Road deterioration determination system and road deterioration determination method

Abstract

In a road deterioration determination system according to the present invention, a position information acquisition unit (1) acquires position information about a vehicle (100). A video acquisition unit (2) acquires road surface video, which is video taken of the road surface around the vehicle (100). A water immersion detection unit (3) detects partially water-immersed road surfaces from the road surface video. A road surface deterioration determination unit (4) determines a deterioration level of the road surface on the basis of the shape and the degree of water immersion of the water-immersed portions of the road surface, and also identifies a road surface deterioration point, which is a point where the road surface has deteriorated, on the basis of the position information about the vehicle (100) when the road surface video of the road surface with detected deterioration was taken. A road surface deterioration information storage unit (8) stores road surface deterioration information including the road surface deterioration point and information about the deterioration level.

Description

Road deterioration determination system and road deterioration determination method

[0001] The present disclosure relates to a road deterioration determination system and a road deterioration determination method for detecting deterioration of a road surface.

[0002] For example, Patent Document 1 below proposes a road surface deterioration diagnosis device that detects road surface deterioration from a captured image of the road surface. The road surface deterioration diagnosis device in Patent Document 1 detects depressions in the road surface by detecting puddles that have formed on the road surface, thereby detecting road surface deterioration.

[0003] International Publication No. 2021 / 193148

[0004] While the technology in Patent Document 1 can detect severe deterioration such as large depressions in the road surface, it has difficulty detecting small depressions such as cracks in the road surface, or linear deterioration such as rutting, which can lead to problems such as delayed detection of road surface deterioration and delayed road repairs.

[0005] The present disclosure has been made to solve the above-mentioned problems, and aims to provide a road deterioration determination system that can detect small depressions and linear deterioration in the road surface.

[0006] The road deterioration determination system according to the present disclosure includes a location information acquisition unit that acquires location information of a vehicle, an image acquisition unit that acquires road surface image, which is an image obtained by capturing the road surface around the vehicle, a water infiltration detection unit that detects a road surface that is partially flooded with water from the road surface image, a road surface deterioration determination unit that determines the level of road surface deterioration based on the shape of the water-soaked portion of the road surface and the degree of water infiltration, and identifies road surface deterioration points, which are points where the road surface has deteriorated, based on the location information of the vehicle at the time the road surface image of the road surface where deterioration has been detected was captured, and a road surface deterioration information storage unit that stores road surface deterioration information including information on the road surface deterioration points and the deterioration level.

[0007] The road deterioration determination system according to the present disclosure can detect small depressions and linear deterioration in the road surface.

[0008] The objects, features, aspects, and advantages of the present disclosure will become more apparent from the following detailed description and the accompanying drawings.

[0009] FIG. 1 is a diagram showing the configuration of a road deterioration determination system according to embodiment 1. FIG. 2 is a diagram showing the relationship between the wetness of a road surface and the luminance distribution of a road surface image. FIG. 3 is a diagram showing an example of probe information. FIG. 4 is a flowchart showing the operation of a probe device in the road deterioration determination system according to embodiment 1. FIG. 5 is a flowchart showing the operation of a server in the road deterioration determination system according to embodiment 1. FIG. 6 is a diagram showing the configuration of a road deterioration determination system according to embodiment 2. FIG. 7 is a diagram showing the configuration of a road deterioration determination system according to embodiment 3. FIG. 8 is a diagram showing an example of the hardware configuration of a probe device and a server. FIG. 9 is a diagram showing an example of the hardware configuration of a probe device and a server.

[0010] <Embodiment 1> Fig. 1 is a diagram showing the configuration of a road deterioration determination system according to embodiment 1. This road deterioration determination system detects deterioration of the road surface of a road paved with asphalt, concrete, or the like.

[0011] The road deterioration determination system includes a probe device 110 mounted on a vehicle 100 and a server 200 capable of communicating with the probe device 110 via a network 300. Hereinafter, a vehicle 100 mounted with the probe device 110 is referred to as a "probe vehicle." The road deterioration determination system may include multiple probe vehicles 100. The probe device 110 does not necessarily have to be permanently installed on the probe vehicle 100, and may be detachable from the probe vehicle 100. In this case, the probe device 110 may be an application that runs on a portable terminal such as a mobile phone, a smartphone, or a PND (Portable Navigation Device).

[0012] As shown in FIG. 1 , the probe device 110 is connected to a locator 101, a camera 102, and a communication device 103 provided in the probe vehicle 100. The locator 101 calculates the current position of the probe vehicle 100 using GNSS (Global Navigation Satellite Systems). The camera 102 is an imaging device that captures images of the road surface around the probe vehicle 100. Hereinafter, the image of the road surface around the probe vehicle 100 captured by the camera 102 will be referred to as a "road surface image." The communication device 103 is a communication means for the probe vehicle 100 to communicate with the server 200.

[0013] The camera 102 may be any type, such as a front camera, a rear camera, or an in-vehicle camera. The locator 101 and the camera 102 may be built into a drive recorder of the probe vehicle 100. The communication device 103 may be a general-purpose communication device such as a mobile phone or a smartphone. Some or all of the locator 101, the camera 102, and the communication device 103 may be built into the probe device 110.

[0014] The probe device 110 includes a location information acquisition unit 1 , an image acquisition unit 2 , a water intrusion detection unit 3 , a road surface deterioration determination unit 4 , and a probe information generation unit 5 .

[0015] The position information acquisition unit 1 acquires position information indicating the current position of the probe vehicle 100 from the locator 101. The image acquisition unit 2 acquires, from the camera 102, a road image obtained by capturing an image of the surroundings of the probe vehicle 100.

[0016] The water intrusion detection unit 3 analyzes the road surface image acquired by the image acquisition unit 2 and detects from the road surface image that the road surface is partially flooded with water. Because the luminance (color shade) in the road surface image differs between a water-covered road surface, a water-flooded road surface, and a dry road surface, the water intrusion detection unit 3 can detect a road surface that is partially flooded with water from the luminance distribution in the road surface image.

[0017] FIG. 2 is a diagram showing the relationship between the wetness of the road surface and the luminance distribution of road surface images. In FIG. 2, graph (a) shows the height distribution of the road surface where a linear crack K (a portion partially seeped with water) exists, and graphs (b) to (e) show the luminance distribution of road surface images captured of the road surface corresponding to graph (a). Graph (a) shows the luminance distribution of road surface images captured when the road surface was covered with water. Graph (c) shows the luminance distribution of road surface images captured when both the road surface and the linear crack K were wet. Graph (d) shows the luminance distribution of road surface images captured when the road surface was dry and the linear crack K was wet. Graph (e) shows the luminance distribution of road surface images captured when both the road surface and the linear crack K were dry. The horizontal axis (x-axis) of each graph represents the distance in the direction perpendicular to the linear crack K.

[0018] For example, a road surface covered with water has a mirror-like appearance, reflecting the scenery, resulting in a large change in brightness in the road surface image, as shown in graph (b). For a while after the water has flowed away from a state in which the road surface was covered with water, both the road surface and the linear crack K remain wet. In a road surface image of a road surface in which both the road surface and the linear crack K are wet, as shown in graph (c), a difference in brightness occurs between the road surface and the linear crack K, but the brightness difference is not large because the brightness of the entire road surface is low. After that, the road surface begins to dry, resulting in a state in which water is partially seeped in (i.e., only the linear crack K is wet). In a road surface image of a road surface in a partially water-stained state, the brightness of the dry road surface increases, resulting in a large difference in brightness between the road surface and the linear crack K, as shown in graph (d). After that, when the linear crack K dries, the brightness of the linear crack K also increases, and as shown in graph (e), the difference between the brightness of the road surface and the brightness of the linear crack K decreases again.

[0019] Therefore, the water infiltration detection unit 3 can detect a road surface that is partially flooded with water from the road surface image based on the distribution of stably low and high brightness areas in the road surface image. Specifically, the water infiltration detection unit 3 can detect a road surface that is partially flooded with water by detecting a road surface image with a brightness distribution like that shown in graph (d), in which the brightness is high overall but there are localized areas where the brightness drops significantly. Road surface images with a brightness distribution like that shown in graph (d) have a large difference between the high and low brightness areas and can be detected relatively easily, so this detection method can be expected to achieve high detection accuracy. A more specific detection method, for example, is pattern matching, in which a template of the brightness distribution of a road surface image that is partially flooded with water is prepared in advance and the brightness distribution of the road surface image acquired by the image acquisition unit 2 is compared with the template.

[0020] The road surface deterioration determination unit 4 determines the deterioration level of the road surface based on the shape of the water-soaked part and the degree of water infiltration in the partially water-soaked road surface detected by the water infiltration detection unit 3. Water is more likely to infiltrate into parts that have deteriorated due to cracks, etc., and the degree of water infiltration increases in parts where road surface deterioration has progressed, so the deterioration level of the road surface can be determined from the shape of the water-soaked part and the degree of water infiltration.

[0021] Since the greater the degree of water penetration, the lower the luminance in the road surface image, the degree of water penetration (deterioration level) can also be determined based on the luminance of the water-soaked portions of the road surface in the road surface image. For example, the road surface deterioration determination unit 4 may determine the degree of water penetration (deterioration level) based on the difference in luminance between the water-soaked portions and the dry portions of the road surface in the road surface image.

[0022] The road surface deterioration determination unit 4 may also determine the degree of water infiltration (deterioration level) by comparing a road surface image of a partially waterlogged road surface with a road surface image of a dry road surface, all of which are taken at the same location. Furthermore, the road surface deterioration determination unit 4 may determine the degree of water infiltration (deterioration level) by comparing a road surface image of a partially waterlogged road surface with a road surface image of a dry road surface, a road surface image of a waterlogged road surface, and a road surface image of a road surface with puddles, all of which are taken at the same location. This improves the accuracy of detecting road surface deterioration.

[0023] When the road surface is completely covered with water or has puddles, it is difficult to accurately extract the waterlogged areas from the road surface image, and when the road surface is completely dry, there are no waterlogged areas. On the other hand, when the road surface is partially waterlogged, it is possible to accurately extract the waterlogged areas. This is why the road surface deterioration determination unit 4 determines the deterioration level of the road surface based on the road surface image of the road surface in a partially waterlogged state.

[0024] The road surface deterioration determination unit 4 may determine the deterioration level of the road surface by taking into consideration one or more of the degree of depression, the size, and the number of water-soaked portions of the road surface.

[0025] Furthermore, the road surface deterioration determination unit 4 identifies a road surface deterioration point, which is a point where the road surface has deteriorated, based on the position information of the probe vehicle 100 at the time when the road surface image of the road surface where deterioration has been detected was taken. Strictly speaking, there is a difference between the position of the probe vehicle 100 and the shooting range of the camera 102, but here, this difference is assumed to be negligibly small, and the position of the probe vehicle 100 at the time when the road surface image of the road surface where deterioration has been detected was taken is regarded as the road surface deterioration point.

[0026] The probe information generation unit 5 creates probe information including the location information and road surface image of the probe vehicle 100 with the same timestamp, and the deterioration level of the road surface determined from the road surface image, and transmits the created probe information to the server 200 via the network 300 using the communication device 103.

[0027] The server 200 is constructed in a cloud environment and has sufficient hardware resources to process probe information transmitted from a large number of probe vehicles 100. The server 200 includes a communication unit 6, a probe information storage unit 7, a road surface deterioration information storage unit 8, a map database 9, and a repair priority determination unit 10.

[0028] The communication unit 6 is a communication means for the server 200 to communicate with the probe device 110 of the probe vehicle 100. The probe information transmitted from the probe device 110 is received by the communication unit 6.

[0029] The probe information storage unit 7 stores the probe information received by the communication unit 6. FIG. 3 shows an example of probe information stored in the probe information storage unit 7. In FIG. 3, "vehicle ID" is the identifier of the probe vehicle 100 that is the sender of the probe information. "Coordinates" is the location information included in the probe information, i.e., information on the location where the road surface image included in the probe information was captured. "Image file" is a data file of the road surface image included in the probe information. "Timestamp" is the location information and timestamp of the road surface image included in the probe information. "Road surface deterioration level" is the deterioration level of the road surface determined from the road surface image included in the probe information. "Repair priority" will be described later. Note that a deterioration level of "0" means that there is no deterioration on the road surface.

[0030] 3 also includes probe information in which no road surface deterioration was detected (deterioration level is "0"), the probe device 110 may transmit only probe information in which road surface deterioration was detected to the server 200. This reduces the amount of probe information transmitted from the probe device 110 and suppresses the consumption of communication resources.

[0031] The road surface deterioration information storage unit 8 extracts and stores road surface deterioration information including information on road surface deterioration points and deterioration levels from the probe information stored in the probe information storage unit 7. In this embodiment, the server 200 has a map database 9 in which map data is stored, and the road surface deterioration information storage unit 8 stores information on road surface deterioration points included in the road surface deterioration information by linking it to the map data in the map database 9. Note that when road surface deterioration progresses or when road surface repairs are performed, the deterioration level of the road surface deterioration point may change or the road surface deterioration point may disappear (the deterioration level becomes 0). In such cases, the road surface deterioration information storage unit 8 updates the road surface deterioration information that it stores.

[0032] The repair priority determination unit 10 determines the priority of repairing the road surface at a road surface deterioration point included in the probe information stored in the probe information storage unit 7 (hereinafter also referred to as "repair priority"). The repair priority is determined based on the deterioration level of the road surface at the road surface deterioration point, the traffic volume at the road surface deterioration point, or both. Information on the traffic volume at each point may be included in the map data in the map database 9, or may be obtained from an external source (such as infrastructure). As shown in FIG. 3 , the probe information storage unit 7 of this embodiment stores the repair priority information determined by the repair priority determination unit 10 in the probe information. Furthermore, the road surface deterioration information storage unit 8 stores the repair priority information in the road surface deterioration information, and also links the priority information to the map data.

[0033] 4 is a flowchart showing the operation of the probe device 110. The operation of the probe device 110 will be described below based on the flowchart of FIG.

[0034] When the probe vehicle 100 starts traveling, the probe device 110 starts the operation shown in Fig. 4. First, the position information acquisition unit 1 acquires the position information of the probe vehicle 100 from the locator 101 (step S10), and the image acquisition unit 2 acquires the road surface image obtained by photographing the surroundings of the probe vehicle 100 from the camera 102 (step S11).

[0035] Next, the water intrusion detection unit 3 analyzes the road surface image acquired in step S11 and searches for road surfaces that are partially inundated with water and that are captured in the road surface image (step S12).

[0036] Next, the road surface deterioration determination unit 4 determines the deterioration level of the road surface based on the shape of the waterlogged portion and the degree of waterlogging in the partially waterlogged road surface detected in step S12. At this time, the road surface deterioration determination unit 4 regards the shooting position of the road surface image where road surface deterioration is detected (i.e., the position of the probe vehicle 100 at the time the road surface image was shot) as a point where the road surface has deteriorated, and identifies that point as a road surface deterioration point (step S13).

[0037] Next, the probe information generation unit 5 generates probe information including the location information acquired in step S10, the road surface image acquired in step S11, and the road surface deterioration level determined from the road surface image in step S12 (step S14).The probe information generation unit 5 then transmits the probe information generated in step S14 to the server 200 using the communication device 103 (step S15).

[0038] 4 ends. If the probe vehicle 100 continues traveling (NO in step S16), the process returns to step S10, and the above-described process is repeated. The repetition period, i.e., the probe information generation period, may be a fixed period, such as every second, or may be a variable period designated by the server 200.

[0039] 5 is a flowchart showing the operation of the server 200. The operation of the server 200 will be described below with reference to the flowchart of FIG.

[0040] The probe information transmitted from the probe device 110 in step S15 of FIG. 4 is received by the communication unit 6 of the server 200 (step S20) and stored in the probe information storage unit 7 (step S21).

[0041] The road surface deterioration information storage unit 8 extracts and stores road surface deterioration information including information on road surface deterioration points and deterioration levels from the probe information stored in the probe information storage unit 7. At this time, the road surface deterioration information storage unit 8 stores the information on the road surface deterioration points included in the road surface deterioration information by linking it to map data in the map database 9 (step S22).

[0042] The repair priority determination unit 10 determines the priority (repair priority) for repairing the road surface at the road surface deterioration point (step S23). The probe information storage unit 7 includes the repair priority information in the probe information, and the road surface deterioration information storage unit 8 includes the repair priority information in the road surface deterioration information linked to the map data.

[0043] The above process is repeatedly executed every time the communication unit 6 receives new probe information.

[0044] The road deterioration determination system according to the first embodiment can detect small deterioration such as cracks and other depressions in the road surface, as well as linear deterioration such as rutting. As a result, road surface deterioration can be discovered at a relatively early stage, preventing delays in road repairs.

[0045] [Variation 1] In the first embodiment, an example has been shown in which the road surface deterioration determination unit 4 is disposed in the probe device 110 mounted on the probe vehicle 100. However, the road surface deterioration determination unit 4 may be disposed in the server 200. Disposing the road surface deterioration determination unit 4 in the server 200 reduces the calculation load on the probe device 110, which can contribute to reducing the cost of the probe device 110. However, it should be noted that because the probe device 110 cannot detect road surface deterioration, it is not possible to, for example, transmit to the server 200 only probe information in which road surface deterioration has been detected in order to reduce the consumption of communication resources.

[0046] [Variation 2] Analysis using machine learning or deep learning may be introduced into the processes executed by the water intrusion detection unit 3 and the road surface deterioration determination unit 4. For example, the server 200 may generate, through learning, logic for the water intrusion detection unit 3 to detect a road surface in a partially water-soaked state from road surface images and logic for the road surface deterioration determination unit 4 to determine the deterioration level of the road surface, and distribute the generated logic to the probe device 110.

[0047] As a specific example, the server 200 may classify the road surface conditions in past road surface images stored in the probe information storage unit 7 into a state where the road surface is completely covered in water, a state where puddles have formed, a state where the road surface is partially flooded with water, and a dry state, learn the characteristics of the road surface images corresponding to each state, and based on the learning results, generate logic for the water inundation detection unit 3 to detect a road surface that is partially flooded with water.

[0048] In addition, the server 200 may learn the brightness characteristics of water-soaked parts of the road surface in past road surface images stored in the probe information storage unit 7, and based on the learning results, generate logic for the road surface deterioration determination unit 4 to determine the level of road surface deterioration.

[0049] The state in which the entire road is covered with water may be further classified into a flooded state and a completely wet state. The past road surface images used in the learning process are not limited to those captured by the camera 102 of the probe device 110. For example, they may be captured by a drive recorder mounted on a vehicle managed by a local government (such as a prefecture, city, ward, town, or village) or a general vehicle, or by any device with a camera function, such as a smartphone. Furthermore, data in which the deterioration level of the road surface, as determined by a road administrator through visual inspection, is associated with the road surface, may be used in the learning process.

[0050] [Variation 3] The method of detecting road surface deterioration points implemented by the road surface deterioration determination unit 4 is particularly effective for roads having a road surface on which cracks, scratches, or ruts become noticeable when water seeps in. Therefore, the probe device 110 may analyze the road surface image captured while the probe vehicle 100 is traveling, and when a road having a road surface on which cracks, scratches, or ruts become noticeable when water seeps in may be detected, the probe device 110 may transmit information about the road to the server 200.

[0051] The server 200 includes the information about the road in the map data of the map database 9. This enables the server 200 to determine the validity of the information about road surface deterioration points included in the probe information stored in the probe information storage unit 7 for each road based on the map data of the map database 9.

[0052] The server 200 may also distribute information about the road to the probe device 110, and the road surface deterioration determination unit 4 may determine the validity of the information about the road surface deterioration points that it has detected for each road.

[0053] [Variation 4] When a road is illuminated by lighting devices such as headlights at night, cracks, scratches, ruts, etc. on the road surface may appear as shadows on the road surface. Therefore, the road surface deterioration determination unit 4 may determine the deterioration level of the road surface by taking into account the shadows that appear on the road surface when the road surface is illuminated by the lighting devices of the probe vehicle 100.

[0054] [Variation 5] Locator 101 may be a high-precision locator with centimeter-level (sub-meter-level) accuracy. Camera 102 may be a high-performance camera capable of capturing high-resolution (e.g., 4K, 8K, or higher resolution), high-frame-rate (e.g., 24 fps or higher), and wide-range (e.g., HDR). Furthermore, the map data stored in map database 9 may be high-precision map data including road shape data for each lane. By improving the performance of these, road surface deterioration points can be detected with higher accuracy, and road surface deterioration points can be more accurately linked to map data.

[0055] For example, the location information of the probe vehicle 100 acquired by the location information acquisition unit 1 may include information on the lane on which the probe vehicle 100 is traveling. In this case, information on the lane where the road surface has deteriorated can be included in information on the road surface deterioration point. This allows the probe information generation unit 5 to generate probe information for each lane. Furthermore, if high-precision map data including road shape data for each lane is stored in the map database 9, the road surface deterioration information storage unit 8 can store information on lanes where the road surface has deteriorated, included in the road surface deterioration information, by linking it to the high-precision map data.

[0056] [Variation 6] Immediately after the rain stops, the entire road surface is covered with water (entirely wet), making it difficult to detect road surface deterioration from road surface images. Even after a long time has passed since the rain stopped and the entire road surface has dried, it becomes even more difficult to detect road surface deterioration from road surface images. Therefore, the reliability of road surface deterioration information (road surface deterioration points and deterioration level) detected from road surface images is low in road surface images immediately after the rain, becomes high in road surface images when the road surface gradually dries and becomes partially waterlogged, and then becomes low again in road surface images when the dry areas of the road surface have spread and the road surface is approaching a completely dry state.

[0057] Therefore, the repair priority determination unit 10 may calculate the elapsed time from the time when the rain stopped at the point where the road surface image was captured to the time when the road surface image was captured, based on past weather information, determine the reliability of the road surface deterioration information corresponding to the road surface image based on the elapsed time, and determine the repair priority taking the reliability into consideration. In other words, when the deterioration level of a road surface deterioration point with high reliability and the deterioration level of a road surface deterioration point with low reliability are the same, the repair priority determination unit 10 may set the repair priority of the road surface deterioration point with high reliability higher than the repair priority of the road surface deterioration point with low reliability.

[0058] Furthermore, the server 200 may calculate and store, for each road, the time it takes for the road surface to become partially waterlogged after rain based on previously acquired road surface images (road surface images stored in the probe information storage unit 7) and past weather information, and may predict the time when the road surface of each road will become partially waterlogged based on current weather information, and provide the prediction result to the user of the probe vehicle 100. The user can efficiently collect road deterioration information by driving the probe vehicle 100 at the time when it is predicted that the road surface will become partially waterlogged.

[0059] Furthermore, the server 200 may formulate a driving plan for the probe vehicle 100 that can efficiently acquire road deterioration information based on the predicted time when the road surface of each road will be partially flooded and the map data in the map database 9, and provide the driving plan to the user of the probe vehicle 100.

[0060] <Embodiment 2> Fig. 6 is a diagram showing the configuration of a road deterioration determination system according to embodiment 2. The configuration of the road deterioration determination system in Fig. 6 is the same as that in Fig. 1 except that a sprinkler device 104 is mounted on the probe vehicle 100.

[0061] The probe device 110 detects road surface deterioration from road surface images of a road that has become partially waterlogged after rain, but rain does not always fall conveniently before the probe vehicle 100 is driven. Therefore, in the second embodiment, the probe vehicle 100 is equipped with a sprinkler device 104 that sprinkles water around the probe vehicle 100, giving the probe vehicle 100 the function of wetting the surrounding road surface. The probe vehicle 100 may capture road surface images with the camera 102 while spraying water on the road with the sprinkler device 104, or may drive on the road again a certain time after spraying water on the road with the sprinkler device 104 and capture road surface images.

[0062] Instead of water, a liquid or powder such as road deicer may be sprayed as long as it can make deteriorated areas of the road surface more noticeable.

[0063] <Embodiment 3> Fig. 7 is a diagram showing the configuration of a road deterioration determination system according to embodiment 2. The configuration of the road deterioration determination system in Fig. 6 is different from the configuration in Fig. 1 in that an information distribution unit 11 is provided in the server 200.

[0064] The information distribution unit 11 is a communication device that distributes map data including road surface deterioration information, i.e., map data linked to information on road surface deterioration points. A user of the road deterioration determination system uses a viewing terminal 400 to log in to the server 200 with a pre-registered account, and then views the map data distributed by the information distribution unit 11 via a VPN (Virtual Private Network) 310 to obtain information such as the location of road surface deterioration points, the deterioration level, and repair priority. Possible users of the road deterioration determination system include, for example, local governments (such as prefectures, cities, wards, towns, and villages) that manage roads and contractors for road repair work.

[0065] <Hardware Configuration Example> The probe device 110 and the server 200 constituting the road deterioration determination system are realized, for example, by a processing circuit 50 shown in FIG. 8 . That is, the probe device 110 acquires location information of the probe vehicle 100, acquires road surface images captured of the road surface around the probe vehicle 100, detects partially waterlogged road surfaces from the road surface images, and determines the road surface deterioration level based on the shape of the waterlogged portions on the road surface and the degree of waterlogging. The processing circuit 50 also identifies road surface deterioration points where the road surface has deteriorated based on location information of the probe vehicle 100 when the road surface image of the road surface where deterioration has been detected was captured. The server 200 also includes a processing circuit 50 that performs processing to store road surface deterioration information including information on road surface deterioration points and deterioration levels. The processing circuit 50 may be dedicated hardware or may be configured using a processor (also called a central processing unit (CPU), processing device, arithmetic unit, microprocessor, microcomputer, or DSP (digital signal processor)) that executes a program stored in memory.

[0066] When the processing circuitry 50 is dedicated hardware, the processing circuitry 50 may be, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or a combination thereof. The functions of the components of the probe device 110 and the server 200 may be realized by individual processing circuits, or the functions may be collectively realized by a single processing circuit.

[0067] The probe device 110 and the server 200 can also be realized by, for example, a processor 51 and a memory 52 shown in FIG. 9 . In this case, the functions of the components of the probe device 110 and the server 200 are realized by software or the like (software, firmware, or a combination of software and firmware). The software or the like is written as a program and stored in the memory 52. ​​The processor 51 realizes the functions of each part by reading and executing the program stored in the memory 52. ​​That is, the probe device 110 includes the memory 52 for storing a program that, when executed by the processor 51, results in the following processes: acquiring position information of the probe vehicle 100; acquiring road surface images that are images of the road surface around the probe vehicle 100; detecting a road surface that is partially submerged in water from the road surface images; determining the deterioration level of the road surface based on the shape of the submerged portion of the road surface and the degree of water penetration; and identifying a road surface deterioration point where the road surface has deteriorated based on the position information of the probe vehicle 100 at the time the road surface image where deterioration has been detected was captured. The server 200 also includes a memory 52 for storing programs that result in the execution of a process for storing road surface deterioration information, including information on road surface deterioration points and deterioration levels. In other words, these programs can be said to cause a computer to execute procedures and methods for the operation of the components of the probe device 110 and the server 200.

[0068] Here, the memory 52 may be, for example, a non-volatile or volatile semiconductor memory such as a RAM (Random Access Memory), a ROM (Read Only Memory), a flash memory, an EPROM (Erasable Programmable Read Only Memory), or an EEPROM (Electrically Erasable Programmable Read Only Memory), a HDD (Hard Disk Drive), a magnetic disk, a flexible disk, an optical disk, a compact disk, a mini disk, a DVD (Digital Versatile Disc), and a drive device for such a disk, or any other storage medium that will be used in the future.

[0069] The above describes a configuration in which the functions of the components of the probe device 110 and the server 200 are realized either by hardware or software, etc. However, this is not a limitation, and the probe device 110 and the server 200 may be configured such that some of the components are realized by dedicated hardware and other components are realized by software, etc. For example, it is possible to realize the functions of some of the components by the processing circuit 50 as dedicated hardware, and to realize the functions of other components by the processing circuit 50 as the processor 51 reading and executing a program stored in the memory 52.

[0070] As described above, the probe device 110 and the server 200 can realize the above-described functions by hardware, software, or a combination of these.

[0071] It is possible to freely combine the embodiments, and to modify or omit the embodiments as appropriate.

[0072] The above description is illustrative in all respects, and it is understood that countless variations not illustrated can be envisioned.

[0073] 100 Probe vehicle, 101 Locator, 102 Camera, 103 Communication device, 104 Sprinkler device, 110 Probe device, 200 Server, 300 Network, 310 VPN, 400 Viewing terminal, 1 Location information acquisition unit, 2 Image acquisition unit, 3 Water infiltration detection unit, 4 Road surface deterioration determination unit, 5 Probe information generation unit, 6 Communication unit, 7 Probe information storage unit, 8 Road surface deterioration information storage unit, 9 Map database, 10 Repair priority determination unit, 11 Information distribution unit, 50 Processing circuit, 51 Processor, 52 Memory.

Claims

1. A road deterioration determination system comprising: a location information acquisition unit that acquires location information of a vehicle; an image acquisition unit that acquires road surface image, which is an image obtained by capturing the road surface around the vehicle; a water infiltration detection unit that detects the road surface in a state where it is partially flooded with water from the road surface image; a road surface deterioration determination unit that determines the deterioration level of the road surface based on the shape of the water-soaked part of the road surface and the degree of water infiltration, and identifies road surface deterioration points, which are points where the road surface has deteriorated, based on the location information of the vehicle at the time the road surface image of the road surface where deterioration has been detected was captured; and a road surface deterioration information storage unit that stores road surface deterioration information including information on the road surface deterioration points and the deterioration level.

2. The road deterioration determination system described in claim 1, wherein the location information acquisition unit, the image acquisition unit, the water infiltration detection unit and the road surface deterioration determination unit are arranged in the vehicle, the road surface deterioration information storage unit is arranged in a server capable of communicating with the vehicle, and the road surface image and the corresponding road surface deterioration information are transmitted from the vehicle to the server as probe information.

3. The road deterioration determination system of claim 1, wherein the location information acquisition unit acquires the location information of the vehicle from a locator of the vehicle's drive recorder, and the image acquisition unit acquires the road surface image from a camera of the drive recorder.

4. The road deterioration determination system of claim 1, wherein the road surface conditions in the past road surface images are classified into a state where the road surface is completely covered with water, a state where puddles have formed, a state where the road surface is partially flooded with water, and a state where the road surface is completely dry, and the characteristics of the road surface images corresponding to each state are learned, and based on the learning results, the water infiltration detection unit generates logic to detect the road surface when it is partially flooded with water.

5. The road deterioration determination system according to claim 1, wherein the road surface deterioration determination unit determines the degree of water penetration based on the brightness of the water-soaked portion of the road surface in the road surface image.

6. A road deterioration determination system as described in claim 5, which learns the brightness characteristics of water-soaked parts of the road surface in past road surface images, and based on the learning results, generates logic for the road surface deterioration determination unit to determine the degree of water soaking.

7. The road deterioration determination system according to claim 1, wherein the road surface deterioration determination unit determines the deterioration level of the road surface by taking into account one or more of the degree of depression, area, and number of water-soaked areas on the road surface.

8. The road deterioration determination system according to claim 1, wherein the road surface deterioration determination unit determines the deterioration level of the road surface by taking into account shadows that appear on the road surface when illuminated by the vehicle's lighting device.

9. The road deterioration determination system of claim 1, wherein the road surface deterioration determination unit determines the deterioration level of the road surface by comparing the road surface image of the road surface in a partially water-soaked state with the road surface image of the road surface in a dry state, both taken at the same location.

10. A road deterioration determination system as described in claim 1, further comprising a map database in which map data is stored, and wherein the road surface deterioration information storage unit stores information on the road surface deterioration points included in the road surface deterioration information by linking it to the map data.

11. The road deterioration determination system according to claim 10, further comprising an information distribution unit that distributes the map data including the road surface deterioration information linked to information on the road surface deterioration points.

12. The road deterioration determination system according to claim 1, further comprising a map database storing map data including information on roads having surfaces that become more noticeable with cracks, scratches, or ruts when waterlogged.

13. The road deterioration determination system according to claim 1, further comprising a repair priority determination unit that determines the priority of repairing the road surface at the road surface deterioration point based on one or both of the deterioration level and traffic volume at the road surface deterioration point.

14. The road deterioration determination system described in claim 13, wherein the repair priority determination unit calculates the elapsed time from the time the rain stopped to the time the road surface image was taken based on past weather information, determines the reliability of the road surface deterioration information corresponding to the road surface image based on the elapsed time, and determines the priority taking into account the reliability.

15. A road deterioration determination system as described in claim 1, wherein the location information of the vehicle acquired by the location information acquisition unit includes information on the lane in which the vehicle is traveling, and the information on the road surface deterioration point includes information on the lane in which the road surface has deteriorated.

16. A road deterioration determination system as described in claim 15, further comprising a map database in which high-precision map data including road shape data for each lane is stored, and the road surface deterioration information storage unit stores information on lanes whose road surfaces have deteriorated, included in the road surface deterioration information, by linking it to the high-precision map data.

17. The road deterioration determination system according to claim 1, further comprising a sprinkler device for sprinkling water around the vehicle.

18. A road deterioration determination method, in which a position information acquisition unit of the road deterioration determination system acquires position information of a vehicle, an image acquisition unit of the road deterioration determination system acquires road surface image, which is image obtained by photographing the road surface around the vehicle, a water infiltration detection unit of the road deterioration determination system detects the road surface in a partially water-infiltrated state from the road surface image, a road surface deterioration determination unit of the road deterioration determination system determines the deterioration level of the road surface based on the shape of the water-infiltrated portion of the road surface and the degree of water infiltration, and identifies a road surface deterioration point, which is a point where the road surface has deteriorated, based on the position information of the vehicle at the time the road surface image of the road surface where deterioration has been detected was taken, and a road surface deterioration information storage unit of the road deterioration determination system stores road surface deterioration information including information on the road surface deterioration point and the deterioration level.

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