Self-position estimation device, navigation device including self-position estimation device, self-position estimation method, and self-position estimation program

Abstract

To estimate its own position with high accuracy even if vehicle speed data by a vehicle speed sensor cannot be obtained, when traveling on a specific path such as a tunnel where radio waves from a positioning satellite cannot be correctly received.SOLUTION: A smartphone (10) according to the present invention functions as a car navigation device when a car navigation app (30) installed in the smartphone (10) is started. When the vehicle on which the smartphone (10) is mounted passes through the tunnel (100a), a change in the orientation of the vehicle is derived based on the angular speed data by the angular speed detection unit (20), and the current position of the vehicle is estimated based on a comparison between the DR data (YD) representing the transition of the derived change in the orientation of the vehicle and the reference data (YS) serving as a reference.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] The present invention relates to a self-location estimation device, a navigation device equipped with the self-location estimation device, a self-location estimation method, and a self-location estimation program, and in particular to a self-location estimation device, a navigation device equipped with the self-location estimation device, a self-location estimation method, and a self-location estimation program that estimates the vehicle's own position when traveling through a specific road such as a tunnel where radio waves from positioning satellites such as GPS satellites cannot be correctly received. [Background technology]

[0002] One example of this type of technology is an on-board device (car navigation device) disclosed in Patent Document 1. This on-board device calculates the distance traveled by the vehicle in a non-reception section where GPS signals cannot be received based on positioning using autonomous navigation, and corrects the position of the vehicle on the screen that has passed through the non-reception section based on this calculated distance and the distance on the map of the non-reception section. Note that a gyro sensor and a vehicle speed sensor are used for positioning using autonomous navigation. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2010-175323 Summary of the Invention [Problem to be solved by the invention]

[0004] Recently, car navigation apps for smartphones (and tablets) and drive recorders with car navigation functions have been distributed on the market. In such modules that are not dedicated to car navigation, vehicle speed data from a vehicle speed sensor cannot be input into the module, so when traveling through a specific road such as a tunnel, the position of the vehicle itself (the vehicle) is estimated using detection data from some sensor equipped in the module.

[0005] One such method is to estimate the vehicle's own position using acceleration data from an acceleration sensor provided in a module. According to this method, the vehicle's speed immediately before entering a specific road is derived by satellite navigation using radio waves from positioning satellites. Then, while traveling on the specific road, the vehicle's own speed is derived based on acceleration data from the acceleration sensor, and the vehicle's own position is estimated based on the derived vehicle's own speed and position data representing the position of each point on the specific road. However, with this method, the acceleration sensor simultaneously detects not only acceleration due to changes in the vehicle's speed but also gravitational acceleration caused by the gradient of the specific road (road), resulting in an error (acceleration error) in the acceleration data from the acceleration sensor. This results in a problem in that the vehicle's own position cannot be estimated with high accuracy.

[0006] Therefore, an object of the present invention is to provide a novel technology that can estimate with high accuracy the vehicle's own position when traveling on a specific road where radio waves from a positioning satellite cannot be received correctly, even if vehicle speed data cannot be obtained from a vehicle speed sensor. [Means for solving the problem]

[0007] To achieve this object, the present invention includes a first invention relating to a self-location estimation device, a second invention relating to a navigation device equipped with the self-location estimation device, a third invention relating to a self-location estimation method, and a fourth invention relating to a self-location estimation program.

[0008] A first aspect of the present invention relating to a self-location estimation device includes a storage unit, an orientation change detection unit, and a self-location estimation unit. The storage unit pre-stores reference transition data representing the transition of changes in the orientation (direction) of a moving object when the moving object travels along a specific road where radio waves from a positioning satellite cannot be received correctly, in association with position data representing the positions of various points on the specific road. The orientation change detection unit detects changes in the moving object's orientation while traveling along the specific road. The self-location estimation unit then estimates the moving object's position based on a comparison between the self-transition data representing the transition of changes in the moving object's orientation detected by the orientation change detection unit and the reference transition data.

[0009] For example, the reference transition data is data that represents the transition of the orientation of a moving object based on the relationship between the cumulative value of the absolute value of the amount of change in the orientation of the moving object and the cumulative value of the amount of change in the orientation of the moving object relative to a predetermined orientation. The self-transition data is data that represents the transition of the object's orientation based on the relationship between the cumulative value of the absolute value of the amount of change in the object's orientation and the cumulative value of the amount of change in the object's orientation relative to a predetermined orientation. The self-location estimation unit then estimates its own location by deriving the correspondence between the individual data values ​​that make up the self-transition data and the individual data values ​​that make up the reference transition data.

[0010] In this case, the self-location estimation unit may derive the correspondence between the individual data values ​​constituting the self transition data and the individual data values ​​constituting the reference transition data, for example, by dynamic time warping.

[0011] Additionally, the self-location estimation unit may use only a portion of the most recent data values ​​among the individual data values ​​that make up the self-estimated data in the calculation according to the dynamic time warping method.

[0012] A second aspect of the present invention relates to a navigation device equipped with a self-location estimation device, which includes the self-location estimation device according to the first aspect of the present invention, and further includes a display unit and a display control unit. The display control unit displays a map including at least a part of the specific road on the display unit, and places an indicator representing the self-location on the map at a position corresponding to the self-location estimated by the self-location estimation unit.

[0013] In the second aspect of the present invention, the vehicle control device may further include a curvature degree deriving means for deriving a curvature degree of the curve of the specific road at the vehicle's own position estimated by the self-position estimating unit. If the curvature degree of the curve of the specific road derived by the curvature degree deriving unit exceeds a predetermined threshold, the display control unit desirably refrains from updating the position of the indicator according to the vehicle's own position estimated by the self-position estimating unit.

[0014] A third aspect of the present invention, relating to a self-location estimation method, is based on the premise that the position of a device equipped with an orientation change detection unit that detects changes in its own orientation is estimated, and includes a storage step, an orientation change detection step, and a self-location estimation step. In the storage step, reference transition data representing the transition of changes in the orientation of a moving object when the moving object travels along a specific road where radio waves from a positioning satellite cannot be properly received is stored in advance in association with position data representing the positions of each point on the specific road. In the orientation change detection step, changes in the orientation of the device while the device is traveling along the specific road are detected by the orientation change detection unit. Then, in the self-location estimation step, the position of the device is estimated based on a comparison between the self-transition data representing the transition of changes in the orientation of the device detected in the orientation change detection step and the reference transition data.

[0015] A fourth aspect of the present invention, relating to a self-location estimation program, is based on the premise that the position of a device equipped with an orientation change detection unit that detects changes in its own orientation is estimated. Under this premise, a storage procedure, an orientation change detection procedure, and a self-location estimation procedure are executed by a computer of the device. In the storage procedure, reference transition data representing the transition of changes in the orientation of a moving object when traveling along a specific road where radio waves from a positioning satellite cannot be properly received is stored in advance in association with position data representing the positions of each point on the specific road. In the orientation change detection procedure, the orientation change detection unit is caused to detect changes in the orientation of the device while the device is traveling along the specific road. In the self-location estimation procedure, the position of the device is estimated based on a comparison between the self-transition data representing the transition of changes in the orientation of the device detected in the orientation change detection procedure and the reference transition data. [Effects of the Invention]

[0016] According to the present invention, when traveling on a specific road where radio waves from a positioning satellite cannot be received correctly, one's own position can be estimated with high accuracy even if vehicle speed data is not obtained from a vehicle speed sensor. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a block diagram showing an electrical configuration of a smartphone according to a first embodiment of the present invention. [Figure 2] FIG. 2 is a diagram for explaining a self-position estimation method in the first embodiment. [Figure 3] FIG. 3 is a diagram conceptually showing the configuration of reference data in the first embodiment. [Figure 4] FIG. 2 is a diagram conceptually illustrating the configuration of DR data in the first embodiment. [Figure 5] FIG. 4 is another diagram for explaining the self-location estimation method in the first embodiment. [Figure 6] FIG. 10 is yet another diagram for explaining the self-location estimation method in the first embodiment. [Figure 7]FIG. 2 is a diagram for explaining a dynamic time warping method used in the first embodiment. [Figure 8] FIG. 10 is another diagram for explaining the dynamic time warping method used in the first embodiment. [Figure 9] FIG. 10 is yet another diagram for explaining the dynamic time warping method used in the first embodiment. [Figure 10] FIG. 10 is yet another diagram for explaining the dynamic time warping method used in the first embodiment. [Figure 11] FIG. 10 is yet another diagram for explaining the dynamic time warping method used in the first embodiment. [Figure 12] FIG. 4 is a diagram for explaining how to derive the correspondence between DR data and reference data in the first embodiment. [Figure 13] FIG. 3 is a diagram showing an example of a map including a vehicle mark displayed on a touch panel in the first embodiment. [Figure 14] FIG. 4 is a flowchart showing the flow of a self-position estimation task in the first embodiment. [Figure 15] FIG. 10 is a flowchart showing a part of the flow of a self-location estimation task in a second embodiment of the present invention. [Figure 16] FIG. 10 is a diagram showing another example of a tunnel to which the present invention can be applied. DETAILED DESCRIPTION OF THE INVENTION

[0018] [First Example] A first embodiment of the present invention will be described using a smartphone 10 shown in FIG. 1 as an example.

[0019] The smartphone 10 according to the first embodiment includes a control unit 12, an input / output interface (I / F) unit 14, an operation display unit 16, a positioning satellite radio wave receiving unit 18, an angular velocity detection unit 20, and an auxiliary storage unit 22. The smartphone 10 also includes various other elements such as a microphone and a speaker, but elements not directly related to the present invention are not shown here.

[0020] The control unit 12 is an element responsible for overall control of the smartphone 10, and includes a computer serving as a control execution unit, such as an application processor (hereinafter referred to as "APP") 12a. The control unit 12 also includes a main memory unit 12b that is directly accessible by the APP 12a. The main memory unit 12b includes, for example, a ROM and a RAM. The ROM stores firmware such as a boot loader and an operating system. The RAM provides a working area and a buffer area for the APP 12a to execute processes based on various programs included in the firmware and various applications (application software).

[0021] The input / output interface unit 14 is an element that acts as a bridge between the control unit 12, particularly the APP 12a, and each element such as the operation display unit 16, and includes, for example, a chipset. Therefore, the input / output interface unit 14 is connected to the control unit 12 as well as each element such as the operation display unit 16.

[0022] The operation display unit 16 has a touch panel 16a. Although not shown in detail, the touch panel 16a is a combination product in which a transparent sheet-like pointing device is attached to the display surface of a flat panel display such as a liquid crystal display or an organic EL display. Such a touch panel 16a serves as both an operation reception unit that receives user operations and a display unit that displays various information. The operation display unit 16 also has hardware buttons such as a power button (not shown) and light-emitting components such as LEDs (not shown).

[0023] The positioning satellite radio wave receiving unit 18 is a component that receives radio waves from a positioning satellite (not shown) and derives the position of itself (the smartphone 10). The positioning satellite here is, for example, a GPS satellite or a GNSS satellite. Naturally, the positioning satellite radio wave receiving unit 18 also has a receiving antenna (not shown).

[0024] The angular velocity detection unit 20 is an element that detects the angular velocity of itself (the smartphone 10), and is capable of detecting angular velocity in the horizontal direction in particular. Therefore, the angular velocity detection unit 20 has an angular velocity sensor (not shown), such as a gyro sensor.

[0025] The auxiliary storage unit 22 is a so-called internal storage, and includes, for example, a flash memory (not shown). Various applications (application software) are stored (installed) in the auxiliary storage unit 22. One of these applications is a car navigation application 30. The car navigation application 30 includes map data for various parts of the country in which the smartphone 10 is used, as well as reference data YS, which will be described later.

[0026] The smartphone 10 according to the first embodiment functions as a car navigation device when the car navigation application 30 described above is activated. When the smartphone 10 functioning as a car navigation device can correctly receive radio waves from positioning satellites, it estimates its own position by satellite navigation using the radio waves from the positioning satellites. However, when it cannot correctly receive radio waves from the positioning satellites, for example, when traveling through a tunnel, the smartphone 10 naturally cannot perform positioning using satellite navigation and instead performs positioning using autonomous navigation. Furthermore, the smartphone 10 cannot acquire vehicle speed data from a vehicle speed sensor provided in the vehicle, so it performs positioning using autonomous navigation using angular velocity data from the angular velocity detection unit 20.

[0027] Specifically, for example, assume that a vehicle (not shown) equipped with a smartphone 10 travels (drives) on a road 100 including a tunnel 100a as shown in Fig. 2(A) in the direction indicated by arrow 100b. Note that the road 100 shown in Fig. 2(A) is the Nagoya Expressway Route 2 Higashiyama Line (Nagoya Municipal Expressway Route 1 Yotsuya Takabari Line), and the tunnel 100a is the Higashiyama Tunnel. Furthermore, the left side in Fig. 2(A) is the east side, and the right side in Fig. 2(A) is the west side. Therefore, assume that the vehicle travels on the road 100 from the east side to the west side.

[0028] When a vehicle travels along such a road 100, particularly when traveling through a tunnel 100a, the orientation of the vehicle when it enters the entrance Pin (high-needle entrance) of the tunnel 100a is used as a reference, e.g., 0 degrees. Then, the amount of change in the vehicle's orientation relative to this reference orientation of 0 degrees is sequentially calculated, e.g., in units of 1 degree, i.e., with a resolution of 1 degree. The vehicle's orientation is calculated based on angular velocity data from the angular velocity detection unit 20, and this calculation is performed by the APP 12a. Whether the vehicle has entered the entrance Pin of the tunnel 100a is determined based on whether the smartphone 10 (positioning satellite radio wave receiving unit 18) correctly receives radio waves from a positioning satellite, specifically, whether the reception level of the radio waves is equal to or greater than a predetermined threshold level. Alternatively, in addition to or instead of the determination based on the reception level of the radio waves, whether the vehicle has entered the entrance Pin of the tunnel 100a may be determined by positioning using satellite navigation. This determination is also made by APP12a.

[0029] Then, each time the amount of change in the vehicle's heading relative to the reference heading is calculated in units of one degree, that is, each time the vehicle's heading changes by one degree, the amount of change in the heading is accumulated, and the absolute value of the amount of change in the heading is also accumulated. These calculations for accumulation are also performed by the APP 12a.

[0030] Then, the relationship between the cumulative value of the absolute value of the amount of change in orientation (hereinafter referred to as "absolute cumulative orientation change") X and the cumulative value of the amount of change in orientation (hereinafter referred to as "cumulative orientation change") Y is summarized as time-series data YX expressed by the following equation 1. Note that m in equation 1 is a variable (index) representing the absolute cumulative orientation change X, and more specifically, is an integer (natural number) greater than or equal to 1 and less than or equal to M. In other words, Y[m] in equation 1 is the cumulative orientation change Y when the absolute cumulative orientation change X is m, that is, the data value (data point) of the time-series data XY. Furthermore, M is the maximum value of the variable m.

[0031] 《Formula 1》 YX={Y[1],Y[2],…,Y[m],…,Y[M]} The time series data YX expressed by Equation 1 is illustrated in Figure 2(B). Figure 2(B) is a graph of the time series data YX, and more specifically, the time series data YX is expanded (plotted) on a two-dimensional Cartesian coordinate system with the absolute cumulative heading change amount X on the horizontal axis and the cumulative heading change amount Y on the vertical axis. The cumulative heading change amount Y takes a positive value when the vehicle's heading relative to the reference heading changes to the right, and takes a negative value when the vehicle's heading relative to the reference heading changes to the left. The units of the absolute cumulative heading change amount X and the cumulative heading change amount Y are both degrees.

[0032] Taking these into consideration, if we look at FIG. 2(B) immediately after the vehicle enters the entrance Pin of tunnel 100a, the vehicle's heading first changes 9 degrees to the left. As a result, the cumulative heading change amount Y becomes -9 degrees (= 0 degrees - 9 degrees), and the absolute cumulative heading change amount X becomes 9 degrees (= 0 degrees + |-9| degrees). Next, the vehicle's heading changes 21 degrees to the right. As a result, the cumulative heading change amount Y becomes 12 degrees (= -9 degrees + 21 degrees), and the absolute cumulative heading change amount X becomes 30 degrees (= |-9| degrees + |21| degrees). In this manner, the absolute cumulative heading change amount X and the cumulative heading change amount Y each change, that is, the time-series data YX changes.

[0033] As can be seen from this, the graph (line) representing the time-series data YX extends to the right in Fig. 2(B) and also extends in either the up or down direction in Fig. 2(B) every time the vehicle's heading changes by one degree. In other words, the graph representing the time-series data YX does not extend only along the left-right direction (horizontal direction) in Fig. 2(B), nor does it extend only along the up and down direction (vertical direction).

[0034] 2(B) represents ideal time-series data YX, or, in other words, reference data YS that serves as a reference for the time-series data YX. This reference data YS is obtained (actually measured, so to speak) based on the amount of change in the direction of a vehicle when the vehicle actually travels (test passes) through a road 100 that includes a tunnel 100a, or is derived (by calculation) from map data for the road 100. This reference data YS is expressed by the following equation 2, which conforms to the above-mentioned equation 1.

[0035] 《Formula 2》 YS={Ys[1],Ys[2],…,Ys[m],…,Ys[M]} The reference data YS is pre-installed in the car navigation application 30 in the manner shown in Fig. 3. Additionally, although it is not clear from Fig. 3 and other figures, each of the data values ​​Ys[1] to Ys[M] constituting the reference data YS is associated with position data that indicates a position within the actual tunnel 100a corresponding to each of the data values ​​Ys[1] to Ys[M]. The reference data YS for (various) tunnels 100a in various locations is pre-installed in the car navigation application 30.

[0036] The dashed-dotted line connecting a suitable point on the road 100 shown in Fig. 2(A) with a suitable point (vertex) on the graph shown in Fig. 2(B) indicates that these two points correspond to each other. In the graph shown in Fig. 2(B), the maximum value M of the variable m representing the absolute cumulative amount of change in heading X is "153." This means that when the tunnel 100a is the Higashiyama Tunnel mentioned above, the heading of the vehicle changes a total of 153 degrees from the entrance Pin of the tunnel 100a to the exit (Fukiage side exit).

[0037] Then, when the smartphone 10 functioning as a car navigation device is in operation and a vehicle equipped with the smartphone 10 passes through the road 100 including the tunnel 100a, time-series data YX is acquired in the same manner as described above, that is, the time-series data YX is acquired based on the amount of change in the vehicle's orientation. The acquired time-series data YX is then temporarily stored as DR (Dead Reckoning) data YD to be used for positioning using autonomous navigation in a manner as shown in FIG. 4, for example, in the main memory unit 12b. This DR data YD is expressed by the following Equation 3, which conforms to the above-mentioned Equation 1.

[0038] 《Formula 3》 YD={Yd[1],Yd[2],…,Yd[m],…,Yd[M]} When this DR data YD is expanded in the above-mentioned orthogonal coordinate system, it becomes as shown in Fig. 5. In Fig. 5, the DR data YD is shown by a graph with a thick dashed line. Also, in Fig. 5, a thick solid line represents the reference data YS.

[0039] As shown in FIG. 5, the DR data YD is roughly similar to the reference data YS, but does not completely match (in most cases). In particular, when the vehicle changes lanes inside the tunnel 100a, the DR data YD will have a different trajectory (shape) from the reference data YS, as shown in the area surrounded by the dashed ellipse 110 in FIG. 5. As a result, the DR data YD will have a trajectory that extends to the right in FIG. 5. Note that the change in vehicle direction caused by a lane change is smaller than the change in vehicle direction caused by the curve in the tunnel 100a, so the trajectory of the DR data YD does not change extremely (i.e., to the extent that it is not similar to the reference data YS). Furthermore, the end point Pd of the trajectory of the DR data YD in FIG. 5 corresponds to the current position of the vehicle.

[0040] From such a relationship between the DR data YD and the reference data YS, as shown in Fig. 6, the DR data YD and the reference data YS can be considered to correspond to each other at a point where their cumulative azimuth change amounts Y are the same and their absolute cumulative azimuth change amounts X are close to each other. That is, in Fig. 6, a certain data value Yd[n] (n: a natural number equal to or less than M) of the DR data YD indicated by each arrow 120 and a certain data value Ys[m] of the reference data YS can be considered to correspond to each other. Therefore, if it is known to which data value Ys[m] of the reference data YS an arbitrary data value Yd[n] of the DR data YD corresponds, it becomes possible to estimate which position in the tunnel 100a the arbitrary data value Yd[n] of the DR data YD corresponds to. Strictly speaking, in the application of a car navigation device, it is sufficient to know which data value Ys[m] of the reference data YS corresponds to the data value Yd[n'] (n': the current absolute cumulative orientation change amount X of the DR data YD) of the end point Pd of the trajectory of the DR data YD, i.e., the latest data value Yd[n'], in other words, to know the current position of the vehicle within the tunnel 100a.

[0041] To this end, pattern matching is performed between the DR data YD and the reference data YS. For example, a well-known dynamic time warping (DTW) algorithm is used to perform this pattern matching. The dynamic time warping algorithm calculates the distance between each data value of two pieces of time series data in a brute-force manner, searches for the combination with the smallest distance among all combinations, and treats it as the similarity. This dynamic time warping algorithm can derive the similarity between the time series data even if they have different lengths or periods, i.e., it can derive the correspondence between each data value.

[0042] This dynamic time warping method will be explained using, for example, the two time series data α and β shown in Figure 7(A). In order to derive the correspondence between these two, a matrix 200 such as that shown in Figure 7(B) is prepared. This matrix 200 has a number of cells equal to the product of the number of data values ​​of one time series data α and the number of data values ​​of the other time series data β. Note that the number of data values ​​of one time series data α is 6, and the number of data values ​​of the other time series data β is also 6. Therefore, matrix 200 has a total of 36 (=6 x 6) cells.

[0043] The rows and columns of matrix 200 are associated with the time series data α and β, respectively. For example, the rows of matrix 200 are associated with one piece of time series data α, and the columns of matrix 200 are associated with the other piece of time series data β. Then, the absolute values ​​of the mutual differences between the data values ​​of each piece of time series data α and β, i.e., the absolute errors, are calculated in a brute-force manner. The first data values ​​of each piece of time series data α and β correspond to each other, and the last data values ​​of each piece of time series data α and β are also treated as corresponding to each other. For one piece of time series data α associated with a row of matrix 200, its first data value corresponds to the first (top) row of matrix 200, and its last data value corresponds to the last (bottom) row of matrix 200. In addition, for the other time series data β associated with a column of matrix 200, its first data value is associated with the first (leftmost) column of matrix 200, and the last data value of the time series data α is associated with the last (rightmost) column of matrix 200.

[0044] 8, first, a value of "0.2" (=|0.8-0.6|) which is the absolute value of the mutual difference between the data value "0.8" of one piece of time-series data α corresponding to that cell and the data value "0.6" of the other piece of time-series data β corresponding to that cell is input into the cell in the upper left corner (first row, first column) of matrix 200, i.e., the absolute error is input. Next, a value of "1.0" (=0.8+0.2) which is obtained by adding the absolute error (=0.8=|0.8-0|) between the data value "0.8" of one piece of time-series data α corresponding to that cell and the data value "0" of the other piece of time-series data β corresponding to that cell to the absolute error between that cell and the data value "0" of the other piece of time-series data β corresponding to that cell to the absolute error is input into the cell adjacent to the left of that cell (i.e., the upper left corner) to the absolute error (=0.2) of the cell adjacent to the left of that cell (i.e., the upper left corner) is input. In a similar manner, the remaining squares in the first row of the matrix 200 are filled in.

[0045] Next, the absolute error (=1.0=|-0.4-0.6|) between the data value "-0.4" of one piece of time series data α corresponding to that cell and the data value "0.6" of the other piece of time series data β corresponding to that cell is added to the absolute error (=0.2) of the cell adjacent above that cell (i.e., in the upper left corner), resulting in a value of "1.2" (=1.0+0.2). In a similar manner, the remaining cells in the first column of matrix 200 are filled in.

[0046] 9, attention is now focused on the cell in the second row and second column of matrix 200 (the cell surrounded by a dashed line). This cell is input with the absolute error (=-0.4=|-0.4-0|) between the data value "-0.4" of one piece of time-series data α corresponding to this cell and the data value "0" of the other piece of time-series data β corresponding to this cell, plus the absolute error (=0.2) of the cell with the smallest absolute error among the three cells (surrounded by dashed lines) to the left, above, and to the upper left of this cell. In a similar manner, the remaining cells in the second row of matrix 200 are filled, and then the remaining cells from the third row onward in matrix 200 are filled.

[0047] As a result, all the squares are filled in as shown in Fig. 10. As mentioned above, the first data values ​​of each piece of time series data α and β correspond to each other, and the last data values ​​of each piece of time series data α and β are also treated as corresponding to each other. To represent this, in Fig. 10, the squares corresponding to the first data values ​​of each piece of time series data α and β (the squares in the upper left corner) and the squares corresponding to the last data values ​​of each piece of time series data α and β (the squares in the lower right corner) are shaded.

[0048] After all the squares are filled in this way, the corresponding relationship between the data values ​​of each of the time series data α and β is derived (searched). In this case, first, the square in the lower right corner (sixth row, sixth column) of matrix 200 is focused on, and the square with the smallest error value (=3.1) (the square in the sixth row, fifth row, surrounded by a dashed line) is identified among the three squares (surrounded by a dashed line) adjacent to the left, above, and to the upper left of this square. This identified square indicates that the data value "0" of one of the time series data α corresponding to this square and the data value "-1" of the other of the time series data β corresponding to this square correspond to each other.

[0049] In a similar manner, the cell identified as above (the cell in the sixth and fifth rows surrounded by the dashed line) is focused on, and the cell with the smallest error value (=2.1) among the three cells adjacent to the left, above, and to the upper left of this cell (the cell in the sixth and fourth rows) is newly identified, that is, it is identified as the cell representing the correspondence between the data values ​​of each of the time series data α and β. Thereafter, in a similar manner, cells representing the correspondence between the data values ​​of each of the time series data α and β are sequentially identified.

[0050] As a result, as shown in Fig. 7(B), the correspondence relationship between the data values ​​of each of the time series data α and β is derived on the matrix 200. That is, in Fig. 7(B), the hatched cells represent the correspondence relationship between the data values ​​of each of the time series data α and β. In Fig. 7(A), the data values ​​of each of the time series data α and β are connected by a dashed line, which indicates that the data values ​​connected by the dashed line correspond to each other.

[0051] That is, as described above, the first data values ​​of each of the time series data α and β correspond to each other, and the last data values ​​of each of the time series data α and β also correspond to each other. For example, a data value of "-0.4" in one of the time series data α corresponds to a data value of "0" in the other of the time series data β. This data value of "0" in the other of the time series data β also corresponds to the data values ​​of "-1" and "-0.3" in the one of the time series data α, but such correspondence of one data value to multiple data values ​​is possible in dynamic time warping. Furthermore, a data value of "1" in one of the time series data α corresponds to a data value of "1" in the other of the time series data β. Furthermore, a data value of "0" in one of the time series data α corresponds to three data values ​​in the other of the time series data β: "0.2," "-1," and "-0.5."

[0052] When such dynamic time warping is used for pattern matching between the DR data YD and the reference data YS, for example, to derive a correspondence between all data values ​​Yd[n] constituting the DR data YD and all data values ​​Ys[m] constituting the reference data YS, a matrix 200 having a corresponding number of cells must be prepared. Specifically, a matrix 200 having a huge number of cells, such as M × M, must be prepared. This matrix 200 is provided in the main storage unit 12b, and as the matrix 200 becomes larger, the storage capacity (memory capacity) of the main storage unit 12b is correspondingly strained. In addition, the APP 12a is responsible for the calculation for pattern matching using the dynamic time warping method, and as the matrix 200 becomes larger, the burden on the APP 12a increases. As mentioned above, in a car navigation device application, it is sufficient to know the current position of the vehicle; in other words, it is not necessary to derive a correspondence between all data values ​​Yd[n] constituting the DR data YD and all data values ​​Ys[m] constituting the reference data YS.

[0053] For these reasons, in order to avoid overloading the storage capacity of the main memory unit 12b and to reduce the burden on the APP 12a, in pattern matching between the DR data YD and the reference data YS using the dynamic time warping method, only a portion of the most recent data among the data values ​​Yd[n] constituting the DR data YD is used in the calculation for the pattern matching. More specifically, for example, only the most recent four (i.e., four degrees' worth) data values ​​Yd[n'], Yd[n'-1], Yd[n'-2], and Yd[n'-3] among the data values ​​Yd[n] constituting the DR data YD are used in the calculation for the pattern matching. Therefore, the number of cells in the matrix 200 is reduced to 4 × M, as shown in FIG. 12.

[0054] 12, the matrix 200 has 4 rows and M columns of cells. The most recent four data values ​​Yd[n'], Yd[n'-1], Yd[n'-2], and Yd[n'-3] of the DR data YD are associated with the rows of the matrix 200. In particular, the data values ​​Yd[n'], Yd[n'-1], Yd[n'-2], and Yd[n'-3] including the most recent data value Yd[n'] are associated with the rows of the matrix 200 so that the most recent data value Yd[n'] is associated with the first row of the matrix 200. In addition, the data values ​​Ys[1] to Ys[M] of the reference data YS are associated with the columns of the matrix 200. In particular, the first data value Ys[1] of the reference data YS is associated with the first column of the matrix 200, and each data value Ys[1] to Ys[M] including the first data value Ys[1] is associated with a column of the matrix 200.

[0055] Then, all the squares of matrix 200 are filled in in the same manner as described with reference to Figures 8 to 10. Then, the square with the smallest error value is identified for each column of matrix 200. In Figure 12, the hatched squares indicate the squares with the smallest error value identified for each column.

[0056] Here, if both of the following two conditions are satisfied, it is considered that the correspondence between the DR data YD and the reference data YS is stable and that it is possible to estimate the current position of the vehicle within the tunnel 100a. The first condition is that the error values ​​(minimum values) of the squares with the smallest error values ​​identified for each column of the matrix 200 are all the same. The second condition is that the squares with the smallest error values ​​identified for each column of the matrix 200 are aligned in a diagonal line (from the upper left to the lower right) on the matrix 200. Note that FIG. 12 shows an example of a state in which both the first and second conditions are satisfied, or more precisely, an example of a state in which at least the second condition is satisfied. In addition, Figure 12 shows an example of a state in which the four most recent data values ​​Yd[n'], Yd[n'-1], Yd[n'-2] and Yd[n'-3] of the DR data YD correspond to four consecutive data values ​​Ys[m'] (m': a value of the absolute cumulative orientation change amount X), Ys[m'-1], Ys[m'-2] and Ys[m'-3] of the reference data YS.

[0057] That is, the satisfaction of the first condition, i.e., the fact that the error values ​​of the cells with the smallest error values ​​specified for each column of the matrix 200 are all the same, means that the current vehicle orientation in the tunnel 100a represented by the DR data YD matches the orientation represented by the reference data YS. And the satisfaction of the second condition, i.e., the fact that the cells with the smallest error values ​​specified for each column of the matrix 200 are aligned diagonally in a straight line on the matrix 200, means that the four most recent data values ​​Yd[n'], Yd[n'-1], Yd[n'-2], and Yd[n'-3] of the DR data YD stably correspond to certain four data values ​​Ys[m'], Ys[m'-1], Ys[m'-2], and Ys[m'-3] of the reference data YS.

[0058] In this way, when it is deemed that the correspondence between the DR data YD and the reference data YS is stable and that it is possible to estimate the current position of the vehicle within the tunnel 100a, the current position of the vehicle is estimated. That is, the position based on the position data associated with the data value Ys[m'] of the reference data YS corresponding to the latest data value Yd[n'] of the DR data YD is estimated as the current position of the vehicle.

[0059] 13 is displayed on the touch panel 16a of the smartphone 10. Specifically, the map 300 includes the current location of the vehicle. Therefore, when the vehicle is traveling through a tunnel 100a, the map 300 includes at least a part of the tunnel 100a. Additionally, a vehicle mark 310 is displayed on the map 300 at a position corresponding to the current location of the vehicle, as an indicator representing the vehicle.

[0060] Then, when the vehicle exits the exit Pout of the tunnel 100a, positioning using autonomous navigation is terminated and switched to positioning using satellite navigation. Whether the vehicle has exited the exit Pout of the tunnel 100a is determined based on whether the smartphone 10 (positioning satellite radio wave receiver 18) is again able to correctly receive radio waves from the positioning satellite, more specifically, based on whether the reception level of the radio waves has again reached or exceeded the aforementioned threshold level. This determination is made by the APP 12a. After switching to positioning using satellite navigation, the position of the vehicle mark 310 on the map 300 is updated based on the results of positioning using the satellite navigation.

[0061] To achieve such positioning using autonomous navigation, the APP 12a of the smartphone 10 executes a self-location estimation task in accordance with a self-location estimation program included in the car navigation application 30. The flow of this self-location estimation task is shown in FIG. 14. Note that the symbols beginning with "S" shown in FIG. 14 are symbols used to identify each step (process), and in the following description, each step will be represented by this symbol. The APP 12a executes the self-location estimation task upon determining that the vehicle has entered the entrance Pin of the tunnel 100a. In addition, before the vehicle enters the entrance Pin of the tunnel 100a, the APP 12a can recognize the location of the tunnel 100a using positioning using satellite navigation, and therefore prepares reference data YS corresponding to the tunnel 100a.

[0062] According to this self-location estimation task, first, in S1, the APP 12a sets the variable m of the absolute cumulative orientation change amount X to "1" as an initial value. Then, in the following S3, the APP 12a sets the cumulative orientation change amount Y to "0" degrees, that is, resets it. Furthermore, in the following S5, the APP 12a starts deriving the cumulative orientation change amount Y. Then, in the following S7, the APP 12a sets the variable j representing the number of stored data values ​​Yd[m] of the DR data YD to "1" as an initial value. Thereafter, the APP 12a advances the processing to S9.

[0063] In S9, the APP 12a sets the current cumulative heading change amount Y as a reference value Y' for calculating a change amount ΔY in the cumulative heading change amount Y, which will be described later. Then, in the following S11, the APP 12a waits until the absolute value |ΔY| of the change amount ΔY in the cumulative heading change amount Y becomes 1 degree or more, that is, until the vehicle heading changes by 1 degree or more (S11: NO). Then, when the absolute value |ΔY| of the change amount ΔY in the cumulative heading change amount Y becomes 1 degree or more, that is, when the vehicle heading changes by 1 degree or more (S11: YES), the APP 12a proceeds to the processing at S13. The change amount ΔY in the cumulative heading change amount Y is calculated by the following equation 4.

[0064] 《Formula 4》 ΔY=Y-Y' In S13, the APP 12a stores the current cumulative orientation change amount Y as a data value Yd[m] of the DR data YD, for example, in the main storage unit 12b. Then, in the following S15, the APP 12a increments the value of the variable m of the absolute cumulative orientation change amount X, and then proceeds to S17.

[0065] In S17, the APP 12a determines whether the value of the variable j, which indicates the number of stored data values ​​Yd[m] of the DR data YD, has reached "4", that is, whether the number of stored data values ​​Yd[m] has reached "4". If the value of the variable j has reached "4", that is, if the number of stored data values ​​Yd[m] has reached "4" (S17: YES), the APP 12a proceeds to S21, which will be described later. On the other hand, if the value of the variable j has not reached "4", that is, if the value of the variable j has not reached "4" (S17: NO), the APP 12a proceeds to S19.

[0066] In S19, the APP 12a increments the value of the variable j, and then returns the process to S9 to set the current cumulative orientation change amount Y as a new reference value Y′ for calculating the change amount ΔY of the cumulative orientation change amount Y.

[0067] On the other hand, when the APP 12a proceeds from S17 to S21, in S21, the APP 12a derives the correspondence between the DR data YD and the reference data YS in the manner described above, and more specifically, derives which data value Ys[m] of the reference data YS corresponds to each of the four most recent data values ​​Yd[n'], Yd[n'-1], Yd[n'-2], and Yd[n'-3] of the DR data YD. Then, the APP 12a proceeds to S23.

[0068] In S23, the APP 12a determines whether the correspondence between the DR data YD and the reference data YS derived in S21 is stable, that is, whether both the first and second conditions described above are satisfied. If the correspondence between the DR data YD and the reference data YS is stable, that is, if both the first and second conditions are satisfied (S23: YES), the APP 12a proceeds to S25. On the other hand, if the correspondence between the DR data YD and the reference data YS is not stable, that is, if at least one of the first and second conditions is not satisfied (S23: NO), the APP 12a proceeds to S29, which will be described later.

[0069] In S25, the APP 12a estimates its own current position, that is, the current position of the vehicle. That is, the APP 12a estimates the current position of the vehicle based on the position data associated with the data value Ys[m'] of the reference data YS corresponding to the latest data value Yd[n'] of the DR data YD. Then, the APP 12a proceeds to S27.

[0070] In S27, the APP 12a updates the display position of the vehicle mark 310 on the map 300 based on the current position of the vehicle estimated in S25, that is, updates the display of the current position of the vehicle. Then, the APP 12a advances the process to S29.

[0071] In S29, the APP 12a determines whether the vehicle has exited the exit Pout of the tunnel 100a. If the vehicle has exited the exit Pout of the tunnel 100a (S29: YES), the APP 12a ends the self-location estimation task. On the other hand, if the vehicle has not exited the exit Pout of the tunnel 100a (S29: NO), the APP 12a returns the process to S9 to set the current cumulative orientation change amount Y as a new reference value Y' for calculating the change amount ΔY of the cumulative orientation change amount Y.

[0072] As described above, according to the first embodiment, positioning by autonomous navigation that focuses particularly on the amount of change in the vehicle's orientation, the position of the vehicle when traveling on a specific road where radio waves from a positioning satellite cannot be received correctly, such as inside tunnel 100a, can be estimated with high accuracy even if vehicle speed data is not available from a vehicle speed sensor.

[0073] As mentioned above, the method using acceleration data from an acceleration sensor cannot estimate the vehicle's position with high accuracy because the acceleration sensor simultaneously detects the gravitational acceleration caused by the gradient of the road in addition to the acceleration caused by changes in the vehicle's speed. For example, if we imagine a vehicle traveling at 100 km / h in a 5 km long tunnel with an average downhill gradient of 1 degree, the vehicle will always have a velocity of -0.1715 m / s 2 This means that even though the actual vehicle speed remains the same, the speed derived from the acceleration data from the acceleration sensor will drop by 6.174 km / h after 10 seconds. It takes three minutes for a vehicle to pass through a 5 km long tunnel, so the speed derived from the acceleration data from the acceleration sensor will be 0 km / h before this time has elapsed. In other words, the vehicle will come to a halt in the tunnel in calculations. In this case, the error at the time the vehicle actually passes through the tunnel will be approximately 2.5 km. This large error occurs because two integrations are performed over time in the process of deriving distance from acceleration data.

[0074] In contrast, according to the first embodiment, the amount of change in the vehicle's heading is derived based on the angular velocity data from the angular velocity detection unit 20, and therefore, integration over time is performed only once in the process of deriving the amount of change in the vehicle's heading. Therefore, even if the angular velocity detection unit 20 is affected by an error, the error is suppressed to a magnitude proportional to time. This means that the error suffered by the angular velocity detection unit 20 is far smaller than the error (noise) suffered by the acceleration sensor. For this reason, according to the first embodiment, the vehicle position can be estimated with high accuracy, and in particular, the vehicle position can be estimated with far higher accuracy than a method that uses acceleration data from an acceleration sensor.

[0075] [Second Example] Next, a second embodiment of the present invention will be described.

[0076] In the method for estimating the current position of a vehicle according to the first embodiment, the accuracy of estimating the current position of the vehicle decreases as the radius of curvature R (degree of curvature) of the curve in tunnel 100a increases. Therefore, if the display position of the vehicle mark 310 on the map 300 is updated based on the current position of the vehicle estimated under such circumstances, the actual position of the vehicle will diverge from the position of the vehicle mark 310, which is undesirable.

[0077] Therefore, in the second embodiment, when the radius of curvature R at the current position of the vehicle is relatively small, more specifically, when the radius of curvature R is equal to or smaller than a predetermined threshold value Rth (R≦Rth), the display position of the host vehicle mark 310 is updated. When the radius of curvature R at the current position of the vehicle is large, that is, when the radius of curvature R exceeds the predetermined threshold value Rth (R>Rth), the display position of the host vehicle mark 310 is not updated, that is, the update of the display position is postponed.

[0078] As mentioned above, each data value Ys[1] to Ys[M] of the reference data YS is associated with position data that indicates a position within the actual tunnel 100a that corresponds to that data value Ys[1] to Ys[M]. Therefore, it is possible to calculate the radius of curvature R at any position within the tunnel 100a from this position data, and therefore it is also possible to calculate the radius of curvature R at the current position of the vehicle.

[0079] To realize such a configuration, in the second embodiment, S101 and S103 as shown in FIG. 15 are provided between S25 and S27 in the self-location estimation task described above.

[0080] That is, after estimating the current position of the vehicle in S25, the APP 12a advances the process to S101, where it calculates the radius of curvature R at the current position of the vehicle. Then, the APP 12a advances the process to S103.

[0081] In S103, the APP 12a compares the radius of curvature R at the current position of the vehicle calculated in S101 with a predetermined threshold value Rth. If the radius of curvature R at the current position of the vehicle is equal to or smaller than the threshold value Rth (S103: YES), the APP 12a proceeds to S27 and updates the display position of the host vehicle mark 310 on the map 300. On the other hand, if the radius of curvature R at the current position of the vehicle exceeds the threshold value Rth (S103: NO), the APP 12a proceeds to S29 and determines whether the vehicle has exited the exit Pout of the tunnel 100a.

[0082] As described above, according to the second embodiment, when the radius of curvature R at the current vehicle position exceeds a predetermined threshold value Rth, that is, under circumstances where there is a possibility that the estimation accuracy of the current vehicle position may be reduced, the display position of the vehicle mark 310 is not updated. This prevents the inconvenience of the actual vehicle position and the position of the vehicle mark 310 on the map 300 becoming separated from each other.

[0083] [Other application examples of the present invention] The above-described embodiments are merely specific examples of the present invention and do not limit the scope of the present invention. The present invention can also be applied in various aspects other than the embodiments.

[0084] For example, the unit (resolution) for deriving the amount of change in the vehicle is set to 1 degree, but this is not limited to this. However, if the unit for deriving the amount of change in the vehicle is too small, the number of cells in the matrix 200 described above will increase excessively, which will put pressure on the storage capacity of the main storage unit 12b and increase the burden on the APP 12a, which performs calculations for pattern matching using the matrix 200. On the other hand, if the unit for deriving the amount of change in the vehicle is too large, the deriving error of the amount of change in the vehicle will increase, and ultimately the estimation accuracy of the current position of the vehicle will decrease. Therefore, it is important that the unit for deriving the amount of change in the vehicle be set to an appropriate value taking these factors into consideration.

[0085] Furthermore, in the pattern matching between the DR data YD and the reference data YS using the dynamic time warping method, only the most recent four (i.e., four-degree) data values ​​Yd[n'], Yd[n'-1], Yd[n'-2], and Yd[n'-3] of each data value Yd[n] constituting the DR data YD are used in the pattern matching calculation, but this is not limited to this. That is, the number of data values ​​Yd[n] of the DR data YD used in the pattern matching calculation is not limited to four. However, if the number of data values ​​Yd[n] used in the pattern matching calculation is excessively large, the vehicle's orientation must change significantly to perform the pattern matching, which may excessively limit the tunnels 100a that satisfy such conditions, i.e., the tunnels 100a to which the present invention can be applied. Furthermore, if the number of data values ​​Yd[n] used in the pattern matching calculation is excessively small, even changes in the vehicle's orientation due to lane changes will be included in the pattern matching, thereby reducing the accuracy of estimating the vehicle's current position. Therefore, it is essential that the number of data values ​​Yd[n] used in the pattern matching calculation be determined to an appropriate value taking these factors into consideration.

[0086] Furthermore, depending on the tunnel 100a, as shown in FIG. 16, a single road 400 may branch into multiple roads, e.g., three roads 410, 420, and 430, within the tunnel 100a. Ohashi Junction in Tokyo is an example of this. In such a case, reference data YS is prepared for each of the three roads 410, 420, and 430. Then, when a vehicle passes through a branch point 450 and it is determined with certainty which of the roads 410, 420, and 430 the vehicle is traveling on, the reference data YS for the road 410, 420, or 430 the vehicle is traveling on is used in the pattern matching calculation.

[0087] In addition, although the dynamic time warping method is used as the algorithm for pattern matching, the algorithm is not limited to this. For example, an algorithm other than dynamic time warping, such as a cross-correlation function (CCF), may be used.

[0088] Furthermore, the present invention can be applied not only to the case where the vehicle's position is estimated inside the tunnel 100a, but also to the case where the vehicle's position is estimated on a specific road other than the tunnel 100a, such as an urban canyon (a valley between buildings) or an elevated road.

[0089] The present invention is not limited to car navigation apps for smartphones, but can also be applied to drive recorders equipped with car navigation functions, etc. In other words, the present invention can be applied to devices that have the function of receiving radio waves from positioning satellites and an angular velocity detection unit such as a gyro sensor, but cannot acquire vehicle speed data from a vehicle speed sensor.

[0090] Furthermore, the present invention is not limited to being provided in the form of a self-location estimation device, but can also be provided in the form of a self-location estimation method or a self-location estimation program.

[0091] The present invention can also be provided in the form of a computer-readable non-transitory recording medium on which a self-location estimation program is recorded. The recording medium referred to here includes semiconductor media and disk media. Alternatively, instead of portable media, built-in (internal) media that are incorporated into devices such as ROMs and hard disk drives can also be used as the recording medium referred to here.

[0092] The reference data YS in each of the above-described embodiments is an example of reference transition data according to the present invention. The DR data YD in each of the embodiments is an example of self-transition data according to the present invention. The angular velocity detection unit 20 in each of the embodiments cooperates with the APP 12a to constitute an orientation change detection unit according to the present invention. Furthermore, the APP 12a in each of the embodiments, particularly the APP 12a that executes S25 of the self-location estimation task, is an example of a self-location estimation unit according to the present invention. Additionally, the map 300 shown in FIG. 13 is displayed on the touch panel 16a by the APP 12a. The APP 12a that displays such map 300 is an example of a display control unit according to the present invention. The calculation of the radius of curvature R in the second embodiment is performed by the APP 12a. The APP 12a that calculates the radius of curvature R is an example of a curve degree derivation unit according to the present invention. [Explanation of symbols]

[0093] 10 … Smartphone 12 ... Control section 12a … APP 12b... Main memory section 16 … Operation display section 16a ... Touch panel 18 ... Positioning satellite radio wave receiver 20...Angular velocity detection section 22 … Auxiliary storage section 30... Car navigation app 100 … road 100a … Tunnel 200... Matrix 300 … Map 310 ... Vehicle mark YD...DR data YS: Normative data

Claims

1. a storage unit in which reference transition data representing the transition of a change in the orientation of a moving object when the moving object travels along a specific road where radio waves from a positioning satellite cannot be received correctly is stored in advance in a state in which the reference transition data is associated with position data representing the positions of each point on the specific road; a direction change detection unit that detects a change in the vehicle's own direction while traveling along the specific road; A self-position estimation device comprising: a self-position estimation unit that estimates its own position based on a comparison between self-transition data representing the transition of changes in the self's orientation detected by the orientation change detection unit and the reference transition data.

2. the reference transition data is data that represents a transition in the orientation of the vehicle based on a relationship between a cumulative value of the absolute value of the amount of change in the orientation of the vehicle and a cumulative value of the amount of change in the orientation of the vehicle relative to a predetermined orientation; the self-transition data is data that represents the transition of the own orientation based on a relationship between an accumulated value of the absolute value of the amount of change in the own orientation and an accumulated value of the amount of change in the own orientation with the predetermined orientation as a reference, The self-location estimation device according to claim 1, wherein the self-location estimation unit estimates the self-location by deriving a correspondence relationship between each data value constituting the self-transition data and each data value constituting the reference transition data.

3. The self-location estimation device according to claim 2 , wherein the self-location estimation unit derives the correspondence relationship by a dynamic time warping method.

4. The self-location estimation device according to claim 3, wherein the self-location estimation unit uses only a portion of the most recent data values ​​among the individual data values ​​constituting the self-estimated data for calculations according to the dynamic time warping method.

5. The self-location estimation device according to claim 1, further comprising: A display unit; A navigation device comprising: a display control unit that displays a map including at least a portion of the specific road on the display unit, and places a display element representing the vehicle's own position at a position on the map corresponding to the vehicle's own position estimated by the vehicle's own position estimation unit.

6. a curve degree deriving unit that derives a curve degree of the specific road at the vehicle's own position estimated by the self-position estimating unit, 6. The navigation device according to claim 5, wherein the display control unit refrains from updating the position of the indicator in accordance with the vehicle's own position estimated by the self-position estimation unit when the degree of curvature of the curve of the specific road derived by the curvature degree derivation unit exceeds a predetermined threshold.

7. A self-position estimation method for estimating a position of a device having an orientation change detection unit that detects a change in its own orientation, comprising: a storage step of storing in advance reference transition data representing the transition of a change in the orientation of a moving object when the moving object travels along a specific road where radio waves from a positioning satellite cannot be correctly received, in a state in which the reference transition data is associated with position data representing the positions of each point on the specific road; a direction change detection step of detecting a change in the direction of the device when the device is traveling on the specific road by the direction change detection unit; A self-position estimation method including a self-position estimation step of estimating the position of the device based on a comparison between self-transition data representing the transition of the change in orientation of the device detected by the orientation change detection step and the reference transition data.

8. A self-position estimation program for estimating a position of a device having an orientation change detection unit that detects a change in its own orientation, a storage step of storing in advance reference transition data representing the transition of a change in the orientation of a vehicle traveling along a specific road where radio waves from a positioning satellite cannot be correctly received, in a state in which the reference transition data is associated with position data representing the positions of each point on the specific road; a direction change detection step of causing the direction change detection unit to detect a change in the direction of the device when the device is traveling on the specific road; A self-location estimation program that causes a computer of the device to execute a self-location estimation procedure that estimates the position of the device based on a comparison between self-transition data that represents the transition of the change in orientation of the device detected by the orientation change detection procedure and the reference transition data.

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