U.S. patent application number 17/471884 was filed with the patent office on 2021-12-30 for methods of determining accident cause and/or fault using telematics data.
This patent application is currently assigned to STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY. The applicant listed for this patent is STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY. Invention is credited to Megan Michal Baumann, Nathan W. Baumann, Atlanta Bonnom, Dustin Ryan Carter, Mark E. Clauss, Craig Cope, Douglas Albert Graff, Jennifer Luella Lawyer, Thomas Michael Potter, Curtis Simpson.
Application Number | 20210407015 17/471884 |
Document ID | / |
Family ID | 1000005830314 |
Filed Date | 2021-12-30 |
United States Patent
Application |
20210407015 |
Kind Code |
A1 |
Potter; Thomas Michael ; et
al. |
December 30, 2021 |
METHODS OF DETERMINING ACCIDENT CAUSE AND/OR FAULT USING TELEMATICS
DATA
Abstract
In systems and methods for facilitating fault determination,
accident data associated with a vehicle accident involving a driver
may be collected. The accident data may include vehicle telematics
and/or other data, and/or the driver may be associated with an
insurance policy. The accident data may analyzed and, based upon
the analysis of the accident data, fault (or lack thereof) of the
driver, other drivers, road or weather conditions, construction, or
other external factors or environment conditions for the vehicle
accident may be determined. The determined fault may be used to
handle an insurance claim associated with the vehicle accident, and
to adjust, generate or update one or more insurance-related items,
including (i) parameters of the insurance policy; (ii) a premium;
(iii) a rate; (iv) a discount; or (v) a reward. As such, drivers
not at a fault for an accident may realize insurance-cost savings
and/or may not be unfairly penalized.
Inventors: |
Potter; Thomas Michael;
(Normal, IL) ; Clauss; Mark E.; (Bloomington,
IL) ; Carter; Dustin Ryan; (Normal, IL) ;
Graff; Douglas Albert; (Mountain View, MT) ; Baumann;
Megan Michal; (Bloomington, IL) ; Bonnom;
Atlanta; (Bloomington, IL) ; Cope; Craig;
(Bloomington, IL) ; Lawyer; Jennifer Luella;
(Bloomington, IL) ; Simpson; Curtis; (Bloomington,
IL) ; Baumann; Nathan W.; (Bloomington, IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY |
BLOOMINGTON |
IL |
US |
|
|
Assignee: |
STATE FARM MUTUAL AUTOMOBILE
INSURANCE COMPANY
BLOOMINGTON
IL
|
Family ID: |
1000005830314 |
Appl. No.: |
17/471884 |
Filed: |
September 10, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14798741 |
Jul 14, 2015 |
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17471884 |
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62145022 |
Apr 9, 2015 |
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62145234 |
Apr 9, 2015 |
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62145027 |
Apr 9, 2015 |
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62145228 |
Apr 9, 2015 |
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62145029 |
Apr 9, 2015 |
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62145232 |
Apr 9, 2015 |
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62145032 |
Apr 9, 2015 |
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62145033 |
Apr 9, 2015 |
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62145024 |
Apr 9, 2015 |
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62145028 |
Apr 9, 2015 |
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62145145 |
Apr 9, 2015 |
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62040735 |
Aug 22, 2014 |
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62027021 |
Jul 21, 2014 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
B60W 40/08 20130101;
G06Q 40/08 20130101 |
International
Class: |
G06Q 40/08 20060101
G06Q040/08; B60W 40/08 20060101 B60W040/08 |
Claims
1.-20. (canceled)
21. A method implemented by a system comprising one or more
processors and one or more memories, the method comprising:
collecting, by one or more sensors, accident data associated with a
vehicle accident involving a driver of a vehicle, the accident data
including driver acuity data and vehicle telematics data before,
during, and after the vehicle accident, the driver acuity data
comprising at least one selected from a group consisting of phone
usage data, audio data, image data, and video data, the vehicle
telematics data comprising data indicating movement of the vehicle;
time-stamping at least a part of the driver acuity data by
associating the driver acuity data with time information;
determining a time of the vehicle accident; selecting the driver
acuity data during the vehicle accident based on the time
information associated with the driver acuity data and the time of
the vehicle accident; analyzing, by the one or more processors, the
selected driver acuity data to determine a driver acuity of the
driver during the vehicle accident; analyzing, by the one or more
processors, the vehicle telematics data to determine a sequence of
movements of the vehicle preceding and during the vehicle accident;
and determining, by the one or more processors, fault of the driver
for the vehicle accident based upon the determined sequence of
movements of the vehicle preceding and during the accident and the
determined driver acuity of the driver during the vehicle
accident.
22. The method of claim 21, wherein the vehicle telematics data
further comprises data indicating at least one selected from a
group consisting of operation status of the vehicle, braking, a
brake light status, turning, a turn signal status, and air bag
status.
23. The method of claim 22, further comprising: analyzing, by the
one or more processors, the accident data to determine driver
behavior of the driver before, during or after the vehicle
accident.
24. The method of claim 23, further comprising: analyzing the
accident data to determine driver behavior of another driver
involved in the vehicle accident before, during or after the
vehicle accident.
25. The method of claim 21, wherein the accident data further
comprises environmental condition data, the environmental condition
data including data indicating at least one selected from a group
consisting of a road condition, a weather condition, and a traffic
condition.
26. The method of claim 25, further comprising: analyzing, by the
one or more processors, the environmental condition data to
determine a condition that is associated with a location of the
vehicle accident before, during or after the vehicle accident, the
conditions including at least one selected from a group consisting
of road conditions, weather conditions, traffic conditions, and
construction conditions.
27. The method of claim 25, further comprising: analyzing, by the
one or more processors, the environmental condition data and data
associated with other vehicle accidents that occurred at the
location of the vehicle accident to determine the fault of the
driver, wherein the fault of the driver includes a percentage
representing the fault of the driver.
28. The method of claim 21, wherein collecting accident data
further includes collecting data generated by the vehicle or a
computer system of the vehicle.
29. The method of claim 28, wherein the accident data further at
least one selected from a group consisting of data associated with
a vehicle other than the insured vehicle, data received via
vehicle-to-vehicle (V2V) communication, and data collected from
roadside equipment or infrastructure located near a location of the
vehicle accident.
30. The method of claim 21, wherein the one or more sensors include
one or more sensors mounted on the vehicle.
31. A system comprising: one or more processors; one or more
sensors configured to collect accident data associated with a
vehicle accident involving a driver of a vehicle, the accident data
including driver acuity data and vehicle telematics data before,
during, and after the vehicle accident, the driver acuity data
comprising at least one selected from a group consisting of phone
usage data, audio data, image data, and video data, the vehicle
telematics data comprising data indicating movement of the vehicle;
and one or more memories storing instructions that, when executed
by the one or more processors, cause the one or more processors to
perform operations comprising: time-stamping at least a part of the
driver acuity data by associating the driver acuity data with time
information; determining a time of the vehicle accident; selecting
the driver acuity data during the vehicle accident based on the
time information associated with the driver acuity data and the
time of the vehicle accident; analyzing the selected driver acuity
data to determine a driver acuity of the driver during the vehicle
accident; analyzing the vehicle telematics data to determine a
sequence of movements of the vehicle preceding and during the
vehicle accident; and determining fault of the driver for the
vehicle accident based upon the determined sequence of movements of
the vehicle preceding and during the accident and the determined
driver acuity of the driver during the vehicle accident.
32. The system of claim 31, wherein the vehicle telematics data
further comprises data indicating at least one selected from a
group consisting of operation status of the vehicle, braking, a
brake light status, turning, a turn signal status, and air bag
status.
33. The system of claim 32, wherein the operations further
comprise: analyzing the accident data to determine driver behavior
of the driver before, during or after the vehicle accident.
34. The system of claim 33, wherein the operations further
comprise: analyzing the accident data to determine driver behavior
of another driver involved in the vehicle accident before, during
or after the vehicle accident.
35. The system of claim 31, wherein the accident data further
comprises environmental condition data, the environmental condition
data including data indicating at least one selected from a group
consisting of a road condition, a weather condition, and a traffic
condition.
36. The system of claim 35, wherein the operations further
comprise: analyzing the environmental condition data to determine a
condition that is associated with a location of the vehicle
accident before, during or after the vehicle accident, the
conditions including at least one selected from a group consisting
of road conditions, weather conditions, traffic conditions, and
construction conditions.
37. The system of claim 35, wherein the operations further
comprise: analyzing the environmental condition data and data
associated with other vehicle accidents that occurred at the
location of the vehicle accident to determine the fault of the
driver, wherein the fault of the driver includes a percentage
representing the fault of the driver.
38. A method implemented by a system comprising one or more
processors and one or more memories, the method comprising:
collecting, by one or more sensors, accident data associated with a
vehicle accident involving a driver of a vehicle, the accident data
including driver acuity data and vehicle telematics data before,
during, and after the vehicle accident, the driver acuity data
comprising at least one selected from a group consisting of phone
usage data, audio data, image data, and video data, the vehicle
telematics data comprising data indicating movement of the vehicle;
time-stamping at least a part of the driver acuity data by
associating the driver acuity data with time information;
determining a time of the vehicle accident; selecting the driver
acuity data during the vehicle accident based on the time
information associated with the driver acuity data and the time of
the vehicle accident; analyzing, by the one or more processors, the
selected driver acuity data to determine a driver acuity of the
driver during the vehicle accident; analyzing, by the one or more
processors, the vehicle telematics data to determine a sequence of
movements of the vehicle preceding and during the vehicle accident;
and determining, by the one or more processors, one or more causes
of the vehicle accident based upon the determined sequence of
movements of the vehicle preceding and during the accident and the
determined driver acuity of the driver during the vehicle accident,
at least one of the one or more causes being attributed to the
driver.
39. The method of claim 38, wherein the vehicle telematics data
further comprises data indicating at least one selected from a
group consisting of operation status of the vehicle, braking, a
brake light status, turning, a turn signal status, and air bag
status.
40. The method of claim 39, further comprising: analyzing, by the
one or more processors, the accident data to determine driver
behavior of the driver before, during or after the vehicle
accident.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This claims the benefit of U.S. Provisional Application No.
62/027,021 (filed Jul. 21, 2014); U.S. Provisional Application No.
62/040,735 (filed Aug. 22, 2014); U.S. Provisional Application No.
62/145,022 (filed Apr. 9, 2015); U.S. Provisional Application No.
62/145,024 (filed Apr. 9, 2015); U.S. Provisional Application No.
62/145,027 (filed Apr. 9, 2015); U.S. Provisional Application No.
62/145,028 (filed Apr. 9, 2015); U.S. Provisional Application No.
62/145,029 (filed Apr. 9, 2015); U.S. Provisional Application No.
62/145,145 (filed Apr. 9, 2015); U.S. Provisional Application No.
62/145,228 (filed Apr. 9, 2015); U.S. Provisional Application No.
62/145,232 (filed Apr. 9, 2015); U.S. Provisional Application No.
62/145,234 (filed Apr. 9, 2015); U.S. Provisional Application No.
62/145,032 (filed Apr. 9, 2015); and U.S. Provisional Application
No. 62/145,033 (filed Apr. 9, 2015). The entirety of each of the
foregoing provisional applications is incorporated by reference
herein.
FIELD
[0002] The present embodiments relate generally to telematics data
and/or insurance policies. More particularly, the present
embodiments relate to performing certain actions, and/or adjusting
insurance policies, based upon telematics and/or other data
indicative of the behavior of an insured and/or others.
BACKGROUND
[0003] During the claims process, insurance providers typically
rely heavily on eyewitness accounts to determine how an accident
occurred (e.g., to determine the cause and the individual(s) at
fault). For example, an employee of the insurance provider may
learn about the sequence of events leading to an accident by
talking to the insured and/or other participants in the accident.
As another example, the insurance provider employee may review a
police report that, by its nature, typically reflects information
garnered by another eyewitness (police officer) observing the
accident scene well after the accident occurred, if at all. As a
result, the insurance provider may obtain inaccurate information,
which may in turn cause the insurance provider to incorrectly
determine cause/fault, and/or fail to appropriately reflect that
cause/fault in future actions (e.g., when adjusting premium levels
for an insured involved in the accident, etc.).
[0004] The present embodiments may overcome these and/or other
deficiencies.
BRIEF SUMMARY
[0005] The present embodiments disclose systems and methods that
may relate to the intersection of telematics and insurance. In some
embodiments, for example, telematics and/or other data may be
collected and used to determine cause and/or fault of a vehicle
accident. The data may be gathered from one or more sources, such
as mobile devices (e.g., smart phones, smart glasses, smart
watches, smart wearable devices, smart contact lenses, and/or other
devices capable of wireless communication); smart vehicles; smart
vehicle or smart home mounted sensors; third party sensors or
sources of data (e.g., other vehicles, public transportation
systems, government entities, and/or the Internet); and/or other
sources of information. The cause and/or fault may be used to
handle an insurance claim, for example. More generally, insurance
claims, policies, premiums, rates, discounts, rewards, programs,
and/or other insurance-related items may be adjusted, generated
and/or updated based upon the cause and/or fault as determined from
the telematics and/or other collected data.
[0006] In one aspect, a computer-implemented method for
facilitating fault determination may comprise (1) collecting, by
one or more remote servers associated with an insurance provider,
accident data associated with a vehicle accident involving a
driver. The accident data may include vehicle telematics data,
and/or the driver may be associated with an insurance policy issued
by the insurance provider. The method may also include (2)
analyzing, by the one or more remote servers, the accident data;
(3) determining, by the one or more remote servers and based upon
the analysis of the accident data, fault of the driver for the
vehicle accident; (4) using the determined fault of the driver to
handle, at the one or more remote servers, an insurance claim
associated with the vehicle accident; and/or (5) using the
determined fault of the driver to adjust, generate or update, at
the one or more remote servers, one or more insurance-related
items. The one or more insurance-related items may include one or
more of (i) parameters of the insurance policy; (ii) a premium;
(iii) a rate; (iv) a discount; and/or (v) a reward. The method may
include additional, less, or alternate actions, including those
discussed elsewhere herein.
[0007] In another aspect, a system for facilitating fault
determination may comprise one or more processors and one or more
memories. The one or more memories may store instructions that,
when executed by the one or more processors, cause the one or more
processors to (1) collect accident data associated with a vehicle
accident involving a driver. The accident data may include vehicle
telematics data, and/or the driver may be associated with an
insurance policy issued by an insurance provider. The instructions
may also cause the one or more processors to (2) analyze the
accident data; (3) determine, based upon the analysis of the
accident data, fault of the driver for the vehicle accident; (4)
use the determined fault of the driver to handle an insurance claim
associated with the vehicle accident; and/or (5) use the determined
fault of the driver to adjust, generate or update one or more
insurance-related items. The one or more insurance-related items
may include one or more of (i) parameters of the insurance policy;
(ii) a premium; (iii) a rate; (iv) a discount; or (v) a reward. The
system may include additional, less, or alternate functionality,
including that discussed elsewhere herein.
[0008] In yet another aspect, a computer-implemented method for
facilitating accident cause determination may comprise (1)
collecting, by one or more remote servers associated with an
insurance provider, accident data associated with a vehicle
accident involving a driver. The accident data may include vehicle
telematics data, and/or the driver may be associated with an
insurance policy issued by the insurance provider. The method may
also include (2) analyzing, by the one or more remote servers, the
accident data; and/or (3) determining, by the one or more remote
servers and based upon the analysis of the accident data, one or
more causes of the vehicle accident. At least one of the one or
more causes may be assigned or attributed to the driver or an
external factor. The method may further include (4) using the one
or more causes of the vehicle accident to handle, at the one or
more remote servers, an insurance claim associated with the vehicle
accident; and/or (5) using the one or more causes of the vehicle
accident to adjust, generate or update, at the one or more remote
servers, one or more insurance-related items. The one or more
insurance-related items may include one or more of (i) parameters
of the insurance policy; (ii) a premium; (iii) a rate; (iv) a
discount; or (v) a reward. The method may include additional, less,
or alternate actions, including those discussed elsewhere
herein.
[0009] Advantages will become more apparent to those skilled in the
art from the following description of the preferred embodiments
which have been shown and described by way of illustration. As will
be realized, the present embodiments may be capable of other and
different embodiments, and their details are capable of
modification in various respects. Accordingly, the drawings and
description are to be regarded as illustrative in nature and not as
restrictive.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] There are shown in the drawings arrangements which are
presently discussed. It is understood, however, that the present
embodiments are not limited to the precise arrangements and
instrumentalities shown.
[0011] FIG. 1 illustrates an exemplary computer system on which the
techniques described herein may be implemented, according to one
embodiment.
[0012] FIG. 2 illustrates an exemplary mobile device or smart
vehicle controller that may collect, receive, generate and/or send
telematics and/or other data for purposes of the techniques
described herein, according to one embodiment.
[0013] FIG. 3 illustrates an exemplary computer-implemented method
of cause and/or fault determination for an insured event, according
to one embodiment.
[0014] FIG. 4 illustrates an exemplary computer-implemented method
of accident scene reconstruction for an insured event, according to
one embodiment.
[0015] FIG. 5 illustrates an exemplary computer-implemented method
of overstated claim or buildup identification, according to one
embodiment.
DETAILED DESCRIPTION
[0016] The present embodiments may relate to, inter alia,
collecting data, including telematics and/or other data, and
analyzing the data (e.g., by an insurance provider server or
processor) to provide insurance-related benefits to insured
individuals, and/or to apply the insurance-related benefits to
insurance policies or premiums of insured individuals. The
insurance-related benefits may include accurate accident or
accident scene reconstructions, and/or more accurate determination
of the causes of, and/or fault for, accidents, which may give rise
to improved claim handling, more accurate/fair adjustments to
insurance policies and/or premiums, and/or other advantages. As
another example, the insurance-related benefits may include
identifying misstated or inaccurate claims, which may lower
individual premiums on the whole for those within a collective
group or pool of insurance customers, for example.
I. Exemplary Telematics Data System
[0017] FIG. 1 illustrates a block diagram of an exemplary
telematics system 1 on which the exemplary methods described herein
may be implemented. The high-level architecture includes both
hardware and software applications, as well as various data
communications channels for communicating data between the various
hardware and software components. The telematics system 1 may be
roughly divided into front-end components 2 and back-end components
4.
[0018] The front-end components 2 may obtain information regarding
a vehicle 8 (e.g., a car, truck, motorcycle, etc.) and/or the
surrounding environment. Information regarding the surrounding
environment may be obtained by one or more other vehicles 6, public
transportation system components 22 (e.g., a train, a bus, a
trolley, a ferry, etc.), infrastructure components 26 (e.g., a
bridge, a stoplight, a tunnel, a rail crossing, etc.), smart homes
28 having smart home controllers 29, and/or other components
communicatively connected to a network 30. Information regarding
the vehicle 8 may be obtained by a mobile device 10 (e.g., a smart
phone, a tablet computer, a special purpose computing device, etc.)
and/or a smart vehicle controller 14 (e.g., an on-board computer, a
vehicle diagnostic system, a vehicle control system or sub-system,
etc.), which may be communicatively connected to each other and/or
the network 30.
[0019] In some embodiments, telematics data may be generated by
and/or received from sensors 20 associated with the vehicle 8. Such
telematics data from the sensors 20 may be received by the mobile
device 10 and/or the smart vehicle controller 14, in some
embodiments. Other, external sensors 24 (e.g., sensors associated
with one or more other vehicles 6, public transportation system
components 22, infrastructure components 26, and/or smart homes 28)
may provide further data regarding the vehicle 8 and/or its
environment, in some embodiments. For example, the external sensors
24 may obtain information pertaining to other transportation
components or systems within the environment of the vehicle 8,
and/or information pertaining to other aspect so of that
environment. The sensors 20 and the external sensors 24 are
described further below, according to some embodiments.
[0020] In some embodiments, the mobile device 10 and/or the smart
vehicle controller 14 may process the sensor data from sensors 20,
and/or other of the front-end components 2 may process the sensor
data from external sensors 24. The processed data (and/or
information derived therefrom) may then be communicated to the
back-end components 4 via the network 30. In other embodiments, the
front-end components 2 may communicate the raw sensor data from
sensors 20 and/or external sensors 24, and/or other telematics
data, to the back-end components 4 for processing. In thin-client
embodiments, for example, the mobile device 10 and/or the smart
vehicle controller 14 may act as a pass-through communication node
for communication with the back-end components 4, with minimal or
no processing performed by the mobile device 10 and/or the smart
vehicle controller 14. In other embodiments, the mobile device 10
and/or the smart vehicle controller 14 may perform substantial
processing of received sensor, telematics, or other data. Summary
information, processed data, and/or unprocessed data may be
communicated to the back-end components 4 via the network 30.
[0021] The mobile device 10 may be a general-use personal computer,
cellular phone, smart phone, tablet computer, or a dedicated
vehicle use monitoring device. In some embodiments, the mobile
device 10 may include a wearable device such as a smart watch,
smart glasses, wearable smart technology, or a pager. Although only
one mobile device 10 is illustrated, it should be understood that a
plurality of mobile devices may be used in some embodiments. The
smart vehicle controller 14 may be a general-use on-board computer
capable of performing many functions relating to vehicle operation,
an on-board computer system or sub-system, or a dedicated computer
for monitoring vehicle operation and/or generating telematics data.
Further, the smart vehicle controller 14 may be installed by the
manufacturer of the vehicle 8 or as an aftermarket modification or
addition to the vehicle 8. Either or both of the mobile device 10
and the smart vehicle controller 14 may communicate with the
network 30 over link 12 and link 18, respectively. Additionally,
the mobile device 10 and smart vehicle controller 14 may
communicate with one another directly over link 16. In some
embodiments, the mobile device 10 and/or the smart vehicle
controller 14 may communicate with other of the front-end
components 2, such as the vehicles 6, public transit system
components 22, infrastructure components 26, and/or smart homes 28,
either directly or indirectly (e.g., via the network 30).
[0022] The one or more sensors 20 referenced above may be removably
or fixedly disposed within (and/or on the exterior of) the vehicle
8, within the mobile device 10, and/or within the smart vehicle
controller 14, for example. The sensors 20 may include any one or
more of various different sensor types, such as an ignition sensor,
an odometer, a system clock, a speedometer, a tachometer, an
accelerometer, a gyroscope, a compass, a geolocation unit (e.g., a
GPS unit), a camera and/or video camera, a distance sensor (e.g.,
radar, LIDAR, etc.), and/or any other sensor or component capable
of generating or receiving data regarding the vehicle 8 and/or the
environment in which the vehicle 8 is located.
[0023] Some of the sensors 20 (e.g., radar, LIDAR, ultrasonic,
infrared, or camera units) may actively or passively scan the
vehicle environment for objects (e.g., other vehicles, buildings,
pedestrians, etc.), traffic control elements (e.g., lane markings,
signs, signals, etc.), external conditions (e.g., weather
conditions, traffic conditions, road conditions, etc.), and/or
other physical characteristics of the environment. Other sensors of
sensors 20 (e.g., GPS, accelerometer, or tachometer units) may
provide operational and/or other data for determining the location
and/or movement of the vehicle 8. Still other sensors of sensors 20
may be directed to the interior or passenger compartment of the
vehicle 8, such as cameras, microphones, pressure sensors,
thermometers, or similar sensors to monitor the vehicle operator
and/or passengers within the vehicle 8.
[0024] The external sensors 24 may be disposed on or within other
devices or components within the vehicle's environment (e.g., other
vehicles 6, infrastructure components 26, etc.), and may include
any of the types of sensors listed above. For example, the external
sensors 24 may include sensors that are the same as or similar to
sensors 20, but disposed on or within some of the vehicles 6 rather
than the vehicle 8.
[0025] To send and receive information, each of the sensors 20
and/or external sensors 24 may include a transmitter and/or a
receiver designed to operate according to predetermined
specifications, such as the dedicated short-range communication
(DSRC) channel, wireless telephony, Wi-Fi, or other existing or
later-developed communications protocols. As used herein, the terms
"sensor" or "sensors" may refer to the sensors 20 and/or external
sensors 24.
[0026] The other vehicles 6, public transportation system
components 22, infrastructure components 26, and/or smart homes 28
may be referred to herein as "external" data sources. The other
vehicles 6 may include any other vehicles, including smart
vehicles, vehicles with telematics-capable mobile devices,
autonomous vehicles, and/or other vehicles communicatively
connected to the network 30 via links 32.
[0027] The public transportation system components 22 may include
bus, train, ferry, ship, airline, and/or other public
transportation system components. Such components may include
vehicles, tracks, switches, access points (e.g., turnstiles, entry
gates, ticket counters, etc.), and/or payment locations (e.g.,
ticket windows, fare card vending machines, electronic payment
devices operated by conductors or passengers, etc.), for example.
The public transportation system components 22 may further be
communicatively connected to the network 30 via a link 34, in some
embodiments.
[0028] The infrastructure components 26 may include smart
infrastructure or devices (e.g., sensors, transmitters, etc.)
disposed within or communicatively connected to transportation or
other infrastructure, such as roads, bridges, viaducts, terminals,
stations, fueling stations, traffic control devices (e.g., traffic
lights, toll booths, entry ramp traffic regulators, crossing gates,
speed radar, cameras, etc.), bicycle docks, footpaths, or other
infrastructure system components. In some embodiments, the
infrastructure components 26 may be communicatively connected to
the network 30 via a link (not shown in FIG. 1).
[0029] The smart homes 28 may include dwellings or other buildings
that generate or collect data regarding their condition, occupancy,
proximity to a mobile device 10 or vehicle 8, and/or other
information. The smart homes 28 may include smart home controllers
29 that monitor the local environment of the smart home, which may
include sensors (e.g., smoke detectors, radon detectors, door
sensors, window sensors, motion sensors, cameras, etc.). In some
embodiments, the smart home controller 29 may include or be
communicatively connected to a security system controller for
monitoring access and activity within the environment. The smart
home 28 may further be communicatively connected to the network 30
via a link 36, in some embodiments.
[0030] The external data sources may collect data regarding the
vehicle 8, a vehicle operator, a user of an insurance program,
and/or an insured of an insurance policy. Additionally, or
alternatively, the other vehicles 6, the public transportation
system components 22, the infrastructure components 26, and/or the
smart homes 28 may collect such data, and provide that data to the
mobile device 10 and/or the smart vehicle controller 14 via links
not shown in FIG. 1.
[0031] In some embodiments, the front-end components 2 communicate
with the back-end components 4 via the network 30. The network 30
may include a proprietary network, a secure public internet, a
virtual private network and/or one or more other types of networks,
such as dedicated access lines, plain ordinary telephone lines,
satellite links, cellular data networks, or combinations thereof.
In embodiments where the network 30 comprises the Internet, data
communications may take place over the network 30 via an Internet
communication protocol.
[0032] The back-end components 4 may use a remote server 40 to
receive data from the front-end components 2, determine
characteristics of vehicle use, determine risk levels, modify
insurance policies, and/or perform other processing functions in
accordance with any of the methods described herein. In some
embodiments, the server 40 may be associated with an insurance
provider, either directly or indirectly. The server 40 may include
one or more computer processors adapted and configured to execute
various software applications and components of the telematics
system 1.
[0033] The server 40 may further include a database 46, which may
be adapted to store data related to the operation of the vehicle 8
and/or other information. As used herein, the term "database" may
refer to a single database or other structured data storage, or to
a collection of two or more different databases or structured data
storage components. Additionally, the server 40 may be
communicatively coupled via the network 30 to one or more data
sources, which may include an accident database 42 and/or a third
party database 44. The accident database 42 and/or third party
database 44 may be communicatively connected to the network via a
communication link 38. The accident database 42 and/or the third
party database 44 may be operated or maintained by third parties,
such as commercial vendors, governmental entities, industry
associations, nonprofit organizations, or others.
[0034] The data stored in the database 46 might include, for
example, dates and times of vehicle use, duration of vehicle use,
speed of the vehicle 8, RPM or other tachometer readings of the
vehicle 8, lateral and longitudinal acceleration of the vehicle 8,
incidents or near-collisions of the vehicle 8, communications
between the vehicle 8 and external sources (e.g., other vehicles 6,
public transportation system components 22, infrastructure
components 26, smart homes 28, and/or external information sources
communicating through the network 30), environmental conditions of
vehicle operation (e.g., weather, traffic, road condition, etc.),
errors or failures of vehicle features, and/or other data relating
to use of the vehicle 8 and/or the vehicle operator. Prior to
storage in the database 46, some of the data may have been uploaded
to the server 40 via the network 30 from the mobile device 10
and/or the smart vehicle controller 14. Additionally, or
alternatively, some of the data may have been obtained from
additional or external data sources via the network 30.
Additionally, or alternatively, some of the data may have been
generated by the server 40. The server 40 may store data in the
database 46 and/or may access data stored in the database 46 when
executing various functions and tasks associated with the methods
described herein.
[0035] The server 40 may include a controller 55 that is
operatively connected to the database 46 via a link 56. It should
be noted that, while not shown in FIG. 1, one or more additional
databases may be linked to the controller 55 in a known manner. For
example, separate databases may be used for sensor data, vehicle
insurance policy information, and vehicle use information. The
controller 55 may include a program memory 60, a processor 62
(which may be called a microcontroller or a microprocessor), a
random-access memory (RAM) 64, and an input/output (I/O) circuit
66, all of which may be interconnected via an address/data bus 65.
It should be appreciated that although only one microprocessor 62
is shown, the controller 55 may include multiple microprocessors
62. Similarly, the memory of the controller 55 may include multiple
RAMs 64 and multiple program memories 60. Although the I/O circuit
66 is shown as a single block, it should be appreciated that the
I/O circuit 66 may include a number of different types of I/O
circuits. The RAM 64 and program memories 60 may be implemented as
semiconductor memories, magnetically readable memories, or
optically readable memories, for example. The controller 55 may
also be operatively connected to the network 30 via a link 35.
[0036] The server 40 may further include a number of software
applications stored in a program memory 60. The various software
applications on the server 40 may include specific programs,
routines, or scripts for performing processing functions associated
with the methods described herein. Additionally, or alternatively,
the various software application on the server 40 may include
general-purpose software applications for data processing, database
management, data analysis, network communication, web server
operation, or other functions described herein or typically
performed by a server. The various software applications may be
executed on the same computer processor or on different computer
processors. Additionally, or alternatively, the software
applications may interact with various hardware modules that may be
installed within or connected to the server 40. Such modules may
implement part of all of the various exemplary methods discussed
herein or other related embodiments.
[0037] In some embodiments, the server 40 may be a remote server
associated with or operated by or on behalf of an insurance
provider. The server 40 may be configured to receive, collect,
and/or analyze telematics and/or other data in accordance with any
of the methods described herein. The server 40 may be configured
for one-way or two-way wired or wireless communication via the
network 30 with a number of telematics and/or other data sources,
including the accident database 42, the third party database 44,
the database 46 and/or the front-end components 2. For example, the
server 40 may be in wireless communication with mobile device 10;
insured smart vehicles 8; smart vehicles of other motorists 6;
smart homes 28; present or past accident database 42; third party
database 44 operated by one or more government entities and/or
others; public transportation system components 22 and/or databases
associated therewith; smart infrastructure components 26; and/or
the Internet. The server 40 may be in wired or wireless
communications with other sources of data, including those
discussed elsewhere herein.
[0038] Although the telematics system 1 is shown in FIG. 1 to
include one vehicle 8, one mobile device 10, one smart vehicle
controller 14, one other vehicle 6, one public transportation
system component 22, one infrastructure component 26, one smart
home 28, and one server 40, it should be understood that different
numbers of each may be utilized. For example, the system 1 may
include a plurality of servers 40 and hundreds or thousands of
mobile devices 10 and/or smart vehicle controllers 14, all of which
may be interconnected via the network 30. Furthermore, the database
storage or processing performed by the server 40 may be distributed
among a plurality of servers in an arrangement known as "cloud
computing." This configuration may provide various advantages, such
as enabling near real-time uploads and downloads of information as
well as periodic uploads and downloads of information. This may in
turn support a thin-client embodiment of the mobile device 10 or
smart vehicle controller 14 discussed herein.
[0039] FIG. 2 illustrates a block diagram of an exemplary mobile
device 10 and/or smart vehicle controller 14. The mobile device 10
and/or smart vehicle controller 14 may include a processor 72,
display 74, sensor 76, memory 78, power supply 80, wireless radio
frequency transceiver 82, clock 84, microphone and/or speaker 86,
and/or camera or video camera 88. In other embodiments, the mobile
device and/or smart vehicle controller may include additional,
fewer, and/or alternate components.
[0040] The sensor 76 may be able to record audio or visual
information. If FIG. 2 corresponds to the mobile device 10, for
example, the sensor 76 may be a camera integrated within the mobile
device 10. The sensor 76 may alternatively be configured to sense
speed, acceleration, directional, fluid, water, moisture,
temperature, fire, smoke, wind, rain, snow, hail, motion, and/or
other type of condition or parameter, and/or may include a gyro,
compass, accelerometer, or any other type of sensor described
herein (e.g., any of the sensors 20 described above in connection
with FIG. 1). Generally, the sensor 76 may be any type of sensor
that is currently existing or hereafter developed and is capable of
providing information regarding the vehicle 8, the environment of
the vehicle 8, and/or a person.
[0041] The memory 78 may include software applications that control
the mobile device 10 and/or smart vehicle controller 14, and/or
control the display 74 configured for accepting user input. The
memory 78 may include instructions for controlling or directing the
operation of vehicle equipment that may prevent, detect, and/or
mitigate vehicle damage. The memory 78 may further include
instructions for controlling a wireless or wired network of a smart
vehicle, and/or interacting with mobile device 10 and remote server
40 (e.g., via the network 30).
[0042] The power supply 80 may be a battery or dedicated energy
generator that powers the mobile device 10 and/or smart vehicle
controller 14. The power supply 80 may harvest energy from the
vehicle environment and be partially or completely energy
self-sufficient, for example.
[0043] The transceiver 82 may be configured for wireless
communication with sensors 20 located about the vehicle 8, other
vehicles 6, other mobile devices similar to mobile device 10,
and/or other smart vehicle controllers similar to smart vehicle
controller 14. Additionally, or alternatively, the transceiver 82
may be configured for wireless communication with the server 40,
which may be remotely located at an insurance provider
location.
[0044] The clock 84 may be used to time-stamp the date and time
that information is gathered or sensed by various sensors. For
example, the clock 84 may record the time and date that photographs
are taken by the camera 88, video is captured by the camera 88,
and/or other data is received by the mobile device 10 and/or smart
vehicle controller 14.
[0045] The microphone and speaker 86 may be configured for
recognizing voice or audio input and/or commands. The clock 84 may
record the time and date that various sounds are collected by the
microphone and speaker 86, such as sounds of windows breaking, air
bags deploying, tires skidding, conversations or voices of
passengers, music within the vehicle 8, rain or wind noise, and/or
other sound heard within or outside of the vehicle 8.
[0046] The present embodiments may be implemented without changes
or extensions to existing communications standards. The smart
vehicle controller 14 may also include a relay, node, access point,
Wi-Fi AP (Access Point), local node, pico-node, relay node, and/or
the mobile device 10 may be capable of RF (Radio Frequency)
communication, for example. The mobile device 10 and/or smart
vehicle controller 14 may include Wi-Fi, Bluetooth, GSM (Global
System for Mobile communications), LTE (Long Term Evolution), CDMA
(Code Division Multiple Access), UMTS (Universal Mobile
Telecommunications System), and/or other types of components and
functionality.
II. Telematics Data
[0047] Telematics data, as used herein, may include telematics
data, and/or other types of data that have not been conventionally
viewed as "telematics data." The telematics data may be generated
by, and/or collected or received from, various sources. For
example, the data may include, indicate, and/or relate to vehicle
(and/or mobile device) speed; acceleration; braking; deceleration;
turning; time; GPS (Global Positioning System) or GPS-derived
location, speed, acceleration, or braking information; vehicle
and/or vehicle equipment operation; external conditions (e.g.,
road, weather, traffic, and/or construction conditions); other
vehicles or drivers in the vicinity of an accident;
vehicle-to-vehicle (V2V) communications; vehicle-to-infrastructure
communications; and/or image and/or audio information of the
vehicle and/or insured driver before, during, and/or after an
accident. The data may include other types of data, including those
discussed elsewhere herein. The data may be collected via wired or
wireless communication.
[0048] The data may be generated by mobile devices (smart phones,
cell phones, lap tops, tablets, phablets, PDAs (Personal Digital
Assistants), computers, smart watches, pagers, hand-held mobile or
portable computing devices, smart glasses, smart electronic
devices, wearable devices, smart contact lenses, and/or other
computing devices); smart vehicles; dash or vehicle mounted systems
or original telematics devices; public transportation systems;
smart street signs or traffic lights; smart infrastructure, roads,
or highway systems (including smart intersections, exit ramps,
and/or toll booths); smart trains, buses, or planes (including
those equipped with Wi-Fi or hotspot functionality); smart train or
bus stations; internet sites; aerial, drone, or satellite images;
third party systems or data; nodes, relays, and/or other devices
capable of wireless RF (Radio Frequency) communications; and/or
other devices or systems that capture image, audio, or other data
and/or are configured for wired or wireless communication.
[0049] In some embodiments, the data collected may also derive from
police or fire departments, hospitals, and/or emergency responder
communications; police reports; municipality information; automated
Freedom of Information Act requests; and/or other data collected
from government agencies and officials. The data from different
sources or feeds may be aggregated.
[0050] The data generated may be transmitted, via wired or wireless
communication, to a remote server, such as a remote server and/or
other processor(s) associated with an insurance provider. The
remote server and/or associated processors may build a database of
the telematics and/or other data, and/or otherwise store the data
collected.
[0051] The remote server and/or associated processors may analyze
the data collected and then perform certain actions and/or issue
tailored communications based upon the data, including the
insurance-related actions or communications discussed elsewhere
herein. The automatic gathering and collecting of data from several
sources by the insurance provider, such as via wired or wireless
communication, may lead to expedited insurance-related activity,
including the automatic identification of insured events, and/or
the automatic or semi-automatic processing or adjusting of
insurance claims.
[0052] In one embodiment, telematics data may be collected by a
mobile device (e.g., smart phone) application. An application that
collects telematics data may ask an insured for permission to
collect and send data about driver behavior and/or vehicle usage to
a remote server associated with an insurance provider. In return,
the insurance provider may provide incentives to the insured, such
as lower premiums or rates, or discounts. The application for the
mobile device may be downloadable off of the internet.
[0053] In some embodiments, the telematics and/or other data
generated, collected, determined, received, transmitted, analyzed,
or otherwise utilized may relate to biometrics. For example,
biometrics data may be used by an insurance provider to push
wireless communications to a driver or an insured related to health
and/or driving warnings or recommendations. In one aspect, a
wearable electronics device may monitor various physical conditions
of a driver to determine the physical, mental, and/or emotional
condition of the driver, which may facilitate identification of a
driver that may have a high risk of accident. Wearable electronics
devices may monitor, for example, blood pressure or heart rate.
Such data may be remotely gathered by an insurance provider remote
server 40 for insurance-related purposes, such as for automatically
generating wireless communications to the insured and/or policy and
premium adjustments.
[0054] In some embodiments, the telematics and/or other data may
indicate a health status of a driver. If biometrics data indicates
that an insured is having a heart attack, for example, a
recommendation or warning to stop driving and/or go to a hospital
may be issued to the insured via the mobile device 10 or other
means, and/or the insurance provider (or mobile device 10 or smart
vehicle controller 14) may issue a request for immediate medical
assistance.
[0055] The biometrics data may indicate the health or status of an
insured immediately after an accident has occurred. The biometrics
data may be automatically analyzed by the remote server 40 to
determine that an ambulance should be sent to the scene of an
accident. In the unfortunate situation that a death and/or a cause
of death (e.g, severe auto accident) is indicated (from the
telematics or other data, or from emergency responder wireless
communication), an insurance provider may remotely receive that
information at a remote server 40, and/or automatically begin
processing a life insurance policy claim for the insured.
III. Cause of Accident and/or Fault Determination
[0056] The present embodiments may determine the cause of a vehicle
accident from analyzing the telematics and/or other data collected
(e.g., any type or types of telematics and/or other data described
above in Section I and/or Section II). An accident may be
determined to have been fully, primarily, or partially caused by a
number of factors, such as weather conditions, road or traffic
conditions, construction, human error, technology error, vehicle or
vehicle equipment faulty operation, and/or other factors.
[0057] In one aspect, the present embodiments may determine who was
at fault (either entirely or partially) for causing a vehicle
collision or accident. Mobile devices, smart vehicles, equipment
and/or sensors mounted on and/or within a vehicle, and/or roadside
or infrastructure systems may detect certain indicia of fault, or
perhaps more importantly (from the insured's perspective), a lack
of fault. An insured may opt-in to an insurance program that allows
an insurance provider to collect telematics and/or other data, and
to analyze that data for low- or high-risk driving and/or other
behavior (e.g., for purposes of fault determination). The analysis
of the data and/or low- or high-risk behavior identified, and/or
the determination of fault, may be used to handle an insurance
claim, and/or used to lower insurance premiums or rates for the
insured, and/or to provide insurance discounts or rewards to the
insured, etc.
[0058] Telematics data and/or other types of data may be generated
and/or collected by, for example, (i) a mobile device (smart phone,
smart glasses, etc.), (ii) cameras mounted on the interior or
exterior of an insured (or other) vehicle, (iii) sensors or cameras
associated with a roadside system, and/or (iv) other electronic
systems, such as those mentioned above, and may be time-stamped.
The data may indicate that the driver was driving attentively
before, during, and/or after an accident. For instance, the data
collected may indicate that a driver was driving alone and/or not
talking on a smart phone or texting before, during, and/or after an
accident. Responsible or normal driving behavior may be detected
and/or rewarded by an insurance provider, such as with lower rates
or premiums, or with good driving discounts for the insured.
[0059] Additionally or alternatively, video or audio equipment or
sensors may capture images or conversations illustrating that the
driver was driving lawfully and/or was generally in good physical
condition and calm before the accident. Such information may
indicate that the other driver or motorist (for a two-vehicle
accident) may have been primarily at fault.
[0060] Conversely, an in-cabin camera or other device may capture
images or video indicating that the driver (the insured) or another
motorist (e.g., a driver uninsured by the insurance provider)
involved in an accident was distracted or drowsy before, during,
and/or after an accident. Likewise, erratic behavior or driving,
and/or drug or alcohol use by the driver or another motorist, may
be detected from various sources and sensors. Telematics data, such
as data gathered from the vehicle and/or a mobile device within the
vehicle, may also be used to determine that, before or during an
accident, one of the drivers was speeding; following another
vehicle too closely; and/or had time to react and avoid the
accident.
[0061] In addition to human drivers, fault may be assigned to
vehicle collision avoidance functionality, such that the insured's
insurance premium or rate may not be negatively impacted by faulty
technology. The telematics and/or other data collected may include
video and/or audio data, and may indicate whether a vehicle, or
certain vehicle equipment, operated as designed before, during,
and/or after the accident. That data may assist in reconstructing a
sequence of events associated with an insured event (e.g., a
vehicle collision).
[0062] For instance, the data gathered may relate to whether or not
the vehicle software or other collision avoidance functionality
operated as it was intended or otherwise designed to operate. Also,
a smart vehicle control system or mobile device may use G-force
data and/or acoustic information to determine certain events. The
data may further indicate whether or not (1) an air bag deployed;
(2) the vehicle brakes were engaged; and/or (3) vehicle safety
equipment (lights, wipers, turn signals, etc.), and/or other
vehicle systems operated properly, before, during, and/or after an
accident.
[0063] Fault or blame, whole or partial, may further be assigned to
environmental and/or other conditions that were causes of the
accident. Weather, traffic, and/or road conditions; road
construction; other accidents in the vicinity; and/or other
conditions before, during, and/or after a vehicle accident (and in
the vicinity of the location of the accident) may be determined
(from analysis of the telematics and/or other data collected) to
have contributed to causing the accident and/or insured event. A
percentage of fault or blame may be assigned to each of the factors
that contributed to causing an accident, and/or the severity
thereof.
[0064] A sliding deductible and/or rate may depend upon the
percentage of fault assigned to the insured. The percent of fault
may be determined to be 0% or 50%, for example, which may impact an
amount that is paid by the insurance provider for damages and/or an
insurance claim.
IV. Accident Reconstruction
[0065] The telematics and/or other data gathered from the various
sources, such as any type or types of telematics and/or other data
described above in Section I and/or Section II (e.g., mobile
devices; smart vehicles; sensors or cameras mounted in or on an
insured vehicle or a vehicle associated with another motorist;
biometric devices; public transportation systems or other roadside
cameras; aerial or satellite images; etc.), may facilitate
recreating the series of events that led to an accident. The data
gathered may be used by investigative services associated with an
insurance provider to determine, for a vehicle accident, (1) an
accident cause and/or (2) lack of fault and/or fault, or a
percentage of fault, that is assigned or attributed to each of the
drivers involved. The data gathered may also be used to identify
one or more non-human causes of the accident, such as road
construction, or weather, traffic, and/or road conditions.
[0066] A. Time-Stamped Sequence of Events
[0067] The series or sequence of events may facilitate establishing
that an insured had no, or minimal, fault in causing a vehicle
accident. Such information may lead to lower premiums or rates for
the insured, and/or no change in insurance premiums or rates for
the insured, due to the accident. Proper fault determination may
also allow multiple insurance providers to assign proper risk to
each driver involved in an accident, and adjust their respective
insurance premiums or rates accordingly such that good driving
behavior is not improperly penalized.
[0068] In one aspect, audio and/or video data may be recorded. To
facilitate accurate reconstruction of the sequence of events, the
audio and video data may capture time-stamped sound and images,
respectively. Sound and visual data may be associated with and/or
indicate, for example, vehicle braking; vehicle speed; vehicle
turning; turn signal, window wiper, head light, and/or brake light
normal or faulty operation; windows breaking; air bags deploying;
and/or whether the vehicle or vehicle equipment operated as
designed, for each vehicle involved in a vehicle accident or other
insured event.
[0069] B. Virtual Accident Reconstruction
[0070] The telematics and/or other data gathered may facilitate
accident reconstruction, and an accident scene or series of events
may be recreated. As noted above, from the series of events leading
up to, during, and/or after the accident, fault (or a percentage of
fault) may be assigned to an insured and/or another motorist. The
data gathered may be viewed as accident forensic data, and/or may
be applied to assign fault or blame to one or more drivers, and/or
to one or more external conditions.
[0071] For example, the telematics and/or other data gathered may
indicate weather, traffic, road construction, and/or other
conditions. The data gathered may facilitate scene reconstructions,
such as graphic presentations on a display of a virtual map. The
virtual map may include a location of an accident; areas of
construction; areas of high or low traffic; and/or areas of bad
weather (rain, ice, snow, etc.), for example.
[0072] The virtual map may indicate a route taken by a vehicle or
multiple vehicles involved in an accident. A timeline of events,
and/or movement of one or more vehicles, may be depicted via, or
superimposed upon, the virtual map. As a result, a graphical or
virtual moving or animated representation of the events leading up
to, during, and/or after the accident may be generated.
[0073] The virtual representation of the vehicle accident may
facilitate (i) fault, or percentage of fault, assignment to one or
more drivers; and/or (ii) blame, or percentage of blame, assignment
to one or more external conditions, such as weather, traffic,
and/or construction. The assignments of fault and/or blame, or lack
thereof, may be applied to handling various insurance claims
associated with the vehicle accident, such as claims submitted by
an insured or other motorists. The insured may be insured by an
insurance provider, and the other motorists may be insured by the
same or another insurance provider. The assignments of fault and/or
blame, or lack thereof, may lead to appropriate adjustments to the
insurance premiums or rates for the insured and/or the other
motorists to reflect the cause or causes of the accident determined
from the data collected.
[0074] The virtual representation of the vehicle accident may
account for several vehicles involved in the accident. The sequence
of events leading up to and including the accident may include
analysis of the telematics and/or other data to determine or
estimate what each of several vehicles and/or respective drivers
did (or did not) do prior to, during, and/or after the
accident.
[0075] As an example, voice data from using a smart phone to place
a telephone call before or during an accident may indicate a
distracted driver. As another example, vehicle sensors may detect
seat belt usage, such as seat belt usage before or during an
accident, and/or the frequency or amount of seat belt usage by a
specific driver. The data may reveal the number of children or
other passengers in a vehicle before or during an accident.
[0076] Moreover, GPS (Global Positioning System) location and speed
data from several vehicles may be collected. Other vehicle data may
also be collected, such as data indicating whether (i) turn signals
were used; (ii) head lights were on; (iii) the gas or brake pedal
for a vehicle was pressed or depressed; and/or (iv) a vehicle was
accelerating, decelerating, braking, maneuvering, turning, in its
respective lane, and/or changing lanes.
[0077] Infrastructure data, such as data from public transportation
systems and/or smart traffic lights, may also be collected. Thus,
for each vehicle accident or insured event, a unique combination of
data may be gathered at the insurance provider remote server (e.g.,
server 40 of FIG. 1) and then analyzed to determine a most likely
series of events leading up to the insured event.
V. Claim Accuracy Verification/Buildup Identification
[0078] The telematics and/or other data gathered from the various
sources (e.g., any type or types of telematics and/or other data
described above in Section I and/or Section II) may also, or
instead, be used to verify accurate insurance claims, and/or to
identify overstated claims and/or buildup. The data may verify an
insured's account of events, the severity of the accident, the
damage to a vehicle, the injuries to passengers riding in the
vehicle, and/or other items to ensure that an insured is properly
compensated and/or that the insured's insurance claim is properly
and efficiently handled.
[0079] Automatic, prompt verification of the veracity of an
insurance claim may speed up claim processing, and lead to quicker
claim payout monies being issued to an insured. The automatic
verification of the claim, such as by an insurance provider remote
server (e.g., server 40 of FIG. 1), may also lead to less hassle
for the insured in resolving the insurance claim, and/or require
less time on the part of the insured in filling out insurance
claim-related paperwork or otherwise getting their insurance claim
resolved.
[0080] The data collected may be used to verify whether a "hit and
run" accident was truly a hit and run, for example. For "hit and
run" accident claims, telematics and/or other data may be used to
determine (i) whether the vehicle was running, or alternatively not
in use, at the time of the accident, and/or (ii) whether the
location at which the insurance claim indicates that the vehicle
was located at the time of the accident is accurate. The data may
indicate whether the car was parked or not moving, and/or indeed
moving (and speed), at the time of the accident. Such information
may indicate whether an insurance claim for an insured event is
accurate, as opposed to including potential buildup.
[0081] The telematics and/or other data gathered may also indicate
the number of persons involved in the accident. For instance, data
may indicate or verify that there were five passengers in the
vehicle at the time of the accident, as reported by the insured. As
another example, the data may reveal that only two passengers were
in the vehicle, and not four injured persons as reported in an
insurance claim.
[0082] As another example, and as noted above, vehicle location may
be verified. An insurance claim for a hit and run accident may
state that the insured vehicle was parked in a certain parking lot
or garage at 2 p.m. The telematics data gathered (e.g., including
GPS data from a mobile device or smart vehicle) may verify the
location of the insured vehicle at that time. Alternatively, the
telematics data gathered may indicate that the insured vehicle was
actually located halfway across town at that time. In this manner,
the data gathered may be used to verify accurate claims, and not
penalize an insured for accurate claim reporting, as well as to
detect potential fraudulent and/or inflated claims that may warrant
further investigation by an insurance provider.
[0083] A. Estimating Likely Damage Associated with Insured
Event
[0084] The telematics and/or other data gathered may relate to
classifying automobile accidents by type and/or estimating a
probability of injury to the insured and/or passengers. The data
gathered may indicate the type of accident, the likely condition of
the vehicle after the accident, and/or the likely health of the
insured and/or passengers after the accident. The data may further
indicate the veracity of an insurance claim to facilitate prompt
and accurate handling of an insurance claim submitted by an insured
for an insured event.
[0085] For a severe accident, major vehicle repair work and/or
medical bills for the passengers involved in the accident may be
anticipated or expected. For instances where the data indicates a
severe accident, the insurance provider may quickly verify the
associated insurance claims. Subsequently, the insurance claims may
be promptly handled and the insured may receive prompt payment.
[0086] On the other hand, for a minor accident, major vehicle
repair work or extensive medical bills may not be anticipated or
expected, and insurance claims for such may indicate potential
buildup. As an example, a request for back surgery resulting from a
minor collision may be indicative of an inflated claim, and may be
flagged for further investigation by the insurance provider.
[0087] B. Police Report Information
[0088] In one embodiment, data pertinent to an insured event that
is generated by government officials may be collected at an
insurance provider remote server (e.g., server 40 of FIG. 1).
Police report information may be collected automatically (e.g.,
with the permission of an insured). The police report information
may have information related to the cause of an insured event
(e.g., vehicle accident and/or fire losses, including home fire
losses). The police report information may include a series of
events leading up to the insured event, witness names, and/or other
information useful to handling insurance claims. The police report
information may be automatically scanned, or otherwise collected
and stored in a database or other memory associated with the
insurance provider.
[0089] Data from the governmental bodies may also be acquired
through Freedom of Information Act (FOIA) requests that may provide
the public with access to public records, including police or
accident reports. The FOIA requests may be automatically generated
and/or submitted by an insurance provider remote server (e.g.,
server 40 of FIG. 1) once an insured event is detected/determined
to have occurred from the telematics and/or other data collected,
and/or analyzed at the insurance provider remote server.
Additionally or alternatively, the FOIA requests may be
automatically generated and/or submitted once an insurance claim is
received from an insured. The public records may facilitate
determining accurate insurance claims and/or verifying insurance
claims submitted, leading to timely processing.
VI. Exemplary Fault Determination Method
[0090] FIG. 3 illustrates an exemplary computer-implemented method
100 for facilitating fault determination for a vehicle accident. In
some embodiments, the method 100 may be implemented in whole or in
part by one or more components of the system 1 depicted in FIG. 1.
For example, the method 100 may be implemented by one or more
servers remote from the components (e.g., sensors, vehicles, mobile
devices, etc.) sourcing telematics data, such as the server 40
(e.g., processor(s) 62 of the server 40 when executing instructions
stored in the program memory 60 of the server 40) or another server
not shown in FIG. 1.
[0091] The method 100 may include collecting accident data
associated with a vehicle accident involving a driver (block 102).
The driver may be associated with an insurance policy issued by the
insurance provider (e.g., an owner of the policy, or another
individual listed on the policy). The accident data may include
telematics data, and possibly other data, collected from one or
more sources. For example, the accident data may include data
associated with or generated by one or more mobile devices (e.g.,
mobile device 10 of FIGS. 1 and 2); an insured vehicle or a
computer system of the insured vehicle (e.g., vehicle 8 or smart
vehicle controller 14 of FIGS. 1 and 2, or one or more sensors
mounted on the vehicle); a vehicle other than the insured vehicle
(e.g., vehicle 6 of FIG. 1); vehicle-to-vehicle (V2V) communication
(e.g., communications between vehicle 8 and vehicle 6 in FIG. 1);
and/or roadside equipment or infrastructure located near a location
of the vehicle accident (e.g., infrastructure components 26 of FIG.
1). Generally, the accident data may include any one or more of the
types of data discussed above in Section I and/or II (and/or other
suitable types of data), and may be collected according to any of
the techniques discussed above in Section I and/or II (and/or other
suitable techniques). The accident data may have been generated by
the respective source(s), and/or collected, before, during and/or
after the accident.
[0092] The method 100 may also include analyzing any or all of the
collected accident data (block 104). As shown in FIG. 3, for
example, insured driver behavior and/or acuity data may be analyzed
(block 104A), road, weather, construction, and/or traffic
conditions data may be analyzed (block 104B), and/or other vehicle
and/or other driver behavior or action data may be analyzed (block
104C). As a more specific example, driver acuity data (e.g., phone
usage data) collected from the insured's vehicle and/or mobile
device may be analyzed to determine precisely when, in relation to
the time of the accident, the insured was or was not likely
distracted (e.g., talking on the phone). As another example,
weather data (e.g., collected by a mobile device or vehicle-mounted
camera, or from a third party server) may be analyzed to determine
weather conditions, such as rain, snow or fog, during and/or just
prior to the accident. As yet another example, other driver
behavior data (e.g., collected by a sensor mounted on the insured's
vehicle, or a roadside camera or other sensor, etc.) may be
analyzed to determine the speed, direction, lane usage, etc., of
one or more drivers other than the insured.
[0093] In some embodiments, other data is also, or instead,
analyzed at block 104. For example, data pertaining to other
vehicle accidents occurring at the same location (e.g., a
particular intersection) may be analyzed. Such an analysis may
indicate that the street configuration, or another characteristic,
of the accident location is likely at least a partial cause of the
accident, for example.
[0094] The method 100 may also include determining, based upon the
analysis of the accident data at block 104 (e.g., at one or more of
blocks 104A through 104C), fault of the driver for the vehicle
accident (blocks 106, 108). As seen in FIG. 3, for example, the
fault for the driver (e.g., the insured) and/or for another driver
may be compared or otherwise analyzed (block 106), and then
assigned to the respective individuals for insurance purposes
(block 108). The fault may be determined as one or more binary
indicators (e.g., "at fault" or "not at fault"), percentages (e.g.,
"25% responsible"), ratios or fractions, and/or any other suitable
indicator(s) or measure(s) of fault. In some embodiments and/or
scenarios, fault for a first individual is implicitly determined
based upon the fault that is explicitly determined for another
individual (e.g., an insured may implicitly be determined to have
0% fault if another driver is explicitly determined to be 100% at
fault).
[0095] The method 100 may also include using the fault determined
at blocks 106, 108 to handle or adjust an insurance claim
associated with the vehicle accident (block 110). For example, the
determined fault of the driver (e.g., insured) may be used to
determine the appropriate payout by the insurance provider, or
whether another insurance provider should be responsible for
payment, etc.
[0096] The method 100 may also include using the fault determined
at blocks 106, 108 to adjust, generate and/or update one or more
insurance-related items (block 112). The insurance-related item(s)
may include, for example, parameters of the insurance policy (e.g.,
a deductible), a premium, a rate, a discount, and/or a reward. As a
more specific example, if it is determined that the driver (e.g.,
insured) is at least partially at fault, the driver's insurance
premium may be increased.
[0097] In other embodiments, the method 100 may include additional,
fewer, or alternate actions as compared to those shown in FIG. 3,
including any of those discussed elsewhere herein. For example, the
method 100 may further include transmitting information indicative
of the adjusted, generated, or updated insurance-related items to a
mobile device associated with the driver (or another individual
associated with the insurance policy), such as mobile device 10 of
FIG. 1, to be displayed on the mobile device for review,
modification, or approval by the driver or other individual.
[0098] As can be seen from the above discussion, the method 100 may
enable fault to be more reliably and/or accurately determined with
respect to a vehicle accident, which may in turn allow more
accurate and efficient claim handling, and/or more accurate and
efficient adjustment, generation and/or updating of
insurance-related items. Moreover, components in the example system
1 may complete their tasks more quickly and/or efficiently, and/or
the resource usage or consumption of components in the example
system 1 may be reduced. For instance, a claim associate may need
to initiate or receive fewer communications with an insured (e.g.,
via mobile device 10 and/or network 30) and/or other individuals,
and/or the processor 62 may consume less time and/or fewer
processing cycles in handling a claim, if the data collected from
some or all of the sources shown in front-end components 2 of FIG.
1 is complete or informative enough to avoid the need for extensive
follow-up investigation.
VII. Additional Exemplary Fault Determination Method
[0099] In one aspect, a computer-implemented method of accident
cause and/or fault determination may be provided. The method may
include (1) collecting or receiving telematics and/or other data at
or via a remote server associated with an insurance provider, the
telematics and/or other data being associated with a vehicle
accident involving a specific driver and/or an insured. The insured
may own an insurance policy issued by the insurance provider,
and/or the telematics and/or other data may be gathered before,
during, and/or after the vehicle accident. The method may include
(2) analyzing the telematics and/or other data at and/or via the
remote server; (3) determining, at and/or via the remote server,
fault or a percentage of fault of the vehicle accident that is
assigned or attributed to the specific driver and/or the insured
from the analysis of the telematics and/or other data; (4) using
the fault or percentage of fault that is assigned or attributed to
the specific driver and/or the insured to handle and/or address, at
and/or via the remote server, an insurance claim associated with
the vehicle accident; and/or (5) using the fault or percentage of
fault that is assigned or attributed to the specific driver and/or
the insured to adjust, generate, and/or update, at and/or via the
remote server, an insurance policy, premium, rate, discount, and/or
reward for the specific driver and/or the insured. The method may
include additional, fewer, or alternate actions, including those
discussed elsewhere herein.
[0100] For instance, the method may further include transmitting
information related to an adjusted, generated, and/or updated
insurance policy, premium, rate, discount, and/or reward from the
remote server to a mobile device associated with the specific
driver and/or insured to facilitate presenting, on a display of the
mobile device, all or a portion of the adjusted, generated, and/or
updated insurance policy, premium, rate, discount, and/or reward to
the specific driver and/or insured for review, modification, and/or
approval.
[0101] Analyzing the telematics and/or other data at the remote
server to determine fault or a percentage of fault of the vehicle
accident may involve analysis of driver behavior and/or acuity
before, during, and/or after the vehicle accident using the
telematics and/or other data received or collected. Additionally or
alternatively, analyzing the telematics and/or other data at the
remote server to determine fault or a percentage of fault of the
vehicle accident may involve analysis of road, weather, traffic,
and/or construction conditions associated with a location of the
vehicle accident before, during, and/or after the vehicle accident
using the telematics and/or other data received or collected.
[0102] Analyzing the telematics and/or other data at the remote
server to determine fault or a percentage of fault of the vehicle
accident may also involve analysis of behavior and/or actions taken
by another driver other than the insured that is involved with the
vehicle accident, and/or other vehicle accidents that occurred at
the location of the accident, such as at a busy intersection.
[0103] The telematics and/or other data may include data associated
with, or generated by, mobile devices, such as smart phones, smart
glasses, and/or smart wearable electronic devices capable of
wireless communication. Additionally or alternatively, the
telematics and/or other data may include data associated with, or
generated by, an insured vehicle or a computer system of the
insured vehicle. The telematics and/or other data may further
include data associated with, or generated by, (i) a vehicle other
than the insured vehicle; (ii) vehicle-to-vehicle (V2V)
communication; and/or (iii) road side equipment or infrastructure
located near a location of the vehicle accident.
VIII. Exemplary Accident Reconstruction Method
[0104] FIG. 4 illustrates an exemplary computer-implemented method
200 of accident or accident scene reconstruction for a vehicle
accident. In some embodiments, the method 100 may be implemented in
whole or in part by one or more components of the system 1 depicted
in FIG. 1. For example, the method 200 may be implemented by one or
more servers remote from the components (e.g., sensors, vehicles,
mobile devices, etc.) sourcing telematics data, such as the server
40 (e.g., processor(s) 62 of the server 40 when executing
instructions stored in the program memory 60 of the server 40) or
another server not shown in FIG. 1.
[0105] The method 200 may include collecting accident data
associated with a vehicle accident involving a driver (block 202).
The driver may be associated with an insurance policy issued by the
insurance provider (e.g., an owner of the policy, or another
individual listed on the policy). The accident data may include
telematics data, and possibly other data, collected from one or
more sources. For example, the accident data may include data
associated with or generated by one or more mobile devices (e.g.,
mobile device 10 of FIGS. 1 and 2); an insured vehicle or a
computer system of the insured vehicle (e.g., vehicle 8 or smart
vehicle controller 14 of FIGS. 1 and 2, or one or more sensors
mounted on the vehicle); a vehicle other than the insured vehicle
(e.g., vehicle 6 of FIG. 1); vehicle-to-vehicle (V2V) communication
(e.g., communications between vehicle 8 and vehicle 6 in FIG. 1);
and/or roadside equipment or infrastructure located near a location
of the vehicle accident (e.g., infrastructure components 26 of FIG.
1). Generally, the accident data may include any one or more of the
types of data discussed above in Section I and/or II (and/or other
suitable types of data), and may be collected according to any of
the techniques discussed above in Section I and/or II (and/or other
suitable techniques). The accident data may have been generated by
the respective source(s), and/or collected, before, during and/or
after the accident.
[0106] The method 200 may also include analyzing any or all of the
collected accident data (block 204), reconstructing the accident
from the accident data (block 206), and creating a virtual accident
scene (block 208). As shown in FIG. 4, for example, insured driver
behavior and/or acuity data may be analyzed to reconstruct the
accident (block 206A), road, weather, construction, and/or traffic
conditions data may be analyzed to reconstruct the accident (block
206B), and/or other vehicle and/or other driver behavior or action
data may be analyzed to reconstruct the accident (block 206C). As a
more specific example, driver acuity data (e.g., phone usage data)
collected from the insured's vehicle and/or mobile device may be
analyzed to determine precisely when, in relation to the time of
the accident, the insured was or was not likely distracted (e.g.,
talking on the phone). As another example, weather data (e.g.,
collected by a mobile device or vehicle-mounted camera, or from a
remote server) may be analyzed to determine weather conditions,
such as rain, snow or fog, during and/or just prior to the
accident. As yet another example, other driver behavior data (e.g.,
collected by a sensor mounted on the insured's vehicle, or a
roadside camera or other sensor, etc.) may be analyzed to determine
the speed, direction, lane usage, etc., of one or more drivers
other than the insured.
[0107] Block 206 may include, for example, determining a sequence
of events for the accident, and block 208 may include generating a
virtual reconstruction of the accident (and/or a scene of the
accident) based upon the sequence of events. The sequence of events
may include events occurring before, during, and/or after the
accident. The events may include any types of occurrences, such as
vehicle movements, driver actions (e.g., stepping on the brake
pedal, talking on a smart phone, etc.), traffic light changes, and
so on. The virtual reconstruction may depict/represent not only the
sequence of events, but also various states/conditions that exist
while the sequence of events occurs. For instance, the virtual
reconstruction may include an animated graphical depiction of two
or more vehicles involved in the vehicle accident before and during
the accident, while also depicting driver acuity, weather
conditions, traffic conditions, and/or construction conditions. The
vehicles and/or conditions may be depicted at the time of the
accident, and at (or in the vicinity of) the vehicle accident, for
example. In some embodiments, the virtual reconstruction may be
superimposed upon a map.
[0108] The method 200 may also include determining (e.g., based
upon a virtual reconstruction of the accident generated at block
208) fault of the driver for the accident. As seen in FIG. 4, for
example, the fault for the driver (e.g., the insured) and/or for
another driver may be compared or otherwise analyzed (block 210).
The fault may be determined by processing/analyzing features of the
generated virtual reconstruction, for example, or by displaying the
virtual reconstruction to a user (e.g., insurance provider
employee) for human interpretation/analysis, for example.
[0109] The fault may be determined as one or more binary indicators
(e.g., "at fault" or "not at fault"), percentages (e.g., "25%
responsible"), ratios or fractions, and/or any other suitable
indicator(s) or measure(s) of fault. In some embodiments and/or
scenarios, fault for a first individual is implicitly determined
based upon the fault that is explicitly determined for another
individual (e.g., an insured may implicitly be determined to have
0% fault if another driver is explicitly determined to be 100% at
fault).
[0110] The method 200 may also include using the fault determined
at block 210 to handle an insurance claim associated with the
accident (block 212). For example, the determined fault of the
driver (e.g., insured) may be used to determine or adjust the
appropriate payout by the insurance provider, or to determine
whether another insurance provider should be responsible for
payment, etc.
[0111] The method 200 may also include using the fault determined
at blocks 210 to adjust, generate and/or update one or more
insurance-related items (block 214). The insurance-related item(s)
may include, for example, parameters of the insurance policy (e.g.,
a deductible), a premium, a rate, a discount, and/or a reward. As a
more specific example, if it is determined that the driver (e.g.,
insured) is at least partially at fault, the driver's insurance
premium may be increased.
[0112] In other embodiments, the method 200 may include additional,
fewer, or alternate actions as compared to those shown in FIG. 4,
including any of those discussed elsewhere herein. For example, the
method 200 may further include transmitting information indicative
of the adjusted, generated, or updated insurance-related items to a
mobile device associated with the driver (or another individual
associated with the insurance policy), such as mobile device 10 of
FIG. 1, to be displayed on the mobile device for review,
modification, or approval by the driver or other individual.
[0113] As can be seen from the above discussion, the method 200 may
enable accurate reconstruction of an accident, which may in turn
allow more accurate and efficient claim handling, and/or more
accurate and efficient adjustment, generation and/or updating of
insurance-related items. Moreover, components in the example system
1 may complete their tasks more quickly and/or efficiently, and/or
the resource usage or consumption of components in the example
system 1 may be reduced. For instance, a claim associate may need
to initiate or receive fewer communications with an insured (e.g.,
via mobile device 10 and/or network 30) and/or other individuals,
and/or the processor 62 may consume less time and/or fewer
processing cycles in handling a claim, if the data collected from
some or all of the sources shown in front-end components 2 of FIG.
1 is complete or informative enough to re-create an accident scene
without the need for extensive follow-up investigation.
IX. Additional Exemplary Accident Reconstruction Method
[0114] In one aspect, a computer-implemented method of accident
scene reconstruction may be provided. The method may include (1)
collecting or receiving telematics and/or other data at or via a
remote server associated with an insurance provider, the telematics
and/or other data being associated with a vehicle accident
involving a specific driver and/or an insured. The insured may own
an insurance policy issued by the insurance provider, and the
telematics and/or other data may be gathered before, during, and/or
after the vehicle accident. The method may include (2) analyzing
the telematics and/or other data at and/or via the remote server;
(3) determining a sequence of events occurring before, during,
and/or after the vehicle accident, at and/or via the remote server,
from the analysis of the telematics and/or other data; (4)
generating a virtual reconstruction of the vehicle accident and/or
accident scene, at and/or via the remote server, from the sequence
of events determined from the analysis of the telematics and/or
other data; (5) determining, at and/or via the remote server, fault
or a percentage of fault of the vehicle accident that is assigned
or attributed to the specific driver and/or the insured from the
virtual reconstruction of the vehicle accident and/or accident;
and/or (6) using the fault or percentage of fault that is assigned
or attributed to the specific driver and/or the insured to handle
and/or address (either entirely or partially), at and/or via the
remote server, an insurance claim associated with the vehicle
accident.
[0115] The method may include using the fault or percentage of
fault that is assigned or attributed to the specific driver and/or
the insured to adjust, generate, and/or update, via the remote
server, an insurance policy, premium, rate, discount, and/or reward
for the specific driver and/or the insured. The method may also
include transmitting information related to the adjusted,
generated, and/or updated insurance policy, premium, rate,
discount, and/or reward from the remote server to a mobile device
associated with the specific driver and/or insured to facilitate
presenting, on a display of the mobile device, all or a portion of
the adjusted, generated, and/or updated insurance policy, premium,
rate, discount, and/or reward to the specific driver and/or insured
for their review, modification, and/or approval.
[0116] The method may include analyzing the telematics and/or other
data at or via the remote server to determine a sequence of events
occurring before, during, and/or after the vehicle accident and
generating a virtual reconstruction. The analysis may involve
analyzing driver behavior and/or acuity of the specific driver
and/or insured before, during, and/or after the vehicle accident
using the telematics and/or other data. The analysis may also
include analyzing road, weather, traffic, and/or construction
conditions associated with a location of the vehicle accident
before, during, and/or after the vehicle accident, and/or of other
vehicle accidents that occurred at the location of the accident,
such as at a busy intersection. The analysis may further include
analyzing behavior and/or actions taken by another driver (other
than the insured) that is involved with the vehicle accident.
[0117] The virtual reconstruction of the vehicle accident and/or
accident scene may include an animated graphical depiction of two
or more vehicles involved in the vehicle accident before and during
the accident, and may also depict weather, traffic, and/or
construction conditions at the time of the accident and/or in the
vicinity of the vehicle accident superimposed upon a map.
Additionally or alternatively, the virtual reconstruction of the
vehicle accident and/or accident scene may include an animated
graphical depiction of a single vehicle involved in the vehicle
accident before and during the accident. The speed, acceleration,
deceleration, traveling direction, route, destination, location,
number of passengers, type of vehicle, and/or other items
associated with each vehicle depicted may also be graphically
depicted by the virtual reconstruction.
[0118] The telematics and/or other data may include the data
described elsewhere herein. The method of accident reconstruction
may include additional, fewer, or alternate actions, including
those discussed elsewhere herein.
X. Exemplary Buildup Identification Method
[0119] FIG. 5 illustrates an exemplary computer-implemented method
300 for identifying buildup of an insurance claim relating to a
vehicle accident. In some embodiments, the method 300 may be
implemented in whole or in part by one or more components of the
system 1 depicted in FIG. 1. For example, the method 300 may be
implemented by one or more servers remote from the components
(e.g., sensors, vehicles, mobile devices, etc.) sourcing telematics
data, such as the server 40 (e.g., processor(s) 62 of the server 40
when executing instructions stored in the program memory 60 of the
server 40) or another server not shown in FIG. 1.
[0120] The method 300 may include collecting accident data
associated with a vehicle accident involving a driver (block 302).
The driver may be associated with an insurance policy issued by the
insurance provider (e.g., an owner of the policy, or another
individual listed on the policy). The accident data may include
telematics data, and possibly other data, collected from one or
more sources. For example, the accident data may include data
associated with or generated by one or more mobile devices (e.g.,
mobile device 10 of FIGS. 1 and 2); an insured vehicle or a
computer system of the insured vehicle (e.g., vehicle 8 or smart
vehicle controller 14 of FIGS. 1 and 2, or one or more sensors
mounted on the vehicle); a vehicle other than the insured vehicle
(e.g., vehicle 6 of FIG. 1); vehicle-to-vehicle (V2V) communication
(e.g., communications between vehicle 8 and vehicle 6 in FIG. 1);
and/or roadside equipment or infrastructure located near a location
of the vehicle accident (e.g., infrastructure components 26 of FIG.
1). Generally, the accident data may include any one or more of the
types of data discussed above in Section I and/or II (and/or other
suitable types of data), and may be collected according to any of
the techniques discussed above in Section I and/or II (and/or other
suitable techniques). The accident data may have been generated by
the respective source(s), and/or collected, before, during and/or
after the accident.
[0121] The method 300 may also include analyzing any or all of the
collected accident data (block 304). The accident data may be
analyzed to identify the type of accident, a classification of the
accident, and/or a severity of the accident. For example, the
accident may be classified as an "x-car accident," where x
represents the number of vehicles involved. As another example, the
accident may be classified as "side impact," "rear-end collision"
or "head-on collision." As yet another example, it may be
determined that the accident qualifies as a "low," "moderate," or
"high" severity accident (e.g., in terms of likely vehicle damage
and/or personal injury).
[0122] An insurance claim associated with the vehicle accident may
be received (block 306). The insurance claim may have been
generated/initiated by a claim associate of the insurance provider
based upon information obtained from the driver (e.g., over the
phone), for example, and/or received from an enterprise claim
system of the insurance provider.
[0123] The insurance claim may be compared with, or otherwise
analyzed in view of, the accident data collected at block 302
(block 308A). Also, or instead, the insurance claim may be compared
with, or otherwise analyzed in view of, comparable accidents and/or
a baseline of accident information (block 308B). For example, the
method 300 may include determining an average/typical insurance
claim for vehicle accidents associated with the same type,
classification and/or severity of accident that was/were identified
at block 304, and at block 308 the insurance claim received at
block 306 may be compared with that average insurance claim.
[0124] The method 300 may also include identifying potential/likely
claim buildup, and modifying the insurance claim accordingly (block
310). The identification of buildup may be based upon the
comparison (e.g., to an average/typical claim of the same type,
classification and/or severity) at block 308B, for example. As a
more specific example, likely buildup may be identified (and an
agent of the insurance provider may investigate further, etc.) if
the accident is identified as being in the class "rear-end
collision, <5 mph," and it is determined that an average/typical
insurance claim for such accidents involves a much lower amount
(and/or much different type) of vehicle damage than was reported to
the insurance provider. The insurance claim may be modified by
changing a damage amount and/or personal injury description
associated with the claim, for example, and/or further
investigation may be initiated.
[0125] The method 300 may also include handling the modified
insurance claim (block 312). For example, a modified vehicle damage
amount may be used to determine the appropriate payout, if any, by
the insurance provider.
[0126] The method 300 may further include using the modified
insurance claim to adjust, generate and/or update one or more
insurance-related items (block 314). The insurance-related item(s)
may include, for example, parameters of the insurance policy (e.g.,
a deductible), a premium, a rate, a discount, and/or a reward.
[0127] In other embodiments, the method 300 may include additional,
fewer, or alternate actions as compared to those shown in FIG. 5,
including any of those discussed elsewhere herein. For example, the
method 300 may further include transmitting information indicative
of the adjusted, generated, or updated insurance-related items to a
mobile device associated with the driver (or another individual
associated with the insurance policy), such as mobile device 10 of
FIG. 1, to be displayed on the mobile device for review,
modification, or approval by the driver or other individual.
[0128] As can be seen from the above discussion, the method 300 may
enable accurate and efficient buildup detection, which may in turn
allow more accurate and efficient claim handling, and/or more
accurate and efficient adjustment, generation and/or updating of
insurance-related items. Moreover, components in the example system
1 may complete their tasks more quickly and/or efficiently, and/or
the resource usage or consumption of components in the example
system 1 may be reduced. For instance, a claim associate may need
to initiate or receive fewer communications with an insured (e.g.,
via mobile device 10 and/or network 30) and/or other individuals,
and/or the processor 62 may consume less time and/or fewer
processing cycles in handling a claim, if the data collected from
some or all of the sources shown in front-end components 2 of FIG.
1 is complete or informative enough to determine what happened
before and/or during an accident without the need for extensive
follow-up investigation.
XI. Additional Exemplary Buildup Identification Method
[0129] In one aspect, a computer-implemented method of buildup
identification may be provided. The method may include (1)
collecting or receiving telematics and/or other data at a remote
server associated with an insurance provider, the telematics and/or
other data being associated with a vehicle accident involving a
specific driver and/or an insured. The insured may own an insurance
policy issued by the insurance provider and the telematics and/or
other data may be gathered before, during, and/or after the vehicle
accident. The method may include (2) analyzing the telematics
and/or other data at and/or via the remote server to identify a
type, classification, and/or severity of the vehicle accident; (3)
determining an average insurance claim for vehicle accidents
associated with the type, classification, and/or severity of the
vehicle accident, such as at and/or via the remote server; (4)
receiving, at and/or via the remote server, an insurance claim
associated with the vehicle accident; (5) comparing, at and/or via
the remote server, the insurance claim with the average insurance
claim for vehicle accidents associated with the type,
classification, and/or severity of the vehicle accident; and/or (6)
identifying likely buildup or overstatement of the insurance claim,
at and/or via the remote server, based upon the comparison such
that investigation and/or adjustment of the insurance claim is
facilitated. The method may include additional, fewer, or alternate
actions, including those discussed elsewhere herein.
[0130] For instance, the method may further comprise adjusting or
updating, at and/or via the remote server, the insurance claim to
account for the likely buildup or overstatement of the insurance
claim, and/or transmitting information related to the adjusted
and/or updated insurance claim from the remote server to a mobile
device associated with the specific driver and/or insured to
facilitate presenting, on a display of the mobile device, all or a
portion of the adjusted and/or updated insurance claim to the
specific driver and/or insured for their review, modification,
and/or approval.
[0131] The telematics and/or other data may include the types of
data discussed elsewhere herein. Also, identifying likely buildup
or overstatement of the insurance claim may involve identifying
buildup of (i) vehicle damage and/or (ii) personal injury or
injuries from analysis of the telematics and/or other data.
XII. Additional Considerations
[0132] The following additional considerations apply to the
foregoing discussion. Throughout this specification, plural
instances may implement operations or structures described as a
single instance. Although individual operations of one or more
methods are illustrated and described as separate operations, one
or more of the individual operations may be performed concurrently,
and nothing requires that the operations be performed in the order
illustrated. These and other variations, modifications, additions,
and improvements fall within the scope of the subject matter
herein.
[0133] Unless specifically stated otherwise, discussions herein
using words such as "processing," "computing," "calculating,"
"determining," "presenting," "displaying," or the like may refer to
actions or processes of a machine (e.g., a computer) that
manipulates or transforms data represented as physical (e.g.,
electronic, magnetic, or optical) quantities within one or more
memories (e.g., volatile memory, non-volatile memory, or a
combination thereof), registers, or other machine components that
receive, store, transmit, or display information.
[0134] As used herein any reference to "one embodiment" or "an
embodiment" means that a particular element, feature, structure, or
characteristic described in connection with the embodiment is
included in at least one embodiment. The appearances of the phrase
"in one embodiment" in various places in the specification are not
necessarily all referring to the same embodiment.
[0135] As used herein, the terms "comprises," "comprising,"
"includes," "including," "has," "having" or any other variation
thereof, are intended to cover a non-exclusive inclusion. For
example, a process, method, article, or apparatus that comprises a
list of elements is not necessarily limited to only those elements
but may include other elements not expressly listed or inherent to
such process, method, article, or apparatus. Further, unless
expressly stated to the contrary, "or" refers to an inclusive or
and not to an exclusive or. For example, a condition A or B is
satisfied by any one of the following: A is true (or present) and B
is false (or not present), A is false (or not present) and B is
true (or present), and both A and B are true (or present).
[0136] In addition, use of "a" or "an" is employed to describe
elements and components of the embodiments herein. This is done
merely for convenience and to give a general sense of the
invention. This description should be read to include one or at
least one and the singular also includes the plural unless it is
obvious that it is meant otherwise.
[0137] Upon reading this disclosure, those of skill in the art will
appreciate still additional alternative structural and functional
designs through the principles disclosed herein. Thus, while
particular embodiments and applications have been illustrated and
described, it is to be understood that the disclosed embodiments
are not limited to the precise construction and components
disclosed herein. Various modifications, changes and variations,
which will be apparent to those skilled in the art, may be made in
the arrangement, operation and details of the methods and systems
disclosed herein without departing from the spirit and scope
defined in the appended claims. Finally, the patent claims at the
end of this patent application are not intended to be construed
under 35 U.S.C. .sctn. 112(f) unless traditional
means-plus-function language is expressly recited, such as "means
for" or "step for" language being explicitly recited in the
claim(s).
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