Information processing method, program, and information processing device

Abstract

To reduce generation cost of map data to be used in removing objects toward the outside of a passing road, such as snow removal.SOLUTION: An information processing method causes a processor to execute the acquisition of point group data within a prescribed space area including an outside of a passing road (S102), the extraction of point group data included in each extraction area set within the prescribed space area (S104), the projection of the extracted point group data to a prescribed surface set from an edge part of the passing road in a height direction (S106), and the specification of a target area including the point group data projected to the prescribed surface (S108).SELECTED DRAWING: Figure 10

Description

[Technical Field]

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

[0002] Conventionally, in a snow removal support system that smoothly performs snow removal work, a technology has been disclosed for generating map data to support snow removal, in which surrounding feature data that recognizes features on the road to be removed is generated. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2013-87469 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the technology disclosed in Patent Document 1 has a problem in that it requires a lot of effort and expense to recognize features around roads that are the target of snow removal.

[0005] Therefore, an object of the present invention is to provide an information processing method, a program, and an information processing device that can reduce the cost of generating map data used when removing objects from the outside of a roadway, such as when clearing snow. [Means for solving the problem]

[0006] An information processing method according to one aspect of the present invention includes a processor acquiring point cloud data within a predetermined spatial region including outside a roadway, extracting point cloud data contained in each extraction region set within the predetermined spatial region, projecting the extracted point cloud data onto a predetermined plane set in the height direction from the edge of the roadway, and identifying a target region including the point cloud data projected onto the predetermined plane. [Effects of the Invention]

[0007] According to the present invention, it is possible to reduce the cost of generating map data used when removing objects from the outside of a roadway, such as when removing snow. [Brief explanation of the drawings]

[0008] [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. [Figure 2] 1 is a diagram illustrating an example of a configuration of an information processing device according to an embodiment of the present invention. [Figure 3] 1 is a diagram showing an example of the configuration of a snowplow according to an embodiment of the present invention. [Figure 4] FIG. 2 is a diagram illustrating an example of an XZ plane according to an embodiment of the present invention. [Figure 5] FIG. 2 is a diagram illustrating an example of a YZ plane according to an embodiment of the present invention. [Figure 6] 10A and 10B are diagrams showing an example of a snow throwing prohibited area and a snow throwing allowed area set on a predetermined surface according to one embodiment of the present invention. [Figure 7A] FIG. 10 is a diagram showing an example of setting an extraction region according to an embodiment of the present invention. [Figure 7B] FIG. 10 is a diagram showing an example of setting an extraction region according to an embodiment of the present invention. [Figure 8] FIG. 2 is a diagram showing an example of a hierarchical structure of high-precision three-dimensional map data according to an embodiment of the present invention. [Figure 9] FIG. 2 is a diagram for explaining an example of a predetermined surface according to an embodiment of the present invention. [Figure 10] 10 is a flowchart illustrating an example of a process related to map generation according to an embodiment of the present invention. [Figure 11] 10 is a flowchart illustrating an example of a process for using map data according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0009] [Embodiment] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS A preferred embodiment of the present invention will be described with reference to the accompanying drawings. In the drawings, components with the same reference numerals have the same or similar configurations.

[0010] <System Overview> Fig. 1 is a diagram showing an example of the configuration of an information processing system 1 according to one embodiment of the present invention. The information processing system 1 shown in Fig. 1 includes an information processing device 10, a snowplow 20, and a positioning satellite 30 used in the Global Navigation Satellite System (GNSS), which are capable of transmitting and receiving data to and from each other via a network N. There may be one or more snowplows 20 and information processing devices 10.

[0011] Information processing device 10 is, for example, a server, which acquires point cloud data including data on the road surface and uses the disclosed technology to generate map data for supporting snowplow 20. With the disclosed technology, there is no need to recognize features on the road surface or road shoulders, which makes it possible to reduce the effort and cost involved in recognizing and identifying features.

[0012] The information processing device 10 acquires, from the snowplow 20, position information of the snowplow 20 acquired by a locator, etc. Furthermore, the information processing device 10 uses the generated map data to assist the snowplow 20 in its snow-throwing work. For example, the information processing device 20 transmits surrounding map data including the position coordinates of the snowplow 20, etc., to the snowplow 20. Note that the information processing device 10 may be composed of multiple information processing devices.

[0013] Snowplow 20 is a vehicle that removes snow from roads by pushing or blowing the snow aside. Snowplow 20 may also be equipped with a locator that can acquire location information from a satellite. For example, snowplow 20 uses this locator to receive signals from GNSS positioning satellites 30 to detect its own vehicle's location information. Snowplow 20 may also be equipped with an automatic driving system and be a vehicle capable of automatic driving (autonomous driving). Snowplow 20 may also be equipped with a measuring device that can measure point cloud data, such as LiDAR (light detection and ranging).

[0014] The locator may be a locator that identifies the vehicle position using a GPS (Global Positioning System) function, or a locator that can measure the position by lane based on signals from a satellite positioning system, and any known locator may be used. Furthermore, a high-precision locator can receive information from a quasi-zenith satellite, for example, and combine it with three-dimensional map data (described later) to obtain lane-by-lane position information for snowplow 20. For example, by using a locator, the accuracy of the vehicle position can be improved to approximately 0.5 m or less. Here, "position information" refers to information related to the position of snowplow 20, and may include, for example, three-dimensional position information of latitude, longitude, and altitude, or two-dimensional position information of latitude and longitude.

[0015] The snowplow 20 throws snow from the road surface onto the road shoulder using the map data generated by the information processing device 10. At this time, the snowplow 20 displays or determines on the screen the road shoulder onto which snow can be thrown, based on the snow throwing prohibited area or snow throwing allowed area included in the map data, and controls snow throwing.

[0016] The map data may also be surrounding map data showing a map of the area around snowplow 20, specifically map data within a predetermined area including the position information of snowplow 20, and map data showing the aforementioned snow throwing prohibited area or snow throwing allowed area. The predetermined area may be, for example, an area within a radius of several tens to several hundreds of meters centered on the position of snowplow 20.

[0017] The positioning satellite 30 is a satellite that transmits signals necessary for measuring position information. For example, the positioning satellite 30 is a satellite used in GNSS, and may include a satellite (for example, a quasi-zenith satellite) that can transmit signals related to highly accurate position information.

[0018] <Map data overview> Here, an overview of the map data used in this embodiment will be described. The disclosed map data is generated by the information processing device 10 by executing, for example, the following processes: acquisition of point cloud data, extraction of point cloud data in each extraction area, point cloud projection, identification of a target area, and generation of map data based on the target area.

[0019] The disclosed map data is generated by the above-described processes, so there is no need to recognize or identify features from point cloud data. Furthermore, with the disclosed map data, there is no need to recognize each of the many features that exist around the road surface, as in the prior art, so it is possible to prevent features from being overlooked when extracted.

[0020] In the disclosed map data, each extraction area of ​​the point cloud data is set based on features present on the roadside. The point cloud data within this extraction area is projected onto a projection plane in the height direction set on the side of the road. If no point cloud data is projected onto the projection plane, the extraction area is determined to be an area where snow can be thrown, since there are no features present in the area. On the other hand, if point cloud data exists in the extraction area, the point cloud data is projected onto the projection plane. A target area (e.g., a circumscribed area) is set based on the point cloud data on the projection plane, and this target area is set as a snow-throwing prohibited area.

[0021] Furthermore, if there is a forest or similar beyond the shoulder of the road, the forest is generally an area where snow can be thrown, but if an extraction area that includes the forest is set, there will be a large amount of point cloud data in this extraction area, and it will not be set as an area where snow can be thrown. Therefore, if an extraction area is set from the shoulder of the road to the forest, and there is nothing in this extraction area, this extraction area will be set as an area where snow can be thrown, and it will be possible to throw snow toward the forest that exists beyond the extraction area.

[0022] The disclosed map data includes, for example, map data that sets at least areas where snow can be thrown or areas where snow can't be thrown on the projection surface. This makes it possible to provide map data that clearly indicates whether snow can be thrown or not, regardless of the nature of the features that exist on the side of the road.

[0023] <Configuration of information processing device> 2 is a diagram showing an example of the configuration of an information processing device 10 according to an embodiment of the present invention. The information processing device 10 includes one or more processors (e.g., CPU: Central Processing Unit) 110, one or more network communication interfaces 120, a storage device 130, a user interface 150, and one or more communication buses 170 for interconnecting these components. Note that the user interface 150 is not necessarily required, and may be connected as an external device.

[0024] Storage device 130 may be, for example, a high-speed random-access memory such as a DRAM, an SRAM, or other random-access solid-state storage device. Storage device 130 may also 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. Storage device 130 may also be a non-transitory computer-readable recording medium.

[0025] Another example of storage device 130 may be one or more storage devices located remotely from processor 110. In one embodiment, storage device 130 stores programs, modules, and data structures, or a subset thereof, that are executed by processor 110.

[0026] The storage device 130 stores data used by the information processing system 1. For example, the storage device 130 stores point cloud data acquired based on MMS (Mobile Mapping System) or satellite images, data generated by a process for generating map data for snow throwing assistance, map data for snow throwing assistance, etc. The storage device 130 may also store dynamic maps, HD maps, etc.

[0027] The following describes processor 110, which executes processes related to work support for snowplow 20 according to this embodiment. By executing programs stored in storage device 130, processor 110 configures map control unit 112, transmission / reception unit 113, acquisition unit 114, setting unit 115, extraction unit 116, projection unit 117, identification unit 118, and generation unit 119.

[0028] The processor 110 is configured to control the processing of each unit described below, and to execute processing related to the generation of map data used to assist the snowplow 20 in its operation.

[0029] The map control unit 112 uses the point cloud data to control the process of generating map data that sets snow throwing permitted areas or snow throwing prohibited areas.

[0030] The transmitter / receiver 113 transmits and receives data to and from external devices via the network communication interface 120. For example, the transmitter / receiver 113 is configured as a receiver that receives data, signals, etc. from the snowplow 20 and each positioning satellite 30, and is also configured as a transmitter that transmits data, signals, etc. to the snowplow 20 and each positioning satellite 30. As a specific example, the transmitter / receiver 113 receives point cloud data from an MMS measurement vehicle, etc. The transmitter / receiver 113 also receives location information acquired by a locator from the snowplow 20. The transmitter / receiver 113 also transmits map data generated by a generator 119 (described later) to the snowplow 20.

[0031] The acquisition unit 114 acquires point cloud data within a predetermined spatial region including an area outside a roadway. For example, the acquisition unit 114 acquires point cloud data measured by an MMS vehicle traveling on a roadway. The acquisition unit 114 may also acquire point cloud data from another database or the like. The predetermined spatial region is, for example, a three-dimensional space set in an open space, a building, a forest, or the like beside a road.

[0032] The setting unit 115 sets one or more extraction areas in a predetermined spatial area including the outside of the roadway, either manually or automatically. The extraction areas may be set according to features on the side of the road.

[0033] The extraction unit 116 extracts point cloud data included in each extraction region set within a predetermined spatial region. For example, the extraction unit 116 cuts out and extracts point cloud data present within each extraction region, each extraction region having a different region size, from the point cloud data within the predetermined spatial region.

[0034] The projection unit 117 projects the point cloud data extracted by the extraction unit 116 onto a predetermined plane set in the height direction from the edge of the road. For example, the projection unit 117 projects the extracted point cloud data onto a projection plane (e.g., a geofence) set on the shoulder or side of the road.

[0035] The identification unit 118 identifies a target area including the point cloud data projected onto a predetermined surface by the projection unit 117. For example, the identification unit 118 sets an area surrounding the area onto which the point cloud data is projected onto the projection surface, and sets this surrounded area as the target area.

[0036] The above process makes it possible to project areas where features exist outward from a predetermined plane set at the edge of a roadway in the height direction, and to identify snow-throwing-prohibited areas as target areas. The above process makes it possible to identify snow-throwing-prohibited areas with simple processing, without the need to recognize or identify features.

[0037] The identification unit 118 may also identify a target area by connecting the outlines of the point cloud data projected onto a predetermined surface. For example, the identification unit 118 may identify edges of the projected point cloud data, connect the point clouds of the edge portions, and set an outline rectangle including the connected edge portions. The identification unit 118 may set this outline rectangle as the target area. The target area does not necessarily have to be rectangular.

[0038] The above process makes it possible to clearly identify the target area using point cloud data on a specified surface. Furthermore, by identifying a rectangular target area, it becomes possible to identify a no-snow-throwing area that is easy to grasp visually.

[0039] The identification unit 118 may also perform noise removal based on the density of the point cloud data projected onto a predetermined surface to identify a target region. For example, point cloud data may exist in a region where no object exists due to reflection or the like, and the density of the point cloud data is used to remove this noise. In this case, if the density of point cloud data included within a predetermined range of the predetermined point cloud data is equal to or less than a predetermined threshold, the identification unit 118 may determine that the point cloud data is noise and remove it.

[0040] The above processing makes it possible to efficiently remove point cloud data generated by reflections, etc., and improve the accuracy of the snow throwing prohibition area.

[0041] The setting unit 115 may further set an edge of a roadway (e.g., a roadway) based on the high-precision three-dimensional map data. For example, the setting unit 115 acquires high-precision three-dimensional map data stored in the storage device 130, and acquires road shoulder edge information corresponding to the edge of the roadway based on the high-precision three-dimensional map data including the target position for generating map data. As a specific example, the setting unit 115 sets a predetermined plane at a predetermined height in the height direction relative to the position of the feature data of the road shoulder, roadside, or curb, based on feature data of the road shoulder, roadside, or curb, etc., in the high-precision three-dimensional map data.

[0042] The above process makes it possible to automatically set the edges of a roadway using other maps, thereby reducing the burden on the creator of the map data.

[0043] The setting unit 115 may also set each extraction area based on map data related to a predetermined spatial region. For example, the setting unit 115 may recognize feature data present at the location for which map data is to be generated by image recognition or the like based on map data including satellite images or known map data, and set the extraction area based on the recognition results. As a specific example, the setting unit 115 may recognize urban areas, mountainous areas, etc. based on satellite images from Google Earth (registered trademark), and set each extraction area smaller than a predetermined size in urban areas, and set each extraction area larger than a predetermined size in mountainous areas.

[0044] The above processing can assist in setting an extraction area based on satellite images and known map data, making it easier to set an extraction area.

[0045] Furthermore, when setting each extraction area, the setting unit 115 may set each extraction area based on the snow throwing capacity of a snowplow traveling on the road. For example, the setting unit 115 sets the extraction area to an area that the thrown snow can reach, based on the snow throwing height and distance included in the snow throwing capacity of the snowplow.

[0046] The above process makes it possible to prevent the extraction area from being set in an area that exceeds the snow throwing capacity of the snowplow, and to set the extraction area in a more appropriate area.

[0047] Furthermore, when acquiring point cloud data, the acquiring unit 114 may acquire point cloud data in the upward direction of the road. For example, the acquiring unit 114 may acquire point cloud data up to a predetermined height above the road.

[0048] The projection unit 117 may also project point cloud data in the upward direction of the road onto a predetermined plane. For example, the projection unit 117 sets an extraction area that includes features on the roadway, such as power lines and pedestrian bridges.

[0049] By the above process, it is possible to set extraction processing for areas above the road surface that affect the amount of snow thrown.

[0050] The generation unit 119 generates map data in which the target area identified by the identification unit 118 is set as a snow throwing prohibited area. For example, the generation unit 119 sets a snow throwing prohibited area on a predetermined surface, and sets an area on the predetermined surface other than the snow throwing prohibited area as a snow throwing permitted area, and generates map data based on the point cloud data. As a specific example, the generation unit 119 sets a predetermined surface including the above-mentioned snow throwing prohibited area or snow throwing permitted area in map data related to roads based on the point cloud data.

[0051] The above process reduces the cost of generating maps and generates map data for snow-throwing support. By using this map data for snow-throwing support in snowplows, operators can easily determine which areas need to be thrown.

[0052] <Snowplow configuration> 3 is a diagram showing an example of the configuration of a snowplow 20 according to an embodiment of the present invention. The snowplow 20 includes one or more processors (e.g., CPU: Central Processing Unit) 210, one or more network communication interfaces 220, a storage device 230, a user interface 250, a point cloud acquisition device 280, a position measurement device 290, and one or more communication buses 270 for interconnecting these components. Note that the user interface 250 may be a portable processing device such as a mobile terminal or tablet device.

[0053] Storage device 230 may be, for example, a high-speed random-access memory such as a DRAM, an SRAM, or other random-access solid-state storage device. Storage device 230 may also 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. Storage device 230 may also be a non-transitory computer-readable recording medium.

[0054] Another example of storage device 230 may be one or more storage devices located remotely from processor 210. In some embodiments, storage device 230 stores programs, modules, and data structures, or a subset thereof, that are executed by processor 210.

[0055] The storage device 230 stores data used by the information processing system 1. For example, the storage device 230 stores data for snow removal control, map data used for snow throwing support, and the like.

[0056] The user interface 250 is a touch panel display or the like that serves as both a display device and an operation device, and receives operations from an operator and displays information in accordance with the operations.

[0057] The point cloud acquisition device 280 is, for example, a laser, LiDAR, etc., and acquires point cloud data. Note that the point cloud acquisition device 280 is not necessarily a necessary component of the snowplow 20.

[0058] The position measuring device 290 measures a highly accurate position, for example, a position in units of lanes, based on a signal from a satellite positioning system, for example. Alternatively, the position measuring device 290 may measure the position information using a general GPS function.

[0059] Next, we will explain the processor 210 that executes processes related to work support for the snowplow 20 according to this embodiment. By executing programs stored in the storage device 230, the processor 210 constitutes an application control unit 212, a transmission / reception unit 213, an acquisition unit 214, a generation unit 215, a display control unit 216, a snow throwing control unit 217, and a travel control unit 218.

[0060] The processor 210 is configured to control the processing of each unit described below and to execute processing used to assist the snowplow 20 in its work.

[0061] The application control unit 212 uses map data transmitted from the information processing device 10 to control processes such as displaying snow throwing permitted areas or snow throwing prohibited areas, controlling snow throwing, and controlling the running of the snowplow 20.

[0062] The transmitting / receiving unit 213 transmits and receives data to and from external devices via the network communication interface 220. For example, the transmitting / receiving unit 213 is configured as a receiving unit that receives data, signals, etc. from the information processing device 10 and each positioning satellite 30, and is also configured as a transmitting unit that transmits data, signals, etc. to the information processing device 10 and each positioning satellite 30. As a specific example, the transmitting / receiving unit 213 receives map data used for snow throwing support from the information processing device 10.

[0063] The acquisition unit 214 acquires map data in which each snow throwing prohibited area or each snow throwing allowed area is set on a predetermined surface in the height direction from the edge of the road. For example, the acquisition unit 214 acquires map data in which a snow throwing prohibited area is set on a predetermined surface (e.g., a geofence) set on the edge of the road shoulder.

[0064] The generation unit 215 generates screen information that enables discrimination between each snow throwing prohibited area and other areas, or between each snow throwing allowed area and other areas, included in the map data acquired by the acquisition unit 214. For example, the generation unit 215 colors or changes the pattern of the snow throwing prohibited area or snow throwing allowed area set on a predetermined surface so that it can be distinguished from other areas.

[0065] The display control unit 216 controls the display on a display device of the screen information generated by the generation unit 215. For example, the display control unit 216 controls the screen information to be displayed on the display of the user interface 250.

[0066] According to the above process, the operator in snowplow 20 can identify the snow throwing area and perform snow throwing work while checking the snow throwing prohibited area or snow throwing permitted area displayed on the screen. At this time, the screen simply displays the snow throwing prohibited area or snow throwing permitted area, so the operator can easily understand the snow throwing permitted area.

[0067] The snow throwing control unit 217 controls the snow throwing of the snowplow 20 based on each snow throwing prohibited area or each snow throwing permitted area and the high-precision positioning information. Here, the map data in which each snow throwing prohibited area or each snow throwing permitted area is set is map data based on point cloud data, so high-precision position control is possible. For example, the snow throwing control unit 217 obtains the position information of the vehicle from the position measurement device 290, and based on this position information, adjusts the angle and height of the snow throwing equipment so that snow is thrown into the snow throwing permitted areas in the map data.

[0068] The above processing allows the snowplow 20 to automatically control snow throwing. It is also preferable that the snowplow 20 allows both automatic and manual snow throwing control.

[0069] Travel control unit 218 controls the travel of snowplow 20 based on map data based on point cloud data including a predetermined surface and high-precision positioning information. For example, travel control unit 218 acquires the vehicle's position information from position measurement device 290 and controls the autonomous travel of snowplow 20 using this position information and map data. For example, if position measurement device 290 is a locator capable of high-precision position measurement, it can enable autonomous travel on a lane-by-lane basis based on map data based on point cloud data.

[0070] Through the above processing, even if the snowplow 20 does not acquire high-precision three-dimensional map data, if the vehicle's position information can be acquired by using map data generated based on point cloud data, it can perform automatic driving (autonomous driving) based on that position information.

[0071] The acquisition unit 214 may also acquire point cloud data acquired by the point cloud acquisition device 280. As a result, the snow throwing control unit 217 may control snow throwing by the snowplow 20 based on the point cloud data acquired by the acquisition unit 214, each snow throwing prohibited area or each snow throwing allowed area, and the high-precision positioning information. For example, the snow throwing control unit 217 identifies an area where snow can actually be thrown based on the point cloud data outside the road and the snow throwing allowed area, and controls the snow throwing equipment to throw snow toward that area from the current location based on the high-precision positioning information.

[0072] The above process makes it possible to control snow throwing according to the current situation while acquiring point cloud data. For example, if the area to be snow thrown is already covered with snow, the angle and height of the snow throwing equipment will be adjusted based on the point cloud data so that the height at which the snow will accumulate will be the same as the height of the accumulated snow.

[0073] <Map data generation process> Next, the map data generation process of the disclosed technology will be described with reference to Figures 4 to 7. Figure 4 is a diagram showing an example of a plane of the X direction in the lateral direction (vehicle width direction) and the Z direction in the height direction according to one embodiment of the present invention. Figure 5 is a diagram showing an example of a plane of the Y direction in the traveling direction (length direction of the roadway) and the Z direction according to one embodiment of the present invention.

[0074] 4 and 5 show an example in which a tree T10, a guardrail T12, and a utility pole T14 are present beside a road D10. Furthermore, an electric wire runs from the utility pole T14 in the vehicle width direction. In the example shown in FIGS. 4 and 5, the acquisition unit 114 acquires point cloud data within a predetermined spatial region, such as the tree T10, the guardrail T12, and the utility pole T14. For example, a predetermined spatial region AR10 set beside the road and a spatial region AR12 on the road are set.

[0075] The setting unit 115 sets extraction regions AR20 and AR22 for cutting out point cloud data from the spatial region AR10. The three-dimensional extraction region AR20 is a region where nothing exists, and its width W20 in the X direction is set based on the snow-throwing capacity, etc. The three-dimensional extraction region AR22 is a region that includes at least a portion of the tree T10 because the tree T10 exists in the three-dimensional extraction region AR22, and its width W22 in the X direction is set. The width W22 is smaller than the width W20 because the tree T10 exists in the extraction region AR22, and therefore point cloud data can be obtained by setting a region that includes the tree T10.

[0076] Furthermore, the setting unit 115 sets an extraction region AR12 for cutting out point cloud data from the spatial region AR12. Note that the setting unit 115 may adjust the lengths in the X direction, Y direction, and Z direction as appropriate depending on features present in the spatial region. Furthermore, the setting unit 115 may set the spatial region and the extraction region to be the same.

[0077] The setting unit 115 sets the predetermined plane F10, for example, at the edge of the roadway. The predetermined plane F10 may be set by an operation of the map creator, or may be set based on information such as the shoulder or side of the road included in the high-precision three-dimensional map data.

[0078] The extraction unit 116 cuts out and extracts point cloud data of each of the extraction areas AR20, AR22, and AR24 from a predetermined plane F10. The projection unit 117 projects the point cloud data within each of the extraction areas AR20, AR22, and AR24 onto the projection plane F10. The identification unit 118 sets a snow throwing prohibition area based on the point cloud data projected onto the projection plane F10.

[0079] Fig. 6 is a diagram showing an example of a snow throwing prohibited area and a snow throwing allowed area set on a predetermined surface according to one embodiment of the present invention. The example shown in Fig. 6 shows a snow throwing allowed area AR30 and a snow throwing prohibited area AR32 within the predetermined surface F10, which are set by the processing described above. Note that it is sufficient that at least either the snow throwing allowed area AR30 or the snow throwing prohibited area AR32 is set on the predetermined surface F10. Furthermore, the snow throwing allowed area AR30 is an area within the predetermined surface F10 other than the snow throwing prohibited area AR32.

[0080] 7A and 7B are diagrams showing examples of setting extraction regions according to an embodiment of the present invention. Fig. 7A is an image showing an example of a roadway. Fig. 7B is a diagram showing examples of extraction regions for point cloud data of the roadway.

[0081] In the example shown in FIG. 7B, area AR40 has a forest in the depth direction (Y direction) from the road shoulder, and point cloud data exists for this forest. However, since the forest is an area where snow can be thrown, the length of extraction area AR40 in the Y direction is set to a short length from the edge of the roadway to just before the forest, in order to make extraction area AR40 an area where snow can be thrown. As a result, this extraction area AR40 does not include point cloud data for the forest, and when projected onto a specified surface, extraction area AR40 becomes an area where snow can be thrown. Furthermore, because extraction area AR42 is an empty lot, the length in the depth direction (X direction) from the road shoulder is set to, for example, the limit distance for snow throwing.

[0082] 4 to 7, in this embodiment, when setting a snow-throwing prohibited area or a snow-throwing permitted area, there is no need to recognize or identify what the feature is, and point cloud data can be used as is, thereby reducing the effort, expense, and other costs involved in generating a map. Furthermore, when a feature beyond the shoulder of the road is identified by image recognition of a satellite image or the like, it is possible to adjust the extracted area so that a specific feature (such as a forest) is set to be set as a snow-throwing permitted area by shortening its length in the depth direction from the shoulder of the road.

[0083] <High-precision 3D map data> Here, an overview of the high-precision 3D map data used in setting the road shoulder edge in this embodiment will be described. The high-precision 3D map data used in this embodiment is, for example, high-precision 3D map data used in autonomous driving, etc. As a specific example, this map data is map data 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 used in this embodiment is classified into, for example, four hierarchical levels.

[0084] Fig. 8 is a diagram showing an example of a hierarchical structure of high-precision 3D map data according to an embodiment of the present invention. In the example shown in Fig. 8, the high-precision 3D map data is classified into static information SI1, quasi-static information SI2, quasi-dynamic information MI1, and dynamic information MI2.

[0085] Static information SI1 is three-dimensional basic map data, including road surface information, lane information, three-dimensional structures, etc., and is composed of three-dimensional position coordinates and linear vector data that indicate features. Quasi-static information SI2, semi-dynamic information MI1, and dynamic information MI2 are dynamic data that change from moment to moment, and are data that are superimposed on static information based on position information.

[0086] 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 information on nearby vehicles, pedestrians, traffic lights, etc.

[0087] To realize a dynamic map, it is important to construct high-precision 3D map data (hereinafter also referred to as "HD (High Definition) map") among the 3D map data corresponding to the static information SI1. HD maps have a predetermined accuracy level that can be managed on a lane-by-lane basis. For example, if the accuracy level is the map information level used to indicate the accuracy of position and height on a digitized map, an HD map is a map with a map information level of about 500 (equivalent scale 1 / 500) or a level more precise than map information level 500.

[0088] HD maps are generated based on point cloud data acquired by, for example, a laser scanner, using a mobile Mapping System (MMS)-equipped measurement vehicle that performs 3D measurements of the surrounding terrain while moving. HD maps identify features on roads and their surroundings, such as traffic lights, road signs, road signs, lane markings, roadside edges, roadside objects, road surface obstacles, underground objects, and utility poles, from the point cloud data. The HD maps then include feature data managed for each identified feature. The feature data may include any information related to the feature, such as feature ID, type, location, size, configuration, and data generation (update) date and time. The features targeted for generating feature data include both real features and virtual features, such as lane links, generated from real features.

[0089] Furthermore, the HD map is not limited to being generated based on point cloud data obtained by MMS measurement, and may be generated by other methods as long as it has the above-mentioned accuracy level. For example, the HD map may be generated based on 3D surveying using airborne lasers or high-resolution images.

[0090] As described above, the high-precision three-dimensional map data includes feature data of the shoulder edge, and therefore the setting unit 115 can set a predetermined surface on the shoulder edge by identifying the shoulder edge in the feature data.

[0091] <Drive control> Fig. 9 is a diagram for explaining an example of a predetermined plane according to one embodiment of the present invention. In Fig. 9, a predetermined plane F20 set at the edge of the road shoulder and a predetermined plane F22 set at the center line of the lane are set. The predetermined plane F20 is an example of a geofence that includes a no-snow-throwing area and indicates that travel outside the area is prohibited. The predetermined plane F30 is a diagram showing an example of a geofence that is, for example, entirely a no-snow-throwing area and indicates that travel outside the area is permitted.

[0092] In the example shown in Figure 9, a geofence F20, which is a predetermined surface, is set at the curb at the roadway's travel limit, and a snow-throwing prohibition area is set at the location of a utility pole, etc. Furthermore, when snow removal is prohibited beyond the road center line, the entire area is set as a snow-throwing prohibition area, but a geofence F22 is set that allows snow removal beyond the center line while driving.

[0093] In addition, if the roadway is a roadway provided on a bridge or the like and there is a railway or roadway under the bridge, the setting unit 115 may set the area as a no-snow-throwing area if it recognizes that it is a roadway on a bridge based on the feature data, even if there is no point cloud data within the extraction area.

[0094] <Operation processing> Next, there will be described each process related to map data generation by the information processing system 1. Fig. 10 is a flowchart showing an example of a process related to map generation according to one embodiment of the present invention.

[0095] In step S102, the acquisition unit 114 acquires point cloud data within a predetermined spatial region including an area outside the roadway. For example, the acquisition unit 114 acquires point cloud data measured by a vehicle of an MMS traveling on the roadway.

[0096] In step S104, the extraction unit 116 extracts point cloud data included in each extraction region set within the predetermined spatial region. For example, the extraction unit 116 cuts out and extracts point cloud data present within each extraction region, each extraction region having a different region size, from the point cloud data within the predetermined spatial region.

[0097] In step S106, the projection unit 117 projects the point cloud data extracted by the extraction unit 116 onto a predetermined plane set in the height direction from the edge of the road. For example, the projection unit 117 projects the extracted point cloud data onto a projection plane (e.g., a geofence) set on the shoulder or side of the road.

[0098] In step S108, the identification unit 118 identifies a target area including the point cloud data projected onto a predetermined surface by the projection unit 117. For example, the identification unit 118 sets an area surrounding the area onto which the point cloud data is projected onto the projection surface, and sets this surrounded area as the target area.

[0099] In step S110, the generation unit 119 generates map data in which the target area identified by the identification unit 118 is set as a snow throwing prohibited area. For example, the generation unit 119 sets a snow throwing prohibited area on a predetermined surface, sets an area on the predetermined surface other than the snow throwing prohibited area as a snow throwing permitted area, and generates map data based on the point cloud data.

[0100] The above process eliminates the need for feature recognition processing, reducing costs associated with map generation and enabling the generation of map data for snow-throwing support. By using this map data for snow-throwing support in snowplows, operators can easily determine which areas need to be thrown snow.

[0101] 11 is a flowchart showing an example of a process using map data according to an embodiment of the present invention. In step S202, the acquisition unit 214 acquires map data in which each snow-throwing prohibited area or each snow-throwing permitted area is set on a predetermined surface in the height direction from the edge of a road. For example, the acquisition unit 214 acquires map data in which a snow-throwing prohibited area is set on a predetermined surface (e.g., a geofence) set on the edge of a road shoulder.

[0102] In step S204, the generation unit 215 generates screen information that enables discrimination between each snow throwing prohibited area and other areas, or between each snow throwing allowed area and other areas, included in the map data acquired by the acquisition unit 214. For example, the generation unit 215 colors or changes the pattern of the snow throwing prohibited area or snow throwing allowed area set on a predetermined surface so that it can be distinguished from other areas.

[0103] In step S206, the display control unit 216 controls the display of the screen information generated by the generation unit 215 on the display device. For example, the display control unit 216 controls the screen information to be displayed on the display of the user interface 250.

[0104] In step S208, travel control unit 218 controls the travel of snowplow 20 based on map data including a predetermined surface and high-precision positioning information. For example, travel control unit 218 acquires the position information of the vehicle itself from position measurement device 290, and controls the autonomous travel of snowplow 20 using this position information and map data.

[0105] In step S210, the snow throwing control unit 217 controls the snow throwing of the snowplow 20 based on each snow throwing prohibited area or each snow throwing allowed area and the high-precision positioning information. For example, the snow throwing control unit 217 acquires the position information of the vehicle from the position measurement device 290, and adjusts the angle and height of the snow throwing equipment based on this position information so that snow is thrown into the snow throwing allowed area in the map data. Note that step S208 and / or step S210 are not necessarily required processes.

[0106] According to the above process, the operator in snowplow 20 can identify the snow throwing area and perform snow throwing work while checking the snow throwing prohibited area or snow throwing permitted area displayed on the screen. At this time, the screen simply displays the snow throwing prohibited area or snow throwing permitted area, so the operator can easily understand the snow throwing permitted area.

[0107] Although one embodiment of the present invention has been described above in detail, it 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, some of the processes executed by the information processing device 10 may be transferred to another information processing device, or multiple information processing devices may be integrated as appropriate.

[0108] <Modification> Although the present invention has been described based on the above embodiment, the following cases are also included in the present invention.

[0109] <Variation 1> Although not shown in the above embodiment, the snowplow 20 can be applied to any vehicle that moves objects on a road to the shoulder, such as a work vehicle that moves trash, fallen leaves, etc. on the road to the shoulder.

[0110] <Variation 2> In the above-described embodiment and modified examples, a roadway has been described, but the technology of the present disclosure can also be applied to roads other than roadways, such as railroad tracks and sidewalks, on which something is passable. [Explanation of symbols]

[0111] 1...information processing system, 10...information processing device, 20...snowplow, 30...positioning satellite, 110...processor, 112...map control unit, 113...transmitting / receiving unit, 114...acquisition unit, 115...setting unit, 116...extraction unit, 117...projection unit, 118...identification unit, 119...generation unit, 120...network communication interface, 130...storage device, 150...user interface, 210...processor, 212...application control unit, 213...transmitting / receiving unit, 214...acquisition unit, 215...generation unit, 216...display control unit, 217...snow throwing control unit, 218...travel control unit, 220...network communication interface, 230...storage device, 250...user interface, 280...point cloud acquisition device, 290...position measurement device

Claims

1. An information processing method executed by an information processing device including a processor, the processor: Acquiring point cloud data within a predetermined spatial region including outside the traffic route; extracting point cloud data included in each extraction region set within the predetermined spatial region; projecting the extracted point cloud data onto a predetermined plane set in a height direction from an edge of the roadway; identifying a region of interest that includes point cloud data projected onto the predetermined plane; An information processing method that performs the above.

2. The identifying step includes: The information processing method according to claim 1 , further comprising: identifying the target area by connecting contours of the point cloud data projected onto the predetermined surface.

3. The identifying step includes: The information processing method according to claim 1 , further comprising: performing noise removal based on the density of the point cloud data projected onto the predetermined surface, and identifying the target region.

4. the processor: The information processing method according to claim 1 , further comprising setting edges of the travel path based on high-precision three-dimensional map data.

5. the processor: The information processing method according to claim 1 , further comprising: setting each of the extraction areas based on map data relating to the predetermined spatial area.

6. Setting each extraction region includes:

6. The information processing method according to claim 5, further comprising the step of setting each of the extraction areas based on the snow throwing capacity of a snowplow traveling on the roadway.

7. The acquiring of the point cloud data includes: acquiring point cloud data in an upward direction of a roadway; The projecting step includes: The information processing method according to claim 1 , further comprising projecting point cloud data in an upward direction of the travel route onto the predetermined surface.

8. The processor: The information processing method according to claim 1 , further comprising generating map data in which the target area is set as a no-snow-throwing area.

9. A processor included in the information processing device Acquiring point cloud data within a predetermined spatial region including outside the traffic route; extracting point cloud data included in each extraction region set within the predetermined spatial region; projecting the extracted point cloud data onto a predetermined plane set in a height direction from an edge of the roadway; identifying a region of interest that includes point cloud data projected onto the predetermined plane; A program that executes the following.

10. An information processing device including a processor, the processor: Acquiring point cloud data within a predetermined spatial region including outside the traffic route; extracting point cloud data included in each extraction region set within the predetermined spatial region; projecting the extracted point cloud data onto a predetermined plane set in a height direction from an edge of the roadway; identifying a region of interest that includes point cloud data projected onto the predetermined plane; An information processing device that executes the above.

11. An information processing method executed by an information processing device including a processor, the processor: Obtaining map data in which each snow throwing prohibited area or each snow throwing allowed area is set using point cloud data of a predetermined area including the outside of a travel route, the target area being specified by point cloud data projected from the edge of the travel route onto a predetermined plane in the height direction; generating screen information that enables the snow throwing prohibited areas and other areas, or the snow throwing permitted areas and other areas, included in the map data, to be distinguished from each other; Controlling the display of the screen information on a display device; An information processing method that performs the above.

12. the information processing device is provided in a snowplow, the processor: The information processing method according to claim 11 , further comprising controlling snow throwing by the snowplow based on the snow-throwing prohibited areas or the snow-throwing permitted areas and high-precision positioning information.

13. the information processing device is provided in a snowplow, the processor: The information processing method according to claim 11 , further comprising controlling travel of the snowplow based on the map data and the high-precision positioning information.

14. the information processing device is provided in a snowplow, the processor: Acquiring point cloud data sequentially acquired by a point cloud acquisition device; Identifying an area where snow can actually be thrown based on the point cloud data acquired in sequence and each of the snow throwing possible areas included in the map data; The information processing method according to claim 11 , further comprising controlling snow throwing by the snowplow based on the identified area where snow can be thrown and high-precision positioning information.

15. A processor included in the information processing device Obtaining map data in which each snow throwing prohibited area or each snow throwing allowed area is set using point cloud data of a predetermined area including the outside of a travel route, the target area being specified by point cloud data projected from the edge of the travel route onto a predetermined plane in the height direction; generating screen information that enables the snow throwing prohibited areas and other areas, or the snow throwing permitted areas and other areas, included in the map data, to be distinguished from each other; Controlling the display of the screen information on a display device; A program that executes the following.

16. An information processing device including a processor, the processor: Obtaining map data in which each snow throwing prohibited area or each snow throwing allowed area is set using point cloud data of a predetermined area including the outside of a travel route, the target area being specified by point cloud data projected from the edge of the travel route onto a predetermined plane in the height direction; generating screen information that enables the snow throwing prohibited areas and other areas, or the snow throwing permitted areas and other areas, included in the map data, to be distinguished from each other; Controlling the display of the screen information on a display device; An information processing device that executes the above.

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