Road edge heightening device

Abstract

To provide a road edge height application apparatus which can apply the road edge height by extracting height information on a road surface in order to solve a problem of the conventional technique.SOLUTION: A road edge height application apparatus according to the prevent invention comprises: three-dimensional terrain model storage means; road edge model storage means; region of interest setting means; target statistical value setting means; and road edge height calculation means. The target statistical value setting means is means for setting a target statistical value. The road edge height calculation means is means for calculating height information related to an interest constitution point equivalent to the target statistical value in the interest constitution points that belong to the region of interest arranged in a three-dimensional terrain model as the road edge height. The road edge height application apparatus applies the road edge height to an interest reference point corresponding to the region of interest.SELECTED DRAWING: Figure 9

Description

[Technical Field]

[0001] The present invention relates to a technology for assigning height information such as elevation to features, and more specifically to a road edge height assigning device that determines the height of a road edge (hereinafter referred to as "road edge height") by performing statistical processing of height information near the road edge. [Background technology]

[0002] Recently, there has been an increasing demand for topographical information (spatial information). For example, an increasing number of managers are requesting spatial information on facilities, such as their shape and location, in order to more effectively manage facilities installed on or along roads. At the same time, advanced maintenance and management of social infrastructure (hereinafter simply referred to as "social infrastructure") is considered an important issue in realizing Society 5.0, which is currently being promoted jointly by the public and private sectors. Furthermore, as autonomous driving technology becomes more practical, there is a strong demand from many quarters for various spatial information related to roads, including road edges (road boundary lines).

[0003] Traditionally, two-dimensional (2D) planar drawings (floor plans), such as topographical maps, have been the mainstream for showing spatial information. Although plan views sometimes show "height information" such as contour lines and endpoint elevations, they are primarily focused on showing planar positions, making it difficult to grasp the target area as a three-dimensional (3D) space. However, in recent years, advances in measurement technology have made it easy to acquire large numbers of three-dimensional measurement points (hereinafter referred to as "3D point clouds"), and advances in information technology have also made it easier to handle these three-dimensional point clouds.

[0004] For example, to obtain a 3D point cloud of terrain including roads, measurement methods such as aerial photogrammetry, airborne laser measurement, terrestrial laser measurement, and MMS (Mobile Mapping System) are used. Among these, MMS is a system in which sensors such as a laser scanner, camera, a satellite positioning system (GNSS: Global Navigation Satellite System) for obtaining self-position, an IMU (Inertial Measurement Unit), and odometry are mounted on a moving vehicle, allowing the laser scanner to obtain a 3D point cloud while moving on the roadway.

[0005] The 3D point cloud obtained by MMS or other methods is generally used as a 3D model of the terrain to be measured (hereinafter referred to as a "3D terrain model"). This 3D terrain model represents the target terrain using 3D coordinates, and is a terrain model typified by DSM (Digital Surface Model) and DEM (Digital Elevation Model).

[0006] Typically, a 3D terrain model is composed of multiple small regions obtained by dividing the target planar area. These small regions, also known as meshes, are formed by dividing the area into, for example, orthogonal grids, and each small region has a representative point. Because the 3D point clouds obtained by measurement are often random data (data that is irregularly arranged on a plane), geometric calculations are often used to assign heights to the representative points of the small regions. Calculation methods include the Triangulated Irregular Network (TIN) method, which calculates height using an irregular triangulation network formed from random data, the Nearest Neighbor method, which uses the nearest laser measurement point, as well as inverse distance weighting (IDW), the Kriging method, and the averaging method.

[0007] 3D terrain models allow users to grasp the target terrain in both two-dimensional and three-dimensional terms, making them more versatile than flat maps. However, 3D terrain models based on measurement results only provide spatial information based on three-dimensional coordinates, and are unable to show the attributes of features. In other words, simply by looking at a 3D terrain model, users cannot tell where the edges of roads are or where the outer edges of office buildings (the so-called edges) are.

[0008] Adding feature attribute information to a 3D terrain model requires a survey of the features. This means extracting feature attributes by visually inspecting aerial photographs, or having workers go directly to the site and record the visual information on a map, which requires human judgment. However, because roads, for example, generally have considerable lengths, the amount of work required for the survey is enormous, and considering the labor and time involved, it requires significant costs.

[0009] As mentioned above, traditionally, flat maps have been the primary method of data collection. In some cases, these maps are used as raster or vector data (i.e., digitized), and in other cases, features are converted into shapes (polylines or polygons) and then assigned attribute information. Furthermore, recent advances in machine learning technology have made it possible to automatically extract features from aerial photographs and flat maps, and then extract their shapes and attribute information. In this way, it is conceivable that a separate "2D terrain model" is available that does not include elevation or other height information but does include feature attribute information. Using such a 2D terrain model can eliminate (or significantly reduce) the need for manual surveys of features when assigning feature attribute information to a 3D terrain model.

[0010] Therefore, Patent Document 1 proposes a technique for generating three-dimensional feature shape lines by adding elevations to the feature shape lines represented in two-dimensional map data using a point cloud obtained by measurement. [Prior art documents] [Patent documents]

[0011] [Patent Document 1] Japanese Patent Publication No. 2020-013351 Summary of the Invention [Problem to be solved by the invention]

[0012] As mentioned above, 3D terrain models only provide spatial information based on three-dimensional coordinates, and it is not possible to understand where the road edge is simply by looking at the 3D terrain model. Therefore, in order to assign road edge height, manual work, such as visually processing aerial photographs, is unavoidable, resulting in significant costs. Therefore, we suggested the possibility of reducing manual work by using "2D terrain models" that digitize plan views such as raster data and vector data.

[0013] For example, a two-dimensional terrain model (hereinafter referred to as a "road edge model") having attribute information of "road edge" and its planar position (coordinate information) can be placed on a 3D terrain model, and then constituent points around the road edge model can be extracted from the three-dimensional points (hereinafter referred to as "constituent points") that make up the 3D terrain model, and these can be used as the road edge height. However, buildings other than roads, such as houses and office buildings, are often located around road edges, and there are cases where a constituent point cannot immediately be used as the road edge height simply because it is located around the road edge model.

[0014] The object of the present invention is to solve the problems associated with the prior art, that is, to provide a road edge height assigning device that can assign a road edge height after extracting height information of the road surface. [Means for solving the problem]

[0015] The present invention is based on the unprecedented idea of ​​assigning height information equivalent to a target statistical value set for each region of interest as the road edge height, by noting that when a region of interest is set based on a road edge model, the pattern of height information for multiple component points differs for each region of interest.

[0016] The road edge height assignment device of the present invention comprises a 3D terrain model storage means, a road edge model storage means, a region of interest setting means, a target statistical value setting means, and a road edge height calculation means. The 3D terrain model storage means stores a 3D terrain model based on a plurality of component points having 3D coordinates, and the road edge model storage means stores a 2D road edge model representing a road edge. The region of interest setting means generates a plurality of reference points of interest on the road edge model and sets a 2D region of interest having a predetermined size and shape based on the reference points of interest. The target statistical value setting means sets a target statistical value. The road edge height calculation means extracts a plurality of component points belonging to the region of interest when the region of interest is placed on the 3D terrain model as reference points of interest and calculates, as the road edge height, height information related to the reference points of interest that corresponds to the target statistical value. The road edge height is then assigned to the reference points of interest corresponding to the region of interest.

[0017] In the road edge height estimation device of the present invention, the target statistical value setting means may include a learning means and a target statistical value output means. The learning means generates a target statistical value setting model for outputting target statistical values ​​by machine learning multiple types of learning datasets, and the target statistical value output means outputs target statistical values ​​related to the input dataset based on the input input dataset and the target statistical value setting model. The learning dataset includes appropriate statistical values ​​set for the learning region of interest and elevation information for multiple constituent points included in the learning region of interest, and the input dataset includes elevation information for multiple constituent points included in the input region of interest. The learning region of interest is a two-dimensional region having the same size and shape as the region of interest, and the input region of interest is a two-dimensional region based on a target reference point and having the same size and shape as the region of interest. The learning means generates the target statistical value setting model by machine learning the appropriate statistical values ​​included in the learning dataset and the elevation information for the multiple constituent points, and the target statistical value output means outputs target statistical values ​​using the elevation information for the multiple constituent points included in the input dataset as input values.

[0018] In the road edge height assignment device of the present invention, the learning data set may include elevation information of the plurality of component points corresponding to a plurality of predetermined selection statistical values ​​among the plurality of component points, and the input data set may include elevation information of the plurality of component points corresponding to the selection statistical values ​​among the plurality of component points. Note that the selection statistical values ​​may be percentile values ​​calculated based on the elevation information of the plurality of component points.

[0019] The road edge height assignment device of the present invention may further comprise a region of interest dividing means and a road side region setting means. The region of interest dividing means divides the region of interest into a plurality of divided regions using the road edge model as a boundary, and the road side region setting means calculates statistical values ​​of height information for a plurality of component points belonging to each divided region as a divided region representative height, and sets the divided region with the lowest divided region representative height as the road side region. In this case, the road edge height calculation means extracts a plurality of component points belonging to the road side region as target component points and then calculates the road edge height. [Effects of the Invention]

[0020] The road edge height providing device of the present invention has the following effects. (1) Because the road edge model is used, road edge height can be automatically assigned. As a result, the burden on the operator (worker) can be reduced, the cost of data creation can be reduced, and so-called human error can be reduced. (2) It is possible to avoid incorrectly extracting the height of roofs and other structures around the road edge, and therefore to assign road edge heights that are in line with the current situation. (3) The target statistical value setting model can select the optimum target statistical value for the target area, that is, can assign a more appropriate road edge height for the target area. (4) Road edges that are appropriate to the current situation are provided, meaning that a 3D terrain model that is appropriate to the current situation is obtained, which allows for more advanced management of road facilities, etc. and provides map information that is more useful for automated driving. [Brief explanation of the drawings]

[0021] [Figure 1] 1 is a block diagram showing the main configuration of a road edge height providing device according to the present invention; [Figure 2] (a) is a planar model diagram that schematically shows the focus reference points generated at regular intervals on the road edge model by the focus area setting means, and (b) is a planar model diagram that schematically shows the focus area set as a circle centered on the focus reference point. [Figure 3] FIG. 10 is a graph showing the distribution of elevations of constituent points arranged around a point on the road edge. [Figure 4] A model diagram that schematically shows a training dataset, training reference points, training areas, and training construction points. [Figure 5] FIG. 1 is a step diagram showing a schematic procedure for generating a training dataset. [Figure 6] 1 is a model diagram showing a schematic diagram of an input dataset, input reference points, input area, and input configuration points. [Figure 7] FIG. 3 is a planar model diagram that schematically shows a first divided area and a second divided area that are set by dividing the area of ​​interest by the area of ​​interest dividing means. [Figure 8] FIG. 3 is a planar model diagram showing a first divided region, a second divided region, a third divided region, and a fourth divided region set by the region-of-interest dividing means. [Figure 9] FIG. 4 is a flowchart showing an example of a main process flow until the road edge height assignment device outputs a target statistical value. [Figure 10] FIG. 4 is a flowchart showing an example of a main process flow until the road edge height calculation device calculates the road edge height. DETAILED DESCRIPTION OF THE INVENTION

[0022] An example of the road edge height providing device of the present invention will be described with reference to the drawings.

[0023] The road edge height assigning device of the present invention has a technical feature that it sets a plurality of reference points (hereinafter referred to as "reference points of interest") at appropriate intervals on the road edge, sets a predetermined area (hereinafter referred to as "area of ​​interest") based on the reference points of interest, and selects, from among the points (hereinafter referred to as "points of interest") included in the area of ​​interest, height information relating to a point of interest (hereinafter referred to as "specific points of interest") that satisfies a specific condition as the road edge height. The present invention employs a specific statistical value (hereinafter referred to as "target statistical value") as a condition for selecting a specific point of interest from among the plurality of points of interest, and furthermore, sets this target statistical value uniquely for each area of ​​interest. Note that height information can refer to various concepts that represent a relative height difference from a certain reference height, but for convenience, the height information will be described as "elevation" here.

[0024] 1 is a block diagram showing the main components of the road edge height assignment device 100 of the present invention. As shown in this figure, the road edge height assignment device 100 comprises a region of interest setting means 101, a target statistical value setting means 102, a road edge height calculation means 103, a 3D topographical model storage means 108, and a road edge model storage means 109, and may further comprise a learning means 104, a target statistical value output means 105, a region of interest division means 106, a road side region setting means 107, a learning dataset storage means 110, and a target statistical value setting model storage means 111.

[0025] The road edge height estimation device 100 comprises a region of interest setting means 101, a target statistical value setting means 102, a road edge height calculation means 103, a learning means 104, a target statistical value output means 105, a region of interest division means 106, and a road side region setting means 107. These can be manufactured as dedicated units, or general-purpose computers can be used. These computers include a processor such as a CPU, memories such as ROM and RAM, and some also include input means such as a mouse and keyboard, and display means such as a display, and can be configured as a personal computer (PC) or a server, for example.

[0026] The 3D terrain model storage means 108, road edge model storage means 109, learning dataset storage means 110, and target statistical value setting model storage means 111 can be implemented as a storage device of a general-purpose computer (for example, a personal computer), or can be implemented as a database server. When implemented as a database server, they can be placed on a local network (LAN: Local Area Network), or can be implemented as a cloud server that stores data via the Internet.

[0027] Hereinafter, each of the main elements constituting the road edge height providing device 100 of the present invention will be described in detail.

[0028] 1. 3D terrain model storage means and road edge model storage means The 3D terrain model storage means 108 is a means for storing 3D terrain models, and the road edge model storage means 109 is a means for storing "road edge models." Here, the "road edge model" is, as mentioned above, two-dimensional data that includes attribute information of "road edges" and their planar positions (coordinate information), and is data of digitized road edges such as polylines.

[0029] 2. Area of ​​interest setting method The area-of-interest setting means 101 is a means for generating a plurality of reference points of interest on a road edge model. The area-of-interest setting means 101 can automatically generate reference points of interest SP at regular intervals (or irregular intervals) on the road edge model EL as shown in Figure 2(a), or can set reference points of interest SP at arbitrary positions by an operator's operation. Furthermore, the positions of reference points of interest SP that have once been generated can also be changed by an operator's operation.

[0030] The region-of-interest setting means 101 also sets a "region of interest RG." This region of interest RG is a region based on the reference point of interest SP, and is a two-dimensional region (planar region) having a predetermined size (dimensions) and shape. For example, as shown in FIG. 2(b), the region of interest RG can be a circle with a predetermined radius (predetermined radius) centered on the reference point of interest SP, or it can be an ellipse or polygon with predetermined dimensions centered on the reference point of interest SP.

[0031] 3.Means for setting target statistical values As described above, the road edge height assignment device 100 of the present invention selects a target component point SP corresponding to a target statistical value from among the component points included in the target region RG (i.e., the target component points SP), as a specific target component point, and sets the height information (here, altitude) possessed by this specific target component point as the road edge height. The target statistical value setting means 102 is a means for setting a target statistical value for selecting the specific target component point. Here, for example, a percentile value when the altitudes are sorted from smallest (lowest) (hereinafter referred to as a "lowest altitude order percentile value") can be used as the target statistical value. That is, the target statistical value is set to N (a positive real number, not limited to an integer), such as the 5th percentile or the 10th percentile. Of course, the target statistical value is not limited to percentile values. Various statistical values ​​can be used as the target statistical value, such as simply using the Mth (natural number) lowest elevation as the target statistical value, or using the elevation closest to the average elevation minus a certain height as the target statistical value.

[0032] The inventors discovered that the combination (pattern) of elevations of multiple constituent points located around a certain point on a road edge (e.g., a target reference point SP) tends to differ depending on the surrounding environment of the target reference point SP. For example, flat road edges, road edges on viaducts, and road edges with nearby street trees each have different elevation patterns of the surrounding constituent points, while the elevation patterns of flat road edges show a consistent trend. Figure 3 is a graph showing the distribution of elevations of constituent points located around points on a road edge, with the horizontal axis representing percentile values ​​in descending order of elevation and the vertical axis representing elevation. As can be seen from this figure, for flat road edges with no surrounding street trees or buildings, a relatively large percentile value (e.g., 5th to 10th percentile) can be used as the target statistical value. On the other hand, for road edges with surrounding street trees or buildings, a relatively small percentile value (e.g., 2nd to 3rd percentile) is required. Therefore, in the present invention, a target statistical value is set uniquely for each region of interest.

[0033] The target statistical value can be input for each region of interest by an operator operating the target statistical value setting means 102. Alternatively, the target statistical value setting means 102 can automatically (mechanically) set the target statistical value according to the region of interest. In this case, the road edge height estimation device 100 of the present invention may be configured to include a learning means 104, a target statistical value output means 105, a learning data set storage means 110, and a target statistical value setting model storage means 111. The following describes in detail how the target statistical value setting means 102 automatically (mechanically) sets the target statistical value.

[0034] (Means for storing learning data sets and means for storing target statistical value setting models) The learning dataset storage means 110 is a means for storing a "learning dataset," and the target statistical value setting model storage means 111 is a means for storing a "target statistical value setting model." This learning dataset is so-called teacher data for the learning means 104 to perform machine learning, as will be described later, and naturally, a large number of learning datasets are prepared. On the other hand, the target statistical value setting model is a model obtained as a result of machine learning performed by the learning means 104, as will also be described later, and when an "input dataset" is input to this target statistical value setting model, it outputs (returns) a target statistical value.

[0035] (Learning tools) The learning means 104 is a means for generating a "target statistical value setting model" by performing machine learning (e.g., deep learning, etc.) using a training dataset as training data. Here, the training dataset includes the elevations and appropriate statistical values ​​of the constituent points NP, as shown in FIG. 4. However, as shown in this figure, the constituent points NP targeted by the training dataset are constituent points NP (hereinafter, specifically referred to as "training constituent points LCP") that belong to (are included in) the training region LRG. Note that "belonging to the training region LRG" here means that when the constituent point NP is planarly projected onto the region of interest RG, the constituent point NP is included within the training region LRG. Furthermore, the training region LRG is set to a point on the road edge as a training reference point LSP, and is set to a predetermined size and shape based on the training reference point LSP. For example, in FIG. 4, the training region LRG is set to be a circle with a predetermined radius (a predetermined radius) centered on the training reference point LSP on the road edge. Of course, the learning area LRG can be set in various shapes, not just a circle, such as a polygon or an ellipse, or the same shape as the divided area described below (the area obtained by dividing the target area RG by the road edge) (semicircle in the example of Figure 7), and it may also be set in two or more types of shapes.

[0036] A large number of learning regions LRG are prepared for each of various environments (flat road edges, road edges with viaducts and buildings nearby, road edges on viaducts with buildings nearby, etc.) Therefore, it is advisable to specify a large number of learning reference points LSP for each characteristic environment and create a large number of learning regions LRG for each characteristic environment.

[0037] FIG. 5 is a step diagram that schematically illustrates the procedure for generating a training dataset. To generate a training dataset, first, training reference points LSP are designated, as shown in FIG. 5(a). At this time, an operator can designate the desired training reference points LSP using a pointing device (such as a mouse, touch panel, pen tablet, touchpad, trackpad, or trackball) or a keyboard while visually viewing the 3D terrain model displayed on a display unit (such as a monitor). Alternatively, the training reference points LSP can be set automatically (mechanically) using image recognition technology or the like. Once the training reference points LSP are designated, a training region LRG is set based on the training reference points LSP, as shown in FIG. 5(b). Furthermore, training construction points LCP included in the training region LRG are extracted, and their elevations are set as training data, as shown in FIG. 5(c).

[0038] The appropriate statistical value included in the training data set is a statistical value corresponding to an elevation value that is appropriate for the training region LRG, i.e., a correct answer. As with the target statistical value, the lowest elevation percentile value can be used as the appropriate statistical value, and various other statistical values ​​can also be used. However, the same statistical value is used for the appropriate statistical value and the target statistical value. For example, if the lowest elevation percentile value is used as the target statistical value, the lowest elevation percentile value is also used as the appropriate statistical value. The appropriate statistical value can be a statistical value (e.g., 5th percentile) related to the elevation of one training point LCP selected from multiple training points LCP included in the training region LRG, or a statistical value related to a value (e.g., average, median, mode, etc.) obtained as a result of statistical processing of the elevations of these training points LCP. The obtained appropriate statistical value is set as training data, for example, by an operator. By performing machine learning using multiple training data sets prepared in this manner, the training means 104 generates a target statistical value setting model.

[0039] (Target statistical value output means) The target statistical value output means 105 is a means for outputting target statistical values ​​related to an input dataset based on the input dataset and the target statistical value setting model generated by the learning means 104. The input dataset includes the elevations of the constituent points NP as shown in FIG. 6. However, as shown in this figure, the constituent points NP targeted by the input dataset are constituent points NP that belong to (are included in) the region of interest RG (i.e., the constituent points of interest CP). Note that the region of interest RG is based on the reference point of interest SP generated by the region of interest setting means 101 as described above. However, it is preferable that the size and shape for setting the region of interest RG (hereinafter referred to as "region specifications") be the same as the region specifications for setting the learning region LRG. 4, the region of interest RG may also be set as a circle having the same diameter as the region of interest LRG and centered on the reference point of interest SP, or, if the region of interest LRG is set as a semicircle like a divided region described below, the region of interest RG may also be set as a semicircle having the same diameter as the region of interest LRG and centered on the reference point of interest SP. In either case, the input data set is generated by extracting the constituent points of interest CP included in the region of interest RG set by the region of interest setting means 101 and setting the elevations associated with these constituent points of interest CP.

[0040] When an input data set is input, the target statistical value output means 105 outputs a target statistical value related to the input data set using a target statistical value setting model. Specifically, the target statistical value output means 105 compares the combination (pattern) of elevations of the target composing points CP included in the input data set with the learned elevation patterns, selects an approximate (or matching) elevation pattern, and outputs a target statistical value associated with the elevation pattern.

[0041] (Elevation of the dataset) Up to this point, it has been explained that the training dataset contains the elevations of multiple training points LCP, and the input dataset contains the elevations of multiple focus points CP. Of course, it is also possible to include the elevations of all training points LCP in the training dataset, and the elevations of all focus points CP in the input dataset. Alternatively, it is also possible to extract training points LCP and focus points CP based on predetermined requirements and include them in the respective datasets. More specifically, training points LCP and focus points CP are extracted according to predetermined statistical values ​​(hereinafter referred to as "selection statistical values"), and only the elevations of the extracted training points LCP are included in the training dataset, and only the elevation of the extracted focus points CP is included in the input dataset. For example, using percentile values ​​sorted in descending order of elevation, percentile values ​​in 5% increments, such as the 0th percentile (the minimum elevation in this case), 5th percentile, 10th percentile, ..., and 100th percentile, are set as selection statistics (i.e., 21-level selection statistics), and only the elevations of the training points LCPs and target points CPs that correspond to these selection statistics (0th percentile, 5th percentile, ...) are included in each dataset from among all the training points LCPs and target points CPs. Note that it is recommended to set the same selection statistics for extracting training points LCPs and the same selection statistics for extracting target points CPs. In this way, by standardizing the multiple elevations included in the training dataset and input dataset, it becomes easier to match the learned elevation patterns with the elevation patterns in the input dataset.

[0042] 4. Region of interest division method The region of interest dividing means 106 is a means for dividing the region of interest RG using the road edge model EL as a boundary, thereby setting a plurality of regions (hereinafter referred to as "divided regions RS"). For example, in FIG. 7, the region of interest dividing means 106 divides the circular region of interest RG in half to set a first divided region RS1 and a second divided region RS2. Note that the region of interest RG shown in FIG. 7 is a circle centered on the reference point of interest SP, and the road edge model EL naturally passes through the reference point of interest SP, so the first divided region RS1 and the second divided region RS2 are each semicircular (with a central angle of 180°) with the same shape and area. Of course, if the road edge model EL is bent, the semicircular regions will not have the same area, and fan-shaped divided regions with different areas will be set. If the road edge model EL is bent at multiple points within the region of interest RG, the divided regions will not be fan-shaped. Furthermore, if the region of interest RG is not circular, the divided regions will not be fan-shaped either. In any case, the region of interest RG is divided by the road edge model EL.

[0043] 5. Road side area setting means As shown in Figure 7, when a region of interest RG is placed on a 3D topographical model, several constituent points NP (i.e., target constituent points CP) belong to (are included in) that region of interest RG. Then, by using the elevation of these target constituent points CP, the elevation of the target reference point SP related to the region of interest RG can be found.

[0044] Usually, one side of the road edge is a road, and the other side is a non-road area (hereinafter referred to as a "general area"). Therefore, when the target area RG is divided into two divided areas RS (a first divided area RS1 and a second divided area RS2) as shown in Figure 7, it is desirable to select one of them as a road, and then find the elevation of the target reference point SP based on the target configuration point CP that belongs to the divided area RS that is determined to be the road (hereinafter referred to as a "road side area").

[0045] The road side area setting means 107 is a means for setting a road side area from among the plurality of divided areas RS. The processing procedure by the road side area setting means 107 for setting a road side area will be explained in detail below. First, the road side area setting means 107 calculates an elevation (hereinafter referred to as "divided area representative height") that represents the divided area RS based on the elevations of the plurality of focus component points CP that belong to each divided area RS. At this time, the divided area representative height can be set to the median value of the elevations of the plurality of focus component points CP, or to another statistical value such as the average value, mode or lowest elevation percentile of the elevations of the focus component points CP, or simply to the minimum value of the elevations of the focus component points CP.

[0046] Once the divided area representative height for each divided area RS is obtained, the road-side area setting means 107 selects one divided area RS from those divided areas RS. Generally, road-side areas have lower elevations than general areas. Therefore, the road-side area setting means 107 extracts the divided area representative height showing the smallest (lowest) value, selects the divided area RS corresponding to the extracted divided area representative height, and sets it as the road-side area.

[0047] Incidentally, in the example of Fig. 7, two divided areas RS (first divided area RS1 and second divided area RS2) are set by the area of ​​interest dividing means 106, but the area of ​​interest dividing means 106 may also set three or more divided areas RS. For example, Fig. 8 shows a so-called T-junction intersection, and the area of ​​interest RG is set by a circle with a relatively large radius, so in this case the area of ​​interest dividing means 106 divides the area of ​​interest RG by three road edge models EL, thereby setting four divided areas RS (first divided area RS1, second divided area RS2, third divided area RS3, and fourth divided area RS4).

[0048] When three or more divided areas RS are set, the road-side area setting means 107 calculates the divided area representative height for each divided area RS, extracts the divided area representative height showing the smallest (lowest) value, and sets the divided area RS related to the extracted divided area representative height as the road-side area. Alternatively, the road-side area setting means 107 can extract a divided area RS that contacts the target reference point SP from the three or more divided areas RS (hereinafter referred to as a "candidate divided area") and set the road-side area. As can be seen from FIG. 8, it is clear that the divided area RS that contacts the target reference point SP is the road-side area. Therefore, when three or more divided areas RS are set, the divided area RS that contacts the target reference point SP is extracted as a "candidate divided area," and a road-side area is set by selecting from these candidate divided areas. In the example of Figure 8, four divided areas RS (first divided area RS1 to fourth divided area RS4) are set, so the road side area setting means 107 extracts the first divided area RS1 and the second divided area RS2 that are adjacent to the target reference point SP as candidate divided areas, calculates the divided area representative heights for these candidate divided areas (i.e., the first divided area RS1 and the second divided area RS2), extracts the divided area representative height that shows the smallest elevation, and then sets the road side area (the second divided area RS2 in Figure 8).

[0049] 6. Road edge height calculation method The road edge height calculation means 103 is a means for calculating the "road edge height" based on the road side area set by the road side area setting means 107. The processing procedure by which the road edge height calculation means 103 calculates the road edge height will be described in detail below. First, the road edge height calculation means 103 extracts a component point NP (i.e., a target component point CP) that belongs to the road side area. Then, the road edge height is calculated using the elevation of the extracted target component point CP. More specifically, from the elevations of a plurality of target component points CP, the target component point CP having an elevation corresponding to the target statistical value set by the target statistical value setting means 102 is selected as a specific target component point, and the elevation of this specific target component point is set as the road edge height. The road edge height obtained here is assigned to the target reference point SP related to the corresponding target area RG.

[0050] So far, we have explained the processing procedure in which the road edge height calculation means 103 calculates the road edge height based on the road side areas selected from the multiple divided areas RS. However, the road edge height calculation means 103 can also be configured to target all of the focus points CP belonging to the region of interest RG, that is, to select an elevation corresponding to the target statistical value from the elevations of all of the focus points CP belonging to the region of interest RG. In other words, in this case, the process of dividing the region of interest RG to set divided areas RS or the process of setting road side areas is not performed, and the road edge height is simply calculated using the focus points CP belonging to the region of interest RG. Therefore, in this case, the region of interest division means 106 and the road side area setting means 107 can be omitted.

[0051] 7. Processing flow The main processing of the road edge height estimation device 100 will be described in detail below with reference to Figures 9 and 10. Figure 9 is a flow diagram showing an example of the main processing flow up to when the road edge height estimation device 100 outputs a target statistical value, and Figure 10 is a flow diagram showing an example of the main processing flow up to when the road edge height estimation device 100 calculates the road edge height. In these flow diagrams, the processing to be performed is shown in the center column, the items necessary for that processing are shown in the left column, and the items resulting from that processing are shown in the right column.

[0052] As shown in Fig. 9, the process up to outputting the target statistical value can be roughly divided into a "learning step" up to generating a target statistical value setting model, and an "output step" in which the target statistical value related to the input dataset is actually set. In the learning step, first, a learning reference point LSP is specified to create a learning dataset (Step 211 in Fig. 9). At this time, the learning reference point LSP can be specified by an operator, or the learning reference point LSP can be set automatically (mechanically) using image recognition technology or the like.

[0053] Once the learning reference point LSP is set, a learning region of interest LRG is generated based on the learning reference point LSP and in accordance with predetermined region specifications (Step 212 in FIG. 9). Next, the elevations of the learning composition points LCP included in the learning region of interest LRG are set, and appropriate statistics for the learning region of interest LRG are set (Step 213 in FIG. 9), thereby creating a learning dataset (Step 214 in FIG. 9). By repeating the series of processes up to this point (Steps 211 to 214 in FIG. 9), numerous learning datasets are prepared for each of various roadside environments. These learning datasets are then subjected to machine learning by the learning means 104, thereby generating a target statistical value setting model (Step 215 in FIG. 8).

[0054] Once the target statistical value setting model has been generated, the road edge model EL is read from the road edge model storage means 109, and the region of interest setting means 101 generates a plurality of reference points of interest SP on the road edge model EL (Step 231 in Fig. 10). Furthermore, the region of interest setting means 101 sets a region of interest RG based on the reference points of interest SP (Step 232 in Fig. 10).

[0055] When the target reference point SP is generated and the target region RG is set, the target statistical value for that target region RG is output in the output step shown in Fig. 9. More specifically, first, the target composing points CP included in the target region RG are extracted, and the elevations for these target composing points CP are set, thereby generating an input dataset (Step 221 in Fig. 9). Then, the target statistical value output means 105 outputs the target statistical value for that input dataset based on the input input dataset and the target statistical value setting model (Step 222 in Fig. 9).

[0056] Once the region of interest RG is set, the region of interest dividing means 106 sets a plurality of divided regions RS (Step 233 in FIG. 10). Then, the road side region setting means 107 calculates a divided region representative height for each divided region RS (Step 234 in FIG. 10), and sets the road side region based on this divided region representative height (Step 235 in FIG. 10).

[0057] Once the road side area is set, the road edge height calculation means 103 extracts a focus configuration point CP belonging to this road side area (Step 236 in FIG. 10). Then, the target statistical value setting means 102 sets the value output by the target statistical value output means 105 as the target statistical value (Step 237 in FIG. 10), and the road edge height calculation means 103 selects, as a specific focus configuration point, a focus configuration point CP having an elevation corresponding to the target statistical value from among the elevations of a plurality of focus configuration points CP belonging to the road side area, and sets the elevation of this specific focus configuration point as the road edge height (Step 238 in FIG. 10). Note that the series of processes consisting of setting the focus area RA (Step 232) to calculating the road edge height (Step 238) can be specified to be executed for all focus reference points PA, or can be specified to be executed for a specific section (for example, from intersection to intersection) as a unit. In this case, this series of processes (Steps 232 to 238) can be performed for only one representative reference point PA of interest within the specific section to set a target statistical value, and that target statistical value can then be applied to the other reference points PA of interest within the specific section. Alternatively, the process can be performed for all or some of the reference points PA of interest within the specific section to set target statistical values, and the target statistical value with the most number (for example, the 5th percentile) can be set as the overall target statistical value. [Industrial Applicability]

[0058] The road curb height adjustment device of the present invention can be particularly suitably used for managing various facilities, including road facilities, and as map information used in automatic driving. Furthermore, according to the present invention, it is possible to provide highly accurate step information that is useful for elderly people and wheelchair users, and it can also be effectively used in disaster prevention plans. Therefore, the road curb height adjustment device of the present invention is not only applicable to industry, but is also expected to make a great contribution to society. [Explanation of symbols]

[0059] 100 Road edge height providing device of the present invention 101 (Road edge height setting device) focus area setting means 102 (Road edge height assigning device) target statistical value setting means 103 (Road edge height calculation means of road edge height assignment device) 104 (Road Edge Height Applicator) Learning Means 105 (Road edge height assigning device) target statistical value output means 106 (Road edge height assigning device) target area dividing means 107 (Road edge height setting device) road side area setting means 108 (Road edge height setting device) 3D terrain model storage means 109 Road edge model storage means (of road edge height assigning device) 110 (Road edge height assigning device) learning data set storage means 111 (Road edge height assigning device) target statistical value setting model storage means CP Focused Configuration Point EL Road Edge Model LCP Learning Composition Points LRG learning region of interest LSP Learning Reference Points NP constituent points RG Area of ​​Interest RS split area SP Reference point of interest

Claims

1. a three-dimensional terrain model storage means for storing a three-dimensional terrain model based on a plurality of constituent points having three-dimensional coordinates; road edge model storage means for storing a two-dimensional road edge model representing a road edge; an area-of-interest setting means for generating a plurality of reference points of interest on the road edge model and setting a two-dimensional area of ​​interest having a predetermined size and shape based on the reference points of interest; a target statistical value setting means for setting a target statistical value; a road edge height calculation means for extracting a plurality of said constituent points belonging to said region of interest as constituent points of interest when said region of interest is arranged on said three-dimensional topographical model, and for calculating height information relating to said constituent points of interest that correspond to said target statistical value among said constituent points of interest as a road edge height, The target statistical value setting means a learning means for generating a target statistical value setting model for outputting the target statistical value by machine learning a plurality of learning data sets; a target statistical value output means for outputting the target statistical value related to the input data set based on the input input data set and the target statistical value setting model, the learning data set includes an appropriate statistical value set for a learning region of interest and height information of a plurality of the constituent points included in the learning region of interest, the input data set includes height information of the plurality of constituent points included in the input region of interest, the learning region of interest is a two-dimensional region that is based on the reference point of interest and has the same size and shape as the region of interest, the input region of interest is a two-dimensional region that is based on the reference point of interest and has the same size and shape as the region of interest, assigning the road edge height to the target reference point corresponding to the target area; A road edge height providing device.

2. the learning data set includes height information of the constituent points corresponding to a plurality of predetermined selection statistical values ​​among the plurality of constituent points, the input data set includes height information of the constituent points corresponding to the selection statistical value among the plurality of constituent points, the selection statistical value is a percentile value calculated based on height information of a plurality of the constituent points; 2. The road edge height providing device according to claim 1.

3. a three-dimensional terrain model storage means for storing a three-dimensional terrain model based on a plurality of constituent points having three-dimensional coordinates; road edge model storage means for storing a two-dimensional road edge model representing a road edge; an area-of-interest setting means for generating a plurality of reference points of interest on the road edge model and setting a two-dimensional area of ​​interest having a predetermined size and shape based on the reference points of interest; a target statistical value setting means for setting a target statistical value; a region-of-interest dividing means for dividing the region of interest into a plurality of divided regions using the road edge model as a boundary; a road-side area setting means for calculating a statistical value of height information relating to the plurality of component points belonging to the divided area as a divided area representative height, and setting the divided area relating to the lowest divided area representative height as a road-side area; a road edge height calculation means for extracting a plurality of said constituent points belonging to said road side area as constituent points of interest when said road side area is arranged on said three-dimensional terrain model, and for calculating height information relating to said constituent points of interest that correspond to said target statistical value among said constituent points of interest as a road edge height, assigning the road edge height to the target reference point corresponding to the target area; A road edge height providing device.

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