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.:
Date of Patent:
Dec. 10, 2024
Filed:
Apr. 23, 2024
Samsara Inc., San Francisco, CA (US);
Akshay Raj Dhamija, Campbell, CA (US);
Abner Ayala, Orlando, FL (US);
Rohit Annigeri, Santa Clara, CA (US);
Cole Jurden, Kansas City, MO (US);
Douglas Boyle, Verdi, NV (US);
Jason Liu, Christiansburg, VA (US);
Kevin Lai, Redmond, WA (US);
Jose Cazarin, Calgary, CA;
Pang Wu, San Francisco, CA (US);
Nathan Hurst, Seattle, WA (US);
Brian Westphal, Livermore, CA (US);
Lucas Doyle, San Francisco, CA (US);
Saurabh Tripathi, San Jose, CA (US);
Shirish Nair, Shoreline, WA (US);
Samsara Inc., San Francisco, CA (US);
Abstract
Methods, systems, and computer programs are presented for the management of lane-departure (LD) events. One method includes training a classifier for LD events and loading the classifier into a vehicle. LD events are detected based on outward images using the classifier, while the turn signal is monitored to prevent false triggers. If an LD event is detected, rules are checked for alerting the driver and deciding whether to alert the driver or not. Subsequently, additional rules are checked for reporting the event and deciding whether to report the event to a Behavior Monitoring System (BMS) or to discard it. The method also includes a solid line departure model that identifies crossing dashed, solid-white, and solid-yellow lanes, delaying alerts and event generation until a significant portion of the vehicle crosses over the lane. The model also outputs a confidence score reflecting the amount of vehicle deviation from the driving lane.