Patent Issued for Context-Based Grading (USPTO 10,633,002)
2020 MAY 12 (NewsRx) -- By a
The assignee for this patent, patent number 10,633,002, is
Reporters obtained the following quote from the background information supplied by the inventors: βThe collection and analysis of driving data, such as the identification of driving behaviors and traffic accidents, has many applications. For example, insurance companies and financial institutions may offer rate discounts or other financial incentives to customers based on safe driving behaviors and accident-free driving records. Law enforcement or government personnel may collect and analyze driving data and traffic accident statistics to identify dangerous driving roads or times, and to detect moving violations and other unsafe driving behaviors. In other cases, driving data may be used for navigation applications, vehicle tracking and monitoring applications, and on-board vehicle maintenance applications, among others.
βVehicle-based computer systems, such as on-board diagnostics (OBD) systems and telematics devices, may be used in automobiles and other vehicles, and may be capable of collecting various driving data and vehicle sensor data. For example, OBD systems may receive information from the vehicleβs on-board computers and sensors in order to monitor a wide variety of information relating to the vehicle systems, such as engine RPM, emissions control, vehicle speed, throttle position, acceleration and braking rates, use of driver controls, etc. Vehicles may also include Global Positioning System (GPS) receivers and devices installed within or operating at the vehicle configured to collect vehicle location and time data. Such vehicle-based systems may be capable of collecting driving data which may be used to perform various driving data analyses such as statistical driving evaluations, driver score calculations, etc. Vehicle-based systems also may be configured to detect the occurrence of traffic accidents, for instance, using vehicle body impact sensors and airbag deployment sensors. However, not all vehicles are equipped with systems capable of collecting, analyzing, and communicating driving data. Moreover, a single vehicle may be used by multiple different drivers, and conversely, a single driver may drive multiple different vehicles. Thus, vehicle driving data and/or accident records collected by vehicle-based systems might not include the vehicle occupants that correspond to the collected driving and accident data.
βIn contrast to vehicle-based systems, mobile devices such as smartphones, personal digital assistants, tablet computers, and the like, are often carried and/or operated by a single user. Some mobile devices may include movement sensors, such as an accelerometer, gyroscope, speedometer, and/or GPS receivers, capable of detecting movement.β
In addition to obtaining background information on this patent, NewsRx editors also obtained the inventorβs summary information for this patent: βThe following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosure. The summary is not an extensive overview of the disclosure. It is neither intended to identify key or critical elements of the disclosure nor to delineate the scope of the disclosure. The following summary merely presents some concepts of the disclosure in a simplified form as a prelude to the description below.
βAspects of the disclosure relate to methods, computer-readable media, and apparatuses for receiving telematics data (e.g., vehicle operation data, driver data, environmental data, etc.) associated with one or more vehicles from one or more mobile devices respectively disposed within the one or more vehicles. Telematics data may include, for example, a geographic location of the vehicle, a route being traversed by the vehicle, driving events or maneuvers (e.g., braking, turning, accelerating) performed by the vehicle, corresponding timestamps or timeframes, etc. In some instances, the telematics data may include sensor data from various sensors operationally coupled to the vehicle and configured to sense the immediate surroundings of the vehicle. A computing device may group the vehicles based on their telematics data. For instance, vehicles may be grouped based on route traversed, geographic proximity with one another, etc. The computing device may also use telematics data to determine driving patterns representative of driving behaviors of each vehicle as well as group driving patterns representative of the normalized driving behavior for a group of vehicles. One or more outlier vehicles for each group may be identified based on the outlier vehiclesβ driving pattern being different from its groupβs group driving pattern beyond a dissimilarity metric threshold (or a similarity metric threshold). A driver score of the outlier vehicle may be positively or negatively adjusted based on whether the outlier was safer or less safe than its group. A measure of safety for a vehicle may be a number of unsafe events (e.g., hard-braking, swerving, excessive speeding, etc.) determined from a vehicleβs telematics data for a particular time period. A measure of safety for a group of vehicles may be an average number of unsafe events determined from the telematics data of the vehicles in the group for the particular time period.
βIn accordance with further aspects of the present disclosure, contextual data (e.g., extenuating circumstances) may be considered in determining a driver score. For instance, a high-risk or unsafe driving event performed by a vehicle and identified from the vehicleβs telematics data may generally result in a negative adjustment to the driverβs score. However, if the driver performed the high-risk or unsafe driving event to avoid an accident with another vehicle that is behaving badly (e.g., performing a high-risk or unsafe driving event), the driverβs score might not be negatively adjusted. Rather, the driver score may be maintained or positively adjusted. As an example, a driver who performs a hard-braking event as a result of going too fast on a curve may result in a negative adjustment to the driverβs score. However, a driver who performs a hard-braking event to avoid a collision with another vehicle that is swerving and cutting off the driverβs vehicle might not result in a negative adjustment to the driverβs score. In some instances, such a driver may receive a positive adjustment to his or her driving score.
βIn accordance with further aspects of the present disclosure, a computing device may determine whether a vehicle that was not involved in a collision or accident determination but nonetheless caused the collision or accident.
βOther features and advantages of the disclosure will be apparent from the additional description provided herein.β
The claims supplied by the inventors are:
βThe invention claimed is:
β1. A driving analysis server comprising: a processing unit comprising a processor; and a memory unit storing computer-readable instructions that, when executed by the processing unit, cause the driving analysis server to: receive telematics data associated with a plurality of vehicles; determine a group of vehicles, wherein a geographic location of each vehicle in the group of vehicles is within a first distance from each other vehicle in the group of vehicles; determine a driving pattern for a first vehicle in the group of vehicles, wherein the driving pattern for the first vehicle is a second vector comprising a plurality of elements representative of telematics data of the first vehicle; determine a normalized group driving pattern representative of driving behavior of the group of vehicles, wherein the normalized group driving pattern of the group of vehicles is a first vector comprising a plurality of elements, wherein each element of the first vector is an average of values of each vector associated with each vehicle of the group of vehicles; identify that the first vector is different than the second vector; compare, based on the identification, a number of one or more unsafe driving events performed by the first vehicle to an average number of unsafe driving events performed by the group of vehicles; adjust, based on the comparison, a driver score of the first vehicle, wherein the driver score is an indication of the driving pattern of the first vehicle compared to the normalized group driving pattern representative of driving behavior of the group of vehicles; and output the driver score for display to a graphical user interface to a driver of the first vehicle.
β2. The driving analysis server of claim 1, wherein adjusting the driver score of the first vehicle comprises positively adjusting the driver score of the first vehicle.
β3. The driving analysis server of claim 1, wherein the computer-readable instructions, when executed by the processing unit, further cause the driving analysis server to: in response to determining that the number of one or more unsafe driving events performed by the first vehicle is greater than the average number of unsafe driving events performed by the group of vehicles, negatively adjust the driver score of the first vehicle.
β4. The driving analysis server of claim 1, wherein the computer-readable instructions, when executed by the processing unit, further cause the driving analysis server to: in response to determining that a second vehicle of the group of vehicles caused the first vehicle to perform at least one unsafe driving event of the one or more unsafe driving events, ignore, in a calculation of the driver score for the first vehicle, the at least one unsafe driving event.
β5. The driving analysis server of claim 1, wherein the computer-readable instructions, when executed by the processing unit, further cause the driving analysis server to: in response to determining that a second vehicle of the group of vehicles caused the first vehicle to perform at least one unsafe driving event of the one or more unsafe driving events, positively account for the at least one unsafe driving event in a calculation of the driver score for the first vehicle.
β6. The driving analysis server of claim 5, wherein the computer-readable instructions, when executed by the processing unit, further cause the driving analysis server to: determine that the second vehicle caused the first vehicle to perform the unsafe driving event based, at least in part, on the second vehicle being within a second distance of the first vehicle and performing another unsafe driving event within a time frame prior to a time at which the first vehicle performed the at least one unsafe driving event.
β7. The driving analysis server of claim 1, wherein an unsafe driving event is one of a hard-braking event, a swerving event, an excessive-speed event, or a hard-acceleration event.
β8. The driving analysis server of claim 1, wherein each vehicle in the group of vehicles traversed the same driving route on a same date.
β9. The driving analysis server of claim 1, wherein the telematics data is received from a plurality of mobile devices respectively disposed within the plurality of vehicles.
β10. A method comprising: receiving, by a computing device, telematics data associated with a plurality of vehicles; determining a group of vehicles, wherein a geographic location of each vehicle in the group of vehicles is within a first distance from each other vehicle in the group of vehicles; determining a driving pattern for a first vehicle in the group of vehicles, wherein the driving pattern for the first vehicle is a second vector comprising a plurality of elements representative of telematics data of the first vehicle; determining a normalized group driving pattern representative of driving behavior of the group of vehicles, wherein the normalized group driving pattern of the group of vehicles is a first vector comprising a plurality of elements, wherein each element of the first vector is an average of values of each vector associated with each vehicle of the group of vehicles; identifying that the first vector is different than the second vector; comparing, based on the identifying, a number of one or more unsafe driving events performed by the first vehicle to an average number of unsafe driving events performed by the group of vehicles; and adjusting, based on the comparison, a driver score of the first vehicle, wherein the driver score is an indication of the driving pattern of the first vehicle compared to the normalized group driving pattern representative of driving behavior of the group of vehicles; and outputting the driver score for display to a graphical user interface to a driver of the first vehicle.
β11. The method of claim 10, further comprising: in response to determining that the number of unsafe driving events performed by the first vehicle is greater than the average number of unsafe driving events performed by the group of vehicles, negatively adjust the driver score of the first vehicle.
β12. The method of claim 10, wherein adjusting the driver score comprises positively adjusting the driver score of the first vehicle.
β13. The method of claim 10, further comprising: in response to determining that a second vehicle of the group of vehicles caused the first vehicle to perform at least one unsafe driving event of the one or more unsafe driving events, ignore, in a calculation of the driver score for the first vehicle, the at least one unsafe driving event.
β14. The method of claim 10, further comprising: in response to determining that a second vehicle of the group of vehicles caused the first vehicle to perform at least one unsafe driving event of the one or more unsafe driving events, positively account for the at least one unsafe driving event in a calculation of the driver score for the first vehicle.
β15. A non-transitory computer-readable medium storing instructions that, when executed by a computing device, cause the computing device to: receive telematics data associated with a plurality of vehicles; determine a group of vehicles, wherein a geographic location of each vehicle in the group of vehicles is within a first distance from each other vehicle in the group of vehicles; determine a driving pattern for a first vehicle in the group of vehicles, wherein the driving pattern for the first vehicle is a second vector comprising a plurality of elements representative of telematics data of the first vehicle; determine a normalized group driving pattern representative of driving behavior of the group of vehicles, wherein the normalized group driving pattern of the group of vehicles is a first vector comprising a plurality of elements, wherein each element of the first vector is an average of values of each vector associated with each vehicle of the group of vehicles; determine that the first vector is different than the second vector; identify a number of one or more unsafe driving events performed by the first vehicle to an average number of unsafe driving events performed by the group of vehicles; adjust, based on the identification, a driver score of the first vehicle, wherein the driver score is an indication of the driving pattern of the first vehicle compared to the normalized group driving pattern representative of driving behavior of the group of vehicles; and output the driver score for display to a graphical user interface to a driver of the first vehicle.
β16. The non-transitory computer-readable medium of claim 15, further storing instructions that, when executed by the computing device, cause the computing device to: in response to determining that the number of unsafe driving events performed by the first vehicle is greater than the average number of unsafe driving events performed by the group of vehicles, negatively adjust the driver score of the first vehicle.
β17. The non-transitory computer-readable medium of claim 15, wherein adjusting the driver score comprises positively adjusting the driver score of the first vehicle.
β18. The non-transitory computer-readable medium of claim 15, further storing instructions that, when executed by the computing device, cause the computing device to: in response to determining that a second vehicle of the group of vehicles caused the first vehicle to perform at least one unsafe driving event of the one or more unsafe driving events, ignore, in a calculation of the driver score for the first vehicle, the at least one unsafe driving event.
β19. The non-transitory computer-readable medium of claim 18, further storing instructions that, when executed by the computing device, cause the computing device to: in response to determining that a second vehicle of the group of vehicles caused the first vehicle to perform at least one unsafe driving event of the one or more unsafe driving events, positively account for the at least one unsafe driving event in a calculation of the driver score for the first vehicle.
β20. The non-transitory computer-readable medium of claim 15, wherein an unsafe driving event is one of a hard-braking event, a swerving event, an excessive-speed event, or a hard-acceleration event.β
For more information, see this patent: Ferguson,
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