# US 11,897,483 B2 · Apparatuses, systems and methods for determining distracted drivers associated with vehicle driving routes > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,897,483 B2 - Document ID: US11897483B2 - Status: Granted - Kind: B2 - Application number: 17/502,439 - Filed: 2021-10-15 - Published: 2022-02-10 - Granted: 2024-02-13 - Earliest claimed: 2017-01-19 - Assignees: State Farm Mutual Automobile Insurance Company - Applicants: State Farm Mutual Automobile Insurance Company - Inventors: Aaron Scott Chan; Kenneth J. Sanchez - Web page: https://patentgrep.com/patent/US11897483B2 ## CPC - B60W 40/09: Driving style or behaviour - B60W 50/14: Means for informing the driver, warning the driver or prompting a driver intervention - G06V 10/34: Smoothing or thinning of the pattern; Morphological operations; Skeletonisation - G06V 20/597: {Recognising the driver's state or behaviour, e.g. attention or drowsiness} - B60R 2300/302: combining image information with GPS information or vehicle data, e.g. vehicle speed, gyro, steering angle data - B60W 2040/0872: {Driver physiology} - B60W 2540/229: Attention level, e.g. attentive to driving, reading or sleeping - B60W 2540/30: Driving style - B60W 2556/45: External transmission of data to or from the vehicle - B60W 2556/50: of positioning data, e.g. GPS [Global Positioning System] data - G07C 5/008: {communicating information to a remotely located station (transmission systems for measured values G08C)} - B60W: CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - B60R: VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR - G07C: TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE ## Abstract Apparatuses, systems and methods are provided for determining vehicle driver distractions. More particularly, apparatuses, systems and methods are provided for determining distracted drivers associated with vehicle driving routes based on postures of vehicle occupants. ## Claims ### Claim 1 A device for determining distracted drivers associated with vehicle driving routes, the device comprising: a vehicle interior data receiving module stored on a memory that, when executed by one or more processors, causes the one or more processors to receive vehicle interior data from at least one vehicle interior sensor, wherein the vehicle interior data is representative of at least one distraction of at least one vehicle occupant that is associated with a position of a body of the at least one vehicle occupant; a vehicle location data receiving module stored on the memory that, when executed by the one or more processors, causes the one or more processors to receive vehicle location data from at least one vehicle location sensor; a vehicle driving route distraction data generation module stored on the memory that, when executed by the one or more processors, causes the one or more processors to generate vehicle driving route distraction data based on the vehicle interior data and the vehicle location data; and a vehicle driving route distraction data transmission module stored on the memory that, when executed by the one or more processors, causes the one or more processors to transmit the vehicle driving route distraction data to a remote computing device. ### Claim 2 (depends on 1) The device as in claim 1, wherein the position of the body of the at least one vehicle occupant includes at least one of: (i) a body posture of at least one vehicle occupant, (ii) a facial expression of at least one vehicle occupant, or (iii) a position of a body part of at least one vehicle occupant. ### Claim 3 (depends on 1) The device as in claim 1, wherein 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. ### Claim 4 (depends on 1) The device as in claim 1, wherein the vehicle interior data is representative of a three-dimensional representation of at least one vehicle occupant. ### Claim 5 (depends on 1) 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 one or more processors, causes the one or more processors 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 one or more processors, causes the one or more processors 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. ### Claim 6 (depends on 1) The device as in claim 1, further comprising a current image data receiving module stored on the memory that, when executed by the one or more processors, causes the one or more processors to receive current image data, 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. ### Claim 7 (depends on 1) The device as in claim 1, further comprising a previously classified image data receiving module stored on the memory that, when executed by the one or more processors, causes the one or more processors to receive previously classified image data, 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. ### Claim 8 A computer-implemented method for determining distracted drivers associated with vehicle driving routes, the method comprising: receiving, at one or more processors of a computing device, vehicle interior data from at least one vehicle interior sensor, wherein the vehicle interior data is representative of at least one distraction of at least one vehicle occupant that is associated with a position of a body of the at least one vehicle occupant; receiving, at the one or more processors of the computing device, vehicle location data from at least one vehicle location sensor; generating, using the one or more processors of the computing device, vehicle driving route distraction data based on the vehicle interior data and the vehicle location data; and transmitting, using the one or more processors of the computing device, the vehicle driving route distraction data to a remote computing device. ### Claim 9 (depends on 8) The method as in claim 8, wherein the position of the body of the at least one vehicle occupant includes at least one of: (i) a body posture of at least one vehicle occupant, (ii) a facial expression of at least one vehicle occupant, or (iii) a position of a body part of at least one vehicle occupant. ### Claim 10 (depends on 8) The method as in claim 8, wherein the vehicle interior data is representative of a three-dimensional representation of at least one vehicle occupant. ### Claim 11 (depends on 8) The method as in claim 8, further comprising: receiving, at the one or more processors 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 one or more processors 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. ### Claim 12 (depends on 8) The method as in claim 8, further comprising receiving, at the one or more processors of the computing device, current image data, 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. ### Claim 13 (depends on 8) The method as in claim 8, further comprising receiving, at the one or more processors of the computing device, previously classified image data, 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. ### Claim 14 A computer-implemented method for determining distracted drivers associated with vehicle driving routes, the method comprising: receiving, at one or more processors of a computing device, vehicle interior data for each of a plurality of vehicles from at least one vehicle interior sensor, wherein the vehicle interior data is representative of distraction of a plurality of vehicle drivers; receiving, at the one or more processors of the computing device, vehicle location data for each of the plurality of vehicles from at least one vehicle location sensor, wherein the vehicle location data is representative of a vehicle location for each of the plurality of veihcles; generating, using the one or more processors of the computing device, vehicle driving route distraction data based on the vehicle interior data for each of the plurality of vehicles and the vehicle location data for each of the plurality of vehicles, wherein the vehicle driving route distraction data is representative of a number of distracted drivers along at least one vehicle driving route; and transmitting, using the one or more processors of the computing device, the vehicle driving route distraction data to a remote computing device. ### Claim 15 (depends on 14) The method as in claim 14, wherein 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. ### Claim 16 (depends on 14) The method as in claim 14, wherein at least one 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. ### Claim 17 (depends on 14) The method as in claim 14, wherein the vehicle driving route distraction data is representative of a level of distraction based on the number of distracted drivers along the at least one vehicle driving route. ### Claim 18 (depends on 14) The method as in claim 14, further comprising: receiving, at the one or more processors of the computing device, previously classified vehicle interior data for each of the plurality of vehicles, wherein the previously classified vehicle interior data is representative of circumstances associated with vehicle occupant distractions; and generating, using the one or more processors of the computing device, vehicle occupant distraction data based on a comparison of the vehicle interior data for each of the plurality of vehicles with the previously classified vehicle interior data for each of the plurality of vehicles, and wherein the vehicle driving route distraction data is further based on the vehicle occupant distraction data. ### Claim 19 (depends on 14) The method as in claim 14, further comprising receiving, at the one or more processors of the computing device, current image data for each of the plurality of vehicles, 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. ### Claim 20 (depends on 14) The method as in claim 14, further comprising receiving, at the one or more processors of the computing device, previously classified image data for each of the plurality of vehicles, 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.