U.S. patent application number 13/830807 was filed with the patent office on 2013-09-19 for event based gps tracking.
This patent application is currently assigned to ZONAR SYSTEMS, INC.. The applicant listed for this patent is ZONAR SYSTEMS, INC.. Invention is credited to CHARLES MICHAEL MCQUADE.
Application Number | 20130245880 13/830807 |
Document ID | / |
Family ID | 49158404 |
Filed Date | 2013-09-19 |
United States Patent
Application |
20130245880 |
Kind Code |
A1 |
MCQUADE; CHARLES MICHAEL |
September 19, 2013 |
EVENT BASED GPS TRACKING
Abstract
System and method for enabling predefined events to be used to
trigger the collection of vehicle position data. A combination GSM
device and GPS device is used to collect vehicle position data and
to convey that position data to a remote computing device for
review and/or analysis. There is a tradeoff between collecting too
much data (cell phone bill is too high) and collecting too little
data (value added analytics cannot be achieved without sufficient
data). The concepts disclosed herein relate to method and apparatus
to enable the data collection/transmission paradigm of such a
GSM/GPS to be varied (or triggered) based on the detection of one
or more predefined events. This enables data which can contribute
to value added analytics to be acquired, without wasting airtime on
unimportant data.
Inventors: |
MCQUADE; CHARLES MICHAEL;
(ISSAQUAH, WA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
ZONAR SYSTEMS, INC. |
Seattle |
WA |
US |
|
|
Assignee: |
ZONAR SYSTEMS, INC.
SEATTLE
WA
|
Family ID: |
49158404 |
Appl. No.: |
13/830807 |
Filed: |
March 14, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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61610975 |
Mar 14, 2012 |
|
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Current U.S.
Class: |
701/32.4 |
Current CPC
Class: |
G07C 5/008 20130101;
G07C 5/085 20130101; G07C 5/0808 20130101 |
Class at
Publication: |
701/32.4 |
International
Class: |
G07C 5/08 20060101
G07C005/08 |
Claims
1. A method for generating non-position parameter encoded position
data from a vehicle equipped with a geographical position system,
the method comprising the steps of: (a) collecting geographical
position data from the vehicle during vehicle operation according
to a defined logging paradigm, the geographical position data being
time indexed; (b) determining if a predefined parameter is present
during vehicle operation, the predetermined parameter comprising a
parameter other than time, speed, direction, and distance; (c) in
response to detecting the predefined parameter during vehicle
operation, modifying the defined logging paradigm to collect
additional geographical position data at the time the predefined
parameter is detected, and (d) collecting data corresponding to the
predefined parameter detected.
2. The method of claim 1, further comprising the steps of combining
the parameter data and the geographical position data together at
the vehicle to produce parameter encoded position data.
3. The method of claim 2, further comprising the step of conveying
the parameter encoded position data to a remote computing device
that is physically spaced apart from the vehicle.
4. The method of claim 1, wherein the predefined parameter
comprises at least one of the following: (a) an increase in an
amount of fuel consumed by the vehicle; (b) a decrease in an amount
of fuel consumed by the vehicle; (c) a change in a status of a
cruise control component; (d) a change in a status of an accessory
component associated with a parasitic load; and (e) a change in
throttle position.
5. The method of claim 1, wherein the predefined parameter
comprises at least one of the following: (a) any change in engine
RPM; (b) a change in engine RPM over a predetermined magnitude; (c)
any change in engine load (d) a change in engine load over a
predetermined magnitude; (e) any change in oil temperature; (f) a
change in oil temperature over a predetermined magnitude; (g) any
change in coolant temperature; and (h) a change in coolant
temperature over a predetermined magnitude.
6. The method of claim 1, wherein the predefined parameter
comprises at least one of the following: (a) any change in brake
temperature; and (b) a change in brake temperature over a
predetermined magnitude.
7. A geographical position system for use in a vehicle, the
geographical position system comprising: (a) a positioning sensing
component for collecting geographical position data from the
vehicle during vehicle operation, the geographical position data
being time indexed; (b) a first data port for receiving
non-position related parameter data from the vehicle during
operation of the vehicle, the non-position related parameter data
being time indexed to correlate the non-position related parameter
data to a specific point in time, the non-position related
parameter comprising at least one of fuel use, engine RPM, engine
load, temperature, cruise control status, and accessory device
status; (c) a processor for implementing the functions of: (i)
triggering the logging of geographical position data according to a
predefined paradigm; and (ii) triggering the logging of
non-position related parameter data and position and geographical
position data based on the detection of an event associated the
non-position related parameter data; and (d) a data link for
conveying the non-position related parameter data and the
geographical position data to a remote computing device.
8. The system of claim 7, wherein the data link comprises a
wireless data link.
9. The system of claim 7, wherein the processor is configured to
combine the parameter data and the geographical position data
together at the vehicle to produce parameter encoded position
data.
10. A method for generating and using event encoded position data
from a vehicle equipped with a geographical position system to
determine at least one event based operational characteristic of
the vehicle, the method comprising the steps of: (a) collecting
geographical position data from the vehicle during vehicle
operation; (b) collecting non-position related parameter data from
the vehicle during operation of the vehicle based on the detection
of a predefined event, the non-position related parameter data
being time indexed to correlate the non-position related parameter
data to a specific point in time, the non-position related
parameter comprising at least one of fuel use, engine RPM, engine
load, temperature, cruise control status, and accessory device
status; (c) conveying the geographical position data and the
non-position related parameter a remote computing device, such data
collectively comprising event encoded position data; and (d)
analyzing the event encoded position data to determine at least one
operating characteristic of the vehicle.
11. The method of claim 10, wherein the step of analyzing the event
encoded position data to determine at least one operating
characteristic of the vehicle comprises the step of displaying a
route traversed by a vehicle with fuel use overlaid on the route,
the fuel use being determined by fuel use included in the event
encoded position data.
12. The method of claim 10, wherein the step of analyzing the event
encoded position data to determine at least one operating
characteristic of the vehicle comprises the step of displaying a
route traversed by a vehicle with cruise control use overlaid on
the route, the cruise control use being determined by cruise
control status included in the event encoded position data.
13. The method of claim 10, wherein the step of analyzing the event
encoded position data to determine at least one operating
characteristic of the vehicle comprises the step of displaying a
route traversed by a vehicle with engine RPM data overlaid on the
route, the engine RMP data having been included in the event
encoded position data.
14. The method of claim 10, wherein the step of analyzing the event
encoded position data to determine at least one operating
characteristic of the vehicle comprises the step of displaying a
route traversed by a vehicle with engine load data overlaid on the
route, the engine load data having been included in the event
encoded position data.
15. The method of claim 10, wherein the step of analyzing the event
encoded position data to determine at least one operating
characteristic of the vehicle comprises the step of displaying a
route traversed by a vehicle with accessory device use overlaid on
the route, the accessory device use being determined by accessory
device status included in the event encoded position data.
16. The method of claim 10, wherein the step of analyzing the event
encoded position data to determine at least one operating
characteristic of the vehicle comprises the step of displaying a
route traversed by a vehicle with throttle position data overlaid
on the route, the throttle position data having been included in
the event encoded position data.
17. The method of claim 10, wherein the step of analyzing the event
encoded position data to determine at least one operating
characteristic of the vehicle comprises the step of displaying a
route traversed by a vehicle with vehicle temperature data overlaid
on the route, the temperature data having been included in the
event encoded position data.
18. The method of claim 10, wherein the step of analyzing the event
encoded position data to determine at least one operating
characteristic of the vehicle comprises the step of displaying a
route traversed by a vehicle with transmission gear selection
position data overlaid on the route, the transmission gear
selection data having been included in the event encoded position
data.
19. The method of claim 10, wherein the step of analyzing the event
encoded position data to determine at least one operating
characteristic of the vehicle comprises the step of enabling a user
to define a geofence, such that only event encoded position data
falling within the confines of the geofence are included within the
analysis.
20. A method for generating non-position parameter encoded position
data from a vehicle equipped with a geographical position system,
the method comprising the steps of: (a) collecting geographical
position data from the vehicle during vehicle operation according
to a defined logging paradigm, the geographical position data being
time indexed; (b) determining if a predefined parameter is present
during vehicle operation, wherein the predefined parameter
comprises at least one of the following: (i) an increase in an
amount of fuel consumed by the vehicle; (ii) a decrease in an
amount of fuel consumed by the vehicle; (iii) a change in a status
of a cruise control component; (iv) a change in a status of an
accessory component associated with a parasitic load; and (v) a
change in throttle position (c) in response to detecting the
predefined parameter during vehicle operation, modifying the
defined logging paradigm to collect additional geographical
position data at the time the predefined parameter is detected, and
(d) collecting data corresponding to the predefined parameter
detected.
21. A non-transitory memory medium having machine instructions
stored thereon for carrying out the steps of claim 20.
22. A method for generating non-position parameter encoded position
data from a vehicle equipped with a geographical position system,
the method comprising the steps of: (a) collecting geographical
position data from the vehicle during vehicle operation according
to a defined logging paradigm, the geographical position data being
time indexed; (b) determining if a predefined parameter is present
during vehicle operation, wherein the predefined parameter
comprises at least one of the following: (i) any change in engine
RPM; (ii) a change in engine RPM over a predetermined magnitude;
(iii) any change in engine load; (iv) a change in engine load over
a predetermined magnitude; (v) any change in oil temperature; (vi)
a change in oil temperature over a predetermined magnitude; (vii)
any change in coolant temperature; and (viii) a change in coolant
temperature over a predetermined magnitude; (c) in response to
detecting the predefined parameter during vehicle operation,
modifying the defined logging paradigm to collect additional
geographical position data at the time the predefined parameter is
detected, and (d) collecting data corresponding to the predefined
parameter detected.
23. A non-transitory memory medium having machine instructions
stored thereon for carrying out the steps of claim 22.
24. A non-transitory memory medium having machine instructions
stored thereon for carrying out the steps of claim 1.
25. The non-transitory memory medium of claim 24, which further
includes machine instructions stored thereon for carrying out the
steps of claim 4.
26. The non-transitory memory medium of claim 24, which further
includes machine instructions stored thereon for carrying out the
steps of claim 5.
27. The non-transitory memory medium of claim 24, which further
includes machine instructions stored thereon for carrying out the
steps of claim 6.
28. A method for generating non-position parameter encoded position
data from a vehicle equipped with a geographical position system,
the method comprising the steps of: (a) collecting geographical
position data from the vehicle during vehicle operation according
to a defined logging paradigm, the geographical position data being
time indexed; (b) determining if a predefined parameter is present
during vehicle operation, wherein the predefined parameter
comprises at least one of the following: (i) any change in brake
temperature; and (ii) a change in brake temperature over a
predetermined magnitude; (c) in response to detecting the
predefined parameter during vehicle operation, modifying the
defined logging paradigm to collect additional geographical
position data at the time the predefined parameter is detected, and
(d) collecting data corresponding to the predefined parameter
detected.
29. A non-transitory memory medium having machine instructions
stored thereon for carrying out the steps of claim 28.
Description
RELATED APPLICATIONS
[0001] This application is based on a prior copending provisional
application Ser. No. 61/610,975, filed on Mar. 14, 2012, the
benefit of the filing date of which is hereby claimed under 35
U.S.C. .sctn.119(e).
BACKGROUND
[0002] As the cost of sensors, communications systems and
navigational systems has dropped, operators of commercial and fleet
vehicles now have the ability to collect a tremendous amount of
data about the vehicles that they operate, including geographical
position data (generally referred to herein as GPS data, noting
that position data can be collected by systems related to but
distinct from the well-known Global Positioning System) collected
during the operation of the vehicle.
[0003] Vehicle fleet operators often operate vehicles along
predefined and generally invariant routes. For example, buses
frequently operate on predefined routes, according to a predefined
time schedule (for example, along a route that is geographically,
as well as temporally defined). It would be desirable to provide
new techniques for analyzing data collected from vehicles
transiting such predefined routes over time, to aid in identifying
vehicle performance problems requiring servicing.
[0004] Vehicle fleet operators often operate vehicles both on road
and off road. Significantly, fuel tax is calculated differently for
on-road and off-road use. It would be desirable to provide new
techniques for analyzing data collected from vehicles operating
both on road and off road, to enable fuel tax calculations to be
performed more accurately.
[0005] Transmitting data from a vehicle to a remote server can be
accomplished using GSM technology. There is a tradeoff between
collecting too much data (cell phone bills are too high) and
collecting too little data (value-added analytics cannot be
achieved without sufficient data). It would be desirable to provide
method and apparatus to collect useful amounts of data without
wasting bandwidth on less valuable data.
SUMMARY
[0006] One aspect of the novel concepts presented herein is a
system and method for enabling predefined events to be used to
trigger the collection of such event data and vehicle position
data. A combination GSM device and GPS device is used to collect
vehicle position data and to convey that position data to a remote
computing device for review and/or analysis. There is a tradeoff
between collecting too much data (cell phone bills are too high)
and collecting too little data (value-added analytics cannot be
achieved without sufficient data). The concepts disclosed herein
relate to method and apparatus to enable the data
collection/transmission paradigm of such a GSM/GPS device to be
varied (or triggered) based on the detection of one or more
predefined events. This enables data which can contribute to value
added analytics to be acquired, without wasting airtime on less
important data.
[0007] One paradigm for collecting and transmitting GPS data using
a GSM/GPS device (or a separate GSM device coupled to a GPS, noting
that the concepts disclosed herein can also be implemented using
other forms of wireless data transfer, including but not limited to
satellite) is to collect GPS data at predetermined intervals, or
according to some algorithm that changes the vehicle position data
collecting paradigm based on changes in vehicle speed or heading
(such an algorithm can enable relatively more data to be collected
during city driving versus traveling along a straight section of
highway with little change in speed or heading). The concepts
disclosed herein are based on modifying the frequency with which
GPS data is collected and transmitted to a remote server, based on
non-position related inputs received from the vehicle (i.e., not
simply a change in speed or heading as in the above described
algorithm). In an exemplary embodiment, those non-position related
inputs are acquired from a vehicle data bus, such as a J1939,
J1708, and/or CAN bus. In certain embodiments, those on
non-position related inputs can be received from an OBD or OBD-II
interface.
[0008] In at least one embodiment, an existing GPS data acquisition
paradigm is modified when an amount of fuel passing through fuel
injectors increases, such that a GPS data point is acquired when
fuel use increases. In an exemplary embodiment, the data point
includes fuel use data and GPS data. Such data will enable vehicle
operators to understand at what vehicle locations their fuel usage
increases.
[0009] In at least one embodiment, an existing GPS data acquisition
paradigm is modified when an amount of fuel passing through fuel
injectors decreases, such that a GPS data point is acquired when
fuel use decreases. In an exemplary embodiment, the data point
includes fuel use data and GPS data. Such data will enable vehicle
operators to understand at what vehicle locations their fuel usage
decrease.
[0010] While relating fuel use to fuel passing through injectors
represents an exemplary technique for determining changes in fuel
use that trigger collection of a GPS data point and fuel use data,
it should be understood that other types of sensors related to fuel
use can be similarly employed (such as a flow sensor in a fuel
line). Any input providing insight into changes (decreases or
increases) in fuel consumption can be used as an input.
[0011] In at least one embodiment, an existing GPS data acquisition
paradigm is modified when a throttle position changes, such that a
GPS data point is acquired when throttle position changes. In an
exemplary embodiment, the data point includes throttle position and
GPS data. Such data will enable vehicle operators to understand at
what vehicle locations their throttle positions change.
[0012] In at least one embodiment, an existing GPS data acquisition
paradigm is modified when engine, oil, coolant and/or brake
temperatures change, such that a GPS data point is acquired when
such temperature changes occur. In an exemplary embodiment, the
data point includes temperature data and GPS data. Such data will
enable vehicle operators to understand at what vehicle locations
their vehicle system's temperatures change.
[0013] In at least one embodiment, an existing GPS data acquisition
paradigm is modified when accessory devices such as fans are
energized, such that a GPS data point is acquired when such
accessory devices are used. In an exemplary embodiment, the data
point includes the identity of the accessory device, any measured
parasitic load, and GPS data. Such data will enable vehicle
operators to understand at what vehicle locations the vehicle's
parasitic loads increase, which generally relates to a decrease in
fuel economy.
[0014] In at least one embodiment, an existing GPS data acquisition
paradigm is modified when cruise control is turned on or off, such
that a GPS data point is acquired when cruise control is turned on
or off. In an exemplary embodiment, the data point includes the
status of the cruise control unit and GPS data. Such data will
enable vehicle operators to understand at what vehicle locations
cruise control is and is not employed.
[0015] In at least one embodiment, an existing GPS data acquisition
paradigm is modified when a driver changes gears, such that a GPS
data point is acquired when such shifting occurs. In an exemplary
embodiment, the data point includes the selected gear (RPM can also
be collected if desired) and GPS data. Such data will enable
vehicle operators to understand at what vehicle locations the
driver shifts gears. Shifting patterns can have a measurable impact
on fuel economy.
[0016] In at least one embodiment, an existing GPS data acquisition
paradigm is modified when engine load changes, such that a GPS data
point is acquired when such engine load changes. Engine load is not
simply RPM, and many vehicle ECU units are configured to calculate
engine load. In an exemplary embodiment, the data point includes
the engine load and GPS data. Such data will enable vehicle
operators to understand at what vehicle locations the engine loads
increase or decrease.
[0017] In at least one embodiment, inclinometers, accelerometers,
or hard braking sensors are used to similarly change an existing
GPS data acquisition paradigm.
[0018] In at least one embodiment, an existing GPS data acquisition
paradigm is modified when engine RPM changes, such that a GPS data
point is acquired when engine RPM increases or decreases. In an
exemplary embodiment, the data point includes RPM data and GPS
data. Such data will enable vehicle operators to understand at what
vehicle locations their RPMs change. It should be understood that
limits can be placed on how much the engine RPMs need to vary to
trigger a change. For example, some operators may wish to track RMP
changes of about 10% or more, while other operators may wish to
track RPM changes of about 50 or more RPMs. It should be recognized
that such RPM changes are intended to be exemplary, and not
limiting. Further, with respect to that other non-position based
event changes disclosed herein (i.e., fuel use, temperature,
throttle position, etc.), it should be recognized that changing an
existing GPS data acquisition paradigm can be based on any change
in the specific parameter, or a certain magnitude of a change in
that parameter. Further, the concepts disclosed herein encompass
embodiments where the magnitude of the change needed to trigger
modification of an existing GPS data acquisition paradigm can
itself be modified (i.e., the magnitude can be modified). The
concepts disclosed herein encompass embodiments where the magnitude
of the change triggering the modification of the existing GPS data
acquisition paradigm can be modified by users; such that users can
reprogram the logic controlling the GPS data acquisition paradigm
to modify the magnitude parameters of non-position based events
that trigger a change in the existing GPS data acquisition
paradigm.
[0019] In addition to being implemented as a method, the concepts
disclosed herein can also be implemented as a memory medium,
storing machine instructions that when executed by a processor
implement the method, and by a system for implementing the method.
In such a system, the basic elements include a vehicle that is to
be operated by a vehicle operator, data collection components in
the vehicle (sensors/controllers for determining parameters such as
fuel use, temperature, RMP, load, shifting patterns, throttle
position, use of accessory components, use of cruise control,
etc.), and a geographical position tracking unit (such as a GPS
tracking device), a processor for combining the different data
types into time indexed parameter (fuel use, temperature, RMP,
load, shifting patterns, throttle position, use of accessory
components, use of cruise control, etc.) encoded position data
(such a processor could be part of the GPS unit), a data link
(which in an exemplary embodiment is integrated into the GPS unit
as a wireless data link), and a remote computing device. In
general, the remote computing device can be implemented by a
computing system employed by an entity operating a fleet of
vehicles. Entities that operate vehicle fleets can thus use such
computing systems to track and process data relating to their
vehicle fleet. It should be recognized that these basic elements
can be combined in many different configurations to achieve the
exemplary method discussed above. Thus, the details provided herein
are intended to be exemplary and not limiting on the scope of the
concepts disclosed herein.
[0020] The above noted method is preferably implemented by a
processor (such as computing device implementing machine
instructions to implement the specific functions noted above) or a
custom circuit (such as an application specific integrated
circuit).
[0021] Other concepts that can be combined with the modification of
a GPS data collection paradigm include the following. A method of
combining fuel use data collected by a vehicle's fuel injector with
position data collected during operation of the vehicle, to
generate fuel use encoded position data. Such fuel use encoded
position data preferably is four dimensional: position (latitude
& longitude), time, fuel injector data, and odometer data.
Generating such data requires the vehicle to be equipped with a
position sensing system (able to determine the vehicle's latitude
& longitude per unit of time), and a sensor incorporated into
at least one fuel injection component, to enable an amount of fuel
introduced into the vehicle's engine to be determined per unit of
time. Diesel engines that include fuel injectors configured to
collect information about the flow of fuel through the injectors
per unit time are currently available. In an exemplary, but not
limiting embodiment, the odometer data is collected from the
vehicle computer using a J-1708 or J-1939 bus. While including the
odometer data is likely to be popular with end users, it should be
understood that the concepts disclosed herein also encompass
embodiments in which the odometer data is not included in the fuel
use encoded position data.
[0022] Such fuel use encoded position data has a number of uses.
The data can be used to determine fuel usage of the vehicle under
many different search parameters. For example, many commercial
trucks are used both on and off road. Diesel fuel for highway use
is taxed at a much higher rate than diesel fuel for non-highway use
(diesel for off road use is generally exempt from Federal and State
road taxes). The fuel use encoded position data can be used to
calculate how much diesel fuel is consumed when the vehicle is not
on the highway (i.e., when the lower tax rate applies). Normally
this metric is calculated using an average MPG. If the off-road
trip was 20 miles round trip and the vehicle MPG averages 10 MPG,
then 2 gallons of diesel were assumed to be used. That calculation
is very error prone. Off road fuel consumption is often higher for
a number of reasons. Road condition is poorer, so fuel consumption
generally is higher. Many commercial vehicles going off road are
maintenance vehicles equipped with power take off units, which use
engine power to do mechanical work other than driving road wheels.
Thus, even when the vehicle is not moving, the engine is often
consuming fuel to power ancillary equipment. A mileage based
calculation will not take into account the fuel consumed off road
when the vehicle is stationary, but consuming fuel to power
equipment.
[0023] The fuel use encoded position data can also be used to
evaluate the mechanical condition of a vehicle. Assume a vehicle
travels from point A to point B consistently. By monitoring fuel
use for that trip over a period of time, a decrease in fuel
efficiency may indicate a mechanical problem (dirty injectors,
fouled spark plugs, etc.).
[0024] Yet another use for the fuel use encoded position data is to
provide a data set to be used to analyze fuel consumption relative
to elevation change. By quantifying how much fuel is consumed
traveling over a route with elevation changes, one can identify
alternate, possibly longer routes, that are more fuel efficient,
due to fewer elevation changes. A related use for the fuel use
encoded position data is to provide a data set to be used to
analyze fuel consumption relative to road surface. Analyzing fuel
consumption relative to the type of road surface will enable
vehicle operators to learn what road type surfaces are associated
with lower fuel consumption. Regularly traveled routes can then be
analyzed to determine if an alternate route over different road
surfaces could lead to lower fuel consumption. Correlating the fuel
use encoded position data with vehicle loading data can also
facilitate analysis of fuel consumption not only based on elevation
and road surface, but vehicle loading as well.
[0025] It should be recognized that one aspect of the concepts
disclosed herein is a method for generating fuel use encoded
position data by combining fuel usage data (per unit of time)
collected by fuel injectors with position data (per unit of time).
Another aspect of the concepts disclosed herein is a method for
collecting fuel use encoded position data at a remote computer, by
wirelessly transmitting the fuel use encoded position data from the
vehicle to a remote computer in real-time. The term real-time is
not intended to imply the data is transmitted instantaneously,
rather the data is collected over a relatively short period of time
(over a period of seconds or minutes), and transmitted to the
remote computing device on an ongoing basis, as opposed to storing
the data at the vehicle for an extended period of time (hour or
days), and transmitting an extended data set to the remote
computing device after the data set has been collected.
Transmitting fuel use encoded position data at a later time, rather
than in real time, is encompassed by the concepts disclosed herein,
although real-time data transmission is likely to be popular with
users.
[0026] Another aspect of the concepts disclosed herein is a method
for using fuel use encoded position data to calculate fuel use
taxes. While the fuel tax calculations could be performed by a
processor in the vehicle, in a preferred but not limiting
embodiment, the fuel use encoded position data will be transferred
to the remote computing device for storage, such that the fuel use
encoded position data for a particular vehicle can be accessed at a
later time to perform the fuel tax calculations. It should be
understood that the term remote computer and the term remote
computing device are intended to encompass networked computers,
including servers and clients, in private networks or as part of
the Internet. The fuel use encoded position data can be stored by
one element in such a network, retrieved for review by another
element in the network, and analyzed by yet another element in the
network. In at least one embodiment, a vendor is responsible for
storing the data, and clients of the vendor are able to access and
manipulate the fuel use encoded position data.
[0027] Still another aspect of the concepts disclosed herein is a
method for using fuel use encoded position data to diagnose a
relative mechanical condition of a vehicle that repetitively
travels a specific route. Fuel use encoded position data for
different trips can be compared. Changes in fuel use encoded
position data can indicate that a fuel efficiency of the vehicle
has decreased over time, indicating that the vehicle should be
inspected for mechanical conditions (such as dirty fuel filter,
dirty air filters, and/or clogged fuel injectors, noting that such
examples are not intended to be limiting) that may be contributing
to a reduction in fuel efficiency.
[0028] Still another aspect of the concepts disclosed herein is a
method for enabling a user to define specific parameters to be used
to analyze such fuel use encoded position data. In an exemplary
embodiment, a user can define a geographical parameter, and analyze
the fuel use encoded position data in terms of the user defined
geographical parameter. In an exemplary embodiment, the
geographical parameter is a geofence, which can be generated by
displaying a map to a user, and enabling the user to define a
perimeter "fence" around any portion of the map. Having defined the
geofence, the user can then analyze the fuel use encoded position
data for the vehicle, such that only the portion of the fuel use
encoded position data whose geographical/position data falls within
the confines of the geofence is included in the analysis. One such
analysis can be fuel tax calculations, where the geofence is used
to define off road vehicular use. Another such analysis can be
determine how fuel use patterns change over time, where the
geofence is used to define a specific route (such as a bus route or
an invariant delivery route). In another exemplary embodiment, the
geographical parameter is a set of geographical coordinates. As
discussed above, some vehicles regularly travel a predefined route,
and the predefined route can be defined by a set of geographical
coordinates that the vehicle encounters whenever transiting that
route. A larger data set will include more geographical
coordinates. A relatively larger set of geographical coordinates
will be generated if the set of geographical coordinates includes
individual geographical coordinates separated from one another by
25 feet. A relatively smaller set of geographical coordinates will
be generated if the set of geographical coordinates includes
individual geographical coordinates from each intersection at which
the vehicle makes a turn or change in direction (such that
geographical coordinates defining where the vehicle enters and
exits a relatively long street can be separated from one another by
relatively long distances). Such a set of geographical coordinates
can be considered to define a fingerprint for a specific route. An
exemplary analysis of fuel use encoded position data where the
geographical parameter is a route fingerprint is to determine how
fuel use patterns change over time as the route is completed at
different times. Changes in fuel use patterns can indicate that a
fuel efficiency of the vehicle has decreased over time, indicating
that the vehicle should be inspected for mechanical conditions,
generally as discussed above.
[0029] In addition to being implemented as a method, the concepts
disclosed herein can also be implemented as a memory medium,
storing machine instructions that when executed by a processor
implement the method, and by a system for implementing the method.
In such a system, the basic elements include a vehicle that is to
be operated by a vehicle operator, data collection components in
the vehicle (injectors that collect fuel use data per unit time,
and a geographical position tracking unit, such as a GPS tracking
device), a processor for combining the different data types into
time indexed fuel use encoded position data (such a processor could
be part of the GPS unit), a data link (which in an exemplary
embodiment is integrated into the GPS unit as a wireless data
link), and a remote computing device. In general, the remote
computing device can be implemented by a computing system employed
by an entity operating a fleet of vehicles. Entities that operate
vehicle fleets can thus use such computing systems to track and
process data relating to their vehicle fleet. It should be
recognized that these basic elements can be combined in many
different configurations to achieve the exemplary method discussed
above. Thus, the details provided herein are intended to be
exemplary and not limiting on the scope of the concepts disclosed
herein.
[0030] The above noted method is preferably implemented by a
processor (such as computing device implementing machine
instructions to implement the specific functions noted above) or a
custom circuit (such as an application specific integrated
circuit).
[0031] This Summary has been provided to introduce a few concepts
in a simplified form that are further described in detail below in
the Description. However, this Summary is not intended to identify
key or essential features of the claimed subject matter, nor is it
intended to be used as an aid in determining the scope of the
claimed subject matter.
DRAWINGS
[0032] Various aspects and attendant advantages of one or more
exemplary embodiments and modifications thereto will become more
readily appreciated as the same becomes better understood by
reference to the following detailed description, when taken in
conjunction with the accompanying drawings, wherein:
[0033] FIG. 1 is a high level logic diagram showing exemplary
overall method steps implemented in accord with the concepts
disclosed herein to combine fuel use data collected from a fuel
injector with geographical position data collected while a vehicle
is in operation, to generate fuel use encoded position data, which
can be subsequently analyzed to determine at least one operational
parameter of the vehicle;
[0034] FIG. 2 is a functional block diagram of an exemplary
computing device that can be employed to implement some of the
method steps disclosed herein;
[0035] FIG. 3 is a functional block diagram of an exemplary
geographical position sensing system employed to implement some of
the concepts disclosed herein;
[0036] FIG. 4 is an exemplary functional block diagram showing the
basic functional components used to implement the method steps of
FIG. 1;
[0037] FIG. 5 is a schematic block diagram of an exemplary vehicle
configured to collect the fuel use encoded position data employed
in the method steps of FIG. 1;
[0038] FIG. 6 is a high level logic diagram showing exemplary
overall method steps implemented in accord with the concepts
disclosed herein, and summarized above, to utilize fuel encoded
position data collected to determine at least one operational
characteristic of the vehicle, where the analysis includes enabling
the user to define a geofence;
[0039] FIG. 7 is a flow chart showing exemplary method steps
implemented to utilize fuel encoded position data collected by a
vehicle to analyze fuel use patterns based on elevation
changes;
[0040] FIG. 8 is a flow chart showing exemplary method steps
implemented to utilize fuel encoded position data collected by a
vehicle to analyze fuel use patterns based on different types of
road surfaces;
[0041] FIG. 9 is a flow chart showing exemplary method steps
implemented to modify a GPS logging paradigm based on the detection
of one or more non-position related parameters;
[0042] FIG. 10A schematically illustrates a GPS logging paradigm
based on GPS logging at predetermined time intervals;
[0043] FIG. 10B schematically illustrates a GPS logging paradigm
based on GPS logging at predetermined time intervals modified based
on position based parameters;
[0044] FIG. 10C schematically illustrates the GPS logging paradigm
of FIG. 10B modified based on detecting a non-position based
parameter;
[0045] FIG. 11 is a screen shot of a web page upon which a vehicle
owner can view fuel use data overlaid upon vehicle route data,
where the fuel use data was collected using the method of FIG. 9;
and
[0046] FIG. 12 is a functional block diagram of an exemplary
telematics device added to an enrolled vehicle to implement one or
more of the methods of FIGS. 1 and 9.
DESCRIPTION
Figures and Disclosed Embodiments are not Limiting
[0047] Exemplary embodiments are illustrated in referenced Figures
of the drawings. It is intended that the embodiments and Figures
disclosed herein are to be considered illustrative rather than
restrictive. Further, it should be understood that any feature of
one embodiment disclosed herein can be combined with one or more
features of any other embodiment that is disclosed, unless
otherwise indicated.
Newly Disclosed Subject Matter
[0048] The concepts disclosed herein relate to both newly disclosed
subject matter and previously disclosed subject matter. The
previously disclosed subject matter provides contextual information
that is relevant to the new disclosure, hence it inclusion. The
newly disclosed subject matter begins with FIG. 9.
Related Subject Matter Disclosed in U.S. patent application Ser.
No. 12/836,487
[0049] As used herein and in the claims that follow, the term off
road is intended to refer to use of a vehicle where fuel consumed
by that vehicle should be assessed a tax using a tax rate different
than fuel consumed by the same vehicle when traveling over a public
highway. The concepts disclosed herein can help vehicle operators
more
[0050] As used herein and in the claims that follow, the term route
is intended to refer to a route between a starting location and an
ending location that is intended to be traversed a plurality of
times. For example, bus operators generally operate buses on a
number of different specific routes, which are generally
differentiated by a route number. A bus Route 51 might connect a
shopping mall and an airport, while a bus Route 52 might connect
the airport to a university. Route 51 and Route 52 are each
different routes. A route may include one or more intermediate
locations disposed between the starting location and the ending
location, such intermediate locations representing geographical
locations that the route intersects. Each route can be defined by a
plurality of geographical coordinates through which a vehicle will
pass when traversing the route. As such, a set of position data
collected during the operation of a vehicle can be used to define a
route.
[0051] FIG. 1 is a high level flow chart showing the overall method
steps implemented in accord with one aspect of the concepts
disclosed herein, to collect fuel use data from a fuel injector
sensor and position data, then combine the data to generate fuel
use encoded position data, which can be analyzed to determine at
least one operating characteristic of the vehicle. In a block 10, a
vehicle is equipped with geographical position sensors (such as a
GPS unit), so that geographical position data can be collected when
the vehicle is being operated, and a fuel injector that measures a
quantity of fuel flowing through the fuel injector. Mercedes Benz
manufactures diesel engines that incorporate fuel injectors capable
of providing such fuel use data. Other vendors will likely offer
engines having similar functionality. In general, each fuel
injector in the vehicle will include such a fuel sensor. However,
it should be recognized that less than all of the fuel injectors
can include such sensors, so long as the data for the engine's fuel
use is adjusted to compensate (i.e., an engine with four injectors,
only one of which includes a fuel sensor, should have its measured
fuel use increased fourfold to account for fuel flow through the
unmonitored injectors). In an exemplary but not limiting
embodiment, fuel injector data is collected from the vehicle
computer using either a J-1708 or J-1939 bus. The data values are
generally in English unit using the J-1708 bus and metric units
using the J-1939 bus. The J-1939 bus provides fuel injector data
with 1/2 liter resolution. In general, the vehicle computer will
receive from usage data from each cylinder's fuel injector. It
would be possible to collect fuel use data from only a single
injector in a multi-cylinder engine, and then increase that data by
a factor corresponding to the number of cylinders. Similarly, data
could be collected from 1/2 of the injectors, and then doubled to
normalize the data for fuel use in all cylinders.
[0052] In a block 12, the vehicle is operated while collecting both
GPS data (i.e., position data, preferably including time indexed
longitude and latitude data) and fuel use data (as measured through
the fuel injectors). The different types of data are combined into
a time indexed data set. In an exemplary embodiment, the different
types of data (position and fuel use) are combined by a
geographical position sensing system, an exemplary implementation
of which is discussed in detail below to generate fuel use encoded
position data.
[0053] In a block 14, the fuel use encoded position data collected
during operation of the vehicle is conveyed to a remote computing
device. In one exemplary, but not limiting embodiment, the fuel use
encoded position data is wirelessly transmitted to the remote
computing device on the fly (i.e., as the information is
generated). In such an embodiment, it may be desirable to store a
copy of the fuel use encoded position data at the vehicle in case
of a failure in the transmission of the data stream. In another
exemplary embodiment, the fuel use encoded position data is stored
in the vehicle, and conveyed to the remote computing device at a
later time.
[0054] In a block 16, the fuel use encoded position data conveyed
to the remote computing device is analyzed to determine at least
one operational characteristic of the vehicle. The fuel use encoded
position data can be used to determine fuel usage of the vehicle
under many different search parameters. In a first exemplary
embodiment, the fuel use encoded position data is used to calculate
the correct fuel tax for the vehicle, based on an analysis of where
the vehicle was located during fuel use. Commercial trucks are
often used both on and off road. Diesel fuel for highway use is
taxed at a much higher rate than diesel fuel for non-highway use.
In this embodiment, the fuel use encoded position data is used to
calculate how much diesel fuel is consumed when the vehicle is not
on the highway (i.e., when the lower tax rate applies). Simple
average MPG estimates are error prone, as off road fuel consumption
is often higher that highway fuel consumption (road condition is
poorer, and off road vehicle use frequently includes using engine
power to do mechanical work other than driving road wheels). It
should also be recognized that the fuel use encoded position data
can also be used to determine how much fuel is used on public
roadways (where the fuel use tax is higher), and to determine the
off road fuel use by subtracting the fuel used on public roadways
from the total fuel use to determine the off road fuel use.
[0055] In a second exemplary embodiment, the fuel use encoded
position data is used to determine how much fuel is used consumed
during idle time (such as when a vehicle is parked and the engine
is not shut off). Fleet operators want to reduce idle time, as idle
time wastes fuel and increases costs. Fuel use during idle time can
be calculated in a number of ways. Certain geographical positions
(fleet yards, truck stops, loading and unloading points) can be
designated for review, such that fuel use from the fuel use encoded
position data is extracted for the designated geographical
positions, and used to determine how much fuel is consumed at those
locations. Alternatively, the fuel use encoded position data can be
analyzed to determine how much fuel is consumed when the vehicle is
on but its position remains the same (this latter technique is
over-inclusive, as it may include fuel use required to power
equipment needed while the vehicle is stationary, as well as fuel
use while the vehicle is stopped for traffic and shutting down the
vehicle is not practical). The over inclusiveness of the latter
technique can be managed by eliminating geographical positions
where fuel was used to power equipment, or geographical positions
where fuel was used while sitting in traffic.
[0056] In a third exemplary embodiment, the fuel use encoded
position data is used to evaluate (or to monitor) changes in fuel
use patterns for a vehicle regularly traveling the same route.
Changes in such fuel use patterns can be indicative of mechanical
problems, such that when such changes are identified, it may be
prudent to schedule maintenance for the vehicle. Assume a vehicle
travels from point A to point B consistently. By monitoring fuel
use for that trip over a period of time, a decrease in fuel
efficiency may indicate a mechanical problem (dirty injectors,
fouled spark plugs, etc). Of course, such fuel use changes may be
attributable to other conditions, such as changes in traffic
patterns (heavy traffic encountered during one trip will increase
fuel use) or changes in vehicle loading (a trip with a heavy load
will likely consume more fuel than a trip for a light load).
Historical traffic data and loading data can be used to more
clearly target fuel use pattern changes likely to be correlated to
mechanical condition.
[0057] In general, analysis of the fuel use encoded position data
will be carried out by a remote computing device. The remote
computing device in at least one embodiment comprises a computing
system controlled or accessed by the fleet operator. The remote
computing device can be operating in a networked environment, and
in some cases, may be operated by a third party under contract with
the fleet operator to perform such services. FIG. 2 schematically
illustrates an exemplary computing system 250 suitable for use in
implementing the method of FIG. 1 (i.e., for executing block 18 of
FIG. 1). Exemplary computing system 250 includes a processing unit
254 that is functionally coupled to an input device 252 and to an
output device 262, e.g., a display (which can be used to output a
result to a user, although such a result can also be stored).
Processing unit 254 comprises, for example, a central processing
unit (CPU) 258 that executes machine instructions for carrying out
an analysis of fuel use encoded position data collected in
connection with operation of the vehicle to determine at least one
operating characteristic of the vehicle. The machine instructions
implement functions generally consistent with those described above
with respect to block 16 of FIG. 1, as well as those described
below in blocks 30-38, with respect to FIG. 6. CPUs suitable for
this purpose are available, for example, from Intel Corporation,
AMD Corporation, Motorola Corporation, and other sources, as will
be well known to those of ordinary skill in this art.
[0058] Also included in processing unit 254 are a random access
memory (RAM) 256 and non-volatile memory 260, which can include
read only memory (ROM) and may include some form of memory storage,
such as a hard drive, optical disk (and drive), etc. These memory
devices are bi-directionally coupled to CPU 258. Such storage
devices are well-known in the art. Machine instructions and data
are temporarily loaded into RAM 256 from non-volatile memory 260.
Also stored in the non-volatile memory are an operating system
software and ancillary software. While not separately shown, it
will be understood that a generally conventional power supply will
be included to provide electrical power at voltage and current
levels appropriate to energize computing system 250.
[0059] Input device 252 can be any device or mechanism that
facilitates user input into the operating environment, including,
but not limited to, one or more of a mouse or other pointing
device, a keyboard, a microphone, a modem, or other input device.
In general, the input device will be used to initially configure
computing system 250, to achieve the desired processing (i.e., to
compare subsequently collected actual route data with optimal route
data, to identify any deviations and/or efficiency improvements).
Configuration of computing system 250 to achieve the desired
processing includes the steps of loading appropriate processing
software into non-volatile memory 260, and launching the processing
application (e.g., loading the processing software into RAM 256 for
execution by the CPU) so that the processing application is ready
for use. Output device 262 generally includes any device that
produces output information, but will most typically comprise a
monitor or computer display designed for human visual perception of
output. Use of a conventional computer keyboard for input device
252 and a computer display for output device 262 should be
considered as exemplary, rather than as limiting on the scope of
this system. Data link 264 is configured to enable data collected
in connection with operation of a vehicle to be input into
computing system 250 for subsequent analysis. Those of ordinary
skill in the art will readily recognize that many types of data
links can be implemented, including, but not limited to, universal
serial bus (USB) ports, parallel ports, serial ports, inputs
configured to couple with portable memory storage devices, FireWire
ports, infrared data ports, wireless data communication such as
Wi-Fi and Bluetooth.TM., network connections via Ethernet ports,
and other connections that employ the Internet.
[0060] It should be understood that the term remote computer and
the term remote computing device are intended to encompass
networked computers, including servers and clients, in private
networks or as part of the Internet. The fuel use encoded data can
be stored by one element in such a network, retrieved for review by
another element in the network, and analyzed by yet another element
in the network. In at least one embodiment, a vendor is responsible
for storing the data, and clients of the vendor are able to access
and manipulate the data. While implementation of the method noted
above has been discussed in terms of execution of machine
instructions by a processor (i.e., the computing device
implementing machine instructions to implement the specific
functions noted above), the method could also be implemented using
a custom circuit (such as an application specific integrated
circuit).
[0061] FIG. 3 is a functional block diagram of an exemplary
geographical position sensing system employed to implement some of
the concepts disclosed herein. A position sensing system 60
includes a GPS component 64 (which, in this embodiment, includes a
transmitter, although it should be recognized that a GPS unit
without a transmitter can be coupled with a transmitter or other
data link to achieve similar functionality). Position sensing
system 60 optionally includes a data port 68 coupled to the
vehicle's odometer (or to the vehicle's on-board computer), so that
odometer data can be collected and combined with the fuel use
encoded position data. Position sensing system 60 includes a data
port 66 coupled to the vehicle's fuel injectors (any fuel injector
that includes a fuel use sensor; noting that data port 66 can also
be coupled to the vehicle's on-board computer, such that the sensor
data from the fuel injectors is first directed to the on-board
computer, and then to position sensing system 60). GPS component 64
includes a processor that combines GPS data, fuel use data from the
fuel injector sensor(s), and if desired, odometer data, to generate
fuel use encoded position data that is time indexed (i.e., such
that for a given point in time, one can determine the vehicle's
position, the vehicle's fuel use, and optionally the vehicle's
odometer reading). In a related embodiment, position sensing system
60 includes a processor separate and distinct from any processor in
the GPS unit, such that the separate processor performs the
function of combining the GPS data, the fuel use data, and
optionally the odometer data. Such a processor can be implemented
by a general purpose processor implementing specific machine
instructions to execute the intended function, or custom hardware
circuit configured to execute the intended function. While odometer
data, fuel use data, and position data each could be collected at a
different frequencies (i.e., at different times), and combined
together to generate the fuel use encoded position data, in an
exemplary and preferred embodiment, the odometer data, fuel use
data, and position data are collected at the same time, so the time
indexing of each data type matches. By collecting the different
data types at the same time, one can ensure that the amount fuel
use attributed to off road use is as accurate as possible. Note
both the fuel use data and the odometer data normally collected by
the vehicle are accumulated numerical values, and to record a
specific data point one reads those accumulated values and combines
them with the time and position data. The purpose of collecting the
odometer data is to facilitate calculation of off road fuel use. As
noted above, the concepts disclosed herein also encompass
embodiments in which the odometer data is not included in the fuel
use encoded position data.
[0062] If desired, position sensing system 60 can include an ID
data input 62 that is used to uniquely identify the vehicle, so
that the fuel use encoded position data can be uniquely correlated
to a specific vehicle (fleet operators will want to be able to
uniquely identify fuel use encoded position data from different
fleet vehicles). In one embodiment, ID data input 62 comprises a
keyboard or function keys logically coupled to GPS component 64 (or
to the separate processor noted above, if implemented). It should
be recognized, however, that other data input structures (i.e.,
structures other than keyboards) can instead be implemented, and
that the concepts disclosed herein are not limited to any specific
identification data input device. It should also be recognized that
GPS component 64 can be configured to include in the GPS data (or
in the fuel use encoded position data) a data component that can be
used to uniquely correlate fuel use encoded position data with a
specific vehicle, such that ID data input 62 is not required. The
inclusion of ID data input 62 facilitates the addition of other
types of data (such as inspection data) in the fuel use encoded
position data.
[0063] FIG. 4 is a functional block diagram of an exemplary system
that can be employed to implement the method steps of FIG. 1. The
components include a sensor component 40, a transmitter 42, which
may also have a corresponding receiver--not shown (or other data
link), and a remote computing device 44 (generally as described
above). Sensor component 40 includes each element needed to collect
the data elements included in the fuel use encoded position data,
and a processing element required to combine the different types of
sensor data together to generate time indexed fuel use encoded
position data. The sensor elements include at least one fuel
injector sensor to determine a quantity of fuel passing through an
engine fuel injector (noting that each fuel injector in the engine
can include the required sensor, or less than all fuel injectors in
the engine can include such sensors, so long as the appropriate
adjustment is made to the fuel use data to account for injectors
that do not include sensors, generally as discussed above). Other
types of data from other sensors can also be included in the fuel
use encoded position data, including but not being limited to
odometer data. As discussed above, the processor for combining the
different data types into time indexed fuel use encoded position
data can be a separate component or a processor included in a GPS
component. Further, it should be recognized that many GPS units are
available that already incorporate a transmitter, such that
separate transmitter 42 may not be required. It should be
understood that the concepts disclosed herein can be used with
other types of geographical position sensors/systems, and the use
of the term GPS is intended to be exemplary, rather than limiting.
Sensor component 40 and a transmitter 42 are part of a vehicle
41.
[0064] FIG. 5 is a schematic block diagram of an exemplary vehicle
configured to collect the fuel use encoded position data employed
in the method steps of FIG. 1. A vehicle 50 includes GPS unit 54
(which in this embodiment, includes a transmitter, although it
should be recognized that a GPS unit without a transmitter can be
coupled with a transmitter or other data link to achieve similar
functionality). GPS unit 54 is coupled to fuel injector sensors 52,
so that geographical position data and fuel injector data are
combined by the GPS unit into fuel use encoded position data. As
discussed above, the vehicle can include other sensors (such as an
odometer) collecting data that is similarly included in the fuel
use encoded position data. Furthermore, the combining of different
data types into fuel use encoded position data can be implemented
by a processor (not shown in FIG. 5, but discussed above) that is
separate from the GPS unit.
[0065] Still another aspect of the concepts disclosed herein is a
method for enabling a user to define specific parameters to be used
to analyze such fuel use encoded data. In an exemplary embodiment,
a user can define a geographical parameter, and analyze the fuel
use encoded data in terms of the user defined geographical
parameter. In a particularly preferred, but not limiting exemplary
embodiment, the geographical parameter is a geofence, which can be
generated by displaying a map to a user, and enabling the user to
define a perimeter "fence" around any portion of the map. FIG. 6 is
a high level logic diagram showing exemplary overall method steps
implemented in accord with the concepts disclosed herein, and
summarized above, to utilize fuel encoded position data collected
to determine at least one operational characteristic of the
vehicle, where the analysis includes enabling the user to define a
geofence. It should be understood that the method of FIG. 6 is
implemented on a computing system remote from the vehicle
collecting the fuel use encoded position data. In at least one
exemplary, but not limiting embodiment, the fuel use encoded
position data is stored in a networked location, and accessed and
manipulated by a user at a different location.
[0066] In a block 30, a map is displayed to a user. In a block 32,
the user is enabled to define a geofence on the map (i.e., by
prompting the user to define such a geofence, or simply waiting
until the user provides such input). In general, a geofence is
defined when a user draws a perimeter around a portion of the
displayed map using some sort of computer-enabled drawing tool.
Many different software programs enable users to define and select
portions of a displayed map, thus detailed techniques for defining
a geofence need not be discussed herein. In a block 34, the user is
enabled to define a specific vehicle and a time period to be
analyzed (i.e., by prompting the user to define such parameters, or
simply waiting until the user provides such input). In a block 36,
fuel use encoded position data for the specified vehicle, location
parameter (as defined by the geofence), and time parameter is
retrieved. In a block 38, the user is enabled to define the
operational characteristic of the vehicle to be determined. As
noted above, exemplary operational characteristics include, but are
not limited to, determining a quantity of fuel consumed off road
(and thus not subject to road taxes) during the specified period,
and monitoring fuel usage for a vehicle traversing the same route a
number of times to identify changes in fuel usage not attributable
to changes in load or traffic.
[0067] Yet another use for the fuel use encoded position data is to
provide a data set to be used to analyze fuel consumption relative
to elevation change. Referring to FIG. 7, in a block 40 previously
generated fuel use encoded position data for a specific vehicle is
acquired. As discussed above, such data is collected during
operation of the vehicle, and generally stored in a database or
memory accessible in a networked environment (public or private).
Accessing such data can, if desired, require entering a password or
other type of credential to ensure that access to such data is
restricted to authorized parties. In a block 42, the accessed data
is analyzed to determine how road elevation affects fuel
consumption (i.e., fuel use). By quantifying how much fuel is
consumed traveling over a route with elevation changes, one can
identify alternate, possibly longer routes, that are more fuel
efficient due to fewer elevation changes. This analysis may include
comparing data collected while traveling different routes
connecting the same starting point and destination, where the
different routes involve different elevation changes. This analysis
may also involve comparing actual data with estimated fuel use over
a hypothetical alternate route, to aid in determining if the
alternate route (for example, a route that includes fewer elevation
changes) is more fuel efficient.
[0068] A related use for the fuel use encoded position data is to
provide a data set to be used to analyze fuel consumption relative
to road surface. Referring to FIG. 8, in a block 44 previously
generated fuel use encoded position data for a specific vehicle is
acquired. In a block 46, the accessed data is analyzed to determine
how road surface parameters affect fuel consumption. Analyzing fuel
consumption relative to the type of road surface will enable
vehicle operators to learn what type of road surfaces are
associated with lower fuel consumption. Regularly traveled routes
can then be analyzed to determine if an alternate route over a
different type of road surface could lead to lower fuel
consumption. This analysis may include comparing data collected
while traveling different routes connecting the same staring point
and destination, where the different routes involve different types
of road surfaces. For example, data collected while the vehicle
travels a first relatively longer route over a road that has been
repaved relatively recently can be compared with data collected
while the vehicle travels over a second relatively shorter route
over a road that has been not been repaved recently, to determine
whether the relatively longer route is more fuel efficient due to
the differences in the road surfaces. Other differences in types of
road surfaces include grooved surfaces verses un-grooved surfaces,
paved surfaces verses unpaved surfaces, and asphalt surfaces verses
concrete surfaces. Specifics regarding road types (paved, unpaved,
grooved, un-grooved, asphalt, concrete, etc.) can be added to the
fuel use encoded position data to help in identifying trends that
correlate surface type to fuel use.
Newly Disclosed Subject Matter
[0069] FIG. 9 is a flow chart showing exemplary method steps
implemented to modify a GPS logging paradigm based on the detection
of one or more non-position related parameters. In a block 50 a GPS
logging paradigm is defined. In general, such logging paradigms
attempt to balance collecting a useful amount of GPS data with
minimizing airtime consumption. GPS logging paradigms can be based
on time, such that GPS data is collected at predetermined time
intervals (such as once a minute, once an hour, or once a day, such
intervals being exemplary and not limiting). GPS logging paradigms
can include collecting additional GPS data upon vehicle start up
(i.e., key on) and/or shut down (i.e., key off). GPS logging
paradigms can be based in part on collecting GPS data according to
predetermine time intervals, combined with collecting additional
GPS data when the vehicle changes speed or heading. Once collected,
the GPS data is generally conveyed to a remote computing device
using a wireless data link, such as a GSM data link or a satellite
data link, noting that such data links are exemplary and not
limiting. GPS data can be stored until such a link becomes
available. GPS data could also be stored at the vehicle for a
period of time and later conveyed to an external computing device
using wireless or hard wired connections. Such a technique will
require relatively more data storage, and memory failures in the
vehicle can result in loss of data. Many users find regularly data
transfer via cellular or satellite to be more convenient.
[0070] Referring to FIG. 9, at least one non-position based
parameter (in addition to key on/key off) is identified in a block
52 to be used to modify the selected GPS logging paradigm. The
concepts disclosed herein specifically encompass using one or more
of the following parameters to change the GPS logging paradigm:
fuel use, brake temperature, oil temperature, coolant temperature,
throttle position, engine load, engine RPM, shift position/gear
selected, cruise control status, and/or accessory device
status.
[0071] In a block 54 GPS data is acquired during vehicle operation
according to the selected GPS logging paradigm. In a decision block
56 a determination is made as to whether one of the parameters
selected in block 52 has been detected. If not, the logic returns
to block 54. If one of the non-position based (nor key on/key off)
parameters is detected in block 56, then the logic moves to a block
58 and parameter encoded GPS data is acquired (i.e., the parameter
data and current GPS data are logged, so that a later analysis can
correlate the parameter data to the GPS data).
[0072] FIG. 10A schematically illustrates a GPS logging paradigm
based on GPS logging at predetermined time intervals. The line
between the start and end labels represents a vehicle route. Each
shaded circle represents a GPS data logging event. The different
GPS logging events are relatively equally spaced, indicating the
vehicle was traveling at a relatively constant speed during the
route. This is but one possibly type of a GPS logging paradigm that
can be defined in block 50 of FIG. 9.
[0073] FIG. 10B schematically illustrates a GPS logging paradigm
based on GPS logging at predetermined time intervals, modified
based on position based parameters. Rather than logging GPS data
according to elapsed time, the GPS data in this paradigm is logged
when the vehicle changes speed or direction. Significantly, note
the relative dearth of GPS logging in the lower portion of the
route, where the vehicle is not making any changes in direction.
Such a route can correspond to a relatively straight section of
highway. Along such a route segment, where there is no change in
speed or heading, there is little need to collect GPS data, and
eliminating some GPS logging events will reduce a quantity of
airtime consumed.
[0074] FIG. 10C schematically illustrates the GPS logging paradigm
of FIG. 10B modified based on detecting a non-position based
parameter. In this case, the non-position based parameter is
turning cruise control on and off. The cruise control was turned on
at a location 60, and was turned off at a location 62. The GPS
logging paradigm was modified at locations 60 and 62, and the
status of the cruise control was recorded at those locations, as
well as the GPS coordinates. When an operator of the vehicle
reviews the route data, the fact that cruise control was not turned
on until location 60, when the route suggests that cruise control
could have been turned on near location 64. This type of data will
enable operators to educate drivers on how to more efficiently
operate vehicles (the use of cruise control generally results in
fuel savings). It should be recognized that while FIG. 10C relates
to modifying the GPS logging paradigm based on cruise control
status, that the concepts disclosed herein specifically encompass
modifying the GPS logging paradigm based parameters such as fuel
use, brake temperature, oil temperature, coolant temperature,
throttle position, engine load, engine RPM, shift position/gear
selected, and/or accessory device status. The term accessory device
encompasses devices that increase parasitic load and are likely to
reduce fuel economy, such as manual cooling fans, air conditioning
units, etc.
[0075] FIG. 11 is a screen shot of a web page upon which a vehicle
owner can view fuel use data overlaid upon vehicle route data,
where the fuel use data was collected using the method of FIG. 9.
In addition to logging GPS data according to a predefined GPS
logging paradigm based, GPS data was also collected when fuel use
increased or decreased. The combination of fuel use data and GPS
data, presented to a user in the format shown in FIG. 11, enables
vehicle operators (such as fleet managers) to quickly review
vehicle routes to determine areas associated with relatively good
and relatively poor fuel economy. That enables vehicle operators to
analyze their routes, to identify conditions associated with
greater or lesser fuel efficiency, which may lead to redesigning
routes that are traversed on a reoccurring basis to maximize fuel
efficiency.
[0076] The route for a commercial diesel truck shown in FIG. 11
includes segments where fuel economy was over 7.0 MPG (generally
segment 80 shown in green), segments where fuel economy was between
5.1 and 6.9 MPG (generally segment 82 shown in yellow), and
segments where fuel economy was under 5.0 MPG (generally segment
84, shown in red, noting the colors are exemplary and not
limiting). Specifically, segment 80a, segment 80b segment 80c, and
segment 80d represent portions of the route associated with good
fuel economy. Segment 82a, segment 82b segment 82c, and segment 82d
represent portions of the route associated with moderate fuel
economy. Segment 84a, segment 84b segment 84c, and segment 84d
represent portions of the route associated with poor fuel economy.
Note segment 84c is the relatively largest poor fuel economy
segment, and the vehicle operator may focus his attention on that
portion of the route first, to see if some rerouting might enable
that area to be bypassed. Further, such a report can also be
analyzed from the aspect of the time of day. For example,
familiarity with this route might suggest that poor economy in
segment 84c is due to traffic volumes, and changing the timing of
the route may result in increasing the fuel efficiency of that
portion of the route, assuming that such time shifting is
practical.
Exemplary GPS Device with Onboard Computing Environment
[0077] FIG. 12 is a functional block diagram of an exemplary
telematics device added to an enrolled vehicle to implement one or
more of the methods of FIGS. 1 and 9.
[0078] An exemplary telematics unit 160 includes a controller 162,
a wireless data link component 164, a memory 166 in which data and
machine instructions used by controller 162 are stored (again, it
will be understood that a hardware rather than software-based
controller can be implemented, if desired), a position sensing
component 170 (such as a GPS receiver), and a data input component
168 configured to extract vehicle data from the vehicle's data bus
and/or the vehicle's onboard controller (noting that the single
input is exemplary, and not limiting, as additional inputs can be
added, and such inputs can be bi-directional to support data output
as well).
[0079] The capabilities of telematics unit 160 are particularly
useful to fleet operators. Telematics unit 160 is configured to
collect position data from the vehicle (to enable vehicle owners to
track the current location of their vehicles, and where they have
been) and to collect vehicle operational data (including but not
limited to engine temperature, coolant temperature, engine speed,
vehicle speed, brake use, idle time, and fault codes), and to use
data link 164 to (wirelessly in an exemplary but not limiting
embodiment) convey such data to vehicle owners. These data
transmission can occur at regular intervals, in response to a
request for data, or in real-time, or be initiated based on
parameters related to the vehicle's speed and/or change in
location, and/or the change in logging parameters discussed above.
The term "real-time" as used herein is not intended to imply the
data are transmitted instantaneously, since the data may instead be
collected over a relatively short period of time (e.g., over a
period of seconds or minutes), and transmitted to the remote
computing device on an ongoing or intermittent basis, as opposed to
storing the data at the vehicle for an extended period of time
(hour or days), and transmitting an extended data set to the remote
computing device after the data set has been collected. Data
collected by telematics unit 160 can be conveyed to the vehicle
owner using data link 164. If desired, additional memory can be
included to temporarily store data when the data link cannot
transfer data. In particularly preferred embodiments the data link
is GSM or cellular technology based.
[0080] In at least one embodiment, the controller is configured to
implement the method of FIG. 1 by using one or more of data
collected from position sensing component 170 (in some embodiments,
a GPS receiver) and data from data input component 168. In a
related embodiment, the controller is configured to implement the
method of FIG. 9 by using one or more of data collected from
position sensing component 170 and data from data input component
168.
Non-Transitory Memory Medium
[0081] Many of the concepts disclosed herein are implemented using
a processor that executes a sequence of logical steps using machine
instructions stored on a physical or non-transitory memory medium.
It should be understood that where the specification and claims of
this document refer to a memory medium, that reference is intended
to be directed to a non-transitory memory medium. Such sequences
can also be implemented by physical logical electrical circuits
specifically configured to implement those logical steps (such
circuits encompass application specific integrated circuits).
[0082] Although the concepts disclosed herein have been described
in connection with the preferred form of practicing them and
modifications thereto, those of ordinary skill in the art will
understand that many other modifications can be made thereto within
the scope of the claims that follow. Accordingly, it is not
intended that the scope of these concepts in any way be limited by
the above description, but instead be determined entirely by
reference to the claims that follow.
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