Information processing method, program, information processing device, and data structure

Abstract

Provided is an information processing method executed by an information processing device that includes a processor, the processor executing the steps of extracting an edge part of a passage on the basis of point group data for a passage other than a roadway (S104), identifying attribute information relating to the passage on the basis of the point group data (S106), generating line segment data relating to the passage on the basis of the attribute information and edge part data relating to the edge part (S108), and associating the passage attribute information that corresponds to the line segment data with the line segment data (S110).

Description

[Technical field]

[0001] The present invention relates to an information processing method, a program, an information processing device, and a data structure. [Background technology]

[0002] In recent years, technological developments have been actively carried out to generate highly accurate map data and use this map data for autonomous driving and the like. In general, when map data to be used for autonomous driving and the like is generated, features are detected by various sensors mounted on a vehicle, feature data relating to the features is generated, and the feature data is associated with the map data. This enables the vehicle to properly grasp features around the vehicle using the feature data associated with the map data. It is known that this feature data includes information on the type of external sensor and environmental information on the environment when the feature is detected for each type of external sensor (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2020-73893 A Summary of the Invention [Problem to be solved by the invention]

[0004] Here, in the conventional technology, highly accurate map data is generated based on point cloud data and various other data collected when a measurement vehicle equipped with an MMS (Mobile Mapping System) measurement system travels along a roadway.

[0005] However, conventional high-precision map data is map data intended for vehicles traveling on roads, and cannot be used by vehicles traveling on roads other than roads.

[0006] SUMMARY OF THE PRESENT EMBODIMENT An object of the present invention is to provide highly accurate map data that can be used by vehicles traveling on roads other than roads. [Means for solving the problem]

[0007] An information processing method according to one embodiment of the present invention is an information processing method executed by an information processing device including a processor, in which the processor executes the following operations: extracting edges of a passageway other than a roadway based on point cloud data, identifying attribute information related to the passageway based on the point cloud data, generating line segment data related to the passageway based on the edge data related to the edges and the attribute information, and associating the line segment data with attribute information of the passageway corresponding to the line segment data. Effect of the Invention

[0008] According to the present invention, it is possible to provide highly accurate map data that can be used by vehicles traveling on roads other than roadways. [Brief description of the drawings]

[0009] [Figure 1] 1 is a diagram illustrating an example of a configuration of an information processing system according to an embodiment of the present invention. [Diagram 2] 1 is a diagram showing an example of a configuration of a vehicle according to an embodiment of the present invention; [Diagram 3] 1 is a diagram illustrating an example of a configuration of an information processing device according to an embodiment of the present invention. [Figure 4] FIG. 2 is a diagram illustrating an example of a database according to an embodiment of the present invention. [Diagram 5] FIG. 2 is a diagram showing an example of information relating to feature data of a route according to an embodiment of the present invention; [Figure 6A] FIG. 1 is a diagram showing an example of a road without a sidewalk. [Figure 6B] 1A and 1B are diagrams showing examples of various obstacles on a walkway. [Figure 6C] FIG. 1 is a diagram showing an example of grating present on a sidewalk. [Figure 6D]FIG. 13 illustrates the cut-off for vehicle entry and exit. [Figure 7A] FIG. 13 is a diagram showing an example of a relationship between sidewalks and paved roads outside a road and attribute information such as road width. [Figure 7B] FIG. 13 is a diagram showing an example of a relationship between sidewalks and paved roads outside a road and attribute information such as road width. [Figure 8] FIG. 2 is a diagram showing an example of a width code according to an embodiment of the present invention. [Figure 9] 11 is a diagram showing an example of attribute information that can be associated with line segment data according to an embodiment of the present invention; FIG. [Figure 10] FIG. 13 is a diagram showing an example of section cutting in case 1. [Figure 11] FIG. 13 is a diagram showing an example of section cutting in Case 2. [Figure 12] FIG. 13 is a diagram showing an example of section cutting in Case 3. [Figure 13] FIG. 13 is a diagram showing another example of section cutting in Case 3. [Figure 14] 5 is a flowchart showing an example of a process related to map data generation according to an embodiment of the present invention. [Figure 15] 10 is a flowchart showing an example of a process from identifying attribute information to associating processing according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] [Embodiment] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will now be described with reference to the accompanying drawings, in which like reference numerals denote like or similar structures.

[0011] <System Overview> Fig. 1 is a diagram showing an example of the configuration of an information processing system 1 according to an embodiment of the present invention. The information processing system 1 shown in Fig. 1 includes a measurement vehicle (hereinafter also referred to as "vehicle") 10 equipped with a Mobile Mapping System (MMS) measurement device, an information processing device 20, a positioning satellite 30 used in a Global Navigation Satellite System (GNSS), and an observation satellite 40 capable of acquiring satellite images, which are capable of transmitting and receiving data to and from each other via a network N. Furthermore, there may be one or more vehicles 10 and information processing devices 20.

[0012] The vehicle 10 is a vehicle that performs three-dimensional measurement using MMS, and performs three-dimensional measurement of the surrounding terrain while moving. The vehicle 10 can also receive signals from GNSS positioning satellites 30 and detect its own vehicle's position information. The position information includes three-dimensional position information of latitude, longitude, and altitude, or two-dimensional position information of latitude and longitude.

[0013] Furthermore, the vehicle 10 is equipped with various sensors and acquires data detected by the various sensors. The various sensors include an on-board camera, a vehicle speed sensor, an acceleration sensor, etc. Although the vehicle 10 shown in FIG. 1 is an example in which measuring instruments and the like are mounted on a personal mobility vehicle, the measuring instruments and the like may be mounted on a general vehicle that runs on a roadway.

[0014] The positioning satellite 30 is a satellite that transmits signals necessary for measuring position information. The observation satellite 40 observes the Earth using, for example, a Synthetic Aperture Radar (SAR) sensor, and acquires satellite images by photographing the Earth.

[0015] The information processing device 20 is, for example, a server, and acquires data measured by the MMS, data detected by various sensors, and the like from the vehicle 10. The information processing device 20 also acquires signals and images from the satellites 30 and 40. The information processing device 20 generates map data using the data acquired from the vehicle 10 and the satellites 30 and 40. Note that the information processing device 20 may be composed of multiple information processing devices.

[0016] <Vehicle configuration> Fig. 2 is a diagram showing an example of the configuration of a vehicle 10 according to an embodiment of the present invention. In the example shown in Fig. 2, the vehicle 10 travels in a measurement area using an MMS and measures three-dimensional coordinate values ​​of features around the traveled roadway and / or around a traffic path other than the roadway. In order to measure these three-dimensional coordinate values, the vehicle 10 includes a GNSS receiver 101, an IMU 102, a laser scanner 103, and a camera 104. For example, these devices are attached to a top plate 100 provided on the top of the vehicle 10.

[0017] The vehicle 10 also has an odometer 105 attached to a wheel, and includes a processor 106, various sensors 107, and a communication device 108 as on-board devices.

[0018] The GNSS receiver 101 includes an antenna that receives positioning signals from GNSS positioning satellites 30. Based on the positioning signals received by the antenna, the GNSS receiver 101 calculates the pseudorange to the positioning satellite 30, the phase of the carrier wave carrying the positioning signal, and three-dimensional coordinate values.

[0019] The IMU 102 includes a gyro sensor that measures angular velocity in three axial directions and an acceleration sensor that measures acceleration. The IMU 102 acquires posture data of the vehicle using these sensors.

[0020] The laser scanner 103 emits a laser beam while changing the emission angle in the width direction of the vehicle 10, and receives the laser beam reflected by a feature located at the destination of the emission. The laser scanner 103 measures the time from when the laser beam is emitted to when it is received, and calculates the distance to the feature.

[0021] The camera 104 captures an image of the outside of the vehicle 10, for example, the front, etc. In addition, the odometer 105 measures the distance traveled by the vehicle 10.

[0022] The processor 106 performs driving control of the vehicle 10, or controls transmission and reception of data with external devices. The various sensors 107 include an on-board camera, a vehicle speed sensor, and an acceleration sensor. The communication device 108 transmits and receives data to and from external devices. For example, the communication device 108 transmits data acquired by an MMS to the information processing device 20. Note that, with regard to measurement of point cloud data, etc., on sidewalks, within facilities, and on premises (including, but not limited to, airports, ports, etc.), measurements may be performed using measuring devices such as handheld types, backpack types, and fixed types.

[0023] <Configuration of information processing device> 3 is a diagram showing an example of the configuration of an information processing device 20 according to an embodiment of the present invention. The information processing device 20 includes one or more processing devices (CPU: Central Processing Unit) 210, one or more network communication interfaces 220, a storage device 230, a user interface 250, and one or more communication buses 270 for interconnecting these components. Note that the user interface 250 is not necessarily required.

[0024] Storage device 230 may be, for example, a high-speed random access memory such as DRAM, SRAM, or other random access solid-state storage device, or may be a non-volatile memory such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid-state storage devices, or may be a non-transitory computer-readable recording medium.

[0025] As another example, storage device 230 may be one or more storage devices located remotely from CPU 210. In one embodiment, storage device 230 stores programs, modules and data structures, or a subset thereof, that are executed by CPU 210.

[0026] The storage device 230 stores data used by the information processing system 1. For example, the storage device 230 stores data related to the generation of high-precision three-dimensional map data (hereinafter also referred to as "HD (High Definition) map"). As specific examples, dynamic maps, HD maps, feature data, etc. are stored in the storage device 230.

[0027] Fig. 4 is a diagram showing an example of a database according to an embodiment of the present invention. In the example shown in Fig. 4, the storage device 230 stores the dynamic map data as a high-precision map database.

[0028] In the example shown in Fig. 4, the map data is, for example, data of a highly accurate three-dimensional map used in an autonomous vehicle or the like. As a specific example, this map data is data of a map called a dynamic map that is provided in real time and to which more dynamic information such as information on surrounding vehicles and traffic information is added. The map data that can be used in this embodiment is classified into, for example, four hierarchical levels. The map data is classified into static information SI1, semi-static information SI2, semi-dynamic information MI1, and dynamic information MI2.

[0029] The static information SI1 is three-dimensional, highly accurate basic map data (HD data) that includes road surface information, lane information, three-dimensional structures, etc., and is composed of three-dimensional position coordinates and linear vector data that indicate features. The quasi-static information SI2, quasi-dynamic information MI1, and dynamic information MI2 are dynamic data that change from moment to moment, and are data that are superimposed on the static information based on position information.

[0030] The quasi-static information SI2 includes traffic regulation information, road construction information, wide-area weather information, etc. The quasi-dynamic information MI1 includes accident information, congestion information, narrow-area weather information, etc. The dynamic information MI2 includes ITS (Intelligent Transport System) information, including surrounding vehicles, pedestrians, traffic lights, etc.

[0031] The static information SI1 included in the dynamic map data includes HD data, and the HD data includes feature data. This feature data is basic information when an application uses a dynamic map, and can contribute to performance improvement when an application uses a dynamic map. Therefore, it is important to decide what information to include in the feature data. For example, the dynamic map can be used in an automatic driving system such as an AGV (Automatic Guided Vehicle) or a PMV (Personal Mobility Vehicle) that can run autonomously. In order to contribute to performance improvement of the automatic driving system, appropriate information is detected from a large amount of information, and the detected information is included in the feature data according to this embodiment.

[0032] In this embodiment, the map data used for routing (route setting) and navigation (route guidance) of an autonomous vehicle or a remote-controlled vehicle traveling on a roadway other than a roadway or a vehicle controlled by a user may be only the static information S11. In other words, if a vehicle can acquire at least the HD map of the static information S11, it can travel autonomously or by remote control. The vehicle may be any vehicle that has wheels and can travel, and includes general vehicles, AGVs, PMVs, wheelchairs, strollers, two-wheeled vehicles, unicycles, and the like.

[0033] Returning to Fig. 3, the CPU 210 that executes the process of generating feature data according to this embodiment will be described. The CPU 210 executes the programs stored in the storage device 230 to configure a map control unit 212, a transmission / reception unit 213, an acquisition unit 214, an extraction unit 215, an identification unit 216, a generation unit 217, and an association unit 218.

[0034] The CPU 210 controls the processing of a map control unit 212, which in turn controls the processing of each unit (to be described later) and executes processing related to the generation of map data.

[0035] The map control unit 212 uses various data to control the generation of map data. For example, the map control unit 212 controls the generation of HD data and also controls the generation of feature data included in the HD data. The map control unit 212 may control the generation of an HD map for a roadway other than a roadway, or may control the generation of an HD map for a roadway other than a roadway in addition to an HD map for a roadway.

[0036] The transmission / reception unit 213 transmits and receives data to and from external devices via the network communication interface 220. For example, the transmission / reception unit 213 is configured as a receiving unit that receives data, signals, and the like from the vehicle 10 and each of the satellites 30 and 40, and is also configured as a transmitting unit that transmits data, signals, and the like to the vehicle 10 and each of the satellites 30 and 40. As a specific example, the transmission / reception unit 213 receives from the vehicle 10 various data measured by the MMS and various data sensed by various sensors mounted on the vehicle 10, and receives a satellite image including a predetermined position from the observation satellite 40.

[0037] The acquisition unit 214 acquires various data received by the transmission / reception unit 213 as necessary, for example, various data used to generate map data including at least roadways other than roads. As a specific example, the acquisition unit 214 acquires at least point cloud data measured by the vehicle 10.

[0038] The extraction unit 215 extracts the edge of the roadway based on at least the point cloud data of the roadway other than the roadway. For example, the extraction unit 215 may extract the edge of the roadway by detecting the edge of a sidewalk or the like included in the point cloud data.

[0039] In addition, when extracting the edges of the road, the extraction unit 215 may extract the following features using various data measured by the MMS and various data sensed by various sensors mounted on the vehicle 10. -Center lines of roads and virtual stop lines, etc. Sidewalks (longitudinal ends) Crosswalks and intersections (where pedestrians cross) - Road edges (pavement edges) on roads without sidewalks - Objects near sidewalks and road edges that may be obstacles to the movement of AGVs, etc. (pillars, facilities, roadside trees, curbs, etc.) - Gutters (whether or not they have covers or gratings) that run longitudinally along the road edge, U-shaped gutters that cross the road, etc. Entrances and exits to facilities, etc. When extracting a virtual line, the extraction unit 215 generates a virtual line based on feature data (e.g., a route, a sidewalk, etc.) extracted from various data, and extracts the generated virtual line. For example, the extraction unit 215 generates a virtual center line from line segment data on both ends of a sidewalk, and generates a virtual stop line based on the presence or absence of a curb, etc.

[0040] The extraction unit 215 may set the following conditions as conditions for extracting features on a route. - Areas where pedestrians pass, such as sidewalks (within a specified distance) (Even if the road does not have a sidewalk, there is an area where people can walk. However, if there is a gutter, AGVs and other vehicles cannot run on it, so the gutter area is not suitable for running on and is not included in the route.)

[0041] The identification unit 216 identifies attribute information related to a roadway based at least on point cloud data of a roadway other than a roadway. For example, the identification unit 216 identifies attribute information for each feature extracted by the extraction unit 215 based on point cloud data, etc. As a specific example, the identification unit 216 may measure at least the width of the roadway and assign the width data to an item of the attribute information, as described below.

[0042] The generating unit 217 generates line segment data related to a passageway based on the edge data related to the edge extracted by the extracting unit 215 and the attribute information identified by the identifying unit 216. For example, the generating unit 217 divides or separates predetermined line segment data along the edge data based on width data, feature data, etc., to generate each piece of line segment data. As a specific example, the generating unit 217 may classify the width data according to the size of the width data, and generate line segment data such that adjacent line segment data have different classification values ​​for the width data.

[0043] The generating unit 217 may generate, as the line segment data, at least one of edge data itself relating to the edge, line segment data along the edge data, line segment data located approximately at the center of the passageway that crosses the passageway, and line segment data approximately parallel to the edge data. The generating unit 217 may also generate the above-mentioned virtual lines of the passageway, such as a center line or a stop line.

[0044] The associating unit 218 associates attribute information of a route corresponding to the line segment data generated by the generating unit 217 with the line segment data. For example, the associating unit 218 may associate attribute information that caused the generation of each generated line segment data with the line segment data. As a specific example, when the line segment data is generated according to the size of the width data, the associating unit 218 may associate a numerical value or a code of the width data with the line segment data.

[0045] The above processing makes it possible to generate feature data included in high-precision map data such as sidewalks, which is used to control the travel of AGVs, PMVs, wheelchairs, baby strollers, and the like (collectively referred to as "small vehicles"). This enables small vehicles to control their travel in the direction of travel along the line segment data of the route, and to perform at least one of control processes of speed, whether or not to travel, and whether to stop, based on attribute information associated with the line segment data. Note that the use of feature data such as sidewalks is not limited to small vehicles, but may also be used by vehicles that can travel on sidewalks, such as motorcycles and vehicles with narrow widths.

[0046] The attribute information identified by the identification unit 216 may include attribute information of a plurality of items of different types. For example, the attribute information may include each of the items of pavement type, average cross gradient, cross gradient direction, average longitudinal gradient, longitudinal gradient direction, road surface special notes, traffic special notes, and obstacle special notes.

[0047] For example, the pavement type can be identified based on at least one of point cloud data, satellite images, images captured by the vehicle 10, etc., and one of options such as asphalt, concrete, colored pavement, gravel (unpaved), etc. is identified.

[0048] For example, the average transverse slope and the average longitudinal slope can be determined based on at least one of point cloud data, an angular velocity sensor mounted on the vehicle 10, images captured by the vehicle 10, etc., and are determined in percentages.

[0049] Similarly, the transverse gradient direction and the longitudinal gradient direction can be identified based on at least one of the point cloud data, the angular velocity sensor mounted on the vehicle 10, the image captured by the vehicle 10, etc. The transverse gradient direction can be identified as the roadway side or the private land boundary side, and the longitudinal gradient direction can be identified as the starting point side or the end point side of the line segment data.

[0050] The road surface special notes, traffic special notes, and obstacle special notes as special notes may be set by the user. For example, the specification unit 216 may set the contents input by the user for each special note using the user interface 250 in the attribute information.

[0051] The above process makes it possible to include appropriate items in the attribute information for roads other than roadways. For example, attribute information suitable for vehicle driving control can be associated with line segment data of a road as feature data. This allows a vehicle to use the line segment data while acquiring attribute information associated with the line segment data and use it for appropriate driving control.

[0052] The generating unit 217 may also include a step of dividing (or partitioning) the edge data of the passageway to generate line segment data when the attribute information of at least one item satisfies a predetermined condition for division. For example, the generating unit 217 may include, as a predetermined condition for division, that the attribute information of at least one item is different. For example, when the width data or gradient included in the attribute information is different, the generating unit 217 may divide the data into edge data showing similar values ​​(values ​​within a predetermined range) and generate each line segment data.

[0053] The above process generates line segment data using edge data extracted from point cloud data or the like, which makes it easy to generate line segment data and reduces the processing load of the information processing device 20. Furthermore, when the center line of a roadway other than a roadway is converted into line segment data, as with roadways, the width of the roadway often differs depending on the area, and so the information indicating the position of the line segment data indicating the center line becomes complicated, which increases the processing load and memory capacity.

[0054] On the other hand, by generating line segment data from edge data, the line segment data is basically data of straight lines, so data such as positional information can be easily identified, and by using differential values ​​or the like for the positional information of each line segment data, it is possible to reduce the storage capacity of the positional information.

[0055] The path may also include a sidewalk or a paved portion from the road outer edge to the road pavement edge. In this case, the extraction unit 215 may extract the edge of the sidewalk on the opposite side of the road (e.g., the private land boundary side) or the pavement edge of the paved portion.

[0056] As described above, the extraction unit 215 extracts the outer edge of the sidewalk (also referred to as the "sidewalk outer edge") and the outermost edge of the paved portion (also referred to as the "road pavement edge") as edges, and the generation unit 217 generates line segment data from edge data based on these edges, thereby making it possible to generate simple and easy-to-manage line segment data.

[0057] The significance of using the outer sidewalk edge and the road pavement edge as line segment data is given below. The extraction unit 215 does not need to acquire new features separately. - There are fewer horizontal and vertical ingresses and egresses and changes in the area where AGVs, etc. travel compared to other existing alignments in the route. -Because horizontal entrances and exits such as bus stops and cut-offs may be installed in some areas near the roadway, the line data for the outer side of the sidewalk and the road pavement edge are straighter. There are many obstructing features near the road, such as bus stop timetables, roadside trees, and tree belts. Therefore, it is possible to operate AGVs and the like more safely and stably by running them along the line segment data of the outer sidewalk edge (or the road pavement edge).

[0058] The path may also include a passageway in a building or on private land. In this case, the extraction unit 215 may extract an edge of the pathway in the building or on private land. For example, the acquisition unit 214 can acquire point cloud data of the pathway in the building or on private land by driving a small vehicle capable of driving in the building or on private land and capable of MMS measurement.

[0059] Through the above processing, the extraction unit 215 extracts edges of passages in buildings or on private property as edges, the generation unit 217 generates line segment data from edge data based on these edges, and the association unit 218 associates attribute information corresponding to the line segment data with the line segment data. This enables travel control of small vehicles even on passages in buildings or on private property.

[0060] The generating unit 217 generates map data of the road including each line segment data associated with each attribute information of the road. For example, the generating unit 217 may generate map data showing only roads other than the roadway, or may generate map data in which the roadway and the roadway other than the roadway are integrated by adding feature data showing the roadway other than the roadway to conventional map data including the roadway.

[0061] The above processing makes it possible to distribute map data including feature data of routes other than roads, thereby increasing the opportunities for using this map data and increasing the availability of map data of routes other than roads.

[0062] <Data example> Next, examples of data related to the map data generated by the above-mentioned process will be described with reference to Figs. 5 to 9. Fig. 5 is a diagram showing an example of information related to feature data of a route according to one embodiment of the present invention. The feature-related information shown in Fig. 5 includes attribute information and various codes for features that can be extracted from point cloud data, for example.

[0063] In the example shown in Figure 5, in the case of a road that does not have a separate sidewalk, features related to the outside of the roadway are extracted, and since it is anticipated that AGVs and other vehicles will also travel on crosswalks, crosswalk features are extracted even if they are inside the roadway.

[0064] In the example shown in Fig. 5, the features extracted as related to the roadway are the outer roadway line, stop line, pedestrian crossing, curb (lower edge of the roadway side), and road pavement edge (part without sidewalk). In addition, the code "1-1" written to the left of the target feature may be used as a feature code to identify the feature.

[0065] Each target feature shown in FIG. 5 is associated with a "Shape name / shape" related to the road, an attribute code "class", and "walkwidth" (width data), which is one attribute item. The "Shape name / shape" includes the type of feature such as roadway, sidewalk, obstacle, build, etc., and the type of shape such as line, poly (surface, point) in parentheses. "Class" includes the attribute type code. "Walkwidth" (width data) includes width data of sidewalks, etc. that can be measured from point cloud data, etc. The code "class" may be used as a feature code.

[0066] The extraction unit 215 extracts target features included in the feature-related information shown in Figure 5 from point cloud data, etc., and the identification unit 216 may identify various codes for the extracted target features by referring to the feature-related information shown in Figure 5 and assign them to the target features as attribute information.

[0067] (Target feature) Next, an example of a target feature used for driving support of a small vehicle will be described with reference to Fig. 6. The extraction unit 215 extracts both edge portions of the sidewalk, the edge of the road pavement, and the road outer edge as the driving range of the small vehicle.

[0068] FIG. 6A is a diagram showing an example of a road without a sidewalk. When there is no sidewalk, as in the example shown in FIG. 6A, the area between the road pavement edge and the road outer edge line is considered to be a drivable area. The road pavement edge may be a curb (bottom edge), and the road outer edge line may not be a line. When there is a sidewalk, both ends of the sidewalk are extracted.

[0069] In addition, the extraction unit 215 may extract areas and lines related to driving and stopping at intersections, etc. For example, the following features are extracted. Crosswalk (area frame) - Sidewalk boundary of the curb (crosswalk connection) - Stop lines on the road only when there is no sidewalk

[0070] The extraction unit 215 may also extract obstacles (area frames) within the driving range as target features. For example, the following obstacles may be extracted: - Pole-shaped objects (signal poles, trees, bus stop timetable poles, etc.) Facilities (electrical facilities, planting frames, post boxes, telephone booths, etc.) - Including level difference removal blocks etc.

[0071] 6B is a diagram showing an example of various obstacles on a sidewalk. In the example shown in FIG. 6B, trees, traffic lights, bus stops, signs, etc. may be extracted as obstacles.

[0072] The extraction unit 215 may also extract unsuitable driving areas (area frames) within the driving range of the small vehicle. For example, the following features may be extracted as unsuitable driving areas: · Gutter cover Grating · Access to sidewalks (cut-offs, passages to private homes, garages, etc.)

[0073] Fig. 6C is a diagram showing an example of gratings present on a roadway. In the example shown in Fig. 6C, there is one grating on the roadway and one at the roadway edge, indicated by a thick frame. Fig. 6D is a diagram showing a cut-off for vehicles to enter and exit. In the example shown in Fig. 6D, an oval part indicates the cut-off. The features shown in Figs. 6A to 6D may be extracted by the extraction unit 215.

[0074] (Generating line data, adding attributes to width data) 7A and 7B are diagrams showing an example of the relationship between sidewalks and paved roads outside the road and attribute information such as road width. The generating unit 217 generates line segment data based on the positions of features (obstacles, etc.) that narrow the width of the sidewalks and paved roads outside the road (collectively referred to as "sidewalks, etc."). The generating unit 217 may use the sidewalk edge (private land boundary) or the road pavement edge as a reference for the line segment data that is the basis for the generated line segment data.

[0075] 7A shows an example in which a sidewalk is located above the road and a paved road outside the road is located below the road. There are obstacles on the sidewalk and in the paved road outside the road, and the width of the paved road outside the road further narrows as it goes to the right.

[0076] In the example shown in Fig. 7A, the line segment data is finally divided into sections A to C (line segment data) by the generating unit 217. Moreover, the type code of the outer edge (also referred to as the "outer edge") of the sidewalk in sections A to C is given the type code of "21" (sidewalk edge (private land side, outer edge)) shown in Fig. 5 by the identifying unit 216. Moreover, the width data (outer width), which is one of the attribute items associated with the line segment data, is given a width code described below by the identifying unit 216.

[0077] For example, the width code of sections A and C is "12" (1 m or more, less than 2 m), and the width code of section B is "11" (0.5 m or more, less than 1 m) which is narrower than the widths of sections A and C due to the presence of an obstacle. In addition, the type code of the inside edge (also called "inner edge") of the sidewalk of sections A to C (line segment data) is "22" (curb (sidewalk edge (sidewalk edge of a curb between the pedestrian and vehicle)) shown in FIG. 5) assigned by the identification unit 216.

[0078] In the example shown in FIG. 7A, the generating unit 217 finally divides the sections J to M (line segment data) into outer roadway edges (also referred to as "road edges"). The type code of sections J to M is given by the identifying unit 216 the type code "11" (outer roadway line) shown in FIG. 5. The width data (road edge width), which is one of the attributes associated with the line segment data, is given by the identifying unit 216 the width code "0" (less than 0.5 m), "11" or "12" based on the width data of each section. The type code "13" may also be given to each section of the road pavement edge of the road outer pavement by the identifying unit 216.

[0079] In the example shown in FIG. 7B, the identification unit 216 uses at least point cloud data and the like to identify the width data and the gradient of the cutoff included in the attribute information. For example, the identification unit 216 identifies the type code "23" corresponding to the cutoff in the area of ​​section H. The identification unit 216 also identifies that the areas of sections O, Q to R have no markings of the outer roadway line and the distance from the outer roadway line to the pavement edge is less than 0.5 m, and assigns a width code "0". Note that the black markings located at the road edge in section O indicate obstacles that are constantly present, such as mailboxes. In this case, the generation unit 217 generates line segment data by dividing the obstacle by 1 m on both ends, and the association unit 218 associates the line segment data with the width data "0" indicating that the vehicle 10 cannot travel.

[0080] Here, the process up to the generation of line segment data will be described with reference to Figs. 7A and 7B. First, the extraction unit 215 extracts edges of the passageway other than the roadway based on at least the point cloud data, and extracts features related to the passageway, such as obstacles. Next, the specification unit 216 specifies attribute information of the features extracted by the extraction unit 215. For example, the specification unit 216 specifies the code of the feature using the feature-related information shown in Fig. 5, and assigns the code to the extracted target feature. Next, the generation unit 217 divides the extracted passageway into sections based on the edges and the specified attribute information, and generates each line segment data. Finally, the association unit 218 associates the corresponding attribute information with each generated line segment data.

[0081] (Width Code) Here, the width code will be described. FIG. 8 is a diagram showing an example of a width code according to an embodiment of the present invention. The advantage of setting a width code is that it reduces the cost of creating and managing width data, and reduces the processing load on the side that uses the width data. For example, if detailed width values ​​are set for each sidewalk section, the processing load and cost increase, and it becomes cumbersome to grasp the width even when setting a route for a small vehicle. Therefore, the specification unit 216 may set an approximate width level (width code) based on the width value, and may assign the width code as attribute information for each section (each line segment data). The width code shown in FIG. 8 is an example, and other levels (ranges) may be set.

[0082] Fig. 9 is a diagram showing an example of attribute information that can be associated with line segment data according to an embodiment of the present invention. In the example shown in Fig. 9, the attribute information includes pavement type, average cross gradient, cross gradient direction, average longitudinal gradient, longitudinal gradient direction, road surface notes, traffic notes, obstacle notes, etc.

[0083] The pavement type, for example, the type of road surface such as asphalt, concrete, colored pavement, gravel, etc., can be identified by the identifying unit 216 based on point cloud data, images captured by the vehicle 10, etc. The average transverse slope and longitudinal average slope can be identified by the identifying unit 216 based on at least one of the point cloud data, an angular velocity sensor mounted on the vehicle 10, and images captured by the vehicle 10. For example, the average transverse slope and longitudinal average slope are identified in %.

[0084] The transverse gradient direction and the longitudinal gradient direction can be identified by the identification unit 216 based on at least one of the point cloud data, the angular velocity sensor mounted on the vehicle 10, and the captured image of the vehicle 10. The transverse gradient direction can be identified as descending toward the roadway side or toward the private land boundary side, and the longitudinal gradient direction can be identified as descending toward the starting point side or the end point side of the line segment data.

[0085] The road surface notes, traffic notes, and obstacle notes as special notes may be set by the user. For example, the specification unit 216 may set the contents input by the user for each special note using the user interface 250 as attribute information. For example, pavement deterioration, cracks, and swelling caused by tree roots may be set as road surface notes. For example, a lot of bicycle traffic, a lot of pedestrians during the day, etc. may be set as traffic notes. For example, a lot of parked bicycles, a lot of parked vehicles, a lot of signs, a lot of step-eliminating blocks, etc. may be set as obstacle notes.

[0086] <Example of dividing a line> Next, some examples of generation of line segment data by the generation unit 217 will be described. In the examples shown below, it is assumed that point cloud data of the sidewalk and the like is also measured while traveling on a roadway, but data measured by the MMS of the vehicle while traveling separately on the sidewalk and the like may also be used.

[0087] (Case 1) Section division of a feature on the road surface that is less than 1m long in the longitudinal direction If there is a feature on the road surface such as a utility pole or a lighting pole that has a longitudinal section of less than 1m (approximately 20 to 40cm) and this feature narrows the width of the sidewalk, it is possible that such a small feature section cut-off may be overlooked when setting a route for a small vehicle. Therefore, in the case of a feature that is less than 1m, the generation unit 217 extends a substantially perpendicular line from the road centerline (if there is none, it is imaginary) to the sidewalk edge (private land boundary) or road pavement edge so as to pass through the position of the pole or the like, and cuts off the sidewalk edge or pavement edge from the intersection by approximately 1m in front and behind (guideline) for a total of approximately 2m.

[0088] Fig. 10 is a diagram showing an example of section division in case 1. In the example shown in Fig. 10, the generation unit 217 sets the intersection point of the sidewalk edge when a substantially perpendicular line is extended from the road centerline through the obstacle O10 as the center, divides the sidewalk edge into a range of a predetermined distance to the left and right (or front and rear) from this center, and generates line segment data. For sections L10 and L14 shown in Fig. 10, the width data (width value) is M10. On the other hand, because obstacle O10 exists in section L12, the width data M12 of this section is narrower than M10.

[0089] (Case 2) Sectioning of a feature on the road surface that is longer than 1m in length For features whose longitudinal section on the road surface exceeds 1 m, the generation unit 217 extends an approximately perpendicular line from the road centerline (which is imaginary if none exists) to the edge of the sidewalk (private land boundary) or the edge of the road pavement, so as to pass through the positions of both ends (maximum parts) of the longitudinal direction of the feature, and divides the section between the points where the width begins to change (from where it starts to narrow).

[0090] Fig. 11 is a diagram showing an example of section division in Case 2. In the example shown in Fig. 11, a sidewalk is narrowed by a bus bay, a parking area, etc. The generation unit 217 sets an approximately perpendicular line passing through both ends (maximum parts) of the feature from the center line, and divides the sidewalk at the intersection point of this perpendicular line and the sidewalk edge. For sections L20 and L24 shown in Fig. 11, the width data (width value) is M20. On the other hand, for section L22, the width data of the narrowest area is measured and set to M22.

[0091] (Case 3) Sectioning when there are continuous ground features at a short distance When ground features that cause the width to narrow are continuously present at a certain interval in the longitudinal direction, the generation unit 217 does not perform sectioning for each individual ground feature, but performs integrated sectioning. The association unit 218 associates the narrowest width information in consideration of the fact that the width information to be assigned to the section cannot pass through. Here, the distance between the continuously present ground features is about 5 to 10 m, but this distance is not limited by the surrounding sidewalk environment.

[0092] For example, in a continuous section where there are entrances and exits to the outside of the sidewalk, such as the frontage of a building or a cut - down of the sidewalk in the middle, and the only use is to pass through, the association unit 218 associates the narrowest width data with the section (for example, Fig. 12).

[0093] Also, even in a continuous section, if there is a cut - down of the sidewalk in the middle, the generation unit 217 performs sectioning based on the sectioning definition of the ground features before and after this (for example, Fig. 13).

[0094] Fig. 12 is a diagram showing an example of sectioning in Case 3. In the example shown in Fig. 12, ground features (such as obstacle O30) that narrow the width of the sidewalk at a certain interval are relatively short - distance apart, and it shows the case where there is a section within that interval where there is no access to the sidewalk, such as a cut - down of the sidewalk.

[0095] For example, as shown in Fig. 12, in a section where there is no access to the sidewalk, ground features that narrow the sidewalk are scattered, and the narrowed widths may vary depending on the section. At this time, if there is no access road (such as a front entrance) to the residential area within this section, the association unit 218 treats the two end sections L32 as a whole and associates the width M32 (M32 < M34 = M36) narrowed the most by the obstacle O30 as the attribute of the section L32. This is to enable the determination of whether a small vehicle can pass through by grasping the narrowest width.

[0096] Fig. 13 is a diagram showing another example of section division in Case 3. In the example shown in Fig. 13, there is a relatively short distance of feature (obstacle O40, etc.) narrowing the width of the sidewalk at a certain interval, but within that section, the sidewalk is lowered, etc., and there is a section L44 where it is possible to enter and exit the sidewalk.

[0097] In the example shown in FIG. 13, the association unit 218 assigns sidewalk width data to sections L40, L44, and L48 as attribute information, and assigns narrowing sidewalk width data M42 and M46 to sections L42 and L46 as attribute information.

[0098] Sections L42 and L44 are not treated as a continuous section because section L44 has a turn-off allowing access to the sidewalk. On the other hand, section L46 including obstacle O40 is treated as a continuous section because there is no turn-off allowing access to the sidewalk. The curved arrows in Fig. 13 indicate that width code M40 corresponds to the attributes of sections L40 and L48, and width code M44 corresponds to the attributes of section L46.

[0099] The above-mentioned cases 1 to 3 show examples of dividing line segment data into sections, but the invention is not limited to the above-mentioned examples, and the generating unit 217 may generate line segment data (sections) using other conditions and the like.

[0100] <Operation> Next, each process related to map data generation by the information processing system 1 will be described. Fig. 14 is a flowchart showing an example of a process related to map data generation according to an embodiment of the present invention. The process shown in Fig. 14 is executed by the information processing device 20.

[0101] In step S102, the acquisition unit 214 of the information processing device 20 acquires at least point cloud data on a roadway other than the roadway. The acquisition unit 214 may acquire data sensed by various sensors in addition to the point cloud data. The acquisition unit 214 may also acquire point cloud data and various data related to the roadway.

[0102] In step S104, the extraction unit 215 extracts edges of the roadway based on at least the point cloud data of the roadway other than the roadway.

[0103] In step S106, the identification unit 216 identifies attribute information related to the route based on the point cloud data acquired by the acquisition unit 214. For example, the identification unit 216 identifies attribute information from the feature extracted by the extraction unit 215 with reference to the feature-related information shown in FIG.

[0104] In step S108, the generating unit 217 generates line segment data related to the path based on the edge data related to the edge of the path extracted by the extracting unit 215 and the attribute information identified by the identifying unit 216. For example, the generating unit 217 may generate the line segment data using the method described with reference to Figs.

[0105] In step S110, the associating unit 218 associates the line segment data generated by the generating unit 217 with attribute information of the route corresponding to the line segment data. For example, the attribute information may be set to at least one of road width data and data of each item shown in Fig. 9. Preferably, road width data is set as the attribute information. This makes it possible to determine whether small vehicles can travel along the road, and can be used for routing and navigation of small vehicles.

[0106] In step S112, the generating unit 217 generates map data of the road including each line segment data associated with each attribute information of the road by the associating unit 218. This map data may include an HD map of the roadway.

[0107] 15 is a flowchart showing an example of processing from identification of attribute information to association processing according to an embodiment of the present invention. The processing shown in FIG. 15 shows a specific example of the processing in steps S106 to S110 shown in FIG.

[0108] In step S202, the identification unit 216 measures the width of each area of ​​a roadway other than a roadway, and identifies width data (a numerical value or a width code).

[0109] In step S204, the generation unit 217 divides edge data of sidewalks, etc. based on feature data (e.g., obstacles) or attribute information (e.g., width data, gradient, etc.) existing in the roadway other than the roadway, and generates data of each line segment.

[0110] In step S206, the association unit 218 associates (sets) each piece of line segment data generated by the generation unit 217 with corresponding attribute information.

[0111] In step S208, which is processed in step S206, the association unit 218 inputs the measured width data of the passage as one item of attribute information to be associated with the line segment data. The items of the attribute information may include the items shown in FIG. 9.

[0112] According to the above process, it is possible to provide highly accurate map data that can be used by vehicles traveling on roads other than roads. Vehicles that use the highly accurate map data do not need to be able to travel autonomously, and may use the map data for route routing, navigation, and the like.

[0113] Although one embodiment of the present invention has been described above in detail, the present invention is not limited to the above embodiment, and various modifications and changes are possible within the scope of the claims. For example, in the present invention, a part of the processing executed by the information processing device 20 may be transferred to another information processing device, or a plurality of information processing devices may be appropriately integrated.

[0114] <Variation 1> In the first modified example, the above-mentioned embodiment may be configured by a data structure of map data. For example, the data structure of map data related to a roadway other than a roadway is used in a computer having a processor and a memory, and is stored in the memory. This data structure includes each line segment data and attribute information. Each line segment data is each line segment generated using edge data related to an edge extracted based on point cloud data on the roadway, and is used by the processor for control processing related to the progress of the vehicle. For example, the vehicle is controlled to run along each line segment data. Therefore, since the line segment data is preferably as straight as possible, it is preferable that the line segment data be generated by dividing the edge data of a straight line such as a sidewalk edge.

[0115] The attribute information is attribute information of a route identified based on point cloud data associated with each corresponding line segment data, and is used by a processor for at least one control process of a vehicle's speed, whether or not it can travel, and whether or not it can stop. For example, the attribute information can be used for route search such as adjusting the vehicle's speed, stopping temporarily, or making a detour. Specifically, the vehicle's speed is adjusted according to the magnitude of the width data in the attribute information, and for example, the vehicle may adjust its speed to be slower as the road width becomes narrower. In addition, the vehicle may be controlled to stop temporarily before the area indicated by the reduction in the attribute information, since there is a possibility that vehicles may enter or exit the area.

[0116] Thus, by providing map data having the above-mentioned data structure to a vehicle or a device that remotely controls the vehicle, the vehicle becomes able to autonomously travel on paths other than roadways.

[0117] <Variation 2> In the above example, the map data of the route is used for a vehicle, but the map data may be used for other applications. For example, the map data may be provided to a mobile device used by a visually impaired person to provide detailed route guidance, to use for VR (Virtual Reality) or AR (Augmented Reality), to use for digital advertising, or to use for games that allow precise location identification.

[0118] <Variation 3> Also, the HD map of a roadway other than a roadway can be associated with a conventional HD map of a roadway. For example, the identification unit 216 identifies the positional relationship between the roadway line segment data and the roadway line segment data, and the association unit 218 associates the roadway line segment data with information capable of identifying the position of the roadway line segment data. For example, for the roadway line segment data that is closest to the positional information of the starting point of the roadway line segment data, a difference value between the positional information of the starting point of this roadway line segment data and the positional information of the starting point of the roadway line segment data is associated with the roadway line segment data. Also, data indicating this difference value may be included in the ID of the feature data of the roadway line segment data.

[0119] <Modification 4> The passageway other than the roadway may include a river on which ships and the like navigate. For example, a measuring device may be mounted on a ship to acquire point cloud data and the like on the river, making it possible to generate a 3D high-precision map of the river. In this case, as described above, line segment data is generated based on attribute information of the river (such as the river width, the position of bridges, and the position of moored ships), and the attribute information is assigned to each of the divided line segment data. For example, in a ship-steering support system that supports the operation of a ship, the HD map of the river is used, and the line segment data and / or the attribute information associated with the line segment data are used to determine whether or not the river is passable. The HD map of the river can also be used for determining the ship's route and for route guidance (navigation). Therefore, in the case of the fourth modification, the above-mentioned passageway may be replaced with a river, and the vehicle may be replaced with a ship. [Explanation of symbols]

[0120] 1...information processing system, 10...measurement vehicle, 20...information processing device, 30...positioning satellite, 40...observation satellite, 100...top plate, 101...GNSS receiver, 102...IMU, 103...laser scanner, 104...camera, 105...odometer, 106...processor, 107...various sensors, 108...communication device, 210...CPU, 212...map control unit, 213...transmitter / receiver unit, 214...acquisition unit, 215...extraction unit, 216...identification unit, 217...generation unit, 218...association unit, 230...storage device, 250...user interface, 220...network communication interface

Claims

1. An information processing method executed by an information processing device including a processor, The processor, Extracting edges of a roadway other than a roadway based on point cloud data of the roadway; Identifying attribute information regarding the travel route based on the point cloud data; generating line segment data for a roadway other than the roadway based on edge data relating to the edge and the attribute information; Associating the line segment data with attribute information of the route corresponding to the line segment data; Run The attribute information includes attribute information of a plurality of items of different types, The generating of the line segment data includes: An information processing method comprising: dividing the edge data to generate the line segment data when attribute information of at least one item satisfies a predetermined condition regarding division.

2. An information processing method executed by an information processing device including a processor, comprising: The processor, Extracting edges of a roadway other than a roadway based on point cloud data of the roadway; Identifying attribute information regarding the travel route based on the point cloud data; generating line segment data for a roadway other than the roadway based on edge data relating to the edge and the attribute information; Associating the line segment data with attribute information of the route corresponding to the line segment data; Run The generating of the line segment data includes: An information processing method comprising generating line segment data based on at least one of attribute information items of pavement type, gradient, and width.

3. The said passageway includes a sidewalk or a paved portion from the road outer line to the road pavement edge, The extracting step comprises: The information processing method according to claim 1 or 2, further comprising extracting an edge of the sidewalk on an opposite side to the roadway, or a pavement edge of the paved portion.

4. The path includes a passageway within a building or private property; The extracting step comprises: A method according to claim 1 or 2, comprising extracting edges of the passageways within the building or edges within the private property.

5. The information processing method according to claim 1 , wherein the attribute information includes at least one item of a road surface condition, a traffic condition, and an obstacle condition, which are set by a user.

6. The processor, The information processing method according to claim 1 , further comprising: generating map data of the route, the map data including line segment data associated with each attribute information of the route.

7. A processor included in the information processing device Extracting edges of a roadway other than a roadway based on point cloud data of the roadway; Identifying attribute information regarding the travel route based on the point cloud data; generating line segment data for a roadway other than the roadway based on edge data relating to the edge and the attribute information; Associating the line segment data with attribute information of the route corresponding to the line segment data; Run the command, The attribute information includes attribute information of a plurality of items of different types, The generating of the line segment data includes: a program for generating the line segment data by dividing the edge data when attribute information of at least one item satisfies a predetermined condition regarding division;

8. A processor included in an information processing device, Extracting edges of a roadway other than a roadway based on point cloud data of the roadway; Identifying attribute information regarding the travel route based on the point cloud data; generating line segment data for a roadway other than the roadway based on edge data relating to the edge and the attribute information; Associating the line segment data with attribute information of the route corresponding to the line segment data; Run the command, The generating of the line segment data includes: A program that generates line segment data based on at least one of the attribute information items of pavement type, gradient, and width.

9. An information processing device including a processor, The processor, Extracting edges of a roadway other than a roadway based on point cloud data of the roadway; Identifying attribute information regarding the travel route based on the point cloud data; generating line segment data for a roadway other than the roadway based on edge data relating to the edge and the attribute information; Associating the line segment data with attribute information of the route corresponding to the line segment data; Run The attribute information includes attribute information of a plurality of items of different types, The generating of the line segment data includes: An information processing device comprising: dividing the edge data to generate the line segment data when attribute information of at least one item satisfies a predetermined condition regarding division.

10. An information processing device including a processor, The processor, Extracting edges of a roadway other than a roadway based on point cloud data of the roadway; Identifying attribute information regarding the travel route based on the point cloud data; generating line segment data for a roadway other than the roadway based on edge data relating to the edge and the attribute information; Associating the line segment data with attribute information of the route corresponding to the line segment data; Run The generating of the line segment data includes: An information processing device that generates line segment data based on at least one item of attribute information of pavement type, gradient, and width.

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