Apparatuses, systems and methods for determining distractions associated with vehicle driving routes

• US 9,944,296 B1
• Filed: 01/13/2016
• Issued: 04/17/2018
• Est. Priority Date: 01/13/2015
• Status: Active Grant

• Current Assignee: State Farm Mutual Automobile Insurance Company (State Farm)
• Original Assignee: State Farm Mutual Automobile Insurance Company (State Farm)
• Inventors: Sanchez, Kenneth Jason, Chan, Aaron Scott, Saeed, Muhammad Ibrahim, Maloney, Dirk Patrick, Anilal, Narendra, Vemulapati, Vibhakar V S, Hseih, William Cho, Zhou, Richard
• Primary Examiner(s): Wang, Jack K
• Application Number:
  US14/994,409
• Time in Patent Office: 825 Days
• Field of Search: 340439
• CPC Class Codes: B60R 21/01542 detecting passenger motion
• CPC Class Codes: B60R 21/01552 detecting position of specific human body parts, e.g. face, eyes or hands
• CPC Class Codes: B60R 2300/30 characterised by the type of image processing
• CPC Class Codes: B60R 2300/8006 for monitoring and displaying scenes of vehicle interior, e.g. for monitoring passengers or cargo
• CPC Class Codes: B60W 2040/0872 Driver physiology
• CPC Class Codes: B60W 2050/0095 Automatic control mode change
• CPC Class Codes: B60W 2420/403 Image sensing, e.g. optical camera
• CPC Class Codes: B60W 2420/54 Audio sensitive means, e.g. ultrasound
• CPC Class Codes: B60W 2540/22 Psychological state; Stress level or workload
• CPC Class Codes: B60W 2540/223 Posture, e.g. hand, foot, or seat position, turned or inclined
• CPC Class Codes: B60W 2540/225 Direction of gaze
• CPC Class Codes: B60W 2540/229 Attention level, e.g. attentive to driving, reading or sleeping
• CPC Class Codes: B60W 2540/26 Incapacity
• CPC Class Codes: B60W 40/08 related to drivers or passengers
• CPC Class Codes: B60W 40/09 Driving style or behaviour
• CPC Class Codes: B60W 50/08 Interaction between the driver and the control system
• CPC Class Codes: B60W 50/14 Means for informing the driver, warning the driver or prompting a driver intervention
• CPC Class Codes: B60W 60/0053 from vehicle to occupant
• CPC Class Codes: B60W 60/0059 Estimation of the risk associated with autonomous or manual driving, e.g. situation too complex, sensor failure or driver incapacity
• CPC Class Codes: B60Y 2400/30 Sensors
• CPC Class Codes: B60Y 2400/3015 : Optical cameras
• CPC Class Codes: B60Y 2400/3017 : Radars
• CPC Class Codes: G01S 19/42 : Determining position
• CPC Class Codes: G05D 1/0061 : for transition from automatic pilot to manual pilot and vice versa
• CPC Class Codes: G06F 16/51 : Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 18/22 : Matching criteria, e.g. proximity measures
• CPC Class Codes: G06F 18/24 : Classification techniques
• CPC Class Codes: G06F 18/2413 : based on distances to training or reference patterns
• CPC Class Codes: G06T 15/005 : General purpose rendering architectures
• CPC Class Codes: G06T 2207/30196 : Human being; Person
• CPC Class Codes: G06T 2207/30268 : Vehicle interior
• CPC Class Codes: G06T 7/337 : involving reference images or patches
• CPC Class Codes: G06T 7/70 : Determining position or orientation of objects or cameras camera calibration G06T7/80
• CPC Class Codes: G06T 7/73 : using feature-based methods
• 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 20/593 : Recognising seat occupancy
• CPC Class Codes: G06V 20/597 : Recognising the driver's state or behaviour, e.g. attention or drowsiness
• CPC Class Codes: G06V 20/64 : Three-dimensional objects
• CPC Class Codes: G06V 40/10 : Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
• CPC Class Codes: G06V 40/11 : Hand-related biometrics; Hand pose recognition
• CPC Class Codes: H04N 19/17 : the unit being an image region, e.g. an object
• CPC Class Codes: H04N 19/423 : characterised by memory arrangements H04N19/433 takes precedence
• CPC Class Codes: H04N 23/45 : for generating image signals from two or more image sensors being of different type or operating in different modes, e.g. with a CMOS sensor for moving images in combination with a charge-coupled device [CCD] for still images

Abstract

Apparatuses, systems and methods are provided for determining vehicle driver distractions. More particularly, apparatuses, systems and methods are provided for determining distractions associated with vehicle driving routes based on postures of vehicle occupants.

Claims

1. A device for determining distractions associated with corresponding geographic locations along vehicle driving routes, 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 distractions of at least one vehicle occupant;

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

a vehicle driving route distraction data generation module stored on the memory that, when executed by the processor, causes the processor to generate vehicle driving route distraction data, using a covariance matrix having a probability distribution vector, based on the vehicle interior data and the vehicle location data, wherein the vehicle driving route distraction data is representative of at least one geographic location along at least one vehicle driving route that includes at least one distraction correlated with the at least one geographic location; and

a risk report generation module stored on the memory that, when executed by the processor, causes the processor to generate a risk report based on the vehicle driving route distraction data, wherein the risk report provides an indication representative of at least one geographic location correlated with a distraction.

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 location sensor is selected from:

at least one global positioning sensor (GPS) sensor, at least one all source positioning and navigation (ASPN) sensor, or at least one global navigation satellite system (GLONASS) sensor.

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 vehicle interior.

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

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 circumstances associated with vehicle occupant distractions; and

a vehicle occupant distraction data generation module stored on the memory that, when executed by the processor, causes the processor to generate vehicle occupant distraction data based on a comparison of the vehicle interior data with the previously classified vehicle interior data, and wherein the vehicle driving route distraction data is further based on the vehicle occupant distraction 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 distractions associated with corresponding geographic locations along vehicle driving routes, the method comprising:

receiving, at a processor or a computing device, vehicle interior data from at least one vehicle interior sensor, wherein the vehicle interior data is representative of distractions of at least one vehicle occupant;

receiving, at the processor or the computing device, vehicle location data from at least one vehicle location sensor, wherein the vehicle location data is representative of a vehicle geographic location;

generating, using the processor of the computing device, vehicle driving route distraction data, using a covariance matrix having a probability distribution vector, based on the vehicle interior data and the vehicle location data, wherein the vehicle driving route distraction data is representative of at least one geographic location along at least one vehicle driving route that includes at least one distraction correlated with the at least one geographic location; and

generating a risk report based on the vehicle driving route distraction data, wherein the risk report provides an indication representative of at least one geographic location correlated with a distraction.

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 vehicle location sensor is selected from:

at least one global positioning sensor (GPS) sensor, at least one all source positioning and navigation (ASPN) sensor, or at least one global navigation satellite system (GLONASS) sensor.

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 vehicle interior.

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

receiving, at the processor of the computing device, previously classified vehicle interior data, wherein the previously classified vehicle interior data is representative of circumstances associated with vehicle occupant distractions; and

generating, using the processor of the computing device, vehicle occupant distraction data based on a comparison of the vehicle interior data with the previously classified vehicle interior data, and wherein the vehicle driving route distraction data is further based on the vehicle occupant distraction 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 distractions associated with corresponding geographic locations along vehicle driving routes, the non-transitory computer-readable medium comprising:

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

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

a vehicle driving route distraction data generation module that, when executed by the processor, causes the processor to generate vehicle driving route distraction data, using a covariance matrix having a probability distribution vector, based on the vehicle interior data and the vehicle location data, wherein the vehicle driving route distraction data is representative of vehicle driving routes that include distractions, wherein the vehicle driving route distraction data is representative of at least one geographic location along at least one vehicle driving route that includes at least one distraction correlated with the at least one geographic location; and

a risk report generation module that, when executed by the processor, causes the processor to generate a risk report based on the vehicle driving route distraction data, wherein the risk report provides an indication representative of at least one geographic location correlated with a distraction.

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

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 circumstances associated with vehicle occupant distractions; and

a vehicle occupant distraction data generation module that, when executed by the processor, causes the processor to generate vehicle occupant distraction data based on a comparison of the vehicle interior data with the previously classified vehicle interior data, and wherein the vehicle driving route distraction data is further based on the vehicle occupant distraction data.

17. The non-transitory computer-readable medium as in claim 15, wherein the vehicle interior 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.