Granted Patent B2

US 8,952,819 · App. 13/755,194 · Granted Feb 10, 2015

Abstract

Claims (42)

1. A system for event triggering, comprising:

an interface configured to:

receive a face tracking data;

receive a sensor data; and

a processor configured to:

determine a degree of drowsiness based at least in part on the face tracking data and the sensor data, wherein the degree of drowsiness is determined using a model;

in the event that the degree of drowsiness is greater than a first threshold, capture data of a drowsy driving event; and

in the event that the degree of drowsiness is greater than a second threshold, provide a warning;

determine a score for the drowsy driving events based at least in part on a review of captured data of drowsy driving events;

update the model for determining the degree of drowsiness based at least in part on the score of the drowsy driving events.

2. A system as in claim 1 , wherein the model is one of the following: rule-based model, statistical model, neural network based model, or an automatically calibrating anomaly detection model.

3. A system as in claim 1 , wherein the model is trained using one or more of the following: driving simulator data, video data, sensor data, audio data, or captured data.

4. A system as in claim 1 , wherein the model is used to determine a drowsy driving behavior using only non-face-data sensors.

5. A system as in claim 1 , wherein the model is used to determine a real-time driver warning using only non-face-data sensors.

6. A system as in claim 1 , wherein the face tracking data comprises a blink duration.

7. A system as in claim 6 , wherein the blink duration is relative to a driver specific blink duration baseline.

8. A system as in claim 7 , wherein the driver specific baseline includes an average blink duration and a standard deviation of blink duration.

9. A system as in claim 1 , wherein the face tracking data comprises head nodding.

10. A system as in claim 9 , wherein the head nodding is relative to a driver specific head nodding baseline.

11. A system as in claim 10 , wherein the driver specific baseline includes an average head nodding and a standard deviation of head nodding.

12. A system as in claim 1 , wherein the face tracking data is captured using a rear-view mirror mounted unit.

13. A system as in claim 1 , wherein sensor data comprises one or more of the following: steering data, acceleration data, braking data, application of gas data, deviations data, jerkiness of responses data, corrections data, or overcorrections data.

14. A system as in claim 1 , wherein capturing data comprises capturing data prior to being triggered by the drowsiness degree, during the trigger by the drowsiness degree, and after the trigger by the drowsiness degree.

15. A system as in claim 1 , wherein the driver is warned using one or more of the following an audible warning, an illumination warning, or a haptic warning.

16. A system as in claim 1 , wherein the processor is further configured to warn one or more of the following: a dispatcher, a system administrator, or a supervisor.

17. A system as in claim 1 , wherein the first threshold or the second threshold is dynamically adjusted.

18. A method of event triggering, comprising:

receiving a face tracking data;

receiving a sensor data;

determining a degree of drowsiness based at least in part on the face tracking data and the sensor data, wherein the degree of drowsiness is determined using a model; and

in the event that the degree of drowsiness is greater than a first threshold, capturing data of a drowsy driving event; and

in the event that the degree of drowsiness is greater than a second threshold, providing a warning;

determining a score for the drowsy driving events based at least in part on a review of captured data of drowsy driving events;

updating the model for determining the degree of drowsiness based at least in part on the score of the drowsy driving events.

19. A computer program product for event triggering, the computer program product being embodied in a non-transitory computer readable storage medium and comprising computer instructions for:

receiving a face tracking data;

receiving a sensor data;

determining a degree of drowsiness based at least in part on the face tracking data and the sensor data, wherein the degree of drowsiness is determined using a model; and

in the event that the degree of drowsiness is greater than a first threshold, capturing data of a drowsy driving event; and

in the event that the degree of drowsiness is greater than a second threshold, providing a warning;

determining a score for the drowsy driving events based at least in part on a review of captured data of drowsy driving events;

updating the model for determining the degree of drowsiness based at least in part on the score of the drowsy driving events.

Assignments (8)