Patent Issued for Apparatuses, systems, and methods for inferring a driving environment based on vehicle occupant actions (USPTO 11003932)

2021 JUN 23 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A patent by the inventors Chan, Aaron Scott (Bloomington, IL, US), Sanchez, Kenneth J. (San Francisco, CA, US), filed on November 13, 2018, was published online on May 11, 2021, according to news reporting originating from Alexandria, Virginia, by NewsRx correspondents.

Patent number 11003932 is assigned to State Farm Mutual Automobile Insurance Company (Bloomington, Illinois, United States).

The following quote was obtained by the news editors from the background information supplied by the inventors: “Vehicles are being provided with more complex systems. For example, vehicles commonly include a plethora of entertainment systems, such as stereos, USB interfaces for mobile telephones, video players, etc. Vehicles often have a host of other operator interfaces, such as emergency calling systems, vehicle navigation systems, heating and air conditioning systems, interior and exterior lighting controls, air bags, seatbelts, etc.

“Vehicle operating environments are becoming more complex as well. For example, some roadways include u-turn lanes, round-a-bouts, no-left turn, multiple lanes one way in the morning and the other way in the afternoon, etc. Increases in traffic are also contributing to increased complexity.

“These additional complexities contribute to increases in driver risk. What is needed are methods and systems for generating data representative of vehicle driving environments.”

In addition to the background information obtained for this patent, NewsRx journalists also obtained the inventors’ summary information for this patent: “A device for inferring a vehicle driving environment may include 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. The previously classified image data may be representative of previously classified vehicle occupant actions. The device may also include 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. The current image data may be representative of current vehicle occupant action. The device may further include 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.

“In another embodiment, a computer-implemented method for inferring a vehicle driving environment may include 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. The previously classified image data may be representative of previously classified vehicle occupant actions. The method may also include 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. The current image data may be representative of current vehicle occupant actions. The method may further include 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.

“In a further embodiment, 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 may include 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. The previously classified image data may be representative of previously classified vehicle occupant actions. The non-transitory computer-readable medium may also include 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. The current image data may be representative of current vehicle occupant actions. The method may further include 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.”

The claims supplied by the inventors are:

“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 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, wherein the vehicle driving environment is associated with at least one distracted driving behavior, and wherein a risk associated with the at least one distracted driving behavior is quantified based on a weighted factor associated with a first duration of time of a first vehicle occupant behavior relative to a second duration of time of a second vehicle occupant behavior.

“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 previously classified image data is representative of scaled vehicle occupant postures that are normalized for a range of different drivers.

“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 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, wherein the vehicle driving environment is associated with at least one distracted driving behavior, and wherein a risk associated with the at least one distracted driving behavior is quantified based on a weighted factor associated with a first duration of time of a first vehicle occupant behavior relative to a second duration of time of a second vehicle occupant behavior.

“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 previously classified image data is representative of scaled vehicle occupant postures that are normalized for a range of different drivers.

“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 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, wherein the vehicle driving environment is associated with at least one distracted driving behavior, and wherein a risk associated with the at least one distracted driving behavior is quantified based on a weighted factor associated with a first duration of time of a first vehicle occupant behavior relative to a second duration of time of a second vehicle occupant behavior.

“16. The non-transitory computer-readable medium as in claim 15, wherein the previously classified image data is representative of scaled vehicle occupant postures that are normalized for a range of different drivers.

“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.”

There are additional claims. Please visit full patent to read further.

URL and more information on this patent, see: Chan, Aaron Scott. Apparatuses, systems, and methods for inferring a driving environment based on vehicle occupant actions. U.S. Patent Number 11003932, filed November 13, 2018, and published online on May 11, 2021. Patent URL: http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=11003932.PN.&OS=PN/11003932RS=PN/11003932

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