US 10,056,008 B1Grant

Using telematics data including position data and vehicle analytics to train drivers to improve efficiency of vehicle use

Issue Date:2018-08-21

•18 Claims

•15 Drawing Sheets

Abstract

A driver efficiency score is based on defining at least metric, collecting data related to the metric during the driver's operation of a vehicle, determining how often the driver's deviated from an optimal standard for that metric, and then reducing the efficiency score based on how often the driver's deviated from the optimal standard, to express the result as an efficiency score of 100% or less (100% meaning the driver never varied from the optimum). The efficiency score for a specific trip is reported along with a loss in dollars due to an efficiency score of less than 100%. Useful metrics include how often the driver deviated from an optimal RPM range (a sweet zone) for the vehicle being operated, how often the driver operated a vehicle at highway speeds without using cruise control, and how often the driver operated a vehicle in excess of a predetermined maximum speed.

Metadata

Assignee

ZONAR SYSTEMS, INC.

Inventors

Tim Sweany
Charles Michael McQuade

Application Information

Application Number:US 14/214,008

Filing Date:2014-03-14

Priority Date:2006-06-20

Art Unit:3715

Classifications

IPC:

G09B9/04G09B19/16

Field of Search:

G09B 19/167G09B 9/04G06F 11/3065G06F 17/00

Patent Drawings (15 sheets)

Description

Related Applications

[0001] This application is based on two prior provisional applications, Ser. No. 61/800,726 and Ser. No. 61/802,191, each filed on Mar. 15, 2013, the benefit of the filing dates of which are hereby claimed under 35 U.S.C. § 119(e). This application is also a continuation-in-part of two prior co-pending application Ser. No. 13/725,128 and Ser. No. 13/725,886, both of which were filed on Dec. 21, 2012, and each of which is based on prior provisional application; Ser. No. 61/580,197, filed on Dec. 24, 2011, the benefits of the filing dates of which are hereby claimed under 35 U.S.C. § 119(e) and 35 U.S.C. § 120. This application is also a continuation-in-part of the following prior co-pending applications: Ser. No. 13/719,208; Ser. No. 13/719,211; and Ser. No. 13/719,218, each of which were filed on Dec. 18, 2012, and each of which is based on prior provisional application; Ser. No. 61/580,190, filed on Dec. 23, 2011, the benefits of the filing dates of which are hereby claimed under 35 U.S.C. § 119(e) and 35 U.S.C. § 120. This application is also a continuation-in-part of prior of the following co-pending applications: Ser. No. 13/725,183 and Ser. No. 13/725,266, each of which were filed on Dec. 21, 2012, and each of which is based on prior provisional application; Ser. No. 61/580,190, filed on Dec. 23, 2011, the benefits of the filing dates of which are hereby claimed under 35 U.S.C. § 119(e) and 35 U.S.C. § 120.

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 how the vehicles are being driven by the drivers operating such vehicles.

[0003] Unfortunately, simply collecting such data does not automatically translate into cost savings. It would be desirable to provide such fleet operators with additional tools in order to derive a benefit from the wealth of data that can be collected. Preferably, such tools can be used to provide feedback to drivers to enable the fleet operator to encourage driving habits leading to cost savings. Such a tool might thus be used to develop objective criteria that could be used encourage and provide incentives to drivers to improve their performance in operating the vehicles in a fleet.

Summary

[0004] One aspect of the novel concepts presented herein is a method of providing feedback regarding driver performance based on empirical data collected during vehicle operation. In at least one embodiment, position and other vehicle performance data is collected at the vehicle and wirelessly conveyed to a remote computing site, where the data is analyzed and a driver efficiency score is generated. In general, GPS data and other vehicle performance data is collected. In at least some embodiments, the data will be obtained from a vehicle data bus or vehicle controller. In particularly preferred (but not limiting) embodiments, fuel injector data is used to acquire fuel use data every time a GPS data point is reported, enabling vehicle fuel use to be analyzed over very small distances, particularly where GPS data is reported frequently (ranging from 18 seconds to about 5 minutes in an exemplary but not limiting embodiment).

[0005] In reference to the description and the claims that follow, driver metrics are analyzed and reported on a per trip basis. A trip can be defined as a key on event to key off event. Multiple key on/key off events for a single day can be combined and reported as a single trip.

[0006] In at least one embodiment, the driver efficiency score is based on defining at least one driver performance metric, collecting data related to the driver performance metric during the driver's operation of a vehicle, determining how often the driver's deviated from an optimal standard for that driver performance metric, assuming the driver had a starting efficiency score of 100%, and then reducing the efficiency score based on how often the driver's deviated from the optimal standard for that driver performance metric, to express the result as an efficiency score of 100% or less (100% would be possible if the driver never varied from the optimal performance metric). In a related embodiment, the efficiency score for a specific trip, the total mileage driven for a specific trip, and an average cost of fuel are used to report a loss in dollars due to an efficiency score of less than 100%.

[0007] In at least one embodiment, the driver efficiency score is based at least in part on how often the driver deviated from an optimal RPM range (a sweet zone) for the vehicle being operated.

[0008] In at least one embodiment, the driver efficiency score is based at least in part on how often the driver operated a vehicle at highway speeds without using cruise control.

[0009] In at least one embodiment, the driver efficiency score is based at least in part on how often the driver operated a vehicle at highway speeds in excess of a predetermined maximum speed.

[0010] In at least one embodiment, the driver efficiency score is based at least in part on how often the driver operated a vehicle at highway speeds in any gear other than a top gear.

[0011] In at least one embodiment, the driver efficiency score is based at least in part on how often the driver allowed the vehicle to idle longer than a predetermined allowable idle time.

[0012] In at least one embodiment, the driver efficiency score is based on a combination of how often the driver deviated from an optimal RPM range (a sweet zone) and did not use cruise control at highway speeds.

[0013] In at least one embodiment, the driver efficiency score is based on a combination of how often the driver deviated from an optimal RPM range (a sweet zone), did not use cruise control at highway speeds, and exceeded a predetermined speed.

[0014] In at least one embodiment, the driver efficiency score is based on a combination of how often the driver did not use cruise control at highway speeds, and exceeded a predetermined speed.

[0015] In at least one embodiment, the driver efficiency score is visually presented on a graphical user interface (GUI) that simultaneously reports the driver's efficiency percentage score and lost dollars. In a related embodiment the GUI is a webpage. In a related embodiment the webpage also simultaneously displays the average RPM for the trip. In a related embodiment the webpage also simultaneously displays the amount of time at highway speeds the vehicle was in top gear, as a percentage for the trip. In a related embodiment the webpage also simultaneously displays the amount of time at highway speeds the vehicle was operating under cruise control, as a percentage for the trip. In a related embodiment the webpage also simultaneously displays the amount of time the vehicle was operating in an optimal RPM range, as a percentage for the trip. In a related embodiment the webpage also simultaneously displays the total miles for the trip. In a related embodiment the webpage also simultaneously displays the amount of fuel used for the trip. In a related embodiment the webpage includes all of the preceding elements noted in this paragraph. In a related embodiment the webpage also simultaneously displays the amount of time the vehicle was allowed to idle. In a related embodiment the webpage also simultaneously displays the amount of fuel the vehicle consumed while idling.

[0016] In at least one embodiment, the driver efficiency score is visually presented on a graphical user interface (GUI) that simultaneously reports the driver's overall efficiency percentage score, the driver's efficiency score based on use of an optimal RPM range, the driver's efficiency score based on use of cruise control at freeway speeds, and lost dollars.

[0017] In at least one embodiment, the driver efficiency score is visually presented on a graphical user interface (GUI) that simultaneously reports the driver's efficiency score based on use of an optimal RPM range and lost dollars.

[0018] In at least one embodiment, the driver efficiency score is visually presented on a graphical user interface (GUI) that simultaneously reports the driver's efficiency score based on use of cruise control at freeway speeds and lost dollars.

[0019] In at least one embodiment, the driver efficiency score for cruise control is visually presented on a graphical user interface (GUI) that provides a graphical depiction of speed versus time, where the graph can be used to visualize portions a trip where cruise control could have been used, and portions a trip where cruise control could not have been used. In at least one related embodiment, cruise control is assumed to be possible at speeds of greater than 55 MPH (noting that such a value is exemplary, and not limiting). In at least one related embodiment, the GUI simultaneously displays a pie chart that enables a viewer to visually determine what portion of a trip cruise control was not possible, what portion of a trip cruise control was possible and not used, and what portion of a trip cruise control was possible and was used. In at least one related embodiment, the GUI simultaneously displays an interactive map of the trip. In at least one related embodiment, the GUI simultaneously displays a chart of fuel cost, including fuel cost for the portion of the trip where cruise control was not possible, fuel cost for the portion of the trip where cruise control was possible but not used, and fuel cost for the portion of the trip where cruise control was possible and was used.

[0020] In at least one embodiment, an optimal efficiency for a specific trip is visually presented on a graphical user interface (GUI) that provides a textual description of each driver performance metric that applied to that trip, and how each metric contributed to an optimal efficiency of 100%. In at least one embodiment the optimal efficiency is based on proper RPM use and proper cruise control use alone. In at least one embodiment the optimal efficiency is based on proper RPM use, proper cruise control use, and overspeed time. In at least one embodiment the optimal efficiency is based on proper RPM use, proper cruise control use, overspeed time and idle time. In at least one related embodiment, the GUI simultaneously displays a pie chart that enables a viewer to visually determine what metric contributed relatively larger amounts to the optimal efficiency. In at least one related embodiment, the data present is not optimal efficiency, but actual driver efficiency for that trip.

[0021] In at least one embodiment, the driver efficiency score for use of top gear is visually presented on a graphical user interface (GUI) that provides a graphical depiction of gear selection versus time, where the graph can be used to visualize portions a trip where top gear was used, and portions a trip where top gear was not used. In at least one related embodiment, top gear information can be viewed as a teaching tool, but is not included in an overall driver efficiency score, if that overall driver efficiency score also uses an optimal RPM as a driver efficiency metric (because using top gear and RPM would overly penalize drivers whose route required the use of a lower gear due to terrain or load, where using a lower gear in the optimal RPM range is actually more efficient). In at least one related embodiment, the GUI simultaneously displays an interactive map of the trip. Selecting a data element in the graph will automatically change the map view to highlight the corresponding location. In at least one related embodiment, the GUI simultaneously displays a chart of fuel cost, including fuel cost for the portion of the trip where top gear was not used, and fuel cost for the portion of the trip where top gear was used.

[0022] In at least one embodiment, the driver performance metric for RPM use for a trip is visually presented on a graphical user interface (GUI) that provides a histogram based on miles traveled and time resent in a plurality of different RPM ranges. In a related embodiment, the RPM ranges are separated by increments of 100 RPMs. In a related embodiment, the RPM ranges are separated by increments of 50 RPMs.

[0023] In at least one embodiment, the driver performance metric for idle time for a trip is visually presented on a graphical user interface (GUI) that provides a histogram based on idle time in minutes and fuel used during idle in gallons for the trip. In at least one related embodiment, the GUI simultaneously displays an interactive map of the trip, enabling a user to quickly determine the locations of excess idle time events.

[0024] In at least one embodiment, the MPG for the trip is visually presented on a graphical user interface (GUI) that provides a graphical depiction of MPG versus time, where the graph can be used to visualize portions a trip where MPG was highest and lowest. In at least one related embodiment, the GUI simultaneously displays an interactive map of the trip. In at least one related embodiment, the GUI simultaneously displays a chart of fuel cost, including fuel cost for the portion of the trip where MPG was relatively high, fuel cost for the portion of the trip where MPG was relatively moderate, and fuel cost for the portion of the trip where MPG was relatively low. In at least one related embodiment, the GUI simultaneously displays a pie chart that enables a viewer to visually determine what portion of a trip was spent at a relatively low MPG, what portion of a trip was spent at a relatively moderate MPG, and what portion of a trip was spent at a relatively high MPG.

[0025] In at least one embodiment, the driver efficiency score for optimal RPM use is visually presented on a graphical user interface (GUI) that provides a graphical depiction of RPM versus time, where the graph can be used to visualize portions of a trip where RPM fell in an optimal range (visually defined on the graph), and portions of a trip where RPM fell outside the optimal range. In at least one related embodiment, the optimal range is uniquely defined for different types of vehicles (i.e., based on the specific power plant, manufacturer, and fuel maps). In at least one related embodiment, the GUI simultaneously displays a summary of total miles and miles spent outside the sweet zone, as well as an efficiency percentage. In at least one related embodiment, the GUI simultaneously displays an interactive map of the trip. In at least one related embodiment, the GUI simultaneously displays a chart of fuel cost, including fuel cost for the portion of the trip where RPM was optimal, and fuel cost for the portion of the trip where RPM was not optimal.

[0026] In at least one embodiment, the driver efficiency score for over speeding is visually presented on a graphical user interface (GUI) that provides a graphical depiction of speed versus time, where the graph can be used to visualize portions a trip where speed exceeded a predetermined setting (generally 55 MPH, but different operators may select different values based on their policies. In at least one related embodiment, the GUI simultaneously displays an interactive map of the trip. In at least one related embodiment, the GUI simultaneously displays a pie chart that enables a viewer to visually determine what portion of a trip was spent at approved speeds, and what portion of a trip was spent at over speeds. In at least one related embodiment, the GUI simultaneously displays a chart of fuel cost, including fuel cost for the portion of the trip where speed was normal, and fuel cost for the portion of the trip where over speeding occurred.

[0027] In at least one embodiment, the data collected from the vehicle includes the altitude for every GPS point the vehicle creates. This allows the ability to check the altitude delta between every GPS point to determine if the vehicle is going uphill, downhill, or traveling over flat terrain. In at least one embodiment where a driver efficiency metric involving optimal RPM range is employed, data corresponding to a downhill portion of the trip is ignored. In at least one related embodiment, anytime the delta between successive GPS points is less than −1%, that section of the trip is excluded from the driver efficiency calculation. That is because drivers may need to exceed the optimal RPM range for engine braking when going downhill (for safety).

[0028] Another concept disclosed is tracking vehicle performance data using a telematics device that wirelessly conveys vehicle and location data to a remote server to analyze turbo boost pressure to determine if maintenance is desirable to increase fuel economy.

[0029] One aspect of such concepts is analyzing vehicle performance, such as MPG, based on route metrics, including terrain, load, driver performance (max use of cruise, low idles, few over speed events, and high operation in an engine sweet zone) to quantitatively analyze performance. Pure MPG does not tell the whole story, as a driver carrying a heavy load uphill will have worse fuel economy that a driver pulling a light load over flat terrain, even if the driving going uphill is using best driving practices, and the driver over the flat is driving poorly and wasting fuel. The lower MPG in this hypothetical is actually related to more efficient vehicle use, and the uphill driver should be rewarded, not the other driver.

[0030] Metrics involved in an exemplary driver fuel efficiency report include over speed, idle, RPM, use of cruise control, use of high or top gear. Empirical data indicate that fleet operators can achieve up to 7% fuel savings when training their drivers based on these analytics.

[0031] Another concept disclosed is tracking vehicle performance data using a telematics device that wirelessly conveys vehicle and location data to a remote server to analyze turbo boost pressure to determine if maintenance is desirable to increase fuel economy.

[0032] Another concept disclosed is tracking vehicle performance data using a telematics device that wirelessly conveys vehicle and location data to a remote server to analyze differential pressure in the engine to detect clogged fuel filters that should be replaced to increase fuel economy.

[0033] Another concept disclosed is tracking vehicle performance data using a telematics device that wirelessly conveys vehicle and location data to a remote server to analyze the diesel regeneration system to determine if an additional regen cycle, which does consume fuel, may actually result in increased fuel economy. The analysis is based on differential pressure measurements on air filter, and empirical data can be collected on different engine types to determine what the pressure threshold should be to indicate the need for a regen cycle.

[0034] Another concept disclosed herein is to include ambient factors, such as route terrain, wind, weather, traffic, head wind, tailwind, to normalize driver scores before comparing them, so drivers facinga headwind or uphill route are not scored lower than drivers with a tailwind or downhill route.

[0035] The above noted methods are preferably implemented by at least one processor (such as a computing device implementing machine instructions to implement the specific functions noted above) or a custom circuit (such as an application specific integrated circuit).

[0036] 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

[0037] 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:

[0038] FIG. 1 is a high level flow chart showing the overall method steps implemented in accord with one exemplary embodiment for achieving the concepts disclosed herein, in which a remote user monitors can evaluate a driver performance based on an efficiency score and a lost fuel cost derived from empirical data collected during the drivers operation of the vehicle;

[0039] FIG. 2 schematically illustrates a vehicle that includes a plurality of sensors configured to collect the required metrics;

[0040] FIG. 3A is a functional block diagram illustrating the functional elements of an embodiment in which at least some data evaluation is processed within the vehicle in order to provide real time coaching to the driver;

[0041] FIG. 3B is a functional block diagram illustrating the functional elements of an embodiment in which the metrics are processed by a computing device remote from the vehicle to obtain the driver's efficiency score for a specific metric and lost fuel costs;

[0042] FIG. 4 schematically illustrates a vehicle that includes a GPS unit with a wireless transmitter utilized in some embodiments disclosed herein;

[0043] FIG. 5 is a functional block diagram of an exemplary telematics device added to an enrolled vehicle to implement one or more of the methods disclosed herein;

[0044] FIG. 6 is a functional block diagram of an exemplary telematics oriented tablet for in vehicle use that may be employed in accord with some aspect of the concepts disclosed herein;

[0045] FIG. 7 is a functional block diagram of an exemplary telematics oriented tablet for in vehicle use implementing a navigation app that is presented to the driver during vehicle operation, such that an info pane is not consumed by the map portion, and any driver coaching can be visually presented to the driver on the info pane;

[0046] FIG. 8 schematically illustrates an accessory display that can be used along with a processor in the vehicle to display any real time driver coaching, where the accessory display can also be used to uniquely log in drivers, so any reports identify the correct driver;

[0047] FIG. 9 is a functional block diagram illustrating exemplary elements in a driver efficiency and lost fuel cost monitoring system in accord with one aspect of the concepts disclosed herein;

[0048] FIG. 10 is an exemplary computing environment for implementing some of the concepts disclosed herein;

[0049] FIG. 11 is a data table illustrating why simply calculating MPG does not inform a fleet operator which drivers are operating most efficiently;

[0050] FIG. 12 is an exemplary screen shot of a webpage accessed by a user to see detailed analytical information that can be used to understand that Driver #2 from FIG. 11 performed less efficiently, even though that driver had a higher MPG for his trip;

[0051] FIG. 13 is an exemplary screen shot of a webpage accessed by a user to see detailed analytical information that can be used to understand how well a driver performed based on an RPM fuel efficiency metric tracking the drivers use of a most efficient RPM range, and how that efficiency rating translated into additional fuel costs;

[0052] FIG. 14 is an exemplary screen shot of a webpage accessed by a user to see detailed analytical information that can be used to understand how well a driver performed based on a cruise control usage fuel efficiency metric tracking the drivers use of cruise control at highway speeds, and how that efficiency rating translated into additional fuel costs;

[0053] FIG. 15 is an exemplary screen shot of a webpage accessed by a user to see detailed analytical information that can be used to understand how well a driver performed based on a top gear usage fuel efficiency metric tracking the drivers use of top gear, and how that efficiency rating translated into additional fuel costs;

[0054] FIGS. 16A and 16B provide details an exemplary technique to generate the top gear metric of FIG. 15 using data collected from the vehicle and an understanding of the vehicle's design characteristics; and

[0055] FIGS. 17A and 17B are portions of an exemplary graphical user interface (or webpage) providing a summary of the fuel efficiency metrics disclosed herein for a plurality of drivers.

Figures and Disclosed Embodiments Are Not Limiting

[0056] 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. No limitation on the scope of the technology and of the claims that follow is to be imputed to the examples shown in the drawings and discussed herein. Further, it should be understood that any feature of one embodiment disclosed herein (even embodiments disclosed in the Summary) can be combined with one or more features of any other embodiment that is disclosed, unless otherwise indicated.

Non-Transitory Memory Medium

[0057] 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). As used herein and in the claims that follow, the terms processor and controller have been used interchangeably with respect to describing an element to implement a specific logical function, and applicant intends the terms to be interpreted broadly, as encompassing elements that implement specifically defined logical functions (which in some cases rely on machine instructions stored in a memory to implement the function). Even where the term processor is used in place of the term controller, applicant believes that the artisan of skill would be able to readily determine from the disclosure provide herein what additional elements, such as peripherals (ports, clock, timers, UARTs, and ADC) and memory (including, but not limited to EEPROM, SRAM, EPROM, and flash) will be used in connection with such a processor to implement the described logical function.

Exemplary Logic for Calculating Driver Efficiency and Lost Fuel Cost

[0058] FIG. 1 is a high level flow chart showing the overall method steps implemented in accord with one aspect of the concepts disclosed herein. In general, the method being disclosed will be used by operators of fleets of vehicles. While it could be used in the context of a single vehicle, it will likely be of particular value to fleet operators. Certain steps of the method are implemented by a remote user accessing an application that allows the remote user to view one or more reports providing information about a driver's efficiency and lost fuel costs associated with efficiency scores of less than 100% for one or more driver performance metrics. Exemplary metrics include one or more of driver use of cruise control at highway speeds, driver's control of engine RPM to maximize time spent in most efficient RPM range, over speeding events (where driver exceeded a predefined maximum speed), and/or idle time. The RPM range can be a generic default range (such as 1300-1350 RPM) and can also be customized based on OEM data for specific power plants and configurations. Certain steps of the method are automatically implemented by a controller remote from the vehicle. Certain steps of the method are automatically implemented by a controller located at the vehicle.

[0059] Referring to FIG. 1, in a block 10 one or more driver performance metrics that relate to fuel consumption are defined. In at least one embodiment only one metric is employed.

[0060] In at least one embodiment at least two metrics are employed. In one particular preferred embodiment a combination of time spent outside of an optimal RPM range and time not spent in cruise control when driving at freeways speeds are used to calculate driver efficiency. In at least one embodiment an individual efficiency is calculated for each metric, and combined driver efficiency is also calculated.

[0061] In at least one embodiment three metrics are employed; time spent outside of an optimal RPM range, time not spent in cruise control when driving at freeways speeds, and idle time over a predetermined limit. In at least one embodiment an individual efficiency is calculated for each metric, and combined driver efficiency is also calculated.

[0062] In at least one embodiment three metrics are employed; time spent outside of an optimal RPM range, time not spent in cruise control when driving at freeways speeds, and time spent in excess of a predetermined speed (such as 55 MPH). In at least one embodiment an individual efficiency is calculated for each metric, and combined driver efficiency is also calculated.

[0063] In at least one embodiment four metrics are employed; time spent outside of an optimal RPM range, time not spent in cruise control when driving at freeways speeds, time spent in excess of a predetermined speed, and idle time over a predetermined limit. In at least one embodiment an individual efficiency is calculated for each metric, and combined driver efficiency is also calculated.

[0064] Referring once again to FIG. 1, in a block 12 data relating to driver fuel efficiency is collected during vehicle operation. The specific data collected will be a function of the metric being used to calculate driver efficiency. Where idle time is a metric, engine run time without motion (or without vehicle in gear) is collected. Where over speed is a metric, speed data is collected. Where top gear use metrics are collected, data related to top gear is collected (FIGS. 16A and 16B provide details on an exemplary top gear calculation, as top gear data is not always readily available from a vehicle data bus). If cruise control metrics are employed, speed data and cruise control status data are collected. If the RPM sweet zone metric is used, RPM data is collected. In a particularly preferred implementation, that enables very detailed efficiency analysis to be conducted, incremental fuel use data is collected from a vehicles fuel injectors. Heavy duty diesel engines are often equipped with fuel injectors that incrementally measures every half liter of fuel delivered. Where such data can be extracted from a vehicle ECU or data bus, that data is collected. Positional (GPS in an exemplary but not limiting embodiment) is also very valuable. The combination of incremental fuel use data and position data enables a trip to be separated into segments, each segment being defined by sequential fuel measurements. For each segment, fuel consumed and mileage traveled will be known. Where RPM data and/or cruise control data are collected, the RPM range and/or use of cruise control will be known for each segment. For segments where optimal RPM range is not used, a calculation can be made on how much fuel was wasted in that segment. For segments where cruise control was not used at speeds of over 55 MPH (noting that predetermined amount can be adjusted upward or downward based on user preference), a calculation can be made on how much fuel was wasted in that segment.

[0065] In at least one embodiment, the function of block 12 will be implemented by equipping enrolled vehicles with a telematics device (noting that this can be simultaneously implemented by a fleet of vehicles) which collects vehicle position data during vehicle operation, as well as data needed to measure the other metrics being employed to measure efficiency (generally as noted above). In at least some embodiments the telematics device is logically coupled with a vehicle data bus to extract data from the vehicle data bus and/or specific vehicle controllers/ECUs. That vehicle position data (and other data) is wirelessly communicated to a remote monitoring service (generally the same remote service employed to implement the steps of block 10, although the functions could be distributed to different computing systems). In general, the vehicle location is updated on a frequent basis (i.e., once every 5 minutes or less during normal vehicle operation, noting that the specific time interval between updates can vary considerably).

[0066] In an optional block 14, some of the data collected at the vehicle can be used to provide real time driver coaching. While some detailed analysis of the data is better suited to be performed remotely, some useful feedback can be readily presented at the vehicle, to provide drivers with the opportunity to improve their driver efficiency scores. Fleet operators provide more incentives for drivers earning better scores, and even disincentives for drivers with lower scores. In general, the coaching is provided visually using a display in the vehicle, although the concepts disclosed herein encompass audible coaching as well. Where idle time affects a driver's efficiency score, an alert can be displayed to the driver when idle time exceeds the predetermined limit (some fleet owners may set the limit to zero, others may adopt a reasonable time period, such as 5 minutes). If a driver is issued a mobile computing device such as a phone, pager, or mobile tablet, the alert can be sent to that mobile device, in case the driver is not in the cab of the vehicle to notice an in cab display. Where a speed event over a predetermined speed affects a driver's efficiency score, an alert can be displayed to the driver when such speed events occur, to remind the driver their score is being lowered. Where RPM usage outside a predefined sweet zone affects a driver's efficiency score, an alert can be displayed to the driver when such RPM events occur, to remind the driver their score is being lowered. Where cruise control is not used when a certain speed is reached and that non-use affects a driver's efficiency score, an alert can be displayed to the driver to remind him or her to use cruise control to improve their efficiency score.

[0067] Note that block 14 is optional, as the data collected and available remotely can be used in driver training sessions after a driver has completed a trip. The data can be used to teach drivers what behavior unique to their driving patterns most negatively contributes to their score (drivers can also be told what they excel at).

[0068] In a block 16, a processor remote from the vehicle automatically analyzes the data collected from the vehicle and determines a driver efficiency score for one or more metrics that were defined in block 10. Considerable variability in the calculation of an efficiency score are possible, in general the score should be based on empirical data and be useful to highlight behavior that reduces efficiency.

[0069] In one exemplary embodiment, an idle efficiency score is calculated based on determining how much fuel was consumed by the vehicle for a trip. Then, a determination is made as to how much fuel was consumed in that trip by idling (any idle over any predetermined limit). An average idle burn time value can be used to calculate how much fuel was consumed during idle for that specific trip. Assume 100 gallons of fuel were used in trip A, and 0 gallons of fuel were consumed idling over the predetermined limit. The driver's idle time score is 100%. Assume 100 gallons of fuel were used in trip B, and 5 gallons of fuel were consumed idling over the predetermined limit. The driver's idle time score is 95%. In some embodiments, no idle time percentage is calculated; rather a report is generated showing a dollar value of the fuel lost due to idle.

[0070] In one exemplary embodiment, an over speed efficiency score is calculated based on determining how much fuel was consumed by the vehicle for a trip. Then, a determination is made as to how much fuel was consumed in that trip by speeding (any speed over any predetermined limit). In an exemplary embodiment, it is assumed that MPG is reduced by 0.1 MPG for every mile over 55 MPH. That value (or some similar value) can be used to calculate how much extra fuel was consumed by over speed events for that specific trip. Assume 100 gallons of fuel were used in trip C, and 0 gallons of fuel were consumed during over speeding. The driver's over speed score is 100%. Assume 100 gallons of fuel were used in trip D, and 3 gallons of fuel were consumed by speeding. The driver's over speed efficiency score is 97%.

[0071] In one exemplary embodiment, an RPM efficiency score is calculated based on separating the trip into segments based on incremental fuel use measurements. GPS data is used to determine the length of each segment in miles, enabling the MPG of discrete segments to be calculated. The trip data is analyzed to determine each segment where RPM use was outside the sweet zone. The actual MPG for that segment is increased by a factor associated with fuel efficiency increase for optimizing RPM to obtain a theoretical RPM MPG. In an exemplary embodiment, that factor is 5%. In some embodiments, users can adjust that up or down based on their experience. The actual MPG and theoretical RPM MPG (for each segment where RPM was outside the sweet zone) and actual miles traveled in that segment are used to determine how much fuel was wasted in that segment. The segments for a single trip are added together to determine how much was fuel wasted due to failure to optimize RPM. In some embodiments, segments known to be downhill segments are ignored, as safety considerations can require RPMs to be increased for engine braking, and that is a best practice, not a practice that should be penalized. Assume 100 gallons of fuel were used in trip E, and no segments indicated RPM use outside of the optimum range. The driver's RPM efficiency score is 100%. Assume 100 gallons of fuel were used in trip F, and 6 extra gallons of fuel were consumed segments with non-optimal RPM use. The driver's RPM efficiency score is 94%.

[0072] In one exemplary embodiment, a cruise control efficiency score is calculated based on separating the trip into segments based on incremental fuel use measurements. GPS data is used to determine the length of each segment in miles, enabling the MPG of discrete segments to be calculated. The trip data is analyzed to determine each segment where cruise control was not used but could have been used (based on speed, an exemplary speed being 55 MPH, such that only no cruise control use over 55 MPH is being scored). The actual MPG for that segment is increased by a factor associated with fuel efficiency increase for using cruise control to obtain a theoretical cruise MPG. In an exemplary embodiment, that factor is 7%. In some embodiments, users can adjust that up or down based on their experience. The actual MPG and theoretical cruise MPG (for each segment where cruise control could have been used but was not used) and the miles traveled for that segment are used to determine how much fuel was wasted in that segment. The segments for a single trip are added together to determine how much fuel was wasted due to failure to use cruise control. In some embodiments, segments known to be downhill segments are ignored, as some operators have safety practices that don't allow cruise use downhill, and safety practices that reduce fuel efficiency should not be penalized. Assume 100 gallons of fuel were used in trip G, and no segments indicated failure to use cruise control. The driver's cruise control efficiency score is 100%. Assume 100 gallons of fuel were used in trip H, and 11 extra gallons of fuel were consumed segments with non-optimal RPM use. The driver's RPM efficiency score is 89%.

[0073] The more often fuel use measurements are made, the smaller the segments will be for a given speed. In general, the common ½ liter increment is sufficiently detailed such that each segment is relatively small. Even at freeway speeds, segments are often less than 1 mile in length. Still, it is possible that RPM use or cruise control use might be good for part of a segment and bad for another part of the same segment. In some embodiments, if any “bad” behavior (RPM out of the sweet zone or no cruise use when cruise was possible) occurs in a segment, the entire segment is ignored (i.e., the segment will not reduce the driver's efficiency score). In still other embodiments, the segment is separated into “good” and “bad” portions, and the bad portion is used to reduce the drivers efficiency score (the bad portion is treated as a mini segment, and efficiency is calculated generally as discussed above, based on the proportional size of the bad segment relative to the overall segment).

[0074] In some embodiments, the above analysis is automatically performed for all trips, for all drivers, and the results are stored for review by a remote user on demand. In some embodiments, threshold filters are used such that drivers having particularly poor efficiency scores are automatically and affirmatively reported (such as via email or a text alert). In other embodiments, the analysis for a particular driver for a particular trip is generated on demand, based on an inquiry. As some companies prefer not to store some of the underlying data in perpetuity (for example, there is an FMCSA requirement to store GPS data that can be used to verify driver logs for 6 months), performing the analysis in advance ensures that some of the data required for the analysis has not been deleted as part of a defined data retention policy for that data.

[0075] Referring once again to FIG. 1, in a block 18 a remote use is enabled to view a report where the driver efficiency and a cost of lost fuel associated with efficiency scores of less than 100% efficiency can be simultaneously viewed. Exemplary graphical user interfaces that can be used to provide driver fuel efficiency and lost fuel costs are provided in FIGS. 12-15, 17A and 17B.

[0076] Where the analysis is based on ignoring downhill segments, various data sources can be used to determine if a segment is downhill. The GPS data can be analyzed to determine elevation changes. Road grade data can also be acquired from third party sources, such as by cross referencing the GPS coordinates with the Shuttle Radar Topography Mission (SRTM) database. Other map vendors, such as BING and Google, can also provide elevation data. In at least one embodiment, downhill is less than a −1% road grade.

Exemplary Vehicle Equipped with Data Collecting Elements

[0077] FIG. 2 schematically illustrates a vehicle including a plurality of sensors configured to collect the required metrics. A vehicle 22, such as a bus or a truck, includes a plurality of sensors 24a-24h. It should be recognized that the specific number of sensors, and the specific types of sensors and types of data collected by the sensors, are not critical, so long as the sensors collect data for the desired metrics. As noted above, a plurality of different metrics have been specifically identified, however it should be recognized that such metrics are intended to be exemplary, and not limiting on the concepts disclosed herein. In the disclosed exemplary embodiment, each sensor is coupled to a CPU 26 (which, as described in greater detail below, may in some of embodiments be replaced with (or provided in addition to) a transmitter).

[0078] FIG. 3A is a functional block diagram 28a illustrating the functional elements of an exemplary embodiment in which at least some of the metrics are processed within the vehicle to provide real time driver coaching related to behavior that will negatively impact the driver's efficiency score (see block 14 of FIG. 1). The vehicle is equipped with sensors 30 configured to collect the required metrics. The sensors are logically coupled with an onboard vehicle CPU 34, which is configured to implement the method step generally described above for block 14 of FIG. 1. CPU 34 is logically coupled to a memory 32 in which are stored the machine instructions that are executed by the CPU to carry out these logical steps. The plurality of metrics collected by sensors 30 can also be stored in memory 32. A (preferably wireless) transmitter 36 (or other data link) can be included to enable the plurality of metrics to be communicated to a remote computing device for detailed analysis. A display or audio device 38 is included to provide real-time feedback to the driver (by displaying coaching to the driver in real-time). As discussed above, the visual or audio presented to the driver will inform the driver what behavior is reducing his efficiency score (no cruise, wrong RPM, overspeed, idle).

[0079] FIG. 3B is a functional block diagram 28b illustrating the functional elements of an exemplary embodiment in which the metrics are processed by a computing device to obtain the driver's efficiency score, where the computing device is remote from the vehicle. Once again, the vehicle is equipped with sensors 30 configured to collect the required metrics. The sensors are logically coupled with an onboard vehicle CPU 34, which is configured to transmit the collected metrics to remote computing device 39 through transmitter 36 (or other data link). In a particularly preferred embodiment, transmitter 36 is a wireless transmitter. In such an embodiment, the method steps generally described above for processing the collected metrics at the remote computing device (i.e., blocks 16 and 18 of FIG. 1). CPU 34 is logically coupled to memory 32 in which the collected metrics can be stored, if the metrics are not to be transmitted to the remote computing device in real-time. Even if the metrics are transmitted to the remote computing device in real-time, such metrics can be stored in memory 32 as a backup in case the transmission is not successful. In such an embodiment, a display is not required.

Exemplary GPS Device with Onboard Computing Environment

[0080] FIG. 4 schematically illustrates a vehicle 23 that includes a GPS unit 27 configured to collect GPS data that can be used to determine if an enrolled vehicle is approaching a location for which a remote user has established zone based driver/vehicle definitions. Generally as discussed above in connection with FIG. 1, a remote monitoring service analyzes GPS data and other data received from enrolled vehicles to calculate a driver efficiency score for metrics related to fuel use. Vehicle 23, such as a bus or a truck (or automobile, or construction equipment, generally as described above) includes GPS unit 27 coupled with an ignition system 25 of the vehicle. In an exemplary embodiment, the GPS unit will be coupled with the ignition switch, such that it is assumed that when the ignition switch is on, the engine of the vehicle is actually running, and the GPS unit will be activated. As described in greater detail below, GPS data can be used for a plurality of metrics, including idle time, cruise control use, speed, and engine RPM, either independently or by being logically coupled to a vehicle data bus. The most basic GPS unit is able to determine a position of the vehicle at a specific time.

[0081] GPS unit 27 preferably includes or is connected to a wireless transmitter (not separately shown), such that the GPS data and other data used in calculating the driver fuel efficiency score can be wirelessly transmitted to a remote computing device, preferably in real-time. As noted above in connection with the method of FIG. 1, the remote computing device can be programmed to use empirical data from the vehicle to determine a driver's fuel efficiency score for one or more parameters, and to calculate how much fuel is lost in dollars based on less than 100% efficiency for any metric. It should be understood that the concepts disclosed herein encompasses coupling such a GPS unit to vehicle sensors and/or a vehicle data bus, such that driver metrics that require data from a vehicle ECU/data bus can be collected. While not specifically shown in FIG. 4, it should be understood that GPS unit 27 can include a processor that uses GPS data to determine if any zone based behaviors apply to the current vehicle location.

[0082] FIG. 5 is a functional block diagram of an exemplary telematics device added to an enrolled vehicle to implement one or more of the methods of disclosed herein. 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 connection component 168 (noting that in some embodiments a plurality of data connections are provided). Exemplary data connections include physical data links, as well as wireless data links such as Wi-Fi, IR, and Bluetooth™. Data connection component 168 enables vehicle data relevant to calculating driver fuel efficiency metrics (such as cruise control use, engine RPM, and incremental fuel use data from fuel injectors or fuel flow meters) to be collected along with GPS data and conveyed to a remote location for analysis. Data connection component 168 can also be used to send coaching data to a display or audio device to provide caching to a driver in real time, generally consistent with block 14 of FIG. 1.

[0083] 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 RPM, gear selection, cruise control use, vehicle speed, and idle time), and to use the RF component to wirelessly convey such data to vehicle owners and/or a third party monitoring service, where such data can be analyzed to determine driver efficiency scores and fuel lost costs due to scores less than 100%, generally as discussed above in connection with FIG. 1. These data transmissions 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. 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/third party service using RF component 164. If desired, additional memory can be included to temporarily store data if the RF component cannot transfer data. In particularly preferred embodiments the RF components is GSM or cellular technology based. Data connection component 168 can also be used to logically couple telematics unit 160 to a driver input unit, which a driver can use to uniquely identify himself, so his efficiency data can be segregated from other drivers. Such an input device can be a keypad (for a PIN), a bar code reader, an RFID card reader, or a biometric scanner. In at least one embodiment the input device is a handheld device used for performing electronic vehicle inspections (such a device is disclosed in commonly owned U.S. Pat. No. 6,671,646).

[0084] In at least one embodiment, the controller is configured to implement steps 12 and 14 of FIG. 1. It should be understood that any of the vehicle based steps of FIG. 1 could be distributed to one or more other processers/controllers at the vehicle.

Exemplary Tablet for in Vehicle Use to Present Fuel Efficiency Coaching to Driver

[0085] FIG. 6 is a functional block diagram of an exemplary mobile computing device 100 for fleet telematics including a display 106 and a controller 102 configured to present at least one telematics application to a user, and to present fuel efficiency coaching to a driver in real time, generally as discussed above in connection with block 14 of FIG. 1. A non-transitory physical memory 104 is included, upon which machine instructions define one or more applications are stored. Note that in embodiments including device 100 a coaching app can be stored in memory 104. Device 100 includes an optional RFID reader 108 (or other sensor) that enables drivers to log into the tablet, so that fuel efficiency related metrics can be tracked to a specific driver. In exemplary but not limiting embodiments, the device includes at least one data input 110 that can be used to logically couple the device to a vehicle data bus or some other device (such as telematics device 160 of FIG. 5). Note that in embodiments where driver coaching is implemented, controller 102 can implement that function so long as the controller has access to the empirical data required (idle time, speed, RPM data, and cruise control stats).

[0086] Device 100 may include additional components, including but not limiting to a GSM component, a Wi-Fi component, a USB component, a rechargeable battery, and in at least one embodiment a GPS component (in which case the GPS devices of FIGS. 4 and 5 are not required).

[0087] Significantly, the display (or speakers) of device 100 can be used to provide the fuel efficiency coaching function of FIG. 1 (block 14). Controller 102 can be employed in some embodiments to implement one of more of the vehicle side steps of FIG. 1.

[0088] FIG. 7 is a functional block diagram of device 100 implementing a navigation app that is presented to the driver during vehicle operation on display 106. Significantly, an info pane 112 is not consumed by the map portion, and remains visible to the driver. Any fuel efficiency driver coaching (slow down, stop idling, change RPM, use cruise control) can be visually presented to the driver on info pane 112 without losing the navigation view.

[0089] In one preferred embodiment, each driver is provided with an RFID tag, which can be scanned into device 100, or a secret pin number to identify him or herself to the tablet. As fuel efficiency scores may be important to a driver's career development, it is important to have a system for unerringly identifying the driver credited with such scoring. Other applications, such as the driver log application and inspection application, will similarly employ verifiable credentials. In at least one embodiment, the tablet cannot be used without first logging onto the tablet using verifiable credentials.

Exemplary Accessory Display for Presenting Fuel Efficiency Coaching to Driver

[0090] Another aspect of the concepts disclosed herein is an accessory display that can be used in connection with a telematics device that itself might not include a display, such as the GPS based devices of FIGS. 4 and 5, to provide a display upon which fuel efficiency coaching (block 14 of FIG. 1) can be presented to the driver when behavior that reduces fuel efficiency scores is detected.

[0091] FIG. 8 schematically illustrates an accessory display 115 that can be used along with a processor in the vehicle to visually present fuel efficiency coaching to a driver, in accord with the concepts disclosed herein, where the accessory display can also be used to uniquely log in drivers, so any empirical data used to generate fuel efficiency scores identify the correct driver. The accessory display does not possess significant processing power, and is used in connection with some other device at the vehicle that provides the required processing of vehicle data in order to what determine what coaching to provide. A data port on the back enables the accessory device to be logically coupled to the device (such as the devices of FIGS. 4 and 5) providing the processing. The accessory device does not need to include a wireless data link when used in connection with other devices having such functionality. The accessory display provides two basic functions (possibly three if equipped with audio). First, the accessory display provides fuel efficiency coaching to the driver in response to a controller in the vehicle identifies empirical data that will tend to lower the drivers fuel efficiency score for a particular fuel efficiency metric. Second, the accessory display enables drivers to uniquely identify themselves using RFID cards (i.e., the accessory display includes an RFID card reader). If desired, the accessory display can include a speaker to provide audible feedback as well. Also if desired, the RFID component can be eliminated, however, it is desirable to provide some other mechanism to enable drivers to uniquely log into to the system (perhaps using a keyboard, biometric device, or other input device in the vehicle).

[0092] Note than an icon of a hand holding a card is shown on the front of the accessory display. The icon provides the driver a visual reference of where the RFID driver card needs to be relative to the accessory display in order to be read.

Exemplary System Environment

[0093] FIG. 9 is a functional block diagram of an exemplary system employed to implement some of the concepts disclosed herein. The functional block diagram illustrates exemplary components used in each vehicle 128 that is enrolled in fuel efficiency and lost fuel cost monitoring service, to implement some of the method steps discussed above. An exemplary driver fuel efficiency and lost fuel cost monitoring service is based on adding a memory 136 and a bi-directional data link 134 to each enrolled vehicle (in an exemplary, but not limiting embodiment, the data buffer and data link are combined into a single component). GPS data and other data can be temporarily stored in the memory in the event an RF connection is not available to upload the data. In an exemplary embodiment, the data link is a combination radio frequency (RF) transmitter and receiver, although separate transmitters and receivers could be used (note the term RF specifically encompasses cellular telephone based data links) A data terminal can optionally be included in the vehicle to facilitate operator entry of information and operator transmission of information that is presented to the operator on a display within the vehicle. While RF data transmission represents an exemplary embodiment, other types of data transmission could be employed. If the vehicle does not already include GPS (i.e. position) data collecting components 130, such components are added (noting that the collection of other types of data, particularly data than can show whether a driver is using cruise control, idle time data, speed data, and RPM data) is implemented in some embodiments. Most vehicles manufactured today include operational data collecting components already, as many of today's vehicles are designed to use such continuously generated operational data to control operation of the vehicle in real-time, and such vehicles generally include data collecting components, data buses, and controllers that use the operational data to control the operation of the vehicle. The vehicle includes at least one processor 132 that performs the function of managing the transmission of GPS data from the vehicle to the remote monitoring service, collecting the fuel efficiency related driver metrics, and implementing any real time driver coaching. In general, processor 132 can be used to implement the vehicle side processor functions of the methods of FIG. 1, noting that multiple processors can be employed as well, such that not all functions are implemented by the same processor.

[0094] An output 138 is also included, to present driver coaching, generally as discussed above in connect ion with block 14 of FIG. 1. Output 138 can be implemented using a speaker providing an audible output, and using a display providing a visual output. Note that output 138 can be combined into a single component with the memory, GPS collecting component, processor and the data link, so only a single additional component is added to the vehicle. In some embodiments, output 138 is a speaker system already installed in the vehicle, such as an OEM stereo system. In some embodiments, the display and or speakers are part of a smart phone or mobile computing device used by the driver in the vehicle (see element 144).

[0095] In a related preferred embodiment the processor, the GPS component, any buffer, and data link are combined into a single telematics device, and an additional display may be required (such as the accessory of FIG. 8). Such a device will send GPS and vehicle/driver data to the remote computing device discussed above at a plurality of different times during the course of the operation of the vehicle. In general, the telematics device will transmit data at intervals ranging from as frequently as every 5 to 15 seconds, or as rarely as every 5 minutes, recognizing that such intervals can vary, and are intended to be exemplary, and not limiting.

[0096] As indicated in FIG. 9, a remote computing device 140 (or network of devices operated by the monitoring service) is logically coupled via a network 142 (such as the Internet) to one or more computing devices 146 accessible to a vehicle operator (noting that in at least some embodiments′, the monitoring service performs the monitoring function for a plurality of different vehicle operators/fleets). Also shown is an optional driver computing device 144 (such as a personal computer, a tablet, or a smart phone). In some embodiments, fleets operators may want to give drivers access to a webpage similar to that shown in FIG. 1, so that drivers can review their fuel efficiency reports (noting only one such driver device is shown in the Figure; however, the monitoring service will likely be monitoring the performance of a plurality of drivers, each likely having access to a different computing device 144). Network 142 facilitates communication between computing devices 140, 144, and 146, enabling the monitoring service to efficiently communicate with drivers and vehicle operators. It should be noted that the concepts disclosed herein encompass embodiments where the monitoring service and vehicle operator are the same entity.

[0097] The concepts disclosed herein are in at least some embodiments intended to be used by fleet owners operating multiple vehicles, and the GPS data conveyed to the remote location for monitoring will include an ID component that enables each enrolled vehicle to be uniquely identified.

Exemplary Computing Environment

[0098] FIG. 10 is a functional block diagram of an exemplary computing device that can be employed to implement some of the method steps disclosed herein. It should be understood that the concepts disclosed herein encompass processing of data collected at a vehicle both in the vehicle and at a remote computing device.

[0099] FIG. 10 schematically illustrates an exemplary computing system 250 suitable for use in implementing the processing functions disclosed herein. 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 GPS data and driver fuel efficiency data collected from enrolled vehicles, to generate one or more driver fuel efficiency reports. Exemplary reports simultaneously display the driver fuel efficiency score and a lost dollar amount due to extra fuel use. The machine instructions implement functions generally consistent with those described above. 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.

[0100] 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 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.

[0101] 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 monitor vehicle position data and driver fuel; efficiency metrics to calculate a driver fuel efficiency score, and in some embodiments a dollar cost due to extra fuel use). 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. In embodiments where computing system 250 is implemented in a vehicle ti implement one or more of the steps of FIG. 1, the computing system 250 can be configured to run autonomously, such that a user input device not regularly employed.

[0102] 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. In embodiments where computing system 250 is implemented in a vehicle, the computing system 250 can be configured to run autonomously, such that a user output device not regularly employed (except for embodiments providing driver fuel efficiency coaching).

[0103] Data link 264 is configured to enable data to be input into computing system 250 for processing. 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™, network connections via Ethernet ports, and other connections that employ the Internet.

[0104] Note that location data from the enrolled vehicles will be communicated wirelessly in at least some embodiments, to the remote computing system that analyzes the data to calculate the driver fuel efficiency metrics and lost dollar costs due to extra fuel use.

[0105] It should be understood that the terms “remote computer”, “computing device”, and “remote computing device” are intended to encompass a single computer as well as networked computers, including servers and clients, in private networks or as part of the Internet. The location data and driver compliance data (compliance with zone based driver behavior rules) received by the monitoring service from the vehicle 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. While implementation of the methods noted above have 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 methods could also be implemented using a custom circuit (such as an application specific integrated circuit or ASIC).

Exemplary User Reports

[0106] FIG. 11 is a data table illustrating why simply calculating MPG does not inform a fleet operator which drivers are operating most efficiently. Driver #1 only had a fuel efficiency of 6.49 for his trip, while Driver #2 had a fuel efficiency of 7.14 for his trip. Driver #2 is the more efficient driver, correct? No! Driver 1 was carrying a heavier load through the mountains. Driver #2 carried a lighter load over flat terrain. Driver #1 frequently used cruise control, had few overspeed events, and operated in the RPM sweet zone for most of the trip. Driver #2 did not use cruise control, had more overspeed events, and did not operate in the RPM sweet zone for as much of the trip. Driver #2 cost the fleet $114 by poor fuel efficient driving techniques. Multiply that by many drivers over an entire year, that is a lot of wasted fuel.

[0107] FIG. 12 is an exemplary screen shot of a webpage (our GUI in a stand-alone software application, noting that when the term webpage is used herein, it should be understood that the same information can be generated in a stand-alone software product as long as the software has access to the data) accessed by a user to see detailed analytical information that can be used to understand that Driver #2 from FIG. 11 performed less efficiently, even though that driver had a higher MPG for his trip. The report of FIG. 12 relatively large amounts of fuel were wasted by not using cruise control and not using the most efficient RPM range. Slightly less fuel was wasted by speeding. While some fuel was lost idling, that amount was negligible. A driver manager can readily use this information as a training to, to quickly point out to a driver what areas need improvement. Some fleets may want to provide copies of this report to each driver on a daily basis.

[0108] FIG. 13 is an exemplary screen shot of a webpage 300 accessed by a user to see detailed analytical information that can be used to understand how well a driver performed based on an RPM fuel efficiency metric tracking the drivers use of a most efficient RPM range, and how that efficiency rating translated into additional fuel costs. Webpage 300 includes an interactive map portion 302 that enables the user to view the trip. Webpage 300 includes a graph 304 of the entire trip, with RMP versus time. A sweet zone for RPM is shown in a shaded portion 306, so the user can easily tell when the driver operated over or under that sweet zone. Selecting a specific portion of the graph will change the view of the map, so the user can understand the geographical location associated with the poor use of RPM. Webpage 300 includes an RPM efficiency measurement 308, enabling the user to understand that this driver has a relatively good efficiency score for his use of RPM (lower percentage scores are better, the lower the score, the less time spent outside of the sweet zone for RPM, the higher the score, the more time spent outside the sweet zone, a fuel wasting behavior). Webpage 300 includes a fuel usage table 310 that provides detail regarding how much fuel was inside the RPM sweet zone and outside the RPM sweet zone. The user is able to quickly understand how much the poor fuel efficiency driving behavior cost in the terms of dollars.

[0109] Referring to RPM efficiency measurement 308, the 25.9% refers to the amount of time spent driving outside the sweet zone (where 100% would be spending all the driving time outside of the sweet zone), whereas fuel usage table 310 shows how much fuel was lost during that time. In terms of this report, a relatively higher number for RPM efficiency measurement 308 indicates relatively poor performance. For example, a 100% efficiency for RPM efficiency measurement 308 would mean the driver spent all of his driving time operating outside of the preferred RPM sweet zone. It should be understood that if desired to RPM efficiency measurement 308 could be reported in an inverse fashion, such that 100% indicated the best performance (i.e., the driver spent 100% of his time operating in the RPM sweet zone). The empirical data used to generate the data shown on webpage 300 reports that 23.45 gallons of fuel was used to travel 172.3 miles. In the RPM fuel efficiency analysis presented here, an assumption was made that about 7% fuel efficiency gain can be made by operating in the RPM sweet zone. Since 25.9% of the trip (44.6 of 172.3 miles) was outside the sweet zone, a rough calculation based on a 7% fuel loss from being outside the sweet zone would be about 7% of ¼ (the 25.9% value) of the fuel used for the trip. In this case one could estimate 25.9%*7%*23.45 gallons=0.43 gallons of fuel lost outside the sweet zone. Referring to fuel usage table 310, the reported amount of fuel lost is 0.463 gallons, which is fairly close to that rough estimate. The amount reported in table 310 is based on looking at the MPG for each segment of the trip, generally as discussed above, so it is more accurate than the rough calculated noted above. While 0.463 gallons does not seem like a lot, it does add up quickly over multiple vehicles and multiple days, and it is based on a discretionary behavior that can be modified with no safety issue. In a worst case scenario (the driver was 100% outside the sweet zone), for the trip data used in FIG. 13, the amount reported in table 310 would be (23.45 gallons*7%=) 1.64 gallons of fuel lost to being outside the sweet zone.

[0110] FIG. 14 is an exemplary screen shot of a webpage accessed by a user to see detailed analytical information that can be used to understand how well a driver performed based on a cruise control usage fuel efficiency metric tracking the driver's use of cruise control at highway speeds, and how that efficiency rating translated into additional fuel costs. Webpage 312 includes an interactive map portion 302 that enables the user to view the trip. Webpage 312 includes a graph 314 of the entire trip, with speed versus time. A dashed line indicates the speed at which cruise ideally should be used (here 55 MPH), so the user can easily tell when the driver had the opportunity to use cruise control. Selecting a specific portion of the graph will change the view of the map, so the user can understand the geographical location associated with the poor use of cruise control. Webpage 312 includes a pie chart 318 showing portions of the trip where cruise was not possible, portions where cruise was possible and not used, and portions where cruise was possible and used, with the relative percentages, enabling the user to understand that this driver had a very poor efficiency score for his use of cruise (no cruise use), and that he could have used cruise 73.8% of the trip. Webpage 312 includes a fuel usage table 316 that provides detail regarding how much fuel was used with cruise enabled (none here), how much fuel was used in segments where cruise was not possible (under 55 MPH; here 10.531 gallons), and how much fuel was used in segments where cruise was possible but not used (here 13.386 gallons). The user is able to quickly understand how much fuel was consumed in segments of the trip where no cruise was employed (i.e., less efficient operation). Preferably, very little fuel should be reported being used as “potential w/o cruise.”

[0111] FIG. 15 is an exemplary screen shot of a webpage accessed by a user to see detailed analytical information that can be used to understand how well a driver performed based on a top gear usage fuel efficiency metric tracking the driver's use of top gear, and how that efficiency rating translated into additional fuel costs. In this report, in contrast to webpage 300 of FIG. 13, a relatively higher percentage mean relatively better driver performance. Webpage 300 includes an interactive map portion 302 that enables the user to view the trip. Webpage 320 includes map portion 302 so users can see the vehicles entire trip. In a default setting, the entire trip is shown, and users can manipulate the map to zoom into portions of the trip. A graph 322 of the entire trip is included on webpage 320, with gear position versus time. Selecting a specific portion of the graph will change the view of the map, so the user can understand the geographical location associated with the poor use of gear (i.e., lower rather than higher). Webpage 320 includes a top gear efficiency measurement 326, enabling the user to understand that this driver has a relatively good efficiency score for his use of top gear (higher percentage scores are better, the higher the score, the less fuel wasting behavior). Webpage 320 includes a fuel usage table 324 that provides detail regarding how much fuel was in top gear and how much fuel was used in less efficient lower gears. The user is able to quickly understand how much the use of poor gear choice (a less fuel efficient driving behavior) cost in the terms of dollars.

[0112] It should be understood that in at least some embodiments, where a combined fuel efficiency percentage score is calculated for a driver using RPM sweet zone data, that the top gear efficiency data is not included in that combined score. That is because if a driver uses good RPM practice, his top gear use might drop (for instance when pulling a heavy load a lower gear selection might be required to keep the engine operating in the RPM sweet zone). Since the RPM sweet zone represents a fuel efficient operating range (not to mention reducing engine wear and tear), such an embodiment does not penalize drivers for operating in a lower gear. Even in such embodiments, the top gear metric of FIG. 15 may still be made available (just not used for the combined efficiency score along with RPM efficiency). The report of FIG. 15 is particularly valuable to long distance fleet operators whose driver's spend a lot of time on interstate highways, where there ought to be a statistically high opportunity to use an efficient top gear.

[0113] FIGS. 16A and 16B provide details an exemplary technique to generate the top gear metric of FIG. 15 using data collected from the vehicle and an understanding of the vehicle's design characteristics. The top gear calculation requires data to be collected from the vehicle, and for specific transmission information to be known about the vehicle. Data collected from the vehicle is sent to a remote server (over the “cloud”) and a top gear efficiency analysis is performed and reported to a user on a webpage such as that shown in FIG. 15. The top gear analysis is based on obtaining RPM, speed, odometer and location data from the vehicle during vehicle operation. That data, in connection with known gear ratio data for the specific transmission in that vehicle, is used to generate the report of FIG. 15, generally using the process shown in FIG. 16B.

[0114] FIGS. 17A and 17B are portions of an exemplary graphical user interface (or webpage) providing a summary of the fuel efficiency metrics disclosed herein for a plurality of drivers. FIGS. 17A and 17B are screen shots of an exemplary webpage (too wide to display on a single page using acceptably sized font) that presents driver efficiency metrics for a plurality of drivers. This webpage is intended to be viewed by fleet managers to gather a quick understanding of empirically measured driver efficiencies. The report is based on an average fuel cost that can be selected by the user (that function not shown in these FIGURES, but the functionality is there). For each vehicle on a specific day (or month, or week, that is a user definable parameter, noting that individual vehicles, or subsets of all fleet vehicles are similarly selectable), various columns of data are provided, including: Distance (i.e., miles driven); Fuel used (in gallons); MPG; Moving MPG (all idle time or PTO time removed); potential MPG (based on empirically measured driver controlled fuel efficiency robbing behavior); Potential Moving MPG; Driver Efficiency Score; $/Mile costs; Total Loss (in $, based on empirically measured driver controlled fuel efficiency robbing behavior); Average RPM; Top Gear Use (percentage); Total Cruise (%); Achievable Cruise (%); Outside Sweet Zone (RPM; %); Idle Fuel (gallons); and Idle Minutes. The information is sortable, so relatively better performing and/or relatively poorer performing drivers can be filtered or sorted. Not shown in this screen shot is a column reporting overspeed percentage efficiency that is discussed above, but that column is available, and in some embodiments is factored into the Driver Efficiency Score.

[0115] Of particular interest is the Driver Efficiency score. A relatively higher score mean more fuel efficient performance. An exemplary calculation is provided below based on a trip that consumed 50 gallons of fuel. That single metric enables fleets operators to quickly identify their most efficient, and least efficient drivers. The additional information in this report can be used to identify which behavior for a specific driver represents the most cost saving opportunity (i.e., what behavior has the most significant costs associated with it). The driver efficiency score is based on a combination of how many gallons of fuel the driver wasted by operating the vehicle outside of the RPM sweet zone, how many gallons of fuel the driver wasted by operating the vehicle without using cruise control (where the vehicle was going fast enough to make cruise reasonable; i.e., not in traffic); how many gallons of fuel the driver wasted by idling (over some predetermined limit, usually 5 minutes), and how many gallons of fuel the driver wasted by exceeding a predetermined speed limit.

[0116] Another column of particular interest is the Total Loss column. A driver that had poor efficiency over a short trip wastes less fuel than a driver with a higher score may waste over a much longer trip. Thus this column allows fleet operator to see where they are losing the most money in what effectively is a controllable expense.

[0117] The average RPM column enables driver managers to quickly determine if the average RMP of the trip varied substantially from the most fuel efficient sweet zone. That column provides qualitative information about the driver's relative performance, without providing any empirical data about how much fuel might have been wasted. The Top Gear percentage column similarly enables driver managers to quickly determine if a driver often used top gear or not. The top gear column provides qualitative information about the driver's relative performance, without providing any empirical data about how much fuel might have been wasted.

[0118] The Total Cruise and Achievable Cruise are viewed together. While it would be possible to derive a cruise efficiency metric where a 100% score was always possible, because the driver always used cruise whenever cruise actually could be used. In the report of FIGS. 17A and 17B, Total Cruise and Achievable Cruise are both reported. If they match, then the driver did as much as he possibly could with respect to using cruise control. If the Total Cruise is 25% and the Achievable Cruise is 50%, that means that cruise could not have been used 50% of the time, and that the driver actually only used cruise ½ of the time he could have. Note that because the driver efficiency metric is based on the gallons wasted, scoring only 50% Total Cruise where only 50% Achievable Cruise applies does not reduce your Driver Efficiency Score at all, because you would have no fuel lost due to failure to use cruise.

[0119] Similar to what is discussed in FIG. 13, the outside sweet zone metric is inversed relative to other metrics, in that a 0% score is the best possible score. So drivers have a relatively higher score performed relatively poorer. Again, it is possible to combine the Total Cruise, Outside Sweet Zone, Over Speed, and Idle efficiency metrics together to achieve the combined Driver Efficiency Score, even though for some of the metrics 100% is the best, and in others 100% is the worst, because the Driver Efficiency Score is based on gallons wasted for each metric (an exemplary Driver Efficiency Score calculation is provided below).

[0120] The Idle Fuel consumed and Idle Minutes enable driver managers to understand which drivers need coaching to reduce fuel wasted during idle.

[0121] It should be noted that in some embodiments, the relatively efficiency scores are not based on all trip segments (i.e., the distance between incremental fuel readings), but only trip segments where the driver's behavior could reduce fuel costs without sacrificing safety. Thus, in some embodiments, downhill segments are ignored, because safe driving practices (engine breaking) may force the driver outside of the RPM sweet zone. Cruise control is also ignored in some embodiments when driving downhill, again for safety reasons. With respect to cruise control, segments where speed is less than about 50 MPH (user selectable), usage of cruise is not scored, because it would be unsafe to use cruise in something like stop and go driving.

Summary of an Exemplary Fuel Efficiency Score

[0122] In general, the trucking industry believes that driver specific behavior can effect up to 30% of the fuel used. For a trip that consumes 50 gallons of fuel, that variance is 15 gallons. In other words, driver behavior could enable that same trip to be completed using 35 gallons.

[0123] The concepts disclosed herein are based on identify a number of different driver behaviors that affect fuel use, and assigning a percentage value to that behavior. The assigned value may not be absolutely correct, but when used to evaluate all drivers in a fleet using empirical data collected during vehicle operation, inefficient operating patterns can be readily identified in specific drivers, such that driver coaching or driver training (alone or in combination with driver incentives) can be used to reduce inefficient driving behavior.

[0124] Referring once again to that 50 gallon hypothetical, and hypothetical data collected from vehicle operation, one may determine that not using cruise control during the trip lost 5 gallons of fuel (5/50=10%), that operating the vehicle outside of the RPM sweet lost 1 gallon of fuel (1/50=2%); that going over the speed threshold lost 2 gallons of fuel (2/50=4%), and for this trip there was no fuel lost due to excess idle (0/50=0%). Combining those efficiency percentage one determines that the driver could have saved 16% (8 gallons) of the total 50 gallons on this trip. So of the 30% (15 gallons) maximum the driver could have saved he lost 16% (8 gallons). This is then used to get the driver combined fuel efficiency metric:

[0125] 15 gallons−8 gallons=7 gallons.

[0126] 30%−16%=14%

[0127] 7 gallons/15 gallons=46.66% Driver Efficiency

[0128] 14%/30%=46.66% Driver efficiency

[0129] In another example also based on 50 gallons, assume a driver had only lost 2% of the fuel (1 gallon) to being outside the sweet zone, and didn't do anything else wrong.

[0130] 15 gallons−1 gallon=14 gallons

[0131] 30%−2%=28%

[0132] 14 gallons/15 gallons=93.33% Driver Efficiency

[0133] 28%/30%=93.33% Driver Efficiency

[0134] So, the more fuel the driver could have saved, the worse their driver efficiency score.

[0135] 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.

Claims

The invention in which an exclusive right is claimed is defined by the following:

1. A method to improve operating efficiency of a driver of a vehicle, comprising:

operating the vehicle over a plurality of selected segments, each segment having an associated segment length;

selecting, with a computing device, one or more driver-controllable fuel efficient behavior metrics on which to generate a driver efficiency score, the one or more driver-controllable fuel efficient behavior metrics drawn from a plurality of driver-controllable fuel efficient behavior metrics including:

an amount of time that the vehicle is idling or not idling within each of the plurality of selected segments,

an amount of time that the vehicle is operated above or below a determined engine speed within each of the plurality of selected segments,

an amount of time that the vehicle is operated above or below a determined vehicle speed within each of the plurality of selected segments, and

an amount of time that the vehicle is operated using or not using cruise control within each of the plurality of selected segments;

within each of the plurality of selected segments, automatically collecting vehicle data associated with each selected driver-controllable fuel efficient behavior metric, the vehicle data automatically collected with a plurality of independent sensors including at least one engine-based sensor and at least one telematics sensor;

selecting at least one model efficiency factor associated with each selected driver-controllable fuel efficient behavior metric;

calculating with the computing device a plurality of interim driver efficiency values, the plurality of interim driver efficiency values including at least one interim driver efficiency value per selected segment and per selected driver-controllable fuel efficient behavior metric, each interim driver efficiency value calculated using automatically collected vehicle data and a selected model efficiency factor;

calculating a driver efficiency score that is between a lowest efficiency value and a highest efficiency value, the driver efficiency score calculated with the computing device from some but not all of the plurality of interim driver efficiency values;

determining how much fuel is consumed by the vehicle within each of the plurality of selected segments;

determining whether or not the driver efficiency score is less than the highest efficiency value;

based on a determination that the driver efficiency score is less than the highest efficiency value, calculating a value representative of the fuel wasted within each of the plurality of selected segments due to the one or more driver-controllable fuel efficient behaviors; and

in response to a user request, concurrently displaying the driver efficiency score and the value representative of the fuel wasted.

2. The method of claim 1, further comprising:

determining an amount of fuel used by the vehicle when the driver could have used cruise control but did not; and

applying a corrective factor based on efficiency gains related to cruise control use to determine an amount of fuel wasted when not using cruise control.

3. The method of claim 1, further comprising:

determining an amount of fuel used by the vehicle when the driver operated the vehicle at an engine speed outside of a predetermined range;

applying a corrective factor based on efficiency gains related to operating the vehicle at an engine speed within the predetermined range; and

determining an amount of fuel wasted by not operating the vehicle at the engine speed within the predetermined range.

4. The method of claim 1, further comprising:

determining an amount of fuel used by the vehicle when the driver operated the vehicle at a speed greater than a predetermined speed value;

applying a corrective factor based on an effect of speed on fuel efficiency; and

determining an amount of fuel wasted by operating the vehicle at the speed greater than the predetermined speed value.

5. The method of claim 1, further comprising:

determining an amount of fuel wasted when the vehicle has idled for longer than a predetermined idle value.

6. The method of claim 1,

wherein automatically collecting vehicle data includes:

collecting incremental fuel use data correlated with location data; and

wherein calculating the driver efficiency score includes:

segmenting operation of the vehicle into a plurality of segments, each segment associated with a sequential incremental fuel use reading;

calculating an amount of fuel consumed during each segment;

calculating a length of each segment using the location data;

determining a vehicle fuel efficiency for each segment;

associating the vehicle fuel efficiency for each segment with at least one of the plurality of driver-controllable fuel efficient behavior metrics; and

producing a per segment cost of fuel wasted.

7. A computing device arranged to improve operating efficiency of a driver of a vehicle, comprising:

a sensor interface to communicatively couple a plurality of independent sensors to the computing device, the vehicle sensors including at least one engine-based sensor and at least one telematics sensor;

a positioning system device arranged to provide geographic data indicating operation of the vehicle over a plurality of selected segments, each segment having an associated segment length;

a logic module including a processor and memory having processor-executable instructions stored thereon, the processor-executable instructions arranged to select one or more driver-controllable fuel efficient behavior metrics on which to generate a driver efficiency score, the one or more driver-controllable fuel efficient behavior metrics drawn from a plurality of driver-controllable fuel efficient behavior metrics including:

an amount of time that the vehicle is idling or not idling within each of the plurality of selected segments,

an amount of time that the vehicle is operated above or below a determined engine speed within each of the plurality of selected segments,

an amount of time that the vehicle is operated above or below a determined vehicle speed within each of the plurality of selected segments, and

an amount of time that the vehicle is operated using or not using cruise control within each of the plurality of selected segments;

wherein the processor-executable instructions are further arranged to:

within each of the plurality of selected segments, automatically collect vehicle data associated with each selected driver-controllable fuel efficient behavior metric, the vehicle data received via the sensor interface;

select at least one model efficiency factor associated with each selected driver-controllable fuel efficient behavior metric;

calculate a plurality of interim driver efficiency values, the plurality of interim driver efficiency values including at least one interim driver efficiency value per selected segment and per selected driver-controllable fuel efficient behavior metric, each interim driver efficiency value calculated using the vehicle data received via the sensor interface and a selected model efficiency factor;

calculate a driver efficiency score that is between a lowest efficiency value and a highest efficiency value, the driver efficiency score calculated from some but not all of the plurality of interim driver efficiency values;

determine how much fuel is consumed by the vehicle within each of the plurality of selected segments;

determine whether or not the driver efficiency score is less than the highest efficiency value;

based on a determination that the driver efficiency score is less than the highest efficiency value, calculate a value representative of the fuel wasted within each of the plurality of selected segments due to the one or more driver-controllable fuel efficient behaviors; and

in response to a user request, concurrently display the driver efficiency score and the value representative of the fuel wasted.

8. The computing device of claim 7, wherein the processor-executable instructions of the logic module are further arranged to:

determine an amount of fuel used by the vehicle when the driver could have used cruise control but did not; and

apply a corrective factor based on efficiency gains related to cruise control use to determine an amount of fuel wasted when not using cruise control.

9. The computing device of claim 7, wherein the processor-executable instructions of the logic module are further arranged to:

determine an amount of fuel used by the vehicle when the driver operated the vehicle at an engine speed outside of a predetermined range;

apply a corrective factor based on efficiency gains related to operating the vehicle at an engine speed within the predetermined range; and

determine an amount of fuel wasted by not operating the vehicle at the engine speed within the predetermined range.

10. The computing device of claim 7, wherein the processor-executable instructions of the logic module are further arranged to:

determine an amount of fuel used by the vehicle when the driver operated the vehicle at a speed greater than a predetermined speed value;

apply a corrective factor based on an effect of speed on fuel efficiency; and

determine an amount of fuel wasted by operating the vehicle at the speed greater than the predetermined speed value.

11. The computing device of claim 7, wherein the processor-executable instructions of the logic module are further arranged to:

determine an amount of fuel wasted when the vehicle has idled for longer than a predetermined idle value.

12. The computing device of claim 7,

wherein an automatic collection of vehicle data includes:

collecting incremental fuel use data correlated with location data; and

wherein a calculation of the driver efficiency score includes:

segmenting operation of the vehicle into a plurality of segments, each segment associated with a sequential incremental fuel use reading;

calculating an amount of fuel consumed during each segment;

calculating a length of each segment using the location data;

determining a vehicle fuel efficiency for each segment;

associating the vehicle fuel efficiency for each segment with at least one of the plurality of driver-controllable fuel efficient behavior metrics; and

producing a per segment cost of fuel wasted.

13. A non-transitory memory medium having machine instructions stored thereon, said machine instructions configured to perform acts comprising:

while operating a vehicle over a plurality of selected segments, each segment having an associated segment length, automatically collecting vehicle data, the vehicle data automatically collected with a plurality of independent sensors including at least one engine-based sensor and at least one telematics sensor;

selecting one or more driver-controllable fuel efficient behavior metrics on which to generate a driver efficiency score for a driver of the vehicle, the one or more driver-controllable fuel efficient behavior metrics drawn from a plurality of driver-controllable fuel efficient behavior metrics including:

an amount of time that the vehicle is idling or not idling within each of the plurality of selected segments, an amount of time that the vehicle is operated above or below a determined engine speed within each of the plurality of selected segments, an amount of time that the vehicle is operated above or below a determined vehicle speed within each of the plurality of selected segments, and an amount of time that the vehicle is operated using or not using cruise control within each of the plurality of selected segments;

selecting at least one model efficiency factor associated with each selected driver- controllable fuel efficient behavior metric;

calculating a plurality of interim driver efficiency values, the plurality of interim driver efficiency values including at least one interim driver efficiency value per selected segment and per selected driver-controllable fuel efficient behavior metric, each interim driver efficiency value calculated using automatically collected vehicle data and a selected model efficiency factor;

calculating a driver efficiency score that is between a lowest efficiency value and a highest efficiency value, the driver efficiency score calculated from some but not all of the plurality of interim driver efficiency values;

determining how much fuel is consumed by the vehicle within each of the plurality of selected segments;

determining whether or not the driver efficiency score is less than the highest efficiency value;

in response to a user request, concurrently displaying the driver efficiency score and the value representative of the fuel wasted.

14. The non-transitory memory medium having machine instructions stored thereon of claim 13, said machine instructions further configured to perform acts comprising:

determining an amount of fuel used by the vehicle when the driver could have used cruise control but did not; and

applying a corrective factor based on efficiency gains related to cruise control use to determine an amount of fuel wasted when not using cruise control.

15. The non-transitory memory medium having machine instructions stored thereon of claim 13, said machine instructions further configured to perform acts comprising:

determining an amount of fuel used by the vehicle when the driver operated the vehicle at an engine speed outside of a predetermined range;

applying a corrective factor based on efficiency gains related to operating the vehicle at an engine speed within the predetermined range; and

determining an amount of fuel wasted by not operating the vehicle at the engine speed within the predetermined range.

16. The non-transitory memory medium having machine instructions stored thereon of claim 13, said machine instructions further configured to perform acts comprising:

determining an amount of fuel used by the vehicle when the driver operated the vehicle at a speed greater than a predetermined speed value;

applying a corrective factor based on an effect of speed on fuel efficiency; and

determining an amount of fuel wasted by operating the vehicle at the speed greater than the predetermined speed value.

17. The non-transitory memory medium having machine instructions stored thereon of claim 13, said machine instructions further configured to perform acts comprising:

determining an amount of fuel wasted when the vehicle has idled for longer than a predetermined idle value.

18. The non-transitory memory medium having machine instructions stored thereon of claim 13, wherein automatically collecting vehicle data includes:

collecting incremental fuel use data correlated with location data; and

wherein calculating the driver efficiency score includes:

segmenting operation of the vehicle into a plurality of segments, each segment associated with a sequential incremental fuel use reading;

calculating an amount of fuel consumed during each segment;

calculating a length of each segment using the location data;

determining a vehicle fuel efficiency for each segment;

associating the vehicle fuel efficiency for each segment with at least one of the plurality of driver-controllable fuel efficient behavior metrics; and

producing a per segment cost of fuel wasted.

Patent Citations (480)

Patent	Date	Inventor	Cited By
US3573620(A)	1971-04-01	Ashley et al.	Applicant
US3990067(A)	1976-11-01	Van Dusen et al.	Applicant
US4025791(A)	1977-05-01	Lennington et al.	Applicant
US4092718(A)	1978-05-01	Wendt	Applicant
US4258421(A)	1981-03-01	Juhasz et al.	Applicant
US4258521(A)	1981-03-01	Fricker et al.	Applicant
US4263945(A)	1981-04-01	Van Ness	Applicant
US4325057(A)	1982-04-01	Bishop	Applicant
US4469149(A)	1984-09-01	Walkey et al.	Applicant
US4602127(A)	1986-07-01	Neely et al.	Applicant
US4651157(A)	1987-03-01	Gray et al.	Applicant
US4658371(A)	1987-04-01	Walsh et al.	Applicant
US4688244(A)	1987-08-01	Hannon et al.	Applicant
US4750197(A)	1988-06-01	Denekamp et al.	Applicant
US4763356(A)	1988-08-01	Day, Jr. et al.	Applicant
US4799162(A)	1989-01-01	Shinkawa et al.	Applicant
US4804937(A)	1989-02-01	Barbiaux et al.	Applicant
US4814711(A)	1989-03-01	Olsen et al.	Applicant
US4846233(A)	1989-07-01	Fockens	Applicant
US4897792(A)	1990-01-01	Hosoi	Applicant
US4934419(A)	1990-06-01	Lamont et al.	Applicant
US4935195(A)	1990-06-01	Palusamy et al.	Applicant
US5006847(A)	1991-04-01	Rush et al.	Applicant
US5058044(A)	1991-10-01	Stewart et al.	Applicant
US5068656(A)	1991-11-01	Sutherland	Applicant
US5072380(A)	1991-12-01	Randelman et al.	Applicant
US5120942(A)	1992-06-01	Holland et al.	Applicant
US5128651(A)	1992-07-01	Heckart, deceased	Applicant
US5132906(A)	1992-07-01	Sol et al.	Applicant
US5204819(A)	1993-04-01	Ryan	Applicant
US5206643(A)	1993-04-01	Eckelt	Applicant
US5223844(A)	1993-06-01	Mansell et al.	Applicant
US5243323(A)	1993-09-01	Rogers	Applicant
US5243343(A)	1993-09-01	Moriyasu	Applicant
US5321629(A)	1994-06-01	Shirata et al.	Applicant
US5337003(A)	1994-08-01	Carmichael et al.	Applicant
US5359522(A)	1994-10-01	Ryan	Applicant
US5359528(A)	1994-10-01	Haendel et al.	Applicant
US5394136(A)	1995-02-01	Lammers et al.	Applicant
US5399844(A)	1995-03-01	Holland	Applicant
US5442553(A)	1995-08-01	Parrillo	Applicant
US5459304(A)	1995-10-01	Eisenmann	Applicant
US5459660(A)	1995-10-01	Berra	Applicant
US5479479(A)	1995-12-01	Braitberg et al.	Applicant
US5488352(A)	1996-01-01	Jasper	Applicant
US5499182(A)	1996-03-01	Ousborne	Applicant
US5541845(A)	1996-07-01	Klein	Applicant
US5546305(A)	1996-08-01	Kondo	Applicant
US5557254(A)	1996-09-01	Johnson et al.	Applicant
US5557268(A)	1996-09-01	Hughes et al.	Applicant
US5557628(A)	1996-09-01	Kuba et al.	Applicant
US5572192(A)	1996-11-01	Berube	Applicant
US5585552(A)	1996-12-01	Heuston et al.	Applicant
US5594650(A)	1997-01-01	Shah et al.	Applicant
US5596501(A)	1997-01-01	Comer et al.	Applicant
US5600323(A)	1997-02-01	Boschini	Applicant
US5610596(A)	1997-03-01	Petitclerc	Applicant
US5623258(A)	1997-04-01	Dorfman	Applicant
US5629678(A)	1997-05-01	Gargano et al.	Applicant
US5657010(A)	1997-08-01	Jones	Applicant
US5671158(A)	1997-09-01	Fournier et al.	Applicant
US5680328(A)	1997-10-01	Skorupski et al.	Applicant
US5711712(A)	1998-01-01	Graf	Applicant
US5719771(A)	1998-02-01	Buck et al.	Applicant
US5731893(A)	1998-03-01	Dominique	Applicant
US5732074(A)	1998-03-01	Spaur et al.	Applicant
US5742915(A)	1998-04-01	Stafford	Applicant
US5745049(A)	1998-04-01	Akiyama et al.	Applicant
US5748106(A)	1998-05-01	Schoenian et al.	Applicant
US5754965(A)	1998-05-01	Hagenbuch	Applicant
US5758299(A)	1998-05-01	Sandborg et al.	Applicant
US5758300(A)	1998-05-01	Abe	Applicant
US5768382(A)	1998-06-01	Schneier et al.	Applicant
US5781871(A)	1998-07-01	Mezger et al.	Applicant
US5794164(A)	1998-08-01	Beckert et al.	Applicant
US5804937(A)	1998-09-01	Sasajima et al.	Applicant
US5808565(A)	1998-09-01	Matta et al.	Applicant
US5809437(A)	1998-09-01	Breed	Applicant
US5815071(A)	1998-09-01	Doyle	Applicant
US5835871(A)	1998-11-01	Smith et al.	Applicant
US5838251(A)	1998-11-01	Brinkmeyer et al.	Applicant
US5839112(A)	1998-11-01	Schreitmueller et al.	Applicant
US5867404(A)	1999-02-01	Bryan	Applicant
US5871871(A)	1999-02-01	Hogan et al.	Applicant
US5874891(A)	1999-02-01	Lowe	Applicant
US5884202(A)	1999-03-01	Arjomand	Applicant
US5890061(A)	1999-03-01	Timm et al.	Applicant
US5890080(A)	1999-03-01	Coverdill et al.	Applicant
US5890520(A)	1999-04-01	Johnson, Jr.	Applicant
US5892346(A)	1999-04-01	Moroto et al.	Applicant
US5894617(A)	1999-04-01	Liou	Applicant
US5913180(A)	1999-06-01	Ryan	Applicant
US5919239(A)	1999-07-01	Fraker et al.	Applicant
US5922037(A)	1999-07-01	Potts	Applicant
US5923572(A)	1999-07-01	Pollock	Applicant
US5928291(A)	1999-07-01	Jenkins et al.	Applicant
US5933844(A)	1999-08-01	Young	Applicant
US5942753(A)	1999-08-01	Dell	Applicant
US5956259(A)	1999-09-01	Hartsell, Jr. et al.	Applicant
US5983154(A)	1999-11-01	Morisawa	Applicant
US5987377(A)	1999-11-01	Westerlage et al.	Applicant
US5995895(A)	1999-11-01	Watt et al.	Applicant
US5995898(A)	1999-11-01	Tuttle	Applicant
US6009355(A)	1999-12-01	Obradovich et al.	Applicant
US6009363(A)	1999-12-01	Beckert et al.	Applicant
US6016457(A)	2000-01-01	Toukura et al.	Applicant
US6016795(A)	2000-01-01	Ohki	Applicant
US6024142(A)	2000-02-01	Bates	Applicant
US6025776(A)	2000-02-01	Matsuura	Applicant
US6038500(A)	2000-03-01	Weiss	Applicant
US6043661(A)	2000-03-01	Gutierrez	Applicant
US6049755(A)	2000-04-01	Lou et al.	Applicant
US6054950(A)	2000-04-01	Fontana	Applicant
US6061614(A)	2000-05-01	Carrender et al.	Applicant
US6064299(A)	2000-05-01	Lesesky et al.	Applicant
US6070118(A)	2000-05-01	Ohta et al.	Applicant
US6070156(A)	2000-05-01	Hartsell, Jr.	Applicant
US6078255(A)	2000-06-01	Dividock et al.	Applicant
US6084870(A)	2000-07-01	Wooten et al.	Applicant
US6092021(A)	2000-07-01	Ehlbeck et al.	Applicant
US6107915(A)	2000-08-01	Reavell et al.	Applicant
US6107917(A)	2000-08-01	Carrender et al.	Applicant
US6112152(A)	2000-08-01	Tuttle	Applicant
US6127947(A)	2000-10-01	Uchida et al.	Applicant
US6128551(A)	2000-10-01	Davis et al.	Applicant
US6128959(A)	2000-10-01	McGovern et al.	Applicant
US6134499(A)	2000-10-01	Goode et al.	Applicant
US6169938(B1)	2001-01-01	Hartsell, Jr.	Applicant
US6169943(B1)	2001-01-01	Simon et al.	Applicant
US6199099(B1)	2001-03-01	Gershman et al.	Applicant
US6202008(B1)	2001-03-01	Beckert et al.	Applicant
US6208948(B1)	2001-03-01	Klingler et al.	Applicant
US6236911(B1)	2001-05-01	Kruger	Applicant
US6240365(B1)	2001-05-01	Bunn	Applicant
US6253129(B1)	2001-06-01	Jenkins et al.	Applicant
US6256579(B1)	2001-07-01	Tanimoto	Applicant
US6259358(B1)	2001-07-01	Fjordbotten	Applicant
US6263273(B1)	2001-07-01	Henneken et al.	Applicant
US6263276(B1)	2001-07-01	Yokoyama et al.	Applicant
US6278928(B1)	2001-08-01	Aruga et al.	Applicant
US6278936(B1)	2001-08-01	Jones	Applicant
US6285953(B1)	2001-09-01	Harrison et al.	Applicant
US6295492(B1)	2001-09-01	Lang et al.	Applicant
US6330499(B1)	2001-12-01	Chou et al.	Applicant
US6339745(B1)	2002-01-01	Novik	Applicant
US6362730(B2)	2002-03-01	Razavi et al.	Applicant
US6370454(B1)	2002-04-01	Moore	Applicant
US6374176(B1)	2002-04-01	Schmier et al.	Applicant
US6396413(B2)	2002-05-01	Hines et al.	Applicant
US6411203(B1)	2002-06-01	Lesesky et al.	Applicant
US6411888(B1)	2002-06-01	Weisman, II	Examiner
US6411891(B1)	2002-06-01	Jones	Applicant
US6417760(B1)	2002-07-01	Mabuchi et al.	Applicant
US6421790(B1)	2002-07-01	Fruehling et al.	Applicant
US6438471(B1)	2002-08-01	Katagishi et al.	Applicant
US6438472(B1)	2002-08-01	Tano et al.	Applicant
US6450411(B1)	2002-09-01	Rash et al.	Applicant
US6456039(B1)	2002-09-01	Lauper et al.	Applicant
US6502030(B2)	2002-12-01	Hilleary	Applicant
US6505106(B1)	2003-01-01	Lawrence et al.	Applicant
US6507810(B2)	2003-01-01	Razavi et al.	Applicant
US6529723(B1)	2003-03-01	Bentley	Applicant
US6529808(B1)	2003-03-01	Diem	Applicant
US6535811(B1)	2003-03-01	Rowland et al.	Applicant
US6539296(B2)	2003-03-01	Diaz et al.	Applicant
US6571168(B1)	2003-05-01	Murphy et al.	Applicant
US6587768(B2)	2003-07-01	Chene et al.	Applicant
US6594579(B1)	2003-07-01	Lowrey et al.	Applicant
US6594621(B1)	2003-07-01	Meeker	Applicant
US6597973(B1)	2003-07-01	Barich et al.	Applicant
US6604033(B1)	2003-08-01	Banet et al.	Applicant
US6608554(B2)	2003-08-01	Lesesky et al.	Applicant
US6609082(B2)	2003-08-01	Wagner	Applicant
US6611740(B2)	2003-08-01	Lowrey et al.	Applicant
US6614392(B2)	2003-09-01	Howard	Applicant
US6616036(B2)	2003-09-01	Streicher et al.	Applicant
US6621452(B2)	2003-09-01	Knockeart et al.	Applicant
US6636790(B1)	2003-10-01	Lightner et al.	Applicant
US6664897(B2)	2003-12-01	Pape et al.	Applicant
US6671646(B2)	2003-12-01	Manegold et al.	Applicant
US6680694(B1)	2004-01-01	Knockeart et al.	Applicant
US6708113(B1)	2004-03-01	Von Gerlach et al.	Applicant
US6714857(B2)	2004-03-01	Kapolka et al.	Applicant
US6714859(B2)	2004-03-01	Jones	Applicant
US6727818(B1)	2004-04-01	Wildman et al.	Applicant
US6732031(B1)	2004-05-01	Lightner et al.	Applicant
US6732032(B1)	2004-05-01	Banet et al.	Applicant
US6735542(B1)	2004-05-01	Burgett et al.	Applicant
US6744352(B2)	2004-06-01	Lesesky et al.	Applicant
US6748318(B1)	2004-06-01	Jones	Applicant
US6754183(B1)	2004-06-01	Razavi et al.	Applicant
US6757606(B1)	2004-06-01	Gonring	Applicant
US6768994(B1)	2004-07-01	Howard et al.	Applicant
US6795761(B1)	2004-09-01	Lee et al.	Applicant
US6801841(B2)	2004-10-01	Tabe	Applicant
US6801901(B1)	2004-10-01	Ng	Applicant
US6804606(B2)	2004-10-01	Jones	Applicant
US6804626(B2)	2004-10-01	Manegold et al.	Applicant
US6816762(B2)	2004-11-01	Hensey et al.	Applicant
US6834259(B1)	2004-12-01	Markwitz et al.	Applicant
US6839619(B2)	2005-01-01	Bellinger	Applicant
US6847887(B1)	2005-01-01	Casino	Applicant
US6856820(B1)	2005-02-01	Kolls	Applicant
US6856897(B1)	2005-02-01	Phuyal et al.	Applicant
US6876642(B1)	2005-04-01	Adams et al.	Applicant
US6879894(B1)	2005-04-01	Lightner et al.	Applicant
US6880390(B2)	2005-04-01	Emord	Applicant
US6894617(B2)	2005-05-01	Richman	Applicant
US6899151(B1)	2005-05-01	Latka et al.	Applicant
US6904359(B2)	2005-06-01	Jones	Applicant
US6909947(B2)	2005-06-01	Douros et al.	Applicant
US6924750(B2)	2005-08-01	Flick	Applicant
US6928348(B1)	2005-08-01	Lightner et al.	Applicant
US6946953(B2)	2005-09-01	Lesesky et al.	Applicant
US6952645(B1)	2005-10-01	Jones	Applicant
US6954689(B2)	2005-10-01	Hanson et al.	Applicant
US6957133(B1)	2005-10-01	Hunt et al.	Applicant
US6972668(B2)	2005-12-01	Schauble	Applicant
US6980093(B2)	2005-12-01	Oursler et al.	Applicant
US6988033(B1)	2006-01-01	Lowrey et al.	Applicant
US7022018(B2)	2006-04-01	Koga	Applicant
US7027955(B2)	2006-04-01	Markwitz et al.	Applicant
US7035733(B1)	2006-04-01	Alwar et al.	Applicant
US7048185(B2)	2006-05-01	Hart	Applicant
US7068301(B2)	2006-06-01	Thompson	Applicant
US7103460(B1)	2006-09-01	Breed	Applicant
US7113127(B1)	2006-09-01	Banet et al.	Applicant
US7117121(B2)	2006-10-01	Brinton et al.	Applicant
US7129852(B2)	2006-10-01	Aslund et al.	Applicant
US7155199(B2)	2006-12-01	Zalewski et al.	Applicant
US7171372(B2)	2007-01-01	Daniel et al.	Applicant
US7174243(B1)	2007-02-01	Lightner et al.	Applicant
US7174277(B2)	2007-02-01	Vock et al.	Applicant
US7177750(B2)	2007-02-01	Schröder	Applicant
US7184866(B2)	2007-02-01	Squires et al.	Applicant
US7202801(B2)	2007-04-01	Chou	Applicant
US7225065(B1)	2007-05-01	Hunt et al.	Applicant
US7228211(B1)	2007-06-01	Lowrey et al.	Applicant
US7254516(B2)	2007-08-01	Case, Jr. et al.	Applicant
US7343252(B2)	2008-03-01	Wiens	Applicant
US7362229(B2)	2008-04-01	Brinton et al.	Applicant
US7366589(B2)	2008-04-01	Habermas	Applicant
US7424414(B2)	2008-09-01	Craft	Applicant
US7447574(B1)	2008-11-01	Washicko et al.	Applicant
US7477968(B1)	2009-01-01	Lowrey et al.	Applicant
US7480551(B1)	2009-01-01	Lowrey et al.	Applicant
US7523159(B1)	2009-04-01	Williams et al.	Applicant
US7532962(B1)	2009-05-01	Lowrey et al.	Applicant
US7532963(B1)	2009-05-01	Lowrey et al.	Applicant
US7590768(B2)	2009-09-01	Gormley	Applicant
US7596437(B1)	2009-09-01	Hunt et al.	Applicant
US7604169(B2)	2009-10-01	Demere	Applicant
US7627546(B2)	2009-12-01	Moser et al.	Applicant
US7640185(B1)	2009-12-01	Giordano et al.	Applicant
US7650210(B2)	2010-01-01	Breed	Applicant
US7660658(B2)	2010-02-01	Sheynblat	Applicant
US7672756(B2)	2010-03-01	Breed	Applicant
US7672763(B1)	2010-03-01	Hunt et al.	Applicant
US7692552(B2)	2010-04-01	Harrington et al.	Applicant
US7774123(B2)	2010-08-01	Schröder	Applicant
US7778752(B1)	2010-08-01	Hunt et al.	Applicant
US7783507(B2)	2010-08-01	Schick et al.	Applicant
US7831368(B2)	2010-11-01	Schröder	Applicant
US7841317(B2)	2010-11-01	Williams et al.	Applicant
US7913664(B2)	2011-03-01	Williams et al.	Applicant
US7925426(B2)	2011-04-01	Koebler et al.	Applicant
US8014915(B2)	2011-09-01	Jeon et al.	Applicant
US8046501(B2)	2011-10-01	Gormley	Applicant
US8090598(B2)	2012-01-01	Bauer et al.	Applicant
US8140265(B2)	2012-03-01	Grush	Applicant
US8140358(B1)	2012-03-01	Ling et al.	Applicant
US8185293(B2)	2012-05-01	Jiang et al.	Applicant
US8204634(B2)	2012-06-01	Schwarz et al.	Applicant
US8219796(B2)	2012-07-01	Weiberle et al.	Applicant
US8280573(B2)	2012-10-01	Sudou et al.	Applicant
US8527132(B2)	2013-09-01	Mineta	Applicant
US8560996(B1)	2013-10-01	Brebner et al.	Applicant
US8577703(B2)	2013-11-01	McClellan et al.	Applicant
US8604920(B2)	2013-12-01	Armitage et al.	Applicant
US8849501(B2)	2014-09-01	Cook et al.	Applicant
US8882634(B2)	2014-11-01	Banker et al.	Applicant
US8914184(B2)	2014-12-01	McQuade et al.	Applicant
US8918229(B2)	2014-12-01	Hunt et al.	Applicant
US8924138(B2)	2014-12-01	Chauncey et al.	Applicant
US8930040(B2)	2015-01-01	Gompert et al.	Applicant
US8949008(B2)	2015-02-01	Krengiel	Applicant
US8996287(B2)	2015-03-01	Davidson et al.	Applicant
US9162573(B2)	2015-10-01	Grajkowski et al.	Applicant
US9170913(B2)	2015-10-01	Hunt et al.	Applicant
US9229906(B2)	2016-01-01	McQuade et al.	Applicant
US9358986(B2)	2016-06-01	Hunt	Applicant
US9393954(B2)	2016-07-01	Gibson et al.	Applicant
US9447747(B2)	2016-09-01	Gibson et al.	Applicant
US9527515(B2)	2016-12-01	Hunt et al.	Applicant
US9631528(B2)	2017-04-01	Bradley et al.	Applicant
US9747254(B2)	2017-08-01	McQuade et al.	Applicant
US2001/0047283(A1)	2001-11-01	Melick et al.	Applicant
US2001/0053983(A1)	2001-12-01	Reichwein et al.	Applicant
US2002/0016655(A1)	2002-02-01	Joao	Applicant
US2002/0022979(A1)	2002-02-01	Whipp et al.	Applicant
US2002/0107833(A1)	2002-08-01	Kerkinni	Applicant
US2002/0107873(A1)	2002-08-01	Winkler et al.	Applicant
US2002/0111725(A1)	2002-08-01	Burge	Applicant
US2002/0116122(A1)	2002-08-01	Satonaka	Applicant
US2002/0132699(A1)	2002-09-01	Bellinger	Applicant
US2002/0133275(A1)	2002-09-01	Thibault	Applicant
US2002/0150050(A1)	2002-10-01	Nathanson	Applicant
US2002/0165669(A1)	2002-11-01	Pinto et al.	Applicant
US2002/0178147(A1)	2002-11-01	Arroyo et al.	Applicant
US2003/0030550(A1)	2003-02-01	Talbot	Applicant
US2003/0033071(A1)	2003-02-01	Kawasaki	Applicant
US2003/0036823(A1)	2003-02-01	Mahvi	Applicant
US2003/0060966(A1)	2003-03-01	MacPhail et al.	Applicant
US2003/0120745(A1)	2003-06-01	Katagishi et al.	Applicant
US2003/0132699(A1)	2003-07-01	Yamaguchi et al.	Applicant
US2003/0146854(A1)	2003-08-01	Jones	Applicant
US2003/0182033(A1)	2003-09-01	Underdahl et al.	Applicant
US2003/0195696(A1)	2003-10-01	Jones	Applicant
US2003/0195697(A1)	2003-10-01	Jones	Applicant
US2003/0195698(A1)	2003-10-01	Jones	Applicant
US2003/0195699(A1)	2003-10-01	Jones	Applicant
US2003/0216847(A1)	2003-11-01	Bellinger	Applicant
US2004/0006421(A1)	2004-01-01	Yanase	Applicant
US2004/0039504(A1)	2004-02-01	Coffee et al.	Applicant
US2004/0054470(A1)	2004-03-01	Farine et al.	Applicant
US2004/0133336(A1)	2004-07-01	Fosseen	Applicant
US2004/0230346(A1)	2004-11-01	Brooks et al.	Applicant
US2004/0236596(A1)	2004-11-01	Chowdhary et al.	Applicant
US2004/0243368(A1)	2004-12-01	Hiemer et al.	Applicant
US2004/0249558(A1)	2004-12-01	Meaney	Applicant
US2005/0010479(A1)	2005-01-01	Hannigan et al.	Applicant
US2005/0021222(A1)	2005-01-01	Minami et al.	Examiner
US2005/0072384(A1)	2005-04-01	Hadley et al.	Applicant
US2005/0107946(A1)	2005-05-01	Shimizu et al.	Applicant
US2005/0131625(A1)	2005-06-01	Birger et al.	Applicant
US2005/0206534(A1)	2005-09-01	Yamane et al.	Applicant
US2005/0209775(A1)	2005-09-01	Entenmann	Applicant
US2005/0273250(A1)	2005-12-01	Hamilton et al.	Applicant
US2006/0041337(A1)	2006-02-01	Augsburger et al.	Applicant
US2006/0047381(A1)	2006-03-01	Nguyen	Applicant
US2006/0047384(A1)	2006-03-01	Robinson et al.	Applicant
US2006/0106510(A1)	2006-05-01	Heffington	Applicant
US2006/0232406(A1)	2006-10-01	Filibeck	Applicant
US2006/0246918(A1)	2006-11-01	Fok et al.	Applicant
US2007/0001831(A1)	2007-01-01	Raz et al.	Applicant
US2007/0050193(A1)	2007-03-01	Larson	Applicant
US2007/0069947(A1)	2007-03-01	Banet et al.	Applicant
US2007/0083314(A1)	2007-04-01	Corigliano et al.	Applicant
US2007/0143002(A1)	2007-06-01	Crowell et al.	Examiner
US2007/0149184(A1)	2007-06-01	Viegers et al.	Applicant
US2007/0156337(A1)	2007-07-01	Yanni	Applicant
US2007/0168125(A1)	2007-07-01	Petrik	Applicant
US2007/0174683(A1)	2007-07-01	Gehring et al.	Applicant
US2007/0179709(A1)	2007-08-01	Doyle	Applicant
US2007/0192012(A1)	2007-08-01	Letang	Applicant
US2007/0256481(A1)	2007-11-01	Nishiyama et al.	Examiner
US2007/0293369(A1)	2007-12-01	Hornbrook et al.	Applicant
US2008/0027591(A1)	2008-01-01	Lenser et al.	Applicant
US2008/0077299(A1)	2008-03-01	Arshad et al.	Applicant
US2008/0086241(A1)	2008-04-01	Phillips et al.	Applicant
US2008/0109122(A1)	2008-05-01	Ferguson et al.	Applicant
US2008/0121443(A1)	2008-05-01	Clark et al.	Applicant
US2008/0154489(A1)	2008-06-01	Kaneda et al.	Applicant
US2008/0154712(A1)	2008-06-01	Wellman	Applicant
US2008/0167767(A1)	2008-07-01	Brooks et al.	Applicant
US2008/0243389(A1)	2008-10-01	Inoue et al.	Applicant
US2008/0262646(A1)	2008-10-01	Breed	Applicant
US2008/0269974(A1)	2008-10-01	Schwarz et al.	Applicant
US2008/0319602(A1)	2008-12-01	McClellan et al.	Applicant
US2008/0319665(A1)	2008-12-01	Berkobin et al.	Applicant
US2009/0037033(A1)	2009-02-01	Phillips et al.	Applicant
US2009/0069999(A1)	2009-03-01	Bos	Applicant
US2009/0093941(A1)	2009-04-01	Drazich	Applicant
US2009/0143923(A1)	2009-06-01	Breed	Applicant
US2009/0156310(A1)	2009-06-01	Fargo	Applicant
US2009/0157267(A1)	2009-06-01	Shin et al.	Examiner
US2009/0160675(A1)	2009-06-01	Piccinini et al.	Applicant
US2009/0164081(A1)	2009-06-01	Meloche et al.	Applicant
US2009/0177350(A1)	2009-07-01	Williams et al.	Applicant
US2009/0186325(A1)	2009-07-01	Kumar	Examiner
US2009/0222200(A1)	2009-09-01	Link, II et al.	Applicant
US2009/0240391(A1)	2009-09-01	Duddle et al.	Applicant
US2009/0254259(A1)	2009-10-01	The	Applicant
US2010/0005280(A1)	2010-01-01	Wagner et al.	Applicant
US2010/0009696(A1)	2010-01-01	Fok et al.	Applicant
US2010/0017236(A1)	2010-01-01	Duddle et al.	Applicant
US2010/0082238(A1)	2010-04-01	Nakamura et al.	Applicant
US2010/0088127(A1)	2010-04-01	Betancourt et al.	Applicant
US2010/0114404(A1)	2010-05-01	Donnelly	Applicant
US2010/0121539(A1)	2010-05-01	Price et al.	Applicant
US2010/0145550(A1)	2010-06-01	Ross-Martin	Applicant
US2010/0145600(A1)	2010-06-01	Son et al.	Examiner
US2010/0152941(A1)	2010-06-01	Skaff et al.	Examiner
US2010/0160013(A1)	2010-06-01	Sanders	Applicant
US2010/0161172(A1)	2010-06-01	Bjelkstål	Applicant
US2010/0191403(A1)	2010-07-01	Krause	Applicant
US2010/0198466(A1)	2010-08-01	Eklund et al.	Applicant
US2010/0204882(A1)	2010-08-01	Giovaresco et al.	Applicant
US2010/0207760(A1)	2010-08-01	Stomski	Applicant
US2010/0209884(A1)	2010-08-01	Lin et al.	Applicant
US2010/0209890(A1)	2010-08-01	Huang et al.	Applicant
US2010/0209891(A1)	2010-08-01	Lin et al.	Applicant
US2010/0211278(A1)	2010-08-01	Craig et al.	Applicant
US2010/0305819(A1)	2010-12-01	Pihlajamaki	Applicant
US2010/0324955(A1)	2010-12-01	Rinehart et al.	Applicant
US2011/0098898(A1)	2011-04-01	Stählin et al.	Applicant
US2011/0106374(A1)	2011-05-01	Margol et al.	Applicant
US2011/0106388(A1)	2011-05-01	Boeckenhoff et al.	Applicant
US2011/0112739(A1)	2011-05-01	O'Dea et al.	Applicant
US2011/0137773(A1)	2011-06-01	Davis, III et al.	Applicant
US2011/0148618(A1)	2011-06-01	Harumoto et al.	Applicant
US2011/0161116(A1)	2011-06-01	Peak et al.	Applicant
US2011/0172895(A1)	2011-07-01	Fukumoto et al.	Applicant
US2011/0178684(A1)	2011-07-01	Umemoto et al.	Applicant
US2011/0184642(A1)	2011-07-01	Rotz et al.	Applicant
US2011/0276209(A1)	2011-11-01	Suganuma et al.	Applicant
US2011/0279255(A1)	2011-11-01	Miyoshi	Examiner
US2011/0307166(A1)	2011-12-01	Hiestermann et al.	Applicant
US2011/0313647(A1)	2011-12-01	Koebler et al.	Applicant
US2012/0041638(A1)	2012-02-01	Johnson et al.	Applicant
US2012/0083958(A1)	2012-04-01	Ballard	Applicant
US2012/0087771(A1)	2012-04-01	Wenzel	Applicant
US2012/0136539(A1)	2012-05-01	Bryant et al.	Applicant
US2012/0143484(A1)	2012-06-01	Sawada	Examiner
US2012/0191269(A1)	2012-07-01	Chen et al.	Applicant
US2012/0221216(A1)	2012-08-01	Chauncey et al.	Applicant
US2012/0221234(A1)	2012-08-01	Sujan et al.	Applicant
US2012/0226391(A1)	2012-09-01	Fryer et al.	Applicant
US2012/0239279(A1)	2012-09-01	Stuart et al.	Applicant
US2012/0239462(A1)	2012-09-01	Pursell	Examiner
US2012/0253744(A1)	2012-10-01	Schmidt	Applicant
US2012/0256770(A1)	2012-10-01	Mitchell	Applicant
US2012/0268587(A1)	2012-10-01	Robbins et al.	Applicant
US2012/0296532(A1)	2012-11-01	Murakami et al.	Applicant
US2012/0296549(A1)	2012-11-01	Adams	Applicant
US2013/0006469(A1)	2013-01-01	Green et al.	Applicant
US2013/0030660(A1)	2013-01-01	Fujimoto	Applicant
US2013/0030667(A1)	2013-01-01	Fujimoto et al.	Applicant
US2013/0046559(A1)	2013-02-01	Coleman et al.	Applicant
US2013/0061044(A1)	2013-03-01	Pinkus et al.	Applicant
US2013/0158838(A1)	2013-06-01	Yorke et al.	Applicant
US2013/0164712(A1)	2013-06-01	Hunt et al.	Applicant
US2013/0164713(A1)	2013-06-01	Hunt et al.	Applicant
US2013/0164715(A1)	2013-06-01	Hunt et al.	Applicant
US2013/0166170(A1)	2013-06-01	Hunt et al.	Applicant
US2013/0184965(A1)	2013-07-01	Hunt et al.	Applicant
US2013/0209968(A1)	2013-08-01	Miller et al.	Applicant
US2013/0238182(A1)	2013-09-01	Osagawa et al.	Applicant
US2013/0261939(A1)	2013-10-01	McQuade et al.	Applicant
US2013/0261942(A1)	2013-10-01	McQuade et al.	Applicant
US2013/0274955(A1)	2013-10-01	Rosenbaum	Applicant
US2013/0345914(A1)	2013-12-01	Love et al.	Applicant
US2013/0345927(A1)	2013-12-01	Cook et al.	Applicant
US2014/0012634(A1)	2014-01-01	Pearlman et al.	Applicant
US2014/0195106(A1)	2014-07-01	McQuade et al.	Applicant
US2014/0257943(A1)	2014-09-01	Nerayoff et al.	Applicant
US2014/0309849(A1)	2014-10-01	Ricci	Applicant
US2014/0350777(A1)	2014-11-01	Kawai et al.	Applicant
US2014/0365070(A1)	2014-12-01	Yano et al.	Applicant
US2015/0291176(A1)	2015-10-01	Jeong et al.	Applicant
US2017/0066453(A1)	2017-03-01	Hunt	Applicant
US2017/0067385(A1)	2017-03-01	Hunt	Applicant
US2017/0084091(A1)	2017-03-01	McQuade et al.	Applicant
CA2138378(A1)	1994-11-01		Applicant
CA2326892(A1)	1999-10-01		Applicant
CA2388572(A1)	2001-05-01		Applicant
EP7027792(B1)	1994-01-01		Applicant
EP755039(A2)	1997-01-01		Applicant
EP814447(A1)	1997-12-01		Applicant
EP926020(A2)	1999-06-01		Applicant
EP1067498(A1)	2001-01-01		Applicant
EP1271374(A1)	2003-01-01		Applicant
EP1005627(B1)	2003-10-01		Applicant
EP2116968(A1)	2009-11-01		Applicant
JP2001-280985(A)	2001-10-01		Applicant
WO97/26750(A1)	1997-07-01		Applicant
WO98/03952(A1)	1998-01-01		Applicant
WO98/30920(A2)	1998-07-01		Applicant
WO3/023550(A2)	2003-03-01		Applicant
WO2007/092711(A2)	2007-08-01		Applicant

Non-Patent Literature (42)

“Nextel, Motorola and Symbol Technologies Offer First Wireless Bar Code Scanner for Mobile Phones,” The Auto Channel, Jun. 11, 2003, URL=https://www.theautochannel.com/news/2003/06/11/162927.html, download date Aug. 28, 2017, 4 pages.Applicant
“ObjectFX Integrates TrackingAdvisor with QUALCOMM's FleetAdvisor System; Updated Version Offers Benefit of Visual Display of Vehicles and Routes to Improve Fleet Productivity,” Business Wire, Oct. 27, 2003, URL=https://www.thefreelibrary.com/_/print/PrintArticle.aspx?id=109283193, download date Aug. 28, 2017, 3 pages.Applicant
“Private fleets moving to wireless communications,” Drivers, May 1, 1997, URL=https://web.archive.org/web/20060511114359/driversmag.com/ar/fleet_private_fleets_moving/index.html, download date Aug. 28, 2017, 4 pages.Applicant
Albright, “Indiana embarks on ambitious RFID roll out,” Frontline Solutions, May 20, 2002, URL=https://web.archive.org/web/20021102141244/http://www.frontlinetoday.com/frontline/article/articleDetail.jsp?id=19358, download date Aug. 28, 2017, 3 pages.Applicant
Anonymous, “Transit agency builds GIS to plan bus routes”, American City & Country 118(4):14-16, Apr. 1, 2003. (4 pages).Applicant
Banks et al., “DATATRAK Automatic Vehicle Location and Position Reporting System,” Vehicle Navigation and Information Systems Conference, Toronto, Canada, Sep. 11-13, 1989, pp. 214-218.Applicant
Banks, “Integrated Automatic Vehicle Location and Position Reporting System,” 2nd International Conference on Road Traffic Monitoring, London, United Kingdom, Feb. 7-9, 1989, pp. 195-199.Applicant
Black, “OBD II Up Close,” MOTOR:28-34, Jul. 1998. (6 pages).Applicant
Child Checkmate Systems Inc., “What is the Child Check-Mate Safety System,” URLs=http://www.childcheckmate.com/what.html, http://www.childcheckmate.com/overview.html, http://www.childcheckmate.com/how.html, download date Apr. 7, 2004, 5 pages.Applicant
Detex, “Detex Announces the Latest Innovation in Guard Tour Verification Technology,” Jan. 1, 2003, URL=https://web.archive.org/web/20031208082505/http://www.detex.com/NewsAction.jspa?id=3, download date Aug. 28, 2017, 1 page.Applicant
Dwyer et al., “Analysis of the Performance and Emissions of Different Bus Technologies on the City of San Francisco,” SAE Commercial Vehicle Engineering Congress and Exhibition, Rosemont, Illinois, USA, Oct. 26-28, 2004. (Abstract only) (2 pages).Applicant
FleeTTrakkeR, “D.O.T. Driver Vehicle Inspection Reports on your wireless phone!,” ReporTTrakkeR, URL=http://www.fleettrakker.com/web/index.jsp, download date Mar. 12, 2004, 3 pages.Applicant
GCS General Control Systems, “The Data Acquisition Unit Escorte,” Nov. 20, 2001, URL=http://www.gcs.at/eng/produkte/hw/escorte.htm, download date Apr. 21, 2005, 4 pages.Applicant
GCS General Control Systems, “The PenMaster,” and “The PSION Workabout,” Nov. 20, 2001, URL=http://www.gcs.at/eng/produkte/hw/penmaster.htm, download date Apr. 5, 2007, 3 pages.Applicant
GCS General Control Systems, News, Dec. 11, 2002, URL=http://www.gcs.at/eng/news/allgemein.htm, download date Apr. 21, 2005, 2 pages.Applicant
Ghilardelli et al., “Path Generation Using η4-Splines for a Truck and Trailer Vehicle,” IEEE Transactions on Automation Science and Engineering 11(1):187-203, 2014.Applicant
Guensler et al., “Development of a Comprehensive Vehicle Instrumentation Package for Monitoring Individual Tripmaking Behavior,” Technical Specifications and Analysis, Georgia Institute of Technology, School of Civil and Environmental Engineering, Atlanta, Georgia, USA, Feb. 1999, 31 pages.Applicant
Jenkins et al., “Real-Time Vehicle Performance Monitoring Using Wireless Networking,” Proceedings of the 3rd IASTED International Conference on Communications, Internet and Information Technology, St. Thomas, US Virgin Islands, Nov. 22-24, 2004, pp. 375-380.Applicant
Kurtz, “Indiana's E-Government: A Story Behind Its Ranking,” INContext 4(1):6-8, 2003.Applicant
Kwon, “Networking Technologies of In-Vehicle,” Seoul National University, School of Electrical Engineering, Seoul, South Korea, Mar. 8, 2008, 44 pages.Applicant
Leavitt, “The Convergence Zone,” FleetOwner, Jun. 1, 1998, URL=http://www.driversmag.com/ar/fleet_convergence_zone/index.html, download date Aug. 24, 2010, 4 pages.Applicant
MIRAS, “About SPS Technologies,” as archived on May 7, 1999, URL=http://replay.waybackmachine.org/19990507195047/http://miras.com/html/about_sps_technologies.html, download date Sep. 29, 2010, 1 page.Applicant
MIRAS, “How MIRAS Works,” as archived on Apr. 29, 1999, URL=http://replay.waybackmachine.org/19990429144910/http://www.miras.com/html/products.html, download date Sep. 29, 2010, 1 page.Applicant
MIRAS, “MIRAS Unit,” as archived on May 4, 1999, URL=http://replay.waybackmachine.org/19990504052250/http://www.miras.com/html/1000unit.html, download date Sep. 29, 2010, 1 page.Applicant
MIRAS, “Monitoring Vehicle Functions,” as archived on Apr. 27, 1999, URL=http://replay.waybackmachine.org/19990427152518/http://www.miras.com/html/monitoring.html, download date Sep. 29, 2010, 1 page.Applicant
MIRAS, “Remote Control,” as archived on Apr. 29, 1999, URL=http://replay.waybackmachine.org/19990429145717/http://www.miras.com/html/remote_control.html, download date Sep. 29, 2010, 1 page.Applicant
MIRAS, “Tracking & Monitoring Software,” as archived on Apr. 29, 1999, URL=http://replay.waybackmachine.org/19990429160322/http://www.miras.com/html/software.html, download date Sep. 29, 2010, 1 page.Applicant
MIRAS, MIRAS 4.0 Screenshot, as archived on May 7, 1999, URL=http://replay.waybackmachine.org/19990507205618/http://www.miras.com/html/largescreen.html, download date Sep. 29, 2010, 1 page.Applicant
“MIRAS GPS vehicle tracking using the Internet,” Business Wire, Nov. 22, 1996, URL=https://www.thefreelibrary.com/_/print/PrintArticle.aspx?id=18878231, download date Aug. 28, 2017, 2 pages.Applicant
Pan et al., “Simulation-Based Optimization for Split Delivery Vehicle Routing Problem: A Report of Ongoing Study,” Proceedings of the 2013 Winter Simulation Conference, Washington D.C., USA, Dec. 8-11, 2013, pp. 1089-1096.Applicant
Papadoglou et al., “Short message service link for automatic vehicle location reporting,” Electronics Letters 35(11):876-877, 1999.Applicant
Quaan et al., Guard Tour Systems, post dates Sep. 16, 2003, Oct. 3, 2003, and Sep. 4, 2004, URL=http://www.securitymanagement.com/ubb/Forum30/HTML/000066.html, download date Mar. 8, 2005, 1 page.Applicant
Rehman et al., “Characterisation of System Performance of GPS Antennas in Mobile Terminals Including Environmental Effects,” 3rdEuropean Conference on Antennas and Propagation, Berlin, Germany, Mar. 23-27, 2009, pp. 1832-1836.Applicant
Senger, “Inside RF/ID: Carving a Niche Beyond Asset Tracking,” Business Solutions, Feb. 1, 1999, 3 pages.Applicant
Sterzbach et al., “A Mobile Vehicle On-Board Computing and Communication System,” Computers & Graphics 20(4):659-667, 1996.Applicant
The Gale Group, “Tracking out of route: software helps fleets compare planned routes to actual miles.(TECHNOLOGY),” Commercial Carrier Journal 162(10):S46, 2005. (4 pages).Applicant
Tiscor, “Inspection Manager 6.0,” Product User Guide, 2004, 73 pages.Applicant
Tiscor, “Inspection Manager: An Introduction,” Sep. 27, 2004, 19 pages.Applicant
Tsakiri et al., “Urban Fleet Monitoring with GPS and GLONASS,” The Journal of Navigation 51(3):382-393, 1998. (Abstract only) (2 pages).Applicant
Tuttle, “Digital RF/ID Enhances GPS,” Proceedings of the 2nd Annual Wireless Symposium, Santa Clara, California, USA, Feb. 15-18, 1994, pp. 406-411.Applicant
Want, “RFID: A Key to Automating Everything,” Scientific American 290(1):56-65, 2004.Applicant
Zujkowski, “Savi Technology, Inc.: Savi Security and Productivity Systems,” Remarks, ATA Security Forum, Chicago, Illinois, USA, May 15, 2002, 21 pages.Applicant

Abstract