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Insurance Abstract
A method and system for determining one or more conditions of a
driving insurance policy for a driver. The system of the invention
comprises a processor configured to receive values of one or more
parameters indicative of a driving profile of the driver and to
calculate a value of each of one or more parameters indicative of
the one or more conditions of the insurance policy based upon the
values of the one or more parameters indicative of the driver's
driving profile. Typically the one or more parameters indicative
of the driving profile are calculated from a data steam generated
by a vehicle sensor utility installed in a vehicle that monitors
the state of the vehicle while being driven by the driver.
Insurance Claims
1. A system for determining one or more conditions of a driving
insurance policy for a driver, comprising a processor configured
to: (a) receive values of one or more parameters indicative of a
driving profile of the driver; and (b) calculate a value of each
of one or more parameters indicative of the one or more conditions
of the insurance policy based upon the values of the one or more
parameters indicative of the driver's driving profile.
2. The system according to claim 1, wherein one or more of the
parameters indicative of the one or more conditions of the insurance
policy includes one or both of a premium for the policy and a deductible
for the policy.
3. The system according to claim 1, further comprising a vehicle
sensor utility operative to monitor the state of the vehicle and
to output a data stream indicative of the driver's driving.
4. The system according to claim 3, wherein the vehicle sensor
utility includes any one or more of the sensors selected from the
group comprising a tachometer, a speedometer, an accelerometers,
a GPS receiver, a foot brake position sensor, an accelerator position
sensor, a steering wheel position sensor, a handbrake position sensor,
an activation of turn signals sensor, a transmission shift position
sensor, and a clutch position sensor.
5. The system according to claim 3, wherein the processor is further
configured to detect one or more driving events in the driver's
driving from the data stream.
6. The system according to claim 5, wherein the processor is further
configured to calculate the values of the one or more parameters
indicative of one or more detected driving events.
7. The system according to claim 6, wherein the processor is further
configured to calculate the values of the parameters indicative
of the driver's driving profile in a calculation involving the values
of the one or more parameters indicative of one or more detected
driving events.
8. The system according to claim 5, wherein the processor is further
configured to identify one or more driving maneuvers executed by
the driver, a driving maneuver being a predetermined sequence of
driving events.
9. The system according to claim 7, wherein the processor is further
configured to calculate the values of the one or more parameters
indicative of one or more detected driving maneuvers.
10. The system according to claim 9, wherein the processor is further
configured to calculate the values of the parameters indicative
of the driver's driving profile in a calculation involving the values
of the one or more parameters indicative of one or more detected
driving maneuvers.
11. The system of claim 5, wherein said at least one driving event
is selected from the group comprising a start event, an end event,
a maximum event, a minimum event, a cross event, a flat event, a
local maximum event, and a local flat event.
12. The system of claim 8, wherein at least one driving maneuver
is selected from the group comprising acceleration, acceleration
before turn, acceleration during lane change, acceleration into
turn, acceleration into turn out from rest, acceleration from rest,
acceleration out of turn, acceleration while passing, braking, braking
after a turn, braking before a turn, stopping, braking out of a
turn, braking within a turn, failed lane change, failed passing,
lane change, lane change and braking, passing, passing and braking,
turning, turning and accelerating, and executing a U-turn.
13. A method for determining one or more conditions of a driving
insurance policy for a driver, comprising a processor configured
to: (a) receiving values of one or more parameters indicative of
a driving profile of the driver; and (b) calculating a value of
each of one or more parameters indicative of the one or more conditions
of the insurance policy based upon the values of the one or more
parameters indicative of the driver's driving profile.
14. The method according to claim 13, wherein one or more of the
parameters indicative of the one or more conditions of the insurance
policy includes one or both of a premium for the policy and a deductible
for the policy.
15. The method according to claim 13, further comprising monitoring
the state of the vehicle and outputting a data stream indicative
of the driver's driving.
16. The method according to claim 15, wherein the monitoring includes
monitoring any one or more sensors sensing the driver's driving,
the one or more sensors being selected from the group comprising
a tachometer, a speedometer, an accelerometers, a GPS receiver,
a foot brake position sensor, an accelerator position sensor, a
steering wheel position sensor, a handbrake position sensor, an
activation of turn signals sensor, a transmission shift position
sensor, and a clutch position sensor.
17. The method according to claim 15, further comprising detecting
one or more driving events in the driver's driving.
18. The method according to claim 17, further comprising calculating
the values of the one or more parameters indicative of one or more
detected driving events.
19. The method according to claim 18, further comprising calculating
the values of the parameters indicative of the driver's driving
profile in a calculation involving the values of the one or more
parameters indicative of one or more detected driving events.
20. The method according to claim 17, further comprising identifying
one or more driving maneuvers executed by the driver, a driving
maneuver being a predetermined sequence of driving events.
21. The method according to claim 19, further comprising calculating
the values of the one or more parameters indicative of one or more
detected driving maneuvers.
22. The method according to claim 21, further comprising calculating
the values of the parameters indicative of the driver's driving
profile in a calculation involving the values of the one or more
parameters indicative of one or more detected driving maneuvers.
23. The method of claim 17, wherein said at least one driving event
is selected from the group comprising a start event, an end event,
a maximum event, a minimum event, a cross event, a flat event, a
local maximum event, and a local flat event.
24. The method of claim 18, wherein at least one driving maneuver
is selected from the group comprising acceleration, acceleration
before turn, acceleration during lane change, acceleration into
turn, acceleration into turn out from rest, acceleration from rest,
acceleration out of turn, acceleration while passing, braking, braking
after a turn, braking before a turn, stopping, braking out of a
turn, braking within a turn, failed lane change, failed passing,
lane change, lane change and braking, passing, passing and braking,
turning, turning and accelerating, and executing a U-turn.
25. A system for determining one or more conditions of a driving
insurance policy for a driver, comprising (a) a vehicle sensor utility
operative to monitor the state of a vehicle and to output a data
stream indicative of a driver's driving; and (b) a processor configured
to: (i) detect one or more driving events in the driver's driving
from the data stream output from the vehicle sensor utility; (ii)
identify one or more driving maneuvers executed by the driver, a
driving maneuver being a predetermined sequence of driving events;
(iii) calculate the values of the one or more parameters indicative
of one or more detected driving maneuvers; (iv) calculate the values
of parameters indicative of the driver's driving profile in a calculation
involving the values of the one or more parameters indicative of
one or more detected driving maneuvers; and (v) calculate a value
of each of one or more parameters indicative of the one or more
conditions of the insurance policy based upon the values of the
one or more parameters indicative of the driver's driving profile.
26. A method for determining one or more conditions of a driving
insurance policy for a driver, comprising: (a) detecting one or
more driving events in the driver's driving in a data stream output
from a vehicle sensor utility; (b) identifying one or more driving
maneuvers executed by the driver, a driving maneuver being a predetermined
sequence of driving events; (c) calculating the values of the one
or more parameters indicative of one or more detected driving maneuvers;
(d) calculating the values of parameters indicative of the driver's
driving profile in a calculation involving the values of the one
or more parameters indicative of one or more detected driving maneuvers;
and (e) calculating a value of each of one or more parameters indicative
of the one or more conditions of the insurance policy based upon
the values of the one or more parameters indicative of the driver's
driving profile.
27. A program storage device readable by machine, tangibly embodying
a program of instructions executable by the machine to perform method
steps for determining one or more conditions of a driving insurance
policy for a driver, comprising calculating a value of each of one
or more parameters indicative of the one or more conditions of the
insurance policy based upon the values of the one or more parameters
indicative of the driver's driving profile.
28. A computer program product comprising a computer useable medium
having computer readable program code embodied therein for determining
one or more conditions of a driving insurance policy for a driver,
the computer program product comprising computer readable program
code for causing the computer to calculate a value of each of one
or more parameters indicative of the one or more conditions of the
insurance policy based upon the values of the one or more parameters
indicative of the driver's driving profile.
29. A computer program comprising computer program code means for
performing all the steps of claim 13 when said program is run on
a computer.
30. A computer program as claimed in claim 27 embodied on a computer
readable medium.
Insurance Description
[0001] This application claims the benefit of prior U.S. provisional
patent application No. 60/688,726 filed Jun. 9, 2005, the contents
of which are hereby incorporated by reference in their entirety.
FIELD OF THE INVENTION
[0002] The present invention relates to a method and system for
devising a driving insurance policy for a driver.
BACKGROUND OF THE INVENTION
[0003] Driver skill and responsible behavior is critical for vehicle
safety. Various methods and systems have therefore been proposed
for automatically monitoring a driver and the manner in which the
vehicle is being driven. Such systems and methods allow objective
driver evaluation to determine the quality of the driver's driving
practices and facilitate the collection of qualitative and quantitative
information related to the contributing causes of vehicle incidents,
such as accidents. These systems and methods help to prevent or
reduce vehicle accidents, and vehicle abuse, and also help to reduce
vehicle operating, maintenance, and replacement costs. The social
value of such devices and systems is universal, in reducing the
impact of vehicle accidents. The economic value is especially significant
for commercial and institutional vehicle fleets.
[0004] Driver monitoring systems vary in their features and functionality
and exhibit considerable variability in their approach to the overall
problem. Some focus on location and logistics, others on engine
diagnostics and fuel consumption, whereas others concentrate on
safety management.
[0005] For example, U.S. Pat. No. 4,500,868 to Tokitsu et al. is
intended as an adjunct in driving instruction. By monitoring a variety
of sensors (such as engine speed, vehicle velocity, selected transmission
gear, and so forth), the system of Tokitsu determines whether certain
predetermined condition thresholds are exceeded, and, if so, to
signal an alarm to alert the driver. Alarms are also recorded for
later review and analysis. The Tokitsu system is valuable, for example,
if the driver were to rapidly depress the accelerator pedal resulting
in an acceleration exceeding a predetermined threshold. This would
result in an alarm, cautioning the driver to reduce the acceleration.
If the driver were prone to such behavior, this is indicated in
the records created by the system.
[0006] U.S. Pat. Nos. 4,671,111 and 5,570,087 to Lemelson teach
the use of accelerometers and data recording/transmitting equipment
to obtain and analyze vehicle acceleration and deceleration.
[0007] U.S. Pat. No. 5,270,708 to Kamishima discloses a system
that detects a vehicle's position and orientation, turning, and
speed, and coupled with a database of past accidents at the present
location and determines whether the present vehicle's driving conditions
are similar to those of a past accident, and if so, alerts the driver.
If, for example, the current vehicle speed on a particular road
exceeds the speed threshold previously stored in the database at
that point of the road, the driver could be alerted. Moreover, if
excessive speed on that particular area is known to be the cause
of many accidents, the system could notify the driver of this.
[0008] U.S. Pat. No. 5,546,305 to Kondo performs an analysis of
vehicle speed and acceleration, engine rotation rate, and applies
threshold tests. Such an analysis can often distinguish between
good driving behavior and erratic or dangerous driving behavior
(via a driving "roughness" analysis). Providing a count
of the number of times a driver exceeded a predetermined speed threshold,
for example, may be indicative of unsafe driving.
[0009] U.S. Pat. No. 6,060,989 to Gehlot describes a system of
sensors within a vehicle for determining physical impairment of
the driver that might interfere with the driver's ability to safely
control his vehicle. Specific physical impairments illustrated include
intoxication, fatigue and drowsiness, or medicinal side-effects.
In Gehlot's system, sensors monitor the driver directly, rather
than the vehicle.
[0010] U.S. Pat. No. 6,438,472 to Tano, et al. describes a system
which statistically analyzes driving data (such as speed and acceleration
data) to obtain statistical aggregates that are used to evaluate
driver performance. Unsatisfactory driver behavior is determined
when certain predefined threshold values are exceeded. A driver
whose behavior exceeds a statistical threshold from what is considered
safe driving, is classified as a "dangerous" driver. Thresholds
can be applied to the statistical measures, such as standard deviation.
[0011] In addition to the above issued patents, there are several
commercially available products for monitoring vehicle driving behavior.
The "Mastertrak" system by Vetronix Corporation of Santa
Barbara, Calif., is intended as a fleet management system which
provides an optional "safety module" that addresses vehicle
speed and safety belt use. A system manufactured by SmartDriver
of Houston, Tex., monitors vehicle speed, accelerator throttle position,
engine and engine RPM, and can detect, count, and report on the
exceeding of thresholds for these variables.
SUMMARY OF THE INVENTION
[0012] The present invention provides a method and system for determining
the terms or conditions of an insurance policy for a driver. In
accordance with the invention, a driver is profiled according to
the risk associated with his driving and one or more conditions
are determined for an insurance policy is based upon the driver's
profile. Profiling the driver involves collecting data on the driver's
driving activity and processing the data to calculate one or more
parameters indicative of the driver's driving skills, his aptitude
in handling driving situations, the general safety of his driving,
and his risk of being involved in an adverse driving event.
[0013] The calculated parameters are used to determine one or more
conditions of a driving insurance policy for the driver such as
calculating the insurance premium for the policy or calculating
a policy deductible (the amount deducted from an indemnification
payment made to the insured driver in accordance with the terms
of the insurance policy).
[0014] The driver's profile may be obtained by any method known
in the art. The profile is typically obtained by recording driving
data of the driver using one or more sensing devices installed in
a vehicle while being driven by the driver. The sensing devices
may be linked to a processor in the vehicle for initial processing
of the data. However, part of the processing of the collected day
may be performed in a remotely located server that receives raw
or partially processed data from a unit in the vehicle.
[0015] The driver's driving data may include, for example, any
one or more of acceleration in the direction of driving, radial
acceleration, speed, and a variety of other factors that relate
to the physical location or movement of the vehicle. The driving
parameter may also include other parameters more directly associated
with the driver such as use of the vehicle's accelerator pedal or
breaks, use of a hand-held mobile communication device while driving,
and many others.
[0016] The invention may be applied to a plurality of drivers,
for example, a plurality of drivers driving one or more joint vehicles,
for example, drivers of a fleet of vehicles, drivers in a family
all jointly sharing one or a few vehicles, etc. In this embodiment,
driving parameters for each driver may be calculated and the conditions
of a driver's insurance policy may be determined for each driver.
Alternatively, the driving parameters obtained for each driver may
be used to determine the conditions for a group insurance policy
for the entire plurality of drivers. As will be appreciated, the
calculation of the conditions of the group insurance policy may
involve the extent of driving each driver. For example, a driver
that spends a relatively large amount of time driving may be assigned
a higher weight in the calculation of the group insurance policy
in comparison to a drive that spends only a relatively small amount
of time driving.
[0017] A system according to the invention comprises one or more
vehicle-installed sensing devices for monitoring the state of the
vehicle and outputting data indicative thereof. The sensing devices
may be linked to a processor located on the vehicle for initial
processing of the data.
[0018] The system in most cases comprises a system server utility
and vehicle-carried processor unit. The communication between the
vehicle and a server utility will typically be wireless, e.g. transmitted
over a cellular network or any other suitable wireless link. A wireless
link between the vehicle-installed utilities and the server, permit
an essentially real time download of data on the driving activity,
and at times partially processed data from the vehicle utilities
to the server. However, the communication may at times be through
a physical link or a short range contact-less communication, for
example, when the a vehicle arrives at a central location such as
a service center or refueling station.
[0019] As stated above, the driver's profile may be obtained from
the driver's driving data which may be collected and initially analyzed
in any manner known in the art. In a preferred embodiment of the
invention, the driving data are collected as described in U.S. patent
application Ser. No. 10/894,345, the contents of which are incorporated
herein in its entirety by reference.
[0020] The method and system of U.S. patent application Ser. No.
10/894,345 is based on the realization that a driver's driving ability
is revealed in the manner that he executes common driving maneuvers.
Such driving maneuvers include passing, lane changing, traffic blending,
making turns, handling intersections, handling off- and on-ramps,
driving in heavy stop-and-go traffic, accelerating, accelerating
before turn, accelerating during lane change, accelerating into
a turn, accelerating into a turn from rest, accelerating from rest,
accelerating out of a turn, accelerating while passing, braking,
braking after a turn, braking before a turn, stopping, braking out
of a turn, braking within a turn, failed lane change, failed passing,
lane change, lane change braking, turning, turning and accelerating,
and executing a U-turn.
[0021] The method of U.S. patent application Ser. No. 10/894,345
calculates the values of parameters of the driver's driving from
parameter values extracted from the driving maneuvers executed by
the driver. Fundamental driving events in the driver's driving are
detected from the data streams from the vehicle's, sensors and driving
maneuvers are identified as predetermined sequences of driving events.
The driving maneuvers are analyzed to calculate the values of parameters
of the driving maneuvers as executed by the driver.
[0022] A driving event handler and the maneuver detector may each,
independently, be a software utility operating in a processor, a
hardware utility configured for that purpose or, typically, a combination
of the two. The event handler and the maneuver detector may both
be included in one computing unit, as hardware and/or software modules
in such unit, each one may constitute a separate hardware and/or
software utility operative in different units. Such different units
may be installed in a vehicle, although, as may be appreciated,
they may also be constituted in a remote location, e.g. in a system
server, or one installed in the vehicle and the other in the remote
location. In case one or more of the system's components is installed
in a remote location, the receipt of input from the upstream vehicle
installed component may be wireless, in which case the input may
be continuous or batch wise (e.g. according to a predefined transmission
sequence) or may be through physical or proximity communication,
e.g. when a vehicle comes for service or refueling.
[0023] The system of U.S. patent application Ser. No. 10/894,345
may include a database characteristic driving maneuver and an anomaly
detector operative to compare at least one driving maneuver as executed
by the driver to a characteristic driving maneuver previously stored
in the database. The database may record driving maneuver representations
representative of an average driver's performance, e.g. an average
performance in a fleet of drivers, in a defined neighborhood, in
a country, drivers of a specific age group, etc. In such a case
the driving maneuver for a driver may be compared to a characteristic
driving maneuver for a plurality of drivers.
[0024] Thus, in its first aspect, the invention provides a system
for determining one or more conditions of a driving insurance policy
for a driver, comprising a processor configured to:
[0025] (a) receive values of one or more parameters indicative
of a driving profile of the driver; and
[0026] (b) calculate a value of each of one or more parameters
indicative of the one or more conditions of the insurance policy
based upon the values of the one or more parameters indicative of
the driver's driving profile.
[0027] In its second aspect, the invention provides a method for
determining one or more conditions of a driving insurance policy
for a driver, comprising a processor configured to:
[0028] (a) receiving values of one or more parameters indicative
of a driving profile of the driver; and
[0029] (b) calculating a value of each of one or more parameters
indicative of the one or more conditions of the insurance policy
based upon the values of the one or more parameters indicative of
the driver's driving profile.
[0030] In its third aspect, the invention provides a system for
determining one or more conditions of a driving insurance policy
for a driver, comprising
[0031] (a) a vehicle sensor utility operative to monitor the state
of a vehicle and to output a data stream indicative of a driver's
driving; and
[0032] (b) a processor configured to: [0033] (i) detect one or
more driving events in the driver's driving from the data stream
output from the vehicle sensor utility; [0034] (ii) identify one
or more driving maneuvers executed by the driver, a driving maneuver
being a predetermined sequence of driving events; [0035] (iii) calculate
the values of the one or more parameters indicative of one or more
detected driving maneuvers; [0036] (iv) calculate the values of
parameters indicative of the driver's driving profile in a calculation
involving the values of the one or more parameters indicative of
one or more detected driving maneuvers; and [0037] (v) calculate
a value of each of one or more parameters indicative of the one
or more conditions of the insurance policy based upon the values
of the one or more parameters indicative of the driver's driving
profile.
[0038] In its fourth aspect, the invention provides a method for
determining one or more conditions of a driving insurance policy
for a driver, comprising:
[0039] (a) detecting one or more driving events in the driver's
driving in a data stream output from a vehicle sensor utility;
[0040] (b) identifying one or more driving maneuvers executed by
the driver, a driving maneuver being a predetermined sequence of
driving events;
[0041] (c) calculating the values of the one or more parameters
indicative of one or more detected driving maneuvers;
[0042] (d) calculating the values of parameters indicative of the
driver's driving profile in a calculation involving the values of
the one or more parameters indicative of one or more detected driving
maneuvers; and
[0043] (e) calculating a value of each of one or more parameters
indicative of the one or more conditions of the insurance policy
based upon the values of the one or more parameters indicative of
the driver's driving profile.
[0044] In its fifth aspect, the invention provides a program storage
device readable by machine, tangibly embodying a program of instructions
executable by the machine to perform method steps for determining
one or more conditions of a driving insurance policy for a driver,
comprising calculating a value of each of one or more parameters
indicative of the one or more conditions of the insurance policy
based upon the values of the one or more parameters indicative of
the driver's driving profile.
[0045] In its sixth aspect, the invention provides a computer program
product comprising a computer useable medium having computer readable
program code embodied therein for determining one or more conditions
of a driving insurance policy for a driver, the computer program
product comprising computer readable program code for causing the
computer to calculate a value of each of one or more parameters
indicative of the one or more conditions of the insurance policy
based upon the values of the one or more parameters indicative of
the driver's driving profile.
[0046] In its seventh aspect, the invention provides computer program
comprising computer program code means for performing all the steps
of the method of the invention when said program is run on a computer.
[0047] In its eighth aspect, the invention provides a computer
program comprising computer program code means for performing all
the steps of the method of the invention when said program is run
on a computer embodied on a computer readable medium.
[0048] It will also be understood that the system according to
the invention may be a suitably programmed computer. Likewise, the
invention contemplates a computer program being readable by a computer
for executing the method of the invention. The invention further
contemplates a machine-readable memory tangibly embodying a program
of instructions executable by the machine for executing the method
of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0049] The invention is herein described, by way of example only,
with reference to the accompanying drawings, wherein:
[0050] FIG. 1 shows a method and system for providing insurance
in accordance with one embodiment of the invention;
[0051] FIG. 2 shows a method and system for providing insurance
in accordance with another embodiment of the invention;
[0052] FIG. 3 shows a graph of a raw data stream from multiple
vehicle accelerometers;
[0053] FIG. 4 shows filtering of the raw data stream of FIG. 3;
[0054] FIG. 5 shows parsing the filtered data stream of FIG. 4
to derive a string of driving events;
[0055] FIG. 6 shows a data and event string analysis for a "lane
change" driving maneuver;
[0056] FIG. 7 shows a data and event string analysis for a "turn"
driving maneuver;
[0057] FIG. 8 shows a data and event string analysis for a "braking
within turn" driving maneuver.
[0058] FIG. 9 shows a data and event string analysis for an "accelerate
within turn" driving maneuver;
[0059] FIG. 10 shows a non-limiting illustrative example of transitions
of a finite state machine for identifying driving maneuvers;
[0060] FIG. 11 is a flowchart of a method for analyzing and evaluating
vehicle driver performance;
[0061] FIG. 12 is schematic diagram of an arrangement for assessing
driver skill according to an embodiment of the present invention;
[0062] FIG. 13 is a schematic diagram of an arrangement for assessing
driver attitude; and
[0063] FIG. 14 is a schematic diagram of an arrangement for determining
whether there is a significant anomaly in the current driver's behavior
and/or performance.
DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS
[0064] The principles and operation of a system and method according
to the present invention may be understood with reference to the
drawings and the accompanying description that illustrate some specific
and currently preferred embodiments. It is to be understood that
these embodiments, while illustrative are non-limiting but rather
illustrative to the full scope of the invention defined above.
[0065] FIG. 1 shows a system for determining one or more conditions
of a driver's insurance policy in accordance with one embodiment
of the invention. A typical set of sensors 101 installed in a vehicle
includes one or more sensors such as a tachometer 103, a speedometer
105, one or more accelerometers 107, a GPS receiver 109, and optional
additional sensors 111. As will be appreciated, the invention is
not limited to a specific type of a sensor set and any currently
available or future available sensing system may be employed in
the present invention. In the case of accelerometers, it is understood
that an accelerometer is typically operative to monitoring the acceleration
along one particular specified vehicle axis, and outputs a raw data
stream corresponding to the vehicle's acceleration along that axis.
Typically, the two main axes of vehicle acceleration that are of
interest are the longitudinal vehicle axis--the axis substantially
in the direction of the vehicle's principal motion ("forward"
and "reverse"); and the transverse (lateral) vehicle axis--the
substantially horizontal axis substantially orthogonal to the vehicle's
principal motion ("side-to-side"). An accelerometer which
is capable of monitoring multiple independent vector accelerations,
along more than a single axis (a "multi-axis" accelerometer)
is herein considered as being equivalent to a plurality of accelerometers,
wherein each accelerometer of the plurality is capable of monitoring
acceleration along a single axis. Additional sensors in the set
of sensors 101 can include sensors for foot brake position, accelerator
position, steering wheel position, handbrake position, activation
of turn signals, transmission shift position, clutch position, and
the like. Some of the sensors, such as tachometer 103 and speedometer
105 may output a continuously varying signal which represents the
magnitude of a measured parameter. Other sensors, such as a transmission
shift position sensor may have a discrete output which indicates
which gear is in use. A more complex output would come from GPS
receiver 109, according to the formatting standards of the manufacturer
or industry. Other sensors can include a real-time clock, a directional
device such as a compass, one or more inclinometers, temperature
sensors, precipitation sensors, ambient light sensors, and so forth,
to gauge actual road conditions and other driving factors.
[0066] The output of sensor set 101 is a stream 102 of raw data,
in analog and/or digital form. The data stream 102 is input into
an analysis and evaluation unit 113. The evaluation unit 113 calculates
the values of one or more parameters of the driver's driving on
the basis of the raw data stream 102. For example, the evaluation
unit 113 may include threshold settings 115 and a threshold discriminator
117. A statistical unit 119 provides report summaries, and an optional
continuous processing unit 121 may be included to preprocess the
raw data. The output of analysis and evaluation unit 113 is a statistically-processed
data stream 124.
[0067] The data stream is input to an insurance policy processor
129, which determines one or more conditions of an insurance policy
of the driver. As stated above, determining the one or more conditions
of the insurance policy may include calculating a premium for the
driver's driving insurance or calculating a deductible for the policy.
[0068] FIG. 2 illustrates a system for determining one or more
conditions of an insurance policy for a driver according to a more
preferred embodiment of the present invention. In this embodiment,
a driver's profile is obtained as disclosed in U.S. patent application
Ser. No. 10/894,345, the contents of which are incorporated herein
in its entirety by reference. The system of this embodiment includes
a sensor set 101 that is similar to the sensor set 101 of the FIG.
1 that monitors states of a vehicle while being driven by the driver,
and outputs a raw data stream 102. The raw data stream 102 is input
into a driving event handler 201, which contains a low-pass filter
202, a driving event detector 203, a driving events stack and driving
event extractor 205 for storing and managing driving events, and
a driving event library 207, which obtains data from a database
209.
[0069] In this embodiment, driving events are fundamental driving
operations that characterize basic moves of driving, as explained
and illustrated in detail below. The driving event handler 201 performs
an analysis on the raw data stream 102 from sensor set 101, and
outputs a string of driving events 206. A driving event string may
be a time-ordered non-empty set of driving event symbols arranged
in order of their respective occurrences. Driving event detector
203 performs a best-fit comparison of the filtered sensor data stream
with event types from event library 207, such as by using a sliding
window technique over the data stream. A real-time clock 208 provides
a reference time input to the system, illustrated here for a non-limiting
embodiment of the present invention as input to driving event handler
201.
[0070] A driving event may be characterized by a symbol that qualitatively
identifies the basic driving operation, and may be associated with
one or more numerical parameters which quantify the driving event.
These parameters may be derived from scaling and offset factors
used in making a best-fit comparison against events from the event
library 207. For example, the scaling of the time axis and the scaling
of the variable value axis which produce the best fit of the selected
segment of the input data stream to the model of the event in event
library 207 can be used as numerical parameters (in most cases,
one or more of these numerical parameters are related to the beginning
and end times of the driving event). If close fits can be obtained
between the string of driving events and the input data stream,
the event string (including the event symbols and associated parameter
set) can replace the original data stream, thereby greatly compressing
the data and providing an intelligent analysis thereof.
[0071] The driving event string 206 is input into a driving maneuver
detector 211. A driving maneuver is recognized as a sequence of
driving events which are executed when the maneuver is executed.
A "lane change", for example, is a driving maneuver that,
in the simplest case, may be represented by a sequence of a lateral
acceleration followed by a lateral deceleration during a period
of forward motion. A lane change during a turn is more involved,
but can be similarly represented by a sequence of driving events.
As in the case of the driving events themselves, driving maneuvers
can contain one or more numerical parameters, which are related
to the numerical parameters of the driving events which make up
the driving maneuver.
[0072] A driving maneuver sequence is a time-ordered non-empty
set of driving maneuvers arranged according to the respective times
of their occurrence. Referring still to FIG. 2, it is seen that
in order to derive a sequence of driving maneuvers from a string
of driving events, maneuver detector 211 contains a maneuver library
213 fed from the database 209, a pattern recognition unit 215 to
recognize sequences of driving events which make up driving maneuvers,
and a maneuver classifier 217 to construct a driving maneuver sequence
output. By comparing the timing and other quantities of the driving
maneuver with those of known skillful drivers, a skill assessor
219 calculates a skill rating for the driver's execution of one
or more driving maneuvers. Furthermore, by analyzing the magnitude
of certain key parameters (such as those related to acceleration
and deceleration during the maneuver), an attitude assessor 221
can develop and assign an attitude rating to the current driver's
execution of the driving maneuver. Moreover, each maneuver may be
assigned a weighting driving risk coefficient for developing and
assigning an aggregate attitude rating for the current driver.
[0073] As a non-limiting example, a simple event is to start the
vehicle moving forward from rest (the "start" event).
A numerical parameter for this event is the magnitude of the acceleration.
A generalized version of this event is a speed increase of a moving
vehicle (the "accelerate" event). Another simple event
is to slow the vehicle to a halt from a moving condition (the "stop"
event).
[0074] The following Table 1 includes non-limiting examples of
some common driving maneuvers, their common meaning in a driving
context, and their suggested driving risk coefficients. It is noted
that there are many possible descriptive terms for the driving events
and driving maneuvers described herein, and the choice of the terms
that are used herein has by itself no significance in the context
of the invention. For example, the "passing" driving maneuver
is herein named after the common term for the maneuver in the United
States, but the same maneuver is also referred to as "bypassing"
or "overtaking" in some locations.
[0075] In the non-limiting example shown in FIG. 1, coefficients
range from 1 to 10, with 10 representing the most dangerous driving
maneuvers. Risk coefficients, of course, are subjective, and according
to other embodiments of the present invention may be redefined to
suit empirical evidence. The coefficients may also be different
for different countries, different driver populations, etc. The
coefficients may be different at different times. For example, driving
at a speed above a given threshold may be assigned a relatively
low risk coefficient during the daylight hours, and a higher risk
coefficient during the night. TABLE-US-00001 TABLE 1 Examples of
Driving Maneuvers and Driving Risk Coefficients Driving Maneuver
Coefficient Accelerate 3 increase vehicle speed Accelerate before
Turn 6 increase vehicle speed prior to a turn Accelerate during
Lane Change 5 increase vehicle speed while moving to a different
travel lane Accelerate into Turn 5 Increase vehicle speed while
initiating a turn Accelerate into Turn out of Stop 6 start moving
vehicle while initiating a turn from a stopped position Accelerate
out of Stop 5 start moving vehicle from a stopped position Accelerate
out of Turn 4 increase vehicle speed while completing a turn Accelerate
while Passing 5 increase vehicle speed while overtaking and bypassing
a leading vehicle when initially traveling in the same travel lane
Braking 5 applying vehicle brakes to reduce speed Braking after
Turn 6 applying vehicle brakes to reduce speed after completing
a turn Braking before Turn 7 applying vehicle brakes to reduce speed
before beginning a turn Braking into Stop 3 applying vehicle brakes
to reduce speed and coming to a stopped position Braking out of
Turn 7 applying vehicle brakes to reduce speed while completing
a turn Braking within Turn 8 applying vehicle brakes to reduce speed
during a turn Failed Lane Change 10 aborting an attempted move to
a different travel lane Failed Passing 10 aborting an attempt to
overtake and bypass a leading vehicle when initially traveling in
the same travel lane Lane Change 4 moving into a different travel
lane Lane Change and Braking 8 moving into a different travel lane
and then applying vehicle brakes to reduce speed Passing 4 overtaking
and bypassing a leading vehicle when initially traveling in the
same travel lane Passing and Braking 8 overtaking and passing a
leading vehicle when initially traveling in the same travel lane
and then applying vehicle brakes to reduce speed Turn 3 substantially
changing the vehicle travel direction Turn and Accelerate 4 substantially
changing the vehicle travel direction and then increasing vehicle
speed U-Turn 5 substantially reversing the vehicle travel direction
[0076] The maneuver detector 211 may include an anomaly detector
223 in which the driving maneuvers executed by the driver are checked
for inconsistencies with a previously obtained driving profile of
the driver. A profile or set of profiles for a driver can be maintained
in the database 209 for comparison with the driver's current driving
profile. A set of profiles for various maneuvers can be maintained
so that whatever the current driving maneuver happens to be, a comparison
can be made with a previously recorded reference maneuver of the
same category (namely, for example, a lane change maneuver with
a recorded lane change maneuver, etc.). If there is a significant
discrepancy between the current driving maneuvers and stored previously
reference profiles for the driver, which are used as reference,
the driving inconsistencies can be reported to an emergency alert
227 for follow-up checking or investigation. As previously noted,
a significant discrepancy or inconsistency may indicate an unsafe
condition (e.g. as a result of a driver's current attitude, as a
consequence of driving under the influence of alcohol and/or drugs,
etc.).
[0077] The output 220 of the maneuver detector 211 icludes a sequence
of driving maneuvers together with the skill ratings of the driver's
execution of the maneuvers. The output 220 is input to an insurance
policy processor 229. The insurance policy processor 229 determines
one or more conditions of an insurance policy of the driver in a
calculation involving the data in the output 220. As stated above,
determining the one or more conditions of the insurance policy may
include calculating a premium for the driver's driving insurance
or calculating a deductible for the policy.
Analysis of Raw Data to Obtain a Driving Event String
[0078] FIG. 3 illustrates an example of raw data stream 307 obtained
from two vehicle accelerometers, as plotted in a 3-dimensional form.
An x-axis 301 represents the longitudinal acceleration of the vehicle
(in the direction in which the vehicle is normally traveling), and
hence represents forward and reverse acceleration and deceleration
data 307. A y-axis 303 represents the transverse (lateral) acceleration
of the vehicle to the left and right of the direction in which the
vehicle is normally traveling,. A time axis 305 is perpendicular
to the x and y-axes. Data 307 are representative of the time-dependent
raw data stream output from sensor set 101 (FIG. 2).
[0079] Note that FIG. 3 is a non-limiting example for the purpose
of illustration. Other raw sensor data streams besides acceleration
can be represented in a similar manner. Other examples include accelerator
(gas) pedal, position, speed, brake pedal position and brake pressure,
gear shifting rate, etc. In other cases, however, the graph may
not need multiple data axes. Acceleration is a vector quantity and
therefore has directional components, requiring multiple data axes.
Scalar variables, however, have no directional components and two-dimensional
graphs may suffice to represent the data stream in time. Speed,
brake pressure, and so forth are scalar variables.
[0080] FIG. 4a shows the data depicted in FIG. 3 in a two-dimensional
form in which the acceleration data in two dimensions (the x and
y axes in FIG. 3), are shown on a common time axis. The longitudinal
acceleration (the x axis in FIG. 3) is shown as a data stream 401a,
and the lateral acceleration (the y axis in FIG. 3) is shown as
a sta stream 140b. FIG. 4b illustrates the effect of the initial
filtering of the data streams x and y in FIG. 4a performed by low-pass
filter 202. After applying low-pass filter 202 to each of the data
streams 401a and 401b, respective filtered data streams 403a, and
403b are output in which noise has been removed is output. In addition
to low-pass filtering, low-pass filter 202 can also apply a moving
average and/or a domain filter.
[0081] FIG. 5 illustrates the parsing each of the filtered data
streams 403a and 403b into a string of driving events. Driving events
are indicated by distinctive patterns in the filtered data stream,
and can be classified according, for example, to the following non-limiting
set of driving events:
[0082] a "Start" event 501, designated herein as S, wherein
the variable has an initial substantially zero value;
[0083] an "End" event 503, designated herein as E, wherein
the variable has a final substantially zero value;
[0084] a maximum or "Max" event 505, designated herein
as M, wherein the variable reaches a substantially maximum value;
[0085] a minimum or "Min" event 507, designated herein
as L, wherein the variable reaches a substantially minimum value;
[0086] a "Cross" event 509, designated herein as C, wherein
the variable changes sign (crosses the zero value on the axis);
[0087] a local maximum or "L. Max" event 511, designated
herein as 0, wherein the variable reaches a local substantially
maximum value;
[0088] a local flat or "L. Flat" event 513, designated
herein as T, wherein the variable has a local (temporary) substantially
constant value; and
[0089] a "Flat" event 515, designated herein as F, wherein
the variable has a substantially constant value.
[0090] As previously mentioned, each of these driving events designated
by a symbolic representation also has a set of one or more numerical
parameters which quantify the numerical values associated with the
event. For example, a "Max" event M has the value of the
maximum as a parameter. In addition, the time of occurrence of the
event is also stored with the event.
[0091] It is possible to define additional driving events in a
similar fashion. For events involving vector quantities, such as
for acceleration (as in the present non-limiting example), the driving
event designations are expanded to indicate whether the event relates
to the x component or the y component. For example, a maximum of
the x-component (of the acceleration) is designated as Mx, whereas
a maximum of the y-component (of the acceleration) is designated
as My.
[0092] Referring again to FIG. 5, it is seen that filtered data
streams 403a and 403b contain the following time-ordered sequence
of driving events:
[0093] an Sx event 521;
[0094] an Lx event 523;
[0095] an Fy event 525;
[0096] an Ex event 527;
[0097] an Sy event 529;
[0098] an Mx event 531;
[0099] an My event 533;
[0100] an Ly event 535;
[0101] a Ty event 537;
[0102] an Ey event 539;
[0103] an Sx event 541; and
[0104] an Mx event 543.
[0105] The above analysis is performed by the event handler 201
(FIG. 2). The resulting parsed filtered data thus results in the
output of the driving event string from event handler 201:
[0106] Sx Lx Fy Ex Sy Mx My Ly Ty Ey Sx Mx
[0107] Once again, each of the symbols of the above event string
has associated parameters which numerically quantify the individual
events.
[0108] According to another embodiment of the present invention,
there are also variations on these events, depending on the sign
of the variable. For example, there may be an Sx positive event
and an Sx negative event, corresponding to acceleration and deceleration,
respectively.
Analysis of a Driving Event String to Obtain a Sequence of Driving
Maneuvers
[0109] FIG. 6 illustrates raw data stream 601 for a Lane Change
driving maneuver, as a 3-dimensional representation of the x- and
y- acceleration components as a function of time. A two dimensional
graph 603 shows the x- and y-acceleration components on a common
time axis. The driving event sequence for this maneuver is: an Sy
event 605; an My event 607; a Cy event 609; an Ly event 611; and
an Ey event 613. Thus, the driving event sequence Sy My Cy Ly Ey
corresponds to a Lane Change driving maneuver.
[0110] FIG. 7 illustrates raw data 701 for a Turn driving maneuver,
The driving event sequence for this maneuver is: an Sy event 703;
an Ly event 705; and an Ey event 707. Thus, the driving event sequence
Sy Ly Ey corresponds to a Turn driving maneuver.
[0111] FIG. 8 illustrates raw data 801 for a Braking within Turn
driving maneuver. The driving event sequence for this maneuver is:
an Sy event 803; an Sx event 805; an My event 807; an Ey event 809;
an Lx event 811; and an Ex event 813. Thus, the driving event sequence
Sy Sx My Ey Lx Ex corresponds to a Braking within Turn driving maneuver.
[0112] It is noted that the Braking within Turn driving maneuver
illustrates how the relative timing between the x-component events
and the y-component events can be altered to create a different
driving maneuver. Referring to FIG. 8, it is seen that the order
of Sx event 805 and My event 807 can in principle be reversed, because
they are events related to different independent variables (the
forward x-component of acceleration versus and the lateral y-component
of acceleration). The resulting driving event sequence, Sy My Sx
Ey Lx Ex thus corresponds to a driving maneuver where the maximum
of the lateral acceleration (My) occurs before the braking begins
(Sx), rather than afterwards as in the original driving maneuver
Sy Sx My Ey Lx Ex, as shown in FIG. 8. This change in timing can
create a related, but different driving maneuver that can, under
some circumstances, have significantly different dynamic driving
characteristics and may represent a completely different level of
risk. Because the timing difference between these two maneuvers
can be only a small fraction of a second, the ability of a driver
to successfully execute one of these maneuvers in preference over
the other may depend critically on his level of driving skill and
experience.
[0113] It is further noted that a similar situation exists regarding
the relative timing of the Ey event 809 and Lx event 811. These
two events are also related to independent variables and in principle
can be interchanged to create another different driving event sequence,
Sy My Sx Lx Ey Ex. All in all, it is possible to create a total
of four distinct, but related event sequences:
[0114] 1. Sy My Sx Ey Lx Ex
[0115] 2. Sy Sx My Ey Lx Ex
[0116] 3. Sy My Sx Lx Ey Ex
[0117] 4. Sy Sx My Lx Ey Ex
[0118] It is noted above that some of these event sequences may
have different characteristics. However, some of these sequences
may not have significant differences in the characteristics of the
resulting driving maneuvers. In this latter case, an embodiment
of the present invention considers such differences to be variations
in a basic driving maneuver, rather than a different driving maneuver.
The alternative forms of the driving event strings for these similar
driving maneuvers are stored in the database in order that such
alternative forms may be recognized.
[0119] It is further noted that the above remarks are not limited
to this particular set of driving maneuvers, but may apply to many
other driving maneuvers as well.
[0120] FIG. 9 illustrates raw data 901 for an Accelerate within
Turn driving maneuver. The driving events indicated are: an Sy event
903; an Sx event 905; an Mx event 907; an Ex event 909; an My event
911; and an Ey event 913. Thus, the driving event sequence Sy Sx
Mx Ex My Ey corresponds to an Accelerate within Turn driving maneuver.
[0121] FIG. 10 illustrates a non-limiting example of the transitions
of a finite state machine for identifying driving maneuvers, according
to a preferred embodiment of the present invention. Such a machine
can perform pattern recognition and function as the pattern recognition
unit 215 (FIG. 2), or can supplement the action thereof. In this
example, the machine of FIG. 10 can recognize four different driving
maneuvers: Accelerate, Braking, Turn, and Turn and Accelerate. The
transitions initiate at a begin point 1001, and conclude at a done
point 1003. The machine examines each driving event in the input
event string, and traverses a tree with the branchings corresponding
to the recognized driving maneuvers as shown. If the first event
is Sx, then the maneuver is either Accelerate or Braking. Thus,
if the next events are Mx Ex, it is an Accelerate maneuver, and
a transition 1005 outputs Accelerate. If the next events are Lx
Ex, however, a transition 1007 outputs Braking. Similarly, if the
first event is Sy, the maneuver is either Turn or Turn and Accelerate.
If the next events are My Ey, a transition 1009 outputs Turn. Otherwise,
if the next events are Mx My Ex Ey, a transition 1011 outputs Turn
and Accelerate. In this illustrative example, if there is no node
corresponding to the next driving event in the event string, the
machine makes a transition to done point 1003 without identifying
any maneuver. In practice, however, the finite state machine will
associate a driving maneuver with each physically-possible input
string.
Method and Processing
[0122] FIG. 11 is an overall flowchart of a method according to
a preferred embodiment of the invention for analyzing and evaluating
vehicle driver performance and behavior. The input to the method
is a raw sensor data stream 1101, such as the output 102 from sensor
set 101 (FIG. 2). The method starts with a filter step 1103 in which
the sensor data stream is filtered to remove extraneous noise. This
is followed by an event-detection step 1105, after which a driving
event string 1107 is generated in a step 1109. After this, a pattern-matching
step 1111 matches the events of event string 1107 to maneuvers in
maneuver library 213 (FIG. 2), in order to generate a maneuver sequence
1113 in a step 1115. Following this, a step 1119 assesses the driver's
skill and creates a skill rating 1117. In addition, a step 1123
assesses the driver's attitude and creates an attitude rating 1121.
The results of the driver skill assessment step 1119, the driver
attitude assessment step 1123, and the driving anomaly detection
step 1127 are then input to is input to an insurance policy processor
229 that determines one or more conditions of an insurance policy
of the driver.
Assessing Skill and Attitude
[0123] FIG. 12 is a schematic diagram of an arrangement or process
according to a preferred embodiment of the present invention for
assessing driver skill for a maneuver 1201. For this assessment,
an executed maneuver 1201 is represented by a driving event sequence,
as described above. The maneuver library 213 (FIG. 2) contains a
poorly-skilled maneuver template 1203, which is a driving event
sequence for the same maneuver, but with parameters corresponding
to those of an inexperienced or poor driver. Maneuver library 213
also contains a highly-skilled maneuver template 1205, which is
a driving event sequence for the same maneuver, but with parameters
corresponding to those of an experienced and skilled driver. Poorly-skilled
maneuver template 1203 and highly-skilled maneuver template 1205
are combined in a weighted fashion by being multiplied by a multiplier
1207 and a multiplier 1209, respectively, with the weighted components
added together by an adder 1211. Multiplier 1209 multiplies highly-skilled
maneuver template 1205 by a factor f which ranges from 0 to 1, whereas
multiplier 1207 multiplies poorly-skilled maneuver template 1203
by a factor (1-f), so that the output of adder 1211 is a weighted
linear combination of poorly-skilled maneuver template 1203 and
highly-skilled maneuver template 1205. This weighted linear combination
is input into a comparator 1213, which also has an input from the
executed maneuver 1201. The output of comparator 1213 adjusts the
value off for both multiplier 1207 and multiplier 1209, such that
the stable value off corresponds to the weighted combination of
poorly-skilled maneuver template 1203 and highly-skilled maneuver
template 1205 that comes closest to being the same as maneuver 1201.
Thus, the factor f serves as a skill ranking of the driver's performance
for maneuver 1201, where a value of f=1 represents the highest degree
of skill, and a value of f=0 represents the lowest degree of skill.
In an embodiment of the present invention, skill ratings corresponding
to several driving maneuvers can be statistically-combined, such
as by analyzer 225 (FIG. 2).
[0124] As noted, FIG. 12 is a schematic diagram of a process to
assess skill level for a maneuver. From the perspective of an algorithm
or method, the procedure involves finding the value off in the interval
[0, 1] for which the f-weighted highly-skilled template added to
a (1-f)-weighted poorly-skilled most closely approximates the maneuver
in question.
[0125] In still another embodiment of the present invention, the
assessing of skill by comparison of the maneuver with various standards
is accomplished through the application of well-known principles
of fuzzy logic.
[0126] A similar assessment regarding driver attitude is illustrated
in FIG. 13. The templates retrieved from the maneuver library 213
are a template 1303 for a safely-executed maneuver corresponding
to maneuver 1201, and a template 1305 for a dangerously-executed
maneuver corresponding to maneuver 1201. These are combined in a
weighted fashion by a multiplier 1309, which multiplies dangerously-executed
maneuver 1305 by a factor g, on the interval [0, 1], and a multiplier
1307, which multiplies safely-executed maneuver 1303 by a factor
of (1-g). The multiplied maneuvers are added together by an adder
1311, and the combination is compared against maneuver 1201 by a
comparator 1313 to find the value of g which yields the closest
value to the original maneuver. Thus, g serves as a ranking of the
driver's attitude for maneuver 1201, where a value of g=1 represents
the greatest degree of danger, and a value of g=0 represents the
lowest degree of danger. An intermediate value of g, such as g=0.5
can be interpreted to represent "aggressive" driving,
where the driver is taking risks.
[0127] As noted, FIG. 13 is a schematic diagram of a process to
assess attitude level for a maneuver. From the perspective of an
algorithm or method, the procedure finds the value of g in the interval
[0, 1] for which the g-weighted dangerously-executed maneuver template
added to a (1-g)-weighted safely-executed maneuver most closely
approximates the maneuver in question.
[0128] In an embodiment of the present invention, attitude ratings
of many driving maneuvers as executed by the driver can be statistically-combined,
such as by analyzer 225 (FIG. 2). When statistically combining attitude
ratings for different maneuvers according to embodiments of the
present invention, note that different maneuvers have different
risk coefficients, as shown in Table 1. The more risk a maneuver
entails, the higher is the risk coefficient. As a non-limiting example,
a driver who performs a Lane Change (risk coefficient=4) with a
g=0.3 and then performs a Braking within Turn (risk coefficient=8)
with a g=0.7 would have an average driving attitude for these two
maneuvers given by: (4*0.3+8*0.7)/2=3.4
[0129] In another embodiment of the present invention, the assessed
attitude of the driver is statistically computed using the maximum
(most dangerous) value of the set of maneuvers. For the example
above, this would be 8*0.7=5.6.
[0130] It is further noted that the factors f and g are arbitrary
regarding the choice of the interval [0, 1], and the assignment
of meaning to the extremes of the interval. A different interval
could be chosen, such as 1-10, for example, with whatever respective
meanings are desired for the value 1 and the value 10. Thus, the
examples above are non-limiting.
Anomaly Detection
[0131] FIG. 14 is a schematic diagram of an arrangement or process
according to an embodiment of the present invention for determining
whether there is a significant anomaly in the behavior and/or performance
of the current driver in comparison to that driver's past behavior
and performance. A particular driving maneuver 1401 is under scrutiny,
and is compared against a previously obtained record 1403 of the
current driver's past execution of the same maneuver. Characteristic
record 1403 is retrieved from database 209 (FIG. 2). The magnitude
of the difference between maneuver 1401 and characteristic maneuver
1403 is obtained by a magnitude subtractor 1405, which outputs the
absolute value of the difference. A discriminator 1409 compares
the difference magnitude from magnitude subtractor 1405 against
a threshold value 1407. If the difference magnitude exceeds threshold
value 1407, discriminator 1409 outputs a driving inconsistency signal.
[0132] As noted, FIG. 14 is a schematic diagram of a process to
assess discrepancies or anomalies in the performance of a maneuver
when compared to a previously-recorded reference. From the perspective
of an algorithm or method, the procedure compares the magnitude
of the difference of the maneuver and the previously-recorded reference
against a threshold value 1407. If the magnitude of the difference
exceeds threshold value 1407, a discrepancy is signaled.
[0133] In some cases, such as for inexperienced drivers, it is
to be expected that over time the quality of driving may steadily
improve. In cases such as this, there may come a point where the
driver's performance and/or attitude may improve to the point where
his or her driving may exhibit significant anomalies (because of
the improvements). Therefore, in an embodiment of the present invention,
the system may update the characteristic records in database 209
to account for improved quality of driving.
[0134] While the invention has been described with respect to a
limited number of embodiments, it will be appreciated that many
variations, modifications and other applications of the invention
may be made.
[0135] While the invention has been described with respect to a
limited number of embodiments, it will be appreciated that many
variations, modifications and other applications of the invention
may be made.
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