Apparatuses, systems, and methods for inferring a driving enviroment based on vehicle occupant actions

• US 10,163,018 B1
• Filed: 06/14/2016
• Issued: 12/25/2018
• 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): Liu, Li
• Application Number:
  US15/181,658
• Time in Patent Office: 924 Days
• CPC Class Codes: B60W 2420/403 Image sensing, e.g. optical camera
• CPC Class Codes: B60W 40/08 related to drivers or passengers
• CPC Class Codes: G06F 16/50 of still image data
• CPC Class Codes: G06F 18/2415 based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 40/08 Insurance
• CPC Class Codes: G06V 10/751 Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• CPC Class Codes: G06V 20/597 Recognising the driver's state or behaviour, e.g. attention or drowsiness
• CPC Class Codes: H04N 23/90 Arrangement of cameras or camera modules, e.g. multiple cameras in TV studios or sports stadiums
• CPC Class Codes: H04N 7/181 for receiving images from a plurality of remote sources

Abstract

Apparatuses, systems and methods are provided for inferring a driving environment. More particularly, apparatuses, systems and methods are provided for inferring a driving environment based on vehicle occupant actions. Vehicle occupant actions may be based on digital image data.

Claims

1. A device for inferring a vehicle driving environment, 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 scaled vehicle occupant postures that are normalized for a range of different drivers, and wherein the previously classified image data is representative of previously classified vehicle occupant actions;

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 occupant action; and

a vehicle driving environment determination module stored on a memory that, when executed by a processor, causes the processor to infer a vehicle driving environment based on a comparison of the current image data with the previously classified image data.

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 driving environment is inferred using a probability function.

4. 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.

5. 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.

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 inferring a vehicle driving environment, 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 scaled vehicle occupant postures that are normalized for a range of different drivers, and wherein the previously classified image data is representative of previously classified vehicle occupant actions;

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 occupant actions; and

inferring, using a processor of a computing device, a vehicle driving environment, based on a comparison of the current image data with the previously classified image data, in response to the processor executing a vehicle driving environment determination 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 previously classified image data is representative of a three-dimensional representation of at least one occupant within the vehicle interior.

12. The method as in claim 8, wherein the vehicle driving environment is inferred using a probability function.

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 inferring a vehicle driving environment, 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 scaled vehicle occupant postures that are normalized for a range of different drivers, and wherein the previously classified image data is representative of previously classified vehicle occupant actions;

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 occupant actions; and

a vehicle driving environment determination module that, when executed by a processor, causes the processor to infer a vehicle driving environment based on a comparison of the current image data with the previously classified image data.

16. The non-transitory computer-readable medium as in claim 15, wherein the vehicle driving environment is inferred 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 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 vehicle interior.