JP2019082377A - Road surface state detection device, and road surface state detection system - Google Patents

Abstract

To improve the detection accuracy of a road surface state.SOLUTION: A road surface state detection device 10 is a device for detecting the state of an object road surface as a detection object. The detection device includes: an acquisition unit 13 for acquiring the distance information indicating the distance between each measurement point on the object road surface and a distance sensor 2; a detection unit 14 for detecting the shape of the object road surface on the basis of the distance information; a determination unit 17 for determining whether, when the object road surface includes a region having a recessed shape or a projecting shape, the region is in an abnormal state or not; and an output unit 18 for outputting a determination result by the determination unit 17. The determination unit 17 determines whether the region is in an abnormal state or not on the basis of the shape in a normal state of the region.SELECTED DRAWING: Figure 1

Description

  The present disclosure relates to a road surface condition detection device and a road surface condition detection system.

  Devices for measuring the shape of the road surface are known. For example, in Patent Document 1, each point on the road surface is irradiated with light such as laser light, and the distance between the point on the road surface and the device is measured to detect the uneven shape of the road surface. Is disclosed.

Unexamined-Japanese-Patent No. 2004-294152

  When the shape of the road surface detected by such a device is not flat, it may be determined that the state of the road surface is abnormal. However, in the road surface property measuring device described in Patent Document 1, for example, even a road surface on which a groove is originally formed is determined to have a concave shape. Thus, even if the road surface is originally normal, it may be determined as abnormal.

  The present disclosure describes a road surface state detection device and a road surface state detection system capable of improving the detection accuracy of the road surface state.

  A road surface state detecting device according to one aspect of the present disclosure is a device that detects a state of a target road surface that is a detection target. The road surface state detecting device comprises: a first acquisition unit for acquiring distance information indicating the distance between each measurement point on the target road surface and the measuring device; a detection unit for detecting the shape of the target road surface based on the distance information; When the road surface includes a concave or convex area, a determination unit that determines whether the area is in an abnormal state and an output unit that outputs a determination result by the determination unit are provided. The determination unit determines whether the area is in an abnormal state based on the shape of the area in the normal state.

  According to the present disclosure, the detection accuracy of the road surface state can be improved.

FIG. 1 is a functional block diagram of a road surface condition detection system according to an embodiment. FIG. 2 is a diagram for explaining measurement by the line scan sensor. FIG. 3 is a hardware configuration diagram of the road surface state detecting device of FIG. (A), (b) of FIG. 4 is a figure which shows the height of the object road surface in a measurement point. FIG. 5 is a diagram showing an example of traveling by a vehicle equipped with the road surface state detection system of FIG. FIG. 6 is a flowchart showing an example of a road surface state detection method performed by the road surface state detection device.

[1] Overview of Embodiment A road surface state detecting device according to one aspect of the present disclosure is a device that detects a state of a target road surface to be detected. The road surface state detecting device comprises: a first acquisition unit for acquiring distance information indicating the distance between each measurement point on the target road surface and the measuring device; a detection unit for detecting the shape of the target road surface based on the distance information; When the road surface includes a concave or convex area, a determination unit that determines whether the area is in an abnormal state and an output unit that outputs a determination result by the determination unit are provided. The determination unit determines whether the area is in an abnormal state based on the shape of the area in the normal state.

  In the road surface state detecting device, the shape of the target road surface is detected based on distance information indicating the distance between each measurement point on the target road surface and the measuring device. When the target road surface includes a concave or convex area, whether or not the area is abnormal is determined based on the shape of the area in the normal state, and the determination result is output. Therefore, for example, when the area in the normal state has a concave shape or a convex shape, the possibility that the area is determined to be abnormal can be reduced. As a result, it is possible to improve the detection accuracy of the road surface state.

  The road surface state detection device may further include a second acquisition unit that acquires a captured image obtained by capturing an area. The determination unit may determine whether the area is in an abnormal state by comparing a reference image indicating a normal state of the area with the captured image. In this case, when the target road surface includes a concave or convex area, it is determined whether the area is abnormal or not by comparing the reference image indicating the normal state of the area with the captured image. Ru. Therefore, for example, when the area in the normal state has a concave shape or a convex shape, the possibility that the area is determined to be abnormal can be reduced. As a result, it is possible to improve the detection accuracy of the road surface state.

  The determination unit identifies the type of the area based on the distance information, and determines that the area is in the normal state if the identified type is an exclusion type that is a predetermined type of exclusion target. It is also good. If the cause of the area being concave or convex is, for example, a type (such as a puddle) that does not affect road surface travel, the area may be regarded as a normal state. As described above, by predetermining the type that does not affect road surface travel as the excluded type, it is possible to suppress overdetection of an abnormal state. As a result, it is possible to improve the detection accuracy of the road surface state.

  The output unit may output position information of the area when it is determined that the area is in an abnormal state. In this case, the position of the abnormal state area can be identified. This makes it possible to efficiently perform maintenance work such as road surface repair and removal of obstacles (falling objects).

  The target road surface may be a runway. On the runway, from the viewpoint of safety, removal of obstacles and repair of road surface damage are desired. Therefore, by improving the detection accuracy of the road surface state, it is possible to improve the safety of the flying object such as an aircraft.

  A road surface condition detection system according to another aspect of the present invention includes the above-described road surface condition detection device and a measurement device that measures a distance. Since the road surface state detection system includes the above-described road surface state detection device, it is possible to improve the detection accuracy of the road surface state.

  The measuring device may be provided in the vehicle. In this case, the distance between the target road surface and the measuring device is measured while the vehicle travels on the target road surface. For this reason, generation of distance information can be facilitated.

[2] Example of Embodiment Hereinafter, an embodiment of the present disclosure will be described with reference to the drawings. In the description of the drawings, the same elements will be denoted by the same reference symbols, without redundant description.

  FIG. 1 is a functional block diagram of a road surface condition detection system according to an embodiment. The road surface state detection system 1 shown in FIG. 1 is a system that detects the state of an object road surface that is a road surface to be detected. The target road surface may be, for example, an airport runway. As the state of the target road surface, the presence or absence of road surface damage and the presence or absence of an obstacle (falling object) are detected. The road surface condition detection system 1 is mounted on, for example, a vehicle. The vehicle on which the road surface state detection system 1 is mounted may be either a manned vehicle or an unmanned vehicle (automatic traveling vehicle), or may be an existing vehicle such as a patrol car and an airport sweeper. The road surface condition detection system 1 is configured to be retrofitted to a vehicle. The road surface condition detection system 1 includes a distance sensor 2, a camera 3, a GPS (Global Positioning System) device 4, a communication device 5, and a road surface condition detection device 10.

  The distance sensor 2 is a measuring device that measures the distance between each of the measurement points on the target road surface and the distance sensor 2. The measurement points are provided at regular intervals with respect to the traveling direction of the road surface. The distance sensor 2 is provided, for example, at the bottom of the body of the vehicle. For example, a millimeter wave radar and a line scan sensor are used as the distance sensor 2. In the present embodiment, a laser type line scan sensor is used as the distance sensor 2.

  FIG. 2 is a diagram for explaining measurement by the line scan sensor. As shown in FIG. 2, the line scan sensor measures the distance between the line scan sensor and the target road surface R by, for example, a light cutting method. Specifically, at each measurement point, the line scan sensor scans the laser light along a scan line extending in a direction (here, a direction perpendicular to the traveling direction of the vehicle). The length of the scan line is, for example, about 1 m. By using a plurality of line scan sensors, a scan line about the width of the vehicle can be obtained. The scanning cycle of the line scan sensor is set so as to obtain the desired resolution for the desired traveling speed of the vehicle. The scanning cycle is set to, for example, about 5 kHz. The interval between measurement points is determined by the traveling speed and the scanning cycle. The line scan sensor emits laser light toward the target road surface R, and receives reflected light reflected by the target road surface R. Based on triangulation, the distance between the line scan sensor and the target road surface R is measured.

  In the example of FIG. 2, the bolt screw B falls on the target road surface R. At each measurement point, the scan line extends in a direction intersecting the extending direction of the bolt screw B. In this case, the distance measured by the distance sensor 2 is the distance between the distance sensor 2 and the target road surface R in the portion where the bolt screw B does not exist in the target road surface R. In the portion where the bolt screw B exists, the distance measured by the distance sensor 2 is the distance between the distance sensor 2 and the surface of the bolt screw B. The distance sensor 2 transmits information indicating the measured distance to the road surface state detecting device 10.

  The camera 3 is an imaging device that generates a captured image by imaging each measurement point on the target road surface. The camera 3 is provided, for example, at the bottom of the body of the vehicle. As the camera 3, for example, a digital camera is used. The captured image may be a still image or a moving image. The camera 3 images the target road surface in synchronization with the distance sensor 2. The imaging cycle of the camera 3 may be the same as the scanning cycle of the distance sensor 2 or may be longer than the scanning cycle of the distance sensor 2. The imaging cycle of the camera 3 may be an integral multiple of the scanning cycle of the distance sensor 2. That is, the measurement points of the camera 3 may be the same as the measurement points of the distance sensor 2 or may be measurement points every predetermined one of the measurement points of the distance sensor 2. The camera 3 transmits the captured image to the road surface state detecting device 10.

  The GPS device 4 is a device that detects the position of a vehicle on which the road surface condition detection system 1 is mounted. In the present embodiment, a GPS / IMU (Global Positioning System / Inertial Measurement Unit) is used as the GPS device 4. The GPS device 4 generates position information indicating the position (latitude, longitude, and altitude) of the vehicle. The GPS device 4 generates position information in synchronization with the distance sensor 2 and the camera 3. That is, the GPS device 4 generates position information indicating the position of each measurement point on the target road surface. The GPS device 4 transmits position information to the road surface condition detecting device 10. The GPS device 4 may generate attitude information indicating the direction of the vehicle.

  The communication device 5 is a device for communicating with an external device. For example, a wireless communication device is used as the communication device 5. The communication device 5 communicates with, for example, a control center (control tower) (not shown).

  The road surface state detection device 10 is a device that detects the state of the target road surface. The road surface state detecting device 10 determines whether an abnormal area is included in the target road surface based on the information indicating the distance, the captured image, and the position information, and transmits the determination result to the control center via the communication device 5 Do. An abnormal area is an area in which a damage or an obstacle exists. The road surface state detecting device 10 is configured of, for example, an information processing device such as a computer.

  FIG. 3 is a hardware configuration diagram of the road surface state detecting device. As shown in FIG. 3, the road surface condition detecting device 10 physically includes one or more processors 101, a main storage device 102 such as a random access memory (RAM) and a read only memory (ROM), a hard disk drive etc. The computer may be configured as a computer including hardware such as the auxiliary storage device 103, the input device 104 such as an operation panel, the output device 105 such as a display, and the communication module 106 which is a data transmission / reception device. Each function shown in FIG. 1 of the road surface condition detecting apparatus 10 is controlled by one or more processors 101 by causing hardware such as the main storage device 102 to read one or more predetermined computer programs. This is realized by operating each hardware and reading and writing data in the main storage device 102 and the auxiliary storage device 103.

  The road surface state detecting device 10 functionally includes a distance information storage unit 11, a captured image storage unit 12, an acquisition unit 13 (first acquisition unit), a detection unit 14, and an acquisition unit 15 (second acquisition unit). , Reference information DB (database) 16, a determination unit 17, an output unit 18, and a detection result DB 19.

  The distance information storage unit 11 stores distance information indicating the distance between each measurement point on the target road surface and the distance sensor 2. The distance information is generated by associating the distance of each measurement point measured by the distance sensor 2 with the position of each measurement point detected by the GPS device 4. The distance information storage unit 11 stores, for example, distance information in order (measurement order) along the traveling route of the vehicle.

  The captured image storage unit 12 stores image information including captured images of measurement points on the target road surface. The image information is generated by associating the captured image of each measurement point generated by the camera 3 with the position of each measurement point detected by the GPS device 4. The captured image storage unit 12 stores, for example, image information in order (measurement order) along a traveling route of a vehicle.

  The acquisition unit 13 acquires distance information stored in the distance information storage unit 11. The acquisition unit 13 acquires, for example, distance information in order along the travel route of the vehicle. The acquisition unit 13 outputs the acquired distance information to the detection unit 14.

  The detection unit 14 detects the shape of the target road surface based on the distance information. Specifically, the detection unit 14 detects the shape of the area obtained by dividing the target road surface at predetermined distances along the travel route. The detection unit 14 detects the three-dimensional shape of the area using distance information of a plurality of measurement points included in the area. For example, on the runway surface, since the most part is flat, the distance between the distance sensor 2 and the runway surface can be regarded as a fixed distance. This fixed distance is preset. The detection unit 14 calculates the height of the target road surface by subtracting the distance measured by the distance sensor 2 from the predetermined distance.

  As illustrated in (a) of FIG. 4, the detection unit 14 calculates the height of the target road surface R at the measurement point P1 based on the distance measured at the measurement point P1 of FIG. 2. Since the scan line at the measurement point P1 passes the head of the bolt screw B, the height of the target road surface R increases at the position where the bolt screw B exists. Similarly, as illustrated in (b) of FIG. 4, the detection unit 14 calculates the height of the target road surface R at the measurement point P2 based on the distance measured at the measurement point P2 of FIG. 2. Since the scan line at the measurement point P2 passes through the shaft portion of the bolt screw B, the height of the target road surface R increases at the position where the bolt screw B exists. Since the diameter of the shank of the bolt screw B is smaller than the diameter of the head of the bolt screw B, the height of the shank of the bolt screw B is smaller than the height of the head of the bolt screw B.

  As described above, the detection unit 14 reproduces the shape (three-dimensional shape) of the target road surface by connecting the heights of the target road surface at a plurality of continuous measurement points along the traveling route in that order. For example, when an obstacle such as a falling object exists on the target road surface, the three-dimensional shape of the target road surface becomes a convex shape, and when the target road surface is damaged and a hole is formed, the three-dimensional shape of the target road surface The shape is concave.

  The detection unit 14 determines whether the shape of each region includes a concave shape or a convex shape or a flat shape. The detection unit 14 compares the distance between the measurement points included in each area and the upper limit threshold to determine whether the shape of the area includes a convex shape. The upper limit threshold is a value of the upper limit that can be regarded as the road surface being flat. When at least one or more of the distances of the measurement points included in the area is larger than the upper limit threshold, the detection unit 14 determines that the shape of the area includes a convex shape.

  Similarly, the detection unit 14 compares the distance between the measurement points included in each area and the lower threshold, and determines whether the shape of the area includes a concave shape. The lower limit threshold is a lower limit value at which the road surface can be considered flat. If at least one or more of the distances of the measurement points included in the area is smaller than the lower limit threshold, the detection unit 14 determines that the shape of the area includes a concave shape. The detection unit 14 determines that the shape of the area is flat when all the distances of the measurement points included in the area are equal to or less than the upper limit threshold and equal to or more than the lower limit threshold. The detection unit 14 sets a region determined to include a concave shape or a convex shape as a candidate region, and outputs position information indicating the position of the candidate region to the acquisition unit 15.

  The acquisition unit 15 acquires a captured image obtained by capturing a candidate area from the image information stored in the captured image storage unit 12. Specifically, the acquisition unit 15 extracts image information corresponding to the position (area) indicated by the position information received from the detection unit 14 from the image information, and acquires a captured image included in the image information. . The acquisition unit 15 outputs the acquired captured image to the determination unit 17.

  The reference information DB 16 stores reference information indicating the shape of the target road surface in the normal state. The reference information is information in which the position (range) of the area on the target road surface, the three-dimensional shape of the area in the normal state, and the reference image that is a captured image in the normal state of the area are associated. . The reference information indicates, for example, the shape of a non-flat region that is not flat in a normal state on the target road surface. Such non-flat areas include areas provided with known equipment. For example, as a known installation, when the target road surface is provided with a draining groove, the grooved area has a concave shape. In this case, the reference information associates the position (range) of the non-flat area, the three-dimensional shape in the normal state of the non-flat area, and the reference image which is a captured image in the normal state of the non-flat area. It is information. Note that, as the reference information, information indicating the shape of a flat region in a normal state in the target road surface may be further included.

  When the target road surface includes a concave or convex area, the determination unit 17 determines the area as a candidate area and determines whether the candidate area is in an abnormal state. Specifically, the determination unit 17 determines whether the candidate area is in an abnormal state by comparing the shape of the candidate area in the normal state with the shape of the candidate area. For example, the determination unit 17 obtains a reference image indicating the normal state of the candidate area from the reference information DB 16, and determines whether the candidate area is in an abnormal state by comparing the reference image and the captured image. For example, the determination unit 17 calculates the degree of similarity between the reference image and the captured image, and determines that the state is normal when the degree of similarity is greater than a predetermined value, and the degree of similarity is less than or equal to the predetermined value. It may be determined that the system is in an abnormal state.

  The determination unit 17 determines whether the candidate area is in an abnormal state by comparing the three-dimensional shape of the candidate area in the normal state with the three-dimensional shape of the candidate area detected by the detection unit 14. May be For example, the determination unit 17 calculates the similarity between the three-dimensional shape of the candidate area in the normal state and the three-dimensional shape of the detected candidate area, and in the normal state when the similarity is larger than a predetermined value. It may be determined that there is an abnormality state if the degree of similarity is equal to or less than a predetermined value.

  The determination unit 17 may identify the type of the candidate area, and may determine that the candidate area is in the normal state when the identified type is an exclusion type that is the type of the exclusion target. The type of candidate area indicates the cause of the concave or convex shape. Such types include road surface damage, obstacles (debris), and water pools. Among these, types that do not affect road surface travel are set in advance as exclusion types. The exclusion type may be, for example, a puddle.

  The determination unit 17 identifies the type of candidate area based on the distance information. Specifically, the determination unit 17 identifies the type of the candidate area using the three-dimensional shape of the candidate area. For example, a plurality of three-dimensional shapes corresponding to each type are set in advance, and the determination unit 17 compares the three-dimensional shape of the candidate area with the set three-dimensional shape to determine the type of the candidate area. Identify The determination unit 17 may identify the type of candidate area by machine learning using the three-dimensional shape of the candidate area.

  The determination unit 17 may identify the type of the candidate area based on the captured image. Specifically, the determination unit 17 may identify the type of the candidate area using the captured image of the candidate area. For example, a plurality of captured images corresponding to each type are set in advance, and the determination unit 17 identifies the type of the candidate region by comparing the captured image of the candidate region with the set captured image. The determination unit 17 may identify the type of candidate area by machine learning using a captured image of the candidate area.

  The determination unit 17 generates a determination result by regarding an area determined as an abnormal state among the candidate areas as an abnormal area and an area determined as a normal state as a normal area. The determination result includes information on the abnormal area and information on the normal area. The information on the abnormal area includes the position, the type, the three-dimensional shape, the size of the concave shape or the convex shape, and the captured image of the area. For example, the type of an abnormal area having a convex shape may be set as an obstacle, and the type of an abnormal area having a concave shape may be set as road surface damage. Similarly, the information on the normal area includes the position, the type, the three-dimensional shape, the size of the concave or convex shape, and the captured image of the area. The determination unit 17 may generate the determination result with the area determined to have a flat shape by the detection unit 14 as a normal area. The determination unit 17 outputs the determination result to the output unit 18.

  The output unit 18 outputs the determination result by the determination unit 17 to the detection result DB 19 and stores the determination result in the detection result DB 19. The output unit 18 may output the determination result to the control center via the communication device 5.

  The detection result DB 19 stores the determination result. The road surface condition detecting device 10 may not have the detection result DB 19, and the detection result DB 19 may be provided outside the control device 10 (control center). Thereby, the state in the detection target area (monitoring range) of the target road surface is databased.

  The vehicle is provided with an ECU (Electronic Control Unit) for overall control of the vehicle. When the vehicle is an automatic traveling vehicle, the ECU controls the automatic traveling of the vehicle. The ECU controls the traveling of the vehicle based on the position information generated by the GPS device 4 and map information (way point) of the runway set in advance. The ECU notifies the control center via the communication device 5 of the state of the vehicle. The state of the vehicle includes, for example, the remaining amount of battery, the remaining amount of fuel, and the state of other on-vehicle devices.

  The control center has map information of the target road surface. The control center adds the location information of the abnormal area to the map information. The control center remotely monitors a vehicle equipped with the road surface condition detection system 1. When an unexpected failure (such as a tire puncture) occurs that causes trouble in traveling in a vehicle equipped with the road surface condition detection system 1, the control center issues an emergency stop command for forcibly stopping the vehicle. Send to the vehicle. When the road surface condition detection system 1 is mounted on an autonomously traveling vehicle, the control center transmits to the vehicle a forcible feedback command for forcibly returning the vehicle if the automatic traveling function is not impaired.

  Next, an example of traveling by a vehicle equipped with the road surface condition detection system 1 will be described. FIG. 5 is a diagram showing an example of traveling by a vehicle equipped with the road surface state detection system of FIG. In the example shown in FIG. 5, the target road surface R is a runway, and the vehicle V is an automatic traveling vehicle. Vehicle V is an electric vehicle and can be charged by non-contact charge. The vehicle V is provided with a power receiving device.

  The road surface state detection system 1 is mounted on the vehicle V. In the vehicle V, position information (latitude and longitude) of a plurality of waypoints WP is set in advance. The plurality of waypoints WP are passing points through which the vehicle V passes, and define a travel route TR of the vehicle V. Waypoint numbers are assigned to the plurality of waypoints WP in the order in which the vehicle V travels. The plurality of waypoints WP are set so that the detection target area of the target road surface R is detected without omission. The ECU of the vehicle V controls the vehicle V to travel toward the waypoint WP in order from the first waypoint WP.

  When the vehicle V has not performed the road surface state detection process, the vehicle V is stopped in the parking area PA. In the parking area PA, a power transmission device for performing contactless charging is installed. For example, the vehicle V starts automatic traveling by receiving a detection command for performing a road surface state detection process from the control center. The vehicle V may start automatic traveling at a preset time. At this time, the ECU operates the road surface state detection system 1 and causes the road surface state detection system 1 to implement a road surface state detection method described later. The vehicle V travels on the traveling route TR at a substantially constant speed (for example, about 60 km per hour). When the vehicle V finishes traveling on the traveling route TR, the vehicle V returns to the parking area PA to perform charging. At this time, the ECU stops the operation of the road surface condition detection system 1.

  Next, a road surface condition detection method performed by the road surface condition detection system 1 will be described. FIG. 6 is a flowchart showing an example of a road surface state detection method performed by the road surface state detection device. For example, while the vehicle V (see FIG. 5) is performing the road surface condition detection process, a series of processes shown in FIG. 6 are performed at predetermined time intervals (traveling distance).

  First, at each measurement point on the target road surface R (travel route TR), the distance sensor 2 measures the distance and the camera 3 captures an image of the target road surface R. At this time, the GPS device 4 generates position information of the vehicle V. Then, the distance sensor 2 transmits information indicating the measured distance to the road surface state detecting device 10, the camera 3 transmits the captured image to the road surface state detecting device 10, and the GPS device 4 detects the road surface state Send to device 10

  The road surface state detecting device 10 generates distance information by correlating the distance of each measurement point measured by the distance sensor 2 with the position of each measurement point detected by the GPS device 4, and the distance information storage unit 11 The distance information is stored in order. Similarly, the road surface state detecting device 10 generates image information by associating the captured image of each measurement point generated by the camera 3 with the position of each measurement point detected by the GPS device 4 The image information is sequentially stored in the storage unit 12.

  Then, the acquiring unit 13 sequentially acquires the distance information stored in the distance information storage unit 11 along the traveling route (step S11). Then, the acquisition unit 13 outputs the acquired distance information to the detection unit 14. Subsequently, the detection unit 14 detects the shape of the target road surface based on the distance information (step S12). Specifically, the detection unit 14 detects the three-dimensional shape of the area obtained by dividing the target road surface at predetermined distances along the travel route.

  Subsequently, the detection unit 14 determines whether the shape of the region includes a concave shape or a convex shape or a flat shape (step S13). If it is determined that the shape of the area is flat (step S13; NO), the area is a normal area, and the series of processing of the road surface state detection method ends for the area. On the other hand, when it is determined in step S13 that the shape of the area includes a concave shape or a convex shape (step S13; YES), the detection unit 14 sets the area as a candidate area and indicates position information indicating the position of the candidate area. Output to the acquisition unit 15. Then, the acquisition unit 15 acquires a captured image obtained by imaging a candidate area from the image information stored in the captured image storage unit 12 (step S14). Then, the acquisition unit 15 outputs the acquired captured image to the determination unit 17.

  Subsequently, the determination unit 17 determines whether the candidate area is in the normal state or in the abnormal state by comparing the shape of the candidate area in the normal state with the shape of the candidate area (step S15). Specifically, the determination unit 17 acquires a reference image indicating the normal state of the candidate area from the reference information DB 16, and compares the reference image with the captured image to determine whether the candidate area is in the normal state or in the abnormal state. Determine if there is. If it is determined that the candidate area is in the normal state (step S15; YES), a series of processing of the road surface state detection method ends for the candidate area.

  On the other hand, when it is determined in step S15 that the candidate area is in the abnormal state (step S15; NO), the determination unit 17 identifies the type of the candidate area and determines whether the identified type is the exclusion type Is determined (step S16). The determination unit 17 identifies the type of the candidate area based on, for example, the distance information and the captured image. If it is determined that the type of the candidate area is an exclusion type (step S16; YES), the candidate area is determined to be in a normal state, and the series of processing of the road surface state detection method ends for the candidate area. On the other hand, when it is determined in step S16 that the type of the candidate area is not an exclusion type (step S16; NO), the determination unit 17 generates a determination result with the candidate area as an abnormal area, and outputs the determination result. Output to Then, the output unit 18 outputs the determination result to the detection result DB 19 or the control center (step S17), and the series of processes of the road surface condition detection method ends for the candidate area.

  As described above, in the road surface state detection system 1 and the road surface state detection device 10, the shape of the target road surface is detected based on distance information indicating the distance between each measurement point on the target road surface and the distance sensor 2. When the target road surface includes a concave or convex area, whether or not the candidate area is abnormal is determined based on the shape of the area (candidate area) in a normal state, and the determination result is output. Be done. Specifically, when the target road surface includes a concave or convex area, whether the candidate area is in an abnormal state by comparing the reference image indicating the normal state of the candidate area with the captured image of the candidate area It is determined whether or not. Therefore, for example, when the candidate area in the normal state has a concave shape or a convex shape, the possibility that the candidate area is determined to be abnormal can be reduced. As a result, it is possible to improve the detection accuracy of the road surface state.

  If the cause of the candidate area having a convex shape or a concave shape is, for example, a type (such as fallen leaves and puddles) that does not affect road surface travel, the candidate area may be regarded as a normal state. Thus, a type that does not affect road surface travel is predetermined as an exclusion type. Then, the determination unit 17 identifies the type of candidate area, and determines that the candidate area is in a normal state when the type of candidate area is the exclusion type. Thereby, over detection of an abnormal state can be suppressed. As a result, it is possible to improve the detection accuracy of the road surface state.

  When it is determined that the candidate area is in an abnormal state, the output unit 18 outputs the position information of the abnormal area with the candidate area as the abnormal area. In this case, the position of the abnormal area can be identified. Thus, for example, the abnormal area can be notified to the control center, and maintenance work such as road surface repair and obstacle removal can be efficiently performed. For example, by linking with another work vehicle (a work vehicle or the like that removes falling objects), efficient maintenance can be achieved.

  On the runway, from the viewpoint of safety, removal of obstacles and repair of road surface damage are desired. Therefore, by using the runway as the target road surface, it is possible to improve the detection accuracy of the road surface condition of the runway, and to improve the safety of the flight vehicle such as an aircraft.

  The road surface condition detection system 1 is provided in a vehicle. Therefore, while the vehicle travels on the target road surface, the distance between the target road surface and the distance sensor 2 is measured. As a result, it is possible to measure in real time the road surface located immediately below the vehicle, and to facilitate generation of distance information.

  Conventionally, since a driver driving a maintenance vehicle visually checks for falling objects on the road surface and road surface damage, it is possible to overlook road surface abnormalities. Moreover, since it is visual confirmation, the traveling speed of the vehicle is about 15 to 20 km / h. Furthermore, at night and in bad weather conditions, visibility is poor, which increases the possibility of overlooking road surface abnormalities, and also reduces travel speed. Thus, the burden on the driver is large. On the other hand, in the road surface state detection system 1 and the road surface state detection device 10, the driver does not have to check the road surface, so the burden on the driver can be reduced. Furthermore, when the road surface condition detection system 1 is mounted on an unmanned vehicle, labor saving can be achieved. It is also conceivable to install a sensor or the like for checking the road surface condition on the ground, but there is a possibility that the sensor or the like may be damaged by being crushed. On the other hand, since the road surface condition detection system 1 is mounted on a vehicle, the possibility of damage can be reduced.

  When a line scan sensor is used as the distance sensor 2, measurement performance can be ensured regardless of day and night and the weather. By shortening the scanning cycle of the distance sensor 2, the measurement speed is increased. Therefore, even if the traveling speed of the vehicle is increased, obstacles and damage of a desired size can be detected. As a result, the work efficiency of the road surface state detection process can be improved, and the use efficiency of the target road surface can be improved.

  As mentioned above, although embodiment of this indication was described, this invention is not limited to the said embodiment.

  For example, all the components of the road surface condition detection system 1 may not be mounted on the vehicle. The distance sensor 2, the camera 3, the GPS device 4, and the communication device 5 may be mounted on the vehicle, and the road surface state detecting device 10 may be provided outside the vehicle. The road surface condition detection device 10 may be provided at the control center.

  The determination unit 17 may not determine whether the type of the candidate area is an exclusion type. In addition, whether the candidate area is in an abnormal state is determined by the determination unit 17 comparing the three-dimensional shape of the candidate area in the normal state with the three-dimensional shape of the candidate area detected by the detection unit 14. In the case of determination, the road surface state detection device 10 may not include the captured image storage unit 12 and the acquisition unit 15, and the road surface state detection system 1 may not include the camera 3.

  When the vehicle equipped with the road surface state detection system 1 detects in advance the state of the target road surface in the normal state, the road surface state detection device 10 acquires the reference information and stores the acquired reference information in the reference information DB 16. It is also good.

Reference Signs List 1 road surface condition detection system 2 distance sensor 3 camera 4 GPS device 5 communication device 10 road surface condition detection device 11 distance information storage unit 12 captured image storage unit 13 acquisition unit 14 detection unit 15 acquisition unit 16 reference information DB
17 determination unit 18 output unit 19 detection result DB
R Target road surface V Vehicle

Claims (7)

A road surface condition detecting device for detecting a condition of a target road surface to be detected,
A first acquisition unit that acquires distance information indicating a distance between each measurement point on the target road surface and the measurement device;
A detection unit that detects the shape of the target road surface based on the distance information;
A determination unit that determines whether the area is in an abnormal state when the target road surface includes a concave or convex area;
An output unit that outputs the determination result by the determination unit;
Equipped with
The road surface state detecting device, wherein the determination unit determines whether or not the area is in an abnormal state based on a shape of the area in a normal state. It further comprises a second acquisition unit for acquiring a captured image obtained by imaging the area,
The road surface condition detecting device according to claim 1, wherein the determination unit determines whether the area is in an abnormal state by comparing a reference image indicating a normal state of the area with the captured image.   The determination unit identifies the type of the area based on the distance information, and the area is in a normal state when the identified type is an exclusion type that is a type of a predetermined exclusion target. The road surface condition detecting device according to claim 1 or 2, which is determined as   The road surface state detecting device according to any one of claims 1 to 3, wherein the output unit outputs position information of the area when it is determined that the area is in an abnormal state.   The road surface condition detection device according to any one of claims 1 to 4, wherein the target road surface is a runway. The road surface state detecting device according to any one of claims 1 to 5;
A measuring device for measuring the distance;
A road surface condition detection system comprising:   The road surface condition detection system according to claim 6, wherein the measurement device is provided in a vehicle.