H04N 19/154 (2014.01); H04N 19/156 (2014.01); H04N 19/172 (2014.01); H04N 19/176 (2014.01); H04N 19/177 (2014.01); H04N 19/42 (2014.01); H04N 19/463 (2014.01); H04N 19/61 (2014.01);
U.S. Cl.
CPC ...
H04N 19/146 (2014.11); G06N 20/00 (2019.01); G06V 20/49 (2022.01); G06V 20/58 (2022.01); H04N 7/183 (2013.01); H04N 19/119 (2014.11); H04N 19/124 (2014.11); H04N 19/14 (2014.11); H04N 19/154 (2014.11); H04N 19/156 (2014.11); H04N 19/172 (2014.11); H04N 19/176 (2014.11); H04N 19/177 (2014.11); H04N 19/42 (2014.11); H04N 19/463 (2014.11); H04N 19/61 (2014.11);
Abstract

Systems and methods are provided for optimizing video compression for remote vehicle control, including capturing, capturing video and sensor data from a vehicle using a plurality of sensors and high-resolution cameras, analyzing the captured video to identify critical regions within frames of the video using an attention-based module. Current network bandwidth is assessed and future bandwidth availability is predicted. Video compression parameters are predicted based on an analysis of the video and an assessment of the current network bandwidth using a control network, and the video is compressed based on the predicted parameters with an adaptive video compression module. The compressed video and sensor data is transmitted to a remote-control center, and received video and sensor data is decoded at the remote-control center. The vehicle is autonomously or remotely controlled from the remote-control center based on the decoded video and sensor data.


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