JPH11304510A - Map matching method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a map matching method for utilizing three-dimensional information from position measurement data and map data for not only the selection of a candidate road but also positioning. SOLUTION: The horizontal direction pattern similarity degree of a present vehicle traveling track pattern and a road pattern at the same time are calculated, and the vertical direction pattern similarity degree of the present vehicle traveling track pattern and the road pattern at the same time are calculated. A weighted horizontal direction pattern similarity degree and a weighted vertical direction pattern similarity degree for which weight set based on the state of a road is respectively multiplied with the horizontal direction pattern similarity degree and the vertical direction pattern similarity degree are calculated and map matching is performed based on the weighted horizontal direction pattern similarity degree and the weighted vertical direction pattern similarity degree.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a map matching method that can be used for a car navigation device.

[0002]

2. Description of the Related Art FIG. 13 shows a block diagram of a conventional car navigation system. In the conventional car navigation device, the GPS unit 1 simultaneously receives radio waves from a plurality of, for example, four GPS satellites, and obtains positioning data of the vehicle. The GPS unit 1 is provided with a GPS receiver, and obtains the absolute position of the vehicle from data received by the GPS receiver. The positioning data includes vehicle traveling direction information and elevation angle information in addition to vehicle position information. include.

The autonomous positioning unit 2 has an autonomous sensor, and calculates a moving distance and a moving direction of the vehicle from output data from the autonomous sensor. Autonomous sensors include a wheel-side sensor that detects a signal corresponding to the number of rotations of a wheel, an acceleration sensor that detects an acceleration of a vehicle, a gyro sensor that detects an angular velocity of a vehicle, and, for example, a pitch movement direction of the vehicle. An autonomous sensor for detecting an attitude angle (also referred to as a pitch angle) at the position is provided, and the positioning data output from the autonomous positioning unit 2 includes the pitch angle of the vehicle. In this car navigation device, the GPS unit 1 and the autonomous positioning unit 2 constitute a positioning unit, and use one or both of the positioning data.

[0004] The map storage unit 3 stores digital map data having three-dimensional road information. For example, a CD-ROM is used as a storage medium. If the data amount is large, it takes a long time to read the map data. Therefore, the map data is divided into a plurality of blocks and stored. In addition, road information
It contains information indicating major points (also referred to as nodes) such as intersections and bends. The node information includes coordinate data at the points, and roads are approximated as straight lines (also referred to as links) connecting the nodes. Is done. In this car navigation device, the three-dimensional road information means that the node information includes three-dimensional coordinate data.

[0005] The positioning control unit 4 reads map data of a predetermined area corresponding to the position of the vehicle from the map storage unit 3 based on the vehicle position information obtained from the GPS unit 1 or the autonomous positioning unit 2. The display unit 5 is a display unit that displays the map data read by the positioning control unit 4, and includes, for example, a liquid crystal display unit.

The map matching unit 6A is a map matching unit that corrects the position of the vehicle on the road based on the three-dimensional road information of the vehicle positioning data and the map data.

The map matching section 6A finds the shortest distance between the vehicle position and the selected road, and a road selection section 7 for selecting a road within a predetermined range of the read map data. A distance consistency determination unit 8 that determines the distance consistency (also referred to as distance consistency), and a direction consistency that compares the traveling direction of the vehicle with the link direction of the selected road and determines the consistency (also referred to as direction consistency). A determination unit 9 for comparing the attitude angle of the vehicle with the road gradient to determine the consistency (also referred to as angle consistency) of the vehicle, an angle consistency determination unit 8, a distance consistency determination unit 8, and a direction consistency determination unit The road storage unit 11 for temporarily storing the roads determined to be consistent by the angle matching unit 9 and the angle consistency determination unit 10 and the roads stored in the road storage unit 11 to be corrected. A road determination unit 12 that determines a road; Road determination unit 12 the position of the vehicle obtained
And a correction unit 13 that corrects on the road determined in.

The attitude angle in the angle consistency determination unit 10 is the inclination of the vehicle in the pitch movement direction with respect to the horizontal plane, and is determined by the elevation angle information obtained from the GPS unit 1 or the pitch angle information obtained from the autonomous positioning unit 2. Can be expressed.

The operation of map matching in the above-described conventional car navigation device will be described with reference to a flowchart shown in FIG. Map matching unit 6
When A obtains the positioning data and the three-dimensional road information of the vehicle (step S1), the road selecting unit 7 reads out the road indicated by the three-dimensional road road information, and selects the road for the later-described consistency determination (step S1). Step S2). At this time, as the road selection range, all the roads in one block where the position of the located vehicle exists are selected from the map data divided into a plurality of blocks.

Each of the roads read in step S2 is checked to determine whether or not the consistency check has been completed (step S3). The road is checked for consistency (step S4).

Next, the determination of consistency will be described. In step S4, a check is made on the distance consistency determination unit 8. The distance consistency determination unit 8 finds the shortest distance between the vehicle and the road, and determines that the distance is equal to or less than a threshold,
If the distance is within 0 m, it is determined that the condition for the distance consistency is satisfied, and then the direction consistency determination unit 9 checks the direction consistency (step S5). If the angle difference between the traveling direction of the vehicle and the link direction of the road is equal to or less than a threshold value, for example, within 30 degrees, the directional consistency determination unit 9 determines that the directional compatibility satisfies the condition. The consistency judgment unit 10 checks the angle consistency (step S6).

The angle consistency determination unit 10 obtains a slope state of a road to be checked from three-dimensional road information, that is, a gradient in a traveling direction of a vehicle on the road. If the difference between this gradient and the attitude angle of the vehicle is equal to or less than a threshold, for example, within ± 5 degrees,
It is determined that the angle matching satisfies the condition.
Here, the attitude angle of the vehicle is represented by a slope of (+) for climbing on a flat road and a slope of (-) for descending on a flat road.

Step S4, Step S5, Step S
The roads determined to be consistent by the respective consistency checks in 6 are regarded as roads where vehicles may exist, and
After being stored in the road storage unit 11 (step S7), the processing is executed from step S3. In step S3, the 〃 of the map data in which the position of the vehicle selected by the road selection unit 7 exists.
When determining that the consistency check has been completed for all the roads in the block, the road determination unit 12 determines the distance between the vehicle position and the road from the roads stored in the road storage unit 11. In consideration of the shortest road, one road to be corrected is determined (step S8).

If the road to be corrected is determined in step S8, the position corrector 13 corrects the position of the vehicle to coordinates on the road (step S9). At this time, as to which position on the road to correct the vehicle position, correction to a position where the distance between the vehicle position and the road is the shortest, that is, a position where the vehicle is drawn perpendicular to the road, etc. Done. The corrected position information of the vehicle is output to the display unit 5 (Step S10).

For the three-dimensional road information, for example, when the link direction of the road is used, the link direction may be calculated each time from each node information, or the link directions corresponding to the nodes may be provided in advance as a storage table. May be used.

In the conventional map matching method as described above, when a road to be corrected is determined from the roads included in the map data, the distance between the position of the vehicle and the road, the traveling direction of the vehicle and the direction of the road are determined. The road to be corrected is determined by comparing the attitude angle of the vehicle with the road gradient in addition to the comparison with the link direction, so that even if there is a road running parallel to the height direction, the road By judging the difference between the gradients, the map matching process can be reliably performed on the road of the map data corresponding to the actual traveling road.

Further, instead of the angle consistency judging section 10 which performs the check in step S6, it is possible to replace the angle consistency judging section with a high consistency checking. Here, the altitude of the road to be checked is read from the three-dimensional road information, and the altitude of the road is compared with the altitude indicated by the positioning data. If the difference is equal to or less than a threshold value, for example, within 30 m, the altitude matching condition is satisfied. It is determined that there is. In this case, it is also possible to use the altitude information from the GPS unit 1 or equip it with an altimeter or the like.

[0018]

However, in the above-described conventional map matching method, the three-dimensional information from the positioning data and the map data is used only for selecting a candidate road. There was a problem that it was not used as an element.

An object of the present invention is to provide a map matching method in which three-dimensional information from positioning data and map data is used not only for selection of candidate roads but also for positioning.

[0020]

Means for Solving the Problems Claim 1 according to the present invention.
The described map matching method calculates a horizontal pattern similarity between the own vehicle travel locus pattern and the road pattern at the same time, and calculates a vertical pattern similarity between the own vehicle travel locus pattern and the road pattern at the same time. Calculating a weighted horizontal pattern similarity and a weighted vertical pattern similarity obtained by multiplying the horizontal pattern similarity and the vertical pattern similarity by weights set based on road conditions, respectively; Map matching is performed based on the pattern similarity and the weighted vertical pattern similarity.

The horizontal pattern similarity between the own vehicle running locus pattern and the road pattern at the same time and the vertical pattern similarity between the own vehicle running locus pattern and the road pattern at the same time are calculated, and the calculated horizontal direction is calculated. The horizontal pattern similarity and the weighted vertical pattern similarity are calculated by weighting the pattern similarity and the vertical pattern similarity based on the road condition, respectively, and the weighted horizontal method pattern similarity and the weighted Map matching is performed based on the vertical pattern similarity, and the three-dimensional information is used not only for selection of candidate roads but also for positioning.

A map matching method according to a second aspect of the present invention is the map matching method according to the first aspect, wherein N (N Is a natural number) of sampling points, and the turning angle at each sampling point on the horizontal vehicle traveling locus pattern is accumulated from a predetermined reference vector to obtain an accumulated angle.
The cumulative angles at the N sampling points are used as the characteristics of the horizontal vehicle running trajectory pattern, and the turning angles at each sampling point on the horizontal road pattern are accumulated from the reference vector to obtain the cumulative angles. The cumulative angle at the point is defined as a horizontal road pattern feature, and a horizontal pattern similarity is obtained based on the horizontal own vehicle traveling trajectory pattern feature and the horizontal road pattern feature.

According to the map matching method according to the second aspect of the present invention, the cumulative angle obtained by accumulating the turning angle at each sampling point on the horizontal vehicle running trajectory pattern from a predetermined reference vector is obtained. The cumulative angles at the N sampling points are used as the characteristics of the horizontal vehicle running trajectory pattern. Similarly, the turning angles at the respective sampling points on the horizontal road pattern are accumulated from the reference vector to obtain the cumulative angles. Since the horizontal pattern similarity is obtained based on the cumulative angles at the sampling points and the horizontal road pattern feature based on the two pattern features, the horizontal vehicle travel trajectory at the time next to or before the time (t) is obtained. The pattern feature and the horizontal road pattern feature are the horizontal vehicle running locus pattern features at time (t). By storing the horizontal road pattern feature and the horizontal direction road pattern feature, it can be obtained by addition and shift, and the calculation processing load for the similarity calculation by the calculation processing device including the computer is reduced, and the high-precision similarity calculation is performed. The degree can be calculated.

A map matching method according to a third aspect of the present invention is the map matching method according to the first aspect, wherein N (N Is a natural number) of sampling points, and the elevation angle at each sampling point on the elevation direction own vehicle traveling locus pattern is accumulated from a predetermined reference vector to obtain a cumulative elevation angle.
The cumulative elevation angle at the N sampling points is used as the vertical vehicle traveling locus pattern feature, and the elevation angles at each sampling point on the elevation direction road pattern are accumulated from the reference vector to obtain a cumulative elevation angle. Is used as a vertical road pattern feature, and a vertical pattern similarity is obtained based on the vertical own vehicle traveling trajectory pattern feature and the vertical road pattern feature.

According to the map matching method according to the third aspect of the present invention, similarly to the case of the map matching method according to the second aspect, the vehicle in the elevation angle direction at the time next to or before the time (t). The running locus pattern feature and the elevation direction road pattern feature can be obtained by adding and shifting by storing the elevation direction own vehicle running locus pattern feature and the elevation direction road pattern feature at time (t). The calculation processing load for the similarity calculation by the calculation processing device including the computer is reduced, and the high-accuracy similarity can be calculated.

[0026]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS A map matching method according to the present invention will be described below with reference to an embodiment.

FIG. 1 is a block diagram showing a configuration of a car navigation system using a map matching method according to an embodiment of the present invention, and FIG. 2 is a diagram illustrating a map matching method according to an embodiment of the present invention. 6 is a flowchart provided for FIG.

In the car navigation apparatus using the map matching method according to the embodiment of the present invention shown in FIG. 1, the same components as those of the car navigation apparatus using the conventional map matching method shown in FIG. Are denoted by the same reference numerals, and descriptions of the same components will be omitted to avoid duplication. Since the car navigation device using the map matching method according to the embodiment of the present invention considers the angular consistency between the road and the vehicle trajectory with the pattern similarity to be determined by the road determination unit 12, Angular consistency determination unit 10 in the car navigation device using the conventional map matching method shown in FIG.
Becomes unnecessary.

Therefore, in the car navigation system using the map matching method according to one embodiment of the present invention, as shown in the flowchart of FIG. 2 showing the operation, the conventional map matching method shown in FIG. Step S6 in the flowchart becomes unnecessary. Other steps are almost the same as those in FIG. 14, but differences will be described.

In step S1, upon receiving the vehicle positioning data and the three-dimensional road information, the map matching unit creates trajectory data appropriately sampled from the positioning data as preprocessing for pattern matching described later. For example, when the vehicle has a minimum turning radius of 5 m, it is necessary to travel about 7.8 m in order to completely change the azimuth in the two-dimensional direction by 90 degrees. Therefore, the sampling interval is set to 10 m.

The processes in steps S2 to S7 are nothing but the creation of road data to be subjected to map matching. Therefore, road data corresponding to the trajectory data created in step S1 is created in any of these steps.
The simplest method for determining the corresponding point on the road is, for example, a line perpendicular to the target road is drawn, and the intersection of the lines is used as the point constituting the road data.

In step S8, the traveling road is determined using the pattern similarity Sim as described below.
The pattern similarity is calculated by weighting the pattern similarity Sθ in the horizontal direction (the turning direction of the vehicle) and the pattern similarity Sφ in the vertical direction (the posture of the vehicle). A specific calculation method will be described below.

First, the pattern feature based on the turning angle will be described.

FIG. 3 shows the turning angle θ of the vehicle. Note that a right turn is plus and a left turn is minus. Assuming that the number of features (or the number of samplings) is N, the pattern feature Sθ at time t is represented using the turning angle θ. A turning angle calculated based on the shape of the trajectory pattern / road pattern is used as a pattern feature by accumulating the turning angle from a reference vector for similarity calculation.

More specifically, N (N is a natural number) sampling points are set on the own vehicle traveling locus pattern and the road pattern at time (t), respectively, and at each sampling point on the own vehicle traveling locus pattern. The turning angle is accumulated from a predetermined reference vector to obtain a cumulative angle, the accumulated angles at N sampling points are used as the characteristics of the own vehicle traveling trajectory pattern, and the turning angle at each sampling point on the road pattern is determined by the reference. The accumulated angle at the N sampling points is determined as a road pattern feature, and the similarity between the own vehicle running locus pattern and the road pattern is determined based on the own vehicle running locus pattern feature and the road pattern feature. Ask for.

The same applies to the calculation of the vehicle running locus pattern feature and the calculation of the road pattern feature.
The former will be described. First, the turning angle θ of the vehicle is shown in FIG. In FIG. 3, the current vehicle position (t) is represented by Pi (x
i, yi), the vehicle position at the immediately preceding time point (t-1) is P
i-1 (xi-1, yi-1), the next time point (t
+1) is represented by Pi + 1 (xi + 1, yi +
1), θi indicates the turning angle from the current own vehicle position to the next own vehicle position in the traveling direction.

Here, the number of features (or the number of samplings)
Is N, the pattern feature (Sθ) at time t is represented by the following equation (1) using the turning angle θ as shown in FIG. FIG. 4 shows a trajectory pattern and a turning angle, and a bold vector indicates a reference vector. FIG.
FIG. 5A shows the turning angle, FIG. 5B shows the cumulative angle, and the cumulative angle forms a pattern feature.

[0038]

(Equation 1)

Therefore, the general form of the pattern feature Sθ (t) is represented by the following equation (2) (where 0 ≦
j ≦ (N−1)).

[0040]

(Equation 2)

The pattern feature Sθ (t + 1) can be represented by the following equation (3) using the pattern feature Sθ (t).

[0042]

(Equation 3)

That is, the update of the pattern feature Sθ is realized simply by addition / subtraction and shift operation.

As described above, the pattern feature Sθ (t-1)
Is expressed as shown in the following equation (4) using the pattern feature Sθ (t).

[0045]

(Equation 4)

As shown in the explanatory diagram of FIG. 6, the pattern feature Sθ (t + 1) at time (t + 1) is represented by θ (t) bias and pattern feature Sθ (t) at time t before time (t + 1). Holds, it is possible to recursively restore simply by adding and shifting operations. 7 to 10 show flowcharts of the updating process of the pattern feature Sθ.

Using the pattern feature Sθ (t) at time (t), the pattern feature Sθ (t +
In the case of the forward process shown in FIG. 7 for calculating 1), θ (t + 1) new at time (t + 1) and θ
(T + 1) bias is read (step S11),
At time (t + 1), i = 0, 1, 2,..., (N−
The pattern feature Sθ (t + 1) (i) up to 2) is represented by Sθ
(T + 1) (i) = Sθ (t) (i + 1) −θ (t +
1) As shown in bias, the pattern feature Sθ (t) at time (t) and θ (t +
1) Calculate using bias (step S12).

After step S12, time (t + 1)
I = (N−1) pattern feature Sθ (t + 1)
(N-1) is calculated as Sθ (t) (N-1) = Sθ (t) (N
-2) + θ (t + 1) new as shown in time (t)
Is calculated using the pattern feature Sθ (t) (N−2) in θ and θ (t + 1) new obtained in step S11 (step S13).

Using the pattern feature Sθ (t) at time (t), the pattern feature Sθ (t−
In the case of the backward process shown in FIG. 8 for calculating 1), θ (t) bias at time (t) is read (step S21), and the pattern feature Sθ (t-1) ( 0) is calculated as Sθ (t-1) (0) =
It is determined by θ (t) bias (step S22).

After step S22, time (t-1)
, The pattern features Sθ (t−1) (i) up to i = 0, 1, 2,..., (N−1) are represented by Sθ (t−1) (i)
= Sθ (t) (i−1) + θ (t) bias, the pattern feature Sθ (t) (i−
It is calculated using 1) and θ (t) bias obtained in step S21 (step S23).

The data update routine is as shown in FIG.
A new point is obtained, and the turning angle θ is calculated from this (step S31). Then, θnew = θ, θbia
According to s = Sθ (0), θnew and θbias are stored in the memory as the latest one (step S32). Next, a forward process is executed to obtain the latest pattern feature Sθ.
(·) Is calculated (step S33). Step S33
Is updated and stored as the latest pattern feature Sθ (•) (step S3).
4). Thus, the pattern feature Sθ (•) is updated to the latest pattern feature Sθ (•).

The routine for calculating the pattern feature Sθ at time T will be described with reference to FIG. In the processing of the calculation routine of the pattern feature Sθ at the time T, the time T ′ is obtained (step S41), and the pattern feature Sθ (t) (i = 01, 2,..., N−1) at an arbitrary time is stored in the memory. Get from. If the temporary data is empty, the latest pattern feature Sθ (t) is copied and used as temporary data (step S42).

After step S42, it is checked whether or not T = T '(step S43). If it is determined in step S43 that T = T', the process ends. Step S4
In T.3, when it is determined that T = T 'is not satisfied, T <T
'(Step S44)
When it is determined at 44 that T <T ', a forward process is executed (step S45), and then T = (T +
1) is executed, and the process is repeated from step S43 (step S46).

If it is determined in step S44 that T <T 'is not satisfied, a backward process is executed (step S47), and then T = (T-1) is executed, and the process is repeated from step S43 (step S48). Through these processes, the pattern feature Sθ at time T is calculated.

Next, the pattern feature based on the elevation angle will be described. The pattern feature calculation method based on the elevation angle is obtained in the same manner as in the case of the turning angle.

FIG. 11A and FIG. 11B show the elevation angle φ of the vehicle.
Shown in Note that the ascending slope is plus and the descending slope is minus. Assuming that the number of features (or the number of samplings) is N, the pattern feature Sφ at time t is represented by the following equation (5) using the elevation angle φ. Sφ is also the cumulative elevation angle.

[0057]

(Equation 5)

Therefore, the general form of the pattern feature Sφ is expressed by the following equation (6) (where 0 ≦ j ≦ (N−
1)).

[0059]

(Equation 6)

The pattern feature Sφ (t + 1) can be expressed by the following equation (7) using the pattern feature Sφ (t).

[0061]

(Equation 7)

That is, the update of the pattern feature Sφ is realized simply by addition and subtraction.

As described above, the pattern feature Sφ (t−1)
Can be expressed by the following equation (8) using the pattern feature Sφ (t).

[0064]

(Equation 8)

This is similar to the turning angle pattern feature.
The pattern feature Sφ before time t is φbi at that time.
If as is held, it is possible to simply recursively restore by addition.

Next, a method of calculating the pattern similarity will be described.

The horizontal correlation value Simθ as the pattern similarity is calculated by the mean square error calculation using Sθ as in the following equation (9). In equation (9), Sθl
ocus indicates a pattern feature of the trajectory pattern in the horizontal direction, and Sθload indicates a pattern feature of the road pattern in the horizontal direction.

[0068]

(Equation 9)

The correlation value Simφ in the vertical direction as the pattern similarity is similarly calculated as in the following equation (10). In Expression (10), Sβφlocus indicates a pattern feature in the vertical direction of the trajectory pattern, and Sφroa
d indicates a pattern feature of the road pattern in the vertical direction.

[0070]

(Equation 10)

As the correlation value (pattern similarity) Sim used for performing the overall discrimination, a value obtained by multiplying the pattern similarity in the horizontal and vertical directions by a weight ω and (1−ω) and adding the result is used. Further, depending on the trajectory, it may be desirable to perform matching in a form shifted forward or backward. Therefore, the pattern similarity Sim in consideration of the matching shift correction in the matching is calculated as in the following equation (11).

[0072]

[Equation 11]

Determination is performed using the correlation value Sim calculated by the above equation. To give an example of the discrimination method, those having a certain threshold value or less can be selected as traveling road selection candidates, and when there are a plurality of traveling road selection candidates, the one with the best value can be selected. Note that the weight ω
Can be set separately for each pattern feature. It is also possible to change the weight ω, for example, to increase the weight ω when traveling on a mountain road and to decrease the weight ω when traveling in an urban area. It is desirable that the determination of such a mountain road travel or city travel be made in steps S1 and S2.

Next, an example of the correction method will be described. Since the combination of the road point and the locus point that gives the minimum value Sim is known, the position correction can be performed by matching the locus point to the corresponding road point.
The azimuth correction is performed by the above-mentioned point (a trajectory point adjusted to a road point) by an angle difference obtained by accumulating a turning angle of a road pattern and a trajectory pattern from a reference vector.
This is performed by rotating about.

When the above method is used, FIG.
As shown in (b) and (b), when there is little difference between patterns in the horizontal direction (in this case, a narrow-angle branch and a road which is difficult to determine by map matching such as a Y-shaped road). In this case, correct discrimination and more accurate correction can be performed. In this example, it is difficult to determine and correct only the horizontal (two-dimensional) pattern features (FIG. 12B). Discrimination and correction that could not be obtained by only information can be performed. This is effective for roads that have heavy road undulations and turns, such as mountain roads.

FIG. 12C shows the trajectory of the vehicle before correction.
FIG. 12 (d) shows the vehicle trajectory after the correction, and the superimposed symbol of ○ and × indicates that there is no elevation change, the superimposed symbol of Δ and × indicates that the elevation angle has changed in the ascending direction, The superimposed symbol indicates that the elevation angle changes in the downward direction.

In the above-described map matching method according to one embodiment of the present invention, the pattern feature calculation method in the vertical direction is obtained by accumulating displacements with respect to the previous posture angle (in the case of a road, the gradient) from the reference vector. Although the sum was added, altitude information can be used instead. That is, the pattern feature is obtained by accumulating the displacement from the immediately preceding altitude from the reference altitude. Also in this case, the same effect as the above-described map matching method according to the embodiment of the present invention can be obtained.

[0078]

As described above, according to the map matching method of the present invention, three-dimensional information from positioning and map data can be used not only for selection of candidate roads, but also for positioning, and is relatively simple. There is an effect that the calculation can be used for specific correction.

[Brief description of the drawings]

FIG. 1 is a block diagram showing a configuration of a car navigation device using a map matching method according to an embodiment of the present invention.

FIG. 2 is a flowchart for explaining the operation of the car navigation device shown in FIG. 1;

FIG. 3 is a schematic diagram of a turning angle for explaining a map matching method according to an embodiment of the present invention.

FIG. 4 is a schematic diagram of a trajectory pattern and a turning angle for explaining a map matching method according to an embodiment of the present invention.

FIG. 5 is a schematic diagram of a turning angle and a cumulative angle for explaining a map matching method according to an embodiment of the present invention.

FIG. 6 is a schematic diagram for explaining a method for calculating a pattern feature in a map matching method according to an embodiment of the present invention.

FIG. 7 is a flowchart for explaining a pattern feature forward process in the map matching method according to the embodiment of the present invention;

FIG. 8 is a flowchart illustrating a backward process of a pattern feature in a map matching method according to an embodiment of the present invention.

FIG. 9 is a flowchart for explaining a pattern feature updating method in the map matching method according to the embodiment of the present invention;

FIG. 10 is a flowchart for explaining a method of calculating a pattern feature at time T in the map matching method according to one embodiment of the present invention;

FIG. 11 is a schematic diagram of an elevation angle and a turning angle for explaining a map matching method according to an embodiment of the present invention.

FIG. 12 is a schematic diagram for explaining a map matching method according to an embodiment of the present invention.

FIG. 13 is a block diagram showing a configuration of a car navigation device using a conventional map matching method.

FIG. 14 is a flowchart for explaining an operation of the car navigation device shown in FIG. 13;

[Explanation of symbols]

 θ Turning angle Sθ Cumulative angle Sθ (t) (i) Pattern feature φ Elevation Sφ Cumulative elevation Sφ (t) (i) Pattern feature

Claims (3)

[Claims] Calculating a horizontal pattern similarity between the own vehicle traveling locus pattern and the road pattern at the same time; calculating a vertical pattern similarity between the own vehicle traveling locus pattern and the road pattern at the same time; Calculate the weighted horizontal pattern similarity and the weighted vertical pattern similarity by multiplying the horizontal pattern similarity and the vertical pattern similarity by the weight set based on the road condition, respectively, and calculate the weighted horizontal method pattern similarity. A map matching method characterized by performing map matching based on the degree of similarity and the weighted vertical pattern similarity. 2. The map matching method according to claim 1, wherein N (N is a natural number) sampling points are set on the horizontal vehicle traveling locus pattern and the horizontal road pattern at the same time, respectively. The turning angle at each sampling point on the direction own vehicle traveling locus pattern is accumulated from a vector serving as a predetermined reference to obtain an accumulated angle, and the accumulated angles at the N sampling points are set as horizontal direction own vehicle traveling locus pattern features, The turning angle at each sampling point on the horizontal road pattern is accumulated from the reference vector to obtain a cumulative angle, and the cumulative angle at the N sampling points is used as a feature of the horizontal road pattern. A map map characterized by calculating a horizontal pattern similarity based on a feature and a horizontal road pattern feature. Pitching method. 3. The map matching method according to claim 1, wherein N (N is a natural number) sampling points are set on the elevation vehicle direction trajectory pattern and the elevation road pattern at the same time, respectively. The elevation angle at each sampling point on the direction vehicle traveling locus pattern is accumulated from a predetermined reference vector to obtain a cumulative elevation angle, and the cumulative elevation angle at the N sampling points is taken as a feature of the vertical vehicle traveling locus pattern. The elevation angle at each sampling point on the direction road pattern is accumulated from the reference vector to obtain a cumulative elevation angle, and the cumulative elevation angle at the N sampling points is used as a vertical road pattern feature, A vertical pattern similarity is calculated based on a vertical road pattern feature. Matching method.