SE1650809A1 - Method and server for reducing fuel consumption in a vehicle - Google Patents

Abstract

23 ABSTRACT Method (400) and server (250) for generating driver ranking values for each respective driverof a set (101) of vehicles (100-1, 100-2) driving on a road segment (110), based on an esti-mated driver related fuel consumption. The method (400) comprises collecting (401) position5 data and vehicle related data (310) associated with the position of vehicles (100-1, 100-2);collecting (402) road data (320) associated with the road segment (110); determining (403)fuel consumption related parameters, associated with the road segment (110) and a timeperiod; connecting (404) the vehicle related data (310) with road data (320) and the set offuel consumption related parameters in a road network model (330); synchronising (405)10 data in the road network model (330) in time; determining (407) the fuel consumption of driverfactors (340, 350, 351, 352, 353, 354, 360); and setting (408) the driver ranking by a com-parison between the fuel consumption of each respective driver. 20 (Pubi. Fig. 2A)

Description

METHOD AND SERVER FOR REDUCING FUEL CONSUMPTION IN A VEHICLE TECHNICAL FIELD This document discloses a server and a method to be performed therein. More particularly,a server and a method are described, for generating driver ranking values for each respectivedriver of a set of vehicles driving on a road segment, based on an estimated driver related fuel consumption.

BACKGROUND Reducing fuel consumption and other costs for maintenance etc., associated with vehicletransportation is for economic reasons important for the vehicle owner. Reduced fuel con- sumption also results in decreased environmental impact.

The fuel consumption of a vehicle is very much depending on the driving style of the driver,i.e. acceleration, usage of the brake, velocity of the vehicle etc., but also of other factors likeair resistance and weight. When the vehicle is a heavy vehicle such as a truck or a bus, thedriver is normally not the owner of the vehicle, nor responsible for paying the fuel bill. Thus the driver may have a low incitement for reducing the fuel consumption.

Today, ranking of drivers cannot be done by comparing the fuel consumption they haveachieved over a route or road segment. The cause of this problem is that there are manyfactors influencing the fuel consumption apart from the driving behaviour. Examples of theseare the loaded weight of the vehicle, inclination of the road and head wind speed amongmany others. This makes it very difficult to compare drivers upon fuel consumption or deter-mine e.g. if driver training of a particular driver or a set of drivers is showing any effect.

Document US8554468 discloses systems and methods for driver performance assessmentand improvement. Various data parameters related to the vehicle are measured and sent toa server where they are stored and analysed. The driver's driving performance from a secu-rity perspective is rated. However, the document does not discuss fuel saving or rating of the driver's fuel consumption.

Document EP2036777 concerns a method and system of providing data to a driver of avehicle. Driving data concerning fuel consumption of the vehicle is extracted, and evaluatedby a computing unit in the vehicle by comparison with reference driving data. The evaluationvalue is then provided to the driver.

No comparison is made between a plurality of drivers of different vehicles. No consideration 2 is taken concerning various factors that influence the fuel consumption, such as e.g. weightof the vehicle, wind conditions, traffic jam, etc. Further, no detection of road segments with a large improvement potential in terms of fuel consumption is made.

Document US20130046449 relates to a fuel optimisation display in a vehicle. ln particular,the document concerns a user interface to guide a driver in selecting accelerator pedal po-sition and gear. A fuel efficiency reference is determined and a comparison between thecurrent fuel consumption of the vehicle and the reference value is made, resulting in a score value, which may be presented to the driver.

No comparison is made between a plurality of drivers of different vehicles. No considerationis taken concerning various factors that influence the fuel consumption, such as e.g. weightof the vehicle, wind conditions, traffic jam, etc. Further, no detection of road segments with a large improvement potential in terms of fuel consumption is made.

Document US2015314789 concerns fuel consumption analysis in a vehicle. l\/leasurementdata is collected from various sensors in the vehicle. Fuel/ energy consumption of the vehicleis divided over a plurality of fuel consumers in the vehicle. The Fuel/ energy consumption ofthe fuel consumers are subdivided into categories; which information may be presented forthe driver. Also in this document, all calculations are made in the vehicle and only concernsthe own vehicle and its driver, i.e. no comparison is made with other vehicle drivers. Further,no detection of road segments with a large improvement potential in terms of fuel consump- tion is made.

Document US2014358430 relates to a driving evaluation system and method. The positionof the vehicle is determined by GPS and various vehicle data is collected and stored in amemory in the vehicle. An evaluation of the road is made by extracting road map data basedon the determined vehicle position. An evaluation report is then generated, rating the driverand suggesting potential improvements in terms of fuel consumption reduction.

All of the cited documents thus concerns computations made in one single vehicle, concern-ing fuel consumption of that vehicle/ driver; without any obvious possibility to compare therating of different drivers, or to detect road segments which may be subjects to improvement. lt would be desired to find a way to encourage and stimulate the vehicle driver to consistentlyreduce the fuel consumption of the vehicle by an objective rating system. Further, it wouldbe desired to find a way of determining an individual driver's fuel consumption before/ aftera driver training. lt would also be desired to discover road segments and/ or driving situationswhere there are an in particular large potential gain to be made in terms of fuel consumption, 3 i.e. where the difference in fuel consumption between a skilled driver and a novice is in par-ticular large. ln addition, it would be desired to be able to identify drivers which are in partic-ular skilled in fuel efficient driving, as these drivers may be selected as driving instructors forbeginners, etc., as well as identify drivers which are in particular need of further training.

SUMMARY lt is therefore an object of this invention to solve at least some of the above problems and encourage a vehicle driver to reduce fuel consumption of a vehicle.

According to a first aspect of the invention, this objective is achieved by a method for use ina server. The method aims at generating driver ranking values for each respective driver ofa set of vehicles driving on a road segment, based on an estimated driver related fuel con-sumption. The method comprises collecting position data and vehicle related data associatedwith the position data from each vehicle in the set of vehicles. Further, the method in additioncomprises collecting road data associated with the road segment. ln further addition, themethod also comprises determining a set of fuel consumption related parameters, associ-ated with the road segment and a time period. Also, the method furthermore comprises con-necting the collected vehicle related data with the collected road data and the determinedset of fuel consumption related parameters in a road network model. The method also com-prises synchronising the connected data in the road network model in time. Further, themethod also comprises determining the fuel consumption of driver related factors of eachdriver at the time period, for passing the road segment. The method also comprises settingthe driver ranking by a comparison between the determined fuel consumption of each re-spective driver for the road segment during the time period.

According to a second aspect of the invention, this objective is achieved by a server, forgenerating driver ranking values for each respective driver of a set of vehicles driving on aroad segment, based on an estimated driver related fuel consumption. The server is config-ured to collect position data and vehicle related data associated with the position data fromeach vehicle in the set of vehicles. Further, the server is configured to collect road data as-sociated with the road segment. ln addition, the server is further configured to determine aset of fuel consumption related parameters, associated with the road segment and a timeperiod. The server is furthermore configured to connect the collected vehicle related datawith the collected road data and the determined set of fuel consumption related parametersin a road network model. Also, the server is configured to synchronise the connected data inthe road network model in time. Furthermore, the server is additionally configured to deter-mine the fuel consumption of driver related factors of each driver at the time period, for pass-ing the road segment. The server is configured to set the driver ranking by a comparisonbetween the determined fuel consumption of each respective driver for the road segment during the time period.

Thanks to the described aspects, by collecting and storing parameter data related to thevehicle and in particular the fuel consumption of the vehicle, associated with geographicalpositions, and combining them with road data and fuel consumption related parameters, as-sociated with the road segment and a time period, and synchronising this connected data intime, it becomes possible to determine fuel consumption of driver related factors of eachdriver on a road segment during a time period. lt thereby becomes possible to rank the driv-ers by comparing the fuel consumption of each respective driver.

This information may be used to determine which drivers to use as instructors for other driv-ers, to select drivers which need further training and/ or to select drivers to drive the first vehicle in a platoon for example.

Further, the fuel consumption during drive for one single driver may be determined before/after an educational training in order to determine the effect of the training.

Furthermore, a fuel saving potential of the road segment may be determined based on a comparison between a difference in fuel consumption between the drivers.

Thereby road segments where it is in particular difficult to pass in a fuel effective mannermay be highlighted and the attention of the driver may be focused on these road segments.

Thereby, the driver is encouraged to reduce fuel consumption of the vehicle.

Other advantages and additional novel features will become apparent from the subsequentdetailed description.

FIGURES Embodiments of the invention will now be described in further detail with reference to theaccompanying figures, in which: Figure 1 illustrates a vehicle according to an embodiment of the invention; Figure 2A illustrates a system comprising a server, databases, a set of vehicles drivingon a road segment and a sensor; Figure 2B illustrates a vehicle interior in a vehicle according to an embodiment; Figure 3A illustrates a set of vehicle related data, road data, and the addition of this data; Figure 3B illustrates a road network model; 5 Figure 3C illustrates fuel consumption of driver related factors on a road segment;Figure 3D illustrates driver ranking according to an embodiment; Figure 3E illustrates fuel saving potential of a road segment according to an embodi- ment;Figure 4 is a flow chart illustrating an embodiment of the method;Figure 5 is an illustration depicting a system according to an embodiment.

DETAILED DESCRIPTION Embodiments of the invention described herein are defined as a method and a server, whichmay be put into practice in the embodiments described below. These embodiments may,however, be exemplified and realised in many different forms and are not to be limited to theexamples set forth herein; rather, these illustrative examples of embodiments are providedso that this disclosure will be thorough and complete.

Still other objects and features may become apparent from the following detailed description,considered in conjunction with the accompanying drawings. lt is to be understood, however,that the drawings are designed solely for purposes of illustration and not as a definition ofthe limits of the herein disclosed embodiments, for which reference is to be made to theappended claims. Further, the drawings are not necessarily drawn to scale and, unless oth-en/vise indicated, they are merely intended to conceptually illustrate the structures and pro-cedures described herein.

Figure 1 illustrates a scenario with a vehicle 100-1 driving in a driving direction 105 on aroad segment 110.

The vehicle 100-1 may comprise e.g. a truck, a bus, a car, a motorcycle, an aircraft, a wa-tercraft, a train, a spacecraft or any similar vehicle or other means of conveyance.

The vehicle 100-1 may typically use diesel for the propulsion of the vehicle 100-1; howeveralso other sources of energy may be utilised such as e.g. electricity, gasoline, kerosene,naphtha, methanol, ethanol, Compressed Natural Gas (CNG), propane, Liquefied PetroleumGas (LPG), methane, Liquefied Natural Gas (LNG), hydrogen, electricity and/ or a fuel cell.

Such fuel cell may comprise e.g. Polymer Electrolyte l\/lembrane (PEM) Fuel Cells, directmethanol fuel cells, phosphoric acid fuel cells, molten carbonate fuel cells, solid oxide fuelcells, reformed methanol fuel cell and/ or regenerative fuel cells, just to mention some non- limiting examples.

The electricity of the vehicle 100-1, when being propelled by electricity, may be stored in abattery, such as a rechargeable battery, e.g. based on lithium-ion and other Iithium-basedvariants such as Lithium iron phosphate and Lithium-titanate. Lead acid batteries, Nickelmetal hydride (NiMH) and/ or zinc-air battery are other possible options. However, electricity may also be obtained from an electricity grid.

According to some embodiments, data is collected from different data sources to create ahigh dimensional model of the road network. This data may then be coupled with connectedvehicle data (spatial data) and used to train a fuel consumption model which accounts fordifferent factors contributing to the fuel consumption of the vehicle 100-1. This part may alsobe referred to as factor separation since it separates the factors contributing to the fuel con-sumption. From this model the fuel consumption factors connected to the drivers are ex-tracted. These factors are a measure on the different drivers' contribution to the fuel con- sumption.

Based on the determined driver factors, the drivers can be ranked, i.e. compared and bench-marked against other drivers on the same road segment 110; or against him/ her self but atdifferent moments in time, based on contribution to the fuel consumption on a specific routeor road segment 110. But mainly the fuel saving potential of the different routes or roadsegments 110 can be estimated using the model output, according to some embodiments.

By being able to rank the drivers, it becomes possible for a haulage contractor to determinewhich drivers that are in need of further driver training, for example. lt is also enabled toselect which driver to drive the first vehicle in a platoon, for example. By letting the driverwhich is most skilled in economic driving driver the first vehicle, the fuel consumption of allvehicles in the platoon could be reduced.

The fuel saving potential is a measure of the difference between drivers with high and lowfuel consumptions on a specific route or road segment. Using this method, it makes it possi-ble to build a sophisticated road network model showing the fuel saving potential. This modelmay then be used as the foundation for further analysis, to get deeper insights in the behav-iours and driving patterns among the vehicle drivers, in some embodiments.

The method also opens up the possibility to use streaming analytics to get great performanceusing distributed calculation methods and continues data insights.

By being able to identify road stretches or areas where differences among the drivers' fuelconsumptions are large, i.e. places where it might require more driving skills to perform a 7 good fuel consumption, it becomes possible to make the drivers aware of such roadstretches/ areas and e.g. provide an advice to the driver; or direct driver training to the par-ticular problems of these road stretches/ areas.

An advantage of the disclosed method of driver ranking is that the ranking is based on thedriver behaviour that actually affects the fuel consumption. The method does not focus onwhich driver behaviours that affect the fuel consumption but instead show how large the totaleffect is. This results in a robust method with no or few assumptions regarding how the fuelconsumption is affected.

By estimating a fuel saving potential of different routes or road segments, i.e., how the dis-tribution of fuel consumptions for a population of drivers compares, new insights of how largethe spread is between good and bad drivers in terms of fuel consumption are achieved.

Further, in some alternative embodiments, the collected and stored parameter data may besent to the vehicle producer, in order for the development department to get a deeper under-standing of how customers use their vehicles 100-1. Thereby future hardware, functionalitiesand/ or services may be developed, based on collected user behaviour.

The disclosed method may leverage from off board technology and big data platforms andis designed to be used in both conventional and streaming analytics applications, in differentembodiments.

Figure 2A illustrates a system comprising a server 250, a first database 210, a second da-tabase 220, a third database 230, a set 101 of vehicles 100-1, 100-2 driving on a road seg-ment 110 and a sensor 240.

The vehicles 100-1, 100-2 driving on the road segment 110 may continuously, or at prede-termined time intervals measuring and transmitting geographical position data and variousvehicle related data at the measured geographical position.

The geographical position of the vehicles 100-1, 100-2 may be determined by a positioningdevice in the respective vehicle 100-1, 100-2 in the set 101, which may be based on a sat-ellite navigation system such as the Navigation Signal Timing and Ranging (Navstar) GlobalPositioning System (GPS), Differential GPS (DGPS), Galileo, GLONASS, or the like.

The geographical position of the positioning device/ vehicle 100-1, 100-2 as well as time,vehicle speed, heading, etc., may be determined continuously, or at a certain predetermined or configurable time interval according to various embodiments.

The geographical position of the vehicles 100-1, 100-2 may alternatively be determined, e.g.by having transponders positioned at known positions around the route and a dedicatedsensor in the vehicle 100-1, 100-2, for recognising the transponders and thereby determiningthe position; by detecting and recognising WiFi networks (WiFi networks along the route 110may be mapped with certain respective geographical positions in a database); by receivinga Bluetooth beaconing signal, associated with a geographical position, or other signal signa-tures of wireless signals such as e.g. by triangulation of wireless signals emitted by a pluralityof fixed base stations with known geographical positions. The position may alternatively beentered manually by the driver.

Further, various parameters of the vehicle 100-1, 100-2 may also be measured and reportedto the database 210; preferably vehicle parameters that are related to the fuel consumptionof the vehicle 100-1, 100-2 such as e.g. speed, acceleration, selected gear, weight of thevehicle 100-1, 100-2, tire pressure, engine load, vehicle slope, vehicle type, usage of brake, usage of retarder; or similar parameters.

The measured position data and vehicle related data may then be sent to the first database210 over a wireless communication interface, such as e.g. Vehicle-to-Structure (V2X) com- munication. ln some embodiments, the communication between the vehicles 100-1, 100-2 and the firstdatabase 210 may be based on Dedicated Short-Range Communications (DSRC) devices.DSRC works in 5.9 GHz band with bandwidth of 75 l\/lHz and approximate range of 1000 m in some embodiments.

The wireless communication may alternatively be made according to any IEEE standard forwireless vehicular communication like e.g. a special mode of operation of IEEE 802.11 forvehicular networks called Wireless Access in Vehicular Environments (WAVE). IEEE802.11p is an extension to 802.11 Wireless LAN medium access layer (MAC) and physicallayer (PHY) specification.

Such wireless communication interface may comprise, or at least be inspired by wirelesscommunication technology such as Wi-Fi, Wireless Local Area Network (WLAN), Ultra Mo-bile Broadband (Ul\/IB), Bluetooth (BT), optical communication such as Infrared Data Asso-ciation (lrDA) or infrared transmission to name but a few possible examples of wireless com- munications in some embodiments. 9 The communication may alternatively be made over a wireless interface comprising, or atleast being inspired by radio access technologies such as e.g. 3GPP LTE, LTE-Advanced,E-UTRAN, UMTS, GSM, GSM/ EDGE, WCDMA, Time Division Multiple Access (TDMA) net-works, Frequency Division Multiple Access (FDMA) networks, Orthogonal FDMA (OFDMA)networks, Single-Carrier FDMA (SC-FDMA) networks, Worldwide lnteroperability for Micro-wave Access (WiMax), or Ultra Mobile Broadband (UMB), High Speed Packet Access(HSPA) Evolved Universal Terrestrial Radio Access (E-UTRA), Universal Terrestrial RadioAccess (UTRA), GSM EDGE Radio Access Network (GERAN), 3GPP2 CDMA technologies,e.g., CDMA2000 1x RTT and High Rate Packet Data (HRPD), or similar, just to mention some few options, via a wireless communication network.

The second database 220 comprises road data associated with different road segments 1 10.Thus the second database 220 may be referred to as a map database comprising variousdata parameters such as topographic data, elevation data, curvature, junctions, speed re-strictions, road surface, road quality, etc.

The third database 230 comprises fuel consumption related parameters, associated with theroad segment 110 and a time period. The fuel consumption related parameters may be forexample wind (direction and strength), temperature (which may influence air resistance),rainfall, fog, snow, iciness on the road, traffic intensity, road works, accidents, etc.

Some such information may be collected by one or more sensors 240 for measuring any, some or all of the above mentioned parameters.

Information measured by the sensor 240 may be sent to the third database 230 via a wired or wireless communication interface, such as e.g. any of the previously described ones.

However, the third database 230 may also comprise fuel consumption related parameterscollected from e.g. a traffic monitoring service, which may be collected e.g. over Internet.

Figure 2B illustrates an example of how the previously scenario in Figure 1 or Figure 2Amay be perceived by the driver of the vehicle 100-1, or by the driver of any other arbitraryvehicle 100-1, 100-2 in the set 101 of vehicles 100-1, 100-2. ln the illustrated embodiment, the vehicle 100-1 comprises a control unit 260. The controlunit 260 is configured for collecting parameter data that are related to the fuel consumptionof the vehicle 100-1, while driving along a route 110 in a driving direction 105. The parameterdata may comprise e.g. speed, acceleration, selected gear, weight of the vehicle 100-1, tire pressure, engine load, vehicle slope, vehicle type, usage of brake, usage of retarder; or sim- ilar parameters.

Further, the vehicle 100-1 comprises a positioning unit 270. The positioning unit 270 may bebased on a satellite navigation system such as the Navigation Signal Timing and Ranging(Navstar) Global Positioning System (GPS), Differential GPS (DGPS), Galileo, GLONASS,or the like. Thus the positioning unit 270 may comprise a GPS receiver.

The geographical position of the vehicle 100-1 may be determined continuously or at certain predetermined or configurable time intervals according to various embodiments.

Positioning by satellite navigation is based on distance measurement using triangulationfrom a number of satellites 280-1, 280-2, 280-3, 280-4. The satellites 280-1, 280-2, 280-3,280-4 continuously transmit information about time and date (for example, in coded form),identity (which satellite 280-1, 280-2, 280-3, 280-4 which broadcasts), status, and where thesatellite 280-1, 280-2, 280-3, 280-4 are situated at any given time. GPS satellites 280-1, 280-2, 280-3, 280-4 sends information encoded with different codes, for example, but not neces-sarily based on Code Division Multiple Access (CDMA). This allows information from an in-dividual satellite 280-1, 280-2, 280-3, 280-4 distinguished from the others' information, basedon a unique code for each respective satellite 280-1, 280-2, 280-3, 280-4. This informationcan then be transmitted to be received by the appropriately adapted positioning unit 270 inthe vehicle 100-1.

Distance measurement can according to some embodiments comprise measuring the differ-ence in the time it takes for each respective satellite signal transmitted by the respectivesatellites 280-1, 280-2, 280-3, 280-4, to reach the positioning unit 270. As the radio signalstravel at the speed of light, the distance to the respective satellite 280-1, 280-2, 280-3, 280-4 may be computed by measuring the signal propagation time.

The positions of the satellites 280-1, 280-2, 280-3, 280-4 are known, as they continuouslyare monitored by approximately 15-30 ground stations located mainly along and near theearth's equator. Thereby the geographical position, i.e. latitude and longitude, of the vehicle100-1 may be calculated by determining the distance to at least three satellites 280-1, 280-2, 280-3, 280-4 through triangulation. For determination of altitude, signals from four satel-lites 280-1, 280-2, 280-3, 280-4 may be used according to some embodiments.

Having determined the geographical position of the vehicle 100-1, and also possibly deter-mined e.g. the driving direction 105 of the vehicle 100-1, the control unit 260 may collect aset of parameters related to fuel consumption of the vehicle 100-1, when driving in the driving 11 direction 105 at each geographical position of the vehicle 100-1, such as e.g. the ones pre- viously enumerated.

The collected set of parameters may then be stored, associated with the geographical posi-tion and the driving direction 105 in a memory or database 210. The memory or database210 may typically be external to the vehicle 100-1.

The illustrated embodiment further comprises a transmitter 290, for transmitting the collectedparameter data to the external database 210 over a wireless interface. The vehicle 100-1may also comprise a receiver in some embodiments, for receiving parameter data stored inthe database 210. Alternatively, the transmitter 290 and the receiver may be combined in one unit, a transceiver.

The mentioned wireless communication of the transmitter 290/ receiver/ transceiver may bebased on, or at least inspired by wireless communication technology such as e.g., 3rd Gen-eration Partnership Project (3GPP) Long Term Evolution (LTE), LTE-Advanced, Vehicle-to-Vehicle (V2V) communication, Wi-Fi, Wireless Local Area Network (WLAN), Ultra MobileBroadband (Ul\/IB), or infrared transmission to name but a few possible examples of wireless communications.

The collected and stored parameter data from the vehicle 100-1 (and a plurality of othervehicles) on the first database 210 may be accessed by the server 250, over a wired or wireless communication interface, such as e.g. some of the previously enumerated.

Figure 3A schematically illustrates a part of the process of connecting vehicle related data310 to road data 320 according to an embodiment.

The collected vehicle related data 310, stored in the first database 210 is combined with roaddata 320 based on geographical coordinates, captured by the respective vehicle 100-1, 100-2 and connected spatially and synchronised in time. This may typically be made by the server250 for a large amount of vehicles, possibly over an extended historical time period. How-ever, in some embodiments, the vehicle related data 310 may be uploaded and collected continuously.

The road data 320 comprises e.g. road data 320 associated with the road segment 1 10, and/or other road segments in a road network model. Such road data 320, besides geographicalcoordinates may comprise e.g. topographic data, elevation data, slope, curvature, junctions,speed restrictions, road surface, road quality, etc. 12 When the vehicle related data 310 has been added, i.e. super positioned on the road data320 based on the respective geographical coordinates, some vehicle related data 310 maybe associated with geographical positions outside the road network, or the road network ofinterest. This vehicle related data 310 may then be identified and removed in some embod- iments.

Figure 3B schematically illustrates a road network model 330, based on the combined vehi-cle related data 310 and road data 320.

According to some embodiments, a fuel consumption model M1, M2, Mn for each roadsegment, using the vehicle related data 310 and road data 320, respectively.

Figure 3C schematically illustrates the fuel consumption models M1, M2, Mn explains thefactors contributing to the fuel consumption.

The total fuel consumption may then be divided into driver related factors 340, 350, 351, 352,353, 354, 360 and other factors 370, 380, not related to the driver such as air resistance ofthe vehicle 100-1, 100-2; roll resistance of the vehicle 100-1, 100-2, etc., for each road seg-ment and driver/ vehicle 100-1, 100-2 at a time period.

The fuel consumption of driver related factors 340, 350, 351, 352, 353, 354, 360 may com-prise determining a total fuel consumption of each vehicle 100-1, 100-2 when passing theroad segment 110; separating the determined total fuel consumption between driver relatedfactors 340, 350, 351, 352, 353, 354, 360 and the other factors 370, 380 and extracting thefuel consumption of driver related factors 340, 350, 351, 352, 353, 354, 360 for further anal- ys|s.

For each driver related factors 340, 350, 351, 352, 353, 354, 360, a fuel consumption esti-mate is made. Further, in some embodiments, an upper uncertainty boundary (UUB) and alower uncertainty boundary (LUB) may be calculated by e.g. the respective fuel consumptionmodels M1, M2, Mn for different road segments 110, or by some statistic method.

Further, one driver, or driver factor, may be selected as reference factor 340. The driver, ordriver factor selected for reference factor 340 may be arbitrary selected in some embodi- mentS.

Figure 3D schematically illustrates a driver factor analysis and a ranking of drivers on oneparticular road segment 110. 13 The distance of the driver related factors 350, 351, 352, 353, 354, 360 to the reference factor340 is computed and determined. Based on this distance, a ranking may be establishedamong the driver related factors 340, 350, 351, 352, 353, 354, 360, i.e. the drivers. ln the arbitrary illustrated example, the driver related factor 350 has the lowest difference tothe reference factor 340, and thus receives the ranking 1 on the particular road segment 110for a particular time period. The driver related factor 353 falls within the same estimate asthe reference factor 340 taking the uncertainty boundaries into account. The driver relatedfactor 353 and the reference factor 340 thus receives the common ranking 2. Neither thedriver related factor 351 and 352, respectively are possible to distinct from each other whenthe uncertainty boundaries are taken into account. Finally, driver related factor 354 has thelargest positive distance to the reference factor 340 and thus receives ranking 4, i.e. thedriver is the least fuel economic driver among the drivers in the set of drivers.

Figure 3E schematically illustrates how the fuel saving potential 395 of a road segment 110may be determined, according to some embodiments.

Firstly, the driver factor 350 having the highest ranking, with the most negative upper uncer-tainty bound may be set as reference level in some embodiments. lf none exists, the refer-ence factor 340 may be used, i.e. the reference will be zero. Distances may then be calcu-lated for all driver factors 340, 350, 351, 352, 353, 354, 360, factors that overlap with thereference level may be given the distance zero.

The computed distances may then be put in a histogram as illustrated in the Figure 3E. lnsome embodiments, the outlier drivers may be excluded from the estimation of the fuel sav-ing potential in order to get a more correct result. Such outliers may be the result of e.g. amalfunctioning vehicle, erroneous calculations, etc., or simply a very low skill driver, not be- ing representative for the driver community in general.

Thus a lower percentile limit 391 and an upper percentile limit 392 may be defined. Thesepercentile limits 391, 392 may be set to the same or distinct values in different embodiments,such as e.g. 5%, etc. (arbitrary, non-limiting example). The factor distance in between thelower percentile limit 391 and the upper percentile limit 392 then may be regarded as ameasure of the fuel saving potential 395 of the read segment 110.

According to the made definition, a road segment 110 having a large fuel saving potential395 is a road segment 110 where the difference between a skilled driver and a novice driveris large; typically, but not exclusively, a topographic intense road segment. On a road seg-ment involving a straight highway without hills, the difference between drivers may be very 14 small or even negligible, as all drivers may drive on auto pilot.

Hereby, road segments having an in particular large fuel saving potential 395 may be de-tected and identified. This information may be used e.g. to design driver education on certainroad segment types which has an in particular large fuel saving potential 395, for example,or possibly in order to alert the drivers when approaching a road segment with a large fuelsaving potential 395, for example in different embodiments.

Figure 4 illustrates an example of a method 400 according to an embodiment. The flow chartin Figure 4 shows the method 400 for use in a server 250, for generating driver rankingvalues for each respective driver of a set 101 of vehicles 100-1, 100-2 driving on a roadsegment 110, based on an estimated driver related fuel consumption. ln order to correctly be able to collect and store the parameters, the method 400 may com-prise a number of steps 401-409. However, some of these steps 401 -409 may be performedsolely in some alternative embodiments, like e.g. steps 406 or step 409. Further, the de-scribed steps 401-409 may be performed in a somewhat different chronological order thanthe numbering suggests. For example, step 402 may be performed before step 401 in someembodiments. The method 400 may comprise the subsequent steps: Step 401 comprises collecting position data and vehicle related data 310 associated withgeographical position data from each vehicle 100-1, 100-2 in the set 101 of vehicles 100-1,100-2.

The geographical position may be determined based on GPS positioning in some embodi-ments, e.g. at certain time intervals. Alternatively, the geographical position may be insertedby the driver. ln some embodiments, the driving direction 105 of the vehicle 100-1, 100-2 may also bedetermined. The driving direction 105 of the vehicle 100-1, 100-2 may be determined basedon the location of the destination of the journey, or by extrapolating the driving directionbased on previously determined geographical positions and possibly knowledge of the roaddirection, e.g. from stored map data.

The vehicle related data 310 may be collected from a first database 210. The vehicle relateddata 310 may comprise parameter data related to fuel consumption of the vehicle 100-1, 100-2 in some embodiments, and/ or identities of the vehicle 100-1, 100-2 and/ or the driver.

The collected set of parameters related to fuel consumption of the vehicle 100-1, 100-2 may comprise e.g.: fuel consumption, velocity, acceleration, selected gear, weight of the vehicle100-1, 100-2, engine load, vehicle slope, vehicle type, tyre pressure, usage of brake, usageof retarder, driver identity, vehicle identity, haulage company of the vehicle 100-1, 100-2 or similar parameters.

The weight of the vehicle 100-1, 100-2 may be measured by a weight sensor on the vehicle100-1, 100-2, or estimated based on the load in some embodiments. ln some embodiments,the weight of the vehicle 100-1, 100-2 may be estimated based on the acceleration capacityof the vehicle 100-1, 100-2. ln some alternative embodiments, also other data may be collected by the vehicle 100-1,100-2, e.g. another road user just in front of the own vehicle 100-1, 100-2 may be detectedby a sensor in the vehicle 100-1, 100-2 based on a camera, a radar, a lidar, a distancedetector, a metal detector, a reflective sensor based on infrared or visible light, or other sim- ilar sensor in different embodiments.

The other road user may be e.g. another vehicle, a human, an animal, or actually any objectappearing on the road segment 110.

Step 402 comprises collecting road data 320 associated with the road segment 110. Theroad data 320 may be extracted from the second database 220, comprising map data. Theroad data 320 may comprise various data parameters such as topographic data, elevationdata, curvature, junctions, speed restrictions, road surface, road quality, etc., associated withgeographical parameters and/ or defined road segments.

Step 403 comprises determining a set of fuel consumption related parameters, associatedwith the road segment 110 and a time period. The set of fuel consumption related parameterssuch as e.g. wind (direction and strength), temperature (which may influence air resistance),rainfall, fog, snow, iciness on the road, traffic intensity, road works, accidents, etc., may beextracted from the third database 230, where they may have been stored associated with aparticular geographical position/ road segment and a time period value.

Step 404 comprises connecting the collected 401 vehicle related data 310 with the collected402 road data 320 and the determined 403 set of fuel consumption related parameters in aroad network model 330.

Step 405 comprises synchronising the connected 404 data in the road network model 330 in time. 16 Step 406 which may be performed only in some embodiments, may comprise removing ve-hicle related data associated with positions outside the road segment 110.

Step 407 comprises determining the fuel consumption of driver related factors 340, 350, 351,352, 353, 354, 360 of each driver at the time period, for passing the road segment 110 at a certain period in time.

The fuel consumption of driver related factors 340, 350, 351 , 352, 353, 354, 360 may in someembodiments be determined by determining a total fuel consumption of each vehicle 100-1,100-2 when passing the road segment 1 10; separating the determined total fuel consumptionbetween driver related factors 340, 350, 351, 352, 353, 354, 360 and other factors 370, 380and extracting the fuel consumption of driver related factors 340, 350, 351, 352, 353, 354,360.

Step 408 comprises setting the driver ranking by a comparison between the determined 407fuel consumption of each respective driver for the road segment 110 during the time period. ln some embodiments, the driver ranking may be set by setting a reference factor 340, de-termining a deviation from the reference factor 340 for each driver and determining a confi-dence interval of the determined deviation for each driver, and thereafter ranking the drivers,based on the determined deviation of each driver from the reference factor 340.

Thereby a ranking between the set of drivers is obtained.

Step 409 which may be performed only in some embodiments, may comprise determining afuel saving potential 395 of the road segment 110 based on a comparison between a differ- ence in fuel consumption between the drivers.

According to some alternative embodiments, the fuel saving potential 395 may be deter-mined 409 by setting the driver with the lowest fuel consumption as a reference level; calcu-lating driver factor distances to the set reference level; determining a first percentile 391 ofthe driver related factors 340, 350, 351, 352, 353, 354, 360, having a factor distance belowthe first percentile 391; determining a second percentile 392 of the driver related factors 340,350, 351, 352, 353, 354, 360, having a factor distance exceeding the second percentile 392;and calculating the factor distances of driver related factors 340, 350, 351, 352, 353, 354,360 in between the first percentile 391 and the second percentile 392.

Figure 5 illustrates an embodiment of a server 250 configured for generating driver rankingvalues for each respective driver of a set 101 of vehicles 100-1, 100-2 driving on a road 17 segment 110, based on an estimated driver related fuel consumption.

The server 250 is configured to collect position data and vehicle related data 310 associatedwith the position data from each vehicle 100-1, 100-2 in the set 101 of vehicles 100-1, 100-2 from a first database 210.

Further the server 250 is also configured to collect road data 320 associated with the roadsegment 110 from a second database 220.

The server 250 is additionally configured to determine a set of fuel consumption related pa-rameters, associated with the road segment 110 and a time period from a third database230.

The server 250 is also configured to connect the collected vehicle related data with the col-lected road data and the determined set of fuel consumption related parameters in a roadnetwork model 330.

Furthermore, the server 250 is configured to synchronise the connected data in the roadnetwork model 330 in time.

The server 250 is additionally configured to determine the fuel consumption of driver relatedfactors 340, 350, 351, 352, 353, 354, 360 of each driver at the time period, for passing theroad segment 110.

The server 250 is also configured to set the driver ranking by a comparison between thedetermined fuel consumption of each respective driver for the road segment 110 during thetime period. ln some embodiments, the server 250 may also be configured to determine a fuel savingpotential 395 of the road segment 110 based on a comparison between a difference in fuel consumption between the drivers.

The server 250 may also in some embodiments be configured to remove vehicle related dataassociated with positions outside the road segment 110.

Additionally, the server 250 may be configured to determine the fuel consumption of driverrelated factors 340, 350, 351, 352, 353, 354, 360 by determining a total fuel consumption ofeach vehicle 100-1, 100-2 when passing the road segment 110; separating the determinedtotal fuel consumption between driver related factors 340, 350, 351, 352, 353, 354, 360 and 18 other factors 370, 380 and extracting the fuel consumption of driver related factors 340, 350,351, 352, 353, 354, 360, in some embodiments.

The server 250 may further be optionally configured to set the driver ranking by setting areference factor 340, determining a deviation from the reference factor 340 for each driverand determining a confidence interval of the determined deviation for each driver, and there-after ranking the drivers, based on the determined deviation of each driver from the referencefactor 340.

The server 250 is thus configured to perform at least some of the steps 401-409 accordingto the method 400.

The server 250 may comprise a receiving circuit 510 configured for receiving data from oneor more databases 210, 220, 230, and/ or from sensors 240.

The server 250 may also comprise a processing circuitry 520 configured for performing atleast some of the calculating or computing of the server 250. Thus the processing circuitry520 may be configured for performing any, some or all of the previously described methodsteps 401-409.

Such processing circuitry 520 may comprise one or more instances of a processing circuit,i.e. a Central Processing Unit (CPU), a processing unit, a processing circuit, a processor, anApplication Specific Integrated Circuit (ASIC), a microprocessor, or other processing logicthat may interpret and execute instructions. The herein utilised expression “processing cir-cuitry” may thus represent a plurality of processing circuits, such as, e.g., any, some or all ofthe ones enumerated above, as well as one single processor.

Furthermore, the server 250 may comprise a memory 525 in some embodiments. The op-tional memory 525 may comprise a physical device utilised to store data or programs, i.e.,sequences of instructions, on a temporary or permanent basis. According to some embodi-ments, the memory 525 may comprise integrated circuits comprising silicon-based transis-tors. The memory 525 may comprise e.g. a memory card, a flash memory, a USB memory,a hard disc, or another similar volatile or non-volatile storage unit for storing data such ase.g. ROIVI (Read-Only Memory), PROIVI (Programmable Read-Only Memory), EPROIVI(Erasable PROIVI), EEPROIVI (Electrically Erasable PROIVI), etc. in different embodiments.

Further, the server 250 may comprise a signal transmitter 530. The signal transmitter 530may be configured for transmitting a control signal over a Wired or wireless interface to be 19 received by a display or any other convenient presentational device for illustrating the out-come of the performed method 400, or a database for storing the results.

The previously described steps 401-409 to be performed in the server 250 may be imple-mented through the processing circuitry 520 within the server 250, together with computerprogram product for performing at least some of the functions of the steps 401-409. Thus acomputer program product, comprising instructions for performing the steps 401-409 in theserver 250 may perform the method 400 comprising at least some of the steps 401-409 forgenerating a driver rating value, based on parameter data, collected and stored while drivingalong a road segment 110, when the computer program is loaded into the processing cir-cuitry 520 of the server 250.

The computer program product mentioned above may be provided for instance in the formof a data carrier carrying computer program code for performing at least some of the steps401-409 according to some embodiments when being loaded into the processing circuitry520 of the server 250. The data carrier may be, e.g., a hard disk, a CD ROIVI disc, a memorystick, an optical storage device, a magnetic storage device or any other appropriate mediumsuch as a disk or tape that may hold machine readable data in a non-transitory manner. Thecomputer program product may furthermore be provided as computer program code on aserver and downloaded to the server 250 remotely, e.g., over an Internet or an intranet con- nection.

Further, some embodiments may comprise a vehicle 100-1, 100-2 configured for communi-cation, directly or indirectly with the previously described server 250, illustrated in Figure 5.

As used herein, the term "and/ or" comprises any and all combinations of one or more of theassociated listed items. The term “or” as used herein, is to be interpreted as a mathematicalOR, i.e., as an inclusive disjunction; not as a mathematical exclusive OR (XOR), unless ex-pressly stated otherwise. ln addition, the singular forms "a", "an" and "the" are to be inter-preted as “at least one", thus also possibly comprising a plurality of entities of the same kind,unless expressly stated othen/vise. lt will be further understood that the terms "includes","comprises", "including" and/ or "comprising", specifies the presence of stated features, ac-tions, integers, steps, operations, elements, or components, but do not preclude the pres-ence or addition of one or more other features, actions, integers, steps, operations, elements,components, or groups thereof. A single unit such as e.g. a processor may fulfil the functionsof several items recited in the claims. The mere fact that certain measures are recited inmutually different dependent claims does not indicate that a combination of these measurescannot be used to advantage. A computer program may be stored/ distributed on a suitablemedium, such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms such as via Internet or other Wired or wireless communication system.

Claims (10)

1. A method (400) in a server (250) for generating driver ranking values for each re-spective driver of a set (101) of vehicles (100-1, 100-2) driving on a road segment (110),based on an estimated driver related fuel consumption, which method (400) comprises: collecting (401) position data and vehicle related data (310) associated with the po-sition data from each vehicle (100-1, 100-2) in the set (101) of vehicles (100-1, 100-2); collecting (402) road data (320) associated with the road segment (110); determining (403) a set of fuel consumption related parameters, associated with theroad segment (110) and a time period; connecting (404) the collected (401) vehicle related data (310) with the collected(402) road data (320) and the determined (403) set of fuel consumption related parametersin a road network model (330); synchronising (405) the connected (404) data in the road network model (330) intime; determining (407) the fuel consumption of driver related factors (340, 350, 351, 352,353, 354, 360) of each driver at the time period, for passing the road segment (110); and setting (408) the driver ranking by a comparison between the determined (407) fuelconsumption of each respective driver for the road segment (110) during the time period.

2. The method (400) according to claim 1, wherein the step of determining (407) thefuel consumption of driver related factors (340, 350, 351, 352, 353, 354, 360) comprisesdetermining a total fuel consumption of each vehicle (100-1, 100-2) when passing the roadsegment (1 10); separating the determined total fuel consumption between driver related fac-tors (340, 350, 351, 352, 353, 354, 360) and other factors (370, 380) and extracting the fuelconsumption of driver related factors (340, 350, 351, 352, 353, 354, 360).

3. The method (400) according to any of claim 1 or claim 2, wherein the driver rankingis set (408) by setting a reference factor (340), determining a deviation from the referencefactor (340) for each driver and determining a confidence interval of the determined deviationfor each driver, and thereafter ranking the drivers, based on the determined deviation of eachdriver from the reference factor (340).

4. The method (400) according to any of claims 1-3, further comprising:determining (409) a fuel saving potential (395) of the road segment (110) based on a comparison between a difference in fuel consumption between the drivers.

5. The method (400) according to claim 4, wherein the fuel saving potential (395) isdetermined (409) by setting the driver with the lowest fuel consumption as a reference level;calculating driver factor distances to the set reference level; determining a first percentile 22 (391) of the driver related factors (340, 350, 351, 352, 353, 354, 360), having a factor dis-tance below the first percentile (391); determining a second percentile (392) of the driverrelated factors (340, 350, 351, 352, 353, 354, 360), having a factor distance exceeding thesecond percentile (392); and calculating the factor distances of driver related factors (340,350, 351, 352, 353, 354, 360) in between the first percentile (391) and the second percentile(392).

6. The method (400) according to any of claims 1-5, further comprising:removing (406) vehicle related data (310) associated with positions outside the road segment (110).

7. A server (250), for generating driver ranking values for each respective driver of aset (101) of vehicles (100-1, 100-2) driving on a road segment (110), based on an estimateddriver related fuel consumption, wherein the server (250) is configured to: collect position data and vehicle related data (310) associated with the position datafrom each vehicle (100-1, 100-2) in the set (101) of vehicles (100-1, 100-2); collect road data (320) associated with the road segment (110); determine a set of fuel consumption related parameters, associated with the roadsegment (110) and a time period; connect the collected vehicle related data with the collected road data and the de-termined set of fuel consumption related parameters in a road network model (330); synchronise the connected data in the road network model (330) in time; determine the fuel consumption of driver related factors (340, 350, 351, 352, 353,354, 360) of each driver at the time period, for passing the road segment (110); and set the driver ranking by a comparison between the determined fuel consumption ofeach respective driver for the road segment (110) during the time period.

8. The server (250) according to claim 7, further configured to:determine a fuel saving potential (395) of the road segment (110) based on a com- parison between a difference in fuel consumption between the drivers.

9. The server (250) according to any of claim 7 or claim 8, further configured to:remove vehicle related data (310) associated with positions outside the road seg-ment (110).

10.cording to any of claims 1-6 when the computer program is executed in the server (250), A computer program comprising program code for performing a method (400) ac- according to any of claims 7-9.