Measuring device, measuring method, program, and storage medium

Abstract

To provide a measuring device, a measuring method, a program, and a storage medium for appropriately adjusting a range for detecting white lines according to a situation, thereby preventing accuracy of estimating an own vehicle's position from decreasing.SOLUTION: A measuring device acquires output data from a sensor unit that detects surrounding features and extracts data corresponding to the detected result within a predetermined range that is in a predetermined positional relationship from a self-position from the output data. The predetermined range is determined by the accuracy of the self-position. Then, the measuring device performs a predetermined process based on 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 an autonomous vehicle, it is necessary to estimate the vehicle position with high accuracy by matching feature positions measured by a sensor such as LiDAR (Light Detection and Ranging) with feature positions in map information for autonomous driving. Examples of features used here include white lines, signs, and billboards. Patent Document 1 describes an example of a method for estimating the vehicle position using feature positions detected by LiDAR and feature positions in map information. Patent Document 2 discloses a technology for transmitting electromagnetic waves to the road surface and detecting white lines based on the reflectance of the waves. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2017-72422 A [Patent Document 2] JP 2015-222223 A 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. For this reason, 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 changes.

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

[0006] The invention described in the claims is a measuring device comprising an acquisition unit that acquires output data from a sensor unit for detecting surrounding road surface lines, an extraction unit that extracts data from the output data that corresponds to the detection results of a specified range that is in a specified positional relationship from the self-position, and a processing unit that performs specified processing based on the extracted data, wherein the extraction unit determines the length in a first direction that intersects with the longitudinal direction of the road surface lines of the specified range in accordance with the accuracy of the self-position.

[0007] The invention described in the claims is a measurement method performed by a measuring device, comprising an acquisition process for acquiring output data from a sensor unit for detecting surrounding road surface lines, an extraction process for extracting data from the output data corresponding to the detection results of a specified range that is in a specified positional relationship from the vehicle's own position, and a processing process for performing a specified processing based on the extracted data, wherein the extraction process determines the length in a first direction intersecting with the longitudinal direction of the road surface lines of the specified range depending on the accuracy of the vehicle's own position.

[0008] The invention described in the claims is a program executed by a measuring device equipped with a computer, which causes the computer to function as an acquisition unit that acquires output data from a sensor unit for detecting surrounding road surface lines, an extraction unit that extracts data from the output data that corresponds to the detection results of a specified range that is in a specified positional relationship from the self-position, and a processing unit that performs specified processing based on the extracted data, and is characterized in that the extraction unit determines the length in a first direction that intersects with the longitudinal direction of the road surface lines of the specified range depending on the accuracy of the self-position. [Brief description of the drawings]

[0009] [Figure 1]FIG. 4 is a diagram illustrating a method for extracting white lines. [Diagram 2] 10A and 10B are diagrams illustrating a method for determining a predicted white line area. [Diagram 3] FIG. 4 is a diagram illustrating a method for calculating the center position of a white line. [Figure 4] 11A and 11B are diagrams illustrating a method of correcting a predicted white line area. [Diagram 5] FIG. 2 is a block diagram showing a configuration of a measuring device. [Figure 6] 13 is a flowchart of a process for estimating a vehicle position using a white line. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] 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 features, an extraction unit that extracts data from the output data that corresponds to the detection results of a specified range that is in a specified positional relationship from the device's own position, and a processing unit that performs specified processing based on the extracted data, wherein the specified range is determined by the accuracy of the device's own position.

[0011] The above-mentioned measuring device acquires output data from a sensor unit for detecting surrounding features, and extracts data from the output data that corresponds to the detection result of a predetermined range that is in a predetermined positional relationship from the self-location. Then, a predetermined process is performed based on the extracted data. Here, the predetermined range is determined by the accuracy of the self-location. Therefore, the predetermined range is appropriately determined according to the accuracy of the self-location, and a predetermined process is performed based on the data extracted from the predetermined range.

[0012] In one aspect of the above-mentioned measuring device, the feature is a road surface line drawn on the road surface, and the predetermined range is determined by the accuracy of the self-position in a first direction intersecting with the longitudinal direction of the road surface line. In this aspect, the predetermined range is determined according to the accuracy of the self-position in the first direction intersecting with the longitudinal direction of the road surface line. Note that in this specification, the "road surface line" refers to the measurement target, such as a dividing line such as a white line or a yellow line, and linear road markings such as a stop line or a pedestrian crossing.

[0013] In a preferred example of the above-mentioned measuring device, the extractor changes the length of the predetermined range in the first direction according to the accuracy of the self-location in the first direction. In another preferred example, the extractor determines the length of the predetermined range in the first direction based on the length of the road surface line in the first direction and the accuracy of the self-location. In another preferred example, the extractor increases the length in the first direction as the accuracy of the self-location decreases.

[0014] In another aspect of the above-mentioned measuring device, the measuring device is mounted on a moving body, and the extracting unit sets the predetermined range at four locations, namely, the right front, right rear, left front, and left rear, based on the position of the moving body. In this aspect, data is extracted from the four locations around the moving body, and a predetermined process is performed based on the data. Preferably, the processing unit detects the position of the feature, and performs a process of estimating the position of the measuring device based on the position of the feature.

[0015] 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 features, an extraction step of extracting data from the output data corresponding to a detection result within a predetermined range that is in a predetermined positional relationship from the self-location, and a processing step of performing a predetermined process based on the extracted data, the predetermined range being determined by the accuracy of the self-location. With this method as well, the predetermined range is appropriately determined according to the accuracy of the self-location, and the predetermined process is performed based on the data extracted from the predetermined range.

[0016] 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 for detecting surrounding features, an extraction unit that extracts data from the output data that corresponds to a detection result within a predetermined range that is in a predetermined positional relationship from the self-location, and a processing unit that performs a predetermined process based on the extracted data, the predetermined range being determined by the accuracy of the self-location. By executing this program on a computer, the above-mentioned measuring device can be realized. This program can be stored in a storage medium and handled. EXAMPLES

[0017] Hereinafter, preferred embodiments of the present invention will be described with reference to the drawings. [White line extraction method] 1 is a diagram for explaining a method for extracting white lines. Extracting white lines means detecting white lines painted on the road surface and calculating their predetermined positions, for example, their center positions.

[0018] (Calculation of predicted white line position) As shown in the figure, 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 v axis, and the direction perpendicular to it is the Y axis of the vehicle coordinate system. v The axis.

[0019] There are white lines, which are lane boundary lines, on the left and right sides of the vehicle 5. The positions of the white lines in the map coordinate system, i.e., the map positions of the white lines, 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 of the white lines is stored in the advanced map as a sequence of coordinate points. In addition, the LiDAR mounted on the vehicle 5 measures scan data along a scan line 2. Note that the scan line 2 indicates the trajectory of the scan by the LiDAR.

[0020] In FIG. 1, the coordinates of the points constituting the white line WL1 on the left side of the vehicle 5, that is, the white line map position WLMP1, is (mx m1 ,my m1 ), and the coordinates of the points constituting 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:

[0021] 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 azimuth angle Ψ' m Using these, it is given by the following equation (1).

[0022]

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[0023] (Determining predicted white line area) Next, a white line prediction range WLPR is determined based on the white line prediction position WLPP. The white line prediction range WLPR indicates a range in which a white line is considered to exist, based on the predicted vehicle position PVP. The white line prediction range WLPR is set to a maximum of four positions, namely, the right front, right rear, left front, and left rear of the vehicle 5.

[0024] FIG. 2 shows a method for determining the white line prediction range WLPR. In FIG. 2(A), 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. v ,0 v ) and the multiple white line predicted positions WLPP1(l'x v1 ,l'y v1 ) the distance D1 is calculated by the following formula (2), and the predicted white line position WLPP1 at which the distance D1 is the minimum value is set as the prediction range reference point Pref1.

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[0025] Similarly, for the white line WL2, the forward reference point (α v ,0 v ) and the multiple white line predicted positions WLPP2(l'x v2 ,l'y v2 ) the distance D2 is calculated by the following equation (3), and the predicted white line position WLPP2 at which the distance D2 is the minimum value is set as the prediction range reference point Pref2.

number

[0026] Then, as shown in FIG. 2B, 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 vThe 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.

[0027] (Calculation of the center position of the white line) Next, the white line predicted position WLPP is used to calculate the white line center position WLCP. Figure 3 shows a method for calculating the white line center position WLCP. Figure 3(A) shows a 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 constitutes the white line. Now, as shown in Figure 3(A), when the white line predicted range WLPR1 is set, white line scan data WLSD1(wx') that exists within the white line predicted range WLPR1 among the scan data output from the LiDAR is calculated. v ,wy' v ) is extracted. Since the reflectivity of white lines is higher than that of normal roads, 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 above a predetermined value is extracted as white line scan data WLSD. If the number of extracted white line scan data WLSD is "n", then the white line center position WLCP1(sx v1 ,sy v1 ) coordinates are obtained.

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[0028] (White line prediction range correction) Next, the correction of the white line prediction range WLPR will be described. As described above, the white line prediction range WLPR is determined based on the white line prediction position WLPP, but if the estimation accuracy of the white line prediction position WLPP is low, the accuracy of the white line prediction range WLPR will decrease, and the white line WL may deviate from the white line prediction range WLPR.

[0029] FIG. 4A shows a case where the estimation accuracy of the white line predicted position WLPP is low, specifically, where the accuracy of the predicted vehicle position PVP of the vehicle 5 is low in the lateral direction of the vehicle 5, i.e., Y v In this case, the predicted vehicle position PVP of the vehicle 5 is Y v Since there is an error of ±1 m in the axial direction, if the predicted vehicle position PVP is used to calculate the predicted white line position WLPP and set the white line prediction range WLPR, the white line prediction range WLPR may deviate from the actual position of the white line WL1, as shown in Figure 4(A).In this case, the number of white line scan data WLSD obtained within the white line prediction range WLPR decreases, so the accuracy of white line extraction decreases, and as a result, the accuracy of vehicle position estimation also decreases.

[0030] In order to solve this problem, in this embodiment, as shown in FIG. 4B, the width of the white line prediction range WLPR is corrected based on the estimation accuracy of the predicted vehicle position PVP of the vehicle 5. In other words, the width of the white line prediction range WLPR is changed by a value according to the accuracy of the predicted vehicle position PVP. As a basic correction method, v The lower the estimation accuracy in the axial direction, the lower the Y of the white line prediction range (WLPR). v Increase the axial length (width).

[0031] For example, the predicted vehicle position PVP Y v When the estimation accuracy in the axial direction is 1 m, the width of the white line prediction range WLPR set by the method described with reference to FIG. 2 is set to Y vA correction is made to widen the axial direction by 1 m to the left and right. As a result, the corrected white line prediction range WLPR will have a width that takes into account the estimation error of the predicted vehicle position PVP, and the possibility that the white line WL will deviate from the white line prediction range WLPR can be reduced. Note that the above example is merely an example, and for example, the value obtained by multiplying the estimation accuracy of the predicted vehicle position PVP by a certain coefficient is used to increase Y v The width in the axial direction may be increased.

[0032] As a specific example, when estimating the vehicle position using an extended Kalman filter, the accuracy of the current predicted vehicle position PVP may be obtained from the value of the covariance matrix calculated sequentially by the extended Kalman filter, and the white line prediction range WLPR may be corrected. The prediction range for a general feature (landmark) is given by the following formula.

[0033]

number

[0034] [Device configuration] FIG. 5 shows a schematic configuration of a vehicle position estimation device to which the measuring 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 an advanced map. The advanced map stored in the database 8 stores landmark map information for each landmark. For white lines, the database 8 also stores white line map positions WLMP indicating 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 relating to white lines around the vehicle's own position.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] The white line position prediction unit 16 calculates the predicted white line position WLPP by 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 by the above-mentioned equations (2) and (3) based on the predicted white line position WLPP, and further corrects the predicted white line range WLPR in accordance with the estimation accuracy of the predicted vehicle position PVP as described above. The white line position prediction unit 16 then supplies the corrected predicted white line range WLPR to the scan data extraction unit 17.

[0040] 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 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 from the scan data SD as white line scan data WLSD and supplies the extracted data to the white line center position calculation unit 18.

[0041] 3, the white line center position calculation unit 18 calculates the white line center position WLCP from the white line scan data WLSD by the formula (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.

[0042] 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 the 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 an advanced map with landmark measurement position information by an external sensor is described in JP 2017-72422 A.

[0043] 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, and the vehicle position estimation unit 19 is an example of a processing unit of the present invention.

[0044] [Vehicle position estimation process] Next, a description will be given of the vehicle position estimation process performed by the vehicle position estimation device 10. Fig. 6 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. 5.

[0045] 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 through the communication unit 14 and acquires white line map information from the advanced map stored in the database 8 (step S12). Note that either step S11 or S12 may be performed first.

[0046] 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, and further corrects the predicted white line range WLPR based on the estimation accuracy of the predicted vehicle position PVP, and supplies the corrected range to the scan data extraction unit 17 (step S14).

[0047] 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 the data to the white line center position calculation unit 18 (step S15).

[0048] 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 (step S18). In this way, the vehicle position estimation process ends.

[0049] [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 surface lines of the present invention. [Explanation of symbols]

[0050] 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 section 16 White line position prediction section 17 Scan Data Extraction Section 18 White line center position calculation section 19 Vehicle position estimation unit

Claims

1. an acquisition unit that acquires output data from a sensor unit for detecting surrounding road surface lines; an extracting unit that extracts data corresponding to a detection result within a predetermined range that is in a predetermined positional relationship from the self-position from the output data; A processing unit that performs a predetermined process based on the extracted data; Equipped with A measurement device characterized in that the extraction unit determines a length of the road surface line in the specified range in a first direction intersecting with the longitudinal direction of the road surface line according to the accuracy of the self-position.

2. The measurement device according to claim 1 , wherein the extraction unit determines the predetermined range depending on the accuracy of the self-location in the first direction.

3. The measuring device according to claim 2 , characterized in that the extraction unit determines the length of the specified range in the first direction based on the length of the road surface line in the first direction and the accuracy of the self-position.

4. 4. The measuring device according to claim 2, wherein the extraction section increases the length in the first direction as the accuracy of the self-location decreases.

5. The measuring device is mounted on a moving body, 5. The measuring device according to claim 1, wherein the extraction unit sets the predetermined range to four locations, namely, a right front, a right rear, a left front, and a left rear, based on the position of the moving body.

6. 6. The measuring device according to claim 1, wherein 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.

7. The measuring device according to claim 1 , further comprising a calculation unit that calculates a center position of the road surface line based on an average value of coordinates of the extracted data.

8. The measuring device according to claim 7 , wherein the calculation unit calculates a center position of the road surface line based on an average value of the coordinates in the longitudinal direction and an average value of the coordinates in the first direction of the extracted data.

9. A measurement method performed by a measurement device, comprising: An acquisition step of acquiring output data from a sensor unit for detecting surrounding road surface lines; an extraction step of extracting data corresponding to a detection result within a predetermined range that is in a predetermined positional relationship from the self-position from the output data; A processing step of performing a predetermined process based on the extracted data; Equipped with A measurement method characterized in that the extraction step determines a length of the road surface line of the specified range in a first direction intersecting with the longitudinal direction of the road surface line according to the accuracy of the self-position.

10. A program executed by a measuring device having a computer, an acquisition unit for acquiring output data from a sensor unit for detecting surrounding road surface lines; an extraction unit that extracts data corresponding to a detection result within a predetermined range that is in a predetermined positional relationship from the self-position from the output data; A processing unit that performs a predetermined process based on the extracted data; and causing the computer to function as The program is characterized in that the extraction unit determines a length of the specified range in a first direction intersecting with the longitudinal direction of the road surface line according to the accuracy of the self-position.

11. A storage medium storing the program according to claim 10.

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