WO2018212283A1 - 測定装置、測定方法およびプログラム - Google Patents
測定装置、測定方法およびプログラム Download PDFInfo
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- WO2018212283A1 WO2018212283A1 PCT/JP2018/019138 JP2018019138W WO2018212283A1 WO 2018212283 A1 WO2018212283 A1 WO 2018212283A1 JP 2018019138 W JP2018019138 W JP 2018019138W WO 2018212283 A1 WO2018212283 A1 WO 2018212283A1
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- white line
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- predetermined range
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
- G01C21/30—Map- or contour-matching
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
- B60W40/04—Traffic conditions
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B21/00—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/005—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 with correlation of navigation data from several sources, e.g. map or contour matching
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
- G01S17/06—Systems determining position data of a target
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
- G01S17/931—Lidar systems specially adapted for specific applications for anti-collision purposes of land vehicles
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2552/00—Input parameters relating to infrastructure
- B60W2552/53—Road markings, e.g. lane marker or crosswalk
Definitions
- the present invention relates to a technique for estimating the position of a moving body based on the position of a feature.
- Patent Document 1 describes an example of a method for estimating a vehicle position using a feature position detected using LiDAR and a feature position of map information.
- Patent Document 2 discloses a technique for transmitting an electromagnetic wave to a road surface and detecting a white line based on the reflectance.
- the number of data that can be measured by LiDAR varies depending on the type of white line (continuous line, broken line, etc.) and paint deterioration. For this reason, when the vehicle position is estimated using the white line, the detection accuracy of the white line changes depending on whether the number of LiDAR data used to detect the white line is small or large, and as a result, the accuracy of the vehicle position estimation changes. Come.
- An object of the present invention is to appropriately adjust the range in which a white line is detected according to the situation, and to prevent a decrease in accuracy of the vehicle position estimation.
- the invention according to claim 1 is a measuring device, an acquisition unit that acquires output data from a sensor unit for detecting surrounding road lines, a self-position, and position information on a broken-line road line A determination unit that determines a predetermined range based on the data, an extraction unit that extracts data corresponding to the detection result of the predetermined range from the output data, and a processing unit that performs a predetermined process based on the extracted data And.
- Invention of Claim 5 is a measuring device, Comprising: The acquisition part which acquires the output data from the sensor part for detecting the surrounding road surface line, a self position, the positional information on the road surface line, A determination unit that determines a predetermined range based on the curvature of the road line, an extraction unit that extracts data corresponding to a detection result of the predetermined range from the output data, and a predetermined unit based on the extracted data And a processing unit that performs processing.
- the invention according to claim 8 is a measuring method executed by the measuring device, wherein an acquisition step of acquiring output data from a sensor unit for detecting a surrounding road surface line, a self-position, and a broken line type A determination step for determining a predetermined range based on the position information of the road surface line, an extraction step for extracting data corresponding to a detection result of the predetermined range from the output data, and a predetermined step based on the extracted data And a processing step for performing the above process.
- the invention according to claim 9 is a measurement method executed by the measurement apparatus, the acquisition step of acquiring output data from a sensor unit for detecting a surrounding road surface line, a self-position, and the road surface line A predetermined step based on the position information and the curvature of the road line, and an extraction step for extracting data corresponding to the detection result of the predetermined range from the output data, And a processing step of performing a predetermined process based on the data.
- the invention according to claim 10 is a program executed by a measuring device including a computer, an acquisition unit for acquiring output data from a sensor unit for detecting a surrounding road surface line, a self-position, and a broken line type A determination unit that determines a predetermined range based on the position information of the road surface line, an extraction unit that extracts data corresponding to the detection result of the predetermined range from the output data, a predetermined range based on the extracted data
- the computer is caused to function as a processing unit that performs processing.
- the invention according to claim 11 is a program executed by a measuring device including a computer, an acquisition unit that acquires output data from a sensor unit for detecting a surrounding road surface line, a self-position, and the road surface A determining unit that determines a predetermined range based on the position information of the line and the curvature of the road surface; an extracting unit that extracts data corresponding to the detection result of the predetermined range from the output data; and the extracted data
- the computer is caused to function as a processing unit that performs predetermined processing based on the above.
- the measurement device includes an acquisition unit that acquires output data from a sensor unit for detecting a surrounding road surface line, a self-position, and position information on a dashed road surface line.
- a determination unit that determines a predetermined range based on the data, an extraction unit that extracts data corresponding to the detection result of the predetermined range from the output data, and a processing unit that performs a predetermined process based on the extracted data And comprising.
- the above measuring device acquires output data from the sensor unit for detecting the surrounding road surface line, and determines a predetermined range based on the self-position and the position information of the dashed road surface line. Then, data corresponding to the detection result in the predetermined range is extracted from the output data, and predetermined processing is performed based on the extracted data.
- the predetermined range can be appropriately determined for the broken road surface.
- the “road line” in the present specification is a marking line 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.
- the determination unit determines the predetermined range based on the position of the self position and the solid line portion of the broken road surface.
- the predetermined range can be appropriately determined using the position information of the solid line portion of the broken-line road surface line.
- the determination unit detects an end portion of the solid line portion, and determines the predetermined range using the end portion as a reference.
- the predetermined range is determined based on the end portion of the solid line portion.
- the said determination part detects the said edge part based on the difference of the positional information of the several point which comprises the said continuous line part.
- the measurement 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 on the road line, A determination unit that determines a predetermined range based on the curvature of the road line, an extraction unit that extracts data corresponding to a detection result of the predetermined range from the output data, and a predetermined unit based on the extracted data A processing unit that performs processing.
- the measuring device acquires output data from a sensor unit for detecting surrounding road lines, and sets a predetermined range based on the self-position, the position information of the road lines, and the curvature of the road lines. decide. Then, data corresponding to the detection result in the predetermined range is extracted from the output data, and predetermined processing is performed based on the extracted data. In this measuring apparatus, even when the road surface is curved, the predetermined range can be appropriately determined in consideration of the curvature.
- the measuring apparatus is mounted on a moving body, and the extraction unit has four locations on the right front, right rear, left front, and left rear on the basis of the position of the moving body.
- the predetermined range is set to.
- data is extracted at four locations around the moving body, and predetermined processing is performed based on the data.
- the processing unit detects a 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.
- the measurement method executed by the measurement apparatus includes an acquisition step of acquiring output data from a sensor unit for detecting a surrounding road surface line, a self-position, and a broken line type A determination step for determining a predetermined range based on the position information of the road surface line, an extraction step for extracting data corresponding to a detection result of the predetermined range from the output data, and a predetermined step based on the extracted data And a process step for performing the process.
- the predetermined range can be appropriately determined for the broken road surface.
- the measurement method executed by the measurement apparatus includes an acquisition step of acquiring output data from a sensor unit for detecting a surrounding road surface line, a self-position, and the road surface line. A predetermined step based on the position information and the curvature of the road line, and an extraction step for extracting data corresponding to the detection result of the predetermined range from the output data, And a processing step for performing a predetermined process based on the data. According to this measurement method, even when the road surface is curved, the predetermined range can be appropriately determined in consideration of the curvature.
- a program executed by a measurement apparatus including a computer includes an acquisition unit that acquires output data from a sensor unit for detecting a surrounding road surface line, a self-position, and a broken line type A determination unit that determines a predetermined range based on the position information of the road surface line, an extraction unit that extracts data corresponding to the detection result of the predetermined range from the output data, a predetermined range based on the extracted data
- the computer is caused to function as a processing unit that performs processing.
- the above measurement apparatus can be realized by executing this program on a computer.
- a program executed by a measurement apparatus including a computer includes an acquisition unit that acquires output data from a sensor unit for detecting a surrounding road surface line, a self-position, and the road surface A determining unit that determines a predetermined range based on the position information of the line and the curvature of the road surface; an extracting unit that extracts data corresponding to the detection result of the predetermined range from the output data; and the extracted data
- the computer is caused to function as a processing unit that performs predetermined processing based on the above.
- the above measurement apparatus can be realized by executing this program on a computer. This program can be stored and handled in a storage medium.
- FIG. 1 is a diagram illustrating a white line extraction method.
- White line extraction refers to detecting a white line painted on a road surface and calculating a predetermined position, for example, a center position.
- the vehicle 5 exists in the map coordinate system (X m , Y m ), and the vehicle coordinate system (X v , Y v ) is defined based on 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.
- white lines that are lane boundary lines on the left and right sides of the vehicle 5.
- the position of the white line in the map coordinate system that is, the white line map position is included in the advanced map managed by the server or the like, and is acquired from the server or the like.
- white line data is stored in the advanced map as a coordinate point sequence.
- the LiDAR mounted on the vehicle 5 measures scan data along the scan line 2.
- the scan line 2 indicates a trajectory of scanning by LiDAR.
- 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, ie, the white line.
- the map position WLMP2 is (mx m2 , my m2 ).
- the predicted host vehicle position PVP in the map coordinate system is given by (x ′ m , y ′ m ), and the predicted host vehicle azimuth angle in the map coordinate system is given by ⁇ ′ m .
- the white line predicted position WLPP (l′ x v , l′ y v ) indicating the predicted position of the white line is the white line map position WLMP (mx m , my m ) and the predicted host vehicle position PVP (x ′ m , y).
- m the white line map position
- PVP the predicted host vehicle position
- 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 by Expression (1). It is done. Thus, 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 are obtained.
- the white line predicted range WLPR is determined based on the white line predicted position WLPP.
- the white line prediction range WLPR indicates a range in which a white line is considered to exist on the basis of the predicted vehicle position PVP.
- the white line prediction range WLPR is set at four locations on the vehicle 5 at the right front, right rear, left front, and left rear at the maximum.
- FIG. 2 shows a method for determining the white line prediction range WLPR.
- A set forward reference point to any position in front of the vehicle 5 (distance alpha v forward position) to ( ⁇ v, 0 v). Then, based on the front reference point ( ⁇ v , 0 v ) and the white line predicted position WLPP, the white line predicted position WLPP closest to the front reference point ( ⁇ v , 0 v ) is searched.
- the white line WL1 based on the forward reference point ( ⁇ v , 0 v ) and a plurality of white line predicted positions WLPP1 (l′ x v1 , l′ y v1 ) constituting the white line WL1, the following The distance D1 is calculated by the 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.
- the white line WL2 based on the forward reference point ( ⁇ v , 0 v ) and a plurality of white line predicted positions WLPP2 (l′ x v2 , l′ y v2 ) constituting the white line WL2, the following formula
- the distance D2 is calculated by (3), and the white line predicted position WLPP2 at which the distance D2 is the minimum value is set as the predicted range reference point Pref2.
- 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
- the white line prediction range WLPR is set.
- white line prediction ranges WLPR1 and WLPR2 are set at the left and right positions in front of the vehicle 5.
- white line prediction ranges WLPR3 and WLPR4 are set at the left and right positions behind the vehicle 5 by setting the rear reference point behind the vehicle 5 and setting the prediction range reference point Pref.
- four white line prediction ranges WLPR1 to WLPR4 are set for the vehicle 5.
- 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 by the average value of the position coordinates of the scan data constituting the white line.
- the white line scan data WLSD1 (wx ′ v , wy) existing in the white line prediction range WLPR1 among the scan data output from the LiDAR. ' v ) is extracted.
- the scan data obtained on the white line is data with high reflection intensity.
- scan data that exists within the white line prediction range WLPR1 and on the road surface and whose reflection intensity is greater than or equal to a predetermined value is extracted as white line scan data WLSD.
- the coordinates of the white line center position WLCP1 (sx v1 , sy v1 ) are obtained by the following equation (4).
- the white line center position WLCP2 is similarly calculated.
- the white line predicted range WLPR is determined based on the white line predicted position WLPP, but when the white line is a broken line (hereinafter, this type of white line is referred to as a “dashed white line”. Also, a situation may occur in which the white line WL is not included in the white line prediction range WLPR.
- FIG. 4A shows an example of a white line prediction range WLPR in the case of a broken line type white line.
- the broken-line white line WL is formed by alternately arranging solid line portions RP and space portions SP.
- the solid line portion RP is a portion where a white line is painted
- the space portion SP is a portion where the white line is not painted between the solid line portions RP.
- the white line map information stored in the advanced map has information on the broken line, and the white line predicted range WLPR based on the white line position information of the solid line part of the broken line type white line.
- the white line map position WLMP included in the white line map information of the broken line type white line stored in the advanced map is only a plurality of white line map positions WLMP constituting the solid line part RP. That is, the white line map information of the broken line type white line includes a plurality of white line map positions WLMP corresponding to the solid line part RP, but does not include the white line map position WLMP corresponding to the space part SP.
- the white line prediction range WLPR is determined so as to include the solid line portion RP of the dashed white line by performing the above-described white line prediction range determination method using a plurality of white line map positions WLMP corresponding to the solid line portion RP. Can do.
- FIG. 4B shows a method for determining the white line predicted position WLPR by this method.
- the white line map position WLMP corresponding to the solid line part RP is acquired from the white line map information of the dashed white line.
- the white line predicted position WLPP corresponding to the solid line part RP is calculated by the above-described equation (1).
- the white line prediction range WLPR is determined using the white line prediction position WLMP corresponding to the solid line part RP by the method described with reference to FIG. Specifically, the white line predicted position WLPP closest to the front reference point is set as the prediction range reference point Pref, and the predetermined range is determined as the white line prediction range WLPR using the prediction range reference point Pref as a reference.
- the white line prediction range WLPR is determined using only the white line map position WLMP corresponding to the solid line part RP without using the white line map position corresponding to the space part SP of the broken line type white line, the solid line part RP is correctly included.
- the white line prediction range WLPR can be determined.
- 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 dashed white line, that is, 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 to determine the white line prediction range WLPR according to the result.
- FIG. 5 shows a method of determining whether the prediction range reference point Pref is the upper end or the lower end of the solid line part RP.
- An ID as identification information is given to the white line map position WLMP stored in the advanced map. Further, the white line predicted position WLPP calculated based on the white line map position WLMP is given the same ID as the white line map position WLMP that is the basis. In the example of FIG. 5A, increasing IDs are assigned from the bottom to the top in the figure.
- of the interval ⁇ (p, q) is larger than a predetermined threshold value ⁇ TH corresponding to the space portion SP, the space portion is between the two white line predicted positions WLPP. SP is present, and the two white line predicted positions WLPP are determined to be the lower end and the upper end of different solid line portions RP.
- of this interval is compared with a predetermined threshold value ⁇ TH corresponding to the length of the space portion SP.
- of this interval is compared with a predetermined threshold value ⁇ TH.
- the white line prediction range WLPR is set according to whether the prediction range reference point Pref is the upper end or the lower end of the solid line portion. For example, if the prediction range reference point Pref is the lower end of the solid portion RP, as shown in FIG. 5 (B), the predetermined distance ⁇ X in the positive direction of the X v-axis from the expected range reference point Pref, the positive and negative Y v-axis A range of a predetermined distance ⁇ Y in each direction is defined as a white line prediction range WLPR. On the other hand, if the prediction range reference point Pref is the upper end of the solid portion RP, as shown in FIG.
- a range of a predetermined distance ⁇ Y in each direction is defined as a white line prediction range WLPR.
- the white line predicted position WLPR is correctly set so as to cover the solid line part RP even when the white line is a broken line type. Can be determined.
- the white line map information in the advanced map includes only the white line map position WLMP of the solid line part RP of the broken line white line.
- the white line map information includes the white line map positions WLMP of both the solid line part RP and the space part SP of the broken line type white line, and each white line map position WLMP corresponds to the solid line part RP or the space part SP.
- a flag or the like indicating may be added. In that case, the flag may be referred to, and only the white line map position WLMP corresponding to the solid line portion RP may be acquired to perform the above processing.
- the white line prediction range WLPR shown in FIG. 1 and the like is set on the assumption that the white line is linear. However, when the white line is curved, sufficient white line scan data is obtained. There is a risk that it cannot be extracted. For example, as shown in FIG. 6A, if the white line WL is curved, the area of the white line WL covered by the white line prediction range WLPR becomes small, and the number of white line scan data that can be acquired decreases.
- the shape of the white line prediction range WLPR can be appropriately determined using the curvature. .
- 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. In this way, more regions of the white line WL are included in the white line predicted range WLPR, and more white line scan data WLSD can be extracted. Therefore, the accuracy of white line extraction can be improved, and the accuracy of vehicle position estimation can also be improved.
- an approximate expression corresponding to the curve of the white line is calculated according to the curvature of the white line stored in the advanced map, and the outer periphery of the white line prediction range WLPR is defined based on the expression. Coordinates can be determined.
- we advance in accordance with such operations in curvature and stores the calculated correction amount of X v-axis direction and Y v-axis direction of the white line prediction range WLPR for the curvature of the curve such as in a look-up table (LUT) It may be left.
- the white line prediction range WLPR may be corrected by referring to the LUT based on the curvature acquired from the white line map information.
- FIG. 7 shows a schematic configuration of a host vehicle position estimation apparatus to which the measurement apparatus of the present invention is applied.
- the own 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 by wireless communication.
- the server 7 is connected to a database 8, and the database 8 stores an advanced map.
- the advanced map stored in the database 8 stores landmark map information for each landmark.
- white line map information including a 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 related to the white line around the own vehicle position of the vehicle.
- the white line map information includes only the white line map position corresponding to the solid line part RP as described above for the broken line type white line, or the white line map position is either the solid line part RP or the space part. It includes a flag indicating whether or not Further, the white line map information includes the curvature at the curve portion of the white line.
- the own vehicle position estimation device 10 includes an inner world sensor 11, an outer world sensor 12, an own 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. Unit 17, white line center position calculation unit 18, and own vehicle position estimation unit 19.
- 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 a CPU or the like. This is realized by a computer executing a program prepared in advance.
- the inner world sensor 11 measures the position of the vehicle as a GNSS (Global Navigation Satellite System) / IMU (Inertia Measurement Unit) combined navigation system, and includes a satellite positioning sensor (GPS), a gyro sensor, a vehicle speed sensor, and the like. Including.
- 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 external sensor 12 is a sensor that detects an object around the vehicle, and includes a stereo camera, LiDAR, and the like.
- the external 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 wirelessly communicating with the server 7.
- the white line map information acquisition unit 15 receives white line map information related to 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. To do.
- the white line position prediction unit 16 calculates the white line predicted position WLPP by the above-described equation (1) based on the white line map position WLMP and the predicted vehicle position PVP acquired from the vehicle position prediction unit 13. Further, the white line position prediction unit 16 determines the white line prediction range WLPR by the above-described equations (2) and (3) based on the white line prediction position WLPP. The white line position prediction unit 16 determines the white line prediction range WLPR for the broken line type white line based on the white line map position WLMP of the solid line part RP as described above. For the white line curve portion, the white line prediction range WLPR is determined based on the curvature of the curve as described above. Then, the white line position prediction unit 16 supplies the determined 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 external sensor 12. Specifically, the scan data extraction unit 17 extracts scan data included in the white line prediction range WLPR and having a reflection intensity equal to or higher than a predetermined value, as white line scan data WLSD, from the scan data SD.
- the white line center position calculation unit 18 is supplied.
- the white line center position calculation unit 18 calculates the white line center position WLCP from the white line scan data WLSD using the 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.
- the own vehicle position estimating unit 19 estimates the own vehicle position and the own vehicle azimuth angle based on the white line map position WLMP in the advanced map and the white line center position WLCP that is white line measurement data by the external sensor 12.
- Japanese Patent Laid-Open No. 2017-72422 discloses an example of a method for estimating the vehicle position by matching the landmark position information of the advanced map and the measured position information of the landmark by the external sensor.
- 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
- the white line position prediction unit 16 is the determination unit of the present invention.
- the vehicle position estimation unit 19 is an example, and is an example of a processing unit of the present invention.
- 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 component shown in FIG.
- the host vehicle position prediction unit 13 acquires the predicted host vehicle position PVP based on the output from the internal sensor 11 (step S11).
- the white line map information acquisition part 15 connects to the server 7 through the communication part 14, and acquires white line map information from the advanced map memorize
- the white line map information acquisition unit 15 acquires the white line map position WLMP corresponding to the solid line part RP for the broken line type white line as described above. Also, the curvature of the white line curve is acquired. Note that either step S11 or S12 may be performed first.
- 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 host 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, for the broken line type white line, the white line predicted range WLPR is determined using the white line map position WLMP of the solid line part RP as described above. For the curve portion of the white line, the white line prediction range WLPR is determined based on the curvature. Then, the white line position prediction unit 16 supplies the white line prediction range WLPR to the scan data extraction unit 17 (step S14).
- the scan data extraction unit 17 converts the scan data SD obtained from the LiDAR as the external sensor 12 into the white line predicted range WLPR, the scan data on the road surface, and the reflection intensity is a predetermined value or more as a white line Extracted as scan data WLSD and supplied to the white line center position calculator 18 (step S15).
- 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 the white line center position WLCP to the own vehicle position estimation unit 19 (step S16). And the own vehicle position estimation part 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). Thus, the own vehicle position estimation process ends.
- the white line that is the lane boundary indicating the 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, a yellow line or the like may be used instead of the white line. These lane markings such as white lines and yellow lines, road markings, and the like are examples of road lines of the present invention.
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Abstract
Description
図1は、白線抽出方法を説明する図である。白線抽出とは、道路面にペイントされた白線を検出し、その所定位置、例えば中心位置を算出することをいう。
図示のように、地図座標系(Xm,Ym)に車両5が存在し、車両5の位置を基準として車両座標系(Xv,Yv)が規定される。具体的に、車両5の進行方向を車両座標系のXv軸とし、それに垂直な方向を車両座標系のYv軸とする。
次に、白線予測位置WLPPに基づいて、白線予測範囲WLPRが決定される。白線予測範囲WLPRは、予測自車位置PVPを基準として、白線が存在すると考えられる範囲を示す。白線予測範囲WLPRは、最大で車両5の右前方、右後方、左前方及び左後方の4か所に設定される。
次に、白線予測位置WLPPを用いて白線中心位置WLCPを算出する。図3は白線中心位置WLCPの算出方法を示す。図3(A)は、白線WL1が実線である場合を示す。白線中心位置WLCP1は、白線を構成するスキャンデータの位置座標の平均値により算出される。いま、図3(A)に示すように、白線予測範囲WLPR1が設定されると、LiDARから出力されるスキャンデータのうち、白線予測範囲WLPR1内に存在する白線スキャンデータWLSD1(wx’v,wy’v)が抽出される。白線上は通常の道路上と比較して反射率が高いので、白線上で得られたスキャンデータは、反射強度の高いデータとなる。LiDARから出力されたスキャンデータのうち、白線予測範囲WLPR1内に存在し、路面上、かつ、反射強度が所定以上値であるスキャンデータが白線スキャンデータWLSDとして抽出される。そして、抽出された白線スキャンデータWLSDの数を「n」とすると、以下の式(4)により、白線中心位置WLCP1(sxv1,syv1)の座標が得られる。
次に、特殊な場合の白線予測範囲WLPRの決定方法について説明する。
(1)白線が破線の場合
前述のように、白線予測範囲WLPRは、白線予測位置WLPPに基づいて決定されるが、白線が破線である場合(以下、この種の白線を「破線型白線」とも呼ぶ。)、白線WLが白線予測範囲WLPRに含まれない状況が生じうる。
図1などに示す白線予測範囲WLPRは、白線が直線的であると想定して設定されているが、白線がカーブしている場合には十分な白線スキャンデータを抽出できなくなる恐れがある。例えば、図6(A)に示すように、白線WLがカーブしていると、白線予測範囲WLPRがカバーする白線WLの領域が小さくなり、取得できる白線スキャンデータの数が減少してしまう。
図7は、本発明の測定装置を適用した自車位置推定装置の概略構成を示す。自車位置推定装置10は、車両に搭載され、無線通信によりクラウドサーバなどのサーバ7と通信可能に構成されている。サーバ7はデータベース8に接続されており、データベース8は高度化地図を記憶している。データベース8に記憶された高度化地図は、ランドマーク毎にランドマーク地図情報を記憶している。また、白線については、白線を構成する点列の座標を示す白線地図位置WLMPを含む白線地図情報を記憶している。自車位置推定装置10は、サーバ7と通信し、車両の自車位置周辺の白線に関する白線地図情報をダウンロードする。
次に、自車位置推定装置10による自車位置推定処理について説明する。図8は、自車位置推定処理のフローチャートである。この処理は、CPUなどのコンピュータが予め用意されたプログラムを実行し、図7に示す各構成要素として機能することにより実現される。
上記の実施例では、車線を示す車線境界線である白線を使用しているが、本発明の適用はこれには限られず、横断歩道、停止線などの線状の道路標示を利用してもよい。また、白線の代わりに、黄色線などを利用しても良い。これら、白線、黄色線などの区画線や、道路標示などは本発明の路面線の一例である。
7 サーバ
8 データベース
10 自車位置推定装置
11 内界センサ
12 外界センサ
13 自車位置予測部
14 通信部
15 白線地図情報取得部
16 白線位置予測部
17 スキャンデータ抽出部
18 白線中心位置算出部
19 自車位置推定部
Claims (12)
- 周囲の路面線を検出するためのセンサ部からの出力データを取得する取得部と、
自己位置と、破線型の路面線の位置情報とに基づいて、所定範囲を決定する決定部と、
前記出力データのうち、前記所定範囲の検出結果に相当するデータを抽出する抽出部と、
抽出されたデータに基づいて所定の処理を行う処理部と、
を備えることを特徴とする測定装置。 - 前記決定部は、前記自己位置と、前記破線型の路面線の実線部の位置情報に基づいて、前記所定範囲を決定することを特徴とする請求項1に記載の測定装置。
- 前記決定部は、前記実線部の端部を検出し、当該端部を基準として前記所定範囲を決定することを特徴とする請求項2に記載の測定装置。
- 前記決定部は、前記実線部を構成する複数点の位置情報の差分に基づいて前記端部を検出することを特徴とする請求項3に記載の測定装置。
- 周囲の路面線を検出するためのセンサ部からの出力データを取得する取得部と、
自己位置と、前記路面線の位置情報と、前記路面線の曲率とに基づいて、所定範囲を決定する決定部と、
前記出力データのうち、前記所定範囲の検出結果に相当するデータを抽出する抽出部と、
抽出されたデータに基づいて所定の処理を行う処理部と、
を備えることを特徴とする測定装置。 - 前記測定装置は、移動体に搭載され、
前記抽出部は、前記移動体の位置を基準として右前方、右後方、左前方、左後方の4か所に前記所定範囲を設定することを特徴とする請求項1乃至5のいずれか一項に記載の測定装置。 - 前記処理部は、前記路面線の位置を検出し、当該路面線の位置に基づいて前記測定装置の位置を推定する処理を行うことを特徴とする請求項1乃至6のいずれか一項に記載の測定装置。
- 測定装置により実行される測定方法であって、
周囲の路面線を検出するためのセンサ部からの出力データを取得する取得工程と、
自己位置と、破線型の路面線の位置情報とに基づいて、所定範囲を決定する決定工程と、
前記出力データのうち、前記所定範囲の検出結果に相当するデータを抽出する抽出工程と、
抽出されたデータに基づいて所定の処理を行う処理工程と、
を備えることを特徴とする測定方法。 - 測定装置により実行される測定方法であって、
周囲の路面線を検出するためのセンサ部からの出力データを取得する取得工程と、
自己位置と、前記路面線の位置情報と、前記路面線の曲率とに基づいて、所定範囲を決定する決定工程と、
前記出力データのうち、前記所定範囲の検出結果に相当するデータを抽出する抽出工程と、
抽出されたデータに基づいて所定の処理を行う処理工程と、
を備えることを特徴とする測定方法。 - コンピュータを備える測定装置により実行されるプログラムであって、
周囲の路面線を検出するためのセンサ部からの出力データを取得する取得部、
自己位置と、破線型の路面線の位置情報とに基づいて、所定範囲を決定する決定部、
前記出力データのうち、前記所定範囲の検出結果に相当するデータを抽出する抽出部、
抽出されたデータに基づいて所定の処理を行う処理部、
として前記コンピュータを機能させることを特徴とするプログラム。 - コンピュータを備える測定装置により実行されるプログラムであって、
周囲の路面線を検出するためのセンサ部からの出力データを取得する取得部、
自己位置と、前記路面線の位置情報と、前記路面線の曲率とに基づいて、所定範囲を決定する決定部、
前記出力データのうち、前記所定範囲の検出結果に相当するデータを抽出する抽出部、
抽出されたデータに基づいて所定の処理を行う処理部、
として前記コンピュータを機能させることを特徴とするプログラム。 - 請求項10又は11に記載のプログラムを記憶した記憶媒体。
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