JP2021060944A - Roadside edge detection method and roadside edge detection device - Google Patents

Abstract

To provide a roadside edge detection method and a roadside edge detection device for improving a roadside edge detection accuracy.SOLUTION: A roadside edge detection method of a driving support system 100 is a roadside edge detection method for detecting a roadside edge provided with a roadside object on a road surface on which a vehicle travels, which is comprised of: a road surface height detection step that detects a height of the road surface in a predetermined range around the vehicle; a total calculation step of calculating a total height of the road surfaces in a predetermined range detected in the road surface height detection step along the traveling direction of the vehicle according to a position in a vehicle width direction with respect to the vehicle; and a roadside edge determination step for detecting a position in a vehicle width direction in which the total sum calculated in the total sum calculation step exceeds a threshold value, and for determining that the roadside edge is located at the detection position in the vehicle width direction where the total sum exceeds the threshold value.SELECTED DRAWING: Figure 1

Description

The present invention relates to a roadside edge detection method and a roadside edge detection device.

In a vehicle driving support system or the like, the position where a roadside object such as a curb is provided on the road surface on which the vehicle travels is detected as a roadside edge, and driving support is provided so as to travel inside the detected roadside edge. ..

For example, according to the driving support system shown in Patent Document 1, position information of a plurality of measurement points along the width direction of the road surface is acquired by a laser radar, and discontinuities in a group of measurement points arranged in the vehicle width direction are obtained. Detected as a roadside edge.

Japanese Unexamined Patent Publication No. 2016-45507

In Patent Document 1, the position information measured at a group of measurement points arranged in the vehicle width direction may be defective. In such a case, the position of the discontinuous portion generated due to the lack of position information and the actual roadside object cannot be matched, and the roadside edge may not be detected accurately.

The present invention has been made to solve the above problems, and provides a roadside edge detection method and a roadside edge detection device for improving the roadside edge detection accuracy.

The roadside edge detection method of the present invention is a roadside edge detection method for detecting a roadside edge provided with a roadside object on a road surface on which a vehicle travels. The roadside edge detection method is a vehicle based on a vehicle based on a road surface height detection step for detecting the height of the road surface in a predetermined range around the vehicle and a road surface height in the predetermined range detected in the road surface height detection step. It is determined that the roadside edge is the position in the vehicle width direction in which the total sum calculated in the total sum calculation step exceeds the threshold value in the total sum calculation step for calculating the total along the traveling direction of the vehicle according to the position in the width direction. , A roadside edge determination step, and the like.

According to the roadside edge detection method of the present invention, it is possible to improve the detection accuracy of the roadside edge.

FIG. 1 is a schematic configuration diagram of a driving support system that executes the roadside edge detection method according to the first embodiment. FIG. 2 is an explanatory diagram showing acquisition control of ranging data. FIG. 3 is an explanatory diagram of a method of generating a grid map from distance measurement data. FIG. 4 is a diagram showing an example of an occupied grid map. FIG. 5 is a flowchart of roadside edge detection control. FIG. 6 is a diagram showing measurement points of distance measurement data from above. FIG. 7 is an explanatory diagram of a part of processing in the roadside edge detection control. FIG. 8 is an explanatory diagram of an example of a detailed specific example of roadside edge detection control. FIG. 9 is a schematic configuration diagram of a driving support system that executes the roadside edge detection method according to the second embodiment. FIG. 10 is a schematic configuration diagram of a driving support system that executes the roadside edge detection method according to the third embodiment. FIG. 11 is a diagram showing a comparison between a case where the traveling lane is straight and a case where the traveling lane is curved. FIG. 12 is a diagram showing a division example of the grid map in the modified example.

Hereinafter, the roadside edge detection method and the roadside edge detection device according to the embodiment of the present invention will be described with reference to the drawings.

(First Embodiment)
FIG. 1 is a schematic configuration diagram of a driving support system that executes the roadside edge detection method according to the first embodiment.

The driving support system 100 is a system mounted on a vehicle for the purpose of assisting the driver in driving the vehicle. The driving support system 100 includes a moving object detection unit 11, a distance measurement sensor 12, a distance measurement data filter unit 13, an odometry measurement unit 14, a distance measurement data storage unit 15, a storage distance measurement data 16, and an occupied grid. It has a map generation unit 17, an occupied grid map 18, an occupied grid map addition unit 19, a discontinuous boundary detection unit 20, a roadside edge determination unit 21, and an output unit 22.

Among these configurations, the distance measurement data filter unit 13, the distance measurement data storage unit 15, the accumulated distance measurement data 16, the occupied grid map generation unit 17, the occupied grid map 18, the occupied grid map adding unit 19, and the discontinuous boundary detection unit. 20, the roadside edge determination unit 21, and the output unit 22 are integrally configured as the roadside edge detection device 10. In the roadside edge detection device 10, the components of the distance measurement data filter unit 13, the distance measurement data storage unit 15, and the storage distance measurement data 16 to the output unit 22 may be configured in the same microcomputer, or may be configured by different microcomputers. It may be configured. Further, the moving object detection unit 11, the distance measuring sensor 12, and the odometry measuring unit 14 are provided as sensors separate from the roadside edge detecting device 10.

Hereinafter, details of each configuration of the driving support system 100 will be described.

The moving object detection unit 11 detects a moving object (moving object) existing around the vehicle, and outputs information indicating the position of the detected moving object to the ranging data filter unit 13. For example, the moving object detection unit 11 is an image processing unit including a camera, and detects the position of a moving object by analyzing the captured time-series images.

The distance measuring sensor 12 is a sensor that measures the distance and direction of an object around the vehicle from the vehicle, that is, the position of the object with respect to the vehicle, and sets a plurality of measurement points on the road surface and the surface of a structure existing on the road surface. Then measure the distance to those measurement points. In the following, the road surface indicated as a distance measurement target by the distance measuring sensor 12 includes roadside objects and the like arranged on the road surface. Therefore, the distance measurement data acquired by the distance measurement sensor 12 shows the undulations caused by the structure or the like in the plane direction in which the road surface exists.

The distance measuring sensor 12 is not limited to the laser radar (LiDAR), and may be a stereo camera or the like. The type of the distance measuring sensor 12 is not limited as long as it can measure the relative distance and direction to the vehicle up to a plurality of measuring points. Therefore, the position as the distance measurement data is shown in the vehicle coordinate system, which is a relative coordinate system based on the position of the vehicle.

The distance measurement data acquired by the distance measurement sensor 12 and the position information of the moving object acquired by the moving object detection unit 11 are input to the distance measurement data filter unit 13. The distance measurement data filter unit 13 erases the distance measurement data in the area where the moving object exists detected by the moving object detection unit 11 from the distance measurement data acquired by the distance measurement sensor 12, and deletes the erased distance measurement data. , Is output to the ranging data storage unit 15.

When the odometry measurement unit 14 acquires the odometry information regarding the traveling vehicle, the odometry measurement unit 14 outputs the odometry information to the distance measurement data storage unit 15. The odometry information includes the traveling speed and the traveling angle of the vehicle, and can be used for creating a traveling locus or the like.

The distance measurement data storage unit 15 receives input of vehicle odometry information acquired by the odometry measurement unit 14 and distance measurement data that has been filtered by the distance measurement data filter unit 13. Then, the distance measurement data storage unit 15 converts the distance measurement data observed in the vehicle coordinate system into the odometry coordinate system in response to these inputs, and uses a plurality of the distance measurement data converted into the odometry coordinate system. The accumulated distance measurement data 16 is created.

The accumulated distance measurement data 16 is the accumulation of distance measurement data in a predetermined period, and each distance measurement data is indicated by an absolute coordinate system based on the position where the vehicle exists at the start of the predetermined period. Then, the accumulated distance measurement data 16 is output to the occupied grid map generation unit 17 by the distance measurement data storage unit 15.

The occupied grid map generation unit 17 generates a grid map of a predetermined two-dimensional area using the accumulated distance measurement data 16, and creates an occupied grid map 18 by cutting out a part of the grid map. Then, the occupied grid map generation unit 17 transmits the created occupied grid map 18 to the occupied grid map adding unit 19.

The grid map generated by the occupied grid map generation unit 17 divides a two-dimensional predetermined area around the vehicle into a grid, and shows the existence of objects in these grids discretely. In each of the grids, the height of the objects present in the grid and the corresponding occupancy value are shown. That is, the grid map is expressed as a set of discretized grids in which the undulations of the surface of the road surface and the existence of structures existing on the road surface are discretized in a predetermined region of the road surface where curbs and the like exist.

Known grid maps include discrete values in the XY Cartesian coordinate system and discrete values in the polar coordinate system of r−θ. The two-dimensional grid map generated by the occupied grid map generation unit 17 in the present embodiment is represented by the XY Cartesian coordinate system. The details of this grid map will be described later with reference to FIG. 3 and the like.

The occupancy grid map addition unit 19 refers to the occupancy value of the grid extending in the traveling direction of the vehicle according to the position from the vehicle in the vehicle width direction with respect to the occupancy grid map 18 generated by the occupancy grid map generation unit 17. Calculate the sum of. As a result, the occupied grid map adding unit 19 generates a distribution of the sum of the occupied values in the traveling direction corresponding to the position in the vehicle width direction, and outputs the distribution to the discontinuous boundary detecting unit 20.

The discontinuous boundary detection unit 20 detects a position in the vehicle width direction in which the total sum exceeds a predetermined threshold value with respect to the distribution of the total sum of the occupied values obtained by the occupied grid map adding unit 19 as a discontinuous boundary. .. Then, the discontinuous boundary detection unit 20 outputs the detected discontinuous boundary to the roadside end determination unit 21. The position where the total sum exceeds a predetermined threshold value is detected from the position where the vehicle exists to the left and right sides in the vehicle width direction.

When the roadside edge determination unit 21 receives the input of the plurality of discontinuous boundaries detected by the discontinuous boundary detection unit 20, the roadside edge determination unit 21 receives the input of the plurality of discontinuous boundaries, and among these discontinuous boundaries, in the vehicle width direction, the left and right sides of the vehicle The two closest discontinuous boundaries are selected, these two discontinuous boundaries are determined as roadside edges, and the output is output to the output unit 22.

The output unit 22 outputs the position of the roadside edge in the vehicle width direction determined by the roadside edge determination unit 21 to an automatic driving system or the like outside the driving support system 100. For example, the automatic driving system uses the received position information of the roadside edge to support the running of the vehicle so that the vehicle travels between the roadside edges.

Here, the outline of the acquisition control of the distance measurement data by the distance measurement sensor 12 will be described.

FIG. 2 is an explanatory diagram showing acquisition control of distance measurement data by the distance measurement sensor 12. This figure shows a situation in which the vehicle 200 travels in a three-dimensional space having an xyz axis. The traveling direction corresponds to the x-axis, the vehicle width direction corresponds to the y-axis, and the height direction corresponds to the z-axis.

The distance measuring sensor 12 sets a plurality of measurement points in a rectangular predetermined region on the xy plane at a predetermined cycle, and measures the distances to these measurement points. In this figure, the distance is measured at four timings of time t-1, t, t + 1, and t + 2. Then, the distance measuring sensor 12 generates distance measuring data using this measurement result. The ranging data indicates the presence or absence of an object at an arbitrary point in a three-dimensional predetermined region in the relative coordinate system with respect to the vehicle 200. Therefore, by using the distance measurement data, it is possible to know the height of the road surface including the roadside object such as a curb.

According to this figure, the distance measurement data at time t-1 includes height information of a columnar obstacle on the left rear side when the traveling direction is set to the front. The distance measurement data at time t includes height information of a rectangular parallelepiped obstacle on the left front side. Further, the distance measurement data at time t + 1 includes height information of a columnar obstacle on the right front side. In this way, the distance measurement data indicates the position of the structure existing on the road surface in the xy plane and the height in the z direction.

The coordinate system of the distance measurement data is converted by the distance measurement data storage unit 15. Specifically, the distance measurement data storage unit 15 uses the odometry information input from the odometry measurement unit 14 to obtain the position of the vehicle 200 at each measurement timing (t-1 to t + 2) using the odometry coordinates. .. Then, the distance measurement data storage unit 15 refers to the distance measurement data that has passed through the distance measurement data filter unit 13 from the relative coordinate system with respect to the vehicle 200 to the absolute coordinates with reference to a predetermined location indicated by the odometry coordinates. Perform conversion to the system.

Finally, the distance measurement data storage unit 15 generates the storage distance measurement data 16 by using the plurality of distance measurement data converted into the absolute coordinate system. Then, the occupied grid map generation unit 17 generates a grid map using the accumulated distance measurement data 16, and generates the occupied grid map 18 by extracting a part of the area of the generated grid map.

Hereinafter, a method of generating a grid map from distance measurement data will be described with reference to FIG. 3, and an example of an occupied grid map 18 which is a part of the generated grid map will be described with reference to FIG.

FIG. 3 is an explanatory diagram of a method of generating a grid map from distance measurement data.

As described above, the distance measuring sensor 12 sets a plurality of measurement points x _j in a predetermined area around the distance measuring sensor 12, acquires the distances to the plurality of measuring points x _j , and obtains the distance measuring data. Ask for. The distance measurement data includes a normal distribution of covariance Σ with the _{measurement point x j} as the average (center of distribution) at each arbitrary point in a predetermined three-dimensional region corresponding to _{one measurement point x j.} Shown. The covariance Σ corresponds to the existence probability of the object. That is, for a single measurement point x _j, such that peaks in the measurement point x _j, normal distribution covariance sigma (Gaussian distribution) is obtained in a three-dimensional region. This means that each of the measurement points x _j defines a normal distribution that peaks at itself and affects the distribution value of the covariance Σ at any point in the three-dimensional space.

Therefore, using the normal distribution of the covariance Σ corresponding to all the measurement points x _{j , each of the grid points x i} provided on the road surface which is a two-dimensional plane (xy plane) is in the z direction (height direction). Calculate the sum of the covariance Σ. Thereby, the occupancy value according to the height of the structure existing at _{the grid point x i} in the xy plane in the z direction can be obtained. In the calculation of the occupancy values, the distance r _j from the distance sensor 12 to the measurement point x _j, and the height h _j from the xy plane at the measurement point x _j is the distribution value of the covariance Σ as a weighted Get on.

As the distance r _j from the distance sensor 12 to the measurement point x _j becomes longer, it becomes sparse distribution of measurement points x _j. Therefore, by multiplying the distribution value of the covariance Σ by the distance r _{j to the measurement point x j} and weighting it, the influence of the normal distribution of the _{measurement point x j can be increased on the obtained occupancy value.} This makes it possible to compensate for the sparse distribution of measurement points x _j.

In the measurement with respect to the measurement point x _j, a laser reflected on the road surface is used. However, in the measurement for a high measurement point x _{j, the} value may be smaller than the correct distribution value depending on the reflection characteristics of the road surface. Therefore, with respect to the distribution value of the covariance sigma, by weighting by multiplying the height h _j of the measurement point x _j, to compensate for inaccuracies in the normal distribution due to the height of the measurement point x _j Can be done.

Therefore, the occupancy value C _{i at} an arbitrary point x _i can be expressed by the following equation. However, N (.; X _{j , Σ) indicates the normal distribution of the covariance Σ with the measurement point x j} of the j-th distance measurement data as the average (center of the distribution). Further, x _i indicates the position of the grid in the xy plane in relative coordinates with respect to the vehicle. i is an index indicating the position of the relative coordinates.

The accumulated distance measurement data 16 finally obtained by superimposing the distance measurement data is represented by a continuous value of the _{occupancy value C i.} Further, the occupied value C _i in each grid may be subjected to a binarization process for determining whether or not it is larger than a certain threshold value.

The expression format of the distance measurement data is not limited to the point cloud format in which the measurement points measured for the distance from the distance measurement sensor 12 as described above are distributed in the three-dimensional space. Like the output result from the stereo camera, the distance distribution from the distance measuring sensor 12 to the measurement point may be expressed as a distance image projected on a two-dimensional plane.

FIG. 4 is a diagram showing an example of the occupied grid map 18 generated by the occupied grid map generation unit 17. For example, with the vehicle 200 as the center, the front and rear in the traveling direction and the left and right in the vehicle width direction are two-dimensional rectangular regions having predetermined lengths, and the rectangular regions are divided in a grid pattern to form a grid.

According to this figure, the rectangular area is assumed to be in the range of 10 m (+ 10 m to -10 m) in the front-rear direction and 20 m (-20 m to + 20 m) in the left-right direction with the vehicle 200 as the center. Further, the grid is set in a square area of, for example, 10 to 20 cm in length and width. In this way, a grid divided by a predetermined grid width is configured in the rectangular area. In such a two-dimensional occupancy grid map 18, an occupancy value C _i indicating the height of an object existing in each grid is shown.

In the following, roadside edge detection control using the _{occupancy value C i will be described.}

FIG. 5 is a flowchart of roadside edge detection control by the driving support system 100. The roadside edge detection device 10 of the driving support system 100 is programmed to repeatedly perform a series of processes of the roadside edge detection control shown in this figure at a predetermined timing. In the following, for the sake of clarification of the description, the blocks that realize the predetermined functions in the roadside edge detection device 10 in FIG. 1 will be described as performing the respective processes. The roadside edge detection device 10 may perform a series of processes.

The roadside edge detection control is mainly shown collectively as step S10, and is collectively shown as the generation control of the accumulated distance measurement data 16 consisting of steps S11 to S15 and the roadside edge consisting of steps S17 to S20. It includes two processes with detection control.

First, the process of generating the accumulated ranging data 16 in step S10 will be described.

In step S11, the distance measurement data filter unit 13 receives the distance measurement data generated by the distance measurement sensor 12. As described above, in the distance measurement data, the occupancy value C _{i of the grid points x i} in a predetermined region around the vehicle 200 is indicated in the vehicle coordinate system.

In step S12, the distance measurement data filter unit 13 associates the distance measurement data acquired from the distance measurement sensor 12 with the position of the moving object detected by the moving object detection unit 11. The moving object exists on the road surface, but is different from the roadside object that defines the roadside edge. Therefore, the distance measurement data filter unit 13 deletes the distance measurement data at the position where the moving object exists, which is detected by the moving object detection unit 11 based on the result of the association.

In step S13, the distance measurement data storage unit 15 acquires the distance measurement data from the distance measurement data filter unit 13 and the odometry information from the odometry measurement unit 14. Then, the distance measurement data storage unit 15 converts the distance measurement data from the vehicle coordinate system to the absolute coordinate system using the odometry information.

As described above, since the distance measurement data shows the position of the vehicle 200 as a zero point, the distance measurement data is a relative coordinate system in which the zero point changes according to the acquisition timing of the distance measurement data. The odometry coordinates are, for example, an absolute coordinate system in which the position of the vehicle 200 at a specific timing such as the start of traveling is set as the zero point. Therefore, by using the odometry information, it is possible to convert the acquired distance measurement data from the relative coordinate system to the absolute coordinate system.

In step S14, the distance measurement data storage unit 15 further records the distance measurement data converted into odometry coordinates in the time buffer.

Here, the time buffer is a memory area for recording a plurality of elements having a predetermined length, and is generally called an array. In this example, the time buffer is configured to be able to record a predetermined number of ranging data. Further, in this time buffer, the distance measurement data acquired at a certain timing is recorded as one element constituting the array of the time buffer, and the distance measurement data in a predetermined period is recorded in the entire time buffer.

More specifically, when the distance measurement data storage unit 15 receives new distance measurement data from the distance measurement data filter unit 13, the distance measurement data storage unit 15 adds new distance measurement data as the final element of the time buffer and the time buffer. Erases the oldest ranging data, which is the first element of. By doing so, since the number of distance measurement data to be stored is kept constant in the time buffer, the distance measurement data having a predetermined time length is stored from the present to the past. For example, the time buffer can store 100 distance measurement data (100 steps). The memory structure in which the oldest element is deleted with the addition of a new element in this way is called a ring buffer.

In step S15, the distance measurement data storage unit 15 generates the storage distance measurement data 16 by superimposing the distance measurement data of the absolute coordinate system recorded in each element of the array in the time buffer.

Here, since the distance measurement data stored in the time buffer are converted into odometry coordinates in step S13, their zero points are equal. Therefore, by superimposing the distance measurement data recorded in the time buffer, it is possible to generate the accumulated distance measurement data 16 indicating the occupied value in the three-dimensional space of the absolute coordinate system with the same zero point as a reference. In this way, it is possible to generate high-density accumulated distance measurement data 16 generated by a plurality of distance measurement data.

Next, the roadside edge detection control in step S16 will be described.

In step S17, the occupied grid map generation unit 17 receives the input of the accumulated distance measurement data 16 from the distance measurement data storage unit 15. The occupied grid map generation unit 17 superimposes the distributed distance measurement data 16 showing the normal distribution of the covariance Σ in the three-dimensional space so as to project the distribution value in the height direction onto the road surface, and obtains the variance value C _i . By calculating, a grid map is generated.

In this grid map, the occupancy value C _{i at each of the grid points x i} formed by dividing the road surface in a grid pattern is shown. Since the occupancy value C _i shown in this grid map is the sum of the normal distributions of the covariance Σ in the height direction, it corresponds to the height of an object that can exist at the _{grid point x i.} Therefore, the grid map shows the height of the road surface according to the undulations of the road surface and the surface of the structure existing on the road surface. Then, the occupied grid map generation unit 17 cuts out a part as shown in FIG. 4 from the generated grid map to generate the occupied grid map 18.

In step S18, the occupied grid map adding unit 19 _{calculates the sum of the occupied values C i in} each of the grid rows in the traveling direction as the total road surface height v in the occupied grid map 18. Thereby, the distribution of the total road surface height v corresponding to the position from the vehicle 200 in the vehicle width direction can be obtained.

In step S19, the discontinuous boundary detection unit 20 receives the input of the distribution of the total road surface height v generated in step S18. The discontinuous boundary detection unit 20 determines whether or not the _{total road surface height v is larger than the threshold value v th} from the vehicle 200 toward the left and right sides in the vehicle width direction, and the total road surface height v is greater than the _{threshold value v th.} It is determined that the position in the vehicle width direction, which is determined to be large, is a discontinuous boundary. A plurality of discontinuous boundaries may be detected.

In step S20, the roadside edge determination unit 21 selects one of the plurality of discontinuous boundaries detected in step S19, one by one, which is the closest discontinuous boundary from the vehicle 200 toward the left and right sides in the vehicle width direction. , It is determined that the place where those discontinuous boundaries are located is the roadside edge. Then, the roadside edge determination unit 21 outputs the determined position of the roadside edge in the vehicle width direction to the output unit 22.

Through such processing, roadside edge detection control is performed. Hereinafter, the roadside edge detection control will be described with reference to FIGS. 6 to 8 with reference to specific examples.

FIG. 6 is a diagram showing the measurement points by the distance measuring sensor 12 from above.

In this figure, the road surface 61 seen from above is shown, and in the center, the vehicle 200 traveling toward the upper part of the drawing is shown. On the road surface 61, curbs 62 are provided on the left side and the right side of the vehicle 200, respectively. Since the vehicle 200 travels between the two curbs 62, the extending direction of the curbs 62 and the traveling direction of the vehicle 200 substantially coincide with each other. The position of the side surface of these curbs 62 on the vehicle 200 side is detected as the roadside edge.

Further, in this figure, the measurement points of the distance by the distance measuring sensor 12 are indicated by circles. Since the radar and the like output from the distance measuring sensor 12 are blocked by the curb 62, the distance measuring data is acquired in the area 63 extending along the curb 62 with a predetermined width on the opposite side of the vehicle 200 with respect to the curb 62. Can not. On the other hand, in the region 64 near the wall surface on the vehicle 200 side of the curb 62, distance measurement data is acquired at a plurality of measurement points. Further, on the road surface 61 in the vicinity of the vehicle 200, there is a region 65 which is a blind spot of the vehicle 200 and cannot acquire distance measurement data.

FIG. 7 is an explanatory diagram of the processing of steps S18 to S20 in the roadside edge detection control shown in FIG.

FIG. 7A shows an occupied grid map 18 corresponding to the region shown in FIG. In the occupied grid map 18 _{, dark hatching is attached to a region where the occupied value C i} is large and the height of the object existing in the grid is high. On the other hand, _{thin hatching is attached to a region where the occupancy value C i} is small and the height of the object existing in the grid is low.

The road surface 61, which serves as a reference in the height direction, is hatched in medium shades. On the other hand, a dark hatching is attached to the region where the tall curb 62 exists. The area 63, which is the shadow of the curb 62, and the area 65, which is the blind spot of the vehicle 200, are shown in white because the distance measurement data cannot be acquired.

FIG. 7B shows the distribution of the total road surface height v according to the position in the vehicle width direction.

The total road surface height v is calculated in step S18 and is the total sum of _{the occupied values C i} of the grid rows in the traveling direction of the vehicle 200 in the occupied grid map 18. Therefore, in the region where the curb 62 exists in the vehicle width direction, the total road surface height v is larger than in the region where only the road surface 61 exists. On the other hand, in the region 63 where the distance measurement data cannot be acquired, the total road surface height v becomes zero. Further, the total road surface height v is relatively low at the position in the vehicle width direction where the region 65 that becomes the blind spot of the vehicle 200 exists.

In step S19, the discontinuous boundary detection unit 20 sequentially determines whether or not the _{total road surface height v is larger than the threshold value v th} from the center of the vehicle 200 toward both sides (left and right) in the vehicle width direction. The position in the vehicle width direction where it is determined that the total road surface height v is _{larger than the threshold value v th is detected as the discontinuous boundary 71.} In this example, two discontinuous boundaries 71 are detected, but three or more may be detected.

In the neglected region 72 where the vehicle 200 exists, it is not necessary to compare the _{total road surface height v with the threshold value v th.} Due to the shape of the vehicle 200 and the location of the distance measuring sensor 12, the neglected region 72 becomes a blind spot of the distance measuring sensor 12, so that it is not necessary to detect the discontinuous boundary 71 in this region.

The threshold value v _th is defined as follows.

First, the average value of the distribution of the total road surface height v at all positions in the vehicle width direction can be calculated as _{v mean by the} following equation using the _{occupancy value C i obtained by equation (1).} However, i (1 ≦ i ≦ N) in the following equation _{is an index indicating the position of the grid point x i} in the vehicle width direction, and is the same as that in the equation (1).

Then, the threshold value v _th can be calculated by the following equation using the average value v _{mean , the maximum value v max} in the total road surface height v, and the coefficient α (0 ≦ α ≦ 1).

In this way, _{in order to determine the threshold value v th} used for detecting the discontinuous boundary 71 by _{the average value v mean of the total road surface height v, the threshold value v th} corresponding to the change in the distribution of the total road surface height v is used. Specifically, if the total road surface height v is large as a whole, the threshold value v _th is large, and if the total road surface height v is small as a whole, the threshold value v _th is small. In this way, _{the detection sensitivity of the discontinuous boundary 71 can be changed by dynamically changing the threshold value vs th} , so that the detection accuracy of the discontinuous boundary 71 changes due to the change in the acquisition status of the distance measurement data. It is suppressed, and it becomes possible to accurately determine the roadside edge.

_{Further, the threshold value v th} is set based on _{the average value v mean} of the total road surface height v, and further, the threshold value v th is set by a value of a constant ratio according to the coefficient α with respect to the difference between the _{average value v mean} and the maximum value v _max. .. Therefore, in the case when the difference of the average value v _mean and the maximum value v _max is small, the threshold value v _th is close to a more average value v _mean, the difference between the average value v _mean and the maximum value v _max is large, the threshold value v _th becomes a value closer to the maximum value v _max. As a result, in addition to the change in the distribution of the total road surface height v, the sensitivity for detecting the discontinuous boundary 71 can be dynamically changed according _{to the maximum value v max, so that the roadside edge can be determined accurately.} It will be possible to do.

The equation (3) is _{a weighted average of the average value v mean} and the maximum value v _max, and can be expressed as the following equation.

FIG. 8 is an explanatory diagram of a more detailed specific example of roadside edge detection control.

FIG. 8A shows a grid map obtained by superimposing the accumulated ranging data 16 in the height direction as viewed from above and behind the vehicle 200, and an occupied grid map 18 which is a part thereof. ing. On the road surface 61, white lines 81 and the like exist in addition to the curb 62, and these objects are shown on the grid map.

FIG. 8B shows the total road surface height v calculated using the occupied grid map 18 of FIG. 8A. In this figure, the neglected region 72 is shown as in FIG. 7B, and the portion where the _{total road surface height v is larger than the threshold value v th is detected as the discontinuous boundary 71A to 71D.} Then, among these discontinuous boundaries 71A to 71D, it is determined that the curb 62 exists at the discontinuous boundaries 71B and 71C which are close to the vehicle 200, and it is detected as the position of the roadside edge.

Thus, to calculate the occupancy value C _i that indicates the height of the road edge and the road surface 61 such as a curb 62 in a predetermined range around the vehicle 200 by using the distance measurement data, the occupancy values C _i along the running direction The total road surface height v is calculated by calculating the total. The total road surface height v is obtained as a distribution according to the position of the vehicle 200 in the vehicle width direction. Then, by comparing the total road surface height v and the threshold value v _th , the discontinuity in the distribution of the total road surface height v is evaluated, and the roadside edge is detected. Due to such a configuration, even if a part of the measurement points of the distance measurement data cannot be detected, the roadside edge can be detected by using the information of the measurement points before and after that, so that the detection system is improved. Can be planned.

According to the first embodiment, the following effects can be obtained.

According to the roadside edge detection method of the first embodiment, the heights of the road surface 61 and the curb 62 are detected in a predetermined range around the vehicle 200, and the occupied grid map 18 showing the _{occupied value C i in each grid is shown.} By calculating the sum of the road surface height detection step (S17) for generating the above and the occupancy value C _i shown in the occupancy grid map 18 along the traveling direction of the vehicle 200, the road surface height according to the position in the vehicle width direction is calculated. A total sum calculation step (S18) for calculating the distribution of the total v, a roadside edge determination step (S20) for determining that the position in the vehicle width direction corresponding to the road surface height total v exceeding _{the threshold value v th is the roadside edge.} To be equipped.

With such a configuration, in a predetermined range around the vehicle 200, the discontinuity of the total road surface height v is evaluated in correspondence with the position in the vehicle width direction, and the position of the roadside end is determined. Therefore, even if a part of the distance measurement data acquired in a predetermined range is missing or erroneous and cannot be detected, the roadside edge can be detected by using the distance measurement data before and after the traveling direction. In this way, since the distance measurement data that has not been detected can be compensated, the detection accuracy of the roadside edge can be improved.

According to the roadside edge detection method of the first embodiment, in the roadside edge determination step (S20), _{the comparison between the total road surface height v and the threshold value v th} is performed from the position of the vehicle 200 to the left and right in the vehicle width direction. It is determined that the position in the vehicle width direction in which the total road surface height v _{exceeds the threshold value v th is determined to be the roadside end.}

With such a configuration, the detected position of the roadside edge can be determined to be the inner wall of the roadside object such as the curb 62 on the vehicle 200 side. It can be detected well.

According to the roadside edge detection method of the first embodiment, in the roadside edge determination step (S20), when there are a plurality of discontinuous boundaries 71 such that the _{total road surface height v exceeds the threshold value v th, the vehicle is in the vehicle width direction.} It is determined that the position of the discontinuous boundary 71 close to 200 is the roadside edge.

The roadside end, which is the side end of the traveling lane of the vehicle 200, is the discontinuous boundary 71 observed on the innermost side of the plurality of discontinuous boundaries 71 when viewed from the vehicle 200. Therefore, when a plurality of discontinuous boundaries 71 are detected, it is determined that the discontinuous boundary 71 closest to the vehicle 200 in the vehicle width direction is the roadside end. As a result, the position of the roadside edge that determines the travelable range of the vehicle 200 can be accurately detected.

According to the roadside edge detection method of the first embodiment, in the roadside edge determination step (S20), the _{larger the average value v mean} of the road surface height total v according to the position in the vehicle width direction, the more the road surface height total v is compared. The threshold value v _th is set large.

_{With such a configuration, the threshold value v th} changes according to the change in the distribution of the total road surface height v. That is, when the total road surface height v is a large distribution as a whole, the threshold value v _th is large, and when the total road surface height v is a small distribution, the threshold value v _th is small. Therefore, the sensitivity of detection of the discontinuous boundary 71, which is the roadside edge, can be dynamically changed according to the distribution of the total road surface height v, so that the measurement accuracy of the distance measurement data changes depending on the weather or the like. However, the influence caused by the change in the distribution of the distance measurement data is suppressed, and the roadside edge can be detected with high accuracy.

According to the roadside edge detection method of the first embodiment, the threshold value v _th is a weighted average of the average value v _mean and the maximum value v _{max at} a predetermined ratio.

With such a configuration, the threshold value _vs. can be set appropriately. For example, when the difference between the average value v _mean and the maximum value v _{max is small, setting the threshold value v th} to a value close to the average value v _mean facilitates the detection of the roadside edge. On the other hand, when _{the difference between the average value v mean} and the maximum value v _max is large, the threshold value v _th can be set to a value closer to the maximum value v _max to suppress erroneous detection of the roadside edge. In this way, _{by using the maximum value v max} in addition to the _mean value v mean, the sensitivity of detection of the discontinuous boundary 71 can be dynamically changed, so that the influence of changes in the distribution of distance measurement data can be suppressed. It becomes possible to detect the roadside edge with high accuracy.

(Second Embodiment)
In the first embodiment, when a plurality of discontinuous boundaries 71 are detected by the discontinuous boundary detecting unit 20, the roadside edge determining unit 21 has a discontinuous boundary 71 that is closer to the vehicle among these discontinuous boundaries 71. It was detected that there was a roadside edge at the position in the vehicle width direction. In the second embodiment, another detection method of the roadside edge when a plurality of discontinuous boundaries 71 are detected will be described.

FIG. 9 is a schematic configuration diagram of a driving support system 100 that executes the roadside edge detection method according to the second embodiment.

According to this figure, as compared with the driving support system 100 of the first embodiment shown in FIG. 1, the roadside edge determination unit 21 has a plurality of discontinuous boundaries 71 detected by the discontinuous boundary detection unit 20. In addition, it accepts inputs from the self-position estimation unit 30 and the map data 31.

The self-position estimation unit 30 estimates the current position of the vehicle using GPS or the like, and outputs the estimated position of the vehicle. Further, the map data 31 is a high-definition map (HD map) showing the position information of the curb 62 and the like. The roadside edge determination unit 21 estimates the approximate position of the curb by associating the current position of the vehicle input from the self-position estimation unit 30 with the high-definition map of the map data 31, and is closest to the estimated position of the curb. The discontinuous boundary is detected as the roadside edge.

In this way, when the extending direction of the curb can be obtained from map information or the like, _{the total occupancy value C i} may be calculated along the extending direction of the curb to calculate the total road surface height v. .. _{That is, the calculation of the total road surface height v is not limited to the total sum of the occupied values C i} along the traveling direction in the relative coordinate system seen from the vehicle as in the first embodiment, and is determined from the position information of the curb in the absolute coordinate system. It may be the sum of _{the occupied values C i} along the traveling direction indicated by the travelable lane.

According to the roadside edge detection method of the second embodiment, in the position information acquisition step, the road information including the position information of the roadside edge and the position information of the vehicle are used to the outside in the vehicle width direction of the vehicle. Get the position of the roadside edge at. When a plurality of discontinuous boundaries are detected in the roadside edge determination step, among those discontinuous boundaries, the discontinuous boundary close to the position of the roadside edge acquired in the position information acquisition step is selected and detected as the roadside edge. To do. By such a process, the detection accuracy of the roadside edge can be improved.

(Third Embodiment)
In the first and second embodiments, the roadside edge detection control is performed when the traveling lane of the vehicle 200 is a straight line, but the present invention is not limited to this. In the third embodiment, an example of performing roadside edge detection control when the traveling lane is curved will be described.

FIG. 10 is a schematic configuration diagram of a driving support system 100 that executes the roadside edge detection method according to the third embodiment.

According to this figure, as compared with the driving support system 100 of the first embodiment shown in FIG. 1, the occupied grid map generation unit 17 is in addition to the accumulated distance measurement data 16 calculated by the distance measurement data storage unit 15. Further, the input from the track curvature detection unit 40 is further accepted.

The track curvature detecting unit 40 detects the radius of curvature r of the traveling lane of the vehicle 200 by using the input from the self-position estimation unit 30 and the map data 31 (not shown in FIG. 10) shown in the second embodiment. To do. Further, the track curvature detection unit 40 may acquire the radius of curvature r by using information such as the white line recognition result, the yaw rate, and the current steering angle.

The occupied grid map generation unit 17 uses the grid map generated from the accumulated distance measurement data 16 to generate an occupied grid map 18 having a length in the traveling direction of l. The traveling direction length l of the vehicle of the occupied grid map 18 is determined according to the following equation.

As shown in the equation (5), the traveling direction length l of the occupied grid map 18 is determined by multiplying the radius of curvature r of the traveling lane by a predetermined proportionality constant ε. Therefore, when the curvature of the traveling lane is large and the radius of curvature r is small, the traveling direction length l of the occupied grid map 18 is short, and when the curvature is small and the radius of curvature r is large, the traveling direction length l is long. It should be _{noted that l max} and l _min indicate the maximum length and the minimum length of the occupied grid map 18 required for the discontinuous boundary detection unit 20 to detect the roadside edge, respectively.

FIG. 11 is a diagram showing a comparison between a case where the traveling lane is straight and a case where the traveling lane is curved. FIG. 11A shows a case where the traveling path is straight, and FIG. 11B shows a case where the traveling path is curved. In both cases, the vehicle 200 and the curb 62 which is the roadside end of the traveling lane are shown together with the occupied grid map 18. In FIG. 11B, it is assumed that the radius of curvature of the traveling lane is r.

When the traveling lane is curved as shown in FIG. 11B, the curb 62 has a wider area in the vehicle width direction as compared with the case where the traveling lane is straight as shown in FIG. 11A. Will be distributed in. Therefore, the peak of the total road surface height v, which is the total sum of _{the occupied values C i} along the traveling direction, becomes low, and the detection accuracy of the roadside edge by comparison with the _{threshold value v th may decrease.} Further, as the curvature of the traveling lane becomes larger and the radius of curvature r becomes smaller, the curb 62 has a wider distribution in the vehicle width direction, and the detection accuracy of the roadside edge is further lowered.

Therefore, as shown in the equation (4), the larger the curvature, that is, the smaller the radius of curvature r, the smaller the traveling direction length l of the vehicle 200 of the occupied grid map 18 in the vehicle width direction of the curb 62. The distribution in can be narrowed. As a result, the decrease in the peak of the total road surface height v can be suppressed, and the roadside edge can be detected with high accuracy.

As described above, according to the roadside edge detection method of the third embodiment, the radius of curvature r of the traveling lane of the vehicle 200 is acquired in the curvature acquisition step, and the smaller the radius of curvature r in the road surface height detection step, the more the occupied grid map. The traveling direction length l of the vehicle 200 of 18 is set short.

Here, when the traveling lane is curved, the portion of the occupied grid map 18 _{where the curb 62 having a large occupied value C i} is arranged is widely distributed in the vehicle width direction. Therefore, the peak of the total road surface height v becomes small, and there is a possibility that the total road surface height v exceeding _{the threshold value v th cannot be detected appropriately.}

However, by shortening the traveling direction length l of the vehicle 200 of the occupied grid map 18, the curbs 62 are distributed in a narrow width in the vehicle width direction. As a result, it is suppressed that the peak of the total road surface height v becomes small, and the _{total road surface height v exceeding the threshold value v th} can be appropriately detected, so that the detection accuracy of the roadside edge is improved. Further, the larger the curvature, that is, the smaller the radius of curvature r, the smaller the traveling direction length l of the vehicle 200 of the occupied grid map 18, so that the detection accuracy of the roadside edge can be further improved.

(Modification example)
In the above-described embodiment, an example of detecting the roadside edge on the occupied grid map 18 having a traveling direction length l corresponding to the radius of curvature r has been shown, but the present invention is not limited to this. The occupied grid map 18 may be divided into a plurality of parts, roadside edges in each divided area may be detected, and these roadside edges may be compared.

FIG. 12 is a diagram showing an example of division of the grid map in this modified example.

In this example, the occupied grid map generation unit 17 further divides the occupied grid map 18 generated in the first embodiment into D pieces to generate divided regions 18-i (0 ≦ i ≦ D), and they are generated. The roadside edge is detected in each of the divided regions 18-i. The divided areas 18-i are arranged side by side from the front to the rear in the traveling direction within a range not exceeding the occupied grid map 18, and the divided areas 18-i are arranged i-th from the front. Shall indicate.

In this figure, l _max indicates the maximum travel direction length that can be taken as the occupied grid map 18. l is a value determined by using the equation (4), and indicates the traveling direction length of the vehicle 200 in one divided region 18-i. In such a case, the number of divisions D of the occupied grid map 18 can be expressed by the following equation.

However, [x] is a Gaussian symbol and indicates the maximum integer that does not exceed x.

By dividing the occupied grid map 18 having the maximum length l _{max by} the number of divisions D, D division areas 18-i are provided. Longitudinal length of the divided regions 18-i are shown as l _i, l _i is regardless of i, are all l.

_{Then, the distance x i} from the center of the vehicle 200 at the front end of the divided region 18-i can be expressed by the following equation.

The occupied grid map generation unit 17 associates the position x _i of the divided area 18-i in the traveling direction with the position of the roadside edge in the vehicle width direction detected in the divided area 18-i, so that the occupied grid map 18 The position of the roadside edge can be detected.

The occupied grid map 18 may be divided by a constant number of divisions D regardless of the radius of curvature r of the runway, and the plurality of division areas 18-i may be evaluated individually. By doing so, on the contrary, the curvature of the traveling lane can be estimated from the detected position of the roadside edge in the vehicle width direction.

As described above, according to the roadside edge detection method of the modified example, the roadside edge in the vehicle width direction is obtained for each of the divided regions 18-i formed by dividing the occupied grid map 18 into D pieces along the traveling direction. Detect the position of. Then, the position of the roadside end in the divided region 18-i in the vehicle width direction is detected in association with the position of the divided region 18-i in the traveling direction. By doing so, even when the traveling lane is curved, the position of the roadside edge on the occupied grid map 18 can be appropriately determined in the traveling direction.

Although the embodiments of the present invention have been described above, the above embodiments are only a part of the application examples of the present invention, and the technical scope of the present invention is limited to the specific configurations of the above embodiments. Absent. In addition, the above embodiments can be combined as appropriate.

10 Roadside edge detection device 12 Distance measurement sensor 14 Odomometry measurement unit 15 Distance measurement data storage unit 16 Accumulation distance measurement data 17 Occupied grid map generation unit 18 Occupied grid map 18-i: Divided area 19 Occupied grid map addition unit 20 Discontinuous boundary Detection unit 21 Roadside edge determination unit 30 Self-position estimation unit 31 Map data 40 Runway curvature detection unit 61 Road surface 62 Edge stone 71 Discontinuous boundary 72 Ignore area 100 Driving support system 200 Vehicle

Claims (9)

It is a roadside edge detection method for detecting a roadside edge provided with a roadside object on a road surface on which a vehicle travels.
A road surface height detection step for detecting the height of the road surface in a predetermined range around the vehicle, and
A total calculation step of calculating the total height of the road surface in the predetermined range detected in the road surface height detection step along the traveling direction of the vehicle according to the position in the vehicle width direction with respect to the vehicle. When,
A roadside edge detection method comprising a roadside edge determination step of determining that a position in the vehicle width direction in which the total sum calculated in the total sum calculation step exceeds a threshold value is the roadside end. The roadside edge detection method according to claim 1.
In the roadside edge determination step, the position where the total exceeds the threshold is detected in order from the position where the vehicle exists in the vehicle width direction toward the side, and when the total exceeds the threshold, it is first detected. A roadside edge detection method for determining that the detection position in the vehicle width direction is the roadside edge. The roadside edge detection method according to claim 1 or 2.
When a plurality of positions in the vehicle width direction in which the total sum exceeds the threshold value are detected in the roadside edge determination step, it is determined that the position closest to the vehicle in the vehicle width direction is the roadside edge. , Roadside edge detection method. The roadside edge detection method according to claim 1 or 2.
Further, a position information acquisition step for acquiring the position information of the roadside edge based on the position information of the vehicle and the road information including the position information of the roadside edge is provided.
When a plurality of positions in the vehicle width direction in which the total sum exceeds the threshold value are detected in the roadside edge determination step, the location closest to the location indicated by the roadside edge position information acquired in the position information acquisition step. A roadside edge detection method for determining that the position is the roadside edge. The roadside edge detection method according to any one of claims 1 to 4.
A roadside edge detection method in which the larger the average value of the sums, the larger the threshold value in the roadside edge determination step. The roadside edge detection method according to claim 5.
A roadside edge detection method in which, in the roadside edge determination step, the threshold value is a weighted average of the average value and the maximum value of the sum at a predetermined ratio. The roadside edge detection method according to any one of claims 1 to 6.
Further, a curvature acquisition step for acquiring the curvature of the traveling lane of the vehicle is provided.
A method for detecting a roadside edge, wherein in the road surface height detection step, the larger the curvature of the traveling lane, the shorter the length of the vehicle in the traveling direction in the predetermined range. The roadside edge detection method according to any one of claims 1 to 6.
In the roadside edge determination step,
The predetermined range is provided by being divided into a plurality of parts along the traveling direction of the vehicle, and each of the divided regions arranged side by side in the traveling direction of the vehicle is the divided region in the traveling direction of the vehicle. The position of the roadside edge in the vehicle width direction is determined in correspondence with the position up to.
A roadside edge detection method for determining that the roadside edge is located at the determined roadside edge position in the vehicle width direction for each of the divided regions. A roadside edge detection device that detects a roadside edge provided with a roadside object on the road surface on which a vehicle travels.
A road surface height detecting unit that detects the height of the road surface in a predetermined range around the vehicle, and a road surface height detecting unit.
A total calculation unit that calculates the total height of the road surface in the predetermined range detected by the road surface height detection unit along the traveling direction of the vehicle according to the position in the vehicle width direction with respect to the vehicle. When,
A roadside edge detection device including a roadside edge determination unit that determines that a position in the vehicle width direction in which the total sum calculated by the total sum calculation unit exceeds a threshold value is the roadside end.

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