Road surface measurement system

Abstract

To improve efficiency when measuring road surface conditions for the second or subsequent times in a measurement range where an existing road surface unevenness model exists. [Solution] In a road surface measurement system in which a measurement vehicle equipped with an imaging device is driven autonomously and multiple image data of the road surface captured by the imaging device are combined using feature point matching processing to generate a road surface unevenness model, a control device that controls the operation of the measurement vehicle and imaging device sets different shooting conditions for areas where the change in road surface condition is greater than a predetermined standard and areas where it is less than a predetermined standard, comparing the existing road surface unevenness model with the measurement range.

Description

[Technical Field]

[0001] The present disclosure relates to a road surface measurement system. [Background technology]

[0002] A technology is known in which a measuring instrument is attached to a moving body, and the measuring instrument photographs the road surface while the moving body is moving, measuring the unevenness of the road surface and the road condition, and generating a road surface unevenness model.For example, Patent Document 1 discloses a measurement device that includes a stereo camera mounted on a vehicle and photographing both a measurement range and a target on the road surface, an extraction means that extracts a reference line from the target in the image photographed by the stereo camera, and a measurement means that measures the road surface condition with a measurement line obtained by shifting the reference line to the measurement range in the photographed image. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-123715 Summary of the Invention [Problem to be solved by the invention]

[0004] Here, because road surfaces wear or deteriorate over time, it is desirable to update the road surface unevenness model periodically or irregularly. At that time, the data obtained by measurement is compared with an already generated road surface unevenness model, and the difference between the two is extracted to determine changes in the road surface condition. However, it is inefficient to perform a detailed analysis of the same entire measurement range every time.

[0005] The present disclosure has been made in consideration of the above-mentioned problems, and an object of the present disclosure is to provide a road surface measurement system that can improve efficiency when performing second or subsequent measurements in a measurement range where an existing road surface unevenness model exists. [Means for solving the problem]

[0006] In order to solve the above problem, according to one aspect of the present disclosure, there is provided a road surface measurement system that generates a road surface unevenness model by combining multiple image data of the road surface captured by an imaging device while the measurement vehicle equipped with the imaging device is driven autonomously, the road surface measurement system including one or more control devices that control the operation of the measurement vehicle and the imaging device, and when there is an existing road surface unevenness model for the measurement range, the one or more control devices set different shooting conditions for areas where the change in road surface condition is greater than a predetermined standard and areas where it is less than the existing road surface unevenness model, and photographs each area. [Effects of the Invention]

[0007] As described above, according to the present disclosure, it is possible to improve efficiency when performing a second or subsequent measurement in a measurement range where an existing road surface unevenness model exists. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram illustrating an overview of a road surface measurement system according to a first embodiment of the present disclosure. [Figure 2] FIG. 2 is an explanatory diagram showing the measuring device according to the embodiment as viewed from the side. [Figure 3] FIG. 2 is an explanatory diagram illustrating the measurement device according to the embodiment as viewed from above. [Figure 4] FIG. 2 is a block diagram showing an example of the configuration of a road surface measurement system according to the embodiment. [Figure 5] 6 is a flowchart showing a processing operation of a processing unit of the control device according to the embodiment; [Figure 6] 10 is a flowchart showing a basic road surface unevenness model generation process performed by a processing unit of the control device according to the embodiment. [Figure 7] 10 is a flowchart showing an autonomous driving control process of the measurement vehicle performed by a processing unit of the control device according to the embodiment. [Figure 8]10 is a flowchart showing a self-position estimation process performed by a processing unit of the control device according to the embodiment. [Figure 9] 10 is a flowchart showing a second or subsequent road surface unevenness model generation process performed by a processing unit of the control device according to the embodiment; [Figure 10] 10 is a flowchart showing a processing operation of the information processing device according to the embodiment. [Figure 11] 10 is a flowchart showing a basic road surface unevenness model generation process performed by the information processing device according to the embodiment. [Figure 12] 10 is a flowchart showing a second or subsequent road surface unevenness model generation process performed by the information processing device according to the embodiment. [Figure 13] 10 is a flowchart showing a processing operation of a processing unit of a control device according to a second embodiment of the present disclosure. [Figure 14] 10 is a flowchart showing a third or subsequent road surface unevenness model generation process performed by a processing unit of the control device according to the embodiment; [Figure 15] 10 is a flowchart showing a third or subsequent road surface unevenness model generation process performed by a processing unit of the control device according to the embodiment; DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, preferred embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In this specification and drawings, components having substantially the same configurations are designated by the same reference numerals, and redundant description will be omitted.

[0010] <<1. First Embodiment>> <1-1. Overview of the road surface measurement system> First, an overview of the road surface measurement system of the present disclosure will be described.

[0011] FIG. 1 is a diagram illustrating an overview of a road surface measurement system according to the present disclosure. 1, the road surface measurement system 1 includes a measurement vehicle 10 equipped with an imaging device, and an information processing device 100 communicatively connected to the measurement vehicle 10. The measurement vehicle 10 and the information processing device 100 are communicatively connected using, for example, cloud computing technology.

[0012] The measurement vehicle 10 captures an image of the road surface of the road R as the measurement target using an imaging device to generate image data Img_α, and transmits the generated image data Img_α to the information processing device 100 sequentially or after the image capturing is completed. The information processing device 100 acquires the image data Img_α, and generates a road surface unevenness model, which is three-dimensional data indicating the unevenness of the road surface, based on the acquired image data Img_α.

[0013] For example, the measurement vehicle 10 divides the road R into three areas D1 to D3 and captures images of the road surface. The measurement vehicle 10 generates time-series image data Img_α while autonomously traveling through each of the areas D1 to D3. Adjacent areas D1 to D3 are set to partially overlap so that the entire measurement range of the road R does not fall outside the imaging range.

[0014] The measurement vehicle 10 also has multiple imaging devices arranged in a direction (left-right direction) perpendicular to the direction of movement (front-back direction). The multiple imaging devices each capture images of the road surface while the measurement vehicle 10 autonomously travels through each of the areas D1 to D3, and generate multiple pieces of image data Img_α. The multiple imaging devices are installed so that the imaging ranges P1 to P6 of adjacent imaging devices partially overlap, so that the entirety of each of the areas D1 to D3 is not excluded from the imaging range.

[0015] Furthermore, the moving speed of the measurement vehicle 10 is set so that the time-series image data Img_α generated by each imaging device partially overlaps. That is, the imaging range at time t_n partially overlaps with the imaging range at the previous time t_n-1 and the imaging range at the next time t_n+1. However, for example, if the moving speed of the measurement device is slow, the imaging range of a certain calculation cycle (time t_n) may overlap with the imaging range of the calculation cycle two cycles before that (times t_n-2, t_n-3,...), and the imaging range of the calculation cycle two cycles after that (times t_n+2, t_n+3,...).

[0016] 1 shows an example in which the road R to be measured extends linearly and the measurement vehicle 10 travels straight, but the shape of the measurement object may be curved, and the example is not limited to the example in which the measurement vehicle 10 travels straight. Furthermore, the division of areas when measuring the road R to be measured, the order in which the areas travel, and the route of the measurement vehicle 10 are not limited to the above example. Furthermore, the number of imaging devices is not particularly limited.

[0017] The information processing device 100 extracts feature points from the acquired multiple pieces of image data Img_α and executes a feature point matching process to align the feature points between the pieces of image data Img_α. The feature point matching process is performed using, for example, SfM (Structure from Motion) processing. Furthermore, the information processing device 100 combines the multiple pieces of image data Img_α through the feature point matching process to generate a road surface unevenness model that indicates information about the unevenness state of the road surface. The road surface unevenness model is generated using, for example, MVS (Multi-View Stereo) processing.

[0018] The measurement vehicle 10 and the information processing device 100 that constitute the road surface measurement system 1 according to this embodiment will be specifically described below.

[0019] <1-2. Measuring equipment> First, a configuration example of the measurement vehicle 10 used in the road surface measurement system 1 according to this embodiment will be described.

[0020] (1-2-1. Overall structure) 2 and 3 are explanatory diagrams showing an example of the configuration of the measurement vehicle 10. FIG. 2 is a diagram of the measurement vehicle 10 as seen from the side, and FIG. 3 is a diagram of the measurement vehicle 10 as seen from above. In FIGS. 2 and 3, the basic forward direction of the measurement vehicle 10 is indicated as "front," and the backward direction is indicated as "rear." Hereinafter, the front-rear direction, left-right direction, and height direction of the measurement vehicle 10 refer to the front-rear direction, left-right direction, and height direction based on the "front" and "rear."

[0021] The measurement vehicle 10 generates a plurality of image data Img_α by capturing images of the road surface as a measurement target, and transmits the generated image data Img_α to the information processing device 100. The measurement vehicle 10 according to this embodiment is configured to be able to move on the road surface unmanned (capable of autonomous driving). The measurement vehicle 10 includes a carriage 3, an imaging unit 5, a surrounding condition detection unit 7, and a control device 50.

[0022] The bogie 3 includes a frame 11, wheels 13 (13F, 13R), a driving force source 15, and a steering device 17. The wheels 13 (13F, 13R), the driving force source 15, and the steering device 17 correspond to a driving device that moves the position of the measurement vehicle 10. The frame 11 forms the skeleton of the bogie 3. The structure and overall shape of the frame 11 shown in Figures 2 and 3 are merely examples, and the configuration of the frame 11 is not particularly limited. The wheels 13 include left and right front wheels 13F and rear wheels 13R, respectively.

[0023] The driving force source 15 outputs a driving force to rotate the rear wheel 13R. A driving motor is a typical example of the driving force source 15, but there is no particular limitation thereto as long as it is capable of outputting power to rotate and drive the rear wheel 13R.

[0024] The steering device 17 adjusts the steering angle of the front wheels 13F. An example of the steering device 17 is a steering device configured to rotate a pinion gear using a motor and move a rack left and right by the rotation of the pinion gear, but the configuration of the steering device 17 is not particularly limited.

[0025] The specific configuration of the dolly 3 is not particularly limited as long as it can support the imaging unit 5 and the surrounding situation detection unit 7 and can travel while adjusting the direction of movement. For example, the number of wheels 13 on the dolly 3 does not need to be four, but may be three, or five or more. Furthermore, the wheels 13 that are rotationally driven by the power output from the driving force source 15 may be the front wheels 13F, or both the front wheels 13F and the rear wheels 13R may be rotationally driven. Furthermore, the driving force source 15 may be four drive motors that respectively drive the four wheels 13.

[0026] Furthermore, the wheels whose steering angles are adjusted by the steering device 17 may be the rear wheels 13R, or all of the front wheels 13F and rear wheels 13R. When four drive motors drive the four wheels 13, respectively, and the direction of travel of the measurement vehicle 10 can be adjusted by controlling the output of the drive motors, the steering device can be omitted.

[0027] The carriage 3 also includes a wheel speed sensor 41. The wheel speed sensor 41 outputs a sensor signal corresponding to the rotation speed of the rear wheel 13R. The wheel speed sensor 41 may also output a sensor signal corresponding to the rotation speed of the front wheel 13F.

[0028] The imaging unit 5 includes a plurality of imaging devices 21a to 21f (hereinafter collectively referred to as imaging devices 21 unless otherwise required), irradiation lamps 25a to 25f (hereinafter collectively referred to as irradiation lamps 25 unless otherwise required), and a reflector 23, and is provided at the rear of the trolley 3.

[0029] The multiple imaging devices 21 are equipped with imaging elements such as CCDs (Charge Coupled Devices) or CMOSs ​​(Complementary Metal-Oxide Semiconductors), and generate image data Img_α (α=1, 2···M) (M is the number of imaging devices 21) by capturing an image of the road surface. The imaging devices 21 are arranged along the left-right direction of the dolly 3. The imaging devices 21 are installed with uniform heights and installation angles (imaging direction orientation). In the measurement vehicle 10 of this embodiment, six imaging devices 21a to 21f are arranged at equal intervals along the left-right direction of the dolly 3. Each imaging device 21 is installed with its imaging direction facing the road surface. The imaging devices 21 are installed so that the imaging ranges of adjacent imaging devices 21 overlap.

[0030] However, the installation positions and installation angles of the multiple imaging devices 21 do not have to be uniform as long as it is possible to maintain the degree of overlap of the imaging ranges of adjacent imaging devices 21. For example, the imaging directions of the imaging devices on both the left and right sides may be oriented toward the center.

[0031] The imaging devices 21 capture images of the road surface at set time intervals and output the generated image data Img_α (α=1, 2...M) to the processing unit 51. The imaging time intervals are set in consideration of the set range of the moving speed of the measurement vehicle 10 so that the imaging ranges of the time-series image data Img_α (α=1, 2...M) of each imaging device 21 overlap with each other. In other words, the time-series image data Img_α (α=1, 2...M) acquired by the processing unit 51 overlap with each other vertically and horizontally. This allows the imaging devices 21 to capture images of the entire road surface to be measured without any omissions.

[0032] The number of imaging devices 21 is not limited to six. Furthermore, the imaging devices 21 do not have to be arranged at equal intervals. However, by arranging the imaging devices 21 at equal intervals, the relative positional relationship of image data generated by adjacent imaging devices 21 becomes the same, which makes it easier to perform calculations.

[0033] The irradiation lamps 25 are light sources for illuminating the road surface of the measurement target, which is imaged by the imaging devices 21. In the measurement vehicle 10 of this embodiment, light emitted from the irradiation lamps 25 is reflected by the reflectors 23 to illuminate the road surface of the measurement target. Note that the number of irradiation lamps 25 is not particularly limited as long as the brightness (illuminance or luminosity) of the road surface imaged by each imaging device 21 is uniform. Furthermore, the means for illuminating the road surface of the measurement target is not limited to the configuration example of this embodiment, and may have any configuration.

[0034] The surrounding condition detection unit 7 has at least one sensor capable of detecting the conditions around the measurement vehicle 10. In this embodiment, the surrounding condition detection unit 7 has distance measurement sensors 31a and 31b (hereinafter collectively referred to as distance measurement sensors 31 unless a distinction is particularly required) and camera sensors 33a and 33b (hereinafter collectively referred to as camera sensors 33 unless a distinction is particularly required). The distance measurement sensor 31 may be, for example, a LiDAR (Light Detection and Ranging) sensor, but may also be a radar sensor or an ultrasonic sensor. The camera sensor 33 has an imaging element such as a CCD or CMOS, and generates image data.

[0035] 2 and 3, the distance measuring sensors 31a and 31b are installed so that the centers of their measurement ranges face forward on the left and right sides of the measurement vehicle 10. The distance measuring sensors 31a and 31b are installed so that they can detect objects, such as targets, mainly on the left and right sides of the measurement vehicle 10. The camera sensors 33a and 33b are installed so that the centers of their imaging ranges face forward on the measurement vehicle 10. The camera sensors 33a and 33b are installed so that they can capture a wide image of the area in front of the measurement vehicle 10.

[0036] The control device 50 includes one or more processors having the function of automatically moving the measurement vehicle 10 and the function of controlling the imaging device 21, and one or more storage devices communicably connected to the one or more processors. The control device 50 also includes a communication interface for communicating with the information processing device 100. The control device 50 is communicably connected to various sensors and electronic control devices provided on the measurement vehicle 10.

[0037] (1-2-3. Control device) Next, the control device 50 provided in the measurement vehicle 10 will be described in detail.

[0038] (Control device configuration) FIG. 4 is a block diagram showing an example of the configuration of the road surface measurement system 1. The control device 50 includes a processing unit 51, a storage unit 53, and a communication unit 57. The processing unit 51 includes one or more processors such as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit), and various peripheral components. Part or all of the processing unit 51 may be configured with updatable firmware or the like, or may be a program module or the like executed by commands from the CPU or the like.

[0039] The processing unit 51 functions as a device that controls the measurement vehicle 10 by having one or more processors execute a computer program. The computer program is a computer program that causes the processor to execute the operations to be performed by the processing unit 51, which will be described later. The computer program executed by the processor may be recorded on a recording medium that functions as a storage unit (memory) 53 provided in the control device 50, or may be recorded on a recording medium built into the processing unit 51 or any recording medium that can be externally attached to the control device 50.

[0040] Recording media for recording computer programs may include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs, DVDs, and Blu-ray (registered trademark), magneto-optical media such as floptical disks, memory elements such as RAMs and ROMs, flash memories such as USB memories and SSDs, and other media capable of storing programs.

[0041] The processing unit 51 is communicatively connected to the driving force source 15, steering device 17, imaging device 21, irradiation lamp 25, distance measurement sensor 31, camera sensor 33, wheel speed sensor 41, input unit 81, and notification unit 83.

[0042] The input unit 81 is a part that accepts input operations by a user. The input unit 81 is configured to include at least one of a keyboard, a mouse, a touchpad, and a microphone, for example. However, the type of the input unit 81 is not particularly limited.

[0043] The notification unit 83 is a part that provides predetermined notifications to the user. The notification unit 83 is configured to include at least one of a display device and a speaker, for example. However, the type of the notification unit 83 is not particularly limited.

[0044] The storage unit 53 is configured with one or more storage elements such as RAM or ROM connected to the processing unit 51 so as to be able to communicate with it. However, the type and number of storage units 53 are not particularly limited. The storage unit 53 stores information such as computer programs executed by the processing unit 51, various parameters used in arithmetic processing, detection data, and arithmetic results. A part of the storage unit 53 is used as a work area.

[0045] The communication unit 57 is an interface for the processing unit 51 to communicate with the information processing device 100. The communication unit 57 may be an interface for communicating with the information processing device 100 via mobile communication, for example, but the communication method with the information processing device 100 is not particularly limited.

[0046] The control device 50 also includes an acceleration sensor 43, an angular velocity sensor 45, a GNSS (Global Navigation Satellite System) sensor 47, and a map data storage unit 55, which are communicatively connected to the processing unit 51. Note that some or all of the acceleration sensor 43, the angular velocity sensor 45, the GNSS sensor 47, and the map data storage unit 55 may be provided separately from the control device 50.

[0047] The acceleration sensor 43 and the angular velocity sensor 45 function as an inertial measurement unit. The acceleration sensor 43 outputs a sensor signal corresponding to the acceleration in each of the three axial directions along the longitudinal, lateral, and height directions of the measurement vehicle 10. The angular velocity sensor 45 outputs a sensor signal corresponding to the angular velocity around each of the three axes along the longitudinal, lateral, and height directions of the measurement vehicle 10. The acceleration sensor 43 and the angular velocity sensor 45 may be integrated sensors as an IMU (inertial measurement unit) sensor, or may be separately provided sensors.

[0048] The GNSS sensor 47 receives satellite signals from positioning satellites such as GPS (Global Positioning System) satellites, and transmits position information of the measurement vehicle 10 contained in the satellite signals to the processing unit 51. The position information may be information on latitude and longitude. Note that the GNSS sensor 47 may be configured to be able to receive satellite signals from satellite systems other than GPS satellites.

[0049] The map data storage unit 55 is configured by a storage element such as RAM or ROM communicably connected to the processing unit 51, or a storage medium such as an HDD, CD, DVD, SSD, USB flash, or storage device. The map data stored in the map data storage unit 55 is associated with latitude and longitude information, and the processing unit 51 can identify the position of the measurement vehicle 10 on the map data based on the latitude and longitude information of the measurement vehicle 10 transmitted from the GNSS sensor 47.

[0050] (Configuration of processing unit) Next, the configuration of the processing unit 51 of the control device 50 will be described.

[0051] The processing unit 51 starts capturing an image of the road surface using the imaging device 21 and executes processing to transmit the image data to the information processing device 100. The processing unit 51 includes an acquisition unit 61, an imaging condition setting unit 63, an autonomous driving control unit 67, a self-position estimation unit 65, an imaging device driving unit 69, and a communication control unit 71. Each of these units is a function realized by execution of a computer program by one or more processors. However, some or all of the acquisition unit 61, the imaging condition setting unit 63, the autonomous driving control unit 67, the self-position estimation unit 65, the imaging device driving unit 69, and the communication control unit 71 may be configured using analog circuits.

[0052] The acquisition unit 61 acquires image data generated by the imaging device 21. The acquisition unit 61 acquires image data generated by the imaging device 21 at set time intervals. The acquisition unit 61 also acquires detection data or sensor signals (hereinafter collectively referred to as "detection information") output from each of the distance measurement sensor 31, camera sensor 33, wheel speed sensor 41, acceleration sensor 43, angular velocity sensor 45, and GNSS sensor 47. The acquisition unit 61 acquires the detection information output from each sensor at each predetermined calculation cycle.

[0053] The photographing condition setting unit 63 sets photographing conditions as operating conditions of the imaging device 21. Specifically, the photographing condition setting unit 63 sets the moving speed of the imaging device 21, i.e., the moving speed of the measurement vehicle 10. The photographing condition setting unit 63 sets a moving speed at which the imaging ranges of the image data acquired in time series partially overlap, based on the length of the road surface corresponding to the pixel size of the image data captured by each imaging device 21 and the time interval between images captured by the imaging devices 21. The photographing condition setting unit 63 also sets the aperture (F-number), shutter speed, and ISO sensitivity of the imaging device 21. The photographing condition setting unit 63 may also set the output (illuminance or brightness) of the irradiation lamp 25. Note that the photographing condition setting unit 63 does not need to perform setting processing for conditions that are fixed regardless of other conditions.

[0054] In this embodiment, if there is no existing road surface unevenness model for the measurement range, the photographing condition setting unit 63 sets the photographing conditions to predetermined basic photographing conditions. When the photographing conditions are set to the predetermined basic photographing conditions, the control device 50 photographs the road surface with the imaging device 21 at a predetermined aperture, shutter speed, ISO sensitivity, and output of the irradiation lamp 25 while moving the measurement vehicle 10 at a predetermined moving speed along a predetermined measurement route.

[0055] Furthermore, if there is a pre-existing road surface unevenness model for the measurement range, the imaging condition setting unit 63 sets different imaging conditions for areas where the change in road surface condition is greater than a predetermined standard compared to the pre-existing road surface unevenness model and areas where it is less than a predetermined standard. Areas where the change in road surface condition is greater than a predetermined standard can be determined based on one or more criteria, such as areas where the height of road surface unevenness has changed by more than a predetermined threshold, or areas where the area of ​​the area where the height of road surface unevenness has changed is more than a predetermined threshold. In this embodiment, the process of determining whether the change in road surface condition is greater than or less than the predetermined standard is performed by an information processing device.

[0056] For example, when the information processing device 100 detects an area where the change in road surface condition is greater than a predetermined standard, the photographing condition setting unit 63 causes the measurement vehicle 10 to return to that area, changes the photographing conditions, and photographs the road surface of that area. The photographing condition setting unit 63 changes the photographing conditions so that the number of photographs taken per unit area for an area where the change in road surface condition is greater than the predetermined standard is greater than the number of photographs taken per unit area for an area where the change in road surface condition is smaller than the predetermined standard.

[0057] For example, the photographing condition setting unit 63 may increase the number of photographs taken of an area where the change in road surface condition is greater than a predetermined standard by slowing down the travel speed of the measurement vehicle 10. The photographing condition setting unit 63 may also increase the number of photographs taken or vary the amount of light used during photographing by changing the travel route of the measurement vehicle 10 and the photographing direction of the area. Additionally, the photographing condition setting unit 63 may increase the definition of the generated image data by increasing the resolution or changing the shutter speed or aperture.

[0058] The self-position estimation unit 65 estimates the position of the measurement vehicle 10 on a predetermined three-dimensional coordinate system (hereinafter also referred to as "self-position"). When the self-position estimation unit 65 cannot acquire position information of the measurement vehicle 10 from the GNSS sensor 47, it estimates the self-position of the measurement vehicle 10 by a predetermined calculation. The self-position of the measurement vehicle 10 indicates the position of the measurement vehicle 10 on a predetermined three-dimensional coordinate system with the starting position of the measurement process as the origin, for example. The self-position of the measurement vehicle 10 may be the position of a reference point (x0, y0, z0) arbitrarily set within the measurement vehicle 10, or may be the position of the center of gravity of the measurement vehicle 10, for example, or may be the position of any one of the multiple imaging devices 21a to 21f.

[0059] The autonomous driving control unit 67 controls the autonomous driving of the measurement vehicle 10. Autonomous driving refers to a driving state in which a processor or the like controls the driving of the driving force source 15 and the steering device 17 based on detection information, causing the measurement vehicle 10 to move along a predetermined target route.

[0060] In this embodiment, the autonomous driving control unit 67 basically moves the measurement vehicle 10 along the measurement route based on the position information of the measurement vehicle 10 detected by the GNSS sensor 47 and information about the measurement route set on map data. As long as the GNSS sensor 47 is in an area where it can receive satellite signals, the autonomous driving control unit 67 can move the measurement vehicle 10 along the preset measurement route using the position information detected by the GNSS sensor 47.

[0061] However, in areas where satellite signals do not reach or are difficult to reach, such as inside tunnels, it is not possible to use the position information detected by the GNSS sensor 47. For this reason, in this embodiment, when road surface measurement is performed in such an area, the autonomous driving control unit 67 moves the measurement vehicle 10 using at least one of information on the relative position with respect to the measurement range and guide information present around the measurement range, and the self-position of the measurement vehicle 10 estimated by the self-position estimation unit 65.

[0062] The imaging device driving unit 69 controls the driving of the imaging device 21. The imaging device driving unit 69 controls the driving of the imaging device 21 in accordance with the shooting conditions (aperture (F value), shutter speed, and ISO sensitivity) set by the shooting condition setting unit 63.

[0063] The communication control unit 71 controls the transmission and reception of information or data to and from the information processing device 100 .

[0064] (1-2-3. Processing operation of the processing unit) Next, the processing operation by the processing unit 51 of the control device 50 will be described.

[0065] FIG. 5 is a flowchart showing the main routine of the processing operation of the processing unit 51. First, the autonomous driving control unit 67 acquires information on a measurement range for measuring the unevenness of the road surface (measurement range information) (step S11). The measurement range information may be set by an operator or may be input from the information processing device 100.

[0066] Next, the photographing condition setting unit 63 determines whether or not there is an existing road surface unevenness model for the measurement range (step S13). For example, the photographing condition setting unit 63 acquires information indicating whether or not there is a road surface unevenness model that has been previously generated for the same measurement range, as a result of communication between the communication control unit 71 and the information processing device 100.

[0067] If the photographing condition setting unit 63 determines that there is an existing road surface unevenness model (S13 / Yes), the processing unit 51 executes processing to generate a second or subsequent road surface unevenness model (road surface unevenness model generation processing) (step S15). On the other hand, if the photographing condition setting unit 63 does not determine that there is an existing road surface unevenness model (S13 / No), the processing unit 51 executes processing to generate a first road surface unevenness model (basic road surface unevenness model generation processing) (step S17).

[0068] (Basic road surface unevenness model generation process) FIG. 6 is a flowchart showing a routine for processing to generate a basic road surface unevenness model. In the basic road surface unevenness model generation process, the photographing condition setting unit 63 sets the photographing conditions to predetermined basic photographing conditions (step S31). The photographing condition setting unit 63 sets the measurement route, the moving speed of the measurement vehicle 10, the aperture (F-number), the shutter speed, the ISO sensitivity, and the output of the irradiation lamp 25 to predetermined conditions.

[0069] Next, the autonomous driving control unit 67 starts the autonomous driving of the measurement vehicle 10 (step S33). For example, as long as the GNSS sensor 47 is in an area where it can receive satellite signals, the autonomous driving control unit 67 moves the measurement vehicle 10 along a preset measurement route using position information detected by the GNSS sensor 47. On the other hand, if the autonomous driving control unit 67 cannot use the position information detected by the GNSS sensor 47, it moves the measurement vehicle 10 using at least one of information on the measurement range and its relative position with respect to guide information present in the vicinity of the measurement range, and the self-position of the measurement vehicle 10 estimated by the self-position estimation unit 65.

[0070] FIG. 7 is a flowchart showing the autonomous driving control process of the measurement vehicle 10 performed by the autonomous driving control unit 67. The autonomous driving control unit 67 acquires information about the measurement route along which the measurement vehicle 10 will travel within the measurement range (step S31). The information about the measurement route may be set in advance by the user, or may be set by the autonomous driving control unit 67 based on the specified measurement range. The measurement route includes information about at least the measurement start position and the measurement end position. For example, the self-position estimation unit 65 records the position coordinates of the reference position (x0, y0, z0) of the measurement vehicle 10 at the start of the measurement process as the start position. The recorded reference position does not need to be specified by specific latitude and longitude information, and may be recorded as an arbitrary point in arbitrary three-dimensional coordinates (for example, the origin of a three-dimensional coordinate system).

[0071] Next, the autonomous driving control unit 67 acquires information on the current position (x, y) and moving direction of the measurement vehicle 10 (step S33). The current position is calculated based on the position information detected by the GNSS sensor 47 or the odometry information estimated by the self-position estimation unit 65.

[0072] FIG. 8 is a flowchart showing an example of the self-position estimation process performed by the self-position estimation unit 65. The self-position estimation unit 65 acquires the distance from the measurement vehicle 10 to the guide information based on the detection information of the distance measurement sensor 31 (step S41). Next, the self-position estimation unit 65 acquires acceleration and angular velocity information obtained from the detection information of the acceleration sensor 43 and the angular velocity sensor 45 acquired in the current calculation cycle (step S43).

[0073] Next, the self-position estimation unit 65 calculates odometry information including the self-position of the measurement vehicle 10 based on information on the distance from the measurement vehicle 10 to the guide information, information on the acceleration and angular velocity of the measurement vehicle 10, and information on the position of the measurement vehicle 10 calculated in the previous calculation cycle (step S45). For example, the self-position estimation unit 65 calculates the position of the measurement vehicle 10 on a three-dimensional coordinate system with the start position recorded in step S31 as the origin.

[0074] Next, the self-position estimation unit 65 records data of the position (position coordinates of the self-position) P_n(x_n, y_n) of the measurement vehicle 10 calculated in the current calculation cycle (t_n) in the storage unit 53 (step S47). In this way, the self-position estimation unit 65 can estimate the position of the measurement vehicle 10 within the measurement range even when the position information detected by the GNSS sensor 47 cannot be used.

[0075] 7, the autonomous driving control unit 67 sets the drive amount of the driving force source 15 and the steering device 17 (step S35). For example, the autonomous driving control unit 67 sets the drive amount of the driving force source 15 so that the moving speed of the measurement vehicle 10 calculated based on the detection information of the wheel speed sensor 41 becomes the moving speed set by the imaging condition setting unit 63. In addition, the autonomous driving control unit 67 sets the angular velocity of the change in the steering angle (steering angular velocity) based on information on the current moving direction of the measurement vehicle 10 and information on the measurement route, and sets the drive amount of the steering device 17 according to the steering angular velocity.

[0076] Next, the autonomous driving control unit 67 controls the driving of the driving force source 15 and the steering device 17 in accordance with the driving amount set in step S35 (step S37). This allows the measurement vehicle 10 to autonomously drive along the measurement route.

[0077] 6, while the autonomous driving control unit 67 is causing the measurement vehicle 10 to drive autonomously, the imaging device driving unit 69 executes a road surface photographing process (step S25). In the basic road surface unevenness model generation process, the imaging device driving unit 69 controls the driving of the imaging device 21 under the set basic photographing conditions to photograph the road surface to be measured. The imaging device driving unit 69 stores the image data Img_α generated by the imaging device 21 in the memory unit 53 together with information on the number α (α=1, 2...M) of the imaging device 21.

[0078] Next, the communication control unit 71 transmits the saved image data Img_α to the information processing device 100 (step S27). While the autonomous driving and road surface photographing process are being executed, the autonomous driving control unit 67 determines whether or not traveling along the measurement route has been completed (step S29). For example, the autonomous driving control unit 67 determines that traveling along the measurement route has been completed when the measurement vehicle 10 reaches the measurement end position. If the autonomous driving control unit 67 does not determine that traveling along the measurement route has been completed (S29 / No), the process returns to step S25, and the processing unit 51 continues executing the autonomous driving and road surface photographing process. On the other hand, if the autonomous driving control unit 67 determines that traveling along the measurement route has been completed (S29 / Yes), it ends the basic road surface unevenness model generation process.

[0079] In the basic road surface unevenness model generation process, image data Img_α obtained by photographing the road surface in the measurement range is transmitted to the information processing device 100, and the information processing device 100 generates a basic road surface unevenness model of the measurement range.

[0080] (Road surface unevenness model generation processing) FIG. 9 is a flowchart showing a routine for the second and subsequent road surface unevenness model generation processes. In the road surface unevenness model generation process, similarly to the basic road surface unevenness model generation process, the photographing condition setting unit 63 sets the photographing conditions to predetermined basic photographing conditions (step S51), and the autonomous driving control unit 67 starts the autonomous driving of the measurement vehicle 10 (step S53). Next, the imaging device driving unit 69 controls the driving of the imaging device 21 to photograph the road surface (step S55). The imaging device driving unit 69 controls the driving of the imaging device 21 under the set basic photographing conditions to photograph the road surface to be measured. The imaging device driving unit 69 stores the image data Img_α generated by the imaging device 21 in the storage unit 53 together with information on the number α (α=1, 2...M) of the imaging device 21.

[0081] Next, the communication control unit 71 transmits the generated image data Img_α to the information processing device 100 (step S57). In the second and subsequent road surface unevenness model generation processes, the generated image data Img_α is transmitted to the information processing device 100 each time an image is captured by the imaging device 21 while the measurement vehicle 10 is traveling, or each time multiple images are captured. Having acquired the image data Img_α, the information processing device 100 combines the image data through feature point matching processing to determine the road surface condition, and returns information to the measurement vehicle 10 indicating whether there is an area where the change in the road surface condition is greater than a predetermined standard compared to an existing road surface unevenness model. The processing by the information processing device 100 will be described in detail later.

[0082] The autonomous driving control unit 67 determines, based on the information acquired from the information processing device 100, whether there is an area where the change in road surface condition is greater than a predetermined standard compared to the existing road surface unevenness model (step S59). If the autonomous driving control unit 67 does not determine that there is an area where the change in road surface condition is greater than a predetermined standard compared to the existing road surface unevenness model (S59 / No), it determines whether traveling along the measurement route has been completed (step S69). If the autonomous driving control unit 67 does not determine that traveling along the measurement route has been completed (S69 / No), the processing unit 51 returns to step S55 and continues executing the autonomous driving and road surface photographing process. On the other hand, if the autonomous driving control unit 67 determines that traveling along the measurement route has been completed (S69 / Yes), it ends the road surface unevenness model generation process.

[0083] On the other hand, if the autonomous driving control unit 67 determines that there is an area where the change in road surface condition is greater than a predetermined standard compared to the existing road surface unevenness model (S59 / Yes), it causes the measurement vehicle 10 to back up to that area (step S61). For example, the autonomous driving control unit 67 controls the torque of the driving force source 15 and the steering angle of the wheels 13 by going back to the previous or previous calculation cycle, and causes the measurement vehicle 10 to back up to a position where it can photograph that area.

[0084] At this time, the steering angle of the wheels 13 is adjusted to the stored value by going back in time to the calculation cycle before the previous one. Also, the drive amount of the driving force source 15 is adjusted to a negative value of the stored value by going back in time to the calculation cycle before the previous one. In this way, the autonomous driving control unit 67 can move the measurement vehicle 10 backward along the trajectory it has traveled.

[0085] Next, the processing unit 51 changes the photographing conditions from the basic photographing conditions and photographs the road surface of the area (step S63). The photographing condition setting unit 63 changes the photographing conditions so that the number of photographs per unit area for an area where the change in road surface condition is greater than a predetermined standard is greater than the number of photographs per unit area for an area where the change in road surface condition is smaller than the predetermined standard.

[0086] For example, the photographing condition setting unit 63 may increase the number of photographs taken of an area where the change in road surface condition is greater than a predetermined standard by slowing down the travel speed of the measurement vehicle 10. The photographing condition setting unit 63 may also increase the number of photographs taken or vary the amount of light used during photographing by changing the travel route of the measurement vehicle 10 and the photographing direction of the area. Additionally, the photographing condition setting unit 63 may increase the definition of the generated image data by increasing the resolution or changing the shutter speed or aperture.

[0087] Next, the communication control unit 71 transmits image data Img_α of an area photographed under changed photographing conditions, in which the change in road surface condition is greater than a predetermined standard, to the information processing device 100 (step S65). After that, the processing unit 51 returns the photographing conditions to the basic photographing conditions, and resumes autonomous traveling along the measurement route (step S67).

[0088] Thereafter, the process returns to step S55, and the processes of the above-mentioned steps are repeated. Then, the autonomous driving control unit 67 determines that measurement has ended when the measurement vehicle 10 reaches the measurement end position (S69 / Yes), and ends the road surface unevenness model generation process. Alternatively, the autonomous driving control unit 67 may determine that measurement has ended when an emergency stop operation is performed by the user or the like.

[0089] As described above, in the second and subsequent road surface unevenness model generation processes, the control device 50 photographs the road surface while driving the measurement vehicle 10 along the measurement route and transmits the generated image data to the information processing device. Furthermore, if the control device 50 detects an area where the change in road surface condition is greater than a predetermined standard, the control device 50 backs the measurement vehicle 10 to that area, changes the photographing conditions, and photographs the road surface again. At this time, the control device 50 changes the photographing conditions so that the number of photographs taken per unit area for the area where the change in road surface condition is greater than the predetermined standard is greater than the number of photographs taken per unit area for the area where the change in road surface condition is smaller than the predetermined standard. This improves the accuracy of the road surface unevenness analysis performed by the information processing device 100. Furthermore, by changing the photographing conditions so as to increase the accuracy of the generated image data, the accuracy of the road surface unevenness analysis performed by the information processing device 100 can also be improved.

[0090] <1-3. Information processing devices> Next, the information processing device 100 used in the road surface measurement system 1 according to this embodiment will be described in detail.

[0091] (1-3-1. Configuration of information processing device) 4, the information processing device 100 includes a communication unit 101, a data processing unit 103, and a storage unit 105. The communication unit 101 is an interface for the data processing unit 103 to communicate with the control device 50. The communication unit 101 may be an interface for communicating with the control device 50 via mobile communication, for example, but the communication method with the control device 50 is not particularly limited.

[0092] The data processing unit 103 is configured with one or more processors such as a CPU or a GPU, and various peripheral components. Part or all of the processing unit 51 may be configured with updatable components such as firmware, or may be a program module executed by instructions from the CPU, etc.

[0093] The data processing unit 103 functions as a device that realizes the functions described below by one or more processors executing a computer program. The computer program is a computer program that causes the processor to execute the operations described below that should be performed by the data processing unit 103. The computer program executed by the processor may be recorded on a recording medium that functions as a storage unit (memory) 105 provided in the information processing device 100, or may be recorded on a recording medium built into the data processing unit 103 or any recording medium that can be externally attached to the information processing device 100.

[0094] Recording media for recording computer programs may include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs, DVDs, and Blu-ray (registered trademark), magneto-optical media such as floptical disks, memory elements such as RAMs and ROMs, flash memories such as USB memories and SSDs, and other media capable of storing programs.

[0095] The storage unit 105 is configured by one or more storage elements such as RAM or ROM, or storage media such as HDD or SSD, communicably connected to the data processing unit 103. However, the type and number of storage units 105 are not particularly limited. The storage unit 105 stores information such as computer programs executed by the data processing unit 103, various parameters used in arithmetic processing, detection data, and arithmetic results. A part of the storage unit 105 is used as a work area.

[0096] The data processing unit 103 receives the image data Img_α, extracts feature points from the received image data Img_α (α=1, 2...M), matches the feature points using SfM processing, and aligns the image data Img_α (α=1, 2...M). The data processing unit 103 also generates a three-dimensional road surface unevenness model that reflects data indicating the unevenness of the road surface that appears as feature points in the aligned image data Img_α (α=1, 2...M).

[0097] FIG. 10 is a flowchart showing the main routine of the processing operation of the information processing device 100. First, the data processing unit 103 acquires information on the measurement range for measuring the road surface unevenness (measurement range information) (step S71). For example, the measurement range information is acquired from the measurement vehicle 10. Next, the data processing unit 103 determines whether or not there is an existing road surface unevenness model for the measurement range (step S73). For example, the data processing unit 103 determines whether or not there is a road surface unevenness model that was previously generated for the same measurement range among the road surface unevenness model data stored in the storage unit 105.

[0098] If the data processing unit 103 determines that there is an existing road surface unevenness model (S73 / Yes), it executes a process to generate a second or subsequent road surface unevenness model (road surface unevenness model generation process) (step S75).On the other hand, if the data processing unit 103 does not determine that there is an existing road surface unevenness model (S73 / No), it executes a process to generate a first road surface unevenness model (basic road surface unevenness model generation process) (step S77).

[0099] (Basic road surface unevenness model generation process) 11 shows a flowchart of the basic road surface unevenness model generation process. Note that the flowchart shown below shows an example in which the control device 50 sequentially transmits image data Img_α captured by the imaging device 21 to the information processing device 100 at predetermined calculation intervals.

[0100] The data processing unit 103 acquires image data Img_α_n (α=1, 2...M) transmitted from the control device 50 (step S81). The measurement vehicle 10 used in the road surface measurement system 1 of this embodiment is equipped with six imaging devices 21a to 21f, and the data processing unit 103 acquires image data Img_α_n (α=1, 2...6) generated by each of the imaging devices 21a to 21f in the current calculation cycle (t_n). The data processing unit 103 stores the acquired image data Img_α_n (α=1, 2...6) in the memory unit 105.

[0101] Next, the data processing unit 103 extracts feature points from the acquired image data Img_α_n (α=1, 2...M) (step S83). A feature point may be a point where the amount of change in luminance (feature amount) between adjacent pixels in the image data Img_α_n is equal to or greater than a predetermined threshold, but other feature amounts may also be used. A method for extracting feature points from image data may be a conventionally known method, and therefore a detailed description thereof will be omitted.

[0102] Next, the data processing unit 103 executes a process of matching the extracted feature points between image data Img_α (α=1, 2...M) (step S85). This matching process includes not only matching of feature points between image data Img_α_n (α=1, 2...M) acquired in the current calculation cycle (t_n), but also matching of feature points between image data Img_α (α=1, 2...M) acquired in calculation cycles previous to the previous one and stored in the storage unit 105. The process of matching feature points between image data may be executed by a conventionally known method, and therefore a detailed description thereof will be omitted.

[0103] Next, the data processing unit 103 uses the image data Img_α (α=1, 2...M) aligned by the feature point matching process to construct three-dimensional point cloud data restored according to the principle of triangulation, and generates a road surface unevenness model (step S87). In this embodiment, the data processing unit 103 constructs three-dimensional point cloud data by MVS (Multi-View Stereo) processing, and generates a road surface unevenness model. However, the method for generating a road surface unevenness model made up of three-dimensional point cloud data is not particularly limited.

[0104] Next, the data processing unit 103 determines whether or not to end the model generation process (step S89). For example, the data processing unit 103 may determine to end the model generation process when a predetermined time has elapsed since the control device 50 stopped transmitting image data Img_α. Alternatively, the data processing unit 103 may determine to end the model generation process when the control device 50 transmits a signal indicating the end of measurement.

[0105] If the data processing unit 103 does not determine that the model generation process should be terminated (S89 / No), the data processing unit 103 returns to step S81 and continues the model generation process. On the other hand, if the data processing unit 103 determines that the model generation process should be terminated (S89 / Yes), the data processing unit 103 terminates the model generation process.

[0106] (Road surface unevenness model generation processing) FIG. 12 shows a flowchart of the road surface unevenness model generation process. Similar to the basic road surface unevenness model generation process, the data processing unit 103 acquires image data Img_α_n (α=1, 2···M) transmitted from the control device 50, extracts feature points from the acquired image data Img_α_n (α=1, 2···M), performs a process of matching the extracted feature points between image data Img_α (α=1, 2···M), constructs three-dimensional point cloud data, and generates a road surface unevenness model (steps S71 to S77).

[0107] Next, the data processing unit 103 compares the generated road surface unevenness model with the basic road surface unevenness model stored in the storage unit 105, and determines whether there is an area where the change in road surface condition is greater than a predetermined standard compared to the existing road surface unevenness model (step S91). For example, the data processing unit 103 makes the determination based on one or more standards, such as an area where the height of the road surface unevenness has changed by more than a predetermined threshold, or an area where the area of ​​the area where the height of the road surface unevenness has changed is more than a predetermined threshold.

[0108] When the data processing unit 103 determines that there is an area where the change in road surface condition is greater than a predetermined standard compared to an existing road surface unevenness model (S91 / Yes), it transmits information about the area to the measurement vehicle 10 (step S93). For example, the data processing unit 103 may transmit to the measurement vehicle 10 position information of the area where the change in road surface condition is greater than a predetermined standard compared to an existing road surface unevenness model, or may transmit to the measurement vehicle 10 information on image data Img_α_n (α=1, 2...M) that includes the area in its shooting range. After transmitting the information about the area to the measurement vehicle 10, the data processing unit 103 returns to step S81 and repeats the processing of each step described above.

[0109] On the other hand, if the data processing unit 103 does not determine that there is an area where the change in road surface condition is greater than a predetermined standard compared to the existing road surface unevenness model (S91 / No), it determines whether the road surface unevenness model generation process has been completed (step S89), as with the basic road surface unevenness model generation process. If the data processing unit 103 does not determine that the model generation process should be terminated (S89 / No), it returns to step S81 and continues the model generation process. On the other hand, if the data processing unit 103 determines that the model generation process should be terminated (S89 / Yes), it terminates the model generation process.

[0110] As described above, in the second and subsequent road surface unevenness model generation processes, the information processing device 100 generates a road surface unevenness model by synthesizing image data acquired from the measurement vehicle 10, and if an area where the change in road surface condition is greater than a predetermined standard is detected, it transmits information about that area to the measurement vehicle 10. This allows the measurement vehicle 10 to back up to that area, change the shooting conditions, and photograph the road surface in that area. Therefore, it is possible to analyze the unevenness state in areas where the change in road surface condition is greater by using more image data or higher-resolution image data.

[0111] <1-4. Effects> As described above, the road surface measurement system 1 according to this embodiment is configured such that, when a pre-existing road surface unevenness model is available for the measurement range, different imaging conditions are set for areas where the change in road surface condition is greater than a predetermined standard compared to the pre-existing road surface unevenness model and areas where it is less than the predetermined standard, and images are captured for each area. In particular, in this embodiment, the number of images captured per unit area for areas where the change in road surface condition is greater than the predetermined standard compared to the pre-existing road surface unevenness model is greater than the number of images captured per unit area for areas where the change in road surface condition is less than the predetermined standard. This allows for detailed analysis only of areas where the change in road surface condition is greater than the predetermined standard compared to the pre-existing road surface unevenness model, thereby improving the efficiency of subsequent measurements.

[0112] Furthermore, in the road surface measurement system 1 according to this embodiment, the existing road surface unevenness model is generated by combining multiple pieces of image data captured under predetermined basic shooting conditions, and when the control device 50 performs a second measurement within the measurement range, it combines the image data captured under the predetermined basic shooting conditions while moving the measurement vehicle 10, calculates the road surface condition, and when it detects an area where the change in road surface condition is greater than a predetermined standard compared to the existing road surface unevenness model, it returns the measurement vehicle 10 to that area, changes the shooting conditions, and photographs the road surface of that area. As a result, if there is no area where the change in road surface condition is greater than the predetermined standard compared to the existing road surface unevenness model, it photographs the entire road surface within the measurement range without backing up the measurement vehicle 10, and a road surface unevenness model is formed, eliminating unnecessary processing.

[0113] The road surface measurement system 1 according to this embodiment is also equipped with an inertial measurement unit, and while moving the measurement vehicle 10, estimates its own position, which is the movement history of the measurement vehicle 10, based on the measurement results of the inertial measurement unit, and returns the measurement vehicle 10 to an area where the change in road surface conditions is greater than a predetermined standard based on the information on the estimated result of its own position. This makes it easy to continue photographing the road surface by backing up the measurement vehicle 10 to a desired position and changing the photographing conditions, thereby improving efficiency.

[0114] <1-5. Variations> So far, the road surface measurement system 1 according to the first embodiment has been described. The road surface measurement system 1 described above can be modified in various ways.

[0115] For example, in the above embodiment, the road surface is photographed while the measurement vehicle 10 is traveling, a road surface unevenness model is generated from the generated image data, and the presence or absence of an area where the change in road surface condition is greater than a predetermined standard is determined by comparing with an existing road surface unevenness model, but the method of determining the presence or absence of an area where the change in road surface condition is greater than a predetermined standard by comparing with an existing road surface unevenness model is not limited to the above example. The measurement vehicle 10 may be equipped with a sensor that detects the attitude of the measurement vehicle 10, and the control device 50 may determine the presence or absence of an area where the change in road surface condition is greater than a predetermined standard based on the detected attitude of the measurement vehicle 10.

[0116] For example, the control device 50 may estimate the road surface unevenness state from changes in the attitude of the bogie 3 of the measurement vehicle 10 detected by one or more sensors selected from an inclination sensor, an acceleration sensor, and an angular velocity sensor provided on the measurement vehicle 10, and compare the estimated unevenness state with the unevenness state associated with the traveling position where the road surface unevenness was detected in a pre-existing road surface unevenness model. This makes it possible to determine whether or not there is an area where the change in the road surface state is greater than a predetermined standard by comparing with the pre-existing road surface unevenness model without generating a road surface unevenness model until the entire measurement range has been photographed. In particular, the method of determination based on changes in the attitude of the bogie 3 is effective when there is a possibility that the road surface has changed significantly, such as when a long time has passed since the previous road surface unevenness model was generated for the same measurement range.

[0117] <<2. Second Embodiment>> The road surface measurement system according to the second embodiment of the present disclosure differs from the road surface unevenness model generation process of the road surface measurement system according to the first embodiment in the processing performed when performing measurements for the third or subsequent times in the same measurement range.

[0118] 13 is a flowchart showing a main routine of the processing operation by the processing unit 51 of the control device 50 of the road surface measurement system according to the second embodiment. In Fig. 13, steps showing the same processes as those in the flowchart shown in Fig. 5 are indicated by the same reference numerals.

[0119] As in the first embodiment, the autonomous driving control unit 67 acquires measurement range information (step S11), and then the photographing condition setting unit 63 determines whether or not a pre-existing road surface unevenness model exists for the measurement range (step S13). If the photographing condition setting unit 63 does not determine that a pre-existing road surface unevenness model exists (S13 / No), the processing unit 51 executes processing to generate an initial road surface unevenness model (basic road surface unevenness model generation processing) as in the first embodiment (step S17).

[0120] On the other hand, in this embodiment, when it is determined that there is an existing road surface unevenness model (S13 / Yes), the photographing condition setting unit 63 determines whether or not it is the second measurement (step S14). Whether or not it is the second measurement can be determined based on, for example, the version or update count information of the road surface unevenness model generated and stored in the information processing device 100. However, the method of determining whether or not it is the second measurement is not particularly limited.

[0121] If the photographing condition setting unit 63 determines that this is the second measurement (S14 / Yes), it executes the road surface unevenness model generation process (second time) as in the first embodiment (step S15). This second road surface unevenness model generation process is executed in accordance with the road surface unevenness model generation process whose flowchart is shown in Fig. 9. On the other hand, if the photographing condition setting unit 63 does not determine that this is the second measurement (S14 / No), that is, if this is the third or subsequent measurement, the processing unit 51 executes the process of generating a road surface unevenness model for the third or subsequent measurement (step S16).

[0122] (Road surface unevenness model generation processing) 14 and 15 are flowcharts showing the routine of the road surface unevenness model generation process from the third time onward. In Fig. 14 and Fig. 15, steps showing the same processes as those in the flowchart shown in Fig. 9 are designated by the same reference numerals.

[0123] In the third and subsequent unevenness model generation processes, similar to the second road surface unevenness model generation process, the photographing condition setting unit 63 sets the photographing conditions to predetermined basic photographing conditions (step S51), and then acquires information on an area (specific area) where the change in road surface condition during the previous or previous measurement was greater than a predetermined standard (step S52). For example, the photographing condition setting unit 63 acquires position information (x, y) of the specific area from the information processing device 100.

[0124] Next, the autonomous driving control unit 67 starts the autonomous driving of the measurement vehicle 10 (step S53). Next, the imaging device driving unit 69 controls the driving of the imaging device 21 to capture an image of the road surface (step S55). Furthermore, the communication control unit 71 transmits the generated image data Img_α to the information processing device 100 (step S57).

[0125] While the measurement vehicle 10 is traveling and repeatedly photographing the road surface and transmitting the image data, the photographing condition setting unit 63 determines whether the measurement vehicle 10 has approached a specific area (step S58). For example, the photographing condition setting unit 63 determines that the measurement vehicle 10 has approached a specific area when the distance from the current position of the measurement vehicle 10 to the specific area becomes less than a predetermined threshold.

[0126] If the photographing condition setting unit 63 does not determine that the measurement vehicle 10 has approached the specific area (S58 / No), the autonomous driving control unit 67 determines whether there is an area where the change in road surface condition is greater than a predetermined standard compared to the existing road surface unevenness model, based on the information acquired from the information processing device 100, as in the second road surface unevenness model generation process (step S59). If the autonomous driving control unit 67 does not determine that there is an area where the change in road surface condition is greater than a predetermined standard compared to the existing road surface unevenness model (S59 / No), it determines whether traveling along the measurement route has been completed (step S69). If the autonomous driving control unit 67 does not determine that traveling along the measurement route has been completed (S69 / No), the processing unit 51 returns to step S55 and continues executing the autonomous traveling and road surface photographing process. On the other hand, if the autonomous driving control unit 67 determines that traveling along the measurement route has been completed (S69 / Yes), it ends the road surface unevenness model generation process.

[0127] On the other hand, if the autonomous driving control unit 67 determines that there is an area where the change in road surface condition is greater than the predetermined standard compared to the existing road surface unevenness model (S59 / Yes), it causes the measurement vehicle 10 to back up to that area (step S61). Next, the processing unit 51 changes the shooting conditions from the basic shooting conditions and captures an image of the road surface in that area (step S63). Next, the communication control unit 71 transmits image data Img_α of the area where the change in road surface condition is greater than the predetermined standard, which was captured under the changed shooting conditions, to the information processing device 100 (step S65). Thereafter, the processing unit 51 returns the shooting conditions to the basic shooting conditions and resumes autonomous driving along the measurement route (step S67).

[0128] On the other hand, in the above-mentioned step S58, if the photographing condition setting unit 63 determines that the measurement vehicle 10 has approached the specific area (S58 / Yes), the processing unit 51 changes the photographing conditions from the basic photographing conditions and photographs the road surface of the specific area (step S63). For example, the photographing condition setting unit 63 changes the photographing conditions so that the number of photographs taken per unit area for an area where the change in road surface condition is smaller than the predetermined standard is greater than the number of photographs taken per unit area for an area where the change in road surface condition is smaller than the predetermined standard, similar to the change method used when it is determined that there is an area where the change in road surface condition is greater than the predetermined standard compared to an existing road surface unevenness model.

[0129] Next, the communication control unit 71 transmits image data Img_α of an area photographed under changed photographing conditions, in which the change in road surface condition is greater than a predetermined standard, to the information processing device 100 (step S65). After that, the processing unit 51 returns the photographing conditions to the basic photographing conditions, and resumes autonomous traveling along the measurement route (step S67).

[0130] Thereafter, the process returns to step S55, and the processes of the above-mentioned steps are repeated. Then, the autonomous driving control unit 67 determines that measurement has ended when the measurement vehicle 10 reaches the measurement end position (S69 / Yes), and ends the road surface unevenness model generation process. Alternatively, the autonomous driving control unit 67 may determine that measurement has ended when an emergency stop operation is performed by the user or the like.

[0131] As described above, in this embodiment, in the third and subsequent road surface unevenness model generation processes, the control device 50 acquires in advance information on specific areas where the change in road surface condition is greater than a predetermined standard compared to the existing road surface unevenness model detected up to the previous time, and changes the imaging conditions to capture images of the road surface of the specific area as the measurement vehicle 10 passes through the specific area. At that time, the control device 50 changes the imaging conditions so that the number of images captured per unit area for areas where the change in road surface condition is greater than the predetermined standard is greater than the number of images captured per unit area for areas where the change in road surface condition is smaller than the predetermined standard. This makes it possible to acquire image data that allows for detailed analysis of specific areas where changes in road surface condition are observed, without having to back up the measurement vehicle 10.

[0132] Although the preferred embodiments of the present disclosure have been described in detail above with reference to the accompanying drawings, the present disclosure is not limited to such examples. It is clear that a person skilled in the art to which the present disclosure pertains can conceive of various modifications or alterations within the scope of the technical ideas set forth in the claims, and it is understood that these also naturally fall within the technical scope of the present disclosure.

[0133] For example, in the above embodiment, the moving speed of the measurement vehicle, the measurement route, the F-number, the shutter speed, and the ISO sensitivity are set as the photographing conditions, but the photographing conditions are not limited to the above examples.

[0134] Furthermore, although the road surface measurement system of the above embodiment is configured by a measurement vehicle that mainly acquires image data and an information processing device that mainly executes data processing, both of which are connected to be able to communicate with each other, the technology of the present disclosure is not limited to the above example. Some of the functions of the data processing unit of the information processing device may be provided in the measurement vehicle. Alternatively, the information processing device may be mounted on the measurement vehicle, or all of the functions of the data processing unit of the information processing device may be provided in a control device of the measurement vehicle, and the road surface measurement system may be configured only by the measurement vehicle.

[0135] In addition, the technology of the present disclosure can also be realized as a measurement vehicle equipped with the control device described in the above embodiments, a method for updating a road surface unevenness model using a road surface measurement system, a road surface measurement processing method using a control device, a computer program that causes a computer to function as at least one of the above control device and information processing device, and a non-temporary tangible recording medium on which the computer program is recorded. [Explanation of symbols]

[0136] 1: Road surface measurement system 10: Measurement vehicle 21: Imaging device 50: Control device 51: Processing section 53: Storage section 55: Map data storage unit 57: Communications Department 61: Acquisition part 63: Shooting condition setting section 65: Self-position estimation part 67: Autonomous driving control unit 69: Imaging device drive unit 71: Communication control unit 100: Information processing device 101: Communications Department 103: Data processing section 105: Storage section

Claims

1. A road surface measurement system generates a road surface unevenness model by combining multiple image data obtained by capturing images of the road surface using a feature point matching process while a measurement vehicle equipped with an image capturing device is driven autonomously, one or more control devices for controlling the driving of the measurement vehicle and the imaging device; The one or more control devices If there is a pre-existing road surface unevenness model for the measurement range, and setting different photographing conditions for an area where the change in road surface condition is greater than a predetermined standard and an area where the change is smaller than a predetermined standard, in comparison with the existing road surface unevenness model, and photographing each area. Road surface measurement system.

2. the existing road surface unevenness model is generated by combining the plurality of image data captured under predetermined basic photographing conditions, The one or more control devices When a second measurement is performed within the measurement range, the image data captured under the predetermined basic photographing conditions is combined while the measurement vehicle is moving, and a calculation of the road surface condition is performed; When an area is detected in which the change in the road surface condition is greater than the predetermined standard in comparison with the existing road surface unevenness model, the measurement vehicle is returned to the area, and the photographing conditions are changed to photograph the road surface in the area. The road surface measurement system according to claim 1 .

3. the road surface measurement system further includes a sensor that detects the attitude of the measurement vehicle; The one or more control devices When an area in which the change in the road surface condition is greater than the predetermined standard is detected based on the detected attitude of the measurement vehicle, the measurement vehicle is returned to the area, and the photographing conditions are changed to photograph the road surface in the area. The road surface measurement system according to claim 2 .

4. The road surface measurement system further includes an inertial measurement unit; The one or more control devices While moving the measurement vehicle, Estimating a self-position, which is a movement history of the measurement vehicle, based on the measurement results of the inertial measurement device; returning the measurement vehicle to an area where the change in the road surface condition is greater than the predetermined standard based on information on the result of the estimation of the vehicle's own position; The road surface measurement system according to claim 2 or 3.

5. the existing road surface unevenness model is generated by combining the plurality of image data captured under the predetermined basic photographing conditions, The one or more control devices When performing a third or subsequent measurement in the measurement range, the road surface is photographed under changed photographing conditions in an area where the change in the road surface condition is greater than the predetermined standard compared with the existing road surface unevenness model detected up to the previous time. The road surface measurement system according to claim 1 .

6. The one or more control devices The number of images taken per unit area in a region where the change in the road surface condition is greater than the predetermined standard compared to the existing road surface unevenness model is made larger than the number of images taken per unit area in a region where the change in the road surface condition is smaller than the predetermined standard. The road surface measurement system according to claim 1 .

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