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:
Sep. 20, 2022

Filed:

Oct. 20, 2020
Applicant:

Toyota Motor Engineering & Manufacturing North America, Inc., Plano, TX (US);

Inventors:

Benjamin P. Austin, Saline, MI (US);

John K. Lenneman, Okemos, MI (US);

Joshua E. Domeyer, Madison, WI (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
B60W 40/09 (2012.01); G06N 3/08 (2006.01); G06N 20/00 (2019.01); G06V 20/56 (2022.01); G06V 20/59 (2022.01); G06V 40/19 (2022.01);
U.S. Cl.
CPC ...
B60W 40/09 (2013.01); G06N 3/084 (2013.01); G06N 20/00 (2019.01); G06V 20/56 (2022.01); G06V 20/597 (2022.01); G06V 40/19 (2022.01); B60W 2540/18 (2013.01); B60W 2540/22 (2013.01); B60W 2540/221 (2020.02); B60W 2540/223 (2020.02); B60W 2540/225 (2020.02);
Abstract

A driver monitor method and system for mitigating cognitive tunneling in a vehicle includes in-cabin video cameras, a heart rate monitor, an audio-visual device, and processing circuitry that detects eye gaze direction, eye lid position, and head position using images from the in-cabin video cameras, and measures heart rate variability using the heart rate monitor. A machine learning device uses the eye gaze direction, eye lid position, head position, and heart rate variability to predict whether a driver is transitioning into a cognitive tunneling state or a fatigue state. The audio-visual device outputs one audio-visual cue to mitigate the cognitive tunneling state and outputs a different cue to mitigate the fatigue state. The machine learning device learns by performing a reinforcement learning algorithm in which the audio-video device outputs a verification request and receives a response to the verification request that is fed back to the machine learning device.


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