Information processing device, information processing method, information processing program, and recording medium

Abstract

This provides a data structure for map data that can determine whether a geographic feature at a given location is deteriorating. [Solution] The map data is structured to include location information indicating the position of a feature and deterioration information relating to the deterioration state of the feature. The map data is used by an information processing device to determine, based on the deterioration information, whether the feature identified by the location information is deteriorated.

Description

Technical Field

[0001] This application relates to the data structure of map data including deterioration information regarding the deterioration state of ground features.

Background Art

[0002] In an autonomous vehicle, it is necessary to match the position of a ground feature measured by a sensor such as LIDAR (Light Detection and Ranging, Laser Imaging Detection and Ranging) with the position of the ground feature described in the map data for autonomous driving to accurately estimate the position of the vehicle itself. Ground features to be used include signs, billboards, white lines drawn on roads, etc. Map data for autonomous driving including the positions of these ground features needs to be maintained and updated in line with reality in order to perform stable autonomous driving. For example, when a part of a white line has disappeared due to aging deterioration or the like, it is necessary to reflect the deterioration information in the data representing the white line in the map data.

[0003] Therefore, conventionally, it has been necessary to drive a map maintenance vehicle to conduct a field survey to check for deteriorated ground features. In such circumstances, with the development of laser measurement technology, the development of technologies that utilize point cloud data obtained by measuring the ground surface for the use and update of map data and the like has been promoted. For example, Patent Document 1 discloses a technique for extracting data that measures a road surface from point cloud data that contains a lot of data other than the road surface, such as buildings and street trees. This technique can be used as a preprocessing for extracting white lines drawn on roads.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] 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 data structure for map data that can determine whether or not a geographical feature at a certain location is deteriorating. [Means for solving the problem]

[0007] One aspect of the present invention is characterized by comprising: an acquisition unit that acquires reflected light information including information on reflected light of light irradiated onto a geographic feature at a location predicted by a geographic feature location prediction means for predicting the location of the geographic feature, and location information of the geographic feature; a processing unit that performs statistical processing based on a plurality of reflected light pieces of information acquired by the acquisition unit; and a determination unit that determines the deterioration state of the geographic feature by comparing the statistically processed information obtained by the processing unit with a threshold value.

[0008] Another aspect of the present invention is an information processing method using an information processing device, characterized by comprising: an acquisition step of acquiring reflected light information including information about reflected light of light irradiated onto a feature at a location predicted by a feature location prediction step of predicting the location of the feature, and location information of the feature; a processing step of performing statistical processing based on a plurality of the reflected light pieces acquired in the acquisition step; and a determination step of determining the deterioration state of the feature by comparing the statistically processed information obtained in the processing step with a threshold value.

[0009] Another aspect of the present invention is characterized in that a computer included in an information processing device functions as an acquisition unit that acquires reflected light information including information on reflected light of light irradiated onto a feature at a location predicted by a feature location prediction means for predicting the location of the feature, and location information of the feature; a processing unit that performs statistical processing based on a plurality of the reflected light pieces acquired by the acquisition unit; and a determination unit that determines the deterioration state of the feature by comparing the statistically processed information obtained by the processing unit with a threshold value.

[0010] Another aspect of the present invention is a computer-readable recording medium that records an information processing program for causing a computer included in an information processing device to function as: an acquisition unit that acquires reflected light information including information about reflected light of light irradiated onto a feature at a location predicted by a feature location prediction means for predicting the location of a feature, and location information of the feature; a processing unit that performs statistical processing based on a plurality of the reflected light pieces acquired by the acquisition unit; and a determination unit that determines the deterioration state of the feature by comparing the statistically processed information obtained by the processing unit with a threshold value. [Brief explanation of the drawing]

[0011] [Figure 1] This is a block diagram showing the configuration of the deteriorated geological feature identification system according to the 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 predicted range of the white line in the embodiment. [Figure 6]This figure shows an example of the data structure of the transmitted data related to the embodiment. [Figure 7] This flowchart shows an example of the operation of the map data management system according to the embodiment for processing reflectance intensity data. [Figure 8] This flowchart shows an example of the operation of the degradation determination process by the server device according to the embodiment. [Figure 9] (A) is a side view showing how the Lidar according to the third modified example emits multiple lights in the vertical direction, and (B) is a diagram showing how the Lidar measures the reflected light intensity at the white line. [Figure 10] (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 third 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 11] This figure shows an example of the data structure of map data related to the fifth modification. [Figure 12] This figure shows an example of the data structure of transmitted data related to the sixth modified example. [Modes for carrying out the invention]

[0012] 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 data structure of the map data according to this embodiment.

[0013] As shown in Figure 1, the data structure 1 of the map data according to this embodiment is configured to include, in association with, location information 1A indicating the location of a feature and deterioration information 1B relating to the deterioration state of the feature.

[0014] The transmitted data is used by the information processing device to determine, based on the deterioration information, whether the feature identified by the location information is deteriorated.

[0015] According to the data structure of the transmission data according to this embodiment, the information processing apparatus can determine whether a ground object at a certain position is deteriorated based on the deterioration information associated with the position information indicating the position.

Example

[0016] The example will be described with reference to FIGS. 2 to 8. The example described below is an example in which the present invention is applied to the map data management system S.

[0017] [1. Configuration and Outline 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 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 FIG. 2 shows one in - vehicle terminal 200, the map data management system S may include a plurality of in - vehicle terminals 200. Also, the server device 100 may be composed of a plurality of devices.

[0018] As shown in FIGS. 3(A) and 4(A), the in - vehicle terminal 200 transmits reflection intensity data D indicating the reflection intensity measured by receiving the reflected light from the white lines W1 and W2 (an example of a "ground object") of the light L irradiated by the Lidar 205 itself to the server device 100 in a vehicle V equipped with the Lidar 205 together with the in - vehicle terminal 200. The bar lines shown as the reflection intensity data D in FIGS. 3(A) and 4(A) represent the magnitude of the reflection intensity at that point by their length (the longer, the greater the reflection intensity). The reflection intensity data D is data including the reflection intensity at each point irradiated by the light L irradiated by the Lidar 205. FIGS. 3(A) and 4(A) show that the reflection intensity at 5 points is measured for each of the white lines W1 and W2.

[0019] 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 the deteriorated features. The server device 100 then updates the map data corresponding to the deteriorated features. Furthermore, the map data may be updated by a device that receives instructions from the server device 100.

[0020] [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.

[0021] 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 location information indicating the position of the features (white lines in this embodiment) that are subject to deterioration judgment, and a feature ID to distinguish them from other features (since the 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 line W1 and white line 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 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.

[0022] The communication unit 203 controls the communication status between the in-vehicle terminal 200 and the server device 100.

[0023] 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.

[0024] Lidar205 is a device that detects objects by mounting on the roof of a vehicle, etc., and emitting infrared laser light (shine downwards from the roof at a certain angle), receiving the light reflected from points on the surface of objects around the vehicle, and repeating this process in a circular motion around the vehicle to generate reflectance intensity data D, which shows the reflected intensity at each point. Since the reflectance intensity data D is data that shows the intensity of light reflected from the ground and objects when the laser light is irradiated horizontally, it includes areas with low reflectance intensity (ground areas where no objects exist) and areas with high reflectance intensity (areas where objects exist). In addition, multiple Lidar205s may be mounted on the front or rear of the vehicle, and the reflectance intensity data acquired from each in their respective fields of view may be combined to generate reflectance intensity data D around the vehicle.

[0025] Lidar 205 immediately transmits the 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 irradiation intensity data indicating the intensity of the infrared laser light irradiated to measure the reflectance intensity data D, measurement position information indicating the position of the vehicle (Lidar 205) at the time of receiving the reflectance intensity data D, and measurement date and time information indicating the date and time of receiving the reflectance intensity data D. The control unit 201 may delete from the storage unit 202 any reflectance intensity data D, irradiation intensity data, measurement position 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.

[0026] 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.

[0027] The camera 207 is mounted on the vehicle and transmits the captured images obtained by photographing the area around the vehicle to the in-vehicle terminal 200 via the interface unit 204.

[0028] 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.

[0029] 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.

[0030] 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 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.

[0031] 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: Xm, Ym Vehicle coordinate system: XV, YV White line map position in map coordinate system: mxm,mym Predicted position of white lines in vehicle coordinate system: lxv, lyv Estimated vehicle position in map coordinate system: xm, ym Estimated vehicle azimuth angle in map coordinate system: Ψm A vehicle coordinate system is a coordinate system that uses the vehicle's position as its reference point (origin).

[0032] The control unit 201 calculates the predicted white line range from the white line map position indicated by the position information of the white line 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.

[0033] 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

[0034] 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.

[0035] The control unit 201 transmits the extracted reflectance intensity data 321 and 322 to the server device 100, associating them with the feature IDs corresponding to the map locations of white line 1 and white line 2, respectively, the illumination intensity data corresponding to the reflectance intensity data D from which the reflectance intensity data 321 and 322 were extracted, and the measurement date and time information. In the following, the reflectance intensity 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 intensity data D, and the reflectance intensity data D showing the reflectance extracted within the white line prediction range will be referred to as post-extraction reflectance intensity data D.

[0036] Here, using Figure 6, the data structure of the transmitted data 500 when the in-vehicle terminal 200 transmits the reflection intensity data D to the server device 100 will be explained. As shown in Figure 6, the transmitted data 500 consists of a basic information unit 510, a recognized object information unit 520, and a specific information unit 530.

[0037] The basic information unit 510 consists of a header 511, vehicle metadata 512, and vehicle location 513. The header 511 stores the Ver (version) of the data format of the transmitted data and a timestamp (the transmission time of the transmitted data 500). The vehicle metadata 512 stores a vehicle ID that identifies the vehicle on which the in-vehicle terminal 200 is installed, information indicating the vehicle size, and information indicating the type of sensor installed on the vehicle (Lidar 205 or camera 207). The vehicle location 513 stores information indicating the vehicle's position when the in-vehicle terminal 200 recognizes a feature.

[0038] The recognition object information unit 520 is configured to include feature IDs 521. Feature IDs 521 store IDs that identify the feature subject to deterioration information (for example, feature IDs corresponding to the map locations of white line 1 and white line 2, respectively). The server device 100 and the in-vehicle terminal 200 identify the feature based on the feature IDs.

[0039] The unique information unit 530 includes acquisition date and time 531, degradation information 532, weather information 533, and sensor type 534. The degradation information 532 stores the extracted reflectance intensity data D and the irradiation intensity data. The acquisition date and time 531 stores measurement date and time information indicating the date and time when the control unit 201 received the reflectance intensity data D from the Lidar 205. The weather information 533 stores information indicating the weather (sunny, rainy, snowy, foggy, etc.) when the control unit 201 received the reflectance intensity data D from the Lidar 205. The weather information may be generated by the control unit 201 based on images taken by the camera 207, for example, or it may be received from a weather information server. In addition to or instead of the weather information 533, the unique information unit 530 may also include road surface information indicating the condition of the road surface (wet, snowy, etc.). The sensor type used 534 stores information indicating the type of sensor that measured the data stored in the degradation information 532. In this embodiment, the degradation information 532 stores the extracted reflectance intensity data D and the irradiation intensity data, so the sensor type 534 stores information indicating Lidar 205.

[0040] [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.

[0041] The storage unit 102 is composed of, for example, an HDD or SSD, and stores the OS, a white line deterioration detection program, map data, transmission data 500 received from the in-vehicle terminal 200, and various other data.

[0042] The communication unit 103 controls the communication status with the in-vehicle terminal 200.

[0043] The display unit 104 is composed of, for example, a liquid crystal display, and displays information such as characters and images.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] [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 7. Although the flowchart in Figure 7 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 7, from step S101 to step 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 step S202.

[0048] First, the control unit 201 of the in-vehicle terminal 200 acquires estimated vehicle position information (step S101).

[0049] Next, the control unit 201 obtains the location 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 the location information of the white line in the direction of travel of the vehicle and the feature ID.

[0050] 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 position indicated by the white line position information (step S103).

[0051] 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 irradiating a range including the white line prediction range with laser light, based on the measurement position of the measurement position information stored in association with the pre-extraction reflectance data D and the irradiation angle (a constant downward angle) when Lidar 205 irradiates with laser light. Then, the control unit 101 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 101 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.

[0052] Next, the control unit 201 generates the transmission data 500 (step S105). At this time, the degradation information 532 stores the extracted reflection intensity data D extracted in the processing of step S104.

[0053] Next, the control unit 201 sends the transmission data 500 generated in step S105 to the server device 100 (step S106), and terminates the reflection intensity data transmission process.

[0054] In response, when the control unit 101 of the server device 100 receives the transmission data 500 from the in-vehicle terminal 200 (step S201), it stores it in the storage unit 102 (step S202) and terminates the reflection intensity data transmission processing. As a result, the storage unit 102 of the server device 100 stores multiple extracted reflection intensity data D transmitted from each of the multiple in-vehicle terminals 200.

[0055] [4.2. Example of operation during degradation detection processing] Next, an example of the operation of the deterioration judgment process by the server device 100 will be explained using the flowchart in Figure 8. The deterioration judgment process is executed, for example, when an operator or the like issues an instruction to judge the deterioration of a white line, and the feature ID of the white line to be judged is specified.

[0056] First, the control unit 101 of the server device 100 acquires a specified feature ID (step S211). Next, the control unit 101 acquires degradation information (extracted reflectance intensity data D) from the storage unit 102 for the transmitted data 500 that includes the feature ID and whose measurement date and time are within a predetermined period (for example, the last three months) (step S212). The reason for limiting the acquisition target to reflectance intensity data D whose measurement date and time are within a predetermined period is that reflectance intensity data D that is too old is not suitable for determining the current degradation state.

[0057] Next, the control unit 101 corrects the extracted reflectivity data D extracted in step S212 (step S213). Specifically, the reflectivity data D is corrected using at least one of the following information: measurement date and time information or weather information (road surface information). The reflectivity measured by Lidar 205 is affected by the influence of sunlight and the condition of the road surface, and therefore differs depending on the measurement date and time and the weather (road surface condition) at the time of measurement. For example, even for the same white line, the reflectivity at dawn or dusk will differ from that during the day. Also, the reflectivity on a sunny day will differ from that on a cloudy, rainy, or snowy day. This allows for the cancellation of differences between reflectivity data due to the time of measurement and weather (road surface condition), enabling appropriate deterioration determination.

[0058] Next, the control unit 101 calculates the average of the extracted reflectance intensity data D corrected in step S213 (step S214), and then calculates the standard deviation (step S215). 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)). Also, 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 point.

[0059] Next, the control unit 101 determines whether the average calculated in step S214 is less than or equal to the first threshold (step S216). If the control unit 101 determines that the average is less than or equal to the first threshold (step S216: YES), it determines that the specified white line is "deteriorated" (step S218), updates the map data corresponding to the white line by adding information indicating that it is "deteriorated" (step S219), and terminates the deterioration determination process. Note that the control unit 101 determining that the specified white line is "deteriorated" in step S218 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 S216: NO), it then determines whether the standard deviation calculated in step S215 is greater than or equal to the second threshold (step S217).

[0060] In this case, if the control unit 101 determines that the standard deviation is greater than or equal to the second threshold (step S217: YES), it determines that the specified white line is "deteriorated" (step S218), updates the map data corresponding to the white line by adding information indicating that it is "deteriorated" (step S219), 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 S217: NO), it determines that the specified white line is "not deteriorated" (step S220), and terminates the deterioration determination process.

[0061] As described above, in this embodiment, the map data management system S has a control unit 101 of the server device 100 that acquires reflection intensity data D measured by receiving reflected light reflected by a white line (an example of a "feature") from light emitted by a Lidar 205 equipped in a vehicle (an example of a "mobile object"), and identifies a white line that is deteriorating based on the acquired reflection intensity data D.

[0062] 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 due to limitations such as changes in brightness at night and in backlight, and camera resolution, it is difficult to make an accurate determination of deterioration. Therefore, using reflectance intensity data D for deterioration determination, as in this embodiment, is superior.

[0063] 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.

[0064] Furthermore, the control unit 101 acquires a feature ID to identify the location 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.

[0065] Furthermore, the control unit 101 identifies deteriorated white lines based on the position information and the extracted reflectance intensity data D measured within the set white line prediction range. This allows for the exclusion of reflectance intensity data D indicating light reflected from sources other than the white lines, enabling more accurate deterioration determination of the white lines.

[0066] Furthermore, the control unit 101 updates the map data corresponding to the white lines that were determined to be "deteriorated" in step S218 of Figure 8 (by 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.

[0067] Furthermore, the control unit 101 receives the reflected light from the white lines that have been reflected by the Lidar illuminating the white lines of one or more vehicles, acquires the 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 the reflected intensity data D acquired from one or more vehicles.

[0068] 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.

[0069] [5. Variant] Next, we will describe some variations of this embodiment. Note that the variations described below can be combined as appropriate.

[0070] [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 traffic guides, 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.

[0071] 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 traffic guides, 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.

[0072] [5.2. Second 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.

[0073] Furthermore, the control unit 101 of the server device 100 may refer to the 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 designated 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.

[0074] [5.3. Third Variation] In the above embodiment, the Lidar 205 is mounted on the roof of the vehicle, and a single infrared laser beam L is irradiated in a circular motion around the vehicle at a fixed downward angle. However, as shown in Figure 9(A), for example, the Lidar 205 may irradiate multiple infrared laser beams L (five in Figure 9(A)) in a circular motion around the vehicle, each with a different downward irradiation angle. This allows for the measurement of reflectance intensity data D along the direction of travel of the vehicle at once, as shown in Figure 9(B). Furthermore, as in the above embodiment, the measurement of reflectance intensity data D by irradiating with a single infrared laser beam L is performed each time the vehicle V moves a predetermined distance, and by 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 9.

[0075] Furthermore, as explained in the first modified example, as shown in Figure 10(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 deterioration judgment based only on the reflectance corresponding to the effective area. Specifically, as shown in Figures 10(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 assessment 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 assessment of features other than dashed white lines, such as traffic guides, lettering, and pedestrian crossings, which consist of painted and unpainted areas.

[0076] [5.4. Fourth 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 fourth modified example, 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 travels, 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 irradiation angle at which the Lidar 205 irradiates the 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 fourth 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.

[0077] [5.5. Fifth Variation] In the degradation information 532 of the transmitted data 500, the reflectance (the ratio of extracted reflectance data D to irradiation intensity data) may be stored instead of the extracted reflectance intensity data D and irradiation intensity data. In this case, the acquisition date and time 531 stores the date and time on which the extracted reflectance intensity data D, which is the basis for the reflectance, was measured. The control unit 101 of the server 100 may determine the degradation level (for example, a 10-level scale) and degradation type (for example, partial peeling of paint, paint fading, dirt adhesion) of the surface based on the extracted reflectance intensity data D and irradiation intensity data, or the reflectance (hereinafter, "extracted reflectance intensity data D and irradiation intensity data, or reflectance" may be collectively referred to as "reflection intensity data D, etc.") received from the in-vehicle terminal 200. In this case, the control unit 101 may also receive the captured image taken by the camera 207 from the in-vehicle terminal 200 and determine the degradation level and degradation type of the surface based on the captured image or at least one of the reflectance intensity data D, etc. For example, the control unit 101 may make a determination by statistically processing the reflection intensity data D, analyzing the captured image, or combining both. When receiving a captured image from the in-vehicle terminal 200, for example, the captured image may be included in the degradation information 532 of the transmitted data 500, or a separate area may be provided for the captured image. In such cases, the acquisition date and time 531 stores the date and time the captured image was taken, and the sensor type used 534 stores information indicating the camera 207. The control unit 101 then reflects the determined degradation level and degradation type of the feature in the map data. Next, the data structure of the map data stored in the storage unit 102 (in particular the data structure of the map data that holds degradation information related to white lines (sometimes called "road lines") which are features) will be explained using Figure 11.

[0078] As shown in Figure 11, the map data 600 includes feature ID (road line ID) 601, location 602, line width 603, affiliated link 604, line type 605, deterioration information acquisition date (time) 606, deterioration information 607, and deterioration type 608. The map data 600 is used by the server device 100, the in-vehicle terminal 200, or other information processing device to determine whether a feature identified by location 602 is deteriorated based on the deterioration information 607. The feature ID (road line ID) 601 stores an ID that identifies the road line and is used by the server device 100 or the in-vehicle terminal 200 when identifying a feature (road line). Location 602 stores latitude and longitude information indicating the location of the road line. Alternatively, latitude and longitude information indicating the location may be stored for each of the multiple points that constitute the longitudinal line passing through the center of the road line. Line width 603 stores the length information in the short direction of the road line. The line type 605 stores information indicating the type of boundary line (dashed line, solid line, etc.).

[0079] The date (time) for acquiring deterioration information 606 stores the date on which the deterioration level stored in deterioration information 607 was acquired (determined). Deterioration information 607 stores the deterioration level of the road markings. Deterioration type 608 stores information indicating the type of deterioration. For the date (time) for acquiring deterioration information 606, instead of the date on which the deterioration level was determined, the date and time of measurement of the reflectance data D etc. used to determine the deterioration level, or a date or time determined therefrom (if the deterioration level was determined based on reflectance data D etc. measured over a certain period, a date or time representing that certain period) may be stored. For example, if a large amount of reflectance data D etc. is received in one day, the predetermined period in step S212 may be set to one day, such as the day before the processing day, in which case that date (the date of the day before the processing day) is stored. Alternatively, the predetermined period may be set to 10:00 AM to 11:00 AM on a certain date, in which case 10:00 AM (or 11:00 AM) on a certain date may be stored in the date (time) for acquiring deterioration information 606. The above is an example of the data structure for map data 600. However, if the target is a white line, additional information such as whether or not retroreflective material is applied, or whether or not it is prone to becoming a puddle during rainfall, may be added.

[0080] Each time the control unit 101 determines the deterioration level and deterioration type for the same feature, it may store the following as historical information in the map data 600: deterioration information acquisition date (time) 606, which stores information indicating the date and time when the deterioration level and deterioration type were determined, associated with the feature ID 601 or location 602 that identifies the feature; deterioration information 607, which stores the deterioration level; and deterioration type 608, which stores information indicating the deterioration type. This historical information can be used to predict the progress of deterioration of the feature. Furthermore, if the deterioration of the feature recovers without repairing the feature, it can be determined that the deterioration type was dirt adhesion.

[0081] [5.6. Sixth Variation] In the fifth modification, the control unit 101 of the server device 100 determines the deterioration level and type of deterioration of the feature based on the reflectance intensity data D and other images received from the in-vehicle terminal 200. However, the control unit 201 of the in-vehicle terminal 200 may determine the deterioration level and type of deterioration of the feature based on the reflectance intensity data D and other images, and transmit the determination result to the server device 100 in the transmission data 500. In the transmission data 500 in the sixth modification, the date and time when the deterioration level was determined is stored in the acquisition date and time 531 (or the date or time that specifies the measurement time of the reflectance intensity data D used to determine the deterioration level may be stored). In addition, the deterioration level determined by the control unit 201 is stored in the deterioration information 532 (however, in this case as well, it is preferable to include the reflectance intensity data D and other images in the transmission data 500). Furthermore, as shown in Figure 12, a deterioration type 535 is provided in the specific information section 530 of the transmission data 500 to store information indicating the deterioration type. Furthermore, the weather information 533 may also store information that identifies the weather or road surface conditions at the time the reflectance data D used to determine the deterioration level was measured. In addition, if the deterioration level and type of deterioration of a feature is determined based on both the reflectance data D measured by Lidar 205 and the captured image taken by camera 207, information indicating Lidar 205 and camera 207 is stored in the sensor type 534 of the transmitted data 500. If the in-vehicle terminal 200 determines the deterioration level and type of deterioration of a feature based on data measured by other sensors, information indicating those other sensors is stored in the sensor type 534 of the transmitted data 500.

[0082] Meanwhile, the control unit 101 of the server device 100 stores the information stored in the acquisition date and time 531 of the transmitted data 500 in the deterioration information acquisition date (time) 606 of the map data 600. The control unit 101 also stores the deterioration level stored in the deterioration information 532 of the transmitted data 500 in the deterioration information 607 of the map data 600. Furthermore, the control unit 101 stores the information stored in the deterioration type 535 of the transmitted data 500 in the deterioration type 608 of the map data 600. In addition, each time the control unit 101 receives transmitted data 500 containing the same feature ID (road boundary ID), it may store the deterioration information acquisition date (time) 606, which stores the information stored in the acquisition date and time 531, the deterioration information 607, which stores the deterioration level stored in the deterioration information 532, and the deterioration type 608, which stores the information stored in the deterioration type 535, as historical information in the map data 600, associated with the feature ID. The historical information can be used to predict the progress of deterioration of the feature. Furthermore, if the deterioration of a geological feature recovers without any repair of the feature, it can be determined that the type of deterioration was due to dirt accumulation.

[0083] [5.7. Seventh Variation] The seventh modification is a modification of the fifth modification. In the seventh modification, the deterioration information 607 of the map data 600 may store the reflectance intensity data D etc. stored in the deterioration information 532 of the transmitted data 500. That is, the control unit 101 of the server device 100 may store the reflectance intensity data D etc. stored in the deterioration information 532 of the transmitted data 500 received from the vehicle terminal 200 without determining the deterioration level and deterioration type, and may also store the measurement date and time stored in the acquisition date and time 531 of the transmitted data 500 in the deterioration information acquisition date (time) 606 of the map data 600. In this case as well, each time transmitted data 500 containing the same feature ID (road line ID) is received, the deterioration information acquisition date (time) 606, which stores the information stored in the acquisition date and time 531, and the deterioration information 607, which stores the reflectance intensity data D etc. stored in the deterioration information 532, may be kept in the map data 600 as history information, associated with the feature ID. The history information can be used to predict the progress of deterioration of the feature. Furthermore, if the deterioration of a geological feature recovers without any repair of the feature, it can be determined that the type of deterioration was due to dirt accumulation. [Explanation of symbols]

[0084] 1. Map data 1A Location information 1B Degradation Information 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 207 Camera

Claims

[Claim 1] Information regarding the reflected light of light irradiated onto a geographical feature, and the location information of the geographical feature, including the reflected light information. The acquisition unit that acquires information, A processing unit that performs statistical processing based on a plurality of reflected light information acquired by the acquisition unit, By comparing the statistically processed information obtained by the processing unit with a threshold, the inferiority of the feature is determined. A determination unit for determining the state of transformation, An information processing device characterized by comprising:

Images