METHOD AND APPARATUS FOR DETERMINING AND ANALYZING A LOCATION OF VISUAL INTEREST

• US 20100033333A1
• Filed: 06/11/2007
• Published: 02/11/2010
• Est. Priority Date: 06/11/2006
• Status: Active Grant

• Current Assignee: Volvo Truck Corporation (AB Volvo)
• Original Assignee: Volvo Technology Corporation
• Inventors: Kronberg, Peter, Victor, Trent
• Granted Patent: US 8,487,775 B2
• Time in Patent Office: Days
• US Class Current: 340/576
• CPC Class Codes: A61B 2503/22 Motor vehicles operators, e.g. drivers, pilots, captains
• CPC Class Codes: A61B 3/113 for determining or recording eye movement
• CPC Class Codes: A61B 5/1114 Tracking parts of the body
• CPC Class Codes: A61B 5/163 by tracking eye movement, gaze, or pupil change
• CPC Class Codes: A61B 5/18 for vehicle drivers or machine operators
• CPC Class Codes: A61B 5/6893 Cars
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7207 of noise induced by motion artifacts
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: G06V 20/597 Recognising the driver's state or behaviour, e.g. attention or drowsiness
• CPC Class Codes: G06V 40/19 Sensors therefor
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A method of analyzing data based on the physiological orientation of a driver is provided. Data is descriptive of a driver'"'"'s gaze-direction is processing and criteria defining a location of driver interest is determined. Based on the determined criteria, gaze-direction instances are classified as either on-location or off-location. The classified instances can then be used for further analysis, generally relating to times of elevated driver workload and not driver drowsiness. The classified instances are transformed into one of two binary values (e.g., 1 and 0) representative of whether the respective classified instance is on or off location. The uses of a binary value makes processing and analysis of the data faster and more efficient. Furthermore, classification of at least some of the off-location gaze direction instances can be inferred from the failure to meet the determined criteria for being classified as an on-location driver gaze direction instance.

Claims

1. A method on analyzing a location of driver visual interest derived from observation of driver physiological orientation in a vehicle, the method comprising:

obtaining data descriptive of a plurality of driver gaze direction instances, wherein each driver gaze direction instance is defined by a data set;

processing the data using a computer-based processor;

classifying a plurality of the driver gaze direction instances as either 00location or off-location in preparation for further analysis related to the location of driver visual interest;

clustering data derived from the classified driver gaze direction instances;

characterized in that

transforming each of the plurality of classified driver gaze direction instances into one of two binary values representative of whether the respective driver gaze direction instance is on-location or off-location thus forming a classification signal which determines in each sample or unit of time whether the location of driver visual interest is on-location or off-location;

computing a glance frequency by counting the number of on-and off intervals in the classification signal; and

computing a glance duration by regarding a start and end of each continuous segment classified in the same way.

2. The method as recited in claim 1, wherein only an area where measurement devices provide optima! performance is regarded for driver gaze direction instances.

3. The method as recited in claim 1, wherein an actual road-center cluster as measured by measurement devices is constrained by a pre-defined road center area for analysis.

4. The method as recited in claim 1, wherein multiple road-center areas are defined in an overlay fashion when different visual behaviour correlating to different road characteristics is desired to be measured

5. The method as recited in claim 1, wherein the binary values are 1 and 0, the 1 and 0 values being more quickly processible in the further analysis than the obtained data from which the 1 and 0 values are derived.

6. The method as recited in claim 1, wherein the classification of at least some of the off-location driver gaze direction instances is inferred from failure to meet the determined criteria for being classified as an on-location driver gaze direction instance.

7. The method as recited in claim 1, wherein the further analysis relates to times of elevated driver workload and not driver drowsiness.

8. The method as recited in claim 1, wherein each data set defining a driver gaze direction instance is derived from a sensed orientation of an abovewaist portion of the driver'"'"'s body.

9. The method as recited in claim 1, wherein each data set defining a driver gaze direction instance is derived from a sensed orientation of an upper torso portion of the driver'"'"'s body.

10. The method as recited in claim 9, wherein the sensed orientation of an upper torso position is sensed using an ultrasonic device.

11. The method as recited in claim 9, wherein the sensed orientation of the head of the driver is sensed using a camera sensor.

12. The method as recited in claim 1, wherein each data set defining a driver gaze direction instance is derived from a sensed orientation of the head of the driver.

13. The method as recited in claim 12, wherein the sensed orientation of the head of the driver is sensed using a camera sensor.

14. The method as recited in claim 12, wherein the sensed orientation of the head of the driver is sensed using a capacitive sensor.

15. The method as recited in claim 1, wherein each data set defining a driver gaze direction instance is derived from a sensed orientation of at least one eye of the driver.

16. The method as recited in claim 1, wherein the location is a road-center location.

17. The method as recited in claim 1, wherein the location is a rear-view mirror location.

18. The method as recited in claim 1, wherein the location is a side-mirror location.

19. The method as recited in claim 1, wherein the location is a car accessory location.

20. The method as recited in claim 1, wherein the location is a personal accessory.

21. The method of claim 1, further comprising analyzing the classified gaze direction instances using a computer-based processor and determining therefrom whether an excessive amount of driver attention is being paid to the off-location based on an assessment of driver workload.

22. The method of claim 21, further comprising initiating stimulation of the driver'"'"'s attention to driving control tasks after determining an excessive amount of driver attention is being paid to the off-location.

23. The method of claim 22, further comprising detecting a driving control task performance decrement prior to stimulating the driver'"'"'s attention.

24. The method of claim 23, wherein the driving control task performance decrement is based on inconsistent steering.

25. The method of claim 23, wherein the driving control task performance decrement is based on inconsistent lane keeping.

26. The method of claim 23, wherein the driving control task performance decrement is based on inconsistent vehicle speed.

27. The method of claim 16, further comprising analyzing the classified gaze-direction instances using a computer-based processor and determining therefrom whether an excessive amount of driver attention is being paid to the off-road-center location based on an assessment of driver workload.

28. The method of claim 27, further comprising initiating stimulation of the driver'"'"'s attention to driving control tasks after determining an excessive amount of driver attention is being paid to the off-location.