Apparatuses, systems and methods for generating a vehicle driver model for a particular vehicle

• US 10,452,933 B1
• Filed: 12/15/2017
• Issued: 10/22/2019
• Est. Priority Date: 01/19/2017
• 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): Chang, Jon
• Application Number:
  US15/717,291
• Time in Patent Office: 676 Days
• CPC Class Codes: G06V 20/59 inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
• CPC Class Codes: G06V 20/597 Recognising the driver's state or behaviour, e.g. attention or drowsiness
• CPC Class Codes: G06V 20/653 by matching three-dimensional models, e.g. conformal mapping of Riemann surfaces
• CPC Class Codes: G06V 40/20 Movements or behaviour, e.g. gesture recognition recognition of facial expressions G06V40/16

Abstract

Apparatuses, systems and methods are provided for generating a vehicle driver model. More particularly, apparatuses, systems and methods are provided for generating a vehicle driver model for a particular vehicle based on vehicle interior image data.

Claims

1. A device for determining a driver model for a particular vehicle, the device comprising:

a vehicle interior data receiving module stored on a memory that, when executed by a processor, causes the processor to receive vehicle interior data from at least one vehicle interior sensor, wherein the vehicle interior data is representative of driver models;

a previously classified vehicle interior data receiving module stored on the memory that, when executed by the processor, causes the processor to receive previously classified vehicle interior data, wherein the previously classified vehicle interior data is representative of known driver models;

a vehicle driver model determination module stored on the memory that, when executed by the processor, causes the processor to determine at least one driver model based on a comparison of the vehicle interior data with the previously classified vehicle interior data, wherein the at least one driver model includes a range of movement of at least one joint location of a driver;

a vehicle data receiving module stored on the memory that, when executed by the processor, causes the processor to receive vehicle data, wherein the vehicle data is representative of a particular vehicle; and

a driver model/vehicle correlation module stored on the memory that, when executed by the processor, causes the processor to correlate the driver model with the particular vehicle.

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, further comprising:

a previously classified vehicle interior data image feature extraction module stored on the memory that, when executed by the processor, causes the processor to extract image features from the previously classified vehicle interior data, wherein the extracted image features from the previously classified vehicle interior data are representative of known vehicle occupant postures within the particular vehicle, wherein execution of the vehicle occupant posture determination module causes the processor to determine at least one vehicle occupant posture based on a comparison of the vehicle interior data with the image features extracted from the previously classified vehicle interior data.

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

5. The device as in claim 1, further comprising:

a vehicle interior data feature extraction module stored on the memory that, when executed by the processor, causes the processor to extract image features from the vehicle interior data, wherein the image features extracted from the vehicle interior data are representative of postures of a vehicle occupant within the particular vehicle, wherein execution of the vehicle occupant posture determination module causes the processor to determine at least one vehicle occupant posture based on a comparison of the image features extracted from the vehicle interior data with the previously classified vehicle interior data.

6. The device as in claim 1, wherein the current image data 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 a driver model for a particular vehicle, the method comprising:

receiving, at a processor, vehicle interior data from at least one vehicle interior sensor, wherein the vehicle interior data is representative of vehicle occupant postures;

receiving, at the processor, previously classified vehicle interior data, wherein the previously classified vehicle interior data is representative of known vehicle occupant postures;

determining, using the processor, at least one vehicle driver model based on a comparison of the vehicle interior data with the previously classified vehicle interior data wherein the at least one driver model includes a range of movement of at least one joint location of a driver;

receiving, at the processor, vehicle data in response to the processor executing a vehicle data receiving module, wherein the vehicle data is representative of a particular vehicle; and

correlating, using a processor, the driver model with the particular vehicle in response to the processor executing a driver model/vehicle correlation 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, further comprising:

extracting, using the processor, image features from previously classified vehicle interior data, wherein the extracted image features from the previously classified vehicle interior data are representative of known vehicle occupant postures within the particular vehicle, wherein execution of the vehicle occupant posture determination module causes the processor to determine at least one vehicle occupant posture based on a comparison of the vehicle interior data with the image features extracted from the previously classified vehicle interior data.

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

12. The method as in claim 8, further comprising:

extracting, using the processor, image features from the vehicle interior data, wherein the image features extracted from the vehicle interior data are representative of postures of a vehicle occupant within the particular vehicle, wherein execution of the vehicle occupant posture determination module causes the processor to determine at least one vehicle occupant posture based on a comparison of the image features extracted from the vehicle interior data with the previously classified vehicle interior data.

13. The method as in claim 8, 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.

14. The method as in claim 8, wherein the previously classified image data includes images and/or 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 a driver model for a particular vehicle, the non-transitory computer-readable medium comprising:

a vehicle interior data receiving module that, when executed by a processor, causes the processor to receive vehicle interior data from at least one vehicle interior sensor, wherein the vehicle interior data is representative of driver models;

a previously classified vehicle interior data receiving module that, when executed by the processor, causes the processor to receive previously classified vehicle interior data, wherein the previously classified vehicle interior data is representative of known driver models;

a driver model determination module that, when executed by the processor, causes the processor to determine at least one driver model based on a comparison of the vehicle interior data with the previously classified vehicle interior data, wherein the at least one driver model includes a range of movement of at least one joint location of a driver;

a vehicle data receiving module that, when executed by the processor, causes the processor to receive vehicle data, wherein the vehicle data is representative of a particular vehicle; and

a driver model/vehicle correlation module that, when executed by the processor, causes the processor to correlate the driver model with the particular vehicle.

16. The non-transitory computer-readable medium as in claim 15, further comprising:

a previously classified vehicle interior data image feature extraction module that, when executed by the processor, causes the processor to extract image features from the previously classified vehicle interior data, wherein the extracted image features from the previously classified vehicle interior data are representative of known vehicle occupant postures within the particular vehicle, wherein execution of the vehicle occupant posture determination module causes the processor to determine at least one vehicle occupant posture based on a comparison of the vehicle interior data with the image features extracted from the previously classified vehicle interior data.

17. The non-transitory computer-readable medium as in claim 15, further comprising:

a vehicle interior data feature extraction module that, when executed by the processor, causes the processor to extract image features from the vehicle interior data, wherein the image features extracted from the vehicle interior data are representative of postures of a vehicle occupant within the particular vehicle, wherein execution of the vehicle occupant posture determination module causes the processor to determine at least one vehicle occupant posture based on a comparison of the image features extracted from the vehicle interior data with the previously classified vehicle interior data.

18. The non-transitory computer-readable medium as in claim 15, wherein the current image data 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, 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.

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 particular vehicle interior.