A device for identifying deteriorated geological features, a system for identifying deteriorated geological features, a method for identifying deteriorated geological features, a program for identifying deteriorated geological features, and a computer-readable recording medium on which the deteriorated geological feature identification program is stored.

Abstract

The present invention provides a device for identifying deteriorated geological features, etc., that can identify deteriorated geological features from among a large number of them. [Solution] A moving object receives reflected light from a geographical feature after emitting light, acquires reflected intensity data measured by the moving object, and identifies the deteriorated geographical feature based on the acquired reflected intensity data.

Description

Technical Field

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[0003]

[0001] This application relates to the technical field of a deteriorated object identification device for identifying deteriorated objects.

Background Art

[0002] <​​​​​​​​​​​​​​​​​​​​​While the technology described in Patent Document 1 allowed for the determination of road areas from point cloud data measured on the ground surface, it was not possible to determine the deterioration state of geological features. However, in autonomous driving technology, processing using geological feature information is extremely important for tasks such as vehicle position estimation, lane keeping, and recognition of drivable areas, and the deterioration state of geological features has a significant impact on these processes. Therefore, it is important to understand the actual deterioration state of geological features and to reflect deteriorated features in map data.

[0006] In view of these circumstances, one of the objectives of the present invention is to provide a device for identifying deteriorated geological features, etc., that can identify deteriorated geological features from among a large number of geological features. [Means for solving the problem]

[0007] The invention described in claim 1 is characterized by comprising: an acquisition unit that acquires reflection intensity data based on the reflected light from a geographical feature of the light emitted by the emission unit; and an identification unit that identifies a geographical feature that is deteriorating based on the reflection intensity data acquired by the acquisition unit.

[0008] The invention described in claim 11 is characterized by comprising: an acquisition unit that acquires reflection intensity data based on the reflected light from a feature of light emitted by an emission unit provided on one or more moving bodies; an identification unit that identifies a feature that is deteriorating based on the reflection intensity data acquired by the acquisition unit; and an update unit that updates map data corresponding to the deteriorating feature identified by the identification unit.

[0009] The invention described in claim 12 is characterized by comprising: an acquisition unit that acquires deteriorated feature information relating to deteriorated feature identified by a mobile device provided on one or more mobile bodies based on the reflected light from the feature emitted by an emission unit provided on the mobile body; and an update unit that updates map data corresponding to the feature indicated by the deteriorated feature information acquired by the acquisition unit.

[0010] The invention described in claim 13 is a deteriorated feature identification system including a mobile device provided on a mobile body and a deteriorated feature identification device, wherein the mobile device includes a transmitting unit that transmits reflection intensity data based on the reflected light from a feature emitted by an emission unit provided on the mobile body to the deteriorated feature identification device, and the deteriorated feature identification device includes an acquisition unit that acquires the reflection intensity data from the mobile device and an identification unit that identifies a deteriorated feature based on the reflection intensity data acquired by the acquisition unit.

[0011] The invention described in claim 14 is a method for identifying deteriorated geological features using a deteriorated geological feature identification device, characterized by comprising: an acquisition step of acquiring reflection intensity data based on the reflected light from geological features of light emitted from an emission unit; and an identification step of identifying deteriorated geological features based on the reflection intensity data acquired in the acquisition step.

[0012] The invention described in claim 15 is characterized in that the computer functions as an acquisition unit that acquires reflection intensity data based on the reflected light from a geographical feature of the light emitted by the emission unit, and an identification unit that identifies a geographical feature that is deteriorating based on the reflection intensity data acquired by the acquisition unit.

[0013] The invention described in claim 16 is a computer-readable recording medium that stores a deteriorated feature identification program, characterized in that the computer functions as an acquisition unit that acquires reflection intensity data based on the reflected light from a feature emitted by an emission unit, and an identification unit that identifies a deteriorated feature based on the reflection intensity data acquired by the acquisition unit. [Brief explanation of the drawing]

[0014] [Figure 1] This is a block diagram showing the configuration of a deteriorated geological feature identification device according to an embodiment. [Figure 2] This is a block diagram showing the outline configuration of the map data management system according to the embodiment. [Figure 3](A) is a diagram showing how the Lidar according to the embodiment measures the light reflection intensity at the white line (no degradation), and (B) is a diagram showing an example of a graph obtained when the said reflection intensity is statistically processed. [Figure 4] (A) is a diagram showing how the Lidar according to the embodiment measures the light reflection intensity at the white line (with degradation), and (B) is a diagram showing an example of a graph obtained when the said reflection intensity is statistically processed. [Figure 5] This is a diagram illustrating the method for calculating the white line prediction range in the embodiment. [Figure 6] This flowchart shows an example of the operation of the map data management system according to the embodiment for processing reflectance intensity data. [Figure 7] This flowchart shows an example of the operation of the degradation determination process by the server device according to the embodiment. [Figure 8] (A) is a side view showing how the Lidar according to the fourth modified example emits multiple beams of light in the vertical direction, and (B) is a diagram showing how the Lidar measures the reflected light intensity at the white line. [Figure 9] (A) is a diagram showing an example of the reflected intensity measured along the dashed white line in the direction of travel of the vehicle in the fourth modified example, and (B) is an example diagram showing the effective area and ineffective area for separating the reflected intensity used for deterioration determination based on the distribution of said reflected intensity. [Figure 10] This diagram illustrates a method for obtaining paint surface reflectivity data 501, which shows the reflectivity of the painted portion of the road surface. [Figure 11] This is a conceptual diagram illustrating the process of generating a reflectance map data 510 by combining multiple paint area reflectance data 501. [Figure 12] (A) and (B) are examples of painted areas on the road surface. [Modes for carrying out the invention]

[0015] An embodiment for carrying out the present invention will be described with reference to Figure 1. Figure 1 is a block diagram showing the schematic configuration of the deteriorated geological feature identification device according to this embodiment.

[0016] As shown in FIG. 1, the deteriorated feature identification device 1 according to the present embodiment includes an acquisition unit 1A and an identification unit 1B.

[0017] The acquisition unit 1A acquires reflection intensity data based on the reflected light from the feature of the light emitted by the emission unit.

[0018] The identification unit 1B identifies the deteriorated feature based on the reflection intensity data acquired by the acquisition unit 1A.

[0019] As described above, according to the deteriorated feature identification device 1 according to the present embodiment, the acquisition unit 1A acquires the reflection intensity data based on the reflected light from the feature of the light emitted by the emission unit, and the identification unit 1B identifies the deteriorated feature based on the reflection intensity data acquired by the acquisition unit 1A. Therefore, the deteriorated feature can be identified based on the reflection intensity data based on the reflected light from the feature of the light emitted by the emission unit. In addition, by reflecting the deterioration information in the map data for the feature determined to be deteriorated, it becomes possible to promptly and efficiently perform the maintenance of the actual feature, and stable automatic driving becomes possible. [Example]

[0020] The example will be described with reference to FIGS. 2 to 7. The example described below is an example when the invention of the present application is applied to the map data management system S.

[0021] [1. Configuration and Outline of Map Data Management System S] As shown in FIG. 2, the map data management system S of the present example includes a server device 100 that manages map data and in-vehicle terminals 200 mounted on each of a plurality of vehicles. The server device 100 and each in-vehicle terminal 200 are connected via a network NW. Although one in-vehicle terminal 200 is shown in FIG. 2, the map data management system S may include a plurality of in-vehicle terminals 200. Also, the server device 100 may be configured by a plurality of devices.

[0022] As shown in Figures 3(A) and 4(A), the in-vehicle terminal 200 transmits reflection intensity data D to the server device 100. This data is measured by receiving reflected light from white lines W1 and W2 (an example of a "landmark") of light L emitted by the Lidar 205 in a vehicle V equipped with the Lidar 205 together with the in-vehicle terminal 200. In Figures 3(A) and 4(A), the bars representing the reflection intensity data D represent the magnitude of the reflection intensity at that point, depending on their length (longer bars indicate greater reflection intensity). The reflection intensity data D includes data on the reflection intensity at each point irradiated by the light L emitted by the Lidar 205. Figures 3(A) and 4(A) show that the reflection intensity is measured at five points for each of the white lines W1 and W2.

[0023] The server device 100 identifies deteriorated features based on multiple reflectance data D received from each of the multiple in-vehicle terminals 200. Specifically, it identifies deteriorated features by statistically processing the multiple reflectance data D. For example, as shown in Figure 3(A), for white lines that have not deteriorated, the reflectance at each point within the white line is high and uniform, so as shown in Figure 3(B), the average value μ calculated based on multiple reflectances becomes high and the standard deviation σ becomes small. On the other hand, as shown in Figure 4(A), for deteriorated white lines, the reflectance at each point within the white line is low or non-uniform, so as shown in Figure 4(B), the average value μ calculated based on multiple reflectances becomes low or the standard deviation σ becomes large. Therefore, the server device 100 determines whether a feature has deteriorated by comparing the average value μ and standard deviation σ calculated based on multiple reflectances with a threshold, and identifies deteriorated features. The server device 100 then updates the map data corresponding to the deteriorated features. Note that the map data update is instructed from the server device 100. The receiving device may perform the action.

[0024] [2. Configuration of the in-vehicle terminal 200] Next, the configuration of the in-vehicle terminal 200 according to this embodiment will be described. As shown in Figure 2, the in-vehicle terminal 200 is broadly composed of a control unit 201, a storage unit 202, a communication unit 203, and an interface unit 204.

[0025] The storage unit 202 is composed of, for example, an HDD (Hard Disk Drive) or SSD (Solid State Drive), and stores the OS (Operating System), a reflectivity data processing program, map data, reflectivity data D, and various other data. The map data contains map location information indicating the position of the feature (white lines in this embodiment) that is the target of deterioration judgment, and a feature ID to distinguish it from other features (since the map location information and feature ID are information linked to a single feature, the feature ID can be said to be one of the location information indicating the position of that single feature). In the example in Figure 3(A), white lines W1 and W2 are each assigned different feature IDs. If the white lines are long, such as several hundred meters, and it is inappropriate to treat them as a single feature, they are divided into sections of a certain length (for example, 5m) and each section is assigned a separate feature ID and treated as a separate feature. Furthermore, map data similar to that stored in the memory unit 202 (map data describing map location information and feature ID for each feature) is also stored in the memory unit 102 of the server device 100, allowing the same feature to be identified by its feature ID in both the in-vehicle device 200 and the server device 100. Moreover, the map data stored in the memory unit 202 may, for example, store map data for the entire country, or it may store map data corresponding to a certain area including the vehicle's current location, which is received in advance from the server device 100 or the like.

[0026] The communication unit 203 controls the communication status between the in-vehicle terminal 200 and the server device 100.

[0027] The interface unit 204 provides an interface function for exchanging data between external devices such as the Lidar 205 and the internal sensor 206 and the in-vehicle terminal 200.

[0028] The Lidar205 is a device that is mounted on the roof of a vehicle and, as one of its functions, continuously emits infrared laser light in a circular pattern around the vehicle (emitted downwards from the roof at a certain angle). It receives the light reflected from points on the surfaces of objects around the vehicle and generates reflection intensity data D, which shows the reflection intensity at each point. Since the reflection intensity data D shows the intensity of the laser light emitted horizontally and reflected by the ground and objects, it includes areas with low reflection intensity (ground areas where no objects exist) and areas with high reflection intensity (areas where objects exist). In addition, multiple Lidar205s may be mounted on the front or rear of the vehicle, and the reflection intensity data acquired from each can be combined to generate reflection intensity data D around the vehicle.

[0029] Lidar 205 immediately transmits reflectance data D (including areas with low and high reflectance) to the in-vehicle terminal 200 via the interface unit 204 after measuring the reflectance intensity. When the control unit 201 receives the reflectance intensity data D from Lidar 205, it stores the received reflectance intensity data D in the storage unit 202, associating it with measurement location information indicating the position of the vehicle (Lidar 205) at the time of reception of the reflectance intensity data D and measurement date and time information indicating the date and time of reception of the reflectance intensity data D. The control unit 201 may delete from the storage unit 202 any reflectance intensity data D, measurement location information, and measurement date and time information that have been stored in the storage unit 202 for a predetermined time after measurement, or that have been transmitted to the server device 100.

[0030] The internal sensors 206 are a general term for sensors mounted on a vehicle, including satellite positioning sensors (GNSS (Global Navigation Satellite System)), gyro sensors, and vehicle speed sensors.

[0031] The control unit 201 consists of a CPU (Central Processing Unit) that controls the entire control unit 201, a ROM (Read Only Memory) in which control programs for controlling the control unit 201 are pre-stored, and a RAM (Random Access Memory) for temporarily storing various data. The CPU then realizes various functions by reading and executing the various programs stored in the ROM and the memory unit 202.

[0032] The control unit 201 acquires estimated vehicle position information. The estimated vehicle position information may be generated by a device outside the in-vehicle terminal 200, or it may be generated by the control unit 201. The estimated vehicle position information can be generated, for example, by matching the location of features measured by the Lidar 205 with the location of features in the map data for autonomous driving, or by generating it based on information detected by the interior sensor 206 and map data, or by a combination of these methods.

[0033] Furthermore, the control unit 201 predicts the actual position of the white lines as seen from the vehicle (Lidar 205) based on the estimated vehicle position information and the map position information of the white lines shown in the map data. At this time, the control unit 201 calculates and sets the white line prediction range that includes the white lines with a certain margin.

[0034] Here, we will specifically explain how to set the white line prediction range using Figure 5. The coordinate system and other details in Figure 5 are as follows. Map coordinate system: TIFF2026123184000002.tif14165 Vehicle coordinate system: TIFF2026123184000003.tif13168 White line map location in map coordinate system: TIFF2026123184000004.tif19162 Predicted position of white lines in vehicle coordinate system: Estimated vehicle position in map coordinate system: TIFF2026123184000005.tif18168 Estimated vehicle azimuth angle in the map coordinate system: TIFF2026123184000006.tif19169 TIFF2026123184000007.tif19165

[0035] The control unit 201 calculates the predicted white line range from the white line map position indicated by the white line map position information in the direction of travel of the vehicle (for example, 10m ahead), based on the estimated vehicle position indicated by the estimated vehicle position information. In this case, as shown in Figure 5, if the lane in which the vehicle V is traveling is separated by a white line 1 on the left and a white line 2 on the right, the predicted white line range is calculated for each of the white lines 1 and 2.

[0036] Since the method for calculating the predicted white line range is the same for both white line 1 and white line 2, this section will explain the case where the predicted white line range is calculated for white line 1. First, the control unit 201 calculates the predicted position 301 of white line 1 (the predicted white line position for white line 1) based on the map position of white line 1 and the estimated position of the vehicle. The predicted white line position is obtained by the following equation (1).

number

[0037] Next, the control unit 201 sets the white line 1 prediction range 311 based on the white line 1 prediction position 301. Specifically, the white line 1 prediction range 311 is defined as a certain range that includes the white line 1 prediction position 301. Then, the control unit 201 extracts white line 1 reflection intensity data 321, which indicates the reflection intensity within the white line 1 prediction range 311, from the reflection intensity data D, which includes the reflection intensity at multiple points.

[0038] The control unit 201 transmits the extracted reflectance data 321 and 322 to the server device 100, associating them with feature IDs corresponding to the map locations of white line 1 and white line 2, respectively, and measurement date and time information corresponding to the reflectance data D from which the reflectance data 321 and 322 were extracted. In the following, the reflectance data D measured by Lidar 205 and stored in the storage unit 202 by the in-vehicle terminal 200 (which may be raw data measured by Lidar 205 or data processed from raw data) will be referred to as pre-extraction reflectance data D, and the reflectance data D showing the reflectance extracted within the white line prediction range will be referred to as post-extraction reflectance data D.

[0039] [3. Configuration of Server Device 100] Next, the configuration of the server device 100 will be described. As shown in Figure 2, the server device 100 is broadly composed of a control unit 101, a storage unit 102, a communication unit 103, a display unit 104, and an operation unit 105.

[0040] The storage unit 102 is composed of, for example, an HDD or SSD, and stores the OS, a white line degradation judgment program, reflection intensity data D received from the in-vehicle terminal 200, and various other data.

[0041] The communication unit 103 controls the communication status with the in-vehicle terminal 200.

[0042] The display unit 104 is composed of, for example, a liquid crystal display, and displays information such as characters and images.

[0043] The control unit 105 is composed of, for example, a keyboard, a mouse, etc., and receives operation instructions from the operator and outputs the content of those instructions as instruction signals to the control unit 101.

[0044] The control unit 101 consists of a CPU that controls the entire control unit 101, a ROM in which control programs for controlling the control unit 101 are pre-stored, and a RAM for temporarily storing various data. The CPU then realizes various functions by reading and executing the various programs stored in the ROM and the memory unit 102.

[0045] The control unit 101 determines the deterioration state of the white lines based on multiple reflection intensity data D received from one or more in-vehicle terminals 200. The control unit 101 then updates the map data corresponding to the deteriorated feature so that it can identify that the feature is deteriorated.

[0046] [4. Example of operation of the map data management system S] [4.1. Example of operation when processing reflectance intensity data] Next, an example of the operation of the map data management system S's reflection intensity data processing will be explained using the flowchart in Figure 6. Although the flowchart in Figure 6 explains the process in which one in-vehicle terminal 200 measures reflection intensity data D and transmits it to the server device 100, the same process is performed for each in-vehicle terminal 200 included in the map data management system S. Furthermore, the processes of the in-vehicle terminal 200 in Figure 6, from steps S101 to S105, are executed periodically (for example, every predetermined time and / or every time the vehicle on which the in-vehicle terminal 200 is installed travels a predetermined distance), and upon receiving the processing of step S105 by the in-vehicle terminal 200, the server device 100 executes the processes of steps S201 to S202.

[0047] First, the control unit 201 of the in-vehicle terminal 200 acquires estimated vehicle position information (step S101).

[0048] Next, the control unit 201 obtains map position information of the white line from the map data corresponding to the estimated vehicle position indicated by the estimated vehicle position information obtained in step S101 (step S102). At this time, as described above, the control unit 201 obtains map position information of the white line in the direction of travel of the vehicle and the feature ID.

[0049] Next, the control unit 201 calculates and sets the predicted white line range from the estimated vehicle position indicated by the estimated vehicle position information and the white line map position indicated by the white line map position information (step S103).

[0050] Next, the control unit 201 extracts the portion of the pre-extraction reflectance data D measured by Lidar 205 that falls within the white line prediction range to obtain post-extraction reflectance data D (step S104). Specifically, the control unit 201 first identifies the pre-extraction reflectance data D measured by emitting laser light within a range that includes the white line prediction range, based on the measurement position of the measurement position information stored in association with the pre-extraction reflectance data D and the emission angle (a constant downward angle) when Lidar 205 emits laser light. Then, the control unit 201 extracts the portion of the identified pre-extraction reflectance data D that falls within the white line prediction range to obtain post-extraction reflectance data D2. For example, the control unit 201 extracts the portion corresponding to the azimuth angles (θ1, θ2 (see Figure 5)) of the white line prediction range with respect to the vehicle direction.

[0051] Next, the control unit 201 transmits the extracted post-reflection intensity data D extracted in step S104 to the server device 100 along with the measurement date and time information stored in association with the pre-extraction reflectance intensity data D from which the extracted post-reflection intensity data D was extracted, and the feature ID acquired in step S102 (step S105), and terminates the reflectance intensity data transmission process.

[0052] In response, when the control unit 101 of the server device 100 receives the extracted reflectance intensity data D, measurement date and time information, and feature ID from the in-vehicle terminal 200 (step S201), it stores these in the storage unit 102 in association with each other (step S202), and terminates the reflectance intensity data transmission processing. As a result, the storage unit 102 of the server device 100 stores multiple extracted reflectance intensity data D transmitted from each of the multiple in-vehicle terminals 200.

[0053] [4.2. Example of operation during degradation detection processing] Next, an example of the operation of the deterioration determination process by the server device 100 will be explained using the flowchart in Figure 7. The deterioration determination process is executed, for example, when an operator or the like issues an instruction to determine the deterioration of a white line, and the feature ID of the white line to be determined is specified.

[0054] First, the control unit 101 of the server device 100 acquires a specified feature ID (step S211). Next, the control unit 101 acquires extracted reflectance intensity data D from the storage unit 102 that is stored in association with the feature ID and whose measurement date and time are within a predetermined period (for example, the most recent three months) (step S212). The reason for limiting the acquisition target to reflectance intensity data D with a measurement date and time within a predetermined period is that reflectance intensity data D that is too old is not suitable for determining the current state of deterioration.

[0055] Next, the control unit 101 calculates the average of the extracted reflectance intensity data D extracted in step S212 (step S213), and then calculates the standard deviation (step S214). When calculating the average and standard deviation, the control unit 101 processes the extracted reflectance intensity data D for each feature ID (for each white line W1 and white line W2 in the examples of Figures 3(A) and 4(A)). In the examples of Figures 3(A) and 4(A), the reflectance intensity is measured at five points for white line W1, so the control unit 101 calculates the average and standard deviation of the reflectance intensity at each of these points. Alternatively, instead of calculating the average and standard deviation of the reflectance intensity at each of the five points, the control unit 101 may calculate the average and standard deviation of the reflectance intensity at the five points or at a predetermined area (part or all of the area) included in the extracted reflectance intensity data D.

[0056] Next, the control unit 101 determines whether the average calculated in step S213 is less than or equal to the first threshold (step S215). If the control unit 101 determines that the average is less than or equal to the first threshold (step S215: YES), it determines that the specified white line is "deteriorated" (step S217), updates the map data corresponding to the white line by adding information indicating that it is "deteriorated" (step S218), and terminates the deterioration determination process. Note that the control unit 101 determining that the specified white line is "deteriorated" in step S217 is one example of identifying deteriorated white lines. On the other hand, if the control unit 101 determines that the average is not less than or equal to the first threshold (step S215: NO), it then determines whether the standard deviation calculated in step S214 is greater than or equal to the second threshold (step S216).

[0057] In this case, if the control unit 101 determines that the standard deviation is greater than or equal to the second threshold (step S216: YES), it determines that the specified white line is "deteriorated" (step S217), updates the map data corresponding to the white line by adding information indicating that it is "deteriorated" (step S218), and terminates the deterioration determination process. On the other hand, if the control unit 101 determines that the standard deviation is not greater than or equal to the second threshold (step S216: NO), it determines that the specified white line is "not deteriorated" (step S219), and terminates the deterioration determination process. Note that instead of the control unit 101 of the server device 100 performing the deterioration determination process, the control unit 201 of the in-vehicle terminal 200 may perform the deterioration determination process. In this case, the control unit 201 transmits information to the server device 100 indicating that the feature corresponding to the white line that has been determined to be deteriorated is "deteriorated".

[0058] As described above, in this embodiment, the map data management system S has a control unit 101 (an example of an "acquisition unit" or "identification unit") of the server device 100 that receives reflected light from white lines (an example of "features") emitted by a vehicle (an example of a "moving object"), acquires reflected intensity data D measured by the vehicle, and identifies deteriorated white lines based on the acquired reflected intensity data D.

[0059] Therefore, according to the map data management system S of this embodiment, deteriorated white lines can be identified by using the reflectance intensity data D acquired from the vehicle. It is also conceivable to determine the deterioration of white lines based on images taken by a camera mounted on the vehicle, but there are limitations such as changes in brightness due to nighttime conditions and backlighting, and camera resolution, making it difficult to accurately determine deterioration. Therefore, using reflectance intensity data D for deterioration determination, as in this embodiment, is superior.

[0060] Furthermore, the control unit 101 acquires measurement date and time information indicating the measurement date and time of the reflectance data D, selects the reflectance data D measured during a predetermined period based on the measurement date and time information, and identifies the deteriorated white lines based on the selected reflectance data D. Therefore, by appropriately setting a predetermined period (for example, the most recent few months), it is possible to appropriately identify deteriorated white lines based on reflectance data that excludes reflectance data D that is unsuitable for determining the deterioration of white lines.

[0061] Furthermore, the control unit 101 acquires a feature ID (an example of "location information") to identify the position of the white line that has reflected light, and identifies the deteriorated white line based on the reflection intensity data D and the feature ID. This makes it possible to identify the location of the deteriorated white line as well.

[0062] Furthermore, the control unit 101 identifies deteriorated white lines based on map location information (an example of "location information") and extracted reflectance intensity data D measured within a set white line prediction range. This allows for deterioration determination by excluding reflectance intensity data D that indicates light reflected from sources other than the white lines, enabling more accurate deterioration determination of the white lines.

[0063] Furthermore, the control unit 101 (an example of an "update unit") updates the map data corresponding to the white lines that were determined to be "deteriorated" in step S217 of Figure 7 (performing an update that adds information indicating that "deterioration is present"). This allows the deterioration information to be reflected in the map data representing the deteriorated white lines.

[0064] Furthermore, the control unit 101 receives reflected light from the white lines emitted by one or more vehicles, acquires reflected intensity data D measured at each of the one or more vehicles, and identifies the deteriorated white lines based on the acquired multiple reflected intensity data D. Therefore, according to the map data management system S of this embodiment, deteriorated white lines can be identified with high accuracy using reflected intensity data D acquired from one or more vehicles.

[0065] In this embodiment, the geological features subject to deterioration assessment were described as white lines, but any geological features that can be assessed for deterioration based on reflectivity can be included in the assessment.

[0066] [5. Variant] Next, we will describe some variations of this embodiment. Note that the variations described below can be combined as appropriate.

[0067] [5.1. First Variation] In the above embodiment, the case where the white line subject to deterioration assessment is a solid line was described, but the white line may also be a dashed line. Furthermore, white lines are used not only to demarcate lanes, but also for guide fluids, letters, pedestrian crossings, etc. Moreover, the deterioration assessment can include not only white lines but also other features such as signs and billboards. In other words, the features subject to deterioration assessment can be classified into various types. Therefore, feature type information indicating the type of feature may be further linked to the feature ID, and the threshold for deterioration assessment using reflectivity data may be changed depending on the type of feature.

[0068] Furthermore, if the white line is a dashed line, a feature ID may be set for each painted section of the white line, and the sections without painted white lines may be excluded from the deterioration assessment without setting a feature ID. In addition, since the direction of the white line may not be parallel to the direction of vehicle travel for guide fluids, letters, pedestrian crossings, etc., a method for setting the white line prediction range may be defined for each type of white line (for example, the position information of the four corner points of the white line prediction range is described in the map data, and the control unit 201 of the on-board device 200 sets the white line prediction range based on this), and the white line prediction range may be set according to the type of white line that is subject to deterioration assessment.

[0069] [5.2. Second Variation] The reflectance measured by Lidar205 is affected by sunlight and therefore varies depending on the date and weather conditions at the time of measurement. For example, the reflectance of the same white line may differ depending on whether it is measured at dawn or dusk. The reflectance intensity at different times of day differs from that during the daytime. Furthermore, the reflectance intensity on sunny days differs from that on cloudy, rainy, or snowy days. Therefore, in the second modified example, when the control unit 201 of the vehicle-mounted device 200 receives the reflectance intensity data D from the Lidar 205, it further associates weather information indicating the weather conditions at the time of measurement with the data and stores it in the storage unit 202. In step S105 of Figure 6, it further associates the weather information with the data and transmits it to the server device 100, and the server device 100 also further associates the weather information with the data and stores it in the storage unit 102. The control unit 101 of the server device 100 may then correct the reflectance intensity data D according to at least one of the measurement date and time information or the weather information (an example of "incidental information") and identify the deteriorated terrain. This allows for appropriate deterioration determination by offsetting the differences between reflectance intensity data due to the time of measurement and weather conditions.

[0070] [5.3. Third Variation] In the above embodiment, the control unit 201 of the in-vehicle terminal 200 periodically transmits reflectance data D to the server device 100. In addition, the control unit 201 may add a condition that it transmits only reflectance data D measured by the in-vehicle terminal 200 (its own vehicle) within a predetermined area (for example, a measurement area specified by the server device 100). This allows the server device 100 to specify an area where white line deterioration needs to be determined as a measurement area, thereby avoiding the reception of reflectance data D measured in areas where white line deterioration does not need to be determined. This reduces the amount of communication data between the in-vehicle terminal 200 and the server device 100, saves storage capacity in the storage unit 102 of the server device 100 that stores the reflectance data D, and reduces the processing load related to deterioration determination.

[0071] Furthermore, the control unit 101 of the server device 100 may refer to map location information (an example of "location information") stored in the storage unit 102 along with the reflectance intensity data D, and identify deteriorated white lines based on the reflectance intensity data measured in a specified area (for example, an area designated by an operator or the like where deterioration of white lines needs to be determined). By specifying the area where deterioration determination needs to be performed, deterioration determination can be performed only on the white lines within that area, reducing the processing burden compared to performing deterioration determination on areas where deterioration determination is not necessary.

[0072] [5.4. Fourth Variation] In the above embodiment, the Lidar 205 is mounted on the roof of the vehicle and emits a single infrared laser beam L that traces a circle around the vehicle downwards at a certain angle. However, as shown in Figure 8(A), for example, the Lidar 205 may emit multiple (five in Figure 8(A)) infrared laser beams L, each with a different downward emission angle, so that they trace a circle around the vehicle. This allows for the measurement of reflectance intensity data D along the direction of travel of the vehicle at once, as shown in Figure 8(B). Furthermore, as in the above embodiment, by emitting a single infrared laser beam L and measuring the reflectance intensity data D each time the vehicle V moves a predetermined distance, and combining these measurements, reflectance intensity data D can be obtained along the direction of travel of the vehicle, similar to the example shown in Figure 8.

[0073] Furthermore, as explained in the first modified example, as shown in Figure 9(A), when the white line is a dashed line, it is preferable to exclude the unpainted portion of the white line from the deterioration judgment, since deterioration of the white line does not occur in the first place. Therefore, the control unit 101 of the server device 100 may, as described above, divide the area where the white line is painted (painted area) and the area where the white line is not painted (unpainted area) into an effective area and an invalid area based on the reflectance data D measured along the direction of travel of the vehicle V, and perform the deterioration judgment based only on the reflectance corresponding to the effective area. Specifically, as shown in Figures 9(A) and (B), the reflectance shown by the reflectance data D measured along the direction of travel of the vehicle V for a dashed white line is generally high in the painted area and low in the unpainted area. Therefore, a threshold is set, and if the reflectance is above the threshold, it is divided into an effective area and the rest into an invalid area. As a result, as explained in the first modification, feature IDs can be set at predetermined intervals, similar to solid white lines, without having to set a feature ID for each painted area of ​​the dashed white line, and deterioration checks can be avoided for unpainted areas. This method of separating painted areas (effective areas) from unpainted areas (ineffective areas) may also be used for deterioration checks of features other than dashed white lines, such as guide fluids, lettering, and pedestrian crossings, which are composed of painted and unpainted areas.

[0074] [5.5. Fifth Variation] In the above embodiment, the predicted range of the white line is calculated in advance, and the reflectance intensity data D included therein is processed as being due to light reflected by the white line. That is, it was guaranteed that the extracted reflectance intensity data D transmitted by the in-vehicle terminal 200 to the server device 100 was data indicating the reflectance intensity at the white line. In the fifth modification, the in-vehicle terminal 200 transmits the pre-extraction reflectance intensity data D received from the Lidar 205 to the server device 100 in association with measurement location information and measurement date and time information. The control unit 101 of the server device 100 then identifies the reflectance intensity based on the reflection by the white line from the distribution of reflectance intensity shown in the pre-extraction reflectance intensity data D and the positional relationship of the white line that demarcates the lane on which the vehicle is traveling, and performs a deterioration judgment based on the identified reflectance intensity. If the deterioration judgment determines that the feature is deteriorated, the location of the deteriorated white line may be identified by combining the measurement location information corresponding to the reflectance intensity data D and information indicating the emission angle at which the Lidar 205 emits laser light L. This reduces the processing load on the in-vehicle terminal 200 for extracting the portion used for determining the deterioration of the white lines from the pre-extraction reflectance intensity data D measured by Lidar 205, and for acquiring the map location of the white lines. Furthermore, in the fifth modified example, the server device 100 can perform the white line deterioration determination even if the map data stored in the memory unit 202 does not contain the location information of the white lines.

[0075] [5.6. Sixth Variation] Using Figures 10 and 11, we will describe a sixth modified example for determining the deterioration of patterns (sometimes referred to as "painted areas") painted on the road surface, such as arrows, guide fluids, letters, and pedestrian crossings. Figure 10 is a diagram illustrating a method for obtaining painted area reflectivity data 501, which shows the reflectivity of the painted area. Figure 11 is a conceptual diagram of a case where reflectivity map data 510 is generated by combining multiple painted area reflectivity data 501.

[0076] First, using Figure 10, we will specifically explain how to obtain paint area reflectance data 501, which shows the reflectance intensity of the right-turn arrow 500 (painted area). The coordinate system in Figure 10 is as follows. Map coordinate system: TIFF2026123184000009.tif16164 Vehicle coordinate system: TIFF2026123184000010.tif17164 Paint area position in map coordinate system (position of paint as seen from the vehicle): TIFF2026123184000011.tif20163 Paint area position in vehicle coordinate system (paint position as seen from the map): Estimated vehicle position in map coordinate system: TIFF2026123184000012.tif18165 Estimated vehicle azimuth angle in map coordinate system: TIFF2026123184000013.tif18168 TIFF2026123184000014.tif18164

[0077] Furthermore, the position of the road surface paint in the map coordinate system can be determined by the following equation (2).

number

[0078] The control unit 201 of the in-vehicle terminal 200 acquires multiple paint area reflectivity data 501 on the right-turn arrow 500 as the vehicle V moves. For example, as shown in Figure 11, paint area reflectivity data 501A, paint area reflectivity data 501B, paint area reflectivity data 501C, and paint area reflectivity data 501D are acquired sequentially as the vehicle V moves. Each paint area reflectivity data 501 includes the reflectivity at multiple positions (points) on the paint area obtained by a single laser beam emission from the Lidar 205. For example, in Figure 11, the paint area reflectivity data 501A is shown in a horizontal shape, which indicates the reflectivity at multiple positions on the paint area obtained by a single laser beam emission. That is, the paint area reflectivity data 501A is data that includes the reflectivity at each of multiple positions (points) arranged horizontally. The control unit 201 then transmits to the server device 100 information indicating the reflectance at multiple locations included in each paint area reflectance data 501, in association with information indicating the road surface paint position in the map coordinate system calculated based on equation (2) above. The control unit 201 may also transmit the paint area reflectance data 501 to the server device 100 only for paint areas that have been specified in advance by the server device 200 or the like.

[0079] As shown in Figure 11, the control unit 101 of the server device 100 generates a reflectance map data 510 (an example of a "reflection intensity distribution") by synthesizing multiple paint area reflectance intensity data 501A to 501D received from one or more in-vehicle terminals 200 based on information indicating the position of the paint area in a map coordinate system associated with the paint area reflectance intensity data 501. The reflectance intensity map data 510 is data that holds the reflectance for each position coordinate, and has a data structure such as (X1, Y1, I1), (X2, Y2, I2), ... (Xn, Yn, In) (where I represents the reflectance intensity, and X and Y represent the position coordinates from which the reflectance intensity I was obtained). The control unit 101 then determines the deterioration location and deterioration state of the paint area by analyzing the reflectance in the reflectance map data 510. Alternatively, instead of the control unit 101 of the server device 100, the control unit 201 of the in-vehicle terminal 200 may generate the reflectance map data 510 and transmit it to the server device 100. Alternatively, the control unit 201 of the in-vehicle terminal 200 may generate reflectivity map data 510, determine the location and state of deterioration, and transmit deterioration information indicating the location and state of deterioration to the server device 100.

[0080] In the sixth modified example, by generating reflectivity map data using the paint area reflectivity data 501 obtained from one or more vehicles V, high-density distribution information of reflectivity can be obtained, making it possible to determine deterioration even for paint areas with more complex shapes (such as letters) than white lines. Furthermore, by statistically processing the paint area reflectivity data 501 obtained from one or more vehicles V by the server device 100, the accuracy of determining the location and state of deterioration can be improved.

[0081] Furthermore, when determining the deterioration of the painted areas, for example, priorities (weightings) may be set according to the content that each painted area represents, and the threshold used for deterioration determination may be changed according to the priority. Specifically, painted areas that present important information may be given a higher priority, and the threshold may be set lower so that they are more likely to be judged as deteriorated. For example, in Figure 12(A), the painted area 551 representing "STOP" and the painted area 552 representing the "Bicycle Mark" may be given a higher priority, while the painted area 553 representing the "Triangle Mark" may be given a lower priority. Similarly, in Figure 12(B), the painted area 561 representing "STOP" may be given a higher priority, and the painted area 562 representing the "Line" may be given a lower priority. As a result, the threshold used for deterioration determination for painted areas 551, 552, and 561 will be set lower, and even minor deterioration may be judged as deterioration. In this way, by setting priorities according to the content that the painted areas represent, areas with low priority may be judged as not deteriorated if the deterioration is minor. In other words, by using priority, it is possible to flexibly determine the degree of deterioration according to what the painted area indicates.

[0082] Furthermore, this paint deterioration assessment can be performed not only on white painted areas but also on painted areas of other colors, such as yellow. In this case, since the reflectivity may differ depending on the color of the painted area, the threshold used for deterioration assessment may be changed depending on the color of the painted area. [Explanation of Symbols]

[0083] 1 Degraded feature identification device 1A Acquisition Department 1B Specific part S Map Data Management System 100 Server Devices 101 Control Unit 102 Storage section 103 Communications Department 104 Display section 105 Operation section 200 In-vehicle terminals 201 Control Unit 202 Storage section 203 Communications Department 204 Interface section 205 Lidar 206 Internal Sensor

Claims

[Claim 1] An acquisition unit acquires reflection intensity data based on the reflected light from a geographic object emitted by the emission unit, An identification unit identifies deteriorated features based on the reflection intensity data acquired by the acquisition unit, A device for identifying deteriorated geological features, characterized by being equipped with the following:

Images