Apparatuses, systems, and methods for determining degrees of risk associated with a vehicle operator

• US 9,990,553 B1
• Filed: 06/14/2016
• Issued: 06/05/2018
• Est. Priority Date: 06/14/2016
• Status: Active Grant

• Current Assignee: State Farm Mutual Automobile Insurance Company (State Farm)
• Original Assignee: State Farm Mutual Automobile Insurance Company (State Farm)
• Inventors: Chan, Aaron Scott, Sanchez, Kenneth J.
• Primary Examiner(s): Baker, Charlotte M
• Application Number:
  US15/181,604
• Time in Patent Office: 721 Days
• Field of Search: 382103, 382104, 382107, 382154, 280735, 180272, 701300, 701 1, 701 293, 701123, 707711, 707741, 340576, 705 4
• CPC Class Codes: G06F 18/22 Matching criteria, e.g. proximity measures
• CPC Class Codes: G06Q 40/08 Insurance
• CPC Class Codes: G06V 10/761 Proximity, similarity or dissimilarity measures
• CPC Class Codes: G06V 20/597 Recognising the driver's state or behaviour, e.g. attention or drowsiness

Abstract

Apparatuses, systems and methods are provided for determining vehicle operator risk. More particularly, apparatuses, systems and methods are provided for determining degrees of vehicle operator risk. A degree of vehicle operator risk may be based on digital image data.

Claims

1. A device for determining degrees of vehicle operator risk, the device comprising:

a previously classified image data receiving module stored on a memory that, when executed by a processor, causes the processor to receive previously classified image data from at least one previously classified image database, wherein the previously classified image data is representative of previously classified vehicle operator risk;

a current image data receiving module stored on a memory that, when executed by a processor, causes the processor to receive current image data from at least one vehicle interior sensor, wherein the current image data is representative of current vehicle operator risk, and wherein the current image data includes images and/or extracted image features that are representative of vehicle occupant locations/orientations, cellular telephone locations/orientations, vehicle occupant eye locations/orientations, vehicle occupant head location/orientation, vehicle occupant hand location/orientation, a vehicle occupant torso location/orientation, a seat belt location, or a vehicle seat location/orientation;

a degree of risk determination module stored on a memory that, when executed by a processor, causes the processor to determine a vehicle operator degree of risk based on a comparison of the current image data with the previously classified image data; and

a driver warning generation module stored on a memory that, when executed by a processor, causes the processor to generate at least one of a driver warning, a driver advisory, a report, or a display, when the vehicle operator degree of risk exceeds a threshold value.

2. The device as in claim 1, wherein the at least one vehicle interior sensor is selected from:

at least one digital image sensor, at least one ultra-sonic sensor, at least one radar-sensor, at least one infrared light sensor, or at least one laser light sensor.

3. The device as in claim 1, wherein the vehicle operator degree of risk is determined using a probability function.

4. The device as in claim 1, wherein the current image data is representative of a three-dimensional representation of at least one occupant within the vehicle interior.

5. The device as in claim 1, wherein the previously classified image data is representative of a three-dimensional representation of at least one occupant within the vehicle interior.

6. The device as in claim 1, wherein the current image data further includes images and/or extracted image features that are representative of a vehicle occupant using a cellular telephone, a vehicle occupant looking out a vehicle side window, a vehicle occupant adjusting a vehicle radio, a vehicle occupant adjusting a vehicle heating, ventilation and air conditioning system, two vehicle occupants talking with one-another, a vehicle occupant reading a book or magazine, a vehicle occupant putting on makeup, or a vehicle occupant looking at themselves in a mirror.

7. The device as in claim 1, wherein the previously classified image data includes images and/or extracted image features that have previously been classified as being representative of a vehicle occupant using a cellular telephone, a vehicle occupant looking out a vehicle side window, a vehicle occupant adjusting a vehicle radio, a vehicle occupant adjusting a vehicle heating, ventilation and air conditioning system, two vehicle occupants talking with one-another, a vehicle occupant reading a book or magazine, a vehicle occupant putting on makeup, or a vehicle occupant looking at themselves in a mirror.

8. A computer-implemented method for determining degrees of vehicle operator risk, the method comprising:

receiving, at a processor of a computing device, previously classified image data from at least one previously classified image database in response to the processor executing a previously classified image data receiving module, wherein the previously classified image data is representative of previously classified vehicle operator risk;

receiving, at a processor of a computing device, current image data from at least one vehicle interior sensor a current image data receiving module, in response to the processor executing a current image data receiving module, wherein the current image data is representative of current vehicle operator risk, and wherein the current image data includes images and/or extracted image features that are representative of vehicle occupant locations/orientations, cellular telephone locations/orientations, vehicle occupant eye locations/orientations, vehicle occupant head location/orientation, vehicle occupant hand location/orientation, a vehicle occupant torso location/orientation, a seat belt location, or a vehicle seat location/orientation;

determining, using a processor of a computing device, a degree of risk associated with a vehicle operator, based on a comparison of the current image data with the previously classified image data, in response to the processor executing a degrees of risk determination module, and

generating, using a processor of a computing device, at least one of a driver warning, a driver advisory, a report, or a display, when the vehicle operator degree of risk exceeds a threshold value, in response to the processor executing a driver warning generation module.

9. The method as in claim 8, wherein the at least one vehicle interior sensor is selected from:

at least one digital image sensor, at least one ultra-sonic sensor, at least one radar-sensor, at least one infrared light sensor, or at least one laser light sensor.

10. The method as in claim 8, wherein the current image data is representative of a three-dimensional representation of at least one occupant within the vehicle interior.

11. The method as in claim 8, wherein the vehicle operator degree of risk is determined using a probability function.

12. The method as in claim 8, wherein the previously classified image data is representative of a three-dimensional representation of at least one occupant within the vehicle interior.

13. The method as in claim 8, wherein the previously classified image data includes images that are representative of vehicle occupant locations/orientations, cellular telephone locations/orientations, vehicle occupant eye locations/orientations, vehicle occupant head location/orientation, vehicle occupant hand location/orientation, a vehicle occupant torso location/orientation, a seat belt location, or a vehicle seat location/orientation.

14. The method as in claim 8, wherein the previously classified image data includes extracted image features that have previously been classified as being representative of known vehicle occupant locations/orientations, known cellular telephone locations/orientations, known vehicle occupant eye locations/orientations, known vehicle occupant head location/orientation, known vehicle occupant hand location/orientation, a known vehicle occupant torso location/orientation, a known seat belt location, or a known vehicle seat location/orientation.

15. A non-transitory computer-readable medium storing computer-readable instructions that, when executed by a processor, cause the processor to determine degrees of vehicle operator risk, the non-transitory computer-readable medium comprising:

a previously classified image data receiving module that, when executed by a processor, causes the processor to receive previously classified image data from at least one previously classified image database, wherein the previously classified image data is representative of previously classified vehicle operator risk;

a current image data receiving module that, when executed by a processor, causes the processor to receive current image data from at least one vehicle interior sensor, wherein the current image data is representative of current vehicle operator risk, and wherein the current image data includes images and/or extracted image features that are representative of vehicle occupant locations/orientations, cellular telephone locations/orientations, vehicle occupant eye locations/orientations, vehicle occupant head location/orientation, vehicle occupant hand location/orientation, a vehicle occupant torso location/orientation, a seat belt location, or a vehicle seat location/orientation;

a degree of risk determination module that, when executed by a processor, causes the processor to determine a vehicle operator degree of risk based on a comparison of the current image data with the previously classified image data; and

a driver warning generation module that, when executed by a processor, causes the processor to generate at least one of a driver warning, a driver advisory, a report, or a display, when the vehicle operator degree of risk exceeds a threshold value.

16. The non-transitory computer-readable medium as in claim 15, wherein the vehicle operator degree of risk is determined using a probability function.

17. The non-transitory computer-readable medium as in claim 15, wherein the current image data is representative of a three-dimensional representation of at least one occupant within the vehicle interior.

18. The non-transitory computer-readable medium as in claim 15, wherein the current image data further includes images and/or extracted image features that are representative of a vehicle occupant using a cellular telephone, a vehicle occupant looking out a vehicle side window, a vehicle occupant adjusting a vehicle radio, a vehicle occupant adjusting a vehicle heating, ventilation and air conditioning system, two vehicle occupants talking with one-another, a vehicle occupant reading a book or magazine, a vehicle occupant putting on makeup, or a vehicle occupant looking at themselves in a mirror.

19. The non-transitory computer-readable medium as in claim 15, wherein the previously classified image data includes images and/or extracted image features that have previously been classified as being representative of a vehicle occupant using a cellular telephone, a vehicle occupant looking out a vehicle side window, a vehicle occupant adjusting a vehicle radio, a vehicle occupant adjusting a vehicle heating, ventilation and air conditioning system, two vehicle occupants talking with one-another, a vehicle occupant reading a book or magazine, a vehicle occupant putting on makeup, a vehicle occupant looking at themselves in a mirror, known vehicle occupant locations/orientations, known cellular telephone locations/orientations, known vehicle occupant eye locations/orientations, known vehicle occupant head location/orientation, known vehicle occupant hand location/orientation, a known vehicle occupant torso location/orientation, a known seat belt location, or a known vehicle seat location/orientation.

20. The non-transitory computer-readable medium as in claim 15, wherein the previously classified image data is representative of a three-dimensional representation of at least one occupant within the vehicle interior.