Deteriorated ground object identification device, deteriorated ground object identification system, deteriorated ground object identification method, deteriorated ground object identification program and computer readable recording medium with deteriorated ground object identification program recorded thereon

Abstract

To provide a deteriorated ground object identification device, etc., capable of identifying a deteriorated ground object from among a number of ground objects.SOLUTION: Reflected light of light which a mobile emits, from a ground object, is received, reflection intensity data measured in the mobile are acquired and a deteriorated ground object is identified on the basis of the acquired reflection intensity data.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] The present application relates to the technical field of a deteriorated feature identifying device for identifying deteriorated features. [Background technology]

[0002] In autonomous vehicles, it is necessary to estimate the vehicle's position with high accuracy by matching the positions of features measured by sensors such as LIDAR (Light Detection and Ranging, Laser Imaging Detection and Ranging) with the positions of features described in map data for autonomous driving. The features used include signs, billboards, and white lines painted on roads, and map data for autonomous driving that includes the positions of these features must be maintained and updated in accordance with reality in order to ensure stable autonomous driving. For example, if a white line has partially disappeared due to deterioration over time, etc., it is necessary to reflect the deterioration information in the data representing that white line in the map data.

[0003] For this reason, in the past, it was necessary to run a map maintenance vehicle and conduct a field survey to check for deteriorated features. In the midst of this, with the advancement of laser measurement technology, technology is being developed that utilizes point cloud data obtained by measuring the ground surface for the use and updating of map data. For example, Patent Document 1 discloses a technology for extracting data measuring the road surface from point cloud data that contains a lot of data other than the road surface, such as buildings and roadside trees. This technology can be used as a preprocessing for extracting white lines painted on roads.

[0004] [Patent Document 1] JP 2017-9378 A Summary of the Invention [Problem to be solved by the invention]

[0005] By using the technology of Patent Document 1, it was possible to determine the road area from point cloud data obtained by measuring the ground surface, but it was not possible to determine the deterioration state of features. However, in autonomous driving technology, the importance of processing using information on features in vehicle position estimation, lane keeping, recognition of drivable areas, etc. is very high, and the deterioration state of features has a large impact on these processing. Therefore, it is important to understand the actual deterioration state of features and reflect deteriorated features in map data.

[0006] In view of the above circumstances, an object of the present invention is to provide a deteriorated feature identification device and the like that can identify deteriorated features from among a large number of features. [Means for solving the problem]

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

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

[0009] The invention described in claim 12 is characterized in that it comprises an acquisition unit that acquires degraded feature information regarding a degraded feature identified by a mobile device provided on one or more mobile bodies based on reflected light from the feature of light emitted by an emission unit provided on the mobile bodies, and an update unit that updates map data corresponding to the feature indicated by the degraded feature information acquired by the acquisition unit.

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

[0013] The invention described in claim 16 is a computer-readable recording medium having recorded thereon a deteriorated feature identification program, characterized in that it causes a computer to function as an acquisition unit that acquires reflection intensity data based on light reflected from a feature by light 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 description of the drawings]

[0014] [Figure 1] 1 is a block diagram showing a configuration of a deteriorated feature specifying device according to an embodiment. [Diagram 2] 1 is a block diagram showing a schematic configuration of a map data management system according to an embodiment; [Diagram 3]FIG. 1A is a diagram showing how a Lidar according to an embodiment measures the reflection intensity of light from a white line (without degradation), and FIG. 1B is a diagram showing an example of a graph obtained by statistically processing the reflection intensity. [Figure 4] FIG. 1A is a diagram showing how a Lidar according to an embodiment measures the reflection intensity of light at a white line (with deterioration), and FIG. 1B is a diagram showing an example of a graph obtained by statistically processing the reflection intensity. [Diagram 5] 5 is a diagram for explaining a method of calculating a predicted white line range according to an embodiment; FIG. [Figure 6] 5 is a flowchart showing an example of an operation of processing reflection intensity data by the map data management system according to the embodiment. [Figure 7] 10 is a flowchart illustrating an example of an operation of a deterioration determination process performed by a server device according to an embodiment. [Figure 8] FIG. 13A is a side view showing a Lidar according to a fourth modified example emitting multiple beams of light in the vertical direction, and FIG. 13B is a diagram showing the Lidar measuring the reflection intensity of light at a white line. [Figure 9] FIG. 13(A) is a diagram showing an example of the reflection intensity measured for a dashed white line along the vehicle travel direction in the fourth modified example, and FIG. 13(B) is an example diagram showing effective and invalid areas for separating the reflection intensity used for deterioration determination based on the distribution of the reflection intensity. [Figure 10] FIG. 5 is a diagram for explaining a method for acquiring paint part reflection intensity data 501 indicating the reflection intensity at a road surface paint part. [Figure 11] FIG. 5 is a conceptual diagram showing a case where a plurality of paint portion reflection intensity data 501 are synthesized to generate reflection intensity map data 510. [Figure 12] (A) and (B) are examples of painted areas on the road surface. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] An embodiment of the present invention will be described with reference to Fig. 1. Fig. 1 is a block diagram showing a schematic configuration of a deteriorated feature specifying device according to this embodiment.

[0016] As shown in FIG. 1, a deteriorated feature identifying device 1 according to this embodiment is configured to include an acquisition unit 1A and an identifying unit 1B.

[0017] The acquiring unit 1A acquires reflection intensity data based on the light emitted by the emitting unit and reflected by features on the ground.

[0018] The identifying unit 1B identifies deteriorated features on the ground based on the reflection intensity data acquired by the acquiring unit 1A.

[0019] As described above, according to the deteriorated feature identifying device 1 of this embodiment, the acquiring unit 1A acquires reflection intensity data based on the light emitted by the emitting unit and reflected from the feature, and the identifying unit 1B identifies the deteriorated feature based on the reflection intensity data acquired by the acquiring unit 1A. Therefore, it is possible to identify the deteriorated feature based on the reflection intensity data based on the light emitted by the emitting unit and reflected from the feature. Furthermore, by reflecting the deterioration information in the map data for the feature determined to be deteriorated, it is possible to quickly and efficiently maintain the actual feature, and stable automatic driving is possible. EXAMPLES

[0020] The following embodiment will be described with reference to Figures 2 to 7. Note that the following embodiment is an embodiment in which the present invention is applied to a map data management system S.

[0021] [1. Configuration and Overview of Map Data Management System S] As shown in Fig. 2, the map data management system S of this embodiment includes a server device 100 that manages map data, and an in-vehicle terminal 200 that is mounted on each of a plurality of vehicles, and 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 be configured to include a plurality of in-vehicle terminals 200. The server device 100 may also be configured with a plurality of devices.

[0022] As shown in Fig. 3(A) and Fig. 4(A), the vehicle-mounted terminal 200 transmits reflection intensity data D indicating the reflection intensity measured by receiving reflected light of the light L emitted by the Lidar 205 itself from the white lines W1 and W2 (examples of "land features") to the server device 100 in the vehicle V equipped with the Lidar 205 together with the vehicle-mounted terminal 200. Note that the bars shown as the reflection intensity data D in Fig. 3(A) and Fig. 4(A) indicate the magnitude of the reflection intensity at that point depending on their length (the longer the bar, the greater the reflection intensity). The reflection intensity data D is data including the reflection intensity at each point where the light L emitted by the Lidar 205 is irradiated. Fig. 3(A) and Fig. 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 a deteriorated feature based on the multiple reflection intensity data D received from each of the multiple vehicle-mounted terminals 200. Specifically, the server device 100 identifies a deteriorated feature by statistically processing the multiple reflection intensity data D. For example, as shown in FIG. 3(A), for a white line that is not deteriorated, the reflection intensity at each point within the white line is high and uniform, so that the average value μ calculated based on the multiple reflection intensities is high and the standard deviation σ is small, as shown in FIG. 3(B). On the other hand, as shown in FIG. 4(A), for a deteriorated white line, the reflection intensity at each point within the white line is low or non-uniform, so that the average value μ calculated based on the multiple reflection intensities is low or the standard deviation σ is large, as shown in FIG. 4(B). Therefore, the server device 100 compares the average value μ and the standard deviation σ calculated based on the multiple reflection intensities with a threshold value to determine whether the feature is deteriorated and identify the deteriorated feature. Then, the server device 100 updates the map data corresponding to the deteriorated feature. Note that the map data is updated when an instruction is received from the server device 100. This may be done by the receiving device.

[0024] 2. Configuration of the vehicle-mounted terminal 200 Next, a description will be given of the configuration of the in-vehicle terminal 200 according to this embodiment. As shown in Fig. 2, the in-vehicle terminal 200 is mainly 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, a hard disk drive (HDD) or a solid state drive (SSD), and stores an operating system (OS), a reflection intensity data processing program, map data, reflection intensity data D, and various data. The map data describes map position information indicating the position of a feature (in this embodiment, a white line) to be subjected to deterioration judgment, and a feature ID for identifying the feature from other features (since the map position information and the feature ID are information associated with one feature, the feature ID can be said to be one piece of location information indicating the position of the one feature). In the example of FIG. 3(A), the white lines W1 and W2 are assigned different feature IDs. Note that, when a white line is long, such as several hundred meters, and it is inappropriate to treat it as a single feature, it is divided into certain lengths (for example, 5 m) and each is assigned a feature ID as a separate feature and treated. Moreover, map data similar to the map data stored in the storage unit 202 (map data in which map position information and a feature ID are described for each feature) is also stored in the storage unit 102 of the server device 100, and the same feature can be identified by the feature ID in each of the in-vehicle device 200 and the server device 100. Furthermore, the map data stored in the storage unit 202 may be, for example, map data for the entire country, or map data corresponding to a certain area including the current position of the vehicle may be received in advance from the server device 100 or the like and stored.

[0026] The communication unit 203 controls the state of communication between the on-board terminal 200 and the server device 100 .

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

[0028] Lidar205 is a device that is attached to the roof of the vehicle or the like, and has one function of constantly emitting infrared laser light in a circular manner around the vehicle (emitting from the roof at a constant angle downward), receiving the light reflected at points on the surface of features around the vehicle, and generating reflection intensity data D that indicates the reflection intensity at each point. The reflection intensity data D is data that indicates the intensity of laser light that is emitted horizontally and reflected by the ground and features, and therefore includes areas with low reflection intensity (ground areas where no features exist) and areas with high reflection intensity (areas where features exist). In addition, multiple Lidar205 may be attached to the front or rear of the vehicle, and the reflection intensity data of the field of view acquired by each of them may be combined to generate the reflection intensity data D around the vehicle.

[0029] Upon measuring the reflection intensity, the Lidar 205 immediately transmits the reflection intensity data D (including a portion with low reflection intensity and a portion with high reflection intensity) to the in-vehicle terminal 200 via the interface unit 204. Upon receiving the reflection intensity data D from the Lidar 205, the control unit 201 stores the received reflection intensity data D in the storage unit 202 in association with measurement position information indicating the position of the vehicle (Lidar 205) at the time of receiving the reflection intensity data D and measurement date and time information indicating the date and time at which the reflection intensity data D was received. Note that the control unit 201 may delete from the storage unit 202, among the reflection intensity data D, measurement position information, and measurement date and time information stored in the storage unit 202, those for which a predetermined time has passed since measurement, or those transmitted to the server device 100.

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

[0031] The control unit 201 is composed of a CPU (Central Processing Unit) that controls the entire control unit 201, a ROM (Read Only Memory) in which a control program for controlling the control unit 201 and the like are stored in advance, and a RAM (Random Access Memory) that temporarily stores various data. The CPU reads out and executes various programs stored in the ROM and the storage unit 202 to realize various functions.

[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 may be generated by the control unit 201. The estimated vehicle position information can be generated, for example, by matching a feature position measured by the Lidar 205 with a feature position in map data for autonomous driving, or can be generated based on information detected by the internal sensor 206 and map data, or a combination of these.

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

[0034] Here, a method for setting the predicted white line area will be specifically described with reference to Fig. 5. The coordinate system in Fig. 5 is as follows. Map coordinate system: TIFF2025010233000002.tif14165 Vehicle coordinate system: TIFF2025010233000003.tif13168 White line map position in map coordinate system: TIFF2025010233000004.tif19162 White line predicted position in vehicle coordinate system: TIFF2025010233000005.tif18168 Estimated vehicle position in map coordinate system: TIFF2025010233000006.tif19169Estimated vehicle azimuth in map coordinate system: TIFF2025010233000007.tif19165

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

[0036] The calculation method of the predicted white line range is the same for white line 1 and white line 2, so here, a case where the predicted white line range is calculated for white line 1 will be described. First, the control unit 201 calculates a predicted white line 1 position 301 (predicted white line position for white line 1) based on the map position of the white line 1 and the estimated vehicle position. The predicted white line position is obtained by the following equation (1).

number

[0037] Next, the control unit 201 sets a white line 1 predicted range 311 based on the white line 1 predicted position 301. Specifically, a certain range including the white line 1 predicted position 301 is set as the white line 1 predicted range 311. Then, the control unit 201 extracts white line 1 reflection intensity data 321 indicating the reflection intensity within the white line 1 predicted range 311 from the reflection intensity data D including the reflection intensities at a plurality of points.

[0038] The control unit 201 transmits the thus extracted reflection intensity data 321, 322 to the server device 100 in association with feature IDs corresponding to the map positions of the white line 1 and the white line 2, respectively, and with measurement date and time information corresponding to the reflection intensity data D from which the reflection intensity data 321, 322 were extracted. Note that, hereinafter, the reflection intensity data D (which may be raw data measured by the Lidar 205 or data obtained by processing the raw data) measured by the Lidar 205 and stored in the storage unit 202 by the in-vehicle terminal 200 is referred to as pre-extraction reflection intensity data D, and the reflection intensity data D indicating the reflection intensity extracted within the white line prediction range may be referred to as post-extraction reflection intensity data D.

[0039] 3. Configuration of Server Device 100 Next, a description will be given of the configuration of the server device 100. As shown in Fig. 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 configured with, for example, an HDD or SSD, and stores an OS, a white line deterioration determination program, reflection intensity data D received from the in-vehicle terminal 200, and various other data.

[0041] The communication unit 103 controls the state of communication with the vehicle-mounted terminal 200 .

[0042] The display unit 104 is configured, for example, with a liquid crystal display or the like, and displays information such as characters and images.

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

[0044] The control unit 101 is composed of a CPU that controls the entire control unit 101, a ROM in which a control program for controlling the control unit 101 and the like are stored in advance, and a RAM that temporarily stores various data. The CPU reads out and executes various programs stored in the ROM and the storage unit 102 to realize various functions.

[0045] The control unit 101 determines the deterioration state of the white lines based on the multiple reflection intensity data D received from one or multiple in-vehicle terminals 200. Then, the control unit 101 updates the map data corresponding to the deteriorated feature so that the deteriorated feature can be identified as being deteriorated.

[0046] [4. Example of operation of map data management system S] [4.1. Example of operation when processing reflection intensity data] Next, an example of the operation of reflection intensity data processing by the map data management system S will be described with reference to the flowchart of Fig. 6. Note that, although the flowchart of Fig. 6 describes a flow in which one vehicle-mounted terminal 200 measures reflection intensity data D and transmits it to the server device 100, similar processing is performed for each vehicle-mounted terminal 200 included in the map data management system S. Furthermore, the processing of steps S101 to S105 of the vehicle-mounted terminal 200 in Fig. 6 is performed periodically (for example, at predetermined time intervals and / or each time the vehicle on which the vehicle-mounted terminal 200 is mounted moves a predetermined distance), and upon receiving the processing of step S105 by the vehicle-mounted terminal 200, the server device 100 performs the processing 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 acquires map position information of the white line from the map data corresponding to the estimated vehicle position indicated by the estimated vehicle position information acquired in the process of step S101 (step S102). At this time, the control unit 201 acquires the map position information and feature ID of the white line in the traveling direction of the vehicle as described above.

[0049] Next, the control unit 201 calculates and sets a 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 pre-extraction reflection intensity data D measured by the Lidar 205 within the white line prediction range to obtain post-extraction reflection intensity data D (step S104). Specifically, the control unit 201 first identifies pre-extraction reflection intensity data D measured by emitting laser light into a range including the white line prediction range, based on the measurement position of the measurement position information stored in association with the pre-extraction reflection intensity data D and the emission angle (a certain downward angle) at which the Lidar 205 emits the laser light. Next, the control unit 201 extracts a portion of the identified pre-extraction reflection intensity data D within the white line prediction range to obtain post-extraction reflection intensity data D2. For example, the control unit 201 extracts a portion corresponding to the azimuth angles (θ1, θ2 (see FIG. 5)) of the white line prediction range based on the vehicle direction.

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

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

[0053] [4.2. Example of operation during deterioration judgment process] Next, an example of the operation of the deterioration determination process by the server device 100 will be described with reference to the flowchart in Fig. 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 specifies the feature ID of the white line to be determined for deterioration.

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

[0055] Next, the control unit 101 calculates the average of the extracted reflection intensity data D extracted in the process of 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 reflection intensity data D for each feature ID (for each of the white lines W1 and W2 in the examples of Figs. 3(A) and 4(A)). In the examples of Figs. 3(A) and 4(A), the reflection intensity is measured at five points for the white line W1, so the control unit 101 calculates the average and standard deviation of the reflection intensity at each point. Instead of calculating the average and standard deviation of the reflection intensity at each of the five points, the control unit 101 may calculate the average and standard deviation of the reflection intensity at the five points or at points in a predetermined area (part or all of the area) included in the extracted reflection intensity data D.

[0056] Next, the control unit 101 judges whether or not the average calculated in the process of step S213 is equal to or less than the first threshold (step S215). At this time, when the control unit 101 judges that the average is equal to or less than the first threshold (step S215: YES), it judges the designated white line as "deteriorated" (step S217), updates the map data corresponding to the designated white line by adding information indicating that the designated white line is "deteriorated" (step S218), and ends the deterioration judgment process. Note that the control unit 101's judgment of the designated white line as "deteriorated" in the process of step S217 is an example of identifying a deteriorated white line. On the other hand, when the control unit 101 judges that the average is not equal to or less than the first threshold (step S215: NO), it then judges whether or not the standard deviation calculated in the process of step S214 is equal to or more than the second threshold (step S216).

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

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

[0059] Therefore, according to the map data management system S of this embodiment, it is possible to identify deteriorated white lines by using the reflection intensity data D acquired from the vehicle. Note that while it is conceivable to judge the deterioration of white lines based on images captured by a camera mounted on the vehicle, there are restrictions such as changes in brightness at night or backlighting, and camera resolution, making it difficult to appropriately judge the deterioration, and therefore it is preferable to judge the deterioration using the reflection intensity data D as in this embodiment.

[0060] Moreover, the control unit 101 further acquires measurement date and time information indicating the date and time when the reflection intensity data D was measured, selects the reflection intensity 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 reflection intensity data D. Therefore, by appropriately setting the predetermined period (for example, the most recent few months), it is possible to appropriately identify the deteriorated white lines based on the reflection intensity data excluding the reflection intensity data D that is inappropriate for determining deterioration of the white lines.

[0061] Furthermore, the control unit 101 further acquires a feature ID (an example of "location information") for identifying the position of the white line that reflects the 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 position of the deteriorated white line.

[0062] Furthermore, the control unit 101 identifies deteriorated white lines based on the extracted reflection intensity data D measured within a set white line prediction range based on map position information (an example of "position information"). This makes it possible to perform deterioration judgment by excluding reflection intensity data D that indicates light reflected by things other than white lines, and therefore makes it possible to perform deterioration judgment on white lines with higher accuracy.

[0063] Furthermore, the control unit 101 (an example of an "updating unit") updates the map data corresponding to the white lines determined to be "deteriorated" in the process of step S217 in Fig. 7 (performs an update to add information indicating that the white lines are "deteriorated"). This makes it possible to reflect the deterioration information in the map data representing the deteriorated white lines.

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

[0065] In this embodiment, the feature to be subjected to deterioration determination has been described as a white line, but any feature for which deterioration determination can be performed based on reflection intensity can be subjected to deterioration determination.

[0066] [5. Modifications] Next, modified examples of this embodiment will be described. Note that the modified examples described below can be combined as appropriate.

[0067] [5.1. First modified example] In the above embodiment, the case where the white line to be subjected to the deterioration judgment is a solid line has been described, but the white line may also be a broken line. Furthermore, white lines are not only used to separate lanes, but are also used for guides, letters, pedestrian crossings, etc. Furthermore, the object of the deterioration judgment may include not only white lines but also features such as signs and billboards. In other words, the features to be subjected to the deterioration judgment may be classified into various types. Therefore, feature type information indicating the type of feature may be further linked to a feature ID, and the threshold value when the deterioration judgment is performed using the reflection intensity data may be changed depending on the type of feature.

[0068] Furthermore, if the white line is a broken line, a feature ID may be set for each portion where the white line is painted, and a feature ID may not be set for portions where the white line is not painted, and these portions may be excluded from the deterioration judgment. Note that, since the direction of white lines for guides, letters, pedestrian crossings, etc. may not be parallel to the traveling direction of the vehicle, a method for setting the predicted white line range may be determined for each type of white line (for example, position information of the four corner points of the predicted white line range may be described in the map data, and the control unit 201 of the in-vehicle device 200 may set the predicted white line range based on that), and the predicted white line range may be set according to the type of white line to be subjected to the deterioration judgment.

[0069] [5.2. Second Modification] The reflection intensity measured by Lidar205 is affected by sunlight and varies depending on the date and time of measurement and the weather at the time of measurement. For example, the reflection intensity of the same white line varies depending on the time of day and the weather at dawn and dusk. The reflection intensity at night differs from the reflection intensity during the day. Also, the reflection intensity during fine weather differs from the reflection intensity during cloudy, rainy, snowy, etc. weather. In the second modification, the control unit 201 of the vehicle-mounted device 200, when receiving the reflection intensity data D from the Lidar 205, further associates the data with weather information indicating the weather at the time of measurement and stores the data in the storage unit 202. In the process of step S105 in FIG. 6, the control unit 101 of the server device 100 further associates the data with weather information and transmits the data to the server device 100, and the server device 100 also associates the data with weather information and stores the data in the storage unit 102. The control unit 101 of the server device 100 may correct the reflection intensity data D according to at least one of the measurement date and time information and the weather information (an example of "associated information"), and then identify the deteriorated feature. This allows the difference between the reflection intensity data due to the time and weather at the time of measurement to be offset, and the deterioration judgment can be performed appropriately.

[0070] [5.3.Third Modification] In the above embodiment, the control unit 201 of the in-vehicle terminal 200 periodically transmits the reflection intensity data D to the server device 100. In addition, the control unit 201 may add a condition that the in-vehicle terminal 200 (own vehicle) transmits only the reflection intensity data D measured within a predetermined area (e.g., a measurement area specified by the server device 100). As a result, for example, the server device 100 can specify an area where deterioration judgment of the white line needs to be performed as the measurement area, thereby avoiding receiving reflection intensity data D measured in an area where deterioration judgment of the white line does not need to be performed. This makes it possible to reduce the amount of communication data between the in-vehicle terminal 200 and the server device 100, save the storage capacity of the storage unit 102 of the server device 100 that stores the reflection intensity data D, and reduce the processing load related to the deterioration judgment.

[0071] Furthermore, the control unit 101 of the server device 100 may refer to map position information (one example of "position information") stored in the memory unit 102 together with the reflection intensity data D, and identify deteriorated white lines based on reflection intensity data measured in a specified area (for example, an area specified by an operator or the like where deterioration determination of white lines is required). In this way, by specifying the area where deterioration determination is required, deterioration determination can be performed only on white lines within that area, and the processing load can be reduced compared to when deterioration determination is performed on areas where deterioration determination is not required.

[0072] [5.4. Fourth Modification] In the above embodiment, the Lidar 205 is attached to the roof of the vehicle or the like, and emits one infrared laser light L at a certain angle downward to draw a circle around the vehicle. However, for example, as shown in FIG. 8(A), the Lidar 205 may emit a plurality of infrared laser lights L (five in FIG. 8(A)) at different downward emission angles so as to draw a circle around the vehicle. This allows the reflection intensity data D to be measured along the traveling direction of the vehicle at one time, as shown in FIG. 8(B). Also, as in the above embodiment, the reflection intensity data D is measured by emitting one infrared laser light L every time the vehicle V moves a predetermined distance, and by combining them, the reflection intensity data D can be obtained along the traveling direction of the vehicle, as in the example shown in FIG. 8.

[0073] As described in the first modified example, as shown in FIG. 9(A), when the white line is a broken line, it is preferable to exclude the portion where the white line is not painted from the deterioration judgment because the deterioration of the white line does not occur in the first place. Therefore, the control unit 101 of the server device 100 may divide the portion where the white line is painted (painted area) into a valid area and the portion where the white line is not painted (non-painted area) into an invalid area based on the reflection intensity data D measured along the traveling direction of the vehicle V as described above, and perform the deterioration judgment based only on the reflection intensity corresponding to the valid area. Specifically, as shown in FIG. 9(A) and (B), the reflection intensity indicated by the reflection intensity data D measured along the traveling direction of the vehicle V for the broken white line is roughly classified as a high value in the painted area and a low value in the non-painted area, so a threshold is set, and if the reflection intensity is equal to or higher than the threshold, the area is divided into a valid area and the other areas are divided into an invalid area. As a result, as explained in the first modified example, feature IDs can be set for each predetermined distance in the same way as for solid white lines, without setting feature IDs for each painted area of ​​dashed white lines, and deterioration determination can be avoided for non-painted areas. Note that this method of separating painted areas (valid areas) from non-painted areas (invalid areas) may also be used to determine deterioration of guides, characters, pedestrian crossings, and the like, which are composed of painted areas and non-painted areas, in addition to dashed white lines.

[0074] [5.5. Fifth Modification] In the above embodiment, the white line prediction range is calculated in advance, and the reflection intensity data D included therein is processed as being due to light reflected by the white line. That is, the post-extraction reflection intensity data D transmitted by the vehicle-mounted terminal 200 to the server device 100 is guaranteed to be data indicating the reflection intensity at the white line. In the fifth modified example, the vehicle-mounted terminal 200 transmits the pre-extraction reflection intensity data D received from the Lidar 205 to the server device 100 in association with the measurement position information and the measurement date and time information. Then, the control unit 101 of the server device 100 specifies the reflection intensity based on the reflection by the white line from the distribution of the reflection intensity indicated by the pre-extraction reflection intensity data D and the positional relationship of the white line separating the lanes on which the vehicle is traveling, and performs the deterioration judgment based on the specified reflection intensity. Then, when it is judged that the feature is deteriorated by the deterioration judgment, the position of the deteriorated white line may be specified by combining the measurement position information corresponding to the reflection intensity data D and the information indicating the emission angle at which the Lidar 205 emits the laser light L. This makes it possible to reduce the processing load on the in-vehicle terminal 200 required for the process of extracting the portion used for determining deterioration of the white lines from the pre-extraction reflection intensity data D measured by the Lidar 205 and the process of acquiring the map positions of the white lines. Also, in the fifth modification, it becomes possible for the server device 100 to determine deterioration of the white lines even if the map data stored in the memory unit 202 does not have position information of the white lines.

[0075] [5.6. Sixth Modification] 10 and 11, a sixth modified example for determining deterioration of designs (sometimes called "painted parts") such as arrows, guides, letters, and pedestrian crossings painted on road surfaces will be described. Fig. 10 is a diagram for explaining a method for acquiring paint part reflection intensity data 501 indicating the reflection intensity in the painted parts. Fig. 11 is a conceptual diagram for generating reflection intensity map data 510 by combining a plurality of paint part reflection intensity data 501.

[0076] First, a method for acquiring paint portion reflection intensity data 501 indicating the reflection intensity of a right-turn arrow 500 (paint portion) will be specifically described with reference to Fig. 10. The coordinate system in Fig. 10 is as follows. Map coordinate system: TIFF2025010233000009.tif16164 Vehicle coordinate system: TIFF2025010233000010.tif17164Paint position in map coordinate system (position of paint as seen from the vehicle): TIFF2025010233000011.tif20163Paint position in vehicle coordinate system (position of paint as seen from map): TIFF2025010233000012.tif18165Estimated vehicle position in map coordinate system: TIFF2025010233000013.tif18168Estimated vehicle azimuth in map coordinate system: TIFF2025010233000014.tif18164

[0077] In addition, the position of the road paint in the map coordinate system can be calculated using the following equation (2).

number

[0078] The control unit 201 of the in-vehicle terminal 200 acquires a plurality of pieces of paint reflection intensity data 501 on the right turn arrow 500 as the vehicle V travels. For example, as shown in FIG. 11, paint reflection intensity data 501A, paint reflection intensity data 501B, paint reflection intensity data 501C, and paint reflection intensity data 501D are acquired in sequence as the vehicle V travels. Each piece of paint reflection intensity data 501 includes reflection intensity at a plurality of positions (points) on the paint obtained by one emission of laser light by the Lidar 205. For example, the paint reflection intensity data 501A in FIG. 11 is shown in a horizontally elongated shape, which indicates reflection intensity at a plurality of positions on the paint obtained by one emission of laser light. In other words, the paint reflection intensity data 501A is data including reflection intensity at each of a plurality of positions (points) arranged in a horizontal direction. Then, the control unit 201 associates information indicating the reflection intensities at the multiple positions included in each paint part reflection intensity data 501 with information indicating the road surface paint position in the map coordinate system calculated based on the above formula (2), and transmits the information to the server device 100. Note that the control unit 201 may transmit the paint part reflection intensity data 501 to the server device 100 only for paint parts designated in advance by the server device 200, etc.

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

[0080] In the sixth modified example, reflection intensity map data is generated using paint part reflection intensity data 501 obtained from one or more vehicles V, thereby making it possible to obtain high-density distribution information on reflection intensity, and therefore it is possible to determine deterioration even for paint parts having shapes (such as letters) that are more complicated than white lines. In addition, the server device 100 statistically processes the paint part reflection intensity data 501 obtained from one or more vehicles V, thereby improving the accuracy of determining the deterioration position and deterioration state.

[0081] In addition, when judging the deterioration of the paint part, for example, a priority (weighting) may be set according to the contents indicated by each paint part, and a threshold value used in judging the deterioration may be changed according to the priority. Specifically, a high priority is set for a paint part that presents important information, and a low threshold value is set so that the paint part is easily judged as deteriorated. For example, a high priority is set for the paint part 551 indicating "Stop" and the paint part 552 indicating the "bicycle mark" in FIG. 12(A), and a low priority is set for the paint part 553 indicating the "triangle mark". Similarly, a high priority is set for the paint part 561 indicating "Stop" in FIG. 12(B), and a low priority is set for the paint part 562 indicating the "bar line". As a result, a low threshold value is set for the paint parts 551 and 552, and the paint part 561 when judging the deterioration, and even slight deterioration is judged as deterioration. By setting a priority according to the contents indicated by the paint parts in this way, if the deterioration of a part with a low priority is slight, it is judged that the part is not deteriorated. In other words, by using the priority, deterioration can be flexibly determined according to the contents indicated by the paint portion.

[0082] Furthermore, such deterioration judgment of the painted portion can be performed not only for white painted portions but also for painted portions of other colors such as yellow, etc. In this case, since the reflection intensity may differ depending on the color of the painted portion, the threshold value used for the deterioration judgment may be changed depending on the color of the painted portion. [Explanation of symbols]

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

Claims

[Claim 1] an acquisition unit that acquires reflection intensity data based on light emitted by the emission unit and reflected by a feature on the ground; an identification unit that identifies a deteriorated feature based on the reflection intensity data acquired by the acquisition unit; A deteriorated feature identification device comprising:

Images