SE539526C2

SE539526C2 - Method and control unit for a self-learning map

Info

Publication number: SE539526C2
Application number: SE1650027A
Authority: SE
Inventors: Sahlholm Per; FABIAN Sebastian
Original assignee: Scania Cv Ab
Priority date: 2016-01-12
Filing date: 2016-01-12
Publication date: 2017-10-10
Also published as: BR112018013213A2; DE112016005693T5; WO2017123130A1; SE1650027A1

Abstract

SUMMARY Methods (400, 600) and control unit (300) for building a database (350) and for predicting aroute of a vehicle (100). The database (350) comprises a plurality of grid-based represen-tations (200-1, 200-2, 200-3, 200-4) of a geographical landscape, each grid-based repre-sentation (200-1, 200-2, 200-3, 200-4) comprising a plurality of discrete pixels (211, 212, 244) and being associated with a range of directions (210-1, 210-2, 210-3, 210-4). Themethod (600) comprises observing (601) geographical position of the vehicle (100); deter-mining (602) a driving direction (105) of the vehicle (100) at the observed (601) geographi-cal position; selecting (603) one grid-based representation (200-2) out of the plurality ofgrid-based representations (200-1, 200-2, 200-3, 200-4), based on the determined (602)driving direction (105); determining (604) a pixel (223) in the selected (603) grid-based rep-resentation (200-2), corresponding to the observed (601) geographical position; and pre-dicting (605) a next position pixel (211, 212, 244) of the vehicle (100), neighbour to thedetermined (604) pixel (223) in the selected (603) grid-based representation (200-2), basedon a frequency counter value of the neighbour pixels (211 , 212, 244). (Pubi. Fig. 25)

Description

METHOD AND CONTROL UNIT FOR A SELF-LEARNING MAP TECHNICAL FIELD This document discloses a control unit and methods therein. More particularly, a controlunit and methods therein are described for predicting a vehicle route by establishing andusing a database, comprising a plurality of grid-based representations of a geographicallandscape, each grid-based representation comprising a plurality of discrete pixels andbeing associated with a distinct direction.

BACKGROUND Various advantages are achieved when a route of a vehicle could be reliably predicted. Abetter choice of gear may be made for example, leading to saved fuel and improved driv-ability. Further, an improved or optimal velocity profile for the vehicle may be calculatedand utilised for the vehicle, thereby further reducing fuel consumption.

A possible solution to this problem is to use a map, provided by a third party. Such mapcomprises information that make it possible to generate a prediction of how the road aheadof the vehicle will look like in terms of road slope.

The maps used today are static, i.e. they are not updated once the map is installed in thevehicle. Vehicle maps may therefore lack coverage in certain regions where accurate mapdata is not available, e.g. on recently built roads or on small roads which are too uneco-nomical to map. ln addition, some particular environments may be very dynamical such as e.g. mines,building sites, deforestation areas, storage areas in a harbour, a load terminal or similar.Even in case map data is available, the data may be outdated and thereby be useless forthe above mentioned purposes. lt is thus a problem for a driver/ driving control unit in a vehicle to get appropriate map in-formation, corresponding to the geographical environment of the vehicle, in order to makecorrect predictions of the vehicle route, and to extract correct road slope data associatedwith the predicted route.

Document US2014278064 concerns an apparatus for providing route information to a userusing e.g. personal computer for health applications, has processing device for comparingdesired route characteristic information with stored route data and suggesting stored routesto the user. The document describes an apparatus for assisting a user in selecting route. A colour coded heat map is generated over an area based on popularity of different routes by various other users.

Document EP1551195 concerns an information acquisition device e.g. car navigation sys-tem, which predicts the destination of the car by determining search conditions for preciseprediction, and acquires information about predicted destination from a database.

However, the document only concerns prediction of destination of the vehicle, not whichroute it takes, neither is it discussed how the information acquisition could be used for gearselection.

Document WO201411f537 discuss a computer-implemented method for creating mapdata, which involves applying threshold calculation to heat map to identify zones on net-work having high concentration of traffic accidents. This collected information is then util-ised for indicating vehicle routes where the risk of accidents is increased.

However, no prediction of the vehicle route is made. Neither is any information extractedfor the purpose of selecting driving gear of the vehicle.

Document US2015177005 presents a computer-implemented method for varying degree ofprecision in navigation data analysis for customising map content. A heat map is generatedto show the amount of users moving in an area.

Document US2014288821 discloses a method for predicting arrival time of a transit vehicleat a transit stop of a transit route, which involves predicting time of arrival of the particularvehicle based on the current position of the vehicle and historical data of other vehicleshaving driven the same route.

However, no prediction of the vehicle route is made. Neither is any information extractedfor the purpose of selecting driving gear of the vehicle. lt may thereby be desired to be able to create and continuously update a database com-prising a representation of a geographical landscape in order to predict route for a vehicle,and utilising a location dependent parameter associated with the predicted route to providea solution to the above discussed problems in connection with vehicle route prediction.

SUMMARY lt is therefore an object of this invention to solve at least some of the above problems andimprove route prediction of a vehicle.

According to a first aspect of the invention, this objective is achieved by a method for build-ing a database, thereby enabling prediction of a vehicle route. The database comprises aplurality of grid-based representations of a geographical landscape, each grid-based repre-sentation comprising a plurality of discrete pixels and being associated with a range of di-rections. The method comprises observing geographical position of a vehicle. Further themethod comprises determining a driving direction of the vehicle at the observed geographi-cal position. Also, the method comprises selecting one grid-based representation out of theplurality of grid-based representations, based on the determined driving direction. ln addi-tion, the method furthermore comprises determining a pixel in the selected grid-based rep-resentation, corresponding to the observed geographical position. The method in additioncomprises incrementing a frequency counter associated with the determined pixel in theselected grid-based representation, counting an amount of times the vehicle has passedthe pixel in the determined driving direction. Further the method also comprises storing theincremented frequency counter value, associated with the determined pixel in the selectedgrid-based representation in the database.

According to a second aspect of the invention, this objective is achieved by a control unit ina vehicle. The control unit aims at building a database for enabling prediction of a vehicleroute. The database comprises a plurality of grid-based representations of a geographicallandscape, each grid-based representation comprising a plurality of discrete pixels andbeing associated with a range of directions. The control unit is configured to observe geo-graphical position of a vehicle. Further, the control unit is configured to determine a drivingdirection of the vehicle at the observed geographical position. Also, the control unit is addi-tionally configured to select one grid-based representation out of the plurality of grid-basedrepresentations, based on the determined driving direction. Furthermore, the control unit isconfigured to determine a pixel in the selected grid-based representation, corresponding tothe observed geographical position. The control unit is in further addition configured to in-crement a frequency counter associated with the determined pixel in the selected grid-based representation, counting an amount of times the vehicle has passed the pixel in thedetermined driving direction. Further the control unit is also configured to store the meas-ured at least one location-dependent parameter and the incremented frequency countervalue, associated with the determined pixel in the selected grid-based representation in thedatabase.

According to a third aspect of the invention, this objective is achieved by a method for pre-dicting a route of a vehicle, by using a database. The database comprises a plurality ofgrid-based representations of a geographical landscape, each grid-based representationcomprising a plurality of discrete pixels and being associated with a range of directions.The method comprises observing geographical position of the vehicle. Further the methodalso comprises determining a driving direction of the vehicle at the observed geographicalposition. The method additionally comprises selecting one grid-based representation out ofthe plurality of grid-based representations, based on the determined driving direction. Fur-ther the method also comprises determining a pixel in the selected grid-based representa-tion, corresponding to the observed geographical position. Additionally, the method fur-thermore also comprises predicting a next position pixel of the vehicle, neighbour to thedetermined pixel in the selected grid-based representation, based on a frequency countervalue of the neighbour pixels.

According to a fourth aspect of the invention, this objective is achieved by a control unit in avehicle. The control unit aims at predicting a route of a vehicle by using a database. Thedatabase comprises a plurality of grid-based representations of a geographical landscape,each grid-based representation comprising a plurality of discrete pixels and being associ-ated with a range of directions. The control unit is configured to observe geographical posi-tion of the vehicle. Further the control unit is configured to determine a driving direction ofthe vehicle at the observed geographical position. ln addition, the control unit is further-more configured to select one grid-based representation out of the plurality of grid-basedrepresentations, based on the determined driving direction. Also the control unit is further-more configured to determine a pixel in the selected grid-based representation, corre-sponding to the observed geographical position. The control unit is in further addition alsoconfigured to predict a next position pixel of the vehicle, neighbour to the determined pixelin the selected grid-based representation, based on a frequency counter value of theneighbour pixels.

Hereby, thanks to the disclosed aspects, reliable route prediction is provided, while keepingmemory usage requirements low and little computational capacity. Since the current solu-tion is based on the historical direction rather than historical discrete pixel travel, the solu-tion becomes more flexible and accepting of perturbations to previous routes, incurred forexample while driving in another highway lane. Further, the directionality provided by theplurality of grid-based representations, means that opposing driving lanes may fall withinthe same pixel and still give a correct prediction, unlike previously known solutions.Thereby the prediction of unlikely U-turns on highways could be avoided, when travelling in the statistically more unusual direction. Thereby, route prediction of the vehicle is im-proved.

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 side view of a vehicle according to an embodiment; Figure 2A illustrates an overview of a vehicle driving on a road and a correspondencebetween the geographical landscape and a grid-based representationthereof; Figure 2B illustrates a view a vehicle driving on a road and a plurality of grid-basedrepresentations, wherein one is selected based on the vehicle direction; Figure 2C illustrates an example of road slopes when the vehicle is passing three cellsof the grid-based representation; Figure 2D illustrates stored parameter values associated with different pixels in a grid-based representation; Figure 2E illustrates an example of route prediction of the vehicle according to an em-bodiment; Figure 3A illustrates an example of a vehicle interior according to an embodiment; Figure 3B illustrates an example of a vehicle interior according to an embodiment; Figure 4 is a flow chart illustrating an embodiment of a method; Figure5 is an illustration depicting a control unit and system according to an em-bodiment; and Figure 6 is a flow chart illustrating an embodiment of a method.

DETAILED DESCRIPTION Embodiments of the invention described herein are defined as methods and a control unit,which may be put into practice in the embodiments described below. These embodimentsmay, however, be exemplified and realised in many different forms and are not to be lim-ited to the examples set forth herein; rather, these illustrative examples of embodimentsare provided so that this disclosure will be thorough and complete.

Still other objects and features may become apparent from the following detailed descrip-tion, 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 adefinition of the limits of the herein disclosed embodiments, for which reference is to bemade to the appended claims. Further, the drawings are not necessarily drawn to scaleand, unless otherwise indicated, they are merely intended to conceptually illustrate thestructures and procedures described herein.

Figure 1A illustrates a scenario with a vehicle 100 driving in a driving direction 105 on aroad 110. The vehicle 100 may be e.g. a truck, a bus, a van, a car, a motorcycle or anyother similar type of vehicle with or without an attached trailer.

The vehicle 100 comprises one or more on-board vehicle sensors for measuring e.g. roadslope, curvature, vehicle velocity, selected gear, height, etc. The vehicle 100 also com-prises a positioning unit for determining a geographical position of the vehicle 100.Thereby, the vehicle 100 may establish a self-learning map which may be updated con-tinuously, or at certain predetermined or configurable time intervals. The established mapmay then be used to predict the route through the map in order to output a road grade pre-diction in real time. ln some embodiments, the map is stored in a raster format, divided into discrete pixels.Each pixel may correspond to e.g. about 10x10 meters, 20x20 meters, 30x30 meters, etc.,in different embodiments.

The map is further divided into several directional heat maps, or grid-based representationsof the geographical landscape, each representing a direction span. lf 4 directions are cho-sen, these may be north, east, south and west and the direction span of each directionalheat map would be 90 degrees.

The concept of having a plurality of grid-based representations associated with differentdirection spans is not limited to 4 grid-based representations associated with directionspans of about 90 degrees each, but may comprise e.g. 2 grid-based representations as-sociated with direction spans of about 180 degrees each; 3 grid-based representationsassociated with direction spans of about 120 degrees each; 5 grid-based representationsassociated with direction spans of about 72 degrees each; 6 grid-based representationsassociated with direction spans of about 60 degrees each; 7 grid-based representations associated with direction spans of about 51 degrees each; 8 grid-based representationsassociated with direction spans of about 45 degrees each, etc.

The pixels may be small, equally sized area elements, which may have any area coveringformat such as e.g. quadratic, rectangular, triangular, pentagonal, hexagonal, etc., or acombination of these or other formats.

The system may estimate road grade and current location data periodically, and use thisdata to build the map. The current heading or driving direction 105 of the vehicle 100 mayin some embodiments be determined by a heading between two previous data points andthe heat map most closely matching this heading may be recalled. The pixel wherein thecurrent location resides may then be recalled and its heat value or frequency value may beincreased. ln some embodiments, only pixels having data recorded, i.e. that have beenvisited are stored. Thereby storage memory is saved, as there is little point in storing pix-els/ data which are never visited (they may e.g. be situated out of the road). lf a pixel isvisited for the first time and thus does not exist, it may be created and stored with the fre-quency counter incremented to one.

To make a route prediction, a similar approach may be deployed. The current heading ordriving direction 105 determines which of the heat maps to recall, and the current vehiclelocation determines which pixel of that selected heat map. Its neighbouring pixels in thesame heat map may then be examined to find the hottest one, and this may be selected asthe next pixel of the predicted path of the vehicle 100. This process may then be iteratedfor the desired prediction length, at each step updating the location of the vehicle 100 tothe latest selected pixel and calculating a new heading based on the heading between theselected pixels in the prediction. After this step is done, the series of pixels may be exam-ined in order to extract their respective road slope values, if these values have been stored.Finally, the travel distance between each point in the horizon may be estimated to generatea final road slope as a function of travel length, in some embodiments.

The disclosed solution requires a low amount of storage, little computational capacity andproduces better results than previous solutions according to simulator implementations,using collected Controller Area Network (CAN) logs from vehicles to test the system. CANis a connection standard for interconnected distributed systems, typically used in vehiclesto communicate between Electronic Control Units (ECUs).

The performance may be measured by the average road grade prediction error at eachpoint in a 2500-meter-long prediction, sampled hundreds of times along a simulated drive.

The result in highway driving is more than 50% smaller average error than previouslyknown solutions based on a single grid based predictive roadmap algorithm while storagerequirements are decreased by almost 30%.

The disclosed method may be in particular advantageous in road crossings in the same ordifferent planes. A conflict in road data at the point where the highway passes over anotherroad arises because the same pixel is sometimes passed through on the highway, andsometimes below, each time updating the map with conflicting data, ultimately leading tothe wrong prediction of “jumping off” the highway.

However, since driving on the highway and driving on the road below it would recall differ-ent heat maps, the method according to the presented solution behaves more as expected,and produces the correct prediction for this case.

Another inherent advantage to the disclosed solution is that each prediction can be as-signed a heat value, the sum of the heat of its pixels, which provides a measure to com-pare the quality of a prediction against other possibilities.

Figure 2A discloses the vehicle 100 driving in the driving direction 105 on the road 110. Amap 200 is stored in a raster format, divided into discrete pixels 211, 212, 213, 214, 221,222, 223, 224, 231, 232, 233, 234, 241, 242, 243, 244. ln this arbitrary example, the sec-ond integer indicates the vertical coordinate while the third integer indicates the horizontalcoordinate. The situation may be the opposite in other examples.

However, as illustrated in Figure 2B a plurality of grid-based representations 200-1, 200-2,200-3, 200-4 of the geographical landscape, each of them associated with a range of direc-tions 210-1, 210-2, 210-3, 210-4, stored in a database.

While driving the vehicle 100, data may be measured with an on board sensor and storedin some embodiments, e.g. road slope. However, in other embodiments wherein it is de-sired to only predict the route of the vehicle 100, only passing frequency may be deter-mined and stored, for each passed pixel 211, 212, 244 in the grid-based representa-tions 200-1, 200-2, 200-3, 200-4.

The vehicle 100 determines its geographical position via a positioning device while drivingon the road 110. The heading between at least two recently determined geographical posi-tions when entering the current pixel may determine the driving direction 105 of the vehicle100.

The determined driving direction 105 of the vehicle 100 is then compared with the rangesof directions 210-1, 210-2, 210-3, 210-4 in an action A. The range of directions 210-2 em-bracing the driving direction 105 is then determined. Having determined the range of direc-tions 210-2, the associated grid-based representation 200-2 is selected out of the pluralityof grid-based representations 200-1, 200-2, 200-3, 200-4. ln an action B, a pixel 223 in the grid-based representation 200-2 wherein the vehicle 100is currently situated is determined, based on the determined geographical position of thevehicle 100. A frequency counter associated with the pixel 223 in the grid-based represen-tation 200-2 wherein the vehicle 100 is determined to be positioned is incremented.

Further, when predicting the route of the vehicle 100, the neighbour pixel 222 with thehighest stored frequency counter value is determined and the vehicle 100 is estimated todrive to this pixel 222. This process may then be repeated for as long as desired. ln casethe vehicle 100 is estimated to change driving direction 105, the actions A and B may thenbe repeated for selecting a new grid-based representation 200-1, 200-2, 200-3, 200-4. ln some embodiments as mentioned before, the vehicle 100 may measure one or morelocation-dependent parameters at the geographical position when driving. The location-dependent parameter may be e.g. road slope, curvature, height, selected driving gear, ve-hicle velocity or similar parameter measured by a system or entered by the driver of thevehicle 100. This or these parameter/ s may be stored associated with the determined pixel223 in the selected grid-based representation 200-2 in the database. The current day of theweek, time of the day, or other status information may additionally be stored for some at-tributes, to enable conditional predictions when recalling data.

Figure 2C discloses the vehicle 100 driving in the driving direction 105 in the pixel 223.The Figure illustrates an example of the topographical differences the vehicle 100 will ex-perience when passing three cells 222, 223 and 234, in an arbitrary example. By combin-ing the past distance in the horizontal plane with the curvature in the vertical plane, thedistance of the predicted route of the vehicle 100 may be estimated, in some embodiments.

Figure 2D illustrates an example of frequency counter values and measured road slope,associated with pixels 211, 212, 244 of the selected grid-based representation 200-2, ina non-limiting example. The road slope and/ or height profile may be of interest when anoptimal velocity profile is derived to reduce the fuel consumption. ln some embodiments, also a time reference value may be determined and stored, associ-ated with each measurement in order to enable aging.

Aging is the process of lowering the heat at each pixel 211, 212, 244 after some timeperiod, for the purpose of keeping all values from overflowing their data type. lf the valuewas simply capped, the steady state may be a map of equally hot pixels 211, 212, 244at the maximum limit, which would defeat the purpose of such a map. Therefore, sometype of aging may be useful to preserve the data and also to allow to get rid of obsoletedata, e.g. in case of changed driving routines or changed vehicle owner.

Several approaches may be possible for achieving aging. One might use a timer to globallydecrease the heat after a pixel 211, 212, 244 has not been passed through in a certaintime, or decrease the heat for border pixels 211, 212, 244 along the road 110. A disad-vantage with the first proposal is it may require a lengthy processing step. lt is perhapsconceivable to perform such a task when the vehicle 100 is ldle, in some embodiments.

An alternative solution may be to perform the aging at a single pixel 211, 212, 244when lt is passed through. To do this, the heat of the current direction 105 may be calcu-lated. lf it is at the maximum bound of its data type, all values may be scaled back by somescalar. This keeps the proportions intact, and prevents overflow in a manner with very lowcomputational overhead. A conditional statement may be added, which is bypassed themajority of time (depending on the scalar chosen and data type). When it is triggered, a fewmultiplication operations are carried out, the same number as the number of directionalheat maps 200-1, 200-2, 200-3, 200-4. Another possibility is to subtract all values by e.g.one for all pixels 211, 212, 244 except the current one.

Figure 2E illustrates an example of route prediction of the vehicle 100 when driving on theroad 110 through the geographical landscape.

When the prediction is made, the corresponding directional heat map 200-1, 200-2, 200-3,200-4 is used to calculate the next likely pixel 211, 212, 244. First, the correct heat map200-1, 200-2, 200-3, 200-4 may be determined by calculating the current heading 105 andassigning it to one of the predefined direction spans 210-1, 210-2, 210-3, 210-4, dependingon the number of directions of the directional heat maps 200-1, 200-2, 200-3, 200-4.

The current direction 105 may be calculated as a heading between the current pixel 223and a passed pixel 211, 212, 244 n places back. This may be kept track of by using aqueue. The distance n between which the heading is calculated may determine the granu- 11 larity due to aliasing, and also willingness to change direction. As such, lower pixel sizemeans better resolution.

When the number of directions is set to, for example, four, the result is four separate heatmaps 200-1, 200-2, 200-3, 200-4, each one associated with a respective direction span210-1, 210-2, 210-3, 210-4, as seen in Figure 2B. Each heat map 200-1, 200-2, 200-3,200-4 represents the frequency through which each pixel 211, 212, 244 is travelled, onthe condition that the direction falls within a threshold. When doing a prediction, one of thee.g. four heat maps 200-1, 200-2, 200-3, 200-4 may be chosen depending on the currentdirection 105 of the vehicle 100, and used to calculate the probability of the next pixel 211,212, ..., 244. ln some embodiments, the desired output of the provided solution may be the road gradeas a function of distance, the length of each point of the predicted route may be deter-mined. The result of the prediction is a series of pixels 211, 212, 244 that the vehicle100 is predicted to pass through, but the exact distance at each point is unknown. Usingthe pixel size for estimating the distance would only yield a correct value for straight travelthrough the pixels 211, 212, 244, perpendicular to one of the pixel sides, although itmay be utilised for a rough estimation of a minimum value in some embodiments.

The desired result may be an approximation of the travel distance for each and every pixel211, 212, 244 in the horizon, in some embodiments. To approximate the distance thatthe vehicle 100 is assumed to travel to reach a given point, a way of converting geographi-cal position coordinates may be made.

A method of determining a plausible fine-grained path between the pixels 211, 212, 244of the horizon can be accomplished in a few ditlerent ways, such as e.g. linear travelthrough each pixel 211, 212, 244 by interpolating linearly between the entry and exitpoint for each pixel 211, 212, 244; linear approximation by calculating the distance be-tween two pixels 211, 212, 244 of some distance in the horizon, and division by thesame number of data points; polynomial fitting to selected pixels 211, 212, 244 and/ orspline fitting to the entire horizon, in different embodiments.

Figure 3A discloses a vehicle interior, according to an embodiment, illustrating an exampleof how the previous scenario may be perceived by the driver of the vehicle 100 when situ-ated at any arbitrary position along the route. 12 A system 300 for vehicle route prediction may comprise a control unit 310, a positioningdevice 330 and a database 350.

The control unit 310 may be configured for predicting the route for the vehicle 100, from acurrent position of the vehicle 100, to a destination. The control unit 310 may comprise, orbe connected to the database 350, which may comprise a plurality of grid-based represen-tations 200-1, 200-2, 200-3, 200-4, each comprising a set of pixels 211, 212, 244 rep-resenting geographical positions.

The database 350 comprises a plurality of grid-based representations 200-1, 200-2, 200-3,200-4 of a geographical landscape. Each grid-based representation 200-1, 200-2, 200-3,200-4 comprises in turn a plurality of discrete pixels 211, 212, 244 and is associatedwith a range of directions 210-1, 210-2, 210-3, 210-4. The database 350 is configured tostore a frequency counter value, associated with a respective pixel 211, 212, 244 in atleast one of the grid-based representations 200-1, 200-2, 200-3, 200-4. Further, in someembodiments, the database 350 may also be configured to store at least one location-dependent parameter such as topographic data or curvature. ln the illustrated embodiment, the database 350 is vehicle external and accessible for thecontrol unit 310 via a transceiver 340 in the vehicle 100, over a wireless communicationinterface. Various strategies for caching and synchronising subsets of the database 350representing the local area around the vehicle 100 and its' predicted route may be em-ployed to optimise wireless resource usage. ln such an embodiment the location depend-ent parameter(s) may be shared between multiple users of the database 350, while thefrequency counters may be specific for each user. ln another embodiment the frequencycounters may be shared as well, or a weighted combination of private and shared fre-quency counters may be used.

Such wireless communication may comprise or be based on e.g. a Vehicle-to-Vehicle(V2V) signal, or any other wireless signal based on, or at least inspired by wireless com-munication technology such as Wi-Fi, Ultra Mobile Broadband (UMB), Wireless Local AreaNetwork (WLAN), Bluetooth (BT), or infrared transmission to name but a few possible ex-amples of wireless communications.

The geographical position of the vehicle 100 may be determined by the positioning device330 in the vehicle 100, which may be based on a satellite navigation system such as theNavigation Signal Timing and Banging (Navstar) Global Positioning System (GPS), Differ-ential GPS (DGPS), Galileo, GLONASS, or the like. 13 The geographical position of the positioning device 330, (and thereby also of the vehicle100) may be made continuously with a certain predetermined or configurable time intervalsaccording to various embodiments.

Positioning by satellite navigation is based on distance measurement using triangulationfrom a number of satellites 360-1, 360-2, 360-3, 360-4. ln this example, four satellites 360-1, 360-2, 360-3, 360-4 are depicted, but this is merely an example. More than four satel-lites 360-1, 360-2, 360-3, 360-4 may be used for enhancing the precision, or for creatingredundancy. The satellites 360-1, 360-2, 360-3, 360-4 continuously transmit informationabout time and date (for example, in coded form), identity (which satellite 360-1, 360-2,360-3, 360-4 that broadcasts), status, and where the satellite 360-1, 360-2, 360-3, 360-4are situated at any given time. The GPS satellites 360-1, 360-2, 360-3, 360-4 sends infor-mation encoded with different codes, for example, but not necessarily based on Code Divi-sion Multiple Access (CDMA). This allows information from an individual satellite 360-1,360-2, 360-3, 360-4 distinguished from the others' information, based on a unique code foreach respective satellite 360-1, 360-2, 360-3, 360-4. This information can then be transmit-ted to be received by the appropriately adapted positioning device comprised in the vehi-cles 100.

Distance measurement can according to some embodiments comprise measuring the dif-ference in the time it takes for each respective satellite signal transmitted by the respectivesatellites 360-1, 360-2, 360-3, 360-4 to reach the positioning device 330. As the radio sig-nals travel at the speed of light, the distance to the respective satellite 360-1, 360-2, 360-3,360-4 may be computed by measuring the signal propagation time.

The positions of the satellites 360-1, 360-2, 360-3, 360-4 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 may be calculated by determining the distance to at least three satellites 360-1, 360-2,360-3, 360-4 through triangulation. For determination of altitude, signals from four satellites360-1, 360-2, 360-3, 360-4 may be used according to some embodiments.

Having determined the geographical position of the positioning device 330 (or in anotherway), it may be presented on a map, a screen or a display device where the position of thevehicle 100 may be marked. 14 ln some embodiments, the geographical position of the vehicle 100, the predicted route ofthe vehicle 100 and other possible information related to the route planning, such as a gearrecommendation, a velocity recommendation, information concerning an upcoming roadslope etc., may be displayed on an interface unit 320. The optional interface unit 320 maycomprise a dashboard, a screen, a display, or any similar device.

Figure 3B discloses a vehicle interior, similar to the embodiment illustrated in Figure 3A.However, in this embodiment, the database 350 is situated in the vehicle 100, why thetransceiver 340 may be redundant.

The system 300 for vehicle route prediction may comprise a control unit 310, a positioningdevice 330 and a database 350, all comprised within the vehicle 100. The functionalities ofthese units 310, 330 and 350 may be the same or similar to the ones already discussedand explained in conjunction with the embodiment disclosed in Figure 3A.

Further, the vehicle 100 may comprise an interface unit 320 such as e.g. a dashboard, ascreen, a display, or any similar device.

The vehicle 100 may in addition in some embodiments comprise one or several devices formeasuring at least one location-dependent parameter at the geographical position, such ase.g. speed limits, curvature, road slope, height profile selected driving gear, vehicle velocityand/ or road type, for example. The road slope and/ or height profile may be of interestwhen an optimal velocity profile is derived to reduce the fuel consumption.

When the position of the vehicle 100 is registered inside a specific pixel 223 in the selectedgrid-based representation 200-2, that pixel 223 may be determined as part of the vehicleroute and various road attributes such as road slope may be assigned to it. ln order to use the recorded road slope, it may be stored associated with a segment of thepredicted road ahead. Further, frequency statistics is generated, continuously updated andstored, associated with a pixel 211 , 212, 244 in a grid-based representation 200-1, 200-2, 200-3, 200-4. The frequency statistics may be used to determine the most probable nextpixel 211, 212, 244 to move into and thereby make a prediction of the vehicle route.

Once the area represented by a pixel 211, 212, 244 has been traversed by the vehicle100, the pixel 211, 212, 244 may be assigned a value and may hence be seen as vis-ited. When making a prediction of the future movement from the pixel 211, 212, 244,the pixels 211, 212, 244 on the edges of the first pixel 211, 212, 244 may be studied in order to determine which of those has been visited before. By doing so a decision maybe made concerning which pixel 211, 212, 244 is the most probable one to be the nextpixel 211, 212, 244 to visit. lf a pixel 211, 212, 244 only has two additionalneighbour pixels 211, 212, 244 that have been visited next to itself, it is straightforwardto determine the most probable path. This is the case since one of the two neighbouringpixels 211, 212, 244 represent the previous pixel 211, 212, 244 and the other onethe probable next pixel 211, 212, 244 to visit. The most probable move in this case isthus to exit the current pixel 211, 212, 244 into the neighbouring pixel 211, 212, 244that the vehicle 100 did not enter through. However, when noisy positioning signals, high-ways with multiple lanes and crossings are a reality in real world road networks, more so-phisticated ways of predicting the most probable path may be an advantage. To be able topredict the future path of the vehicle 100, statistics thus may be stored in order to generatea prediction of the road ahead for the vehicle 100.

Figure 4 illustrates an example of a method 400 for building a database 350, according toan embodiment. The method 400 aims at enabling prediction of a vehicle route of a vehicle100, based on the established database 350. The database 350 comprises a plurality ofgrid-based representations 200-1, 200-2, 200-3, 200-4, or heat maps, of a geographicallandscape. Each grid-based representation 200-1, 200-2, 200-3, 200-4 comprises a plural-ity of discrete pixels 211, 212, 244 and is associated with a range of directions 210-1,210-2, 210-3, 210-4.

The vehicle 100 may be any arbitrary kind of means for conveyance, such as a truck, abus, a car, a motorcycle or similar. The vehicle 100 may be driven by a driver, or beautonomous in different embodiments. ln order to correctly be able to build the database 350, the method 400 may comprise anumber of steps 401-407. However, some of these steps 401 -407 may be performed solelyin some alternative embodiments, like e.g. step 405. Further, the described steps 401-407may be performed in a somewhat different chronological order than the numbering sug-gests. The method 400 may comprise the subsequent steps: Step 401 comprises observing geographical position of the vehicle 100.The current vehicle position may be determined by a geographical positioning device 330, such as e.g. a GPS. However, the current position of the vehicle 100 may alternatively bedetected and registered by the driver of the vehicle 100. 16 The geographical position may be determined continuously while driving the vehicle 100along the road 110, or at predetermined time intervals in different embodiments.

Step 402 comprises determining a driving direction 105 of the vehicle 100 at the observed401 geographical position.

The driving direction 105 of the vehicle 100 at the observed 401 geographical position maybe determined 402, in some embodiments, based on a heading between the observed 401geographical position of the vehicle 100 and a previously observed 401 geographical posi-tion of the vehicle 100.

Step 403 comprises selecting one grid-based representation 200-2 out of the plurality ofgrid-based representations 200-1, 200-2, 200-3, 200-4, based on the determined 402 driv-ing direction 105.

The grid-based representation 200-2 having a range of directions 210-2 corresponding withthe heading between the observed 401 geographical position of the vehicle 100 and a pre-viously observed 401 geographical position of the vehicle 100, may be selected.

Step 404 comprises determining a pixel 223 in the selected 403 grid-based representation200-2, corresponding to the observed 401 geographical position.

Step 405 which may be performed only in some particular embodiments, comprises meas-uring at least one location-dependent parameter at the observed 401 geographical positionwhen driving in the determined 402 driving direction 105.

The at least one location-dependent parameter may comprise e.g. road slope, curvature,height profile, selected driving gear, vehicle velocity and/ or road type, for example. Theroad slope and/ or height profile may be of interest when an optimal velocity profile is de-rived to reduce the fuel consumption. Other attributes of interest to predict may compriseentities such as arrival time, stored energy consumption, driving hazards etc. ln some such embodiments, the combination of the previously stored slope and the cur-rently determined 405 slope may be made by computing a weighted mean value, giving thecurrently determined 404 slope a higher weight than the previously stored slope.

An average value of the at least one location-dependent parameter may be calculated andstored, based on a previously stored at least one location-dependent parameter and the 17 measured 405 at least one location-dependent parameter. For some parameter types otherauxiliary information such as the fraction of passes past the current position a feature hasbeen observed may be stored.

An advantage is that the road slope may vary within the pixel 211, 212, 244 but by cal-culating an average value, the difference between a predicted road slope and an experi-enced road slope may be reduced or minimised.

Step 406 comprises incrementing a frequency counter associated with the determined 404pixel 223 in the selected 403 grid-based representation 200-2, counting an amount of timesthe vehicle 100 has passed the pixel 223 in the determined 402 driving direction 105.

The frequency counter associated with the determined 404 pixel 223 may be incrementedup to a threshold limit. The threshold limit may be predetermined or configurable. Furtherthe method 400 may comprise, when the frequency counter reaches the threshold limit,decreasing frequency counters associated with the determined 404 pixel 223 in all grid-based representations 200-1, 200-3, 200-4, except the selected 403 grid-based represen-tation 200-2.

Step 407 comprises storing the incremented 406 frequency counter value, associated withthe determined 404 pixel 223 in the selected 403 grid-based representation 200-2 in thedatabase 350. ln some embodiments wherein step 405 has been performed, the measured 405 at leastone location dependent parameter may be stored, associated with the determined 404pixel 223 in the selected 403 grid-based representation 200-2 in the database 350.

Further, in some embodiments, a generated time reference value may be stored associ-ated with the determined 404 pixel 223 in the selected 403 grid-based representation 200- 2.

The method 400 may further comprise decreasing the frequency counter associated withthe determined 404 pixel 223 after a time limit in some embodiments.

Thereby a self-learning map is created in the database 350.

Figure 5 illustrates an embodiment of a system 300 for building a database 350, enablingprediction of a route of a vehicle 100. The database 350 comprises a plurality of grid-based 18 representations 200-1, 200-2, 200-3, 200-4 of a geographical landscape, each grid-basedrepresentation 200-1, 200-2, 200-3, 200-4 comprising a plurality of discrete pixels 211,212, 244 and being associated with a range of directions 210-1, 210-2, 210-3, 210-4.

The system 300 comprises a control unit 310 in the vehicle 100, a geographical positioningdevice 330 and a database 350. The control unit 310 may perform at least some of thepreviously described steps 401-407 according to the method 400 described above andillustrated in Figure 4.

The control unit 310 is configured to observe geographical position of the vehicle 100, e.g.via a positioning unit 330, based on e.g. GPS. Further the control unit 310 is configured todetermine a driving direction 105 of the vehicle 100 at the observed geographical position.The control unit 310 is in addition configured to select one grid-based representation 200-2out of the plurality of grid-based representations 200-1, 200-2, 200-3, 200-4, based on thedetermined driving direction 105. Furthermore, the control unit 310 is also configured todetermine a specific pixel 223 in the selected grid-based representation 200-2, correspond-ing to the observed geographical position. Also, in addition, the control unit 310 is config-ured to increment a frequency counter associated with the determined pixel 223 in the se-lected grid-based representation 200-2, counting an amount of times the vehicle 100 haspassed the pixel 223 in the determined driving direction 105, e.g. within a time period. Fur-thermore, the control unit 310 is additionally configured to store the measured at least onelocation-dependent parameter and the incremented frequency counter value, associatedwith the determined pixel 223 in the selected grid-based representation 200-1, 200-2, 200-3, 200-4 in the database 350.

The control unit 310 may in some embodiments also be configured to measure at least onelocation-dependent parameter at the observed geographical position when driving in thedetermined driving direction 105. The location-dependent parameter may comprise e.g.road slope, curvature, selected driving gear, height, vehicle velocity, etc. The road slopeand/ or height profile may be of interest when an optimal velocity profile is derived to re-duce the fuel consumption. Other attributes of interest to predict may comprise entitiessuch as arrival time, stored energy consumption, driving hazards etc.

The location-dependent parameter may be measured by a sensor and provided to the con- trol unit 310 via a wired or wireless communication interface.

Such sensor may comprise e.g. accelerometer, levelling instrument or other slope sensor based on e.g. laser for measuring road slope. A pressure sensor, barometer, altimeter or 19 other type of elevation meter may measure height profile. The sensor may also comprise acurvature sensor, a speedometer, a gear detector etc., in various different embodiments.

Further the measured at least one location dependent parameter may be stored, associ-ated with the determined pixel 223 in the selected grid-based representation 200-2 in thedatabase 350. ln addition, the control unit 310 may determine the driving direction 105 of the vehicle 100at the observed geographical position based on a heading between the observed geo-graphical position of the vehicle 100 and a previously observed geographical position of thevehicle 100, and wherein the grid-based representation 200-2 having a range of directions210-2 corresponding with said heading, may be selected in some embodiments.

Furthermore, the control unit 310 may generate control signals for the frequency counterassociated with the determined pixel 223 may be incremented up to a threshold limit. Thethreshold limit may be predetermined or configurable. The control unit 310 may also insome embodiments generate control signals for modifying the frequency counter when itreaches the threshold limit, by decreasing frequency counters associated with the deter-mined pixel 223 in all grid-based representations 200-1, 200-3, 200-4, except the selectedgrid-based representation 200-2.

The control unit 310 may additionally via a time measurement instrument like a clock, achronometer, a chronograph or other similar horological instrument determine and gener-ate a time reference value for any determined parameter data such as road slope. Thecontrol unit 310 may also be configured to store the generated time reference value asso-ciated with the determined pixel 223 in the selected grid-based representation 200-2.

Additionally, the control unit 310 may also be configured to generate control signals fordecreasing the frequency counter associated with the determined pixel 223 after a timelimit. Thereby aging of the stored parameter values is enabled.

Furthermore, the control unit 310 may also be configured to calculate and store an averagevalue of the at least one location-dependent parameter, based on a previously stored atleast one location-dependent parameter and the measured at least one location-dependentparameter. ln some embodiments, a weighted mean value of the determined slope may becomputed, giving the currently determined slope a higher weight than the previously storedslope. ln some embodiments, the control unit 310 may be comprised in the vehicle 100. However,in some other alternative embodiments, the control unit 310 may be comprised in a vehicleexternal structure.

The control unit 310 may comprise a processor 520 configured for performing at leastsome of the previously described steps 401-407 according to the method 400, in someembodiments.

Such processor 520 may comprise one or more instances of a processing circuit, i.e. aCentral 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 “processor” maythus represent a processing circuitry comprising a plurality of processing circuits, such as,e.g., any, some or all of the ones enumerated above.

The control unit 310 may further comprise a receiving circuit 510 configured for receiving asignal from the positioning device 330, and/ or the database 350 in different embodiments.

Furthermore, the control unit 310 may comprise a memory 525 in some embodiments. Theoptional 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 em-bodiments, the memory 525 may comprise integrated circuits comprising silicon-basedtransistors. The memory 525 may comprise e.g. a memory card, a flash memory, a USBmemory, a hard disc, or another similar volatile or non-volatile storage unit for storing datasuch as e.g. FlOlVl (Read-Only Memory), PFlOM (Programmable Read-Only Memory),EPROM (Erasable PFlOM), EEPROM (Electrically Erasable PFlOM), etc. in different em-bodiments.

Further, the control unit 310 may comprise a signal transmitter 530. The signal transmitter530 may be configured for transmitting signals to be received by the database 350.

Furthermore, the control unit 310 is configured to predict a vehicle route by using the data-base 350. The database 350 comprises a plurality of grid-based representations 200-1,200-2, 200-3, 200-4 of a geographical landscape, each grid-based representation 200-1,200-2, 200-3, 200-4 comprising a plurality of discrete pixels 211, 212, 244 and beingassociated with a range of directions 210-1, 210-2, 210-3, 210-4. 21 Thus the control unit 310 is configured to observe geographical position of the vehicle 100.Also, the control unit 310 is configured to determine the driving direction 105 of the vehicle100 at the observed geographical position. Further, the control unit 310 is configured toselect one grid-based representation 200-2 out of the plurality of grid-based representa-tions 200-1, 200-2, 200-3, 200-4, based on the determined driving direction 105. ln addi-tion, the control unit 310 is configured to determine a specific pixel 223 in the selected grid-based representation 200-2, corresponding to the observed geographical position. Also, infurther addition, the control unit 310 is configured to predict a next position pixel 211, 212, 244 of the vehicle 100, neighbour to the determined pixel 223 in the selected grid-based representation 200-2, based on a frequency counter value of the neighbour pixels211,212, ..., 244. ln some embodiments, the control unit 310 may in addition be configured to extract at leastone location-dependent parameter, associated with the predicted next position pixel 211,212, 244 in the selected grid-based representation 200-2 from the database 350.

Furthermore, the control unit 310 may also be configured to predict a sequence of nextposition pixels 211, 212, 244 of the vehicle 100, based on frequency counter values ofthe respective neighbour pixel 211, 212, 244. Also, the control unit 310 may be config-ured to select the grid-based representation 200-1, 200-2, 200-3, 200-4, based on the pre-dicted sequence of next position pixels 211, 212, 244 of the vehicle 100.

The control unit 310 may in addition be configured to exclude the neighbour pixel 211, 212, 244 most recently passed by the vehicle 100 when predicting the next position pixel211 , 212, 244 of the vehicle 100.

Additionally, the control unit 310 may be further configured to select driving gear of the ve-hicle 100, based on the extracted at least one location-dependent parameter, in some em-bodiments.

Furthermore, the control unit 310 may also be configured to release vehicle throttle whenthe vehicle 100 is predicted to approach a downhill.

Thereby, an adaptive cruise control of the vehicle 100 may use this information for reduc-ing vehicle velocity in the above described situation, or when e.g. passing a roundabout,without braking the vehicle 100. Thereby energy consumption of the vehicle 100 may bereduced. 22 The previously described steps 401-407 to be performed in the control unit 310 may beimplemented through the one or more processors 520 within the control unit 310, togetherwith computer program product for performing at least some of the functions of the steps401-407. Thus a computer program product, comprising instructions for performing thesteps 401-407 in the control unit 310 may perform the method 400 comprising at leastsome of the steps 401-407 for building a database 350, enabling prediction of the route ofthe vehicle 100, and estimation of the length of the predicted route, when the computerprogram is loaded into the one or more processors 520 of the control unit 310.

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 step401-407 according to some embodiments when being loaded into the one or more proces-sors 520 of the control unit 310. The data carrier may be, e.g., a hard disk, a CD ROM disc,a memory stick, an optical storage device, a magnetic storage device or any other appro-priate medium such as a disk or tape that may hold machine readable data in a non-transitory manner. The computer program product may furthermore be provided as com-puter program code on a server and downloaded to the control unit 310 remotely, e.g., overan Internet or an intranet connection.

Figure 6 illustrates an example of a method 600 according to an embodiment. The flowchart in Figure 6 shows the method 600, for predicting a vehicle route by using a database350. The database 350 comprises a plurality of grid-based representations 200-1, 200-2,200-3, 200-4 of a geographical landscape, each grid-based representation 200-1, 200-2,200-3, 200-4 comprising a plurality of discrete pixels 211, 212, 244 and being associ-ated with a range of directions 210-1, 210-2, 210-3, 210-4.

The route may be predicted based on a database 350, established e.g. by the previouslydescribed method 400 for building a database 350, enabling prediction of a route of a vehi-cle 100.

The vehicle 100 may be any arbitrary kind of means for conveyance, such as a truck, abus, a car, a motorcycle or similar. The vehicle 100 may be driven by a driver, or beautonomous in different embodiments. ln order to correctly be able to predict the vehicle route, the method 600 may comprise anumber of steps 601-608. However, some of these steps 601 -608 may be performed solelyin some alternative embodiments. Further, the described steps 601-608 may be performedin a somewhat different chronological order than the numbering suggests. Steps 606-608 23 may be performed only in some particular embodiments. The method 600 may comprisethe subsequent steps: Step 601 comprises observing geographical position of the vehicle 100.

The current vehicle position may be determined by a geographical positioning device 330,such as e.g. a GPS. However, the current position of the vehicle 100 may alternatively bedetected and registered by the driver of the vehicle 100.

The geographical position may be determined continuously while driving the vehicle 100along the road 110, or at predetermined time intervals in different embodiments.

Step 602 comprises determining a driving direction 105 of the vehicle 100 at the observed601 geographical position.

Step 603 comprises selecting one grid-based representation 200-2 out of the plurality ofgrid-based representations 200-1, 200-2, 200-3, 200-4, based on the determined 602 driv-ing direction 105.

Step 604 comprises determining a pixel 223 in the selected 603 grid-based representation200-2, corresponding to the observed 601 geographical position.

Step 605 comprises predicting a next position pixel 211, 212, 244 of the vehicle 100,neighbour to the determined 604 pixel 223 in the selected 603 grid-based representation200-2, based on a frequency counter value of the neighbour pixels 211, 212, 244.

Thus the next position pixel 211, 212, 244 with the highest frequency counter valuemay be predicted as the next position for the vehicle 100. 24 ln some embodiments, a sequence of next position pixels 211, 212, 244 of the vehicle100 may be iteratively predicted, based on frequency counter values of the respectiveneighbour pixel 211, 212, 244, and wherein grid-based representation 200-1, 200-2,200-3, 200-4 is selected 603, based on the iteratively predicted 605 sequence of next posi-tion pixels 211, 212, 244 of the vehicle 100. ln some such embodiments, the prediction may be iterated until a predetermined or config-urable horizon limit (like e.g. 10 pixels, 20 pixels etc.) is reached.

The iterated prediction may comprise repeating steps 601-605, but using the most recentpredicted next position pixel 211, 212, 244 instead of the current position pixel as inputvalue in step 601. ln some further embodiments, the neighbour pixel 211, 212, 244 most recently passedby the vehicle 100 may be excluded when predicting the next position pixel 211, 212, 244 of the vehicle 100. Thereby, an endless loop predicted of U-turns may be avoided.

Step 606, which only may be performed in some particular embodiments, may compriseextracting at least one location-dependent parameter, associated with the predicted 605next position pixel 211, 212, 244 in the selected 603 grid-based representation 200-2from the database 350, in some embodiments.

Step 607, which only may be performed in some particular embodiments where step 606has been performed, may comprise selecting driving gear of the vehicle 100, based on theextracted 606 at least one location-dependent parameter.

Step 608, which only may be performed in some particular embodiments, may comprisereleasing vehicle throttle when the vehicle 100 is predicted 605 to approach a downhill. Thevehicle 100 may for example be driving uphill and when it is approaching the hill, the vehi-cle throttle may be released, for saving fuel/ energy.

The previously described steps 601-608 to be performed in the control unit 310 may beimplemented through the one or more processors 520 within the control unit 310, togetherwith computer program product for performing at least some of the functions of the steps601-608. Thus a computer program product, comprising instructions for performing thesteps 601-608 in the control unit 310 may perform the method 600 comprising at leastsome of the steps 601 -608 for predicting a route of a vehicle 100, when the computer pro-gram is loaded into the one or more processors 520 of the control unit 310.

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 step601-608 according to some embodiments when being loaded into the one or more proces-sors 520 of the control unit 310. The data carrier may be, e.g., a hard disk, a CD ROM disc,a memory stick, an optical storage device, a magnetic storage device or any other appro-priate medium such as a disk or tape that may hold machine readable data in a non-transitory manner. The computer program product may furthermore be provided as com-puter program code on a server and downloaded to the control unit 310 remotely, e.g., over an Internet or an intranet connection.

The terminology used in the description of the embodiments as illustrated in the accompa-nying drawings is not intended to be limiting of the described methods 400, 600; the controlunit 310; the database 350; the computer program and/ or the vehicle 100, comprising thecontrol unit 310. Various changes, substitutions and/ or alterations may be made, withoutdeparting from invention embodiments as defined by the appended claims.

As used herein, the term "and/ or" comprises any and all combinations of one or more ofthe associated listed items. The term “or“ as used herein, is to be interpreted as a mathe-matical OR, i.e., as an inclusive disjunction; not as a mathematical exclusive OR (XOR),unless expressly stated otherwise. ln addition, the singular forms "a", "an" and "the" are tobe interpreted as “at least one", thus also possibly comprising a plurality of entities of thesame kind, unless expressly stated otherwise. lt will be further understood that the terms"includes", "comprises", "including" and/ or "comprising", specifies the presence of statedfeatures, actions, integers, steps, operations, elements, and/ or components, but do notpreclude the presence or addition of one or more other features, actions, integers, steps,operations, elements, components, and/ or groups thereof. A single unit such as e.g. aprocessor may fulfil the functions of several items recited in the claims. The mere fact thatcertain measures are recited in mutually different dependent claims does not indicate that acombination of these measures cannot be used to advantage. A computer program may bestored/ distributed on a suitable medium, such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but may also be distrib-uted in other forms such as via Internet or other wired or wireless communication system.

Claims

1. A method (400) for building a database (350), enabling prediction of a vehicleroute, wherein the database (350) comprises a plurality of grid-based representations (200-1, 200-2, 200-3, 200-4) of a geographical landscape, each grid-based representation (200-1, 200-2, 200-3, 200-4) comprising a plurality of discrete pixels (211, 212, 244) andbeing associated with a range of directions (210-1, 210-2, 210-3, 210-4), wherein themethod (400) comprises: observing (401) geographical position of a vehicle (100); determining (402) a driving direction (105) of the vehicle (100) at the observed(401) geographical position; selecting (403) one grid-based representation (200-2) out of the plurality of grid-based representations (200-1, 200-2, 200-3, 200-4), based on the determined (402) drivingdirection (105); determining (404) a pixel (223) in the selected (403) grid-based representation(200-2), corresponding to the observed (401) geographical position; incrementing (406) a frequency counter associated with the determined (404) pixel(223) in the selected (403) grid-based representation (200-2), counting an amount of timesthe vehicle (100) has passed the pixel (223) in the determined (402) driving direction (105);and storing (407) the incremented (406) frequency counter value, associated with thedetermined (404) pixel (223) in the selected (403) grid-based representation (200-2) in thedatabase (350).

2. The method (400) according to claim 1, further comprising: measuring (405) at least one location-dependent parameter at the observed (401)geographical position when driving in the determined (402) driving direction (105); andwherein the measured (405) at least one location dependent parameter is stored (407),associated with the determined (404) pixel (223) in the selected (403) grid-based represen-tation (200-2) in the database (350).

3. The method (400) according to any of claim 1 or claim 2, wherein the driving direc-tion (105) of the vehicle (100) at the observed (401) geographical position is determined(402) based on a heading between the observed (401) geographical position of the vehicle(100) and a previously observed (401) geographical position of the vehicle (100), andwherein the grid-based representation (200-2) having a range of directions (210-2) corre-sponding with said heading, is selected (403). 27

4. The method (400) according to any of claims 1-3, wherein the frequency counterassociated with the determined (404) pixel (223) is incremented (406) up to a thresholdlimit; and wherein the method (400) further comprises, when the frequency counterreaches the threshold limit, decreasing frequency counters associated with the determined(404) pixel (223) in all grid-based representations (200-1, 200-3, 200-4), except the se-lected (403) grid-based representation (200-2).

5. The method (400) according to any of claims 1-4, wherein a generated time refer-ence value is stored (407) associated with the determined (404) pixel (223) in the selected(403) grid-based representation (200-2); and wherein the method (400) further comprisesdecreasing the frequency counter associated with the determined (404) pixel (223) after a time limit.

6. The method (400) according to any of claims 2-5, wherein an average value of theat least one location-dependent parameter is calculated and stored (407), based on a pre-viously stored (407) at least one location-dependent parameter and the measured (405) atleast one location-dependent parameter.

7. A control unit (310), for building a database (350), enabling prediction of a vehicleroute, wherein the database (350) comprises a plurality of grid-based representations (200-1, 200-2, 200-3, 200-4) of a geographical landscape, each grid-based representation (200-1, 200-2, 200-3, 200-4) comprising a plurality of discrete pixels (211, 212, 244) andbeing associated with a range of directions (210-1, 210-2, 210-3, 210-4), wherein the con-trol unit (310) is configured to: observe geographical position of a vehicle (100); determine a driving direction (105) of the vehicle (100) at the observed geographi-cal position; select one grid-based representation (200-2) out of the plurality of grid-based rep-resentations (200-1, 200-2, 200-3, 200-4), based on the determined driving direction (105); determine a pixel (223) in the selected grid-based representation (200-2), corre-sponding to the observed geographical position; increment a frequency counter associated with the determined pixel (223) in theselected grid-based representation (200-2), counting an amount of times the vehicle (100)has passed the pixel (223) in the determined driving direction (105); and store the measured at least one location-dependent parameter and the incre-mented frequency counter value, associated with the determined pixel (223) in the selectedgrid-based representation (200-1, 200-2, 200-3, 200-4) in the database (350). 28

8. A method (600) for prediction of a vehicle route by using a database (350), com-prising a plurality of grid-based representations (200-1, 200-2, 200-3, 200-4) of a geo-graphical landscape, each grid-based representation (200-1, 200-2, 200-3, 200-4) compris-ing a plurality of discrete pixels (211, 212, 244) and being associated with a range ofdirections (210-1, 210-2, 210-3, 210-4), wherein the method (600) comprises: observing (601) geographical position of the vehicle (100); determining (602) a driving direction (105) of the vehicle (100) at the observed(601) geographical position; selecting (603) one grid-based representation (200-2) out of the plurality of grid-based representations (200-1, 200-2, 200-3, 200-4), based on the determined (602) drivingdirection (105); determining (604) a pixel (223) in the selected (603) grid-based representation(200-2), corresponding to the observed (601) geographical position; and predicting (605) a next position pixel (211, 212, 244) of the vehicle (100),neighbour to the determined (604) pixel (223) in the selected (603) grid-based representa-tion (200-2), based on a frequency counter value of the neighbour pixels (211, 212, 244).

9. The method (600) according to claim 8, further comprising: extracting (606) at least one location-dependent parameter, associated with thepredicted (605) next position pixel (211, 212, 244) in the selected (603) grid-based rep-resentation (200-2) from the database (350).

10.next position pixels (211, 212, 244) of the vehicle (100) are iteratively predicted (605), The method (600) according to any of claim 8 or claim 9, wherein a sequence of based on frequency counter values of the respective neighbour pixel (211, 212, 244),and wherein grid-based representation (200-1, 200-2, 200-3, 200-4) is selected (603),based on the iteratively predicted (605) sequence of next position pixels (211, 212, 244)of the vehicle (100).

11.(211, 212, 244) most recently passed by the vehicle (100) is excluded when predicting(605) the next position pixel (211 , 212, 244) of the vehicle (100). The method (600) according to any of claims 8-10, wherein the neighbour pixel

12. The method (600) according to any of claims 9-11, further comprising:selecting (607) driving gear of the vehicle (100), based on the extracted (606) at least one location-dependent parameter. 29

13. The method (600) according to any of claims 9-12, further comprising:releasing (608) vehicle throttle when the vehicle (100) is predicted (605) to ap- proach a downhill. 14.comprising a plurality of grid-based representations (200-1, 200-2, 200-3, 200-4) of a geo- A control unit (310) for prediction of a vehicle route by using a database (350), graphical landscape, each grid-based representation (200-1, 200-2, 200-3, 200-4) compris-ing a plurality of discrete pixels (211, 212, 244) and being associated with a range ofdirections (210-1, 210-2, 210-3, 210-4), wherein the control unit (310) is configured to: observe geographical position of the vehicle (100); determine a driving direction (105) of the vehicle (100) at the observed geographi-cal position; select one grid-based representation (200-2) out of the plurality of grid-based rep-resentations (200-1, 200-2, 200-3, 200-4), based on the determined driving direction (105); determine a pixel (223) in the selected grid-based representation (200-2), corre-sponding to the observed geographical position; and predict a next position pixel (211, 212, 244) of the vehicle (100), neighbour tothe determined pixel (223) in the selected grid-based representation (200-2), based on afrequency counter value of the neighbour pixels (211, 212, 244). 15.according to any of claims 1-6 or any of claims 8-13 when the computer program is exe- A computer program comprising program code for performing a method (400, 600) cuted in a control unit (310) according to any of claim 7 or claim 14. 16.2, 200-3, 200-4) of a geographical landscape, each grid-based representation (200-1, 200- A database (350) comprising a plurality of grid-based representations (200-1, 200- 2, 200-3, 200-4) comprising a plurality of discrete pixels (211, 212, 244) and being as-sociated with a range of directions (210-1, 210-2, 210-3, 210-4), wherein the database(350) is configured to store a frequency counter value, associated with a respective pixel(211, 212, 244) in at least one of the grid-based representations (200-1, 200-2, 200-3,200-4). 17.

14. A vehicle (100) comprising a control unit (310) according to any of claim 7 or claim