Road surface state detection device and road surface state detection program

Abstract

To provide a technique for enabling detection by excellently discriminating road surface unevenness such as a deceleration zone from a road surface gradient.SOLUTION: A road surface state detection device (10) includes a regression line generation section (14), a regression line attribute determination section (15), and a non-road surface point detection section (13). The regression line generation section generates a regression line in a two-dimensional coordinate system of a depth direction and a height direction concerning a plurality of distance measuring points. The regression line attribute determination section determines whether the regression line is a road surface constitution line corresponding to a road surface or a non-road surface constitution line not corresponding to the road surface based on a gradient change of the regression line within a predetermined distance in the depth direction. The non-road surface point detection section detects a non-road surface point being the distance measuring point not constituting the road surface based on the determination result of the regression line attribute determination section and a position relation between the regression line and the distance measuring point.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present invention relates to a road surface condition detection device and a road surface condition detection program that detect the road surface condition in the traveling direction of a host vehicle equipped with such a distance measuring sensor based on distance measuring points at which position information is detected by the distance measuring sensor.

Background Art

[0002] The road surface area detection device described in Patent Document 1 extracts, for each individual distance measurement point, that is, a point of interest, obtained by a laser sensor, distance measurement points adjacent to the point of interest in the upward and downward depression angles as a set of adjacent points, one point each. Then, the angle formed by the adjacent point and the point of interest is calculated, and it is determined whether the point of interest exists on the straight line connecting the adjacent points. Further, for the points determined to exist on the straight line, using the determination result for the distance measurement points below the point being focused on as a determination material, the distance measurement points highly likely to constitute the road surface and the distance measurement points that are candidates for constituting the road surface are classified. As a next step, road surface points, that is, distance measurement points that constitute the road surface, are selected from the extracted distance measurement points based on the surrounding determination results, and data indicating the road surface area is output. According to such a road surface area detection device, since it is possible to classify the distance measurement points into three types: road surface points, candidate points, and distance measurement points that are not road surface points based on the shape determination result of the point of interest, the road surface shape can be accurately determined.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, various functions for assisting driving have been installed in vehicles. As one of such functions, for example, there is an increasing need to detect road surface shapes and unevenness for suspension control to improve the riding comfort of passengers. In particular, speed bumps installed on private land or in urban areas cause a large impact on passengers. Therefore, by implementing a speed bump detection function, it becomes possible to realize a comfortable riding comfort. In this regard, for example, the road surface shape of the front side portion of a speed bump is similar to that of an uphill road surface. Therefore, according to the technique described in Patent Document 1, since the angle formed by an adjacent point and a point of interest has the same value in the case of a gradient road surface and in the case of a speed bump, there is a possibility of misdetecting a gradient road surface as a speed bump.

[0005] The present invention has been made in view of the circumstances exemplified above. That is, the present invention provides a technique that enables, for example, good discrimination between road surface unevenness such as a speed bump and a road surface gradient and detection thereof.

Means for Solving the Problem

[0006] The road surface state detection device (10) is configured to detect the road surface state ahead of the host vehicle (V) on which the distance measurement sensor is mounted based on the distance measurement points (P) whose position information is detected by the distance measurement sensor (3). The road surface state detection device according to claim 1, a regression line generation unit (14) that generates a regression line (L) in a two-dimensional coordinate system defined by the depth direction and the height direction along the road surface (R) and the traveling direction of the host vehicle for a plurality of the distance measurement points; a regression line attribute determination unit (15) that executes an attribute determination which is a determination as to whether the regression line is a road surface configuration line (Lgr) corresponding to the road surface or a non-road surface configuration line (Lgu) not corresponding to the road surface; a non-road surface point detection unit (13) that detects a non-road surface point (Pu) which is a distance measurement point not constituting the road surface based on the determination result by the regression line attribute determination unit and the positional relationship between the regression line and the distance measurement points; and the regression line attribute determination unit, A gradient difference calculation unit (151) that calculates a change in the gradient of the regression line; A road surface attribute determination unit (152) that executes the attribute determination based on the change in the gradient within a predetermined distance in the depth direction; It is provided with. The road surface state detection program is a computer program executed by a road surface state detection device (10) that detects the road surface state ahead of the host vehicle (V) on which the distance measurement sensor (3) is mounted based on the distance measurement points (P) at which position information is detected by the distance measurement sensor (3). The road surface state detection program according to claim 8, as a process executed by the road surface state detection device, For a plurality of the distance measurement points in an arbitrary horizontal azimuth, a regression line generation process (S103) of generating a regression line (L) in a two-dimensional coordinate system by the depth direction and the height direction along the road surface (R) and the traveling direction of the host vehicle; A regression line attribute determination process (S104) of executing an attribute determination as to whether the regression line is a road surface configuration line (Lgr) corresponding to the road surface or a non-road surface configuration line (Lgu) not corresponding to the road surface; A non-road surface point detection process (S105) of detecting a non-road surface point (Pu) that is the distance measurement point that does not constitute the road surface based on the determination result by the regression line attribute determination process and the positional relationship between the regression line and the distance measurement point; Including In the regression line attribute determination process, Calculate the change in the gradient of the regression line, Execute the attribute determination based on the change in the gradient within a predetermined distance in the depth direction.

[0007] According to such a configuration, the regression line generation unit and the regression line generation process generate a regression line in a two-dimensional coordinate system based on the depth direction and the height direction for a plurality of distance measurement points. The regression line attribute determination unit and the regression line attribute determination process calculate the gradient change of the regression line, and perform an attribute determination that determines whether the regression line is a road surface configuration line corresponding to the road surface or a non-road surface configuration line not corresponding to the road surface based on the gradient change within a predetermined distance in the depth direction. The non-road surface detection unit and the non-road surface detection process detect non-road surface points, which are distance measurement points that do not constitute the road surface, based on the determination result by the regression line attribute determination unit and the positional relationship between the regression line and the distance measurement points. As a result, it becomes possible to detect road surface irregularities such as speed bumps by well distinguishing them from the road surface gradient.

[0008] In addition, in each column of the application documents, each element may be given a reference sign with parentheses. However, such reference signs merely show an example of the correspondence relationship between the same element and the specific means described in the embodiments described later. Therefore, the present invention is not limited in any way by the description of the above reference signs.

Brief Description of the Drawings

[0009]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Figure 10

Figure 11

Figure 12

Figure 13

Figure 14

Figure 15

Figure 16

Figure 17

Figure 18

Figure 19

Figure 20

Figure 21

Figure 22

Figure 23

Figure 24

Figure 25

Figure 26

Figure 27

Figure 28

Figure 29

Figure 30

Figure 31

Figure 32

Figure 33

Figure 34

Figure 35

Figure 36

Figure 37

Figure 38

Figure 39

Figure 40

Mode for Carrying Out the Invention

[0010] (Embodiment) Hereinafter, exemplary embodiments and specific examples of the present invention will be described with appropriate reference to the drawings. Referring to FIG. 1, the in-vehicle system 1 is mounted on the host vehicle V. The host vehicle V is a so-called automobile that travels on the ground, i.e., on the road, and has a box-shaped vehicle body. By being mounted on the host vehicle V, the in-vehicle system 1 is configured to be able to realize various functions such as driving control and behavior control in such a host vehicle V (for example, suspension control and ADAS control). ADAS is an abbreviation for Advanced Driver-Assistance Systems. Specifically, the in-vehicle system 1 includes a driving state sensor 2, a distance measuring sensor 3, an electronic control unit 4, an alarm device 5, and a vehicle behavior control device 6.

[0011] The running state sensor 2 is communicably connected to the electronic control unit 4 via an in-vehicle communication line. The running state sensor 2 is provided to generate information or signals corresponding to various quantities related to the running state of the host vehicle V and output them to the electronic control unit 4. The "various quantities related to the running state" include, for example, quantities related to the driving operation state of the host vehicle V by the driver or the driving automation system (e.g., the automatic driving system), such as the accelerator operation amount, the brake operation amount, the shift position, the steering angle, etc. Further, the "various quantities related to the running state" include physical quantities related to the physical behavior of the host vehicle V, such as the vehicle speed, the angular velocity, the longitudinal acceleration, the lateral acceleration, etc. That is, the running state sensor 2 is a general term for various well-known sensors required for the driving control of the host vehicle V, such as a shift position sensor, a vehicle speed sensor, an accelerator opening sensor, a steering angle sensor, an angular velocity sensor, an acceleration sensor, a yaw rate sensor, etc., for the sake of simplification of illustration and description.

[0012] The distance measurement sensor 3 is communicably connected to the electronic control unit 4 via an in-vehicle communication line. Referring to FIG. 2, the distance measurement sensor 3 is configured to detect the position information of the distance measurement point P, which is a point on the target Tg around the host vehicle V, by being mounted on the host vehicle V. The target Tg includes a moving object, a stationary object, and the road surface R of the road surface. The "moving object" includes, for example, pedestrians, other vehicles in motion, etc. The "stationary object" includes, for example, road fixtures such as road signs and road studs, parked vehicles, buildings, etc. The distance measurement point P is a point at which the position information is acquired by the distance measurement sensor 3 as a point on the target Tg. The position information of the distance measurement point P is hereinafter referred to as "distance measurement point data". The aggregate of the distance measurement points P is referred to as the distance measurement point group PG. The aggregate of the distance measurement point data for each distance measurement point P included in the distance measurement point group PG is hereinafter referred to as "point group data".

[0013] In this embodiment, as shown in FIG. 2, the distance measurement sensor 3 is configured to be able to acquire position information at least in the depth direction and the height direction as distance measurement point data. The "depth direction" is a direction along the road surface R and the traveling direction of the host vehicle V. In the depth direction, the side close to the host vehicle V may be referred to as the "front side", while the side separated from the host vehicle V may be referred to as the "rear side". The "height direction" is a direction intersecting both the depth direction and the vehicle lateral position direction. Typically, the "height direction" is vertically upward, orthogonal to both the depth direction and the vehicle lateral position direction. Vertically upward is the direction opposite to the direction of the gravitational force. The "vehicle lateral position direction" is a direction parallel to the vehicle width direction of the host vehicle V. The vehicle lateral position direction is a direction intersecting the depth direction. When the depth direction is parallel to the vehicle full length direction of the host vehicle V in a plan view seen from vertically above, the vehicle lateral position direction is a direction orthogonal to the depth direction.

[0014] Specifically, the distance measurement sensor 3 includes, for example, at least any one of a radar sensor, a lidar sensor, an ultrasonic sensor, and a camera. The camera may be a so-called compound eye camera, i.e., a stereo camera, or may utilize a technology of monocular moving stereo, i.e., SFM. SFM is an abbreviation for Structure from Motion. For the acquisition of the distance measurement point data, a so-called "sensor fusion" technology that integrates detection information from multiple types of sensors may be used.

[0015] The electronic control device 4 is provided to control the operations of the warning device 5 and the vehicle behavior control device 6 based on the detection results by at least the driving state sensor 2 and the distance measurement sensor 3. Specifically, in this embodiment, the electronic control device 4 has a configuration as an in-vehicle microcomputer, that is, an ECU. ECU is an abbreviation of Electronic Control Unit. That is, the electronic control device 4 includes a processor composed of a CPU or an MPU, and a storage medium communicably connected to such a processor, and is configured to realize a predetermined function by reading and executing a computer program from the storage medium. The storage medium includes at least a ROM or a non-volatile rewritable memory among various non-transitory physical storage media such as a ROM and a non-volatile rewritable memory. The non-volatile rewritable memory is a storage device that can rewrite information while the power is on and holds the information in a non-rewritable state while the power is off, and is, for example, a flash memory or the like. In the storage medium, together with the above computer program, various data such as initial values, maps, and look-up tables necessary for executing this are stored in advance.

[0016] The warning device 5 and the vehicle behavior control device 6 are communicably connected to the electronic control device 4 via an in-vehicle communication line for information or signals. The warning device 5 is configured to notify various information to the passengers of the host vehicle V by voice or display. That is, the warning device 5 includes a display device such as a meter panel or a head-up display, a speaker as a voice output device, and an ECU that controls the operations of these. The vehicle behavior control device 6 is configured to be able to execute acceleration / deceleration control, braking control, steering control, suspension control, etc. in the host vehicle V. That is, the vehicle behavior control device 6 includes at least an ECU that controls the operations of operation units such as a power mechanism, a braking mechanism, and a suspension mechanism.

[0017] (Road surface condition detection device) Figure 3 shows a schematic functional configuration of the road surface state detection device 10 according to the present embodiment. The road surface state detection device 10 is configured to detect the road surface state ahead of the host vehicle V based on the distance measurement points P at which the position information is detected by the distance measurement sensor 3. In the present embodiment, the road surface state detection device 10 is functionally realized by the electronic control device 4 shown in FIG. 1 when the road surface state detection program according to the present invention is read out from the storage medium by the processor and executed. Specifically, the road surface state detection device 10 includes, as a functional configuration realized on the electronic control device 4, that is, a microcomputer, a data acquisition unit 11, a road surface estimation unit 12, and a non-road surface point detection unit 13. Hereinafter, each of these functional configurations will be described.

[0018] The data acquisition unit 11 is configured to acquire point cloud data using the distance measurement sensor 3. Specifically, the data acquisition unit 11 receives the point cloud data from the distance measurement sensor 3 via the in-vehicle communication line. The road surface estimation unit 12 is configured to estimate the shape of the road surface R based on the point cloud data acquired by the data acquisition unit 11. The non-road surface point detection unit 13 is configured to detect the distance measurement points P that do not constitute the road surface R based on the shape of the road surface R estimated by the road surface estimation unit 12. The distance measurement points P that do not constitute the road surface R are distance measurement points P that are higher or lower than the road surface R, and typically, for example, as shown in FIG. 4, are distance measurement points P corresponding to the deceleration strip Da. The distance measurement points P that constitute the road surface R are hereinafter referred to as road surface points Pr. When the distance measurement sensor 3 is a radar sensor, the road surface point Pr is the distance measurement point P corresponding to the reflection position of the exploration wave when the exploration wave, which is the radar wave transmitted from the distance measurement sensor 3, is reflected on the road surface R and the reflected wave is received by the distance measurement sensor 3. On the other hand, the distance measurement points P that do not constitute the road surface R are hereinafter referred to as non-road surface points Pu. Similar to the above example, when the distance measurement sensor 3 is a radar sensor, the non-road surface point Pu is the distance measurement point P corresponding to the reflection position of the exploration wave when the exploration wave is reflected on the surface of the unevenness such as the deceleration strip Da that does not constitute the road surface R and the reflected wave is received by the distance measurement sensor 3.

[0019] FIG. 4 shows an example of the road surface state when a speed bump Da is provided on a substantially flat road surface R. FIG. 5 shows an example of the road surface state on a sloped road surface R that has no unevenness like the speed bump Da shown in FIG. 4. Here, as in the technique described in Patent Document 1, in FIG. 4, taking the distance measurement point P at the boundary position on the front side in the depth direction between the road surface R and the speed bump Da as the attention point Pd, the angle formed by such an attention point Pd and its adjacent point in the depth direction is defined as the speed bump formation angle αb. On the other hand, in FIG. 5, taking the distance measurement point P with a large slope change on the road surface R as the attention point Pd, the angle formed by such an attention point Pd and its adjacent point in the depth direction is defined as the road slope formation angle αc. That is, the speed bump formation angle αb and the road slope formation angle αc are angles formed by a pair of line segments Ld connecting the attention point Pd and the adjacent point. At this time, the speed bump formation angle αb and the road slope formation angle αc may be close values. In this case, according to the technique described in Patent Document 1, it becomes difficult to distinguish well between the case of the speed bump Da in FIG. 4 and the case of the road slope in FIG. 5, and there is a possibility of erroneously detecting the sloped road surface as the speed bump Da.

[0020] Therefore, in the road surface state detection device 10 according to the present embodiment, the road surface estimation unit 12 includes a regression line generation unit 14 and a regression line attribute determination unit 15. And the non-road surface detection unit 13 is configured to detect a non-road surface point Pu based on the road surface estimation result by such a road surface estimation unit 12. Hereinafter, the configuration and operation of the regression line generation unit 14 and the regression line attribute determination unit 15 that constitute the road surface estimation unit 12 and the non-road surface detection unit 13 will be described.

[0021] As shown in FIG. 6, the regression line generation unit 14 generates a regression line L in a two-dimensional coordinate system defined by the depth direction and the height direction for a plurality of distance measurement points P. The regression line L can be generated using a well-known regression method such as the least squares method. In the present embodiment, the regression line generation unit 14 generates a regression line L composed of a plurality of regression line segments Lg arranged in the depth direction. One regression line segment Lg can be understood as one of the regression lines L formed by the arrangement of a series of distance measurement points P in the depth direction, which is divided into a plurality of segments in the depth direction. Here, as shown in FIG. 6, for the regression line segments Lg in the depth direction, starting from the front side in the depth direction, the (n - 1)-th regression line segment Lg is denoted as Lg(n - 1), the n-th regression line segment Lg is denoted as Lg(n), and the (n + 1)-th regression line segment Lg is denoted as Lg(n + 1). In the present embodiment, the regression line generation unit 14 generates the regression line segment Lg as a line segment by linear regression.

[0022] Also, the difference in the inclination angle between adjacent regression line segments Lg in the depth direction is defined as the gradient difference θ. Specifically, for Lg(n - 1), Lg(n), and Lg(n + 1), which are the (n - 1)-th, n-th, and (n + 1)-th regression line segments Lg, the gradient difference θ between Lg(n - 1) and Lg(n) is denoted as θ(n - 1), and the gradient difference θ between Lg(n) and Lg(n + 1) is denoted as θ(n). The gradient difference θ is a positive value when the inclination angle increases in the upward direction, that is, for example, when changing from horizontal to an upward gradient or when the downward gradient decreases during a downhill section. The gradient difference θ is a negative value when the inclination angle increases in the downward direction, that is, for example, when changing from horizontal to a downward gradient or when the upward gradient decreases during an uphill section. Then, when plotting the change in the gradient difference θ along the depth direction, in the case of the deceleration zone Da in FIG. 4, as shown in FIG. 7, characteristics such as a large decrease from a positive value to a negative value and a large increase from a negative value to a positive value can be observed. On the other hand, in the case of the inclined road surface in FIG. 5, as shown in FIG. 8, no change characteristics between the positive and negative values of the gradient difference θ can be observed.

[0023] Therefore, in this embodiment, the regression line attribute determination unit 15 determines whether the regression line L is a road surface configuration line corresponding to the road surface R or a non-road surface configuration line corresponding to unevenness or the like that does not correspond to the road surface R based on the gradient change of the regression line L. Specifically, referring to FIG. 9, the regression line attribute determination unit 15 determines whether the regression line segment Lg is a road surface segment Lgr corresponding to the road surface configuration line or a non-road surface segment Lgu corresponding to the non-road surface configuration line based on the change characteristics of the gradient difference θ. The gradient difference θ can be considered to correspond to the second derivative of the regression line L.

[0024] Then, the non-road surface detection unit 13 detects the non-road surface point Pu based on the determination result by the regression line attribute determination unit 15 and the positional relationship between the regression line L and the ranging point P. That is, the non-road surface detection unit 13 can determine, for example, whether the ranging point P is a road surface point Pr or a non-road surface point Pu based on the positional relationship in the height direction between the ranging point P and the road surface segment Lgr closest in the depth direction. Alternatively, the non-road surface detection unit 13 can determine, for example, whether the ranging point P is a road surface point Pr or a non-road surface point Pu based on which of the road surface segment Lgr and the non-road surface segment Lgu the ranging point P is closest to. More specifically, for example, it is possible to determine that the ranging point P constituting the road surface segment Lgr, that is, the ranging point P used in the regression calculation of the road surface segment Lgr, is a road surface point Pr. On the other hand, it is possible to determine that the ranging point P constituting the non-road surface segment Lgu is a non-road surface point Pu.

[0025] FIGS. 10 to 15 show an overview of the operation of the road surface state detection device 10 according to this embodiment. As shown in FIG. 3, the regression line generation unit 14 includes a point group extraction unit 141, a representative point extraction unit 142, and a regression calculation unit 143. The regression line attribute determination unit 15 includes a gradient difference calculation unit 151 and a road surface attribute determination unit 152. Hereinafter, an overview of the configuration and operation of the regression line generation unit 14 and the regression line attribute determination unit 15 in this embodiment will be described.

[0026] As shown in FIG. 10, the point group extraction unit 141 is configured to acquire point group data within a predetermined horizontal azimuth region Sg. FIG. 10 shows, as an example, a case where the distance measuring sensor 3 is a so-called wide-angle sensor with a wide horizontal azimuth angle in the distance measuring range. In this case, for each of the horizontal azimuth regions Sg1, Sg2, … Sgn divided at regular horizontal azimuth angles, the point group extraction unit 141 sequentially extracts point group data and determines whether the extracted distance measuring point P is a road surface point Pr or a non-road surface point Pu. FIG. 11 schematically shows the detection status of the distance measuring point P in the depth direction and the height direction when point group data in one of the plurality of horizontal azimuth regions Sg1, Sg2, … Sgn in FIG. 10 is extracted.

[0027] As shown in FIG. 12, the representative point extraction unit 142 is configured to extract a representative point Px from among a plurality of distance measuring points P corresponding to the point group data extracted by the point group extraction unit 141. In FIGS. 12 and 13, the representative point Px is indicated by a black circle, and the distance measuring points P other than the representative point Px are indicated by white circles. As shown in FIG. 13, the representative point Px is a distance measuring point P for generating a regression line L, that is, a regression line segment Lg. In this way, by reducing the number of distance measuring points P for generating the regression line L compared to the case of generating the regression line L using all the distance measuring points P, it is possible to reduce the computational load.

[0028] In this embodiment, the representative point extraction unit 142 sets a plurality of bins B in the depth direction. The bin width Bw, which is the width of the bin B in the depth direction, may be a constant value, or may be variable according to the driving state such as the vehicle speed or the distance from the host vehicle V. Then, the representative point extraction unit 142 extracts a predetermined number of representative points Px for each of the plurality of bins B. When extracting one representative point Px for one bin B, the selection criteria can be, for example, the one with the lowest height, the one with the highest height, the one with the median value, the one with the highest reception intensity of the reflected wave, and so on. Note that increasing the number of representative points Px extracted in one bin B improves the accuracy in the regression calculation while increasing the computational load. Therefore, the number of representative points Px extracted is sufficient to be one per bin B, but may be a small number such as two or three.

[0029] The regression calculation unit 143 is configured to generate a regression line L based on the representative points Px extracted by the representative point extraction unit 142. Specifically, as shown in FIG. 13, the regression calculation unit 143 generates a plurality of line segments, that is, regression line segments Lg, so that the regression line L fits finely to the unevenness of the road surface shape. A detailed specific example of the generation of the regression line segment Lg will be described later.

[0030] In the regression line attribute determination unit 15, the gradient difference calculation unit 151 is configured to calculate the gradient change of the regression line L. Specifically, the gradient difference calculation unit 151 calculates a gradient difference θ, which is the gradient change between adjacent regression line segments Lg in the depth direction. Based on the gradient difference θ calculated by the gradient difference calculation unit 151, as shown in FIG. 14, the road surface attribute determination unit 152 determines whether the regression line segment Lg is either a road surface segment Lgr or a non-road surface segment Lgu. Then, as shown in FIG. 15, the non-road surface detection unit 13 determines whether the ranging point P is a non-road surface point Pu by comparing the distance in the height direction between the road surface segment Lgr and the ranging point P with a threshold value. That is, when the distance in the height direction between the road surface segment Lgr and the ranging point P is equal to or greater than the threshold value, the non-road surface detection unit 13 determines that such a ranging point P is a non-road surface point Pu. On the other hand, when the distance in the height direction between the road surface segment Lgr and the ranging point P is less than or less than the threshold value, the non-road surface detection unit 13 determines that such a ranging point P is a road surface point Pr.

[0031] (Specific Example) Hereinafter, a specific example of the operation of the road surface state detection device 10 according to the present embodiment and the road surface state detection program executed by such a road surface state detection device 10 will be described with reference to the respective drawings. Hereinafter, the road surface state detection device 10 according to the present embodiment and the road surface state detection program executed thereby will be collectively referred to as "the present embodiment". The road surface state detection device 10 executes the processing shown in the flowchart of FIG. 16. In the flowchart shown in FIG. 16, "S" is an abbreviation for "step". The same applies to the flowcharts shown in other figures.

[0032] The road surface state detection device 10 executes the processes of steps 101 to 105 for each of the horizontal azimuth regions Sg1, Sg2, … Sgn. That is, the road surface state detection device 10 first selects the horizontal azimuth region Sg1 and executes the processes of steps 101 to 105. Next, the road surface state detection device 10 selects the horizontal azimuth region Sg2 and executes the processes of steps 101 to 105. In this way, the road surface state detection device 10 loops through steps 101 to 105 from 1 to n.

[0033] In step 101, as shown in FIGS. 10 and 11, the road surface state detection device 10 acquires the point cloud data within one horizontal azimuth region Sg selected in the current loop process. Step 101 corresponds to the point cloud extraction process according to the present invention. Next, in step 102, the road surface state detection device 10 extracts a representative point Px for each bin B from among a plurality of ranging points P in the point cloud data acquired in step 101, as shown in FIG. 12. Step 102 corresponds to the representative point extraction process according to the present invention. Subsequently, in step 103, as shown in FIG. 13, the road surface state detection device 10 sequentially generates a regression line L, that is, a regression line segment Lg, from the near side to the far side in the depth direction based on the extracted representative points Px.

[0034] (Regression line generation process) FIGS. 17 to 20 show a specific example of the regression line generation process by step 103, that is, the generation of the regression line segment Lg. First, the variables and terms shown in the flowchart of FIG. 17 will be described. N is the total number of representative points Px. The vertex list PL is a list of segment constituent points Pxx that are representative points Px constituting the target segment Lg_t shown in FIG. 18. The target segment Lg_t is a regression line segment Lg during generation and corresponds to the regression calculation result of the segment constituent points Pxx. k(PL) is the number of representative points Px, that is, segment constituent points Pxx, included in the vertex list PL. The regression line segment list is a list of generated regression line segments Lg.

[0035] The road surface state detection device 10 sequentially selects one by one from among the N extracted representative points Px in the depth direction starting from the front side, and executes the processes after step 201. That is, the road surface state detection device 10 loops the steps after step 201 from 1 to N. The representative point Px selected in the current loop process is hereinafter referred to as the target representative point Px_t. The target representative point Px_t is the target ranging point to be determined whether to be added to the segment constituent points Pxx in the current loop process.

[0036] Specifically, the road surface state detection device 10 first selects the first representative point Px as the target representative point Px_t in the first loop process, that is, the first time, and executes the process of step 201. In step 201, the road surface state detection device 10 determines whether the number k(PL) of representative points Px included in the vertex list PL is 2 or more. At this point, since k(PL)=0, the determination result of step 201 is "NO". Therefore, the road surface state detection device 10 advances the process to step 202. In step 202, the road surface state detection device 10 adds the target representative point Px_t to the vertex list PL. As a result, k(PL)=1. Then, the road surface state detection device 10 ends the first loop process and executes the second loop process. That is, the road surface state detection device 10 selects the second representative point Px as the target representative point Px_t and executes the process of step 201.

[0037] At the time of the second loop process, since k(PL)=1, the determination result of step 201 is again "NO". Therefore, the road surface state detection device 10 advances the process to step 202 again. In step 202, the road surface state detection device 10 adds the target representative point Px_t to the vertex list PL. As a result, k(PL)=2. Then, the road surface state detection device 10 ends the second loop process and executes the third loop process. That is, the road surface state detection device 10 selects the third representative point Px as the target representative point Px_t and executes the process of step 201 again.

[0038] At the time of the third loop process, since k(PL) = 2, the determination result of step 201 becomes "YES". Therefore, the road surface state detection device 10 advances the process to step 203. In step 203, the road surface state detection device 10 generates a target segment Lg_t composed of the current vertex list PL and a target segment Lg_t composed of adding a target representative point Px_t to the current vertex list PL. After the process of step 203, the road surface state detection device 10 executes the process of step 204. In step 204, the road surface state detection device 10 determines whether the point addition condition, which is the condition for adding the target representative point Px_t to the current vertex list PL, i.e., the segment composition point Pxx, is satisfied.

[0039] The point addition condition will be described with reference to FIGS. 19 and 20. Referring to FIG. 19, the gradient difference δ is the angle formed by the target segment Lg_t and the virtual line segment Ldt. The virtual line segment Ldt is a line segment extending from the target representative point Px_t toward the end of the target segment Lg_t on the side close to the target representative point Px_t. One end of the virtual line segment Ldt is the end point on the side close to the target representative point Px_t in the target segment Lg_t, or the segment composition point Pxx closest to such an end point. FIG. 20 shows a virtual segment Lg_t1, which is the target segment Lg_t newly generated by the target representative point Px_t and the segment composition point Pxx when the target representative point Px_t is added to the current vertex list PL. The point addition condition can be set as the AND condition or the OR condition of the following condition 1 and condition 2. Note that the statistical value Zv is a statistical measure corresponding to the error of the regression calculation, for example, the root mean square error RMSE. (Condition 1) Gradient difference δ < gradient difference threshold δth (Condition 2) Statistical value Zv in the virtual segment Lg_t1 < threshold Zv_th

[0040] When the point addition condition is satisfied (i.e., step 204 = YES), after executing the process of step 202, the road surface state detection device 10 ends the current loop process. As a result, the target representative point Px_t is added to the segment composition point Pxx, and a new target representative point Px_t is selected in the next loop process. On the other hand, when the point addition condition is not satisfied (i.e., step 204 = NO), the road surface state detection device 10 executes a process for determining the target segment Lg_t based on the current vertex list PL as the regression line segment Lg. That is, after executing the processes of step 205 and step 206, the road surface state detection device 10 ends the current loop process. In step 205, the road surface state detection device 10 adds the regression line segment Lg based on the current vertex list PL to the regression line segment list. In step 206, after deleting all but the segment composition point Pxx at the farthest side from the vertex list PL, the road surface state detection device 10 adds the target representative point Px_t to the vertex list PL.

[0041] (Regression line attribute determination process) After generating the regression line L using all the representative points Px, the road surface state detection device 10 proceeds to step 104 shown in FIG. 16. In step 104, the road surface state detection device 10 calculates the gradient difference θ as the relationship between the regression line segments Lg, and as shown in FIG. 14, executes an attribute determination which is a determination as to whether the regression line segment Lg is either the road surface segment Lgr or the non-road surface segment Lgu. Step 104 corresponds to the regression line attribute determination process according to the present invention.

[0042] The outline of the attribute determination will be described using the specific examples shown in FIGS. 21 to 27. FIGS. 21 to 27 show the generation status of the representative point Px and the regression line L in the representative road surface shapes, and the change mode of the gradient difference θ corresponding thereto. FIG. 21 shows the case of a relatively flat road surface R without unevenness such as the deceleration zone Da. FIG. 22 shows the case of a gradient road surface without unevenness such as the deceleration zone Da. FIGS. 23 to 26 show the cases where unevenness exists. Specifically, FIG. 23 shows the case where a trapezoidal hump Db exists on the road surface R. FIG. 24 shows the case where an arc hump Dc exists on the road surface R. FIG. 25 shows the case where the tip of the step Dd is a cliff De. FIG. 26 shows the case where a groove Df exists. FIG. 27 shows the case where the road surface R without unevenness such as the deceleration zone Da is a slope sandwiching a flat road.

[0043] As described with reference to FIGS. 7 and 8, regarding the change mode of the gradient difference θ along the depth direction, when there is unevenness such as the deceleration zone Da, characteristics such as a large decrease from the positive value to the negative value and a large increase from the negative value to the positive value of the gradient difference θ are observed. On the other hand, such change characteristics are not observed in the case of a sloped road surface without unevenness. Therefore, as shown in FIGS. 21 to 27, a decrease characteristic maximum threshold THd1, an increase characteristic maximum threshold THu1, an increase characteristic minimum threshold THu2, and a decrease characteristic minimum threshold THd2 are set for the gradient difference θ. Then, the road surface state detection device 10 determines the attribute of the regression line L, that is, the regression line segment Lg, by determining the change characteristics of the gradient difference θ using these thresholds.

[0044] Specifically, the gradient difference reduction feature for detecting the presence of unevenness is defined as a feature in which the gradient difference θ transitions from a value greater than the maximum threshold THd1 of the reduction feature to a value smaller than the minimum threshold THd2 of the reduction feature. Also, the gradient difference increase feature for detecting the presence of unevenness is defined as a feature in which the gradient difference θ transitions from a value smaller than the minimum threshold THu2 of the increase feature to a value greater than the maximum threshold THu1 of the increase feature. Note that due to the nature of the distance measuring sensor 3, as shown in FIG. 11 and the like, the density of the distance measuring points P tends to be sparser on the back side than on the front side in the depth direction. Also, on the back side rather than the front side in the depth direction, the distance measuring points P tend to be more likely to appear farther away than they actually are. For this reason, on the front side in the depth direction, a large inclination is likely to occur, and conversely, on the back side, a small inclination is likely to occur. And when the host vehicle V is traveling toward unevenness such as the deceleration zone Da, in many cases, the gradient difference reduction feature occurs on the front side and the gradient difference increase feature occurs on the back side. Therefore, in the present embodiment, the maximum threshold THd1 of the reduction feature > the maximum threshold THu1 of the increase feature > the minimum threshold THu2 of the increase feature > the minimum threshold THd2 of the reduction feature is set.

[0045] In the lower graphs of the gradient difference θ in FIGS. 21 to 27, the cases where the gradient difference reduction feature and the gradient difference increase feature are observed are indicated by thick lines. In the case of the road surface R without unevenness such as the trapezoidal hump Db and the groove Df, as shown in FIGS. 21 and 22, neither the gradient difference increase feature nor the gradient difference reduction feature is observed. On the other hand, as shown in FIGS. 23 and 24, when there are the trapezoidal hump Db and the arc hump Dc corresponding to the deceleration zone Da, the gradient difference reduction feature occurs on the front side and the gradient difference increase feature occurs on the back side. The same applies to the case where the tip of the step Dd due to the protrusion is the cliff De as shown in FIG. 25. As shown in FIG. 26, when the unevenness is not a protrusion such as the trapezoidal hump Db but a recess, that is, the groove Df, although a plurality of distance measuring points P can occur in the height direction on the back wall surface Df1 of such a groove Df, by setting the representative point Px as the point with the lowest height, the gradient difference reduction feature can be confirmed after the gradient difference increase feature.

[0046] As described above, when the road surface R has irregularities such as a speed bump Da or a groove Df as shown in FIGS. 23 to 26, both a gradient difference increase feature and a gradient difference decrease feature can be observed. However, as shown in FIG. 27, even when the road surface R is a slope sandwiching a flat road and there are no irregularities such as a speed bump Da or a groove Df, both a gradient difference increase feature and a gradient difference decrease feature can be observed. However, in this case, the gradient difference increase feature and the gradient difference decrease feature are large-scale. On the other hand, the gradient difference increase feature and the gradient difference decrease feature when the road surface R has irregularities as shown in FIGS. 23 to 26 are local. Therefore, in the present embodiment, the road surface attribute determination unit 152 executes attribute determination based on the gradient change within a predetermined distance in the depth direction.

[0047] FIGS. 28 to 37 show specific examples of the attribute determination in step 104 in FIG. 16. FIG. 28 shows an outline of a specific example of the attribute determination process in a flowchart. FIGS. 29 to 35 show specific examples when there is a hump Dg that is a protrusion on the road surface R. FIG. 36 shows a specific example of a gradient road surface without irregularities. FIG. 37 shows a specific example when the road surface R without irregularities is a slope sandwiching a flat road. First, the variables and terms shown in FIG. 28 and the like will be described. The flag F is a flag indicating whether the regression line segment Lg is a road surface segment Lgr or a non-road surface segment Lgu. In FIGS. 29 and the like, when it is a road surface segment Lgr, the "road surface flag" is denoted as "road", and when it is a non-road surface segment Lgu, the "non-road surface flag" is denoted as "non". Let the total number of regression line segments Lg to be determined be m, and the regression line segments Lg be Lg1, Lg2,... Lgm in order from the front side, and the corresponding flags F be F1, F2,... Fm. That is, i = 1 to m in Fi.

[0048] In the regression line attribute determination process, after initializing all the flags F in step 301 shown in FIG. 28, steps 302 and the like are loop-processed from 1 to m. FIG. 29 shows a state where all the flags F are initialized in step 301 in a specific example when there is a hump Dg that is a protrusion on the road surface R.

[0049] In step 302 of the i-th loop process, the road surface state detection device 10 determines whether the i-th flag F = Fi is a road surface flag. If it is a road surface flag (i.e., step 302 = YES), the road surface state detection device 10 proceeds with the process to steps 303 and 304. On the other hand, if it is a non-road surface flag (i.e., step 302 = NO), the road surface state detection device 10 skips the processes after step 303 and ends the current i-th loop process. FIG. 30 shows the case where the process proceeds to step 302 from the state of FIG. 29. That is, i = 1. In this case, immediately after all the flags F are initialized, since F1 is a road surface flag, the process proceeds to steps 303 and 304.

[0050] In step 303, the road surface state detection device 10 searches for a gradient difference increase feature and a gradient difference decrease feature within a predetermined distance Xd from the depth position of the gradient difference θ = θi. The predetermined distance Xd is a distance for determining whether the gradient difference increase feature and the gradient difference decrease feature are local or global, and may be set in advance by an optimization experiment or a computer simulation, or may be corrected later by learning. Typically, the predetermined distance Xd is a value corresponding to the average size in the depth direction of irregularities such as the groove Df and the hump Dg, and can be, for example, about several to 10 m. That is, the predetermined distance Xd corresponds to a threshold value for distinguishing between the case where there are irregularities such as the groove Df and the hump Dg and the case where the road surface R is a slope sandwiching a flat road.

[0051] In step 304, the road surface state detection device 10 determines whether both a gradient difference increase feature and a gradient difference decrease feature exist within a predetermined distance Xd. That is, the process of step 304 is to determine whether there are irregularities such as a groove Df or a hump Dg. FIG. 31 shows the result of searching for the gradient difference increase feature and the gradient difference decrease feature within the predetermined distance Xd from the state of FIG. 30 when i = 1. As shown in FIG. 31, when both the gradient difference increase feature and the gradient difference decrease feature exist within the predetermined distance Xd (that is, step 304 = YES), the road surface state detection device 10 advances the process to steps 305 to 307. On the other hand, when the determination result of step 304 is "NO", the road surface state detection device 10 skips the processes after step 305 and ends the current i-th loop process.

[0052] In step 305, the road surface state detection device 10 obtains the gradient difference θ = θs corresponding to the starting point position of the gradient difference increase feature and the gradient difference decrease feature and the gradient difference θ = θe corresponding to the ending point position, and sets the flags F from the (s + 1)-th to the e-th as non-road surface flags. FIG. 32 shows the result of assigning non-road surface flags to the irregularity positions by the process of step 305 from the state of FIG. 31 when i = 1. From θs = θ1 and θe = θ3, the flags F from the second to the third, that is, F2 and F3, become non-road surface flags.

[0053] In step 306, the road surface state detection device 10 calculates the height difference ΔH before and after crossing the unevenness such as the hump Dg. Then, in step 307, the road surface state detection device 10 determines whether the height difference ΔH exceeds a threshold value. That is, the process of step 307 is a determination as to whether the host vehicle V can travel after crossing the unevenness. The height difference ΔH can be, for example, as shown in FIG. 33, the height difference between the farthest point Pxz, which is the point farthest from among the distance measurement points P from the (s + 1)-th regression line segment Lg, i.e., Lgs+1, to the e-th regression line segment Lg, i.e., Lge, and the reference segment Lgz, which is the (e + 1)-th regression line segment Lg. Alternatively, the height difference ΔH can be, for example, as shown in FIGS. 34 and 35, the height difference between the immediately preceding road surface segment Lgru, which is the road surface segment Lgr immediately before the unevenness such as the hump Dg, and the immediately following road surface segment Lgrd, which is the road surface segment Lgr immediately after the unevenness.

[0054] As shown in FIG. 34, when the height difference ΔH is small and the host vehicle V can travel even after crossing the unevenness, the determination result of step 307 is "NO". In this case, the road surface state detection device 10 skips the process of step 308 and ends the current i-th loop process. On the other hand, as shown in FIG. 35, when the height difference ΔH is large, the determination result of step 307 is "YES". In this case, after the road surface state detection device 10 executes the process of step 308, it ends the process of the flowchart shown in FIG. 28. In step 308, the road surface state detection device 10 sets all the flags F after the (e + 1)-th one to non-road surface flags.

[0055] As shown in FIG. 36, in the case of a gradient road surface, since neither the gradient difference increase feature nor the gradient difference decrease feature exists, the determination result of step 304 is "NO", and no non-road surface flag is assigned. Also, as shown in FIG. 37, when the road surface R is a slope sandwiching a flat road, although both the gradient difference increase feature and the gradient difference decrease feature exist, they do not fit within the predetermined distance Xd, so the determination result of step 304 is "NO", and no non-road surface flag is assigned.

[0056] (Off-road point detection process) After performing the attribute determination as described above, as shown in FIG. 15, the road surface state detection device 10 determines whether the ranging point P is an off-road point Pu. That is, referring to FIG. 16 again, in step 105, the road surface state detection device 10 detects the off-road point Pu from the height difference from the estimated road surface, that is, the road surface segment Lgr. Step 105 corresponds to the off-road point detection process according to the present invention.

[0057] An example of a method for determining whether the ranging point P is a road surface point Pr or an off-road point Pu is shown in FIGS. 38 to 40. These examples show which of the front-side first road surface segment Lgr1 and the rear-side second road surface segment Lgr2 of the adjacent road surface segments Lgr in the depth direction is used as a reference to determine the height of the ranging point P.

[0058] There may be a case where there is a road surface segment Lgr at which the intersection point ZP with the perpendicular line ZL from the ranging point P to be determined is an internal division point. In the example of FIG. 38, the second road surface segment Lgr2 corresponds to this. In this case, it is possible to determine whether the ranging point P is a road surface point Pr or an off-road point Pu based on the height difference with respect to such a road surface segment Lgr.

[0059] On the other hand, as shown in FIG. 39, there may be a case where there is no road surface segment Lgr in which the intersection point ZP with the perpendicular line ZL from the distance measurement point P to be determined is an internal division point. In this case, the distance between the first intersection point ZP1 of the extension line of the first road surface segment Lgr1 from the first end point Z11, which is the end point on the side close to the distance measurement point P to be determined, and the perpendicular line ZL, and the first end point Z11, is defined as W1. Also, the distance between the second intersection point ZP2 of the extension line of the second road surface segment Lgr2 from the second end point Z12, which is the end point on the side close to the distance measurement point P to be determined, and the perpendicular line ZL, and the second end point Z12, is defined as W2. Then, among the first road surface segment Lgr1 and the second road surface segment Lgr2 on the back side thereof, when W1 < W2, the first road surface segment Lgr1 is used as a reference, and when W1 > W2, the second road surface segment Lgr2 is used as a reference to determine whether the distance measurement point P is a road surface point Pr or a non-road surface point Pu.

[0060] Note that if the length of the extension line becomes too large, the reliability of the determination decreases. Therefore, as shown in FIG. 40, the road surface state detection device 10 designates a distance measurement point P at which the perpendicular line ZL cannot be drawn so that the intersection point ZP occurs on the maximum extrapolation line ZX, which is the extension line with the upper limit length, for any road surface segment Lgr, as an unclassified point Pz. The unclassified point Pz is a distance measurement point P that is not classified as either a road surface point Pr or a non-road surface point Pu. The length Wx of the maximum extrapolation line ZX may be set in advance by an optimization experiment or a computer simulation, or may be corrected by learning thereafter. Typically, the length Wx of the maximum extrapolation line ZX is about half the average size in the depth direction of irregularities such as the groove Df and the hump Dg, and can be, for example, several to about 5 m.

[0061] (Effect) As described above, in this embodiment, the regression line generation unit 14 generates a regression line L in a two-dimensional coordinate system defined by the depth direction and the height direction for a plurality of distance measurement points P. The regression line attribute determination unit 15 calculates the gradient change of the regression line L, and based on the gradient change within a predetermined distance in the depth direction, determines whether the regression line L is a road surface configuration line corresponding to the road surface R, that is, a road surface segment Lgr, or a non-road surface configuration line not corresponding to the road surface R, that is, a non-road surface segment Lgu. The non-road surface detection unit 13 detects a non-road surface point Pu, which is a distance measurement point P that does not constitute the road surface R, based on the determination result by the regression line attribute determination unit 15 and the positional relationship between the regression line L and the distance measurement point P. As a result, it becomes possible to accurately distinguish and detect road surface irregularities such as a deceleration zone Da from the road surface gradient.

[0062] (Modification example) The present invention is not limited to the above-described embodiments and examples. Therefore, the above-described embodiments and the like can be appropriately modified. Hereinafter, representative modification examples will be described. In the description of the following modification examples, the differences from the above-described embodiments and the like will be mainly described. Also, in the above-described embodiments and the following modification examples, the same or equivalent parts are denoted by the same reference numerals. Therefore, in the description of the following modification examples, regarding the components having the same reference numerals as those in the above-described embodiments and the like, the descriptions in the above-described embodiments and the like can be appropriately incorporated as long as there is no technical contradiction or special additional explanation.

[0063] The present invention is not limited to the specific applications and device configurations shown in the above embodiments. That is, for example, all or part of the configuration of the road surface state detection device 10 shown in FIG. 3 may be realized by a microcomputer built into the distance measurement sensor 3. Alternatively, the road surface state detection device 10 may be realized by a microcomputer built into the vehicle behavior control device 6.

[0064] All or part of the electronic control device 4 may be configured with a digital circuit, such as an ASIC or an FPGA, that enables the above-described operations. ASIC is an abbreviation for Application Specific Integrated Circuit. FPGA is an abbreviation for Field Programmable Gate Array. That is, in the electronic control device 4, the in-vehicle microcomputer part and the digital circuit part can coexist.

[0065] The program according to the present invention, which enables execution of various operations, procedures, or processes described in the above embodiment, can be downloaded or upgraded via V2X communication. V2X is an abbreviation for Vehicle to X. Alternatively, such a program can be downloaded or upgraded via a terminal device provided at a manufacturing plant, a maintenance plant, a dealership, etc. of the host vehicle V. The storage destination of such a program may be a memory card, an optical disk, a magnetic disk, etc.

[0066] As described above, each of the above functional configurations and processes may be realized by a dedicated computer provided by configuring a processor and a memory programmed to execute one or more functions embodied by a computer program. Alternatively, each of the above functional configurations and processes may be realized by a dedicated computer provided by configuring a processor with one or more dedicated hardware logic circuits. Alternatively, each of the above functional configurations and processes may be realized by one or more dedicated computers configured by a combination of a processor and a memory programmed to execute one or more functions and a processor configured by one or more hardware logic circuits. Further, the computer program may be stored in a computer-readable non-transitory tangible storage medium as instructions to be executed by a computer. That is, each of the above functional configurations and processes can also be represented as a computer program including procedures for realizing the same, or as a non-transitory tangible storage medium storing the program.

[0067] The present invention is not limited to the specific operation modes shown in the above embodiments. That is, for example, the point cloud data may be received by the data acquisition unit 11 from the distance measurement sensor 3 that has generated it, or the data acquisition unit 11 may receive the transmission and reception data in the distance measurement sensor 3 from the distance measurement sensor 3 and generate it in the data acquisition unit 11. Also, the order of the horizontal azimuth regions Sg1, Sg2,... Sgn shown in FIG. 10, which are divided for each fixed horizontal azimuth angle, can be changed. That is, for example, the order may be set in the order from the center side to the outside in the vehicle lateral position direction. Further, when the distance measurement sensor 3 is not a wide-angle sensor, the horizontal azimuth region Sg may not be divided into a plurality for each fixed horizontal azimuth angle as shown in FIG. 10, but may be a single one. In this case, in the flowchart of FIG. 16, the processes of steps 101 to 105 do not become loop processes.

[0068] In the above embodiment, the regression line L was composed of a plurality of regression line segments Lg generated as line segments, but the present invention is not limited to such an aspect. Specifically, the regression line L or the regression line segment Lg can be formed by any multidimensional function such as a polynomial, that is, non-linear regression. In other words, the regression equation is not limited to a linear function, but may be a multidimensional function such as a cubic function, or may use an exponential function or a trigonometric function. Further, the regression line L may be generated so as to be continuous in the depth direction. And the gradient difference θ can be calculated by the second derivative of the regression line L. Also, instead of generating the regression line L using the representative point Px, the regression line L may be generated using all the distance measurement points P.

[0069] The relationship of the decrease feature maximum threshold THd1 > increase feature maximum threshold THu1 > increase feature minimum threshold THu2 > decrease feature minimum threshold THd2 in FIGS. 21 to 27 can be changed as appropriate. Specifically, for example, the decrease feature maximum threshold THd1 may be equal to the increase feature maximum threshold THu1. Or, for example, the increase feature minimum threshold THu2 may be equal to the decrease feature minimum threshold THd2.

[0070] Similar expressions such as "acquisition", "calculation", "estimation", "detection", "detection", and "decision" can be replaced with each other as appropriate within the range where there is no technical contradiction. "Detection" or "detection" and "extraction" can also be replaced as appropriate within the range where there is no technical contradiction.

[0071] It goes without saying that the elements constituting the above-described embodiments are not necessarily essential, except in cases where it is explicitly stated that they are particularly essential and cases where they are considered to be clearly essential in principle. Also, when numerical values such as the number of components, numerical values, quantities, ranges, etc. are mentioned, the present disclosure is not limited to that specific number, except in cases where it is explicitly stated that it is particularly essential and cases where it is clearly limited to a specific number in principle. Similarly, when the shape, direction, positional relationship, etc. of components, etc. are mentioned, the present disclosure is not limited to that shape, direction, positional relationship, etc., except in cases where it is explicitly stated that it is particularly essential and cases where it is clearly limited to a specific shape, direction, positional relationship, etc. in principle.

[0072] The modification examples are not limited to the above examples. For example, all or part of one of the plurality of specific examples and all or part of another one can be combined with each other as long as there is no technical contradiction. There is no particular limitation on the number of combinations. Similarly, all or part of one of the plurality of modification examples and all or part of another one can be combined with each other as long as there is no technical contradiction. Furthermore, all or part of the above specific examples and all or part of the above modification examples can be combined with each other as long as there is no technical contradiction.

Explanation of Reference Numerals

[0073] 3 Distance Measuring Sensor 10 Road Surface Condition Detection Device 13 Non-Road Surface Detection Unit 14 Regression Line Generation Unit 15 Regression Line Attribute Judgment Unit 151 Gradient Difference Calculation Unit 152 Road Surface Attribute Judgment Unit L Regression Line Pr Road Surface Point Non-road point of Pu

Claims

1. A road surface condition detection device (10) that detects the road surface condition in the travel direction of a host vehicle (V) equipped with the distance measurement sensor based on distance measurement points (P) whose position information is detected by the distance measurement sensor (3), A regression line generation unit (14) that generates a regression line (L) in a two-dimensional coordinate system based on the depth direction and the height direction along the road surface (R) and the travel direction of the host vehicle for a plurality of the distance measurement points, A regression line attribute determination unit (15) that performs an attribute determination that determines whether the regression line is a road surface configuration line (Lgr) corresponding to the road surface or a non-road surface configuration line (Lgu) not corresponding to the road surface, A non-road surface point detection unit (13) that detects a non-road surface point (Pu) that is the distance measurement point that does not constitute the road surface based on the determination result by the regression line attribute determination unit and the positional relationship between the regression line and the distance measurement point, Comprising, The regression line attribute determination unit, A gradient difference calculation unit (151) that calculates a gradient change of the regression line, A road surface attribute determination unit (152) that performs the attribute determination based on the gradient change within a predetermined distance in the depth direction, Comprising, Road surface condition detection device.

2. The regression line generation unit generates the regression line composed of a plurality of regression line segments (Lg) arranged in the depth direction, The gradient difference calculation unit calculates the gradient change between the adjacent regression line segments in the depth direction, The road surface condition detection device according to claim 1.

3. The regression line generation unit generates the regression line segment by a line segment, The gradient difference calculation unit calculates the gradient difference between the adjacent line segments in the depth direction, The road surface condition detection device according to claim 2.

4. The regression line generation unit determines whether to add the attention measurement point (Px_t) which is the distance measurement point to be determined whether to add to the segment configuration point or not to the attention segment (Lg_t) which is the regression line segment formed by the segment configuration points (Pxx) as a plurality of the distance measurement points, based on the gradient difference between the virtual line segment (Ldt) toward the end on the side close to the attention measurement point in the attention segment or a statistical measure when the attention measurement point is added to the segment configuration point, The road surface condition detection device according to claim 2 or 3.

5. The regression line generation unit includes a point cloud extraction unit (141) that acquires the position information about the distance measurement points within a predetermined horizontal azimuth region (Sg). The road surface state detection device according to claim 1.

6. The regression line generation unit includes a representative point extraction unit (142) that extracts a representative point (Px) for generating the regression line from among the plurality of distance measurement points, and generates the regression line based on the representative point extracted by the representative point extraction unit. The road surface state detection device according to claim 1.

7. The non-road surface point detection unit determines whether the distance measurement point is a non-road surface point by comparing the distance in the height direction between the road surface configuration line and the distance measurement point with a threshold value. The road surface state detection device according to claim 1.

8. A road surface state detection program executed by a road surface state detection device (10) that detects the road surface state in the travel direction of a host vehicle (V) equipped with a distance measurement sensor (3) based on distance measurement points (P) whose position information is detected by the distance measurement sensor, wherein the processing executed by the road surface state detection device includes a regression line generation process (S103) that generates a regression line (L) in a two-dimensional coordinate system defined by the depth direction and the height direction along the road surface (R) and the travel direction of the host vehicle for a plurality of the distance measurement points at an arbitrary horizontal azimuth, a regression line attribute determination process (S104) that executes an attribute determination as to whether the regression line is a road surface configuration line (Lgr) corresponding to the road surface or a non-road surface configuration line (Lgu) not corresponding to the road surface, and a non-road surface point detection process (S105) that detects non-road surface points (Pu) that are the distance measurement points not constituting the road surface based on the determination result of the regression line attribute determination process and the positional relationship between the regression line and the distance measurement points. The road surface state detection program wherein in the regression line attribute determination process the gradient change of the regression line is calculated, and the attribute determination is executed based on the gradient change within a predetermined distance in the depth direction. Road surface state detection program.

9. In the regression line generation process, the regression line is generated from a plurality of regression line segments (Lg) arranged in the depth direction, and in the regression line attribute determination process, the gradient change between adjacent regression line segments in the depth direction is calculated. The road surface state detection program according to claim 8.

10. In the regression line generation process, the regression line segments are generated by line segments. In the regression line attribute determination process, calculate the gradient difference between the adjacent line segments in the depth direction. The road surface state detection program according to claim 9.

11. In the regression line generation process, based on the gradient difference between a virtual line segment (Ldt) extending from a target measurement point (Px_t), which is a measurement point to be determined whether to be added to the segment constituent points, to an end portion on the side closer to the target measurement point in the target segment (Lg_t), which is the regression line segment formed by the segment constituent points (Pxx) as a plurality of the measurement points, or based on a statistical measure when the target measurement point is added to the segment constituent points, determine whether to add the target measurement point to the segment constituent points. The road surface state detection program according to claim 9 or 10.

12. The regression line generation process includes a point group extraction process (S101) of acquiring the position information about the measurement points within a predetermined horizontal azimuth region (Sg). The road surface state detection program according to claim 8.

13. The regression line generation process includes a representative point extraction process (S102) of extracting a representative point (Px) for generating the regression line from among the plurality of the measurement points, and generates the regression line based on the representative point extracted in the representative point extraction process. The road surface state detection program according to claim 8.

14. In the non-road surface point detection process, determine whether the measurement point is a non-road surface point by comparing the distance in the height direction between the road surface constituent line and the measurement point with a threshold value. The road surface state detection program according to claim 8.

Images