Measurement device, method for measurement, and program

Abstract

To properly adjust the range of detecting a white line according to situations and prevent reduction of the accuracy of estimating the position of an owned vehicle.SOLUTION: A measurement device acquires output data from a sensor unit for detecting a road surface line around the measurement device, and determines a predetermined range on the basis of the self position and positional information on the road surface line of a dashed line type. The measurement device also extracts data on the result of detection in the predetermined range from the output data, and performs predetermined processing on the basis of the extracted data.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present invention relates to a technique for estimating the position of a moving object based on the positions of features. [Background technology]

[0002] In autonomous vehicles, it is necessary to estimate the vehicle's position with high accuracy by matching the positions of features measured by sensors such as LiDAR (Light Detection and Ranging) with the positions of features in map information for autonomous driving. Examples of features used here include white lines, road signs, and billboards. Patent Document 1 describes an example of a method for estimating the vehicle's position using the positions of features detected using LiDAR and the positions of features in map information. Patent Document 2 also discloses a technology for transmitting electromagnetic waves to the road surface and detecting white lines based on their reflectivity. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2017-72422 [Patent Document 2] Japanese Patent Application Laid-Open No. 2015-222223 Summary of the Invention [Problem to be solved by the invention]

[0004] When estimating vehicle position using white lines, the amount of data that can be measured by LiDAR varies depending on the type of white line (continuous line, broken line, etc.), paint deterioration, etc. Therefore, when estimating vehicle position using white lines, the accuracy of white line detection changes depending on whether the amount of LiDAR data used to detect the white lines is small or large, and as a result, the accuracy of vehicle position estimation also changes.

[0005] The present invention aims to solve the above-mentioned problems by appropriately adjusting the range in which white lines are detected depending on the situation, thereby preventing a decrease in the accuracy of vehicle position estimation. [Means for solving the problem]

[0006] The invention described in claim 1 is a measuring device characterized by comprising an acquisition unit that acquires output data from a sensor unit for detecting surrounding road lines, a determination unit that determines a predetermined range based on the vehicle's own position and position information of dashed road lines, an extraction unit that extracts data from the output data that corresponds to the detection result of the predetermined range, and a processing unit that performs predetermined processing based on the extracted data.

[0007] The invention described in claim 5 is a measuring device comprising an acquisition unit that acquires output data from a sensor unit for detecting surrounding road surface lines, a determination unit that determines a predetermined range based on the device's own position, position information of the road surface lines, and the curvature of the road surface lines, an extraction unit that extracts data from the output data that corresponds to the detection result of the predetermined range, and a processing unit that performs predetermined processing based on the extracted data.

[0008] The invention described in claim 8 is a measurement method executed by a measurement device, characterized in that it comprises an acquisition process for acquiring output data from a sensor unit for detecting surrounding road lines, a determination process for determining a predetermined range based on the vehicle's own position and position information of dashed road lines, an extraction process for extracting data from the output data corresponding to the detection result of the predetermined range, and a processing process for performing predetermined processing based on the extracted data.

[0009] The invention described in claim 9 is a measurement method executed by a measurement device, characterized in that it comprises an acquisition process for acquiring output data from a sensor unit for detecting surrounding road surface lines, a determination process for determining a predetermined range based on the device's own position, position information of the road surface lines, and the curvature of the road surface lines, an extraction process for extracting data from the output data corresponding to the detection result of the predetermined range, and a processing process for performing predetermined processing based on the extracted data.

[0010] The invention described in claim 10 is a program executed by a measuring device equipped with a computer, characterized in that it causes the computer to function as an acquisition unit that acquires output data from a sensor unit for detecting surrounding road lines, a determination unit that determines a predetermined range based on the vehicle's own position and position information of dashed road lines, an extraction unit that extracts data from the output data that corresponds to the detection result of the predetermined range, and a processing unit that performs predetermined processing based on the extracted data.

[0011] The invention described in claim 11 is a program executed by a measuring device equipped with a computer, characterized in that it causes the computer to function as an acquisition unit that acquires output data from a sensor unit for detecting surrounding road surface lines, a determination unit that determines a specified range based on the device's own position, position information of the road surface lines, and the curvature of the road surface lines, an extraction unit that extracts data from the output data that corresponds to the detection result of the specified range, and a processing unit that performs specified processing based on the extracted data. [Brief explanation of the drawings]

[0012] [Figure 1] FIG. 10 is a diagram illustrating a white line extraction method. [Figure 2] FIG. 10 is a diagram illustrating a method for determining a predicted white line area. [Figure 3] FIG. 4 is a diagram illustrating a method for calculating the center position of a white line. [Figure 4] 10A and 10B are diagrams illustrating a method for determining a predicted white line range for a broken white line; [Figure 5] FIG. 10 is a diagram illustrating a method for searching for the end of a solid line portion. [Figure 6] 10A and 10B are diagrams illustrating a method for determining a predicted white line range for a curved white line; [Figure 7] FIG. 2 is a block diagram showing the configuration of a measurement device. [Figure 8] 10 is a flowchart of a process for estimating a vehicle position using a white line. DETAILED DESCRIPTION OF THE INVENTION

[0013] In one preferred embodiment of the present invention, the measuring device comprises an acquisition unit that acquires output data from a sensor unit for detecting surrounding road lines, a determination unit that determines a specified range based on the device's own position and position information of dashed road lines, an extraction unit that extracts data from the output data that corresponds to the detection result of the specified range, and a processing unit that performs specified processing based on the extracted data.

[0014] The above-mentioned measuring device acquires output data from a sensor unit for detecting surrounding road lines, and determines a predetermined range based on its own position and position information of the broken road lines. Then, data corresponding to the detection result of the predetermined range is extracted from the output data, and predetermined processing is performed based on the extracted data. This measuring device can appropriately determine the predetermined range for the broken road lines. Note that, in this specification, "road lines" refers to the measurement target, such as dividing lines (e.g., white lines and yellow lines), and linear road markings (e.g., stop lines and pedestrian crossings).

[0015] In one aspect of the above-described measuring device, the determiner determines the predetermined range based on the self-position and the position information of the solid line portion of the broken road line. In this aspect, the predetermined range can be appropriately determined by using the position information of the solid line portion of the broken road line.

[0016] In another aspect of the above-described measuring device, the determination unit detects an end of the solid line portion and determines the predetermined range based on the end. In this aspect, the predetermined range is determined based on the end of the solid line portion. Preferably, the determination unit detects the end based on a difference in positional information of multiple points that make up the solid line portion.

[0017] In another preferred embodiment of the present invention, the measuring device comprises an acquisition unit that acquires output data from a sensor unit for detecting surrounding road surface lines, a determination unit that determines a specified range based on the device's own position, position information of the road surface lines, and the curvature of the road surface lines, an extraction unit that extracts data from the output data that corresponds to the detection result of the specified range, and a processing unit that performs specified processing based on the extracted data.

[0018] The above-mentioned measuring device acquires output data from a sensor unit for detecting surrounding road surface lines, and determines a predetermined range based on its own position, position information of the road surface lines, and the curvature of the road surface lines.The measuring device then extracts data from the output data that corresponds to the detection results of the predetermined range, and performs predetermined processing based on the extracted data.With this measuring device, even if the road surface lines are curved, the predetermined range can be appropriately determined taking into account the curvature.

[0019] In another aspect of the above-mentioned measuring device, the measuring device is mounted on a moving body, and the extraction unit sets the predetermined range at four locations based on the position of the moving body: right front, right rear, left front, and left rear. In this aspect, data is extracted from the four locations around the moving body, and predetermined processing is performed based on the extracted data. Preferably, the processing unit detects the positions of the road surface lines and performs processing to estimate the position of the measuring device based on the positions of the road surface lines.

[0020] In another preferred embodiment of the present invention, a measurement method executed by a measurement device includes an acquisition step of acquiring output data from a sensor unit for detecting surrounding road lines, a determination step of determining a predetermined range based on the vehicle's own position and position information of the dashed road lines, an extraction step of extracting data from the output data corresponding to the detection results of the predetermined range, and a processing step of performing predetermined processing based on the extracted data. This measurement method makes it possible to appropriately determine the predetermined range for the dashed road lines.

[0021] In another preferred embodiment of the present invention, a measurement method executed by a measurement device includes: an acquisition step of acquiring output data from a sensor unit for detecting surrounding road surface lines; a determination step of determining a predetermined range based on the vehicle's own position, position information of the road surface lines, and the curvature of the road surface lines; an extraction step of extracting data from the output data corresponding to the detection results of the predetermined range; and a processing step of performing predetermined processing based on the extracted data. According to this measurement method, even if the road surface lines are curved, the predetermined range can be appropriately determined taking into account the curvature of the road surface lines.

[0022] In another preferred embodiment of the present invention, a program executed by a measuring device equipped with a computer causes the computer to function as an acquisition unit that acquires output data from a sensor unit that detects surrounding road lines, a determination unit that determines a predetermined range based on the vehicle's own position and position information of dashed road lines, an extraction unit that extracts data from the output data that corresponds to the detection result of the predetermined range, and a processing unit that performs predetermined processing based on the extracted data. By executing this program on a computer, the above-mentioned measuring device can be realized.

[0023] In another preferred embodiment of the present invention, a program executed by a measuring device equipped with a computer causes the computer to function as an acquisition unit that acquires output data from a sensor unit that detects surrounding road surface lines, a determination unit that determines a predetermined range based on the vehicle's own position, position information of the road surface lines, and the curvature of the road surface lines, an extraction unit that extracts data from the output data that corresponds to the detection result of the predetermined range, and a processing unit that performs predetermined processing based on the extracted data.The above-mentioned measuring device can be realized by executing this program on a computer.This program can be stored and used on a storage medium. [Example]

[0024] Preferred embodiments of the present invention will now be described with reference to the drawings.

[0025] [White line extraction method] 1 is a diagram illustrating a method for extracting white lines. Extracting white lines involves detecting white lines painted on the road surface and calculating their predetermined positions, such as their center positions.

[0026] (Calculation of predicted white line position) As shown, the map coordinate system (X m ,Y m ), and the vehicle coordinate system (X v ,Y v Specifically, the traveling direction of the vehicle 5 is defined as the X coordinate of the vehicle coordinate system. v axis, and the direction perpendicular to it is the Y axis of the vehicle coordinate system. v The axis.

[0027] White lines, which are lane boundary lines, are present on the left and right sides of the vehicle 5. The positions of the white lines in the map coordinate system, i.e., the white line map positions, are included in an advanced map managed by a server or the like and are acquired from the server or the like. In this embodiment, it is assumed that the data for the white lines is stored in the advanced map as a sequence of coordinate points. Furthermore, the LiDAR mounted on the vehicle 5 measures scan data along scan line 2. Note that scan line 2 indicates the trajectory of the LiDAR scan.

[0028] In FIG. 1, the coordinates of the points that make up the white line WL1 on the left side of the vehicle 5, that is, the white line map position WLMP1, are (mx m1 ,my m1 ), and the coordinates of the points that make up the white line WL2 on the right side of the vehicle 5, that is, the white line map position WLMP2, are (mx m2 ,my m2 ) and the predicted vehicle position PVP in the map coordinate system is (x' m ,y' m ) and the predicted vehicle azimuth in the map coordinate system is given by Ψ' m is given by

[0029] Here, the predicted position of the white line WLPP(l'x v ,l'y v ) is the white line map position WLMP(mx m ,my m ) and predicted vehicle position PVP(x' m ,y' m ) and the predicted vehicle heading angle Ψ' m Using the above, it is given by the following equation (1).

[0030]

number

[0031] (Determining the predicted white line area) Next, a predicted white line range WLPR is determined based on the predicted white line position WLPP. The predicted white line range WLPR indicates the range in which a white line is believed to exist, based on the predicted host vehicle position PVP. The predicted white line range WLPR is set to a maximum of four locations: the front right, rear right, front left, and rear left of the vehicle 5.

[0032] 2A and 2B show a method for determining the white line prediction range WLPR. In FIG. 2A, a white line prediction range WLPR is determined at an arbitrary position (distance α v The forward reference point (α v ,0 v ) and set the front reference point (α v ,0 v ) and the predicted white line position WLPP, the front reference point (α v ,0 v ) to search for the nearest predicted white line position WLPP. Specifically, for the white line WL1, v ,0 v ) and the predicted positions WLPP1(l'x v1 ,l'y v1 ) and calculates the distance D1 using the following equation (2), and the predicted white line position WLPP1 at which the distance D1 is minimum is set as the prediction range reference point Pref1.

[0033]

number

[0034] Similarly, for the white line WL2, the forward reference point (α v ,0 v ) and the predicted positions WLPP2(l'x v2 ,l'y v2 ) and calculates the distance D2 using the following equation (3), and the predicted white line position WLPP2 at which the distance D2 is minimum is set as the prediction range reference point Pref2.

[0035]

number

[0036] As shown in FIG. 2(B), an arbitrary range based on the prediction range reference point Pref, for example, a range from the prediction range reference point Pref to X v ±ΔX, Y in the axial direction v The range of ±ΔY in the axial direction is set as the white line prediction range WLPR. In this way, as shown in FIG. 1, white line prediction ranges WLPR1 and WLPR2 are set at the left and right positions in front of the vehicle 5. Similarly, by setting a rear reference point behind the vehicle 5 and setting the prediction range reference point Pref, white line prediction ranges WLPR3 and WLPR4 are set at the left and right positions behind the vehicle 5. In this way, four white line prediction ranges WLPR1 to WLPR4 are set for the vehicle 5.

[0037] (Calculation of the center position of the white line) Next, the white line center position WLCP is calculated using the white line predicted position WLPP. Figure 3 shows how to calculate the white line center position WLCP. Figure 3(A) shows the case where the white line WL1 is a solid line. The white line center position WLCP1 is calculated from the average value of the position coordinates of the scan data that make up the white line. Now, as shown in Figure 3(A), once the white line predicted range WLPR1 is set, the white line scan data WLSD1(wx') that exists within the white line predicted range WLPR1 from the scan data output from the LiDAR is calculated. v ,wy' v ) is extracted. Since the reflectivity on white lines is higher than on normal roads, the scan data obtained on white lines has a high reflection intensity. Of the scan data output from the LiDAR, scan data that is within the white line prediction range WLPR1, is on the road surface, and has a reflection intensity greater than a predetermined value is extracted as white line scan data WLSD. Then, if the number of extracted white line scan data WLSD is "n", the white line center position WLCP1(sx v1 ,sy v1 ) coordinates are obtained.

[0038]

number

[0039] (Determining the predicted white line range in special cases) Next, a method for determining the white line prediction range WLPR in special cases will be described. (1) When the white line is broken As described above, the white line prediction range WLPR is determined based on the white line predicted position WLPP. However, if the white line is a broken line (hereinafter, this type of white line will also be referred to as a "broken white line"), a situation may arise in which the white line WL is not included in the white line prediction range WLPR.

[0040] FIG. 4A shows an example of a white line prediction range WLPR for a broken white line. The broken white line WL is composed of solid line portions RP and space portions SP arranged alternately. Here, the solid line portions RP are the areas where the white line is painted, and the space portions SP are the areas between the solid line portions RP where the white line is not painted. If the forward reference point is specified and the white line prediction range WLPR is determined as shown in FIG. 4A, the white line prediction range WLPR will be located at a position corresponding to the space portions SP of the broken white line, and the solid line portions RP of the broken white line will hardly be included within the white line prediction range WLPR. In such a case, a sufficient amount of white line scan data WLSD cannot be obtained from the white line prediction range WLPR, making it impossible to accurately calculate the white line center position WLCP.

[0041] To address this issue, in this embodiment, the white line map information stored in the advanced map includes information about broken lines, and the predicted white line range WLPR is determined based on the white line position information of the solid line portion of the broken white line. Specifically, the white line map positions WLMP included in the white line map information of the broken white line stored in the advanced map are limited to the multiple white line map positions WLMP that make up the solid line portion RP. In other words, the white line map information of the broken white line includes multiple white line map positions WLMP that correspond to the solid line portion RP, but does not include the white line map positions WLMP that correspond to the space portion SP. In this way, by performing the method for determining the predicted white line range described above using the multiple white line map positions WLMP that correspond to the solid line portion RP, the predicted white line range WLPR can be determined to include the solid line portion RP of the broken white line.

[0042] FIG. 4B shows a method for determining a predicted white line position WLPR using this method. First, a white line map position WLMP corresponding to the solid line portion RP is obtained from white line map information for dashed white lines. Next, the white line map position WLMP corresponding to the solid line portion RP is used to calculate a predicted white line position WLPP corresponding to the solid line portion RP according to the above-mentioned equation (1). Then, using the method described with reference to FIG. 2, a predicted white line range WLPR is determined using the predicted white line position WLMP corresponding to the solid line portion RP. Specifically, the predicted white line position WLPP closest to the forward reference point is set as the prediction range reference point Pref, and a predetermined range is determined as the predicted white line range WLPR based on the prediction range reference point Pref.

[0043] In this method, the predicted white line area WLPR is determined using only the white line map positions WLMP corresponding to the solid line areas RP, without using the white line map positions corresponding to the space areas SP of the broken white lines. Therefore, the predicted white line area WLPR can be determined so as to correctly include the solid line areas RP.

[0044] However, in this method, the prediction range reference point Pref obtained when determining the white line prediction range WLPR is the end of the solid line portion RP of the broken white line, i.e., either the upper end or the lower end. Therefore, it is necessary to determine whether the obtained prediction range reference point Pref is the upper end or the lower end of the solid line portion RP, and then determine the white line prediction range WLPR based on the result.

[0045] FIG. 5 shows a method for determining whether the prediction range reference point Pref is at the upper or lower end of the solid line portion RP. The white line map position WLMP stored in the advanced map is assigned an ID as identification information. The predicted white line position WLPP calculated based on the white line map position WLMP is assigned the same ID as the original white line map position WLMP. In the example of FIG. 5(A), IDs are assigned in increasing order from bottom to top.

[0046] As can be seen from Fig. 5A, there is a gap corresponding to the space portion SP between the predicted white line position WLPP at the bottom end of a certain solid line portion RP1 and the predicted white line position WLPP at the top end of the adjacent solid line portion RP2 below it. In the example of Fig. 5A, there is a gap corresponding to the space portion SP between the predicted white line position WLPP at ID = (i-1) and the predicted white line position WLPP at ID = (i-2). Therefore, the X of the two adjacent predicted white line positions WLPP v Calculate the distance ΔX in the axial direction. In this case, the distance is calculated by dividing the upper side (X v From the predicted position of the white line (ID=p) on the positive side of the axis, v The predicted position of the white line (ID=q) on the negative side of the axis is subtracted, and the interval Δ (p、q) The interval Δ (p、q) Absolute value of |ΔX (p,q) If | is greater than a predetermined threshold value ΔTH corresponding to the space portion SP, it is determined that a space portion SP exists between the two predicted white line positions WLPP, and that the two predicted white line positions WLPP are the lower and upper ends of different solid line portions RP.

[0047] Specifically, in the example of FIG. 5A, the X between the predicted white line position WLPP of ID=i and the predicted white line position WLPP of ID=(i-1) is v Axial spacing ΔX (i,i-1) is obtained by the following equation (5).

[0048]

number

[0049] Similarly, the X between the predicted white line position WLPP of ID=(i-1) and the predicted white line position WLPP of ID=(i-2) v Axial spacing ΔX (iー1,i-2) is obtained by the following equation (6).

[0050]

number

[0051] Here, which is the upper end and which is the lower end is determined by the way the IDs are assigned to the white line map positions. As shown in FIG. 5(A), the IDs are assigned from bottom to top, i.e., X v If the force is applied so that it increases from the negative side to the positive side of the axis, ΔX (p,q) The value of is positive. Therefore, ΔX(p,q) If >0, ID is X v The predicted position WLPP of the white line with the larger ID is the lower end of one solid line portion RP, and the predicted position WLPP of the white line with the smaller ID is the upper end of the lower solid line portion RP. In the example of FIG. 5(A), ΔX (iー1,i-2) >0, the predicted white line position WLPP for ID=(i-1) is determined to be the lower end of the solid line portion RP1, and the predicted white line position WLPP for ID=(i-2) is determined to be the upper end of the solid line portion RP2.

[0052] On the other hand, contrary to FIG. 5(A), ID is from top to bottom, that is, X v When applied so that it increases from the positive side to the negative side of the axis, ΔX (p,q) The value of is negative. Therefore, ΔX (p,q) If <0, ID is X v This means that the axis is shifted from the positive side to the negative side, and it can be determined that the predicted white line position WLPP with the smaller ID is the bottom end of one solid line section, and the white line map position WLPP with the larger ID is the top end of the solid line section RP below that.

[0053] Then, the white line prediction range WLPR is set depending on whether the prediction range reference point Pref is at the upper end or the lower end of the solid line portion. For example, when the prediction range reference point Pref is at the lower end of the solid line portion RP, as shown in FIG. 5(B), the white line prediction range WLPR is set to X v A given distance ΔX, Y in the positive direction of the axis v The range of a predetermined distance ΔY in each of the positive and negative directions of the axis is defined as the white line prediction range WLPR. On the other hand, when the prediction range reference point Pref is at the upper end of the solid line portion RP, as shown in FIG. 5(C), v A predetermined distance ΔX, Y in the negative direction of the axis v The range of a predetermined distance ΔY in each of the positive and negative directions of the axis is defined as a white line prediction range WLPR.

[0054] In this way, by using the white line map position WLMP of the solid line portion RP stored in the advanced map, it is possible to correctly determine the predicted white line position WLPR so as to cover the solid line portion RP, even if the white line is a broken line.

[0055] In the above example, the white line map information in the advanced map includes only the white line map position WLMP of the solid line portion RP of the broken white line. Alternatively, the white line map information may include the white line map positions WLMP of both the solid line portion RP and the space portion SP of the broken white line, and a flag may be assigned to each white line map position WLMP to indicate whether it corresponds to the solid line portion RP or the space portion SP. In this case, the flag may be referenced, and only the white line map position WLMP corresponding to the solid line portion RP may be obtained and the above processing may be performed.

[0056] (2) When the white line is broken The white line prediction range WLPR shown in Figure 1 and elsewhere is set assuming that the white line is straight, but if the white line is curved, it may not be possible to extract sufficient white line scan data. For example, if the white line WL is curved as shown in Figure 6(A), the area of ​​the white line WL covered by the white line prediction range WLPR becomes smaller, and the amount of white line scan data that can be obtained decreases.

[0057] To address this issue, if the curvature of the curved portion of the white line is stored in the white line map information stored in the advanced map, this can be used to appropriately determine the shape of the white line prediction range WLPR. Specifically, as shown in FIG. 6(B), the shape of the white line prediction range WLPR is curved along the white line based on the curvature of the white line included in the white line map information. This allows a larger area of ​​the white line WL to be included in the white line prediction range WLPR, making it possible to extract more white line scan data WLSD. This improves the accuracy of white line extraction and also improves the accuracy of vehicle position estimation.

[0058] As a specific method, for example, an approximation formula corresponding to the curve of the white line can be calculated according to the curvature of the white line stored in the advanced map, and the coordinates defining the outer periphery of the white line prediction range WLPR can be determined based on the formula. Also, such a calculation can be performed in advance according to the curvature, and the X of the white line prediction range WLPR relative to the curvature of the curve can be calculated. v Axial and Y directions v The amount of correction in the axial direction may be calculated and stored in a look-up table (LUT), etc. Then, while the vehicle 5 is traveling, the predicted white line range WLPR may be corrected by referring to the LUT based on the curvature acquired from the white line map information.

[0059] [Device configuration] FIG. 7 shows a schematic configuration of a vehicle position estimation device to which the measurement device of the present invention is applied. The vehicle position estimation device 10 is mounted on a vehicle and configured to be able to communicate with a server 7, such as a cloud server, via wireless communication. The server 7 is connected to a database 8, which stores advanced maps. The advanced maps stored in the database 8 store landmark map information for each landmark. For white lines, the database 8 also stores white line map information including white line map positions WLMP that indicate the coordinates of the sequence of points that make up the white lines. The vehicle position estimation device 10 communicates with the server 7 and downloads white line map information related to white lines around the vehicle's own position.

[0060] In this embodiment, for broken white lines, the white line map information includes only the white line map position corresponding to the solid line portion RP as described above, or includes a flag indicating whether the white line map position corresponds to the solid line portion RP or a space portion. Furthermore, the white line map information also includes the curvature of the curved portion of the white line.

[0061] The vehicle position estimation device 10 includes an internal sensor 11, an external sensor 12, a vehicle position prediction unit 13, a communication unit 14, a white line map information acquisition unit 15, a white line position prediction unit 16, a scan data extraction unit 17, a white line center position calculation unit 18, and a vehicle position estimation unit 19. Note that the vehicle position prediction unit 13, the white line map information acquisition unit 15, the white line position prediction unit 16, the scan data extraction unit 17, the white line center position calculation unit 18, and the vehicle position estimation unit 19 are actually realized by a computer such as a CPU executing a program prepared in advance.

[0062] The internal sensor 11 measures the vehicle's own position as a GNSS (Global Navigation Satellite System) / IMU (Inertia Measurement Unit) hybrid navigation system, and includes a satellite positioning sensor (GPS), a gyro sensor, a vehicle speed sensor, etc. The vehicle position prediction unit 13 predicts the vehicle's own position by the GNSS / IMU hybrid navigation based on the output of the internal sensor 11, and supplies the predicted vehicle position PVP to the white line position prediction unit 16.

[0063] The external sensor 12 is a sensor that detects objects around the vehicle, and includes a stereo camera, LiDAR, etc. The external sensor 12 supplies the scan data SD obtained by measurement to the scan data extraction unit 17.

[0064] The communication unit 14 is a communication unit for wireless communication with the server 7. The white line map information acquisition unit 15 receives white line map information relating to white lines existing around the vehicle from the server 7 via the communication unit 14, and supplies the white line map positions WLMP included in the white line map information to the white line position prediction unit 16.

[0065] The white line position prediction unit 16 calculates the predicted white line position WLPP using the above-mentioned equation (1) based on the white line map position WLMP and the predicted vehicle position PVP acquired from the vehicle position prediction unit 13. The white line position prediction unit 16 also determines the predicted white line range WLPR using the above-mentioned equations (2) and (3) based on the predicted white line position WLPP. For dashed white lines, the white line position prediction unit 16 determines the predicted white line range WLPR based on the white line map position WLMP of the solid line portion RP, as described above. For curved white lines, the predicted white line range WLPR is determined based on the curvature of the curve, as described above. The white line position prediction unit 16 then supplies the determined predicted white line range WLPR to the scan data extraction unit 17.

[0066] The scan data extraction unit 17 extracts white line scan data WLSD based on the white line prediction range WLPR supplied from the white line position prediction unit 16 and the scan data SD acquired from the external sensor 12. Specifically, the scan data extraction unit 17 extracts, from the scan data SD, scan data that is included in the white line prediction range WLPR, is on the road surface, and has a reflection intensity equal to or greater than a predetermined value, as white line scan data WLSD and supplies the extracted data to the white line center position calculation unit 18.

[0067] 3, the white line center position calculation unit 18 calculates the white line center position WLCP from the white line scan data WLSD using equation (4). Then, the white line center position calculation unit 18 supplies the calculated white line center position WLCP to the vehicle position estimation unit 19.

[0068] The vehicle position estimation unit 19 estimates the vehicle position and vehicle azimuth based on the white line map position WLMP on the advanced map and the white line center position WLCP, which is measurement data of the white line by the external sensor 12. Note that an example of a method for estimating the vehicle position by matching landmark position information on the advanced map with measured position information of the landmark by the external sensor is described in JP 2017-72422 A.

[0069] In the above configuration, the external sensor 12 is an example of a sensor unit of the present invention, the scan data extraction unit 17 is an example of an acquisition unit and extraction unit of the present invention, the white line position prediction unit 16 is an example of a determination unit of the present invention, and the vehicle position estimation unit 19 is an example of a processing unit of the present invention.

[0070] [Vehicle position estimation processing] Next, a description will be given of the vehicle position estimation process performed by the vehicle position estimation device 10. Fig. 8 is a flowchart of the vehicle position estimation process. This process is realized by a computer such as a CPU executing a program prepared in advance and functioning as each of the components shown in Fig. 7.

[0071] First, the vehicle position prediction unit 13 acquires the predicted vehicle position PVP based on the output from the internal sensor 11 (step S11). Next, the white line map information acquisition unit 15 connects to the server 7 via the communication unit 14 and acquires white line map information from the advanced map stored in the database 8 (step S12). Here, as described above, for dashed white lines, the white line map information acquisition unit 15 acquires the white line map position WLMP corresponding to the solid line portion RP. The unit also acquires the curvature of the curved portion of the white line. Note that either step S11 or S12 may be performed first.

[0072] Next, the white line position prediction unit 16 calculates a predicted white line position WLPP based on the white line map position WLMP included in the white line position information obtained in step S12 and the predicted vehicle position PVP obtained in step S11 (step S13). The white line position prediction unit 16 also determines a predicted white line range WLPR based on the predicted white line position WLPP. For dashed white lines, the predicted white line range WLPR is determined using the white line map position WLMP of the solid line portion RP, as described above. For curved white lines, the predicted white line range WLPR is determined based on their curvature. The white line position prediction unit 16 then supplies the predicted white line range WLPR to the scan data extraction unit 17 (step S14).

[0073] Next, the scan data extraction unit 17 extracts, from the scan data SD obtained from the LiDAR as the external sensor 12, scan data that belongs to the white line prediction range WLPR, is on the road surface, and has a reflection intensity equal to or greater than a predetermined value as white line scan data WLSD, and supplies this to the white line center position calculation unit 18 (step S15).

[0074] Next, the white line center position calculation unit 18 calculates the white line center position WLCP based on the white line prediction range WLPR and the white line scan data WLSD, and supplies it to the vehicle position estimation unit 19 (step S16).Then, the vehicle position estimation unit 19 estimates the vehicle position using the white line center position WLCP (step S17), and outputs the vehicle position and vehicle azimuth angle (step S18).This completes the vehicle position estimation process.

[0075] [Variations] In the above embodiment, white lines are used as lane boundary lines indicating lanes, but the application of the present invention is not limited to this, and linear road markings such as pedestrian crossings and stop lines may also be used. Also, yellow lines may be used instead of white lines. These dividing lines such as white and yellow lines and road markings are examples of road lines in the present invention. [Explanation of symbols]

[0076] 5 vehicles 7 Server 8 Database 10 Vehicle position estimation device 11 Internal Sensors 12 External Sensors 13 Vehicle position prediction unit 14 Communications Department 15 White line map information acquisition unit 16 White line position prediction unit 17 Scan data extraction section 18 White line center position calculation section 19 Vehicle position estimation unit

Claims

[Claim 1] an acquisition unit that acquires output data from a sensor unit for detecting surrounding road surface lines; A determination unit that determines a predetermined range based on a self-position and position information of a broken road line; an extracting unit that extracts data corresponding to the detection result in the predetermined range from the output data; A processing unit that performs a predetermined process based on the extracted data; A measuring device comprising:

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