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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.:

US 12272138 B1

PDFFull Text
Date of Patent:
Apr. 08, 2025

Filed:

Jun. 21, 2024

Forward collision warning

Applicant:

Samsara Inc., San Francisco, CA (US);

Inventors:

Rohit Annigeri, Santa Clara, CA (US);

Sharan Srinivasan, Sunnyvale, CA (US);

Kevin Lai, Redmond, WA (US);

Jose Cazarin, Calgary, CA;

Brian Westphal, Livermore, CA (US);

Shiva Bala, San Diego, CA (US);

Ivan Stoev, Santa Barbara, CA (US);

Douglas Boyle, Verdi, NV (US);

Cole Jurden, Kansas City, MO (US);

Margaret Irene Finch, Austin, TX (US);

Rachel Demerly, New York, NY (US);

Maya Krupa, Souh Lake Tahoe, CA (US);

Shirish Nair, Shoreline, WA (US);

Nathan Hurst, Seattle, WA (US);

Yan Wang, Mercer Island, WA (US);

Shaurye Aggarwal, Evanston, IL (US);

Akshay Raj Dhamija, Campbell, CA (US);

Assignee:

Samsara Inc., San Francisco, CA (US);

Attorney:

Schwegman Lundberg & Woessner, P.A.

Primary Examiner:

Marnie A Matt

Int. Cl.
CPC ...
G06V 20/40 (2022.01); B60Q 9/00 (2006.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 10/94 (2022.01); G06V 20/58 (2022.01);
U.S. Cl.
CPC ...
G06V 20/44 (2022.01); B60Q 9/008 (2013.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 10/945 (2022.01); G06V 10/95 (2022.01); G06V 20/46 (2022.01); G06V 20/58 (2022.01);
Abstract

Techniques are presented for the detection and management of collision warning (CW) events. A training dataset comprising videos of vehicle collisions and non-collisions, sensor readings, environmental conditions, and more is utilized to train a CW classification model for detecting potential collision events in vehicles. A backend CW classification model, with greater computational resources, employs a more complex neural network to review CW events received by the Behavioral Monitoring System (BMS) based on video data, achieving higher precision and reducing false positives. The CW model is installed in vehicles for real-time detection, while the backend model is deployed at the BMS. The BMS validates detected CW events, filters out false positives, and streamlines the review process for fleet administrators and customers. Additional BMS filtering operations include assessing non-proximity-related CW events and camera impairments, with the filtered CW events presented for review in the safety inbox.

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