JP2021179443A - Measuring device, measuring method and program - Google Patents

Abstract

To appropriately adjust a range of detecting a white line according to a situation, to prevent accuracy deterioration for own vehicle position estimation.SOLUTION: A measuring device acquires output data from a sensor unit for detecting a surrounding road surface line, and determines a predetermined range based on a self-position, position information of the road surface line, and a moving speed of the self-position. Then, from the output data, data corresponding to the detection result in the predetermined range is extracted, and a predetermined process is performed based on the extracted data.SELECTED DRAWING: Figure 4

Description

The present invention relates to a technique for estimating the position of a moving body based on the position of a feature.

In an autonomous vehicle, it is necessary to match the position of a feature measured by a sensor such as LiDAR (Light Detection and Ringing) with the position of a feature in map information for autonomous driving to estimate the position of the own vehicle with high accuracy. Examples of the features used here include white lines, signs, and signboards. Patent Document 1 describes an example of a method of estimating the position of the own vehicle using the position of the feature detected by using LiDAR and the position of the feature in the map information. Further, Patent Document 2 discloses a technique of transmitting an electromagnetic wave to a road surface and detecting a white line based on its reflectance.

Japanese Unexamined Patent Publication No. 2017-72422 JP-A-2015-222223

When estimating the position of the own vehicle using the white line, the number of data that can be measured by LiDAR differs depending on the type of the white line (continuous line, broken line, etc.) and deterioration of the coating. Therefore, when estimating the position of the own vehicle using the white line, the detection accuracy of the white line changes depending on whether the number of LiDAR data used for detecting the white line is small or large, and as a result, the accuracy of the own vehicle position estimation changes. Come on.

Examples of the problems to be solved by the present invention include the above. It is an object of the present invention to appropriately adjust the range in which the white line is detected according to the situation and prevent the accuracy of the own vehicle position estimation from being lowered.

The invention according to the claim is a measuring device mounted on a moving body, and is an acquisition unit that acquires output data including a road surface line around the moving body from a first sensor which is a scan-type external sensor. And the prediction unit that calculates the predicted position of the moving body based on the output of the second sensor mounted on the moving body, the predicted position, and the position information of the road surface line included in the map data. The predicted range of the road surface line is calculated based on the predicted range of the road surface line, and the predicted range of the road surface line is corrected based on the calculated predicted range of the road surface line and the moving speed of the moving body. Based on the determination unit that determines the data, the extraction unit that extracts the data detected in the prediction range from the output data, the extracted data, and the position information of the road surface line included in the map data. , An estimation unit for estimating the current position of the moving body.

The invention according to claim is a measurement method performed by a measuring device mounted on a moving body, and is an output from a first sensor, which is a scan-type external sensor, including a road surface line around the moving body. An acquisition step of acquiring data, a prediction step of calculating a predicted position of the moving body based on the output of a second sensor mounted on the moving body, the predicted position, and the road surface line included in the map data. By calculating the predicted range of the road surface line based on the position information of the above, and correcting the predicted range of the road surface line based on the calculated predicted range of the road surface line and the moving speed of the moving body. A determination step for determining the predicted range of the road surface line, an extraction step for extracting the data detected in the predicted range from the output data, the extracted data, and the road surface line included in the map data. It includes an estimation step of estimating the current position of the moving body based on the position information.

The invention according to claim is a program mounted on a moving body and executed by a measuring device including a computer, and a road surface line around the moving body is drawn from a first sensor which is a scan type external sensor. An acquisition unit that acquires output data including, a prediction unit that calculates a predicted position of the moving body based on the output of a second sensor mounted on the moving body, the predicted position, and the road surface line included in the map data. By calculating the predicted range of the road surface line based on the position information of the above, and correcting the predicted range of the road surface line based on the calculated predicted range of the road surface line and the moving speed of the moving body. A determination unit that determines the prediction range of the road surface line, an extraction unit that extracts data detected in the prediction range from the output data, the extracted data, and position information of the road surface line included in the map data. Based on the above, the computer functions as an estimation unit that estimates the current position of the moving object.

It is a figure explaining the white line extraction method. It is a figure explaining the method of determining a white line prediction range. It is a figure explaining the calculation method of the white line center position. It is a figure explaining the correction method of the white line predicted position in consideration of the speed of a vehicle. An example of the white line predicted position considering the speed of the vehicle is shown. It is a block diagram which shows the structure of a measuring device. It is a flowchart of the own vehicle position estimation process using a white line.

In one preferred embodiment of the present invention, the measuring device includes an acquisition unit that acquires output data from a sensor unit for detecting a surrounding road surface line, a self-position, position information of the road surface line, and the self. A determination unit that determines a predetermined range based on the movement speed of the position, an extraction unit that extracts data corresponding to the detection result of the predetermined range from the output data, and a predetermined unit based on the extracted data. It is provided with a processing unit that performs processing.

The above-mentioned measuring device acquires output data from the sensor unit for detecting the surrounding road surface line, and sets a predetermined range based on the self-position, the position information of the road surface line, and the moving speed of the self-position. decide. Then, from the output data, data corresponding to the detection result in a predetermined range is extracted, and a predetermined process is performed based on the extracted data. As a result, a predetermined range for extracting data can be appropriately set according to the moving speed of the self-position. The "road surface line" in the present specification is a lane marking such as a white line or a yellow line to be measured, and a linear road marking such as a stop line or a pedestrian crossing.

In one aspect of the above-mentioned measuring device, the determination unit determines the predetermined range based on the self-position and the position information of the solid line portion of the road surface line, and the predetermined range is set according to the movement speed. It shifts in the direction of movement of its own position. In this aspect, the predetermined range is shifted according to the moving speed.

In another aspect of the above measuring device, the measuring device is mounted on a moving body, the determination unit determines a plurality of predetermined ranges based on the position of the moving body, and the sensor unit determines the plurality of predetermined ranges. Each of the predetermined ranges is shifted by a shift amount according to the scan order of the predetermined range. In this aspect, a plurality of predetermined ranges are shifted according to the scanning order by the sensor unit. Preferably, the determination unit shifts with a larger shift amount by a predetermined range in which the scan order by the sensor unit is later. Further, preferably, the faster the scanning speed by the sensor unit is, the smaller the shift amount of the determination unit is.

Preferably, the determination unit sets the predetermined range at four locations, right front, right rear, left front, and left rear, with reference to the position of the moving body. Further, preferably, the processing unit detects the position of the road surface line and performs a process of estimating the position of the measuring device based on the position of the road surface line.

In another preferred embodiment of the invention, the measurement method performed by the measuring device is an acquisition step of acquiring output data from a sensor unit for detecting the surrounding road surface line, a self-position, and a road surface line. A determination step of determining a predetermined range based on the position information and the movement speed of the self-position, an extraction step of extracting data corresponding to the detection result of the predetermined range from the output data, and extraction. It includes a processing step of performing a predetermined process based on data. According to this method, a predetermined range for extracting data can be appropriately set according to the moving speed of the self-position.

In another preferred embodiment of the invention, the program executed by the measuring device including the computer is an acquisition unit, self-position, and a road surface line that acquires output data from the sensor unit for detecting the surrounding road surface line. A determination unit that determines a predetermined range based on the position information of the above and the movement speed of the self-position, an extraction unit that extracts data corresponding to the detection result of the predetermined range from the output data, and extracted data. The computer is made to function as a processing unit that performs a predetermined process based on the above. The above measuring device can be realized by executing this program on a computer. This program can be stored and handled in a storage medium.

Hereinafter, preferred embodiments of the present invention will be described with reference to the drawings.
[White line extraction method]
FIG. 1 is a diagram illustrating a white line extraction method. White line extraction means to detect a white line painted on a road surface and calculate a predetermined position, for example, a center position thereof.

(Calculation of white line predicted position)
As shown in the figure, the vehicle 5 exists in the map coordinate system (X _m , Y _m ), and the vehicle coordinate system (X _v , Y _v ) is defined with reference to the position of the vehicle 5. Specifically, the traveling direction of the vehicle 5 and X _v-axis of the vehicle coordinate system, a direction perpendicular to it and Y _v axis of the vehicle coordinate system.

There are white lines, which are lane boundaries, on the left and right sides of the vehicle 5. The position of the white line in the map coordinate system, that is, the position of the white line map is included in the advanced map managed by the server or the like, and is acquired from the server or the like. In this embodiment, it is assumed that the white line data is stored in the advanced map as a coordinate point sequence. Further, the LiDAR mounted on the vehicle 5 measures scan data along the scan line 2. The scan line 2 shows the trajectory of scanning by LiDAR.

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. It is assumed that the map position WLMP2 is (mx _m2 , my _m2 ). Further, the predicted vehicle position PVP in the map coordinate system is given by _{(x 'm, y' m} ), the predicted vehicle azimuth in the map coordinate system is given by [psi _'m.

Here, the white line predicted position WLPP indicating the predicted position of the white line _{(l'x v, l'y v} ) is the white line map position WLMP _{(mx m,} my m) and the predicted vehicle position PVP (x _'m, y 'and _m), the predicted vehicle azimuth [psi' by using the _m, is given by the following equation (1).

よって、式（１）により、白線ＷＬ１について白線予測位置ＷＬＰＰ１（ｌ’ｘ_ｖ１，ｌ’ｙ_ｖ１）が得られ、白線ＷＬ２について白線予測位置ＷＬＰＰ２（ｌ’ｘ_ｖ２，ｌ’ｙ_ｖ２）が得られる。こうして、白線ＷＬ１、ＷＬ２のそれぞれについて、白線ＷＬ１、ＷＬ２に対応する複数の白線予測位置ＷＬＰＰ１、ＷＬＰＰ２が得られる。

Therefore, according to the equation (1), the white line predicted position WLPP1 (l'x _v1 , l'y _v1 ) is obtained for the white line WL1, and the white line predicted position WLPP2 (l'x _v2 , l'y _v2 ) is obtained for the white line WL2. Will be. In this way, for each of the white lines WL1 and WL2, a plurality of white line predicted positions WLPP1 and WLPP2 corresponding to the white lines WL1 and WL2 can be obtained.

(Determining the white line prediction range)
Next, the white line prediction range WLPR is determined based on the white line prediction position WLPP. White line prediction range WLPR indicates a range in which a white line is considered to exist with reference to the predicted own vehicle position PVP. The white line prediction range WLPR is set at a maximum of four locations, right front, right rear, left front, and left rear of the vehicle 5.

FIG. 2 shows a method for determining the white line prediction range WLPR. In FIG. 2A, a front reference point (α _v , 0 _v ) is set at an arbitrary position (distance α _{v front position) in front of the vehicle 5.} Then, based on the front reference point (α _v , 0 _v ) and the white line predicted position WLPP, the nearest white line predicted position WLPP is searched from the _{front reference point (α v} , 0 _v). Specifically, the white line WL1 is described below based on the _{front reference point (α v} , 0 _v ) and the plurality of white line predicted positions WLPP1 (l'x _v1 , l'y _{v1) constituting the white line WL1.} The distance D1 is calculated by the equation (2) of the above equation (2), and the white line predicted position WLPP1 at which the distance D1 is the minimum value is set as the prediction range reference point Pref1.

Similarly, for the white line WL2, the following equation is obtained based on the _{front reference point (α v} , 0 _v ) and the plurality of white line predicted positions WLPP2 (l'x _v2 , l'y _{v2) constituting the white line WL2.} The distance D2 is calculated according to (3), and the white line predicted position WLPP2 at which the distance D2 is the minimum value is set as the prediction range reference point Def2.

Then, as shown in FIG. 2 (B), any range based on the expected range reference point Pref, for example ± [Delta] X from the expected range reference point Pref in X _v-axis direction, a range of ± [Delta] Y to Y _v-axis direction White line prediction range WLPR is set. In this way, as shown in FIG. 1, the white line prediction ranges WLPR1 and WLPR2 are set at the left and right positions in front of the vehicle 5. Similarly, by setting the rear reference point behind the vehicle 5 and setting the prediction range reference point Pref, the 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 4 are set for the vehicle 5.

また、図３（Ｂ）に示すように、白線が破線である場合も同様に白線中心位置ＷＬＣＰ２が算出される。 (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. FIG. 3 shows a method of calculating the white line center position WLCP. FIG. 3A 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 constituting the white line. Now, as shown in FIG. 3 (A), the white line prediction range WLPR1 is set, among the scan data output from LiDAR, white line scan data exists in the white line expected range WLPR1 WLSD1 (wx _'v, wy ' _v ) is extracted. Since the reflectance on the white line is higher than that on a normal road, the scan data obtained on the white line is data with high reflection intensity. Among the scan data output from LiDAR, the scan data existing in the white line prediction range WLPR1 on the road surface and having a reflection intensity of a predetermined value or more is extracted as the white line scan data WLSD. Then, assuming that the number of the extracted white line scan data WLSD is "n", _{the coordinates of the white line center position WLCP1 (sx v1} , sy _v1 ) can be obtained by the following equation (4).

Further, as shown in FIG. 3B, when the white line is a broken line, the white line center position WLCP2 is calculated in the same manner.

(Correction of white line prediction range according to vehicle speed)
Next, a method of correcting the white line prediction range WLPR according to the speed of the vehicle 5 will be described. FIG. 4A shows the white line prediction ranges WLPR1 and WLPR2 set in front of the vehicle 5 when the vehicle 5 is stopped. Here, it is assumed that LiDAR scans one lap (360 °) clockwise around the position of the vehicle 5 and accumulates scan data for one lap. When the vehicle 5 is stopped, the scan line 2 by LiDAR becomes a circle centered on the position of the vehicle 5, so that the white line prediction ranges WLPR1 and WLPR2 may be equidistant from the vehicle 5. That is, as shown, by arranging a white line estimated range WLPR1 and white expected range WLPR2 a position axisymmetric with respect to the _{X v} axis, it is possible to extract the scan data by LiDAR efficiently.

However, as shown in FIG. 4B, the vehicle moves during the scanning by LiDAR while the vehicle 5 is traveling, particularly when the vehicle is traveling at high speed. In this example, since LiDAR scans clockwise, the vehicle 5 moves forward between the time LiDAR scans the white line WL1 on the left front of the vehicle 5 and the time it scans the white line WL2 on the right front. .. Therefore, as shown in FIG. 4B, the position where LiDAR scans the white line WL2 is ahead of the position where the white line WL1 is scanned. Therefore, when the white line prediction range WLPR2 on the right front side of the vehicle 5 is set to a position symmetrical with the white line prediction range WLPR1 on the left front as in the case of the vehicle stop shown in FIG. 4 (A), the white line prediction range WLPR2 The number of scan lines 2 included in is reduced.

Therefore, in this embodiment, the position of the white line prediction range WLPR is corrected according to the speed of the vehicle 5. Specifically, in the example of FIG. 4B, first, the white line prediction ranges WLPR1 and WLPR2 are set symmetrically in the same manner as in FIG. 4A by the method shown in FIG. 2, and then the speed of the vehicle 5 is adjusted. The white line prediction range WLPR2 is shifted forward by the amount of shift, and is set at the position of the white line prediction range WLPR2x. As a result, as shown in FIG. 4B, the white line prediction range WLPR2x can be set so as to appropriately include the scan line 2 that has moved forward due to the movement of the vehicle 5.

FIG. 5 shows a correction method of the white line prediction range when four white line prediction ranges WLPR1 to 4 are set with reference to the position of the vehicle 5. Also in the example of FIG. 5, the scan by LiDAR is clockwise. The white line prediction range WLPR1 on the left front of the vehicle 5 is considered as a reference point for scanning by LiDAR, and the scanning time is set to t0. Further, the time difference between LiDAR scanning the white line prediction range WLPR1 on the left front and scanning the white line prediction range WLPR2 on the right front is set as Δt1, and the white line on the right rear after scanning the white line prediction range WLPR2 on the right front. The time difference until the prediction range WLPR3 is scanned is Δt2, and the time difference from scanning the white line prediction range WLPR3 on the right rear to scanning the white line prediction range WLPR4 on the left rear is Δt3.

Now, it is assumed that the vehicle 5 is stopped. In this case, the right front of the white line prediction range WLPR2 is set symmetrically with respect to the _{X v-axis} front left white line expected range WLPR1. Also, rear right white line estimated range WLPR3 is set symmetrically with respect to the white line expected range WLPR2 and _{Y v} axes of the right front. Further, the white line expected range WLPR4 left rear is set symmetrically with respect to the white line expected range WLPR3 and _{X v-axis} of the right rear.

Next, it is assumed that the vehicle 5 is traveling at high speed. In this case, the white line prediction range WLPR2 on the right front is shifted forward (in the traveling direction of the vehicle) by Δd1 and set at the position of the white line prediction range WLPR2x. The white line prediction range WLPR3 on the right rear side is shifted forward by Δd2 and set at the position of the white line prediction range WLPR3x. The white line prediction range WLPR4 on the left rear side is shifted forward by Δd3 and set at the position of the white line prediction range WLPR4x.

Here, the shift amounts Δd1 to Δd3 depend on the speed of the vehicle 5. Generally, the faster the speed of the vehicle 5, the larger the shift amount Δd1 to Δd3, and the slower the speed of the vehicle 5, the smaller the shift amount Δd1 to Δd3. More specifically, assuming that the speed of the vehicle 5 is "V", the shift amount Δd1 of the white line prediction range WLPR2x is
Δd1 = Δt1 × V (5)
Therefore, the shift amount Δd2 of the white line prediction range WLPR3x is
Δd2 = (Δt1 + Δt2) × V (6)
Therefore, the shift amount Δd3 of the white line prediction range WLPR4x is
Δd3 = (Δt1 + Δt2 + Δt3) × V (7)
Will be.

In this way, when the white line prediction ranges are provided at four locations around the vehicle 5, the shift amount Δd becomes larger as the scan order from the reference white line prediction range becomes later. When the scanning direction of LiDAR is clockwise as in the example of FIG. 5, the shift amount is Δd1 <Δd2 <Δd3. That is, the shift amount has a relationship of white line prediction range WLPR2x <white line prediction range WLPR3x <white line prediction range WLPR4x. When the scanning direction of LiDAR is counterclockwise, the shift amount has a relationship of white line prediction range WLPR4x <white line prediction range WLPR3x <white line prediction range WLPR2x.

Further, the shift amounts Δd1 to Δd3 also depend on the scanning speed of the LiDAR mounted on the vehicle 5. Generally, the faster the scan speed of LiDAR, the smaller the shift amount Δd1 to Δd3, and the slower the scan speed, the larger the shift amount Δd1 to Δd3. This is because the faster the scanning speed, the smaller the values of Δt1 to Δt3 in the above equations (5) to (7).

The time difference Δt1 to Δt3 of the scan time in the white line prediction range can be calculated based on the distance between the white line prediction ranges and the LiDAR scan speed, or the angle between the white line prediction ranges and the LiDAR scan angle speed. ..

[Device configuration]
FIG. 6 shows a schematic configuration of a vehicle position estimation device to which the measuring device of the present invention is applied. The own vehicle position estimation device 10 is mounted on a vehicle and is configured to be able to communicate with a server 7 such as a cloud server by wireless communication. The server 7 is connected to the database 8, and the database 8 stores the advanced map. The advanced map stored in the database 8 stores landmark map information for each landmark. Further, for the white line, the white line map information including the white line map position WLMP indicating the coordinates of the point sequence constituting the white line is stored. The own vehicle position estimation device 10 communicates with the server 7 and downloads white line map information regarding the white line around the own vehicle position of the vehicle.

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, and scan data extraction. A unit 17, a white line center position calculation unit 18, and a vehicle position estimation unit 19 are provided. The own 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 own vehicle position estimation unit 19 are actually CPUs and the like. This is achieved by the computer executing a program prepared in advance.

The internal sensor 11 is a GNSS (Global Navigation Satellite System) / IMU (Inertial Measurement Unit) combined navigation system that positions the vehicle's own vehicle position, and includes a satellite positioning sensor (GPS), a gyro sensor, a vehicle speed sensor, and the like. include. The own vehicle position prediction unit 13 predicts the own vehicle position of the vehicle by GNSS / IMU combined navigation based on the output of the internal sensor 11, and supplies the predicted own vehicle position PVP to the white line position prediction unit 16.

The outside world sensor 12 is a sensor that detects an object around the vehicle, and includes a stereo camera, LiDAR, and the like. The outside world sensor 12 supplies the scan data SD obtained by the measurement to the scan data extraction unit 17.

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 regarding white lines existing around the vehicle from the server 7 via the communication unit 14, and supplies the white line map position WLMP included in the white line map information to the white line position prediction unit 16. do.

The white line position prediction unit 16 calculates the white line predicted position WLPP by the above equation (1) based on the white line map position WLMP and the predicted own vehicle position PVP acquired from the own vehicle position prediction unit 13. Further, the white line position prediction unit 16 determines the white line prediction range WLPR by the above equations (2) and (3) based on the white line prediction position WLPP. Further, the white line position prediction unit 16 corrects each white line prediction position WLPR1 to WLPR4 as described above according to the vehicle speed, the LiDAR scan speed, and the scan direction (clockwise or counterclockwise). Then, the white line position prediction unit 16 supplies the corrected white line prediction range WLPR to the scan data extraction unit 17.

The scan data extraction unit 17 extracts the 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 outside world sensor 12. Specifically, the scan data extraction unit 17 extracts scan data among the scan data SD, which is included in the white line prediction range WLPR, is on the road surface, and has a reflection intensity of a predetermined value or more, as white line scan data WLSD. , Supply to the white line center position calculation unit 18.

As described with reference to FIG. 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 equation (4). Then, the white line center position calculation unit 18 supplies the calculated white line center position WLCP to the own vehicle position estimation unit 19.

The own vehicle position estimation unit 19 estimates the own vehicle position and the own vehicle azimuth of the vehicle based on the white line map position WLMP in the advanced map and the white line center position WLCP which is the measurement data of the white line by the outside world sensor 12. .. Japanese Patent Application Laid-Open No. 2017-72422 describes an example of a method of estimating the position of the own vehicle by matching the landmark position information of the advanced map with the landmark measurement position information by the external sensor.

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

[Own vehicle position estimation process]
Next, the own vehicle position estimation process by the own vehicle position estimation device 10 will be described. FIG. 7 is a flowchart of the own vehicle position estimation process. This process is realized by executing a program prepared in advance by a computer such as a CPU and functioning as each component shown in FIG. It is assumed that the own vehicle position estimation device 10 always detects the speed of the vehicle 5 by a vehicle speed sensor or the like. Further, it is assumed that the scanning speed and scanning direction of LiDAR are predetermined.

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 the white line map information from the advanced map stored in the database 8 (step S12). Either step S11 or S12 may come first.

Next, the white line position prediction unit 16 calculates the white line predicted position WLPP based on the white line map position WLMP included in the white line position information obtained in step S12 and the predicted own vehicle position PVP obtained in step S11. (Step S13). Further, the white line position prediction unit 16 determines the white line prediction range WLPR based on the white line prediction position WLPP. At this time, the white line position prediction unit 16 corrects the positions of the white line prediction ranges WLPR1 to WLPR4 based on the speed of the vehicle 5, the scan speed of LiDAR, and the scan direction as described above. Then, the white line position prediction unit 16 supplies the white line prediction range WLPR to the scan data extraction unit 17 (step S14).

Next, the scan data extraction unit 17 includes scan data SD obtained from LiDAR as an external sensor 12 that belongs to the white line prediction range WLPR, is on the road surface, and has a reflection intensity of a predetermined value or more. It is extracted as scan data WLSD and supplied to the white line center position calculation unit 18 (step S15).

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 own vehicle position estimation unit 19 (step S16). Then, the own vehicle position estimation unit 19 estimates the own vehicle position using the white line center position WLCP (step S17), and outputs the own vehicle position and the own vehicle azimuth (step S18). In this way, the vehicle position estimation process is completed.

[Modification example]
In the above embodiment, a white line which is a lane boundary line indicating a lane is used, but the application of the present invention is not limited to this, and even if a linear road marking such as a pedestrian crossing or a stop line is used. good. Further, instead of the white line, a yellow line or the like may be used. These lane markings such as white lines and yellow lines and road markings are examples of road surface lines of the present invention.

5 Vehicles 7 Servers 8 Database 10 Own vehicle position estimation device 11 Internal world sensor 12 External world sensor 13 Own vehicle position prediction unit 14 Communication unit 15 White line map information acquisition unit 16 White line position prediction unit 17 Scan data extraction unit 18 White line center position calculation unit 19 Own vehicle position estimation unit

Claims (9)

It is a measuring device mounted on a moving body.
An acquisition unit that acquires output data including the road surface line around the moving body from the first sensor, which is a scan-type external sensor, and an acquisition unit.
A prediction unit that calculates the predicted position of the moving body based on the output of the second sensor mounted on the moving body, and
The predicted range of the road surface line is calculated based on the predicted position and the position information of the road surface line included in the map data, and the calculated predicted range of the road surface line, the moving speed of the moving body, and A determination unit that determines the predicted range of the road surface line by correcting the predicted range of the road surface line based on
An extraction unit that extracts data detected in the prediction range from the output data,
An estimation unit that estimates the current position of the moving body based on the extracted data and the position information of the road surface line included in the map data.
A measuring device equipped with. The determination unit determines the predicted range of the road surface line based on the predicted position and the position information of the solid line portion of the road surface line, and sets the predicted range in the moving direction of the predicted position according to the moving speed. The measuring device according to claim 1, wherein the measuring device is shifted. The determination unit determines a plurality of the prediction ranges based on the position of the moving body, and shifts each of the prediction ranges by a shift amount according to the scanning order of the plurality of prediction ranges by the external world sensor. The measuring device according to claim 2. The measuring device according to claim 3, wherein the determination unit shifts the scan order by the external sensor with a larger shift amount as the prediction range becomes later. The measuring device according to claim 3 or 4, wherein the determination unit reduces the shift amount as the scanning speed by the external sensor increases. One of claims 3 to 5, wherein the determination unit sets the prediction range at four locations of front right, rear right, front left, and rear left with reference to the position of the moving body. The measuring device described in. A measurement method performed by a measuring device mounted on a moving body.
The acquisition process of acquiring output data including the road surface line around the moving body from the first sensor, which is a scan-type external sensor, and
A prediction step of calculating the predicted position of the moving body based on the output of the second sensor mounted on the moving body, and
The predicted range of the road surface line is calculated based on the predicted position and the position information of the road surface line included in the map data, and the calculated predicted range of the road surface line, the moving speed of the moving body, and A determination step of determining the predicted range of the road surface line by correcting the predicted range of the road surface line based on
An extraction step for extracting data detected in the predicted range from the output data, and
An estimation process for estimating the current position of the moving body based on the extracted data and the position information of the road surface line included in the map data, and an estimation step.
A measurement method that comprises. A program mounted on a mobile body and executed by a measuring device equipped with a computer.
An acquisition unit that acquires output data including the road surface line around the moving body from the first sensor, which is a scan-type external sensor.
A prediction unit that calculates the predicted position of the moving body based on the output of the second sensor mounted on the moving body.
The predicted range of the road surface line is calculated based on the predicted position and the position information of the road surface line included in the map data, and the calculated predicted range of the road surface line, the moving speed of the moving body, and A determination unit that determines the predicted range of the road surface line by correcting the predicted range of the road surface line based on
An extraction unit that extracts data detected in the prediction range from the output data,
An estimation unit that estimates the current position of the moving body based on the extracted data and the position information of the road surface line included in the map data.
A program that makes the computer function as. A storage medium in which the program according to claim 8 is stored.

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