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    27 SUMMARY Methods (400, 600) and control unit (300) for building a database (320) and for predicting aroute of a vehicle (100), and estimating length of the predicted route. The method (600)comprises determining (601) geographical position of the vehicle (100); detecting (602) acell border (222A) of a cell (222) in a grid-based representation (200) of a landscape, in adatabase (320), corresponding to the geographical position; determining (603) that the ve-hicle (100) is entering the cell (222) at the cell border (222A); extracting (604) a stored driv-ing direction at the cell border (222A) from the database (320); detecting (605) a cell border(232B) of a neighbour cell (232), in the driving direction at the cell border (222A); repeating(606) step (604) and (605); predicting (607) the route of the vehicle (100); and estimating(608) the length of the predicted (607) route by adding an estimated distance through eachcell (211, 212, 244) of the predicted (607) route. (Publ. Fig. 2A)
    公开号:SE1551062A1
    申请号:SE1551062
    申请日:2015-08-11
    公开日:2017-02-12
    发明作者:Sahlholm Per；Ekstrand Oskar；PIVÉN Patrik
    申请人:Scania Cv Ab；
    IPC主号:
    专利说明:

    METHOD AND CONTROL UNIT FOR ESTIMATING LENGTH OF PREDICTED ROUTE TECHNICAL FIELD This document disclose a control unit and methods therein. More particularly, a control unitand methods therein are described, for building a database, enabling prediction of a routeof a vehicle, and estimating the length of the predicted route based on information stored inthe database.
    BACKGROUND Advanced driver functions in vehicles rely on accurate maps from which a prediction maybe made about how the upcoming route of the vehicle will look like.
    A map may be provided by a third party. Such map comprises information that make itpossible to generate a prediction of how the road ahead of the vehicle will look like in termsof road slope. This prediction is of interest since certain functions rely on information aboutthe upcoming road at a further distance than sensors such as radar or cameras can pro-vide. Such functions can be used to save fuel by calculating an optimal velocity profile forthe vehicle. By having such a map many other functions can be realised.
    The maps used today are static, i.e. they are not updated once the vehicle is delivered.Thus vehicle maps may therefore lack coverage in certain countries where accurate mapdata is not available or just on smaller roads which are too uneconomical to map.
    Further, recently built roads may not be comprised on such map. Additionally, some par-ticular environments may be very dynamical such as e.g. mines, building sites, deforesta- tion areas, storage areas in a harbour, a load terminal or similar. lt is thus a problem for a driving control unit in a vehicle to get appropriate map information,corresponding to the geographical environment, in order to make correct predictions of thevehicle route.
    Document DE102010042065 describes a method for combining route network data from adigital map with data of a digital terrain model. The network data represents coordinatesand curvature of the road and the model represents geodetic height information. However,no prediction of the route of the vehicle may be made, in case there is no road on the digi-tal map, or no digital map at all. Further the document does not discuss estimation of routedistance using statistic data.
    Documents US2010114474 and US2010324752 disclose methods for calculating surfaceelevation of the road in relation to distance of the route to be travelled by a vehicle. How-ever, no prediction of the route of the vehicle may be made, in case there is no road on thedigital map, or no digital map at all. Further the documents do not discuss estimation ofroute distance using statistic data.
    Document EP2623932 discloses a method of creating a route of a vehicle using a grid net,where measurements are made at each grid of the net. However, no prediction of the routeof the vehicle may be made, in case there is no road on the digital map, or no digital mapat all.
    Document .JP3966097 discloses a vehicular road surface altitude estimation device thatestimates the altitude of a travel route based on linear interpolation. However, no predictionof the route of the vehicle is made, in case there is no road on the digital map, or no digitalmap at all. lt may thereby be desired to be able to create and continuously update a map and 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, enabling prediction of a route of a vehicle, and estimating the length of thepredicted route. The method comprises establishing a grid-based representation of a geo-graphical landscape, comprising a plurality of cells. Also the method comprises determiningcurrent geographical position of the vehicle. The method further comprises detecting whenthe determined geographical position of the vehicle corresponds to a cell border of a cell inthe established grid-based representation. Further the method also comprises determininga driving direction of the vehicle when passing the cell border for entering the cell. Themethods also comprises storing the determined driving direction, associated with the cellborder of the cell 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 route of a vehicle, and estimation of the length of the predicted route. The control unit is configuredfor establishing a grid-based representation of a geographical landscape, comprising aplurality of cells. Further the control unit is additionally configured for determining currentgeographical position of the vehicle. Also the control unit is further configured for detectingwhen the determined geographical position of the vehicle corresponds to a cell border of acell in the established grid-based representation. The control unit is also configured fordetermining a driving direction of the vehicle when passing the cell border for entering thecell. ln addition the control unit is also configured for storing the determined driving direc-tion, associated with the cell border of the cell in the database.
    According to a third aspect of the invention, this objective is achieved by a method for pre-dicting a route of a vehicle, and estimating length of the predicted route. The method com-prises determining current geographical position of the vehicle. Also the method furthercomprises detecting a stored cell border of a cell in a grid-based representation of a land-scape, comprising a plurality of cells in a database, corresponding to the determined geo-graphical position. Further the method comprises determining that the vehicle is enteringthe cell at the detected cell border of the cell. The method further comprises extracting astored driving direction at the detected cell border of the cell from the database. Themethod additionally also comprises detecting a cell border of a neighbour cell to the cell, inthe direction of the extracted stored driving direction at the detected cell border of the cell.Furthermore, the method also comprises repeating the steps of extracting the stored driv-ing direction and detecting a cell border of a neighbour cell until a condition is fulfilled. Themethod in further addition also comprises predicting the route of the vehicle by creating aseries of linear segments through the cells, bounded by the detected cell borders. Furtherthe method in addition also comprises estimating the length of the predicted route of thevehicle by adding an estimated distance through each cell of the predicted route of the ve-hicle.
    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, and estimating length of thepredicted route. The control unit comprises determining current geographical position of thevehicle. Further the control unit comprises detecting a stored cell border of a cell in a grid-based representation of a landscape, comprising a plurality of cells in a database, corre-sponding to the determined geographical position. The control unit also comprises deter-mining that the vehicle is entering the cell at the detected cell border of the cell. Further thecontrol unit in addition comprises extracting a stored driving direction at the detected cellborder of the cell from the database. Also the control unit comprises detecting a cell border of a neighbour cell to the cell, in the direction of the extracted stored driving direction at thedetected cell border of the cell. The control unit furthermore comprises repeating the stepsof extracting the stored driving direction and predicting the route of the vehicle until a condi-tion is fulfilled. Further, the control unit also comprises predicting the route of the vehicle bycreating a series of linear segments through the cells, bounded by the detected cell bor-ders. The control unit also comprises estimating the length of the predicted route of thevehicle by adding an estimated distance through each cell of the predicted route of the ve-hicle.
    Hereby, thanks to the disclosed aspects, route planning is simplified. A more accurate dis-tance estimation of the road ahead of the vehicle is possible since it is approximated by afirst order polynomial. The use of directions instead of counters allows the statics to beimplemented with 50% less memory usage. Thus 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 a grid-based representation of a geographical Iandscape and di-rections associated with a cell border; Figure 2B illustrates an example of stored driving directions associated with differentcell borders; Figure 2C illustrates an example of stored road slopes associated with different cellborders; Figure 2D illustrates a grid-based representation of a geographical Iandscape and di-rections associated with a cell border; Figure 2E illustrates a grid-based representation of a geographical Iandscape, direc-tions associated with a cell border and a predicted vehicle route; Figure 2F illustrates an example of road slopes when the vehicle is passing three cells of the grid-based representation; Figure 3 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; andFigure 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. Thevehicle 100 may be e.g. a truck, a bus, a van, a car, or any other similar type of vehiclewith or without an attached trailer.
    The vehicle 100 comprises a self-learning map which may be updated continuously, or atcertain predetermined or configurable time intervals. ln some embodiments, such self-learning map system may be implemented by dividing thecoverage area of the electronic map into small, equally sized area elements or cells. Theentire coverage area then form a grid of many small cells. The cells may have any areacovering format such as e.g. quadratic, rectangular, triangular, pentagonal, hexagonal, etc., or a combination of formats.
    When the position of the vehicle 100 is registered inside a specific cell, or at a cell borderof the cell, that cell is marked as a part of a road and various road attributes such as roadslope 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. The offset from the starting point to each segment with constantroad slope may be detected and determined in order to determine where an inclination ordeclination of the road slope starts and ends, respectively. To reconstruct the road profileof the most probable path, the travelled distance through each cell may also be estimated.This produces an output in form of the road slope as a function of the travelled distance. ln order to generate a probable path statistics may be stored, concerning how the vehicle100 has moved through each cell. One possible implementation solution comprises apply-ing 16 counters, four for each possible side of entry into the cell. The four counters mayrepresent a normalised value of the number of times a certain cell side has been exitedthrough by the vehicle 100, or vehicles. The counters may be used to determine the mostprobable next cell to move into and thereby make a prediction of the vehicle route.
    However, a disadvantage of the above described solution based on 16 counters of eachcell is that it is not possible to calculate the travelled distance though each cell of the grid-based representation of the geographical landscape.
    Even if an accurate estimation of the road slope along the upcoming road may be madewith the above solution, there is an uncertainty about where a road slope or hill actuallybegins and ends. Such errors causes the performance of functions using the generatedroute prediction to deteriorate.
    Thus, an improved approach may be applied in order to overcome the above stated disad-vantages. ln some embodiments, memory storage entities may be used to store informa-tion about the most probable entry/ exit direction of each cell border of each cell. Therebymemory may be saved and a more accurate distance estimation may be made. This isachieved by storing a unique direction for each possible side of entry of the respective cell.This may be visualised as an arrow, pointing in the most likely direction of travel throughthe cell, as will be further discussed and illustrated in Figure 2A.
    Since the road slope is direction dependent, the slope may be stored for each side of entryof each respective cell. The different attributes of a cell is illustrated in Figure 2A. The dis-closed solution may produce a more accurate approximation of the travelled distancethrough the cell by using e.g. linear approximation.
    The current geographical position and driving direction of the vehicle 100 is used as aninitial value to start the prediction algorithm. lf there is previously recorded information reg-istered at the corresponding cell, the direction may be used to compute the trajectorythrough the cell. The point where the straight line is intersecting the border of the cell maybe used as a starting point for the following cell. Cells may be traversed in this manner untilthe desired length of the horizon is reached or there are no more data available as will befurther illustrated and discussed in Figure 2E and Figure 2F.
    Thereby a more accurate distance estimation of the road ahead of the vehicle 100 is pos-sible since it may be approximated by a first order polynomial. The use of directions at thecell borders instead of counters allows the statics to be implemented with 50% less mem-ory usage.
    Figure 2A discloses a grid-based representation 200 of a horizontal geographical land-scape, i.e. the road or other driving environment of the vehicle 100. The grid-based repre-sentation 200 comprises a plurality of cells 211, 212, 213, 214, 221, 222, 223, 224, 231,232, 233, 234, 241, 242, 243, 244.
    The vehicle 100 is currently in its way to enter one cell 222 in the grid-based representation200, at a cell border 222A. The cell 222 has, when the cells 211, 212, 244 are quadraticor rectangular, four cell borders; 222A, 222B, 222C and 222D.
    A driving direction is stored at each cell border 222A, 222B, 222C and 222D of the cell 222.Thereby a driving direction is determined and stored with an angle u, ß, y, ö to a reference,such as the respective cell border 222A, 222B, 222C and 222D.
    Thus the driving direction of the vehicle 100 may be stored when entering the cell border222A, 222B, 222C and 222D of the cell 222. The driving direction may thus be updatedeach time the vehicle 100 enter the respective cell 211, 212, 244. The currently de-tected driving direction of the vehicle 100 when entering the cell 211, 212, 244 may becombined with the previously stored driving directions (if any) and then stored. The combi-nation may be made by calculating a mean average value, or weighted average value ac-cording to different embodiments.
    Once the area represented by a cell 211, 212, 244 has been traversed by the vehicle100, the cell 211, 212, 244 may be assigned a value and may hence be seen as visited.When making a prediction of the future movement from the cell 211, 212, 244, the cells 211 , 212, 244 on the edges of the first cell 211, 212, 244 may be studied in order todetermine which of those has been visited before. By doing so a decision may be madeconcerning which cell 211, 212, 244 is the most probable one to be the next cell 211,212, 244 to visit. lf a cell 211, 212, 244 only has two additional neighbour cells 211,212, 244 that have been visited next to itself, it is straightforward to determine the mostprobable path. This is the case since one of the two neighbouring cells 211, 212, 244represent the previous cell 211, 212, 244 and the other one the probable next cell 211,212, 244 to visit. The most probable move in this case is thus to exit the current cell211, 212, 244 into the neighbouring cell 211, 212, 244 that the vehicle 100 did notenter through. However when noisy positioning signals, highways with multiple lanes andcrossings are a reality in real world road networks, more sophisticated ways of predictingthe most probable path may be an advantage. To be able to predict the future path of thevehicle 100, statistics thus may be stored in order to generate a prediction of the roadahead for the vehicle 100.
    The statistics from previous passes in a cell 211, 212, 244 may be implemented in theform of arrows, pointing in the most common direction of movement through each cell 211,212, 244. One direction through a cell 211, 212, 244 may not be enough for predict-ing the upcoming road, since it is possible to enter a cell 211, 212, 244 from any of itseight surrounding neighbour cells 211, 212, 244. To reduce the complexity and memoryusage, a simplification regarding the transition between two cells 211, 212, 244 may bemade in some embodiments. Such simplification may state that it is only possible to enter acell 211, 212, 244 from one of the four neighbour cells 211, 212, 244 located in theNorth, South, East and West directions, in an example with quadratic cells 211, 212, 244 in a grid-based representation 200 oriented in compass direction.
    The simplification may be reasonable in some embodiments since in practical applicationsit would be very unlikely to enter a cell 211, 212, 244 through a corner of the cell 211,212, 244 because the area of contact is infinitely small. lf two following position sam-ples are located in the cells 211, 212, 244 diagonally next to each other, a linear inter-polation may be made and any cells 211, 212, 244 that are intersected are consideredas a part of the road. This linear interpolation may be conducted in order to find all cells211, 212, 244 that have been crossed by the vehicle 100. To add some robustness tothe system, the case where the interpolation gives more crossed cells 211, 212, 244than possible, based on the maximum speed of the vehicle 100, may be disregarded ac-cording to some embodiments. The directions may be identified based on from which sideA, B, C, D the cell 211, 212, 244 was entered.
    This gives the individual cell 211, 212, 244 the theoretical possibility to represent a sec-tion of a unidirectional road, a semi directional road, a four-way intersection or similar situa-tion.
    Theoretically a wide variety of road attributes can be associated with certain road seg-ments, i.e. cells 211, 212, 244 stored in the map, i.e. the grid-based representation 200of the reality. Such attributes may be e.g. speed limits, curvature, road slope, height profileand/ 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.
    Such road slope and/ or height profile may either be generated from stored heights in amap database or by integrating the road slope over a distance in different embodiments.When a map containing altitude information is built while driving, the system will suffer frominaccuracies such as bias errors in the height signal from a navigation system in the vehi-cle 100. This may be overcome by storing the road grade instead of the height profile,however an additional issue arises. The slope of a road segment is dependent of the direc-tion of movement while the height is not. The road slope may therefore be combined withan identifier of its associated direction. The extra data may be stored for every cells 211,212, 244 and therefore may be minimised in order to reduce memory storage volume. Asimplification similar to the one concerning the directions may be made that limits the dif-ferent road slopes to one in each of the North, South, East and West directions, in someembodiments.
    Figure 2B illustrates an example of how different driving directions may be stored associ-ated with cell borders A, B, C, D of different cells 211, 212, 244 of the grid-based repre-sentation 200.
    The illustrated examples are non-limiting and merely comprises some arbitrary examples.Furthermore, road slope direction and/ or road slope values may be determined and storedassociated with cell borders A, B, C, D of different cells 211, 212, 244 of the grid-based representation 200, as illustrated and exemplified in Figure 2C.
    Figure 2D illustrates the grid-based representation 200, where the driving direction of eachcell border along a predicted route of the vehicle 100 is illustrated.
    The purpose of the system developed is to predict how the road in front of a moving vehicle100 looks like. The desired output may be a prediction of the profile of the upcoming road.This implies that the system may estimate or predict how the route that will be driven by thevehicle 100 will most probably look like. A system module may be used to generate a pre-diction of the road ahead of the vehicle 100 in some embodiments. The core of the systemis an algorithm that determines how the vehicle 100 will most likely navigate between thebuffered cells 211, 212, 244 in the memory or database. With this information the dis-tance of a predicted path can be estimated and the registered road slope can be associ-ated to the predicted path. Since each of the four directions stored in a cell 211, 212, 244 at each cell border A, B, C, D may be associated with a road slope, the road profilecan be extracted together with the direction of the cell 211, 212, 244. lf it is possible topredict a 2 dimensional road, then the road grade profile may also automatically be deter-mined for the prediction, given that the stored road grade is available. ln the magnified cell 222, the entrance point 250 of the vehicle 100 into the cell 222 is de-termined. By assuming that the vehicle 100 is passing the cell 222 in the direction associ-ated with the cell border 222A through the cell, starting from the entrance point 250, theexit point 260 may be determined. lt is to be noted that the exit point 260 also is the en-trance point into the predicted next cell 232 of the route. The above procedure may then berepeated for a plurality of cells 211 , 212, 244 in order to predict the vehicle route.
    Further, the distance passed in each cell 211, 212, 244, like in cell 222 may be ap-proximated. As the entrance point 250, the exit point 260 and the lengths of the cell sidesA, B, C, D are known, the distances C1 and C2 are also known. Thus the distance H maybe computed by using Pythagorean Theorem.
    An issue that may arise when attempting to predict a road profile is that of creating an ac-curate distance estimation to associate the road grade to. lt may in some embodiments beimportant for a predicted hill to not have an offset in terms of distance, since this may limitthe performance of the functions using the output. The most basic approach to calculatethe distance of the horizon is to simply approximate the trajectory with a series of straightlines between the midpoints of the visited cells 211, 212, 244. This yields an uncertaindistance estimation since some cells 211, 212, 244 are traversed shorter than the cellwidth and some longer. A more accurate approach may be to calculate the distance thatthe vehicle 100 actually travelled through a cell 211, 212, 244 and store the distance inthe database. This value may then be incrementally updated every time the cell 211, 212, 244 is traversed. 11 An issue related to this method is that the stored length in some cases may be differentdepending on where the cell 211, 212, 244 is entered. This would for example be thecase if a cell 211, 212, 244 is located across a multi-lane highway where the highwaycrosses the cell 211, 212, 244 at a 45 degree angle. Then the stored distance wouldvary depending on which lane was used. The approach actually implemented is utilisingthe stored direction statistics. Given an entry point in the cell 211, 212, 244 as well asthe most likely direction of travel, the exit as well as the distance of travel may be estimated using a linear approximation as shown in Figure 2D.
    A difficulty related to creating the system may be developing an algorithm that can accu-rately create a predicted road segment from the stored data at the same time as being ro-bust to errors in stored directions. The fundamental idea is to use the latest known positionand bearing sample of the vehicle 100 as a starting point for creating a horizon from thestored cell information. This may be done by extending a straight line from the original posi-tion with the current bearing. When this line crosses into a new cell 211, 212, 244 thestored data in the new cell 211 , 212, 244 is extracted and the directional information forthe specific entry side may be used to give the line a new direction. The line will then beextended until crossing a new cell border A, B, C, D and the procedure will be repeated. ln this way a long piecewise linear road prediction may be created. The corresponding roadgrade data may be extracted at the same time in some embodiments and the distance ineach cell 211, 212, 244 along the route of the vehicle 100 may be calculated. The pro-cedure of creating a predicted road segment is displayed in Figure 2E. lt may be noted that no starting point of the directions is stored but only the directionsthemselves, associated with the entrance cell border A, B, C, D. The arrows on the lefthand side in Figure 2E are all placed in the middle of their respective entry side A, B, C, Dfor illustrative purposes. The entry point 250 of a cell 211, 212, 244 may be determinedfrom where the horizon being generated intersects the cell 211, 212, 244 boundary.The stored direction may then be used to make the horizon continue from this point andfind the next exit. The horizon generation may then proceed until one of three events oc-curs: a predefined maximum length of the horizon is obtained; the algorithm cannot findany probable continuation of the predicted road and/ or the predicted road hits the edge ofthe available buffer around the vehicle 100. 12 The case described above is an embodiment where all necessary information is stored asexpected. This is however not always the case since an offset in the starting point of thealgorithm or disturbances in the recorded directions might result in a predicted horizoncrossing into a previously not visited cells 211, 212, 244 and consequently not beingable to find the necessary directional data. lnstead of terminating the prediction and con-cluding that a longer prediction cannot be achieved, some alternative approaches may beused to increase the robustness and predict longer horizons, according to some embodi-ments.
    The first solution to the problem may be to investigate an alternative exit from the last validcell 211, 212, 244. Depending on the direction data stored of the last valid cell 211,212, 244, an alternative candidate cell 211, 212, 244 may be chosen and investi-gated. This is generally the cell 211, 212, 244 closest to the expected exit point 260 ofthe last valid cell 211 , 212, 244. lf the data required to proceed with the horizon genera-tion can be found in the alternate cell 211, 212, 244 the generated horizon may bemodified to enter into this cell 211, 212, 244 instead and the horizon generation contin-ues in the original manner from the alternative cell 211, 212, 244. This alternative em-bodiment may catch the most cases when the horizon segment is terminated to early. lf theattempt is unsuccessful, a pattern of alternative cells 211, 212, 244 may be sequentiallyconsidered as candidates to be used in the horizon. Three candidate cells 211, 212, 244 may be evaluated if the first choice of next cells 211, 212, 244 for the horizon isunavailable. The first choice of next cells 211, 212, 244 along with the three candidatesmay be described in turn in the list below. 1. The cell 211, 212, 244 that shares the cellborder A, B, C, D where the exit point 260 is calculated to be located. 2. The cell 211, 212, 244 that is located alongside the last valid cell 211, 212, 244 and is the closest tothe calculated exit point 260. 3. The cell 211, 212, 244 that is located alongside the firstchoice of next cell 211, 212, 244 and is closest to the exit point 260. 4. The other cell211 , 212, 244 that is located alongside the first choice of next cell 211, 212, 244 butnot is the closest to the exit point 260. A complementary approach according to yet an al-ternative embodiment, in order to find the problem of finding a longer horizon may be tobacktrack the last steps in the found horizon and look for a different possible route. Thisapproach may be illustrated as a decision tree were different pixels are represented bynodes which may be traversed in order to achieve the longest horizon. The purpose of thisapproach is to eliminate the case when the found horizon represents a dead end. The rea-son for such behaviour may be an actual dead end or be caused by incorrect data stored inthe map. This may be the case when the vehicle 100 approaches e.g. a T- or Y-junction. lfboth of the roads are visited several times each the directional information might indicate 13 that the most probable direction of travel lies in the middle of the two roads where thereactually is no road. Normally in this case the cell 211, 212, 244 that lies between thetwo roads has not been not visited and the algorithm will chose one of the two real roads.However if a cell 211, 212, 244 that lies outside the two roads has been assigned avalue due to incorrect positional data being stored earlier a "phantom road" might be de-tected according to some embodiments. That is a road that does not exist in reality. lf thisnon actual road only has a few cells 211 , 212, 244 registered in its direction, it would bepossible to step back a few cells 211, 212, 244 to find an alternate road which in thiscase would be one of the two real roads in the crossing. ln order to make the specific in-cell calculations standardised a standard cell 211, 212, 244 may be used for all opera-tions on this level. This means that independent of the side A, B, C, D on which the cell211, 212, 244 is entered, the calculations within the cell 211, 212, 244 are con-ducted in the same manner. The entry side A, B, C, D of the cell 211, 212, 244 is de-fined as the base of the cell 211, 212, 244.
    From this side A, B, C, D, a relative coordinate system may be defined in which the entryposition 250 and exit position 260 are defined. A relative angle d, ß, y, ö defined from thebase line may be used to describe the direction of travel. This makes it possible to calcu-late a distance through the cell 211, 212, 244 and determine the exit point 260 of thecell 211, 212, 244 independently of the entry side A, B, C, D of the cell 211, 212, 244. ln order to integrate this relative cell 211, 212, 244 with its corresponding valuesand the calculations in the real map, a transformation from the absolute coordinates intorelative coordinates is necessary. The transformation may be seen as a simple rotation ofthe cell 211, 212, 244. lt is rotated so that the entry side A, B, C, D of the cell 211, 212, 244 is the base. Calculations are conducted and the results is transformed back intoabsolute values and stored in that format.
    Figure 2F illustrates an example of the topographical differences the vehicle 100 will ex-perience when passing three cells 214, 224 and 223, 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 3 presents a vehicle interior, according to an embodiment, illustrating an example ofhow the previous scenario may be perceived by the driver of the vehicle 100 when situatedat any arbitrary position along the route. 14 A control unit 310 may be configured for predicting a route for the vehicle 100, from a cur-rent position of the vehicle 100, to a destination. The control unit 310 may comprise, or beconnected to a database 320, which database 320 may comprise data associated withgeographical positions. ln the illustrated embodiment, the control unit 310 and the data-base 320 are comprised within the vehicle 100. However, in other embodiments, anotherplacing of the control unit 310 and/ or the database 320 may be made, such as for example in a vehicle external structure, accessible via a wireless communication interface.
    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 a positioning device330 in the vehicle 100, which may be based on a satellite navigation system such as theNavigation Signal Timing and Ftanging (Navstar) Global Positioning System (GPS), Differ-ential GPS (DGPS), Galileo, GLONASS, or the like.
    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 340-1, 340-2, 340-3, 340-4. ln this example, four satellites 340-1, 340-2, 340-3, 340-4 are depicted, but this is merely an example. More than four satel-lites 340-1, 340-2, 340-3, 340-4 may be used for enhancing the precision, or for creatingredundancy. The satellites 340-1, 340-2, 340-3, 340-4 continuously transmit informationabout time and date (for example, in coded form), identity (which satellite 340-1, 340-2,340-3, 340-4 that broadcasts), status, and where the satellite 340-1, 340-2, 340-3, 340-4are situated at any given time. The GPS satellites 340-1, 340-2, 340-3, 340-4 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 340-1,340-2, 340-3, 340-4 distinguished from the others' information, based on a unique code foreach respective satellite 340-1, 340-2, 340-3, 340-4. 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 340-1, 340-2, 340-3, 340-4 to reach the positioning device 330. As the radio sig-nals travel at the speed of light, the distance to the respective satellite 340-1, 340-2, 340-3,340-4 may be computed by measuring the signal propagation time.
    The positions of the satellites 340-1, 340-2, 340-3, 340-4 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 340-1, 340-2,340-3, 340-4 through triangulation. For determination of altitude, signals from four satellites340-1, 340-2, 340-3, 340-4 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. 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, may be dis-played on an interface unit. The interface unit may comprise a dashboard, a screen, a dis-play, or any similar device.
    Figure 4 illustrates an example of a method 400 according to an embodiment. The flowchart in Figure 4 shows the method 400, for building a database 320, enabling prediction ofa route of a vehicle 100, and estimating the length of the predicted route.
    The vehicle 100 may be any arbitrary kind of means for conveyance, such as a truck, abus, a car, or similar. The vehicle may be driven by a driver, or be autonomous in differentembodiments. ln order to correctly be able to build the database 320, 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 405-406. Further, the described steps 401-407 may be performed in a somewhat different chronological order than the numberingsuggests. Step 402 may be performed before step 401 for example in some embodiments.The method 400 may comprise the subsequent steps: 16 Step 401 comprises establishing a grid-based representation 200 of a geographical land-scape, comprising a plurality of cells 211, 212, 244.
    Step 402 comprises determining the current 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.
    Step 403 comprises detecting when the determined 402 geographical position of the vehi-cle 100 corresponds to a cell border 222A of a cell 222 in the established 401 grid-basedrepresentation 200.
    Step 404 comprises determining a driving direction of the vehicle 100 when passing thecell border 222A for entering the cell 222.
    Step 405 which may be performed only in some particular embodiments, comprises de-termining a slope associated with the cell border 222A.
    The slope associated with the cell border 222A may be e.g. an average slope of the cell222, an average slope of the cell border 222A, or the determined slope at the current seg-ment of the cell border 222A, in different embodiments.
    Furthermore, according to some embodiments, it may be checked if there is any previouslystored slope associated with the cell border 222A of the cell 222 in the database 320, and ifit is, the previously stored value or values may be combined with the determined slope andstored associated with the cell border 222A of the cell 222 in the database 320. in somesuch embodiments, the combination of the previously stored slope and the currently de-termined 405 slope may be made by computing a weighted mean value, giving the cur-rently determined 404 slope a higher weight than the previously stored slope.
    Step 406 which may be performed only in some particular embodiments, comprises check-ing if there is any previously stored driving direction associated with the cell border 222A ofthe cell 222 in the database 320, and if it is, combining the previously stored driving direc-tion and the determined 404 driving direction, and wherein the combined driving direction isstored 407 associated with the cell border 222A of the cell 222 in the database 320. 17 The combination of the previously stored driving direction and the currently determined 404driving direction may be made by computing a weighted mean value, giving the currentlydetermined 404 driving direction a higher weight than the previously stored driving direc- tion, in some embodiments.
    Step 407 comprises storing the determined driving direction, associated with the cell bor-der 222A of the cell 222 in the database 320.
    Figure 5 illustrates an embodiment of a system 500 for building a database 320, enablingprediction of a route of a vehicle 100, and estimation of the length of the predicted route.
    The system 500 comprises a control unit 310 in a vehicle 100, and a geographical position-ing device 330 and a database 320. 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 for building a database 320 and thereby enable predic-tion of a route of a vehicle 100, and estimation of the length of the predicted route. Thecontrol unit 310 is further configured for establishing a grid-based representation 200 of ageographical landscape, comprising a plurality of cells 211, 212, 244. Also, the controlunit 310 is additionally configured for determining current geographical position of the vehi-cle 100. ln addition, the control unit 310 is also configured for detecting that the determinedgeographical position of the vehicle 100 corresponds to a cell border 222A of a cell 222 inthe established grid-based representation 200. Further, the control unit 310 is configuredfor determining a driving direction of the vehicle 100 when passing the cell border 222A forentering the cell 222. Also, the control unit 310 is configured for storing the determineddriving direction, associated with the cell border 222A of the cell 222 in the database 320. ln some embodiments, the control unit 310 may be further configured for determining aslope and/ or other road attribute related to the road, associated with the cell border 222A.
    Further, according to some embodiments, the control unit 310 may also be configured forstoring the determined slope associated with the cell border 222A of the cell 222 in thedatabase 320. ln some embodiments, a weighted mean value of the determined slope maybe computed, giving the currently determined slope a higher weight than the previouslystored slope. 18 The control unit 310 may further be configured for checking if there is any previously storeddriving direction associated with the cell border 222A of the cell 222 in the database 320,and if it is, combining the previously stored driving direction and the determined drivingdirection, and wherein the combined driving direction is stored associated with the cell bor-der 222A of the cell 222 in the database 320.
    Also, the control unit 310 may be configured for combining the previously stored drivingdirection and the currently determined driving direction by computing a weighted meanvalue, giving the currently determined driving direction a higher weight than the previouslystored driving direction, in some embodiments. 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 310 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. ROIVI (Read-Only Memory), PROM (Programmable Read-Only Memory), 19 EPFšOM (Erasable PROM), EEPROM (Electrically Erasable PROM), 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 320.
    Furthermore, the control unit 310 is configured for predicting a route of a vehicle 100, andestimating length of the predicted route. Further the control unit 310 is configured for de-termining current geographical position of the vehicle 100. Also the control unit 310 is con-figured for detecting a stored cell border 222A of a cell 222 in a grid-based representation200 of a landscape, comprising a plurality of cells 211, 212, 244 in a database 320,corresponding to the determined geographical position. ln addition, the control unit 310 isfurther configured for determining when the vehicle 100 is entering the cell 222 at the de-tected cell border 222A of the cell 222. Also, the control unit 310 is configured for extractinga stored driving direction at the detected cell border 222A of the cell 222 from the database320. Furthermore, the control unit 310 is also configured for detecting a cell border 232B ofa neighbour cell 232 to the cell 222, in the direction of the extracted stored driving directionat the detected cell border 222A of the cell 222. The control unit 310 is furthermore config-ured for repeating the steps of extracting the stored driving direction and predicting theroute of the vehicle 100 until a condition is fulfilled. Also, the control unit 310 is configuredfor predicting the route of the vehicle 100 by creating a series of linear segments throughthe cells 211, 212, 244, bounded by the detected cell borders 222A, 232B. The controlunit 310 is configured for estimating the length of the predicted route of the vehicle 100 byadding an estimated distance through each cell 211, 212, 244 of the predicted route ofthe vehicle 100.
    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 320, enabling prediction of a route of avehicle 100, and estimation of the length of the predicted route, 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 step 401-407 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., over an 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 route of a vehicle 100, and esti-mating length of the predicted route.
    The route may be predicted based on a database 320, established e.g. by the previouslydescribed method 400 for building a database 320, enabling prediction of a route of a vehi-cle 100, and estimating the length of the predicted route.
    The vehicle 100 may be any arbitrary kind of means for conveyance, such as a truck, abus, a car, or similar. The vehicle may be driven by a driver, or be autonomous in differentembodiments. 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. Step 602 maybe performed before step 601 for example in some embodiments. The method 600 maycomprise the subsequent steps: Step 601 comprises determining current 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.
    Step 602 comprises detecting a stored cell border 222A of a cell 222 in a grid-based rep-resentation 200 of a landscape, comprising a plurality of cells 211, 212, 244 in a data-base 320, corresponding to the determined 601 geographical position. 21 Step 603 comprises determining that the vehicle 100 is entering the cell 222 at the de-tected 602 cell border 222A of the cell 222.
    Step 604 comprises extracting a stored driving direction at the detected 602 cell border222A of the cell 222 from the database 320.
    Step 605 comprises detecting a cell border 232B of a neighbour cell 232 to the cell 222, inthe direction of the extracted 604 stored driving direction at the detected cell border 222Aof the cell 222.
    Step 606 comprises repeating the steps of extracting 604 the stored driving direction anddetecting 605 a cell border 232B of a neighbour cell 232 until a condition is fulfilled.
    Step 607 comprises predicting the route of the vehicle 100 by creating a series of linearsegments through the cells 211, 212, 244, bounded by the detected 605 cell borders222A, 232B.
    Step 608 comprises estimating the length of the predicted 607 route of the vehicle 100 byadding an estimated distance through each cell 211, 212, 244 of the predicted 607route of the vehicle 100. ln some embodlments, the estimation of the length of the predicted 607 route is performedby estimating a point of entrance 250 and a point of exit 260 of each cell 211, 212, 244along the predicted 607 route, wherein the point of exit is situated in the extracted 604 driv-ing direction associated with the cell border 222A of the point of entrance 250 of the cell222, calculating the length of the distance between the point of entrance 250 and the pointof exit 260 for each of the cells 211, 212, 244, and adding the calculated lengths toeach other.
    The driver of the vehicle 100 may be informed about the estimated 608 length of the pre-dicted 607 route via an app, presented e.g. in a mobile device of the driver such as a mo-bile phone, computer, computer tablet or similar device.
    The information may however be provided to the driver via a display in the vehicle 100, viaa loud speaker in the vehicle 100 or similar device in different embodiments. 22 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, and estimating length ofthe predicted route, when the computer program is loaded into the one or more processors520 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., overan Internet or an intranet connection.
    The terminology used in the description of the embodiments as illustrated in the accompa-nylng drawings is not intended to be limiting of the described methods 400, 600; the controlunit 310; the computer program and/ or the vehicle 100, comprising a control unit 310.Various changes, substitutions and/ or alterations may be made, without departing frominvention 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 OFl (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 a 23 combination 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.
    权利要求:
    Claims (10)
    [1] 1. A method (400) for building a database (320), enabling prediction of a route of avehicle (100), and estimating the length of the predicted route, wherein the method (400)comprises: establishing (401) a grid-based representation (200) of a geographical landscape,comprising a plurality of cells (21 1 , 212, 244); determining (402) current geographical position of the vehicle (100); detecting (403) when the determined (402) geographical position of the vehicle(100) corresponds to a cell border (222A) of a cell (222) in the established (401) grid-basedrepresentation (200); determining (404) a driving direction of the vehicle (100) when passing the cellborder (222A) for entering the cell (222); and storing (407) the determined driving direction, associated with the cell border(222A) of the cell (222) in the database (320).
    [2] 2. The method (400) according to claim 1, further comprising: determining (405) a slope associated with the cell border (222A); and wherein the determined (405) slope associated with the cell border (222A) isstored (407) associated with the cell border (222A) of the cell (222) in the database (320).
    [3] 3. The method (400) according to any of claim 1 or claim 2, further comprising: checking (406) if there is any previously stored driving direction associated withthe cell border (222A) of the cell (222) in the database (320), and if it is, combining the pre-viously stored driving direction and the determined (404) driving direction, and wherein thecombined driving direction is stored (407) associated with the cell border (222A) of the cell(222) in the database (320).
    [4] 4. The method (400) according to claim 3 wherein the combination of the previouslystored driving direction and the currently determined (404) driving direction is made bycomputing a weighted mean value, giving the currently determined (404) driving direction ahigher weight than the previously stored driving direction.
    [5] 5. A control unit (310), for building a database (320), enabling prediction of a route ofa vehicle (100), and estimation of the length of the predicted route, wherein the control unit(310) is configured for: establishing a grid-based representation (200) of a geographical landscape, com-prising a plurality of cells (21 1, 212, 244); determining current geographical position of the vehicle (100); detecting that the determined geographical position of the vehicle (100) corre-sponds to a cell border (222A) of a cell (222) in the established grid-based representation(200); determining a driving direction of the vehicle (100) when passing the cell border(222A) for entering the cell (222); and storing the determined driving direction, associated with the cell border (222A) ofthe cell (222) in the database (320).
    [6] 6. A method (600) for predicting a route of a vehicle (100), and estimating length ofthe predicted route, wherein the method (600) comprises: determining (601) current geographical position of the vehicle (100); detecting (602) a stored cell border (222A) of a cell (222) in a grid-based repre-sentation (200) of a landscape, comprising a plurality of cells (211, 212, 244) in a data-base (320), corresponding to the determined (601) geographical position; determining (603) that the vehicle (100) is entering the cell (222) at the detected(602) cell border (222A) of the cell (222); extracting (604) a stored driving direction at the detected (602) cell border (222A)of the cell (222) from the database (320); detecting (605) a cell border (232B) of a neighbour cell (232) to the cell (222), inthe direction of the extracted (604) stored driving direction at the detected (605) cell border(222A) of the cell (222); repeating (606) the steps of extracting (604) the stored driving direction and de-tecting (605) a cell border (232B) of a neighbour cell (232) until a condition is fulfilled; predicting (607) the route of the vehicle (100) by creating a series of linear seg-ments through the cells (211, 212, 244), bounded by the detected (605) cell borders(222A, 232B); and estimating (608) the length of the predicted (607) route of the vehicle (100) byadding an estimated distance through each cell (211, 212, 244) of the predicted (607)route of the vehicle (100).
    [7] 7. The method (600) according to claim 6, wherein the estimation (608) of the lengthof the predicted (607) route is performed by estimating a point of entrance (250) and apoint of exit (260) of each cell (211, 212, 244) along the predicted (607) route, whereinthe point of exit is situated in the extracted (604) driving direction associated with the cellborder (222A) of the point of entrance (250) of the cell (222), calculating the length of the 26 distance between the point of entrance (250) and the point of exit (260) for each of the cells(211, 212, 244), and adding the calculated lengths to each other.
    [8] 8. A control unit (310), for predicting a route of a vehicle (100), and estimating lengthof the predicted route, wherein the control unit (310) is configured for: determining current geographical position of the vehicle (100); detecting a stored cell border (222A) of a cell (222) in a grid-based representation(200) of a landscape, comprising a plurality of cells (211, 212, 244) in a database (320),corresponding to the determined geographical position; determining that the vehicle (100) is entering the cell (222) at the detected cellborder (222A) of the cell (222); extracting a stored driving direction at the detected cell border (222A) of the cell(222) from the database (320); detecting a cell border (232B) of a neighbour cell (232) to the cell (222), in thedirection of the extracted stored driving direction at the detected cell border (222A) of thecell (222); repeating the steps of extracting the stored driving direction and predicting theroute of the vehicle (100) until a condition is fulfilled; predicting the route of the vehicle (100) by creating a series of linear segmentsthrough the cells (211, 212, 244), bounded by the detected cell borders (222A, 232B);and estimating the length of the predicted route of the vehicle (100) by adding an esti-mated distance through each cell (211, 212, 244) of the predicted route of the vehicle(100).
    [9] 9. A computer program comprising program code for performing a method (400, 600)according to any of claims 1-4 or any of claims 6-7 when the computer program is exe-cuted in a control unit (310) according to any of claims 5 or 8.
    [10] 10. A vehicle (100) comprising a control unit (310) according to any of claims 5 or 8.
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    同族专利:
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    US20180372501A1|2018-12-27|
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    WO2017026936A1|2017-02-16|
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    US10739143B2|2020-08-11|
    EP3335008B1|2021-08-18|
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    优先权:
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    US15/747,101| US10739143B2|2015-08-11|2016-06-30|Methods and control units for building a database and for predicting a route of a vehicle|
    PCT/SE2016/050670| WO2017026936A1|2015-08-11|2016-06-30|Methods and control units for building a database and for predicting a route of a vehicle|
    KR1020187006045A| KR102046028B1|2015-08-11|2016-06-30|Method and control unit for building a database and predicting the route of the vehicle|
    EP16835539.4A| EP3335008B1|2015-08-11|2016-06-30|Methods and control units for building a database and for predicting a route of a vehicle|
    BR112018000985A| BR112018000985A2|2015-08-11|2016-06-30|methods and control units for building a database and predicting a vehicle route|
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