# Patent

## Title
Method for updating a digital road map

## Abstract

Methods are provided for updating a digital road map in which road segments having segment lengths are stored. At least two vehicles are equipped with at least one respective onboard unit generating position readings. If a first set of position readings of a first onboard unit can be associated with a road segment, a first path length is determined based on the position readings of the first set. If a second set of position readings of a second onboard unit can be associated with the road segment, a second path length is determined based on the position readings of the second set. An average path length is calculated based on at least the first and second path lengths. If the average path length deviates from the stored segment length, the average path length is stored as an updated stored segment length of this road segment in the digital road map.

## Background
CROSS-REFERENCE TO RELATED APPLICATION(S)
This application claims priority to European Patent Application No. 12 180 293.8, filed on Aug. 13, 2012, the entirety of which is incorporated by reference herein.
BACKGROUND
1. Technical Field
The present application relates to a method for updating a digital road map which represents a road system and in which road segments having segment lengths are stored.
2. Background Art
Digital road maps are required in road toll systems, for example, which are based on a map matching process. In such systems, vehicles carry onboard units (OBUs), which continually generate readings of their own positions, so as to compare these to the digital road map and determine the respective nearest road segments as having been driven. The segment lengths of the driven road segments are then added so as to calculate and thereafter impose tolls on the length of the path traveled by the vehicle.
So as to generate the position readings, an OBU determines its own location, for example in a global navigation satellite system (GNSS) or in a public land mobile network using several stationary transceivers. Depending on the configuration, the OBU either transmits the position readings to a central unit of the road toll system, where the map matching is carried out (“thin-client OBU”), or the digital road map is stored directly in the OBU, which carries out the map matching itself (“thick-client OBU”).
Map matching methods have the drawback that the accuracy of the path length determination is limited by the segment resolution of the digital road map. The lengths of the paths in fact traveled by a vehicle frequently deviate from the lengths of the digitally modeled road segments: for example in the region of curves, intersections or traffic circles, the path actually driven is dependent on the driving direction of the vehicle, while the segment lengths of the digital road map are generally modeled independently thereof. Construction sites, temporary detours and the like also cause errors in the path lengths of the vehicles determined by way of map matching.
BRIEF SUMMARY
Aspects of the present application are based on the new finding that it is desirable to be able to adjust the digital road map of a map matching method adaptively to temporary changes of the road system or particular driving habits of the users.
For this purpose, the patent application creates a method for updating a digital road map which represents a road system and in which road segments having segment lengths are stored, comprising:
equipping at least two vehicles moving about in the road system with at least one respective onboard unit generating position readings;
if a first set of position readings of a first onboard unit can be associated with a road segment of the digital road map, determining a first path length based at least on mutual distances of consecutive position readings of the first set;
if a second set of position readings of a second onboard unit can be associated with the same road segment, determining a second path length based at least on mutual distances of consecutive position readings of the second set;
calculating an average path length for this road segment based on at least the first and second path lengths; and
if the calculated average path length deviates from the stored segment length of this road segment by at least a predetermined minimum amount and/or by no more than a predetermined maximum amount, storing the average path length as an updated stored segment length of this road segment in the digital road map.
The digital road map is thus dynamically adapted to local variances, even those of a temporary nature, for example due to construction sites, detours or the like, and more particularly based on averaged path length messages from vehicles that measure the segments locally with the aid of their onboard units.
The corrected updated segment lengths can then, in turn, be used by the same—or different—vehicles for map matching purposes, so as to determine driven road segments and the lengths thereof and, based thereon, be able to calculate a path-dependent road toll, for example. According to a first variant of the method, the path length traveled by the first or second vehicle is thus determined based on at least the updated stored segment length; according to an alternative variant of the method, the path length traveled by a third vehicle is determined based on at least the updated stored segment length if position readings of a third onboard unit of the third vehicle can be associated with this road segment.
So as to minimize measurement errors at the segment boundaries due to scarce or sparse position readings, the respective path length may be determined based on the aforementioned mutual distances, plus the distances of the first and last position readings of the respective set from the respective neighboring segment boundary.
The position readings may be transmitted from the onboard units to a central unit, where the digital road map is stored and updated. The map matching can also be carried out directly at the central unit, or the digital road map is distributed to thick-client OBUs for decentralized map matching processes. This central unit itself can be the billing center of a road toll system or it may only be a map matching proxy provided upstream thereof, which is dedicated to the map matching of position readings of thin-client OBUs.
In a further embodiment, the first and second sets are associated with the road segment by locating the respective nearest road segment. The nearest road segment can, for example, be one from which the position readings in each case have the shortest normal distance.
As an alternative, the digital road map may include a respective separate road segment for different driving directions on a road, and the association can take place by locating the respective nearest road segment having the same driving direction. Especially for road segments with curves, a drastically different segment length may result depending on the driving direction, with the segment being able to be automatically measured in this way and considered in map matching.
In an advantageous variant, the mutual distances are determined directly from the position readings. In an alternative variant, the onboard unit generates a related pair comprising speed and measuring time for each position reading, and the mutual distances of two consecutive position readings are determined based on the speed at the first of the two position readings and the difference of the measuring times of the two position readings. Because particularly precise values are generated for the speed and time, in particular in satellite navigation receivers, the latter variant allows the distances of consecutive position readings to be determined particularly precisely.
Another embodiment is characterized in that the onboard units also generate a related position quality index for each position reading, and only those position readings are used for determining a path length, the position quality index of which exceeds a predetermined first threshold value. “Outlier” readings can thus be suppressed, and the digital road map is only updated based on “good” position readings.
In a further embodiment, it is also possible to form a set quality index based on the number, or the position quality indices, of position readings of an onboard unit that can be associated with a road segment, and to determine a path length based on these positions readings, or to use these readings for calculating the average path length, only if the set quality index exceeds a predetermined second threshold value. An additional, second quality criterion is thus established for the use of readings of an onboard unit: only if an entire set of position readings meets a common quality index (set quality index) are these readings used to calculate the path length and can be included in the calculation of the average path length as a basis for updating the digital road map.
In an advantageous embodiment, the set quality index is determined based on the position quality indices of the set as an average thereof. As a result, the position quality index of each position reading that is considered in the set is included in the set quality index. As an alternative, the set quality index can be determined as a ratio of the number of position readings that are considered in the set to the sum of considered plus not-considered position readings. In this way, measuring inaccuracies of the position readings that are not considered are also indirectly taken into account and thus form a broader basis for a decision.
If desired, the position readings can be generated by way of a mobile radio receiver of the onboard unit by localization in a public land mobile network. It may be particularly advantageous for the position readings to be generated by way of a satellite navigation receiver of the onboard unit by localization in a global navigation satellite system (GNSS).
The position quality index can be determined in various variants, which may also be combined with each other or employed consecutively. According to a first embodiment, which is suited specifically for GNSS-based OBUs, the satellite navigation receiver issues a related DOP (“dilution of precision”) value, such as an HDOP (“horizontal dilution of precision”) value, for each position reading as a basis for the position quality index thereof DOP and HDOP values are natively provided for every position reading by many commercially available satellite navigation receivers and indicate the geometric relationships of the current satellite constellation, which here serve as a basis of a possible range of the respective position reading.
In an alternative or supplemental embodiment, the position quality index of a position reading is generated depending on the distance thereof from a nearest road segment of the digital road map. Such a procedure corresponds to a simplified map matching method, in which no complex plausibility checks are carried out based on neighboring position readings, but only the distance from the nearest road segment is determined. The robustness of the map matching method is thus already incorporated in the method as part of the generation of the position quality index, so that, for example, individual position readings located far off (“outliers”) are not taken into consideration. This embodiment can be directly implemented in thick-client OBUs, where the digital road map is present in the OBU, and is therefore particularly advantageous. However, it is also possible to use thin-client OBUs for this purpose, which outsource this step to an external map matching proxy.
In a further alternative or additional embodiment, the position quality index of a position reading is generated depending on the distance thereof from an expected value, which is formed for this position reading by way of interpolation based on neighboring position readings in the sequence. Isolated “outliers” can thus be eliminated from the method without map matching. It is also possible to use more than two neighboring position readings for interpolation, in particular for interpolations of a higher (non-linear) order, which increases the accuracy.
Alternatively, the position quality index of a position reading can be generated depending on the distance thereof from an expected value, which is formed for this position reading by way of extrapolation based on preceding position readings in the sequence. Contrary to interpolation, subsequent position readings do not have to be known for this purpose, so that this variant is particularly suitable for real-time implementation. Again, more than one preceding position reading may be used for extrapolation purposes, in particular for extrapolations of a higher (non-linear) order or, for example, when employing a Kalman filter for extrapolation, which again increases the accuracy.
Today, many satellite navigation receivers can natively determine their respective speed and movement direction as well as their acceleration with high precision. In such cases, it is particularly advantageous if the satellite navigation receiver issues a related movement vector for each position reading, the vector being used to extrapolate the expected value. Such a movement vector that is formed of the speed and movement direction—optionally using the acceleration value—allows a particularly precise estimation of the expected value for the following position reading and provides a good starting point for generating the position quality index.
As an alternative, the onboard unit may receive a movement reading from a vehicle sensor for every position reading, the reading being used to extrapolate the expected value. The movement reading may be transmitted by a speed or inertia sensor, optionally also by a tachograph of the vehicle, and may also be a combination of the readings from such sensors. This likewise results in a precise estimation of the expected value, and more particularly without being dependent on a GNSS.



BRIEF DESCRIPTION OF THE DRAWINGS/FIGURES
Embodiments will be described in more detail hereafter with reference to the accompanying drawings. In the drawings:
FIG. 1 shows a schematic top view onto a section of a road system modeled by a digital road map, according to an example embodiment
FIG. 2 is a schematic block diagram of an onboard unit suitable for use in the method, according to an exemplary embodiment.
FIG. 3 is an enlarged detail view of one of the turns of the road system of FIG. 1, according to an example embodiment.
FIG. 4a is a flowchart of a part of the method, in which position and set quality indices are used to determine the path length of an OBU, according to an example embodiment.
FIG. 4b shows a flowchart of a part of the method, in which the segment length of a road segment in the digital map is updated, according to an example embodiment.
FIGS. 5 and 6 show different variants for generating a position quality index for a position reading, once in the form of a functional block diagram (FIG. 5) and once based on exemplary position readings in the road system (FIG. 6), according to example embodiments.
FIG. 7 shows in detail a step of extracting sets of position readings in keeping with the variants of FIGS. 5 and 6, according to an example embodiment.
FIG. 8 shows in detail a set quality-dependent path length determination based on the example of FIGS. 5 to 7, according to embodiments.



Embodiments will now be described with reference to the accompanying drawings.
DETAILED DESCRIPTION
FIG. 1 shows a road system 1, which is modeled in the form of a digital road map 1′ comprising a network graph of road segments s1, s2, . . . , or in general terms sn. Several vehicles A1, A2, . . . , or in general terms Ar, which are shown by way of example, are moving about in the road system 1 and carry a respective onboard unit (OBU) O1, O2, . . . , or in general terms Or, which is capable of locating itself in the road system 1.
In addition to a microprocessor 4 and a memory 5, each OBU Or comprises a satellite navigation receiver 6 for this purpose (FIG. 2), by way of which the OBU receives navigation signals 7 of a global navigation satellite system 8 (GNSS) such as GPS (global positioning system), GLONASS (Global Navigation Satellite System), GALILEO or the like, and based thereon continually generates readings of its own position (“position fixes”) p1, p2, . . . , or in general terms pi (more precisely: pi,r; however, to simplify matters, the annexation of the OBU-specific index r will be dispensed with hereafter when speaking of an OBU Or). Alternatively, the OBU Or could also determine the position readings pi thereof in another manner, for example by way of radio triangulation, cell identifier evaluation or the like in a public land mobile network (PLMN) such as a GSM, UMTS or another 3G/4G/5G network.
According to FIG. 2, each OBU Or is also equipped with a transceiver 9, by way of which the OBU can transmit the position readings pi via a mobile communication network 10 to a toll central unit or center 11. The mobile communication network 10 can be of the type that is described, for example a GSM, UMTS or another 3G/4G/5G network, or a network of geographically distributed short-range radio beacons, for example according to the wireless access for vehicular environments (WAVE) standard, or even an ad-hoc network of mobile network nodes, for example a vehicular ad-hoc network (VANET). As an alternative or in addition, the position readings pi could also be derived from such networks.
In the toll center 11, those road segments sn,r on which the vehicle Ar has driven can be determined, for example based on the position readings pi thus obtained by way of map matching with the digital road map 1′. The map matching could also be carried out in a separate map matching proxy provided upstream of the toll center 11, or directly in the OBU Or if the digital road map is stored in the memory 5. Based on the known segment lengths ln of the determined road segments sn,r, which are stored in the digital road map 1′, it is then possible to calculate the length lr of the path traveled by the vehicle Ar in the road system 1.
FIG. 3 illustrates one of the problems of such path length determination by map matching. Because of construction sites, temporary detours, curves, which drivers frequently “cut” (shorten), or simply because of the limited resolution of digital road maps 1′, which frequently model multi-lane road sections or sections with opposing traffic only with a single road segment sn, the path lengths la, lb of the paths wa, wb traveled by drivers in practical experience deviate from the segment lengths ln (here: l3, l4) stored therefor in the road map 1′. The method described hereafter takes this problem into consideration so as to achieve a more accurate path length description for map matching methods.
FIGS. 4a and 4b show the method in different phases and FIGS. 5 to 8 show them in detail. The method phases of FIGS. 4a and 5 to 8 can take place both entirely in an OBU Or or partially in an OBU Or and partially in a toll center 11 and/or a map matching proxy provided upstream thereof; the method phase of FIG. 4b can take place in the toll center 11 and/or a map matching proxy provided upstream thereof
In optional embodiments, the method of FIGS. 4a and 4b uses a special configuration of the OBUs Or, according to which these generate a respective related quality index qi of the position reading pi for each position reading pi, which is to say two mutually associated sequences {pi} (hereafter also referred to as F) and {qi} of position readings pi and position quality indices qi or a sequence {(pi, qi)} of pairs of position readings pi and position quality indices qi. FIGS. 5 and 6 show a variety of variants of generating a position quality index qi for a position reading pi, wherein each of the exemplary road segments s1 to s5 of FIG. 5 shows a different variant.
As is shown in road segment s1 of FIG. 6, a first option is that an OBU Or uses a satellite navigation receiver 6, which on its own (“intrinsically”) provides a measurement error value or range value bi for each position reading pi, which in the simplest case can directly be used as a basis for the position quality index qi, for example in the form qi{circumflex over (=)}1/bi. Such range values b, can, for example, be so-called DOP (“dilution of precision”), PDOP (“positional DOP”), TDOP (“time DOP”), GDOP (“geometric DOP”) or HDOP (“horizontal DOP”) values, as they are generated by commercially available GNSS receivers, for example GPS receivers, based on the current satellite constellation and the resulting measurement errors.
If an OBU Or utilizes a mobile communication network transceiver 9 for position determination instead of (or in addition to) the satellite navigation receiver 6, the range value bi could, for example, also be intrinsically generated by the same, for example based on the currently receivable base stations of the mobile communication network 10 or the current localization accuracy of corresponding mobile communication network services (“location-based services”).
Path 12 of FIG. 5 is a schematic illustration of such a direct use of the range values bi of the receivers 6, 9 for generating the position quality indices qi in a block 13.
A second variant of generating the position quality indices q, is illustrated in road segment s2 of FIG. 6. In this variant, the satellite navigation receiver 6 also supplies a respective current movement vector vj for each position reading pj (see path 14 of FIG. 5), the vector comprising the direction and speed gj of an OBU Or, and perhaps also the acceleration thereof, wherein the values can also be determined with particularly high precision by commercially available satellite navigation receivers 6, for example by way of Doppler measurements using the received satellite navigation signal 7. Commercially available mobile communication receivers 9 could also determine such a movement vector vj, for example based on Doppler measurements using the signals received from the surrounding base stations. In both variants, the movement vector vj could additionally or exclusively be determined by way of an inertial measurement unit (IMU) in conjunction with an electronic compass system, for example.
Proceeding from a current position reading pj, it is possible, based on the current movement vector vj, to estimate (extrapolate) an expected value ej+1 for the respective next position reading pj+1 of the sequence F or {pi} and the deviation δj+1 of the next position reading pj+1 from the expected value ej+1 again constitutes a range index—assuming, for example, that a movement vector vj determined by way of Doppler measurement has very high accuracy—that can serve as a basis for calculating the position quality index qj+1 of the position reading pj+1 in block 13, for example in the form of




  
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As an alternative (or in addition) to the movement vector vj, an OBU Or could also receive movement values mj from a vehicle sensor of the vehicle Ar, for example a speed or inertia sensor, tachometer, odometer, tachograph or the like, see path 15 of FIG. 5, wherein these values can also be used to determine an expected value ej+1 for the next position reading pj+1 and thus the deviation δj+1 and the position quality index qj+1.
Another variant for creating the position quality index qi is shown based on road segments s3 and s4 of FIG. 6 and block 16 of FIG. 5, in particular by way of interpolation or extrapolation of expected values ek, el+1 based on the sequence F of position readings pi. In road segment s3, an expected value ek is formed by way of example by interpolation between two position readings pk−1 and pk+1 adjoining the position reading pk, and the deviation δk of the position reading pk is determined. In road segment s4, an expected value el+1 is formed for a next position reading pl+1 by way of extrapolation based on preceding position readings . . . , pl−2, pl−1, p1, and the deviation δl+1 of the position reading pl+1 is again determined.
The interpolation or extrapolation can also be carried out in each case based on two or more preceding or subsequent position readings pi of the sequence F, and both linear interpolations and extrapolations as well as those of higher orders (“wavelet fitting”, “spline fitting”) may be considered and carried out in a corresponding interpolating or extrapolating unit 16 of FIG. 5.
Based on the deviations δk, δl, it is then again possible in block 13 to determine the corresponding position quality indices qi (here: qk, ql), for example in the form of




  
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Road segment s5 of FIG. 6 and block 17 of FIG. 5 show a variant in which the distance or the deviation δm from the nearest road segment sk, here s5, is determined for every position reading pm by map matching comparison with the digital road map 1′. When using a thick-client OBU, the map matching of block 17 can be carried out in the OBU Or; in the case of a thin-client OBU, this step may be outsourced to an external map matching proxy or the toll center 11. The deviation δm can then again be used to determine the position quality index




  
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    m
  
  ⁢
  
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for the position reacting pm in block 13.

Of course, the position quality index qi in block 13 can also be calculated using any arbitrary function F, for example the weighted sum, of the range values bi and deviations δj, δk, δl, δm, which is to say qi=f(bi, δj, δk, δl, δm).
The values obtained via paths 12 and 15 to 17 could optionally also be evaluated in multiple stages in block 13 so as to generate the quality index qi, for example initially with the aid of the range values bi and, if these are sufficiently small, subsequently based on the expected values ei, ej, ek, el. It would also be possible to generate the position quality indices qi differently for different position readings p for example if, during the GNSS localization, DOP values existed that are not created with intermediate mobile communication localization, or if the position quality indices qi are formed based on manufacturer-specific quality indicators that can be generated by commercially available GNSS receivers.
To come back to FIG. 4a, the figure shows that section of the method which is carried out in each OBU Or—or in the central unit 11 (or an upstream proxy) for each OBU Or—and is used to determine a path length ln′ traveled in the road segment sn by the OBU Or for a road segment sn based on the position readings pi generated by an OBU Or. To this end, in a first step 18, the position readings {pi} of an OBU Or are associated (“matched”) with the respective neared road segment sn of the digital road map 1′. The association is carried out, for example, by searching for that road segment sn for every position reading pi from which the distance δm, for example the normal distance, is minimal. In addition, it may be provided for in the association that the driving direction of the vehicle Ar or of the OBU Or is also taken into consideration, which is to say the history or chronological sequence of the position readings pi in the sequence {pi} if the road segments sn in the digital road map 1′ are dedicated to particular driving directions. It is thus possible, for example, to store a dedicated road segment sn, including directional information, for a road of the road system 1 that has two lanes for opposing driving directions and, in the association use only the nearest road segment sn that corresponds to the driving direction of the vehicle Ar or of the OBU Or.
The position readings pi that can be associated, or that are associated, with a road segment sn in this way are thereafter regarded or extracted as a set Sn of position readings pi from the sequence F or {pi}. In this extraction, it may optionally be provided that position readings pi, the position quality indices qi of which do not achieve or fall below a predetermined first threshold value σ1, are not included in the sets Sn, which is to say they are not considered. FIG. 7 shows the result of step 18 for the exemplary position readings of FIG. 6.
As is shown in FIG. 7, the position reading pi from road segment s1 of FIG. 6 that was not taken into consideration in set S1 was the reading, the large range bi of which had resulted in a position quality index qi smaller than the threshold value σ1. Likewise, the exemplary position readings pj+1, pk, pl+1 of FIG. 6 were not considered in sets S2, S3 and S4 because the deviations δj+1, δk, δl+1 thereof from the respective expected values ej+1, ek, el+1 was large and the position quality indices qj+1, qk, ql+1 thereof were thus smaller than the threshold value σ1. The exemplary position reading pm—in addition to others—from road segment s5 of FIG. 6 was not included in set S5, since the large distance δm of this value from road segment s5 had resulted in a position quality index qm that did not reach the threshold value δ1.
The sets Sn of position readings pi exceeding the threshold value σ1 that were extracted in step 18 are then supplied, together with the position quality indices {qi}n associated with sets Sn—to a method step 19, in which a set quality index Qn is determined for every set Sn, in particular in the following manner.
In one embodiment, the set quality index Qn is determined based on the respective position quality indices qi of the position readings pi included in set Sn, for example as an average or a weighted sum thereof, for which purpose all methods known in mathematics or statistics for linking reading are suited.
In a simplified embodiment, the set quality index Qn is formed only of the number A of position readings pi that are included in set Sn, for example the absolute number of position readings pi in set Sn or a “relative” number in relation to the original position readings pi of the sequence F, which is to say as a ratio of the number of position readings pi considered in set Sn to the sum of position readings pi considered and not considered in set Sn. To this end, it is also possible to specify a minimum number of position readings pi in set Sn, below which the set quality index Qn must be set to zero.
The sets Sn and the set quality indices Qn thereof are then supplied to a comparison step 20, in which the set quality index Qn of every set Sn is compared to a predetermined second threshold value σ2. If the set quality index Qn exceeds the second threshold value σ2, which means that the quantity or overall quality of the position readings pi of the Sn is sufficiently good, the process branches off to a calculating step 21 (branch “y”), in which the path traveled by the vehicle Ar in the road segment sn is determined based on the mutual distances ai of the consecutive position readings pi of set Sn, see for example FIG. 8:

l′1=a1+a2+a3 

l′3=ai+ai+1+ai+2 

l′4=ai+3+ai+4+ai+5 

or in general terms




  
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or more precisely (written with the OBU-specific index r):




  
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This is shown in FIG. 8 by way of example for road segments s1, s3 and s4 and for sets S1, S3 and S4, the set quality indices Q1, Q3 and Q4 of which exceeded the threshold value σ2. The length l′1 of the path traveled in road segment s1 and set S1 here follows from the sum of the mutual distances a1, a2 and a3 of the consecutive position readings p1, p2, p4 and p5 of set S1, which is to say by not taking into consideration the position reading p3 (pi in FIG. 6) that was already excluded in step 18.
So as to minimize measurement errors at the segment boundaries, optionally the distances of the respective first and last position readings of a set Sn from the respective neighboring end points of the road segment sn can be taken into consideration, as is shown by way of example in road segment s1 for the segment boundaries ps1+a, Ps1+b thereof: the distance aA of the first position reading p1 of set Sn from the nearest segment boundary ps1A and the distance aB of the last position reading p5 from the nearest segment boundary ps1B are additionally considered here in the path length l′1, which is to say:

l′1=aA+a1+a2+a3+aB 

or in general terms for road segment n and with the OBU-specific index r:




  
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      r
    
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To this end, the distances aA and aB from the segment boundaries ps1A and ps1B can be calculated both as the shorted point distances (as shown for aA) and as normal distances (as shown for aB).
The distances a1, a2 and a3, or in general terms a (including aA, aB), can be determined directly based on the geometric distances of the position readings p1, p2, p4 and p5 (or the respective segment boundaries psnA, PsnB), which is to say




  
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If the onboard unit Or has also generated a related pair (gi, ti) comprising speed gi and measuring time ti for every position reading p for example with the aid of the satellite navigation receiver 6, the distances a1, a2 and a3 can alternatively also be determined based on the speeds g1, g2 and g4 and the respective differences of the measuring times t1, t2, t4 and t5 of the position readings p1, p2, p4 and p5, which is to say in the form of





  
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In the present example, the set quality index Q1 of set S1 was formed as an average of the position quality indices qi of the position readings pi included in set S1. The lengths l′3 and l′4 for sets S3 and S4 were determined in the same manner.
However, if in comparison step 20 the set quality index Qn does not reach the second threshold value σ2, the OBU Or does not generate a path length l′n for the road segment sn that the OBU determined itself This, in turn, is shown in FIG. 8 by way of example for road segments s2 and s5 and for sets S2 and S5, the set quality indices Q2 and Q5 of which did not reach the threshold value σ2. In the case of set S2, for example, the number A of position readings pi included in set S2 fell below a minimum number (here: four), and in the case of set S5, the relative number A=4/7 (four out of a total of seven position readings pi were considered) was below the second threshold value σ2.
In a simplified embodiment of the method section of FIG. 4a, of course, the comparisons to the threshold values σ1 and σ2, and thus steps 19 and 20, can be eliminated; in the simplest case, only the position readings pi that can be associated with a road segment sn of the digital road map 1′ are associated with this road segment sn as a set Sn and, based thereon, in step 21 the path length l′n(more precisely: l′n,r) of road segment sn measured by the OBU Or is determined
FIG. 4b shows the further phase of updating the digital road map l′ taking into consideration the path lengths l′n determined by two or more OBUs Or for one and the same road segment sn according to FIG. 4a, hereafter provided with the OBU-specific index r, which is to say hereafter denoted by l′n,r. The method section of FIG. 4b is advantageously carried out centrally, for example in the central unit 11 or a proxy provided upstream thereof, because in a first step 22 first the path lengths l′n,r determined by the various OBUs Or for a road segment sn are collected (received). The method section of FIG. 4b can, for example, be carried out or repeated at predetermined times, for example once or several times a day, week or month, or after a particular number of path lengths l′n,r are collected for a road segment sn.
If the method section of FIG. 4a takes place remotely in one of the OBUs Or, these can transmit the determined path lengths l′n,r via the mobile communication network 10, for example (arrow 23). If the method sections of FIG. 4a are carried out centrally for all OBUs Or, for example in the central unit 11 or a proxy provided upstream thereof, the OBUs Or could, for example, also optionally transmit only the position readings pi thereof (and optionally the position quality indices qi thereof) via the mobile communication network 10, which is to say the data path 24 takes place via the mobile communication network 10.
After the data collection or reception step 22 of FIG. 4b, in step 25 an average path length l′n is calculated using




  
    
      l
      n
      ′
    
    _
  
  =
  
    
      1
      R
    
    ⁢
    
      
        ∑
        
          r
          =
          1
        
        R
      
      ⁢
      
        l
        
          n
          ,
          r
        
        ′
      
    
  




where
R . . . is the number of OBUs Or.
The average path length l′n is thus the average value of the path lengths l′n,r measured by two or more OBUs Or in the road system 1 for a road segment sn, and more particularly only based on those position readings pi that exceeded the first threshold value σ1 and those sets Sn,r that exceeded the second threshold value σ2.
A subsequent comparison step 26 is used to check whether the calculated average path length l′n deviates from the segment length ln that is stored in the digital road map 1′ for the road segment sn by at least one predetermined minimum amount σ3. As an alternative or in addition, comparison step 26 can also be used to check whether the calculated average path length l′n deviates from the stored segment length ln by no more than a predetermined maximum amount σ4. If so (branch “y” of step 26), the segment length ln stored in the digital road map 1′ for the road segment sn is corrected or updated, which is to say the calculated average path length l′n is stored as the new segment length ln of road segment sn in the digital road map 1′ (step 27).
If no significant deviation exists, which is to say one that exceeds the minimum amount σ3 (branch “n” of step 26), the segment length ln stored in the digital road map 1′ remains untouched.
Thereafter, the updated road map 1′, comprising the updated segment lengths ln of the road segments sn, can again be used to calculate a path length traveled by a vehicle Ar by way of map matching, and more particularly both for vehicles Ar or OBUs Or which just contributed to updating the road map 1′ and for other (“third) vehicles Ar or OBUs Or. To this end, the position readings pi generated by an OBU Or of a vehicle Ar are again associated with the respective nearest road segments sn of the digital road map 1′ (to the extent this has not already been done in step 18), and the corresponding segment lengths ln of the road segments sn that are considered as having been driven on are added to form a total path length




  
    l
    r
  
  =
  
    
      ∑
      n
    
    ⁢
    
      l
      
        n
        ,
        r
      
    
  




traveled by the vehicle Ar. The traveled path length lr thus added is more accurate than before the digital road map 1′ was updated and also takes into consideration short-cuts, detours, “cut curves” as well as temporary alternative routes, construction sites or the like in the road system 1 used predominantly by vehicles.

Conclusion

The invention is not limited to the shown embodiments, but encompasses all variants, combinations and modifications that are covered by the scope of the accompanying claims. While various embodiments have been described above, it should be understood that they have been presented by way of example only, and not limitation. It will be apparent to persons skilled in the relevant art that various changes in form and detail can be made therein without departing from the spirit and scope of the embodiments. Thus, the breadth and scope of the described embodiments should not be limited by any of the above-described exemplary embodiments, but should be defined only in accordance with the following claims and their equivalents.

## Claims

1. A method for updating a digital road map which represents a road system and in which road segments having segment lengths are stored, comprising:
equipping at least two vehicles moving about in the road system with at least one respective onboard unit generating position readings;
when a first set of position readings of a first onboard unit can be associated with a road segment of the digital road map, determining a first path length traveled in the associated road segment based at least on mutual distances of consecutive position readings of the first set;
when a second set of position readings of a second onboard unit can be associated with the same road segment, determining a second path length traveled in the associated road segment based at least on mutual distances of consecutive position readings of the second set;
calculating an average path length for the road segment based on at least the first and second path lengths; and
when the calculated average path length deviates from the stored segment length of the road segment by at least a predetermined minimum amount and/or by no more than a predetermined maximum amount, storing the average path length as an updated stored segment length of the road segment in the digital road map.

2. The method according to claim 1, wherein the path length traveled by the first or second vehicle is determined based on at least the updated stored segment length.

3. The method according to claim 1, wherein the path length traveled by a third vehicle is determined based on at least the updated stored segment length if position readings of the third onboard unit of a third vehicle can be associated with the road segment.

4. The method according to claim 1, wherein the respective path length is determined based on the aforementioned mutual distances, plus the distances of the first and last position readings of the respective set from the respective neighboring segment boundary.

5. The method according to claim 1, wherein the position readings are transmitted from the onboard units to a central unit, where the digital road map is stored and updated.

6. The method according to claim 5, wherein the updated digital road map is returned by the central unit to at least one onboard unit.

7. The method according to claim 1, wherein the association of the first and second sets with the road segment is done by locating the respective nearest road segment.

8. The method according to claim 1, wherein the digital road map includes a respective separate road segment for different driving directions on a road, and that the association takes place by locating the respective nearest road segment having the same driving direction.

9. The method according to claim 1, wherein the mutual distances are determined directly based on the position readings.

10. The method according to claim 1, wherein the onboard units generate a related pair comprising a speed and measuring time for every position reading, and the mutual distances of two respective consecutive position readings of an onboard unit are determined based on the speed at the first of the two position readings and the difference of the measuring times of the two position readings.

11. The method according to claim 1, wherein the onboard units also generate a related position quality index for each position reading, and only those position readings are used for determining a path length, the position quality index of which exceeds a predetermined first threshold value.

12. The method according to claim 1, wherein a set quality index is formed based on a number, or on position quality indices, of position readings of an onboard unit that can be associated with a road segment, and that a path length is determined based on these position readings, or these readings are used for calculating the average path length, only if the set quality index exceeds a predetermined second threshold value.

13. The method according to claim 12, wherein the set quality index is determined based on the position quality indices of the set as an average thereof, or as a ratio of the number of position readings that are considered in the set to the number of considered and not-considered position readings.

14. The method according to claim 1, wherein the position readings are generated by way of a satellite navigation receiver of the onboard unit by localization in a satellite navigation system.

15. The method according to claim 14, wherein the satellite navigation receiver issues a related DOP value for each position reading as a basis for the position quality index thereof.

16. The method according to claim 1, wherein the position quality measure of a position reading is generated depending on the distance thereof from the located nearest road segment of the digital road map.

17. The method according to claim 1, wherein the position quality index of a position reading is generated depending on the distance thereof from an expected value, which is formed for the position reading by way of interpolation based on neighboring, or by way of extrapolation based on preceding, position readings.

18. An onboard unit included in a vehicle moving about a road system, the onboard unit comprising:
a memory that stores a digital road map which represents the road system;
a satellite navigation receiver that receives navigation signals used to generate a first set of position readings, wherein the onboard unit generates a related pair comprising a speed and measuring time for every position reading of the first set, and the mutual distances of two respective consecutive position readings of the first set are determined based on the speed at the first of the two position readings and the difference of the measuring times of the two position readings;
a microprocessor configured to associate the first set of position readings with a road segment of the digital road map, and, responsive to an association of the first set of position readings with the road segment of the digital map, to determine a first path length traveled in the associated road segment based at least on mutual distances of consecutive position readings of the first set; and
a transceiver configured to transmit the first path length from the onboard unit to be used to update a segment length of the road segment in the digital road map.

19. The onboard unit of claim 18, wherein the onboard unit generates a related position quality index for each position reading, and only those position readings having a related position quality index that exceeds a predetermined first threshold value are used for determining the first path length.

20. An apparatus for updating a digital road map which represents a road system and in which road segments having segment lengths are stored, comprising:
a central unit configured to receive sets of position readings from at least two vehicles moving about in the road system with at least one respective onboard unit generating position readings;
when a first set of position readings of a first onboard unit can be associated with a road segment of the digital road map, the central unit is configured to determine a first path length traveled in the associated road segment based at least on mutual distances of consecutive position readings of the first set;
when a second set of position readings of a second onboard unit can be associated with the same road segment, the central unit is configured to determine a second path length traveled in the associated road segment based at least on mutual distances of consecutive position readings of the second set;
wherein the central unit is configured to calculate an average path length for the road segment based on at least the first and second path lengths; and
when the calculated average path length deviates from the stored segment length of the road segment by at least a predetermined minimum amount and/or by no more than a predetermined maximum amount, the central unit is configured to store the average path length as an updated stored segment length of the road segment in the digital road map.