Information Processing Method, Information Processing Program, Information Processing Apparatus, and Data Structure

Abstract

Provided is an information processing method capable of generating highly reliable and consistent visibility risk data. 【Solution means】An information processing method that is executed by a computer and generates visibility risk data indicating the visibility risk at each point on a road includes: a step of obtaining peripheral environment data indicating the peripheral environment of a first virtual point among a plurality of virtual points set on a plurality of road links; a step of automatically determining the visibility risk at the first virtual point based on the peripheral environment data; and a step of generating the visibility risk data in which the visibility risk at the first virtual point and at least one road link are associated with each other.

Description

Technical Field

[0001] The present disclosure relates to an information processing method, an information processing program, an information processing apparatus, and a data structure. In particular, the present disclosure relates to a method for generating visibility risk data indicating the visibility risk at each point on a road.

Background Art

[0002] Patent Document 1 discloses a route search method capable of performing route search according to the priority of safety or drivability. In the route search method described in Patent Document 1, a weight coefficient of a cost parameter for calculating the link cost of each road link is set according to the priority of safety or drivability. Thus, since the link cost of each road link varies according to safety or drivability, it is possible to perform an optimal route search according to the priority of safety or drivability.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] By the way, the route search method described in Patent Document 1 does not perform route search in consideration of the visibility risk of the surrounding environment at each point on the road (for example, an index indicating the visibility at intersections and sharp curves). On the other hand, according to the statistical survey of traffic accidents, there is a current situation where there are many collision accidents caused by poor visibility due to buildings and the like (that is, visibility risk). Therefore, in order to reduce collision accidents, route search considering visibility risk can become an increasingly important technology in the future. In particular, 1) route search considering visibility risk, 2) visualization of visibility risk on a map, and 3) notification of information regarding visibility risk to a traveling vehicle can become increasingly important technologies in a society where automated driving (AD) and advanced driver assistance systems (ADAS) are generally popularized in the future.

[0005] On the other hand, in order to achieve the above three technologies, visibility risk data indicating the visibility risk at each point on the road is essential. In this regard, comprehensively acquiring visibility risk data has current issues in terms of cost. Furthermore, since a method for ensuring the objectivity and consistency of visibility risk is not sufficiently established at present, there is also an issue that it is difficult to provide highly reliable visibility risk data. Thus, there is room for considering a new method for providing highly reliable and consistent visibility risk data.

[0006] From the above viewpoints, a first object of the present disclosure is to provide an information processing method capable of generating highly reliable and consistent visibility risk data.

[0007] A second object of the present disclosure is to provide an information processing method capable of performing route search considering visibility risk.

[0008] A third object of the present disclosure is to provide an information processing method for visualizing visibility risk on a map.

[0009] A fourth object of the present disclosure is to provide an information processing method capable of providing information regarding visibility risk to a vehicle.

[0010] A fifth object of the present disclosure is to provide an information processing method that enables driving control of a vehicle in consideration of visibility risks.

[0011] Furthermore, an object of the present disclosure is to provide an information processing program for causing a computer to execute the above information processing method and an information processing device that executes the information processing method.

Means for Solving the Problems

[0012] An information processing method according to an aspect of the present disclosure is an information processing method that is executed by a computer and for generating visibility risk data indicating a visibility risk at each point on a road, the method including: obtaining peripheral environment data indicating a peripheral environment of a first virtual point among a plurality of virtual points set on a plurality of road links; automatically determining a visibility risk at the first virtual point based on the peripheral environment data; and generating the visibility risk data in which the visibility risk at the first virtual point and at least one road link are associated with each other.

[0013] An information processing method according to an aspect of the present disclosure is an information processing method that is executed by a computer and for generating visibility risk data indicating a visibility risk at each point on a road, the method including: obtaining image data indicating a peripheral environment at each point on the road; automatically determining a visibility risk at each point based on the image data; and generating visibility risk data indicating the visibility risk at each point.

[0014] An information processing method according to an aspect of the present disclosure is an information processing method executed by a computer and performing route search, including: a step of acquiring information on a departure point and a destination; and a step of performing route search from the departure point to the destination considering the visibility risk based on road network data for route search and visibility risk data indicating the visibility risk at each point on the road. The visibility risk data is associated with the road network data via a road link. In the visibility risk data, the visibility risk at a virtual point set on the road link and the road link are associated with each other.

[0015] An information processing method according to an aspect of the present disclosure is an information processing method executed by a computer and visualizing the visibility risk at each point on the road on a map, including: a step of causing a map to be displayed on a display unit; and a step of visualizing the visibility risk on the map displayed on the display unit based on visibility risk data indicating the visibility risk. In the visibility risk data, the visibility risk at a virtual point set on the road link and the road link are associated with each other.

[0016] An information processing method according to an aspect of the present disclosure is an information processing method executed by a computer and presenting predetermined information to a vehicle, including: a step of acquiring position information indicating the position of the vehicle; and a step of presenting information regarding the visibility risk of the roads around the vehicle to the vehicle based on the position information and visibility risk data indicating the visibility risk at each point on the road. In the visibility risk data, the visibility risk at a virtual point set on the road link and the road link are associated with each other.

[0017] An information processing method according to one aspect of the present disclosure is an information processing method that is executed by a computer and controls the driving of a vehicle based on the visibility risk at each point on a road, the method including: obtaining position information indicating the position of the vehicle; specifying information regarding the visibility risk around the vehicle based on the position information and visibility risk data indicating the visibility risk; and executing driving control of the vehicle considering the visibility risk based on the information regarding the visibility risk. In the visibility risk data, the visibility risk at a virtual point set on the road link is associated with the road link.

[0018] An information processing program according to one aspect of the present disclosure causes a computer to execute the above information processing method. Further, an information processing apparatus according to one aspect of the present disclosure includes at least one processor and at least one memory storing computer-readable instructions, and when the computer-readable instructions are executed by the processor, the information processing apparatus executes the above information processing method.

[0019] A data structure according to one aspect of the present disclosure is a data structure indicating the visibility risk at each point on a road, and has identification information of a virtual point set on a road link, the visibility risk at the virtual point, and identification information of the road link. The identification information of the virtual point, the visibility risk at the virtual point, and the identification information of the road link are associated with each other. The visibility risk at the virtual point is automatically determined based on surrounding environment data indicating the surrounding environment of the virtual point.

Advantages of the Invention

[0020] According to the present disclosure, it is possible to provide an information processing method capable of generating highly reliable and consistent visibility risk data. Further, according to the present disclosure, it is possible to provide an information processing method capable of performing route search considering visibility risk, an information processing method capable of visualizing visibility risk on a map, and an information processing method capable of providing information regarding visibility risk to a vehicle. Also, in the present disclosure, it is possible to provide an information processing program for causing a computer to execute the above information processing method and an information processing apparatus for executing the above information processing method. Furthermore, it is possible to provide a data structure indicating visibility risk at each point on a road that is highly reliable and consistent.

Brief Description of the Drawings

[0021]

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Embodiments for Carrying Out the Invention

[0022] (Overview of this Embodiment) The overview of this embodiment is shown below.

[0023] An information processing method according to an aspect of the present disclosure is an information processing method that is executed by a computer and for generating visibility risk data indicating visibility risks at each point on a road, the method including: obtaining surrounding environment data indicating the surrounding environment of a first virtual point among a plurality of virtual points set on a plurality of road links; automatically determining the visibility risk at the first virtual point based on the surrounding environment data; and generating the visibility risk data in which the visibility risk at the first virtual point and at least one road link are associated with each other.

[0024] According to the above method, after automatically determining the visibility risk at a virtual point based on the surrounding environment data, visibility risk data indicating the visibility risk at the virtual point is generated. In this way, it is possible to generate highly reliable and consistent visibility risk data.

[0025] Further, the step of automatically determining the visibility risk at the first virtual point may include: generating a three-dimensional object corresponding to an object existing around the first virtual point based on the surrounding environment data; arranging the three-dimensional object in a geographical coordinate system; arranging a plurality of virtual point objects corresponding to a plurality of virtual points existing around the first virtual point in the geographical coordinate system; obtaining a field-of-view image captured by a virtual camera corresponding to the first virtual point; and determining the visibility risk at the first virtual point by evaluating the visibility of at least some of the plurality of virtual point objects shown in the field-of-view image.

[0026] According to the above method, the visibility risk at the first virtual point is automatically determined by evaluating the visibility of at least some of the plurality of virtual point objects shown in the field-of-view image captured by the virtual camera. In this way, since the visibility of the virtual point objects based on the influence of the arrangement of the three-dimensional objects is evaluated, it is possible to generate visibility risk data that conforms to the real world, which is difficult to evaluate with map data. Therefore, it is possible to generate highly reliable and consistent visibility risk data.

[0027] Also, at least some of the virtual point objects may be virtual point objects corresponding to N (N is a natural number) virtual points existing near the first virtual point. The visibility risk at the first virtual point may be determined by evaluating the visibility of the N virtual point objects shown in the field-of-view image. The visibility of a predetermined virtual point object among the N virtual point objects may be evaluated based on the ratio of the visible area of the predetermined virtual point object to the total area of the predetermined virtual point object in the field-of-view image.

[0028] According to the above method, the visibility of a predetermined virtual point object is evaluated based on the ratio of the visible area of the predetermined virtual point object to the total area of the predetermined virtual point object. In this way, it becomes possible to generate consistent visibility risk data with guaranteed objectivity.

[0029] Further, the surrounding environment data may be image data.

[0030] According to the above method, after the visibility risk at the virtual point is automatically determined based on the image data, visibility risk data indicating the visibility risk of the virtual point is generated. In this way, compared with point cloud data obtained by a LiDAR unit or the like, it is possible to generate highly reliable and consistent visibility risk data at a relatively low cost.

[0031] Further, the image data may be captured by a camera mounted on a vehicle traveling around the position of the first virtual point.

[0032] According to the above method, after the visibility risk at the virtual point is automatically determined based on the image data captured by a camera mounted on a vehicle, visibility risk data indicating the visibility risk of the virtual point is generated. In this way, through the traveling vehicle equipped with a camera, the update frequency of the visibility risk data can be improved, so that it is possible to obtain high-fidelity and comprehensive visibility risk data.

[0033] Further, the three-dimensional object may include at least one of a ground object corresponding to a ground feature existing around the position of the first virtual point and a road surface object corresponding to a road surface existing around the position of the first virtual point.

[0034] According to the above method, since the visibility of the virtual point object based on the influence of the arrangement of three-dimensional objects such as ground objects and road surface objects is evaluated, it becomes possible to generate visibility risk data that conforms to the real world and cannot be evaluated by map data.

[0035] Further, the surrounding environment data is image data captured by a camera mounted on a vehicle traveling around the position of the first virtual point, and the step of arranging the three-dimensional object in the geographic coordinate system may include the step of arranging the three-dimensional object in the geographic coordinate system based on the shooting direction, shooting position, and field of view angle of the camera.

[0036] According to the above method, since the three-dimensional object is arranged in the geographic coordinate system based on the shooting direction, shooting position, and field of view angle of the camera, it is possible to generate visibility risk data with high reliability and consistency.

[0037] Also, a plurality of virtual points associated with a predetermined road link among the plurality of road links may be arranged at equal intervals on the predetermined road link.

[0038] Further, the three-dimensional object may include a road surface object corresponding to the road surface existing around the position of the first virtual point. The plurality of virtual point objects are arranged on the road surface object, and the virtual camera may be arranged directly above the road surface object existing at the position of the first virtual point.

[0039] According to the above method, since the virtual camera is arranged directly above the road surface object existing at the position of the first virtual point, it is possible to generate visibility risk data that conforms to the line of sight of the viewer.

[0040] Also, when visibility risk data indicating the visibility risk related to the first viewer of the first attribute is generated, the height position of the virtual camera with respect to the road surface object may be set according to the line of sight of the first viewer. When visibility risk data indicating the visibility risk related to the second viewer of the second attribute different from the first attribute is generated, the height position of the virtual camera with respect to the road surface object may be set according to the line of sight of the second viewer.

[0041] According to the above method, since the height position of the virtual camera with respect to the road surface object is set according to the line of sight of viewers with different attributes, it is possible to provide visibility risk data for each viewer with different attributes. In this way, it is possible to provide optimal visibility risk data for each user (viewer) who utilizes the visibility risk data.

[0042] Further, the one or more road links may include a road link associated with the first virtual point and a road link associated with a virtual point corresponding to at least some of the virtual point objects for which visibility has been evaluated.

[0043] An information processing method according to an aspect of the present disclosure is an information processing method that is executed by a computer and for generating visibility risk data indicating the visibility risk at each point on a road, the method including: a step of acquiring image data indicating the surrounding environment at each point on the road; a step of automatically determining the visibility risk at each point based on the image data; and a step of generating visibility risk data indicating the visibility risk at each point.

[0044] According to the above method, by using the image data, it is possible to generate highly reliable and consistent visibility risk data.

[0045] An information processing method according to an aspect of the present disclosure is an information processing method that is executed by a computer and for performing route search, the method including: a step of acquiring information regarding a departure point and a destination; and a step of performing a route search from the departure point to the destination considering the visibility risk based on route search road network data and visibility risk data indicating the visibility risk at each point on the road. The visibility risk data is associated with the road network data via a road link. In the visibility risk data, the visibility risk at a virtual point set on the road link and the road link are associated with each other.

[0046] According to the above method, by utilizing the visibility risk data, it becomes possible to execute a route search that takes into account the visibility risk at each point on the road. In this way, it becomes possible to perform a safer route search that takes into account the visibility risk.

[0047] Further, the step of executing the route search may include a step of executing a route search from the departure point to the destination that takes into account the visibility risk and the surrounding environment risk based on the road network data, the visibility risk data, and the surrounding environment risk data indicating the surrounding environment risk at each point on the road. In the surrounding environment risk data, a surrounding environment risk factor indicating a risk factor around the virtual point, the virtual point, and the road link are associated with each other.

[0048] According to the above method, by utilizing the visibility risk data and the surrounding environment risk data, it becomes possible to execute a route search that takes into account the visibility risk and the surrounding environment risk at each point on the road. In this way, it becomes possible to perform a safer route search that takes into account the visibility risk and the surrounding environment risk.

[0049] Further, the step of executing the route search may include a step of extracting a plurality of road links included between the departure point and the destination based on the road network data, a step of calculating the total visibility risk in a predetermined route constituted by at least some of the plurality of extracted road links based on the visibility risk data, a step of calculating the total surrounding environment risk in the predetermined route based on the visibility risk data and the surrounding environment risk data, and a step of calculating a comprehensive risk that takes into account both the visibility risk and the surrounding environment risk in the predetermined route based on the total visibility risk and the total surrounding environment risk.

[0050] According to the above method, based on the total visual recognition risk and the total surrounding environment risk, a comprehensive risk considering both the visual recognition risk and the surrounding environment risk on a predetermined route is calculated. In this way, it becomes possible to perform a safer route search considering the visual recognition risk and the surrounding environment risk, and it is possible to contribute to the reduction of traffic accidents caused by the visual recognition risk.

[0051] An information processing method according to an aspect of the present disclosure is an information processing method executed by a computer and for visualizing a visual recognition risk at each point on a road on a map, including a step of causing a display unit to display a map, and a step of visualizing the visual recognition risk on the map displayed on the display unit based on visual recognition risk data indicating the visual recognition risk. In the visual recognition risk data, the visual recognition risk at a virtual point set on the road link is associated with the road link.

[0052] According to the above method, by utilizing the visual recognition risk data, it becomes possible to visualize the visual recognition risk at each point on the road on a map. In this way, through the information regarding the visual recognition risk visualized on the map, it becomes possible to efficiently implement traffic safety measures (for example, installation of curved mirrors and danger warning signs, etc.) for reducing the visual recognition risk at each point, and it is possible to contribute to the reduction of traffic accidents caused by the visual recognition risk.

[0053] An information processing method according to an aspect of the present disclosure is an information processing method executed by a computer and for presenting predetermined information to a vehicle, including a step of acquiring position information indicating the position of the vehicle, and a step of presenting information regarding the visual recognition risk around the vehicle to the vehicle based on the position information and visual recognition risk data indicating the visual recognition risk at each point on the road. In the visual recognition risk data, the visual recognition risk at a virtual point set on the road link is associated with the road link.

[0054] According to the above method, by utilizing the visibility risk data, it becomes possible to provide information regarding the visibility risk of the road around the vehicle to the vehicle (especially the passengers of the vehicle). In this way, it is possible to prompt the driver and others to drive safely considering the visibility risk, and contribute to reducing traffic accidents caused by the visibility risk.

[0055] Furthermore, it may further include the step of presenting information regarding the visibility risk and the surrounding environment risk around the vehicle to the vehicle based on the visibility risk data and the surrounding environment risk data indicating the surrounding environment risk at each point on the road. In the surrounding environment risk data, the surrounding environment risk factor indicating the risk factor around the virtual point, the virtual point, and the road link are associated with each other.

[0056] According to the above method, by utilizing the visibility risk data and the surrounding environment risk data, it becomes possible to provide information regarding the visibility risk and the surrounding environment risk around the vehicle to the vehicle (especially the passengers of the vehicle). In this way, it is possible to prompt the driver and others to drive safely considering the visibility risk and the surrounding environment risk, and contribute to reducing the occurrence of traffic accidents caused by the visibility risk and the surrounding environment risk.

[0057] An information processing method according to an aspect of the present disclosure is an information processing method executed by a computer and for controlling the driving of a vehicle based on the visibility risk at each point on the road, including the steps of acquiring position information indicating the position of the vehicle, specifying information regarding the visibility risk around the vehicle based on the position information and the visibility risk data indicating the visibility risk, and executing driving control of the vehicle considering the visibility risk based on the information regarding the visibility risk. In the visibility risk data, the visibility risk at a virtual point set on the road link and the road link are associated with each other.

[0058] According to the above method, by utilizing the visibility risk data, it is possible to realize the driving control of the vehicle considering the visibility risk of the road around the vehicle, thus contributing to the reduction of traffic accidents caused by visibility risks.

[0059] Also, an information processing program for causing a computer to execute the above information processing method is provided. Further, an information processing apparatus including at least one processor and at least one memory storing computer-readable instructions is provided. When the computer-readable instructions are executed by the processor, the information processing apparatus executes the above information processing method.

[0060] A data structure according to an aspect of the present disclosure is a data structure indicating the visibility risk at each point on a road, having identification information of a virtual point set on a road link, the visibility risk at the virtual point, and identification information of the road link. The identification information of the virtual point, the visibility risk at the virtual point, and the identification information of the road link are associated with each other. The visibility risk at the virtual point is automatically determined based on peripheral environment data indicating the peripheral environment of the virtual point.

[0061] According to the above, it is possible to provide a highly reliable and consistent data structure regarding visibility risk.

[0062] Further, the data structure may be associated with road network data for route search via the road link. Based on the data structure and the road network data, route search considering the visibility risk may be executed.

[0063] According to the above, by utilizing the visibility risk data and the road network data, it is possible to execute route search considering the visibility risk at each point on the road. In this way, it is possible to perform safer route search considering the visibility risk, contributing to the reduction of traffic accidents caused by visibility risks.

[0064] In addition, the automatic determination of the visibility risk at a predetermined virtual point among the virtual points may include generating a three-dimensional object existing around the predetermined virtual point based on the surrounding environment data, arranging the three-dimensional object in a geographical coordinate system, arranging a plurality of virtual point objects corresponding to a plurality of virtual points existing around the predetermined virtual point in the geographical coordinate system, obtaining a field-of-view image captured by a virtual camera corresponding to the predetermined virtual point, and evaluating the visibility for at least some of the plurality of virtual point objects shown in the field-of-view image.

[0065] According to the above, by evaluating the visibility for at least some of the plurality of virtual point objects shown in the field-of-view image captured by the virtual camera, the visibility risk at a predetermined virtual point is automatically determined. In this way, it is possible to provide a data structure regarding the visibility risk that is highly reliable and consistent.

[0066] (Configuration of Information Processing System 1) Hereinafter, the information processing system 1 according to the present embodiment will be described with reference to the drawings. FIG. 1 is a diagram showing an example of the information processing system 1 according to the present embodiment. As shown in FIG. 1, the information processing system 1 includes vehicles 2 and 5, a server 3, and a user terminal 6. These are communicably connected to a communication network 4. The vehicles 2 and 5 and the user terminal 6 are communicably connected to the server 3 via the communication network 4. The communication network 4 is configured by, for example, the Internet or the like. In FIG. 1, for convenience of explanation, a single vehicle 2 or 5 and a user terminal 6 are shown, but a plurality of vehicles 2 and 5 and user terminals 6 may be provided in the information processing system 1.

[0067] (Configuration of Vehicle 2) Next, the configuration of the vehicle 2 will be described below with reference to FIG. 2. FIG. 2 is a diagram showing an example of the configuration of the vehicle 2 according to the present embodiment. As shown in FIG. 2, the vehicle 2 includes a vehicle control unit 20, a camera 21, a wireless communication unit 22, a GNSS (Global Navigation Satellite System) receiver 23, an HMI (Human Machine Interface) 24, a storage device 25, a drive system 26, and an azimuth sensor 27. The GNSS receiver 23 may be, for example, a GPS (Global Positioning System) receiver. In the present embodiment, it is assumed that the vehicle 5 has the same configuration as the vehicle 2. That is, the vehicle 5 includes a vehicle control unit, a camera, a wireless communication unit, a GNSS receiver, an HMI (including a navigation device), a storage device, a drive system, and an azimuth sensor.

[0068] The vehicle 2 may be a vehicle capable of traveling in an automatic driving mode (for example, an autonomous vehicle). In this example, a four-wheel vehicle is cited as an example of the vehicle, but the number of wheels of the vehicle 2 is not particularly limited. The vehicle control unit 20 is configured to control various components provided in the vehicle 2, and is constituted by, for example, at least one electronic control unit (ECU: Electronic Control Unit). The electronic control unit includes a computer system including one or more processors and one or more memories.

[0069] The camera 21 is configured to image the surrounding environment of the vehicle 2. The camera 21 is arranged at a predetermined position of the vehicle 2 so as to photograph the surrounding environment of the vehicle 2 through the glass of the vehicle 2, for example. The image captured by the camera 21 may be a still image or a moving image. Further, the camera 21 may be detachably mounted on the vehicle 2. In this regard, the camera 21 may be portable by a driver or the like.

[0070] The wireless communication unit 22 is configured to connect the vehicle 2 to the communication network 4, and includes a transmission / reception antenna and a wireless transmission / reception circuit. The wireless communication unit 22 may be a wireless communication module compatible with a short-range wireless communication standard such as Wi-Fi (registered trademark) or Bluetooth (registered trademark), or may be a wireless communication module compatible with a fourth-generation mobile communication system such as LTE or a fifth-generation mobile communication system.

[0071] The GNSS receiver 23 is configured to acquire information regarding the position of the vehicle 2. In this example, it is assumed that the shooting position of the camera 21 coincides with the position of the vehicle 2. Therefore, the information regarding the position of the vehicle 2 detected by the GNSS receiver corresponds to the information regarding the shooting position of the camera 21. The HMI 24 is composed of an input unit that receives input operations from the driver and an output unit that outputs information regarding the running of the vehicle 2 to the driver. The HMI 24 may include a navigation device configured to provide map information and route information to the driver. The storage device 25 is an external storage device such as an HDD (Hard Disc Drive) or an SSD (Solid State Drive). Map data and vehicle control programs may be stored in the storage device 25. The drive system 26 is configured to control the running state of the vehicle 2. For example, the drive system 26 is configured to control the running of the vehicle 2 by controlling the accelerator, brake, and steering of the vehicle 2 respectively.

[0072] The azimuth sensor 27 is configured to detect the traveling direction of the vehicle 2. In this example, it is assumed that the shooting direction of the camera 21 coincides with the traveling direction of the vehicle 2. Therefore, the information regarding the traveling direction of the vehicle 2 detected by the azimuth sensor 27 corresponds to the information regarding the shooting direction of the camera 21. The vehicle 2 transmits image data, shooting position data of the camera 21, shooting direction data, and field of view angle data to the server 3 through the communication network 4. The image data includes a plurality of images and a plurality of time information. Each of the plurality of images is associated with one of the plurality of time information. Each of the plurality of time information indicates the shooting time of the corresponding image.

[0073] The shooting position data of camera 21 includes the shooting position information of a plurality of cameras 21 and a plurality of time information. The shooting position information is information indicating the position (longitude and latitude) of camera 21. The longitude is displayed within the range of -180 degrees to +180 degrees. The latitude is displayed within the range of -90 degrees to +90 degrees. Each of the plurality of shooting position information is associated with one of the plurality of time information. Each of the plurality of time information indicates the detection time of the corresponding shooting position.

[0074] The shooting direction data includes the shooting direction information of a plurality of cameras 21 and a plurality of time information. The shooting direction of camera 21 is indicated within the range of 0 degrees to 360 degrees. In this case, 0 degrees indicates the north direction, 90 degrees indicates the east direction, 180 degrees indicates the south direction, and 270 degrees indicates the west direction. Each of the plurality of shooting direction information is associated with one of the plurality of time information. Each of the plurality of time information indicates the detection time of the corresponding shooting direction.

[0075] In addition to camera 21, vehicle 2 may be equipped with a LiDAR unit configured to acquire point cloud data indicating the surrounding environment of vehicle 2.

[0076] (Configuration of server 3) Next, the hardware configuration of server 3 will be described below with reference to FIG. 3. FIG. 3 is a diagram showing an example of the configuration of server 3 according to the present embodiment. Server 3 may be composed of a plurality of servers physically separated from each other. Server 3 may be constructed on-premises or as a cloud server. As shown in FIG. 3, server 3 includes a control unit 30, a storage device 31, an input / output interface 32, a communication unit 33, an input operation unit 34, and a display unit 35. These elements are connected to a communication bus 36.

[0077] The control unit 30 includes a memory and a processor. The memory is configured to store computer-readable instructions (programs). For example, the memory is composed of a ROM (Read Only Memory) storing various programs and the like, a RAM (Random Access Memory) having a plurality of work areas storing various programs and the like executed by the processor, and the like. The processor is composed of, for example, at least one of a CPU (Central Processing Unit), an MPU (Micro Processing Unit), and a GPU (Graphics Processing Unit). The CPU may be composed of a plurality of CPU cores. The GPU may be composed of a plurality of GPU cores. The processor may be configured to expand a program specified from various programs incorporated in the storage device 31 or the ROM onto the RAM and execute various processes in cooperation with the RAM. The memory may store an information processing program for causing the processor to execute a series of processes (information processing method according to the present embodiment) executed by the server 3.

[0078] The storage device 31 is, for example, a storage device (storage) such as an HDD or an SSD, and is configured to store programs and various data. The storage device 31 stores map data, visibility risk data, surrounding environment risk data, and virtual point data. Further, the storage device 31 stores image data transmitted from the vehicle 2 and metadata associated with the image data. The map data includes at least road network data for route search, background data, annotation data, address data, and store information data.

[0079] As shown in FIG. 4, the road network data is composed of a plurality of road links (lines) and a plurality of road nodes (points). The road nodes are connected to a plurality of road links. Each road node is connected to another road node via one road link. Each road link is assigned unique identification information (ID). Each road link is associated with, for example, road attribute information and road regulation information (e.g., one-way traffic, etc.). The road attribute information includes, for example, road type information, number of lanes information, and width information. Also, each road link is associated with a link cost (e.g., distance cost, required time cost, etc.) used for route search. Each road node is assigned unique identification information (ID). Each road node is associated with, for example, lane information, direction guidance information, traffic signal information, and intersection name information. Thus, the road network data has information related to road links and information related to road nodes. The server 3 can execute route search from the departure point to the destination by using the road network data.

[0080] The background data includes illustration data regarding the visual background of the map (e.g., map shapes of buildings, sea, mountains, forests, roads, etc.). The annotation data includes character information (e.g., names of buildings and mountains, etc.) to be marked on the map. The address data includes address information associated with each building on the map. The store information data includes information regarding stores on the map.

[0081] The visibility risk data is data indicating the visibility risk at each point on the road. The surrounding environment risk data is data indicating the surrounding environment risk at each point on the road. The virtual point data is data indicating the position of each virtual point. Details of these data will be described later.

[0082] Returning to FIG. 3, the input / output interface 32 is an interface that enables connection between an external device and the server 3, and includes an interface according to a predetermined communication standard such as the USB standard or the HDMI (registered trademark) standard. The communication unit 33 may include various wired communication modules for communicating with an external terminal on the communication network 4. The input operation unit 34 is, for example, a touch panel, a mouse, and / or a keyboard, etc., configured to receive an input operation of an operator and generate an operation signal according to the input operation of the operator. The display unit 35 is, for example, composed of a video display and a video display circuit.

[0083] (Configuration of User Terminal 6) The user terminal 6 is a terminal operated by the user U, and is, for example, a personal computer, a smartphone, a tablet, a wearable device (e.g., a smartwatch or AR glasses), etc. The user terminal 6 includes a control unit, a storage device, a GNSS receiver, a wireless communication unit, an input operation unit, and a display unit. The control unit includes a memory and a processor. The memory is configured to store computer-readable instructions (programs). A map application or a web browser for displaying a map may be installed in the user terminal 6.

[0084] (Configuration of Road Link and Virtual Point) Next, with reference to FIG. 5, the road link L and the virtual points will be described below. FIG. 5(a) is a diagram for explaining the road link L. FIG. 5(b) is a diagram for explaining the virtual points K1 to K4 set on the road link L. In the following description, the virtual points K1 to K4 may be collectively referred to simply as the virtual point K. As shown in FIG. 5(a), each road link L is set with a starting point F, an ending point T, and one or more constituent points M existing between the starting point F and the ending point T. The shape of the road link L is constituted by a plurality of constituent points M. As shown in FIG. 5(b), each road link L is set with virtual points K1 to K4. The plurality of virtual points K1 to K4 are arranged at equal intervals on the road link L. An example of the interval between adjacent virtual points K is 5 m. In the present embodiment, the position of the first virtual point K0 corresponds to the position of the starting point F. Each virtual point K has identification information (ID). In this example, the IDs of the virtual points K0 to K4 are 0 to 4 respectively. That is, the ID of the virtual point K0 overlapping the starting point F is 0, and the virtual point ID increases as it moves away from the starting point F.

[0085] (Visibility risk of each partial path A to C at a specific virtual point) Next, with reference to FIGS. 6 and 7, the visibility risks of each partial path A to C at a specific virtual point K will be described below. Here, the visibility risk at the virtual point K is an index for evaluating the visibility with respect to the surrounding environment at the virtual point K (for example, an object such as another vehicle existing around the virtual point K). FIG. 6 is a diagram showing each virtual point K set on each road link 100 to 103 at an intersection. FIG. 7(a) is a diagram for explaining the visibility risk of the partial path A at the virtual point K0 set on the road link 100. FIG. 7(b) is a diagram for explaining the visibility risk of the partial path B at the virtual point K0. FIG. 7(c) is a diagram for explaining the visibility risk of the partial path C at the virtual point K0. FIG. 7(d) is a diagram for explaining the visibility risks in the partial paths A to C.

[0086] As shown in FIG. 6, virtual points K are set at each of the four road links 100 to 103 at the intersection. Specifically, virtual points K1, K2, and K3 are set on the road link 100, and virtual points K0 and K1 are set on each of the road links 101, 102, and 103.

[0087] In this example, the visibility risk at the virtual point K0 on the road link 100 corresponds to an evaluation index that summarizes the visibility with respect to the virtual point K0 and N virtual points K existing in the vicinity of the virtual point K0 (more specifically, N consecutive virtual points K from the virtual point K0). In the example of FIG. 7, N = 4. That is, the visibility risk at the virtual point K0 on the road link 100 corresponds to an evaluation index that summarizes the visibility with respect to each of the virtual point K0 and the four consecutive virtual points K therefrom.

[0088] As shown in FIGS. 6 and 7, the four consecutive virtual points K from the virtual point K0 on the road link 100 are the four virtual points K existing in each of the partial paths A to C. Specifically, in the partial path A shown in FIG. 7(a), the virtual points K1 and K2 on the road link 100 and the virtual points K0 and K1 on the road link 103 are the four consecutive virtual points K from the virtual point K0 on the road link 100. In the partial path B shown in FIG. 7(b), the virtual points K1 and K2 on the road link 100 and the virtual points K0 and K1 on the road link 101 are the four consecutive virtual points K from the virtual point K0 on the road link 100. In the partial path C shown in FIG. 7(c), the virtual points K1 and K2 on the road link 100 and the virtual points K0 and K1 on the road link 102 are the four consecutive virtual points K from the virtual point K0 on the road link 100.

[0089] As shown in FIG. 7(a), since the visibility with respect to the five virtual points K of the partial path A at the virtual point K0 on the road link 100 (specifically, the virtual points K0 to K2 on the road link 100 and the virtual points K0 to K1 on the road link 103) is good, the visibility risk is (5). Here, the partial path A is composed of a road link sequence including the road links 100 and 103.

[0090] As shown in Fig. 7(b), since the visibility for the five virtual points K of the partial path B at the virtual point K0 on the road link 100 (the virtual points K0 to K2 on the road link 100 and the virtual points K0 to K1 on the road link 101) is good, the visibility risk is (5). Here, the partial path B is composed of a road link sequence consisting of the road links 100 and 101.

[0091] As shown in Fig. 7(c), the visibility for the virtual points K0 to K2 on the road link 100 among the five virtual points K of the partial path C at the virtual point K0 on the road link 100 is good. On the other hand, since the visibility for the virtual points K1 and K0 on the road link 102 at the virtual point K0 is obstructed by the building 80, the visibility for the virtual points K1 and K0 on the road link 102 is poor. As a result, the visibility for the five virtual points K of the partial path C at the virtual point K0 is (3, -2). The visibility risk (3, -2) at the virtual point K0 means that the visibility for the first to third virtual points K including its own virtual point K0 is good, while the visibility for the fourth to fifth virtual points K is poor. The partial path C is composed of a road link sequence consisting of the road links 100 and 102.

[0092] As shown in Fig. 7(d), the visibility risk at the virtual point K0 on the road link 100 has three visibility risks, namely the visibility risk of the partial path A, the visibility risk of the partial path B, and the visibility risk of the partial path C. Here, each of the partial paths A to C is composed of a road link sequence consisting of a plurality of road links. Thus, the visibility risk at the virtual point K0 (road link 100, virtual point ID: 0) has the visibility risk for each road link sequence. In this example, the road link sequence (100, 103) corresponding to the partial path A, the virtual point ID (ID: 0), and the visibility risk (5) are associated with each other. Here, the road link sequence (100, 103) means the partial path from the road link 100 (road link ID: 100) to the road link 103 (road link ID: 103).

[0093] In this example, the virtual point ID is not a unique value, and the virtual point can be identified by the combination of the virtual point ID and the first road link included in the road link sequence. For example, the virtual point K0 (virtual point ID: 0) on the road link 100 can be identified by the combination of the virtual point ID: 0 and the first road link 100 (road link ID: 100) included in the road link sequence. In the visibility risk data (see Fig. 15(b)) showing the visibility risk at each point on the road described later, the virtual point ID, the road link sequence (the IDs of one or more road links constituting the road link sequence), and the visibility risk are associated with each other.

[0094] In addition, a unique value may be assigned to each virtual point ID. In this case, the virtual point K can be identified only by the virtual point ID. Also, in this example, the visibility risk at the virtual point K0 is defined as an evaluation index that summarizes the visibility for the virtual point K0 and the four consecutive virtual points K starting from the virtual point K0, but the number of consecutive virtual points K is not limited to 4. The visibility risk at each virtual point K has the visibility risk of each partial route. The visibility risk is associated with the road link sequence constituting the partial route and the virtual point ID.

[0095] (Visibility risk at each virtual point) Referring to FIGS. 8 and 9, the visibility risk at each virtual point K will be described below. In the example shown in FIG. 8, for the sake of simplicity of explanation, the partial path at each virtual point is one. FIG. 8(a) is a diagram for explaining the visibility risk of the partial path T1 at the virtual point K0 (road link 100, virtual point ID: 0). FIG. 8(b) is a diagram for explaining the visibility risk of the partial path T2 at the virtual point K1 (road link 100, virtual point ID: 1). FIG. 8(c) is a diagram for explaining the visibility risk of the partial path T3 at the virtual point K2 (road link 100, virtual point ID: 2). FIG. 8(d) is a diagram for explaining the visibility risk of the partial path T4 at the virtual point K1 (road link 102, virtual point ID: 1). FIG. 9(a) is a table for explaining the visibility risk at each virtual point K. FIG. 9(b) is a table for explaining the surrounding environment risk.

[0096] As shown in FIG. 8, the partial paths T1 to T4 are composed of a road link sequence consisting of at least two or more of the road links 200 to 203. As shown in FIG. 8(a), when the traveling vehicle V exists at the virtual point K0 on the road link 200, the visibility of the five virtual points K of the partial path T1 composed of the road link sequence consisting of the road links 200 and 201 is evaluated. In this case, the visibility of the three virtual points K0 to K2 on the road link 200 among the five virtual points K of the partial path T1 at the virtual point K0 on the road link 200 is good. On the other hand, the visibility of the two virtual points K0 and K1 on the road link 201 at the virtual point K0 is inhibited by the building 70, so it becomes poor. As a result, the visibility of the five virtual points K of the partial path T1 at the virtual point K0 on the road link 200 is (3, -2) (see FIG. 9(a)).

[0097] Next, as shown in FIG. 8(b), when the traveling vehicle V exists at the virtual point K1 on the road link 200, the visibility of the five virtual points K of the partial path T2 constituted by the road link series composed of the road links 200, 201, and 202 is evaluated. In this case, the visibility with respect to the two virtual points K1 and K2 on the road link 200 among the five virtual points K of the partial path T2 at the virtual point K1 on the road link 200 is good. On the other hand, the visibility with respect to the two virtual points K0 and K1 on the road link 201 at the virtual point K1 is inhibited by the building 70, so it becomes poor. Furthermore, the visibility with respect to the virtual point K1 on the road link 202 is also inhibited by the building 70, so it becomes poor. As a result, the visibility with respect to the five virtual points K of the partial path T2 at the virtual point K1 on the road link 200 is (2, -3) (see FIG. 9(a)).

[0098] Next, as shown in FIG. 8(c), when the traveling vehicle V exists at the virtual point K2 on the road link 200, the visibility of the five virtual points K of the partial path T3 constituted by the road link series composed of the road links 200, 201, and 202 is evaluated. In this case, the visibility with respect to the virtual point K2 on the road link 200 among the five virtual points K of the partial path T3 at the virtual point K2 on the road link 200 is good. On the other hand, the visibility with respect to the two virtual points K0 and K1 on the road link 201 is inhibited by the building 70, so it becomes poor. Furthermore, the visibility with respect to the virtual points K1 and K0 on the road link 202 is also inhibited by the building 70, so it becomes poor. As a result, the visibility with respect to the five virtual points K of the partial path T3 at the virtual point K2 on the road link 200 is (1, -4) (see FIG. 9(a)).

[0099] Next, as shown in FIG. 8(d), when the traveling vehicle V exists at the virtual point K1 on the road link 201, the visibility of the five virtual points K of the partial route T4 composed of the road link sequence consisting of the road links 201, 202, and 203 is evaluated. In this case, the visibility with respect to the two virtual points K0 and K1 on the road link 201 among the five virtual points K of the partial route T4 at the virtual point K1 on the road link 201 is good. Furthermore, the visibility with respect to the virtual points K1 and K0 on the road link 202 and the virtual point K1 on the road link 203 is also good. As a result, the visibility with respect to the five virtual points K of the partial route T4 at the virtual point K1 on the road link 201 is (5) (see FIG. 9(a)).

[0100] In FIG. 9(a), the relationship between the visibility risk at each virtual point K, the road link sequence, and the virtual point ID is shown. Also, as shown in FIG. 8, there is a traffic signal 72 which is a peripheral environment risk factor around the virtual point K0 of the road link 201. Risk factors in the peripheral environment such as traffic signals, intersections, crosswalks, railroad crossings, increase or decrease in the number of lanes, and increase or decrease in width can also be risk factors for rear-end collision accidents and the like. In the present embodiment, the peripheral environment risk factors indicating the risk factors around the virtual point are associated with the road link and the virtual point, and are stored in the server 3 as peripheral environment risk data. In the example shown in FIG. 9(b), the ID (0x0C2) of the peripheral environment risk factor indicating the traffic signal, the ID (102) of the road link, and the virtual point ID (0) are associated with each other. In particular, in the peripheral environment risk data (see FIG. 15(c)) indicating the peripheral environment risk at each point on the road described later, the virtual point ID, the ID of the road link, and the ID of the peripheral environment risk factor are associated with each other.

[0101] Also, based on the virtual point data (see FIG. 15(a)) indicating the position of each virtual point and the road network data, the virtual point closest to the position of the peripheral environment risk factors such as traffic signals and intersections is specified, and then the specified virtual point, the road link associated with the virtual point, and the peripheral environment risk factor are associated with each other. In this way, the peripheral environment risk data is generated.

[0102] As shown in FIGS. 8(a) to 8(c), the virtual point K0 on the road link 201 is a virtual point associated with the traffic signal 72 that is a peripheral environment risk factor. Therefore, the virtual point K0 on the road link 201 is evaluated as a virtual point with poor visibility and having a peripheral environment risk factor. This point will be described later.

[0103] (Visibility risk of a partial route consisting of a single road link) Next, with reference to FIG. 10, the visibility risk of a partial route consisting of a single road link will be described below. FIG. 10(a) is a diagram for explaining the visibility risk in the partial route D (sharp curve) composed of a single road link 204. FIG. 10(b) is a diagram for explaining the visibility risk in the partial route E (crank) composed of a single road link 205. FIG. 10(c) is a table for explaining the visibility risks in the partial routes D and E.

[0104] In this example, the partial routes D and E are different from the partial routes shown in FIGS. 7 and 8 in that they are composed of a road link sequence consisting of a single road link. Similarly, for the partial routes in this example, the visibility risk at the virtual point Kn (n is an integer) corresponds to an evaluation index that summarizes the visibility with respect to the virtual point Kn and the N virtual points K existing in the vicinity of the virtual point Kn (more specifically, the N consecutive virtual points K starting from the virtual point Kn).

[0105] As shown in Fig. 10(a), when the traveling vehicle V exists at the virtual point K2 on the road link 204, the visibility of five virtual points K on the partial path D (sharp curve) constituted by the road link 204 is evaluated. In this case, the visibility with respect to three of the five virtual points K, namely K2 to K4, of the partial path D at the virtual point K2 is good. On the other hand, the visibility with respect to the two virtual points K5 and K6 is impaired by the building 74 existing in the middle of the sharp curve, resulting in poor visibility. As a result, the visibility with respect to the five virtual points K of the partial path D at the virtual point K2 is (3, -2). As shown in Fig. 10(c), after the road link 204 (road link ID: 204), the virtual point (virtual point ID: 2), and the visibility risk (3, -2) are associated with each other, these data are stored in the server 3 as visibility risk data.

[0106] On the other hand, as shown in Fig. 10(b), when the traveling vehicle V exists at the virtual point K4 on the road link 205, the visibility of five virtual points K of the partial path E (crank) constituted by the road link 205 is evaluated. In this case, the visibility with respect to three of the five virtual points K, namely K4 to K2, of the partial path E at the virtual point K4 is good. On the other hand, the visibility with respect to the two virtual points K1 and K0 is impaired by the buildings 74a to 74c (mainly building 74c), resulting in poor visibility. As a result, the visibility with respect to the five virtual points K of the partial path E at the virtual point K4 is (3, -2). As shown in Fig. 10(c), after the road link 205 (road link ID: 205), the virtual point (virtual point ID: -4), and the visibility risk (3, -2) are associated with each other, these data are stored in the server 3 as visibility risk data. Note that in this example, since the traveling direction of the partial path E is opposite to the forward direction from the start point F to the end point T of the road link 205, the virtual point ID is stored as -4 instead of 4 in the visibility risk data.

[0107] That is, in the visibility risk of a partial route consisting of a single road link, when the traveling direction of the partial route coincides with the forward direction from the start point F to the end point T of the road link, the value of the virtual point ID on the visibility risk data is set to a positive value. On the other hand, when the traveling direction of the partial route is opposite to the forward direction from the start point F to the end point T of the road link, the value of the virtual point ID on the visibility risk data is set to a negative value. In this regard, in the example of Fig. 10(a), since the traveling direction of the partial route D coincides with the forward direction from the start point F to the end point T of the road link 204, the virtual point ID on the visibility risk data is the positive value 2.

[0108] (A series of processes for determining the visibility risk at a predetermined virtual point) Next, with reference to Figs. 11 and 12, a series of processes for automatically determining the visibility risk at a predetermined virtual point Kn (an example of the first virtual point) will be described below. In the following description, how the visibility of the virtual point K around the predetermined virtual point Kn is evaluated by the server 3 will be described in detail. Fig. 11 is a flowchart for explaining a series of processes for determining the visibility risk at a predetermined virtual point Kn. Fig. 12(a) is a diagram showing the original image. Fig. 12(b) is a diagram showing the depth image. Fig. 12(c) is a diagram showing a three-dimensional object arranged in the geographic coordinate system. Fig. 12(d) is a diagram showing a three-dimensional object arranged in the geographic coordinate system and a virtual point object.

[0109] As shown in Fig. 11, in step S1, the control unit 30 of the server 3 acquires a plurality of image data (an example of the surrounding environment data) indicating the surrounding environment of a predetermined virtual point Kn (an example of the first virtual point) from the storage device 31. Here, the image data shown in Fig. 12(a) is photographed by the camera 21 mounted on the vehicle 2 traveling around the predetermined virtual point Kn and then transmitted to the server 3 via the communication network 4. In particular, the control unit 30 acquires a plurality of image data photographed around the position of the predetermined virtual point Kn from the storage device 31. The storage device 31 stores the image data transmitted from the vehicle 2 and the metadata associated with the image data.

[0110] The metadata has information regarding the shooting direction, shooting position, and field of view angle of the camera 21 mounted on the vehicle 2. That is, each piece of image data indicating the surrounding environment of the vehicle 2 transmitted from the vehicle 2 is stored in the storage device 31 in a state associated with the information regarding the shooting direction, shooting position, and field of view angle of the camera 21. In this regard, each image can be associated with the shooting direction and shooting position of the camera 21 via time information. In this way, after the shooting position near the position of the predetermined virtual point Kn is specified, image data indicating the surrounding environment of the predetermined virtual point Kn can be extracted based on the specified shooting position.

[0111] In step S2, the control unit 30 acquires depth image data based on the image data. Specifically, the control unit 30 executes depth estimation processing on each piece of image data by using a depth estimation model (a learned model) capable of estimating the depth of a subject such as a feature shown in the image. In this way, the control unit 30 can acquire depth image data (see FIG. 12(b)) corresponding to each piece of image data by estimating the depth of the subject in each image. Here, the depth is assumed to indicate the distance between the camera 21 and the subject (object).

[0112] In step S3, the control unit 30 generates a three-dimensional object of an object such as a feature shown in the image based on one or more pieces of depth image data. Here, the three-dimensional object of the object existing around the predetermined virtual point Kn may be constituted by a polygon. In the present embodiment, the three-dimensional object may include at least one of a feature object corresponding to a feature existing around the position of the predetermined virtual point Kn and a road surface object existing around the position of the predetermined virtual point Kn. Here, a feature is an immovable object on the ground such as a building, a traffic sign, a traffic signal, a tree, a guardrail, or a wall. On the other hand, a moving object such as a vehicle or a pedestrian may be excluded from the features.

[0113] In addition, when a predetermined virtual point Kn exists near an intersection, a three-dimensional object may be generated based on a plurality of depth image data corresponding to a plurality of image data captured from a plurality of imaging directions.

[0114] In the present embodiment, a three-dimensional object is generated based on the image data captured by the camera 21. However, a three-dimensional object may also be generated based on the point cloud data (another example of the surrounding environment data) acquired by the LiDAR unit mounted on the vehicle 2.

[0115] Next, in step S4, the control unit 30 arranges the generated three-dimensional object in the geodetic coordinate system S based on the imaging direction, imaging position, and field of view angle of the camera 21 in a plurality of metadata associated with one or more image data (see FIG. 12(c)). The geodetic coordinate system S is formed as a three-dimensional space, and the position of an object in the geodetic coordinate system S is defined by latitude, longitude, and altitude. In this way, it is possible to accurately arrange the three-dimensional object within the geodetic coordinate system S based on the imaging direction, imaging position, and field of view angle of the camera 21. In the geodetic coordinate system S shown in FIG. 12(c), a ground object O1 and a three-dimensional road link L among the three-dimensional objects are arranged. In the figure, although the road surface object O3 (see FIG. 13) among the three-dimensional objects is not displayed, the road surface object O3 may also be arranged in the geodetic coordinate system S. The three-dimensional road link L is arranged in the geodetic coordinate system S along the road surface shape of the road surface object O3.

[0116] In step S5, after generating a plurality of virtual point objects O2 corresponding to a plurality of virtual points K existing around the predetermined virtual point Kn, the control unit 30 arranges the plurality of virtual point objects O2 in the geodetic coordinate system S. As shown in FIG. 12(d), each virtual point object O2 is arranged at equal intervals along the three-dimensional road link L or the road surface object O3 arranged in the geodetic coordinate system S.

[0117] Based on the virtual point data indicating the planar positions (latitude and longitude) of each virtual point K (refer to Fig. 15(a)), the planar positions (latitude and longitude) of each virtual point object O2 are determined. That is, the planar position of each virtual point object O2 coincides with the planar position of the corresponding virtual point K. On the other hand, as shown in Fig. 13, since each virtual point object O2 is arranged on the road surface object O3 or the three-dimensional road link L, the height position of each virtual point object O2 is determined based on the height position of the three-dimensional road link L or the road surface object O3 at the planar position (latitude and longitude) of each virtual point K.

[0118] For example, the three-dimensional position (latitude, longitude, height) of the virtual point object O2 corresponding to a specific virtual point K is determined based on the latitude and longitude of the specific virtual point K and the height position of the road surface object O3 or the three-dimensional road link L at the latitude and longitude. In this way, each virtual point object O2 is arranged in the geographic coordinate system S.

[0119] As shown in Fig. 13, the shape of each virtual point object O2 is spherical. The diameter R of each virtual point object O2 may be set to, for example, 1.5 m, which is the average height of ordinary vehicles. When the virtual point object O2 is spherical, it is preferable in that the visibility with respect to the virtual point object O2 described later can be objectively evaluated easily.

[0120] Returning to FIG. 11, in step S6, the control unit 30 arranges the virtual camera CAM corresponding to the predetermined virtual point Kn in the geographical coordinate system S. As shown in FIG. 13, the virtual camera CAM is arranged directly above the road surface object O3 or the three-dimensional road link L existing at the position of the predetermined virtual point Kn. In particular, the planar position (latitude·longitude) of the virtual camera CAM coincides with the planar position (latitude·longitude) of the predetermined virtual point Kn. On the other hand, the height position of the virtual camera CAM is determined based on the height position of the road surface object O3 or the three-dimensional road link L at the planar position (latitude·longitude) of the predetermined virtual point Kn. More specifically, the height position D1 of the virtual camera CAM with respect to the road surface object O3 at the planar position (latitude·longitude) of the predetermined virtual point Kn may be set to, for example, 1.2 m, which is the average height of the line of sight of a driver of a normal vehicle.

[0121] In this regard, when visibility risk data indicating the visibility risk associated with a driver of a normal vehicle (an example of a first viewer of the first attribute) is generated, the height position D1 of the virtual camera CAM with respect to the road surface object O3 or the three-dimensional road link L may be set to, for example, 1.5 m, which is the average height of the line of sight of a driver of a normal vehicle. On the other hand, when visibility risk data indicating the visibility risk associated with a driver of a large vehicle such as a truck or a bus (an example of a second viewer of the second attribute) is generated, the height position D1 of the virtual camera CAM with respect to the road surface object O3 or the three-dimensional road link L may be set to, for example, 1.8 m, which is the average height of the line of sight of a driver of a large vehicle.

[0122] In this way, since the height position D1 of the virtual camera CAM is set according to the height of the viewer's line of sight, it is possible to provide optimal visibility risk data that conforms to the viewer's line of sight. Furthermore, since the height position D1 of the virtual camera CAM with respect to the road surface object O3 or the three-dimensional road link L is set according to the line of sight for each viewer, it is possible to provide visibility risk data for viewers with different attributes. Therefore, it is possible to provide optimal visibility risk data for each user (viewer) who utilizes the visibility risk data.

[0123] Also, the horizontal field of view FOV of the virtual camera CAM may be set within a range of, for example, 60 degrees to 180 degrees. The vertical field of view FOV of the virtual camera CAM may be set within a range of, for example, 30 degrees to 120 degrees.

[0124] Returning to FIG. 11, in step S7, the control unit 30 generates a field-of-view image captured by the virtual camera CAM arranged in the geographical coordinate system S. Next, in step S8, the control unit 30 evaluates the visibility with respect to each virtual point object O2 in the field-of-view image captured by the virtual camera CAM.

[0125] In this example, the visibility risk at a predetermined virtual point Kn is evaluated for the visibility with respect to the virtual point Kn and N virtual points K continuously arranged from the front of the virtual point Kn. Here, the visibility with respect to its own virtual point Kn is evaluated as good. Next, regarding the visibility with respect to the N virtual points K continuously arranged from the front of the virtual point Kn, the visibility of each virtual point object O2 corresponding to the N virtual points K shown in the field-of-view image is evaluated.

[0126] In this regard, the visibility with respect to a specific virtual point object O2 in the field-of-view image may be evaluated based on the ratio (%) of the visible area of the specific virtual point object O2 to the total area of the specific virtual point object O2 in the field-of-view image.

[0127] For example, as shown in FIG. 14(a), the visibility of a specific virtual point object O2 is not affected by the ground object O1 at all, and as a result, the ratio of the visible area to the total area of the specific virtual point object O2 is evaluated as 100%. Also, as shown in FIG. 14(b), the visibility of the specific virtual point object O2 is affected by the ground object O1, and as a result, the ratio of the visible area to the total area of the specific virtual point object O2 is evaluated as 50%. As shown in FIG. 14(c), the visibility of the specific virtual point object O2 is affected by the ground object O1, and as a result, the ratio of the visible area to the total area of the specific virtual point object O2 is evaluated as 25%.

[0128] In this example, when the ratio of the visible area to the total area of the specific virtual point object O2 is 50% or more, it may be determined that the visibility of the specific virtual point object O2 is good. On the other hand, when the ratio of the visible area to the total area of the specific virtual point object O2 is less than 50%, it may be determined that the visibility of the specific virtual point object O2 is poor. For example, the ratio of the visible area of the virtual point object O2 to the total area of the virtual point object O2 can be calculated by dividing the number of pixels of the visible area of the virtual point object O2 displayed in the field of view image by the number of pixels of the total area of the virtual point object O2 in the field of view image. In this regard, the number of pixels of the total area of the virtual point object O2 in the field of view image may be determined based on the radius of the virtual point object O2, the field of view angle of the virtual camera CAM, the distance between the virtual point object O2 and the virtual camera CAM, and the pixel width of the screen on which the virtual point object O2 is two-dimensionally projected. The number of pixels of the visible area of the virtual point object O2 displayed in the field of view image is determined by counting.

[0129] Furthermore, the ratio of the visible area of the virtual point object O2 to the total area of the virtual point object O2 may be determined based on the visible shape of the virtual point object O2 within the field of view image and the ratio estimation model. In this case, in the ratio estimation model (trained model), the visible shape of the virtual point object may be input in the input layer, and the ratio of the visible area to the total area of the virtual point object may be output in the output layer.

[0130] Next, in step S9, the control unit 30 determines the visibility risk at the predetermined virtual point Kn by evaluating the visibility of the virtual point object O2 corresponding to N virtual points K continuously arranged from in front of the predetermined virtual point Kn. For example, when N = 20 and the visibility of all 20 virtual point objects O2 is good, the visibility risk at the predetermined virtual point Kn is (21). Also, when the visibility of the virtual point objects O2 from the 1st to the 17th consecutive from the predetermined virtual point Kn is good, while the visibility of the virtual point objects O2 from the 18th to the 20th is poor, the visibility risk at the predetermined virtual point Kn is (18, -3). In this way, it becomes possible for the server 3 to automatically determine the visibility risk at the predetermined virtual point Kn through each process from step S1 to S9.

[0131] Also, when automatically determining the visibility risk at the virtual point Kn+1 adjacent to the predetermined virtual point Kn, each process from step S1 to S9 may be repeatedly executed. On the other hand, when there is no need to update the three-dimensional object arranged in the geographical coordinate system S (particularly, when the visibility risk at the virtual point Kn+1 can be determined with the already generated three-dimensional object and the virtual point object), steps S6 to S9 may be repeatedly executed. In this way, based on the image data showing the surrounding environment at each point on the road, after the visibility risk of each partial route (road link sequence) at each point (virtual points Kn, Kn+1, Kn+2...) is automatically determined, visibility risk data indicating the visibility risk at each point can be generated.

[0132] In addition, in steps S1 to S5 shown in FIG. 11, based on a plurality of image data indicating the surrounding environment of a predetermined virtual point Kn, a three-dimensional object and a virtual point object around the predetermined virtual point Kn are generated. However, the present embodiment is not limited to this. In this regard, after first preparing a plurality of image data indicating the surrounding environment of all virtual points K for which the visibility risk is evaluated, based on the plurality of image data, the three-dimensional objects and virtual point objects around each virtual point K may be generated collectively. In this case, through the processes of steps S1 to S5, the three-dimensional objects and virtual point objects existing in the arrangement area of each virtual point K to be evaluated are arranged on the geographical coordinate system S. Thereafter, the processes of steps S6 to S9 are repeatedly executed. That is, after the virtual camera CAM is sequentially arranged at the planar position of each virtual point K, the visibility risk at each virtual point K is sequentially determined through the field-of-view image captured by the virtual camera CAM.

[0133] Next, with reference to FIG. 15, each of the virtual point data, visibility risk data, and surrounding environment risk data will be described below.

[0134] (Virtual point data) The virtual point data will be described below with reference to FIG. 15(a). As shown in FIG. 15(a), the virtual point data stored in the storage device 31 of the server 3 includes information regarding each road link (each road link ID) and the positions (latitude and longitude) of one or more virtual points, and they are associated with each other. For example, the information regarding the virtual points set on the road link 100 (road link ID: 100) includes the latitude and longitude (x1, y1) of the virtual point K0 (virtual point ID: 0), the latitude and longitude (x2, y2) of the virtual point K1 (virtual point ID: 1), and the latitude and longitude (x3, y3) of the virtual point K2 (virtual point ID: 2). Here, in the information regarding the position of each virtual point, at the left end, the position (x1, y1) of the virtual point K0 (ID: 0) is shown, and the position (x2, y2) of the virtual point K1 (ID: 1) is shown to the right adjacent thereto.

[0135] (Visibility risk data) With reference to FIG. 15(b), the visibility risk data indicating the visibility risk at each point (each virtual point) on the road will be described below. As shown in FIG. 15(b), the visibility risk data stored in the storage device 31 of the server 3 includes information regarding a road link sequence (a combination of one or more road link IDs) composed of one or more road links, a virtual point ID, and a visibility risk, and they are associated with each other. For example, the visibility risk data includes information regarding the visibility risk (3, -2) of a partial route composed of a road link sequence (road link IDs: 100, 102) at the virtual point K0 (road link ID: 100, virtual point ID: 0). In this way, the visibility risk data stores information indicating the visibility risk of each partial route (road link sequence) of each virtual point K. Since the road link ID is included in the visibility risk data, the visibility risk data and the road network data for route search are associated with each other via the road link ID. Therefore, by using the visibility risk data and the road network data, it becomes possible to execute a route search considering the visibility risk.

[0136] (Surrounding environment risk data) Referring to FIG. 15(c), the surrounding environment risk data indicating the surrounding environment risk at each point (each virtual point) on the road will be described below. As shown in FIG. 15(c), the surrounding environment risk data stored in the storage device 31 of the server 3 includes information regarding a road link (road link ID), a virtual point ID, and a surrounding environment risk factor (surrounding environment risk factor ID), and they are associated with each other. For example, the surrounding environment risk data includes information regarding the surrounding environment risk factor ID associated with the virtual point K0 (virtual point ID: 0) on the road link 102. The surrounding environment risk ID indicates the type of surrounding environment risk factors such as intersections and traffic signals. Since the road link ID is included in the surrounding environment risk data, the surrounding environment risk data is associated with the visibility risk data and the road network data for route search via the road link ID. Therefore, by using the visibility risk data, the surrounding environment risk data, and the road network data, it becomes possible to execute route search considering both the visibility risk and the surrounding environment risk.

[0137] According to this embodiment, after the visibility risk at a predetermined virtual point Kn is automatically determined based on the image data, visibility risk data including information regarding the visibility risk at the predetermined virtual point Kn is generated. In this way, it becomes possible to generate highly reliable and consistent visibility risk data. In particular, by evaluating the visibility with respect to the N virtual point objects O2 shown in the field-of-view image captured by the virtual camera CAM, the visibility risk at the predetermined virtual point Kn is objectively and automatically determined. In this way, since the visibility with respect to the virtual point object O2 based on the influence of the arrangement of the ground object O1 is evaluated, it becomes possible to efficiently and automatically generate visibility risk data that conforms to the real world where it is difficult to evaluate in the map data. Therefore, it becomes possible to generate highly reliable and consistent visibility risk data.

[0138] Furthermore, since the visibility of the virtual point object O2 is evaluated based on the ratio of the visible area of the virtual point object O2 to the total area of the virtual point object O2, it is possible to generate consistent visibility risk data with guaranteed objectivity.

[0139] Also, in this embodiment, after the visibility risk at the virtual point Kn is automatically determined based on the image data, visibility risk data indicating the visibility risk at the virtual point Kn is generated. In this way, compared with point cloud data obtained by a LiDAR unit or the like, it is possible to generate highly reliable and consistent visibility risk data at a relatively low cost. Also, after the visibility risk at the virtual point Kn is automatically determined based on the image data captured by the camera 21 mounted on the vehicle 2, visibility risk data indicating the visibility risk of the virtual point Kn is generated.

[0140] In this way, by way of a plurality of vehicles 2 (a plurality of general vehicles, etc.) equipped with the camera 21, the update frequency of the visibility risk data can be improved, so that it is possible to obtain high-fidelity and comprehensive visibility risk data. In particular, in order to make use of the image data provided by a plurality of general vehicles, it is possible to further improve the update frequency of the visibility risk data by giving rewards such as tokens or points to the general vehicles that provide the image data.

[0141] (Route search considering visibility risk and surrounding environment risk) Next, with reference to FIG. 16, route search considering visibility risk and surrounding environment risk will be described below. FIG. 16 is a flowchart for explaining an example of a process of performing route search from a starting point to a destination considering visibility risk and surrounding environment risk.

[0142] As shown in FIG. 16, in step S10, the server 3 acquires information regarding the departure place and the destination (particularly, the position information of the departure place and the position information of the destination) from the vehicle 5 equipped with the route search navigation device via the communication network 4. Here, the position information of the departure place may be the current position information of the vehicle 5. In step S11, the server 3 executes a route search from the departure place to the destination in consideration of the link cost, the visibility risk, and the surrounding environment risk of each road link based on the road network data for route search, the visibility risk data, and the surrounding environment risk data included in the map data stored in the storage device 31.

[0143] The road network data has at least data indicating the connection relationship between each road link via a road node and cost data indicating the link cost of each road link (for example, distance cost, required time cost, etc.). Further, the road network data is associated with the visibility risk data and the surrounding environment risk data shown in FIG. 15 via a road link (road link ID). Therefore, by using each of the road network data, the visibility risk data, and the surrounding environment risk data, it is possible to execute a route search from the departure place to the destination in consideration of the link cost of each road link and the visibility risk and the surrounding environment risk at each point (virtual point) of the road.

[0144] For example, the server 3 extracts a plurality of candidate routes with a low route cost based on the link cost of each road link, and then determines an optimal route with the lowest overall risk considering both the visibility risk and the surrounding environment risk among the extracted plurality of routes or an optimal route with the overall risk being equal to or lower than a predetermined threshold based on the visibility risk data and the surrounding environment risk data. Alternatively, the server 3 extracts a plurality of candidate routes with a low overall risk considering both the visibility risk and the surrounding environment risk (for example, the overall risk is equal to or lower than a predetermined threshold) based on the visibility risk data and the surrounding environment risk data, and then determines an optimal route with the lowest route cost from among the extracted plurality of routes based on the link cost of each road link.

[0145] Furthermore, since various methods of route search based on the three types of data, namely road network data, visibility risk data, and surrounding environment risk data, are assumed, the route search is not limited to the above. Also, in this example, both visibility risk data and surrounding environment risk data are used. However, when performing a route search from a starting point to a destination considering only visibility risk, the surrounding environment risk data may not be utilized.

[0146] Returning to FIG. 16, in step S12, the server 3 transmits information regarding the route search result to the vehicle 5 via the communication network 4. Thereafter, the route search result is displayed on the display unit of the vehicle 5. In this example, after the route search is executed by the server 3, the route search result is transmitted to the vehicle 5 via the communication network 4. However, the vehicle 5 may execute the route search. In this case, map data including road network data, visibility risk data, surrounding environment risk data, and virtual point data are transmitted from the server 3 to the vehicle 5, and these data are stored in the storage device of the vehicle 5. Thereafter, the vehicle control unit of the vehicle 5 can execute a route search from the starting point to the destination considering visibility risk and surrounding environment risk based on these data stored in the storage device. The map data, visibility risk data, and surrounding environment risk data may be transmitted to the vehicle 5 at a predetermined update cycle.

[0147] (Specific example of comprehensive risk considering both visibility risk and surrounding environment risk) Next, with reference to FIGS. 17 to 21, specific examples of the comprehensive risk considering both the visibility risk and the surrounding environment risk in two overall routes F and G will be described below. In route search considering both the visibility risk and the surrounding environment risk, the route with the smallest calculated comprehensive risk can be selected as the optimal route. As described above, first, a plurality of candidate routes are extracted based on the link cost of each road link, and then the route with the smallest comprehensive risk among the extracted candidate routes may be selected as the optimal route. Alternatively, a route having a route cost equal to or less than the first threshold value and a comprehensive risk equal to or less than the second threshold value may be selected as the optimal route.

[0148] FIG. 17(a) is a diagram showing each road link and virtual point existing between the departure point and the destination. FIG. 17(b) is a diagram showing the overall route F from the departure point to the destination. FIG. 17(c) is a diagram showing the overall route G from the departure point to the destination. FIGS. 20(a) to (f) are diagrams for explaining a series of processes (particularly, steps (1) to (10)) for calculating the total visibility risk in the route G shown in FIG. 17. FIG. 21 is a table showing the visibility risk and the surrounding environment risk in each road link sequence of each virtual point on the route G.

[0149] As shown in FIG. 17, there are five road links, namely road links 100, 102, 200, 300, and 400, between the departure point and the destination, and a virtual point K is set at each road link. In addition, there is a building 170 that obstructs visibility near the virtual point K on the road links 100, 102, and 300. Further, there is a traffic signal 174 (surrounding environment risk ID: 900) as a surrounding environment risk factor, and the virtual point K0 on the road link 102 is associated with the traffic signal 174. In this example, it is assumed that the comprehensive risks of the two routes F and G are calculated.

[0150] In this example, for the sake of convenience of explanation, it should be noted that the visibility risk at a predetermined virtual point K on the road link corresponds to an evaluation index obtained by summarizing the visibility with respect to the predetermined virtual point K and three consecutive virtual points K from the predetermined virtual point K (that is, four virtual points K).

[0151] (Method for calculating the total risk of route F) First, by referring to FIGS. 18 and 19, a method for calculating the total risk based on the sum of the visibility risks and the sum of the surrounding environment risks of route F will be described in detail below. FIGS. 18(a) to (f) are diagrams for explaining a series of processes (particularly, from step (1) to (6)) for calculating the sum of the visibility risks in route F shown in FIG. 17. FIG. 19 is a table showing the visibility risks and the surrounding environment risks in each road link sequence of each virtual point on route F.

[0152] (Visibility risk in step (1) of route F) As shown in FIGS. 18(a) and 19, when the traveling vehicle V exists at the virtual point K0 (starting point, virtual point ID: 0) on the road link 100, the visibility risks of the road link sequences (road link ID: 100, 102) and (road link ID: 100, 400) are evaluated. The visibility risk of the road link sequence (100, 102) at the virtual point K0 on the road link 100 is (2, -2). At this point, the visibility of the virtual points K0 and K1 on the road link 102 is blocked by the building 170. Also, the visibility of the virtual point K0 on the road link 102 associated with the traffic signal 174 is poor, and in route F, the traveling vehicle V is scheduled to pass through the virtual point K0. Therefore, one surrounding environment risk factor (surrounding environment risk factor ID: 900) is counted. On the other hand, the visibility risk of the road link sequence (100, 400) at the virtual point K0 on the road link 100 is (4).

[0153] (Visibility risk in step (2) of route F) As shown in FIGS. 18(b) and 19, when the traveling vehicle V exists at the virtual point K1 (virtual point ID: 1) on the road link 100, the visibility risks of the road link sequences (road link IDs: 100, 102, 200), the road link sequence (road link IDs: 100, 102, 300), and the road link sequence (ID: 100, 400) are evaluated. The visibility risk of the road link sequence (100, 102, 200) at the virtual point K1 on the road link 100 is (2, -2). Also, the visibility of the virtual point K0 on the road link 102 associated with the traffic signal 174 is poor, and the traveling vehicle V is scheduled to pass through the virtual point K0. On the other hand, since the surrounding environment risk factor ID is 900 as described above, the surrounding environment risk factor is not newly counted. The visibility risk of the road link sequence (100, 102, 300) at the virtual point K1 on the road link 100 is (2, -2). Furthermore, the visibility risk of the road link sequence (100, 400) at the virtual point K0 on the road link 100 is (4).

[0154] (Visibility Risk in the Order (3) of Route F) As shown in FIGS. 18(c) and 19, when the traveling vehicle V exists at the virtual point K1 (virtual point ID: 1) on the road link 102, the visibility risks of the road link sequences (road link IDs: 102, 200) and the road link sequence (road link IDs: 102, 300) are evaluated. The visibility risk of the road link sequence (102, 200) at the virtual point K1 on the road link 102 is (4). Also, the visibility risk of the road link sequence (102, 300) at the virtual point K1 on the road link 102 is (2, -1).

[0155] (Visibility Risk in the Order (4) of Route F) As shown in FIGS. 18(d) and 19, when the traveling vehicle V is present at the virtual point K0 (virtual point ID: 0) on the road link 102, the visibility risk between the road link sequence (road link ID: 102, 200) and the road link sequence (road link ID: 102, 300) is evaluated. The visibility risk of the road link sequence (102, 200) at the virtual point K0 on the road link 102 is (3). Also, the visibility risk of the road link sequence (102, 300) at the virtual point K0 on the road link 102 is (2).

[0156] (Visibility risk in the order (5) of route F) As shown in FIGS. 18(e) and 19, when the traveling vehicle V is present at the virtual point K1 (virtual point ID: 1) on the road link 200, the visibility risk of the road link sequence (road link ID: 200) is evaluated. The visibility risk of the road link sequence (200) at the virtual point K1 on the road link 200 is (2).

[0157] (Visibility risk in the order (6) of route F) As shown in FIGS. 18(f) and 19, when the traveling vehicle V is present at the virtual point K0 (destination) on the road link 200, the visibility risk of the road link sequence (road link ID: 200) is evaluated. The visibility risk of the road link sequence (200) at the virtual point K0 on the road link 200 is (1).

[0158] From the above, the total visual recognition risk in the entire path F is evaluated as 7. Here, it should be noted that the total visual recognition risk is the sum of negative values where visual recognition is poor (2 + 2 + 2 + 1 = 7). Furthermore, since there are 6 virtual points K in path F, the average visual recognition risk is 7 / 6 = 1.16. Also, in path F, the visual recognition of virtual point K0 associated with traffic signal 174 (peripheral environment risk factor ID: 900) as a peripheral environment risk factor is poor. Moreover, in path F, the traveling vehicle V passes through the virtual point K0. Therefore, the total peripheral environment risk in path F is 1. In this regard, when the visual recognition of N virtual points K associated with different types of peripheral environment risk factors is poor, and the traveling vehicle passes through these N virtual points K in path F, the total peripheral environment risk is N. Thus, in this example, the overall risk of path F is 2.16 (= 1.16 + 1) by adding the average value of the visual recognition risk and the total of the peripheral environment risk factors.

[0159] Note that the overall risk may also be calculated by the following formula. Overall risk R = (average of visual recognition risk) + Σ (total T of various types of peripheral environment risk factors) × (weighting coefficient γ) Here, the above weighting coefficient γ may be set according to the type of peripheral environment risk factor. For example, a weighting coefficient regarding intersections may be prepared for the total T of the peripheral environment risk factor indicating intersections.

[0160] (Calculation method for the overall risk of path G) Next, with reference to FIGS. 20 and 21, a method for calculating the overall risk based on the total visual recognition risk and the total peripheral environment risk of path G will be described in detail below. FIGS. 20(a) to (f) are diagrams for explaining a series of processes (particularly, steps (1) to (10)) for calculating the total visual recognition risk in path G shown in FIG. 17. FIG. 21 is a table showing the visual recognition risk and the peripheral environment risk in each road link sequence of each virtual point on path G.

[0161] (Visibility risk in the order (1) of Route G) As shown in FIGS. 20(a) and 21, when the traveling vehicle V exists at the virtual point K0 (starting point, virtual point ID: 0) on the road link 100, the visibility risks of the road link sequence (road link IDs: 100, 102) and the road link sequence (road link IDs: 100, 400) are evaluated. The visibility risk of the road link sequence (100, 102) at the virtual point K0 on the road link 100 is (2, -2). Also, the visibility at the virtual point K0 on the road link 102 associated with the traffic signal 174 is poor, while in Route G, the traveling vehicle V does not pass through the virtual point K0. Therefore, the surrounding environment risk factor (surrounding environment risk factor ID: 900) is not counted. Also, the visibility risk of the road link sequence (100, 400) at the virtual point K0 on the road link 100 is (4).

[0162] (Visibility risk in the order (2) of Route G) As shown in FIGS. 20(b) and 21, when the traveling vehicle V exists at the virtual point K1 (virtual point ID: 1) on the road link 100, the visibility risks of the road link sequence (road link IDs: 100, 102, 200), the road link sequence (road link IDs: 100, 102, 300), and the road link sequence (IDs: 100, 400) are evaluated. The visibility risk of the road link sequence (100, 102, 200) at the virtual point K1 on the road link 100 is (2, -2). The visibility risk of the road link sequence (100, 102, 300) at the virtual point K1 on the road link 100 is (2, -2). The visibility risk of the road link sequence (100, 400) at the virtual point K0 on the road link 100 is (4).

[0163] (Visibility risk in the order (3) of Route G) As shown in FIGS. 20(c) and 21, when the traveling vehicle V exists at the virtual point K7 (virtual point ID: 7) on the road link 400, the visibility risk of the road link sequence (road link ID: 400) is evaluated. The visibility risk of the road link sequence (400) at the virtual point K7 on the road link 400 is (4).

[0164] (Visibility risk in the order (4) to (7) of route G) As shown in FIG. 21, when the traveling vehicle V exists at virtual points K6 to K3 (virtual point IDs: 6 to 3) on the road link 400, the visibility risk of the road link sequence (400) at each of the virtual points K6 to K3 on the road link 400 is (4).

[0165] (Visibility risk in the order (8) of route G) As shown in FIGS. 20(d) and 21, when the traveling vehicle V exists at the virtual point K2 (virtual point ID: 2) on the road link 400, the visibility risk of the road link sequence (road link ID: 400) is evaluated. The visibility risk of the road link sequence (400) at the virtual point K2 on the road link 400 is (3).

[0166] (Visibility risk in the order (9) of route G) As shown in FIGS. 20(e) and 21, when the traveling vehicle V exists at the virtual point K1 (virtual point ID: 1) on the road link 400, the visibility risk of the road link sequence (road link ID: 400) is evaluated. The visibility risk of the road link sequence (400) at the virtual point K1 on the road link 400 is (2).

[0167] (Visibility risk in the order (10) of route G) As shown in FIGS. 20(f) and 21, when the traveling vehicle V exists at the virtual point K0 (destination) on the road link 400, the visibility risk of the road link sequence (road link ID: 400) is evaluated. The visibility risk of the road link sequence (400) at the virtual point K0 on the road link 400 is (1).

[0168] From the above, the total visibility risk in the entire route G is evaluated as 6 (6 = 2 + 2 + 2). Furthermore, since there are 10 virtual points K in route G, the average visibility risk is 6 / 10 = 0.6. Also, in route G, the total of the surrounding environment risks is 0. Thus, in this example, the overall risk of route G is 0.6 by adding the average value of the visibility risk and the total of the surrounding environment risk factors.

[0169] Since the overall risk of route G is lower than that of route F, from the perspectives of visibility risk and surrounding environment risk, route G is more suitable than route F. Note that the method for calculating the overall risk is not limited to the above method, and various calculation methods can be assumed. Also, when route search considering only the visibility risk is executed, the optimal route may be selected by comparing the average visibility risk of each route. For example, since the average visibility risk of route G is 0.6, which is lower than the average visibility risk of route F, which is 1.16, from the perspective of visibility risk, route G is more suitable than route F.

[0170] Note that when the control unit 30 of the server 3 calculates the overall risk of each route, the control unit 30 extracts a plurality of road links existing between the departure point and the destination based on the road network data and the information regarding the departure point and the destination. Thereafter, the control unit 30 calculates the overall risk of each route constituted by some of the plurality of extracted road links based on the visibility risk data and the surrounding environment risk data stored in the storage device 31.

[0171] According to the present embodiment, by utilizing the visibility risk data, it becomes possible to execute a route search considering the visibility risk at each virtual point K on the road. In this way, it becomes possible to perform a safer route search considering the visibility risk. In particular, according to the present embodiment, based on the sum of the visibility risks and the sum of the surrounding environment risks, an overall risk considering both the visibility risk and the surrounding environment risk is calculated for each route. In this way, it becomes possible to perform a safer route search considering the visibility risk and the surrounding environment risk, and it can contribute to reducing traffic accidents caused by the visibility risk and the surrounding environment risk.

[0172] (Process of visualizing the visibility risk on the map) Next, with reference to FIGS. 22 and 23, the process of visualizing the visibility risk on the map will be described below. FIG. 22 is a flowchart for explaining a series of processes for visualizing the visibility risk on the map. FIG. 23 is a diagram showing an example of a map on which the visibility risk is visualized. In this example, it is assumed that a map on which the visibility risk is visualized is displayed on the display unit of the user terminal 6 that is communicably connected to the server 3 via the communication network 4.

[0173] As shown in FIG. 22, in step S20, the server 3 acquires, from the user terminal 6, current position information indicating the current position of the user terminal 6 via the communication network 4. Next, the server 3 extracts map information around the current position of the user terminal 6 from the map data. In particular, the server 3 extracts road links around the current position of the user terminal 6 from the road network data (step S21).

[0174] In step S22, the server 3 identifies the virtual point K and the position of the virtual point K associated with the extracted road link based on the virtual point data (see FIG. 15(a)) stored in the storage device 31. Next, the server 3 identifies the visibility risk of each virtual point K from the visibility risk data (see FIG. 15(b)) (step S23). In step S24, the server 3 determines the color scheme of the plot points corresponding to the positions of the respective virtual points K according to the visibility risk of each virtual point K. Here, since the visibility risk data stores information regarding the visibility risk of each road link sequence of each virtual point K, the color scheme of the plot points corresponding to the positions of the respective virtual points K may be determined according to the average visibility risk or the maximum visibility risk of each virtual point K.

[0175] For example, as shown in FIG. 21, at the virtual point K0 (virtual point ID: 0) on the road link 100, the visibility risks (2, -2) associated with the road link sequence (100, 102) and the visibility risk (4) associated with the road link sequence (100, 400) are stored in the visibility risk data. In this case, the average visibility risk of the virtual point K0 is 1, while the maximum visibility risk is 2. Thus, the color scheme of the plot point corresponding to the position of the virtual point K0 may be determined based on the average visibility risk (1) or the maximum visibility risk (2).

[0176] In step S25, the server 3 arranges on the map each plot point colored with the color scheme determined in step S24 based on the position of each virtual point K included in the virtual point data. The server 3 generates map image data in which each colored plot point is arranged on the map (step S25). Thereafter, the server 3 transmits the map image data to the user terminal 6 via the communication network 4. As a result, a map in which the visibility risks at each point on the road are visualized is displayed on the display unit of the user terminal 6 (step S26).

[0177] In the map 500 shown in FIG. 23, plot points 520 corresponding to virtual points around the current position P of the user U who holds the user terminal 6 are displayed. The color scheme of the plot points 520 is set according to the visibility risk of the corresponding virtual point K. The color scheme of the plot points may be, for example, the color scheme of a heat map. The larger the value of the visibility risk at the virtual point K, the closer the color scheme of the plot point corresponding to the virtual point K may be to red.

[0178] According to the present embodiment, by utilizing the visibility risk data, it becomes possible to visualize the visibility risks at each point on the road on the map. Thus, through the information regarding the visibility risks visualized on the map, it becomes possible to efficiently implement traffic safety measures (for example, installation of curved mirrors and danger warning signs, etc.) for reducing the visibility risks at each point, and it is possible to contribute to the reduction of traffic accidents caused by visibility risks.

[0179] Further, in this example, although the visibility risk around the current position of the user terminal 6 is visualized, the visibility risk at each virtual point along the route from the departure point to the destination may be visualized on the map. In this case, the route from the departure point to the destination is visualized on the map, and the visibility risk may be visualized on the map by the color scheme of the plot points arranged at the positions corresponding to the respective virtual points on the route.

[0180] Also, both the visibility risk and the surrounding environment risk may be visualized on the map. In this case, the surrounding environment risk can be visualized on the map by the color scheme of the plot points arranged at the positions corresponding to the virtual points associated with the surrounding environment risk factors such as intersections and traffic lights.

[0181] Also, in this example, a series of processes for visualizing the visibility risk on the map is executed by the server 3, but this series of processes may be executed by the control unit of the user terminal 6 or the vehicle control unit of the vehicle 5. In this case, the map data and the visibility risk data are pre-transmitted from the server 3 to the user terminal 6 or the vehicle 5 via the communication network 4.

[0182] (Process of presenting information on the visibility risk and the surrounding environment risk of the road around the vehicle to the passengers of the vehicle) Next, with reference to FIG. 24, a series of processes for presenting information on the visibility risk and the surrounding environment risk of the road around the vehicle to the passengers of the vehicle will be described below. FIG. 24 is a flowchart for explaining a series of processes for presenting information on the visibility risk and the surrounding environment risk of the road around the vehicle to the passengers of the vehicle.

[0183] In this example, it is assumed that the vehicle 5 executes a series of processes for presenting information on the visibility risk and the surrounding environment risk to the passengers of the vehicle. In this case, it is assumed that the map data including the road network data, the visibility risk data, the surrounding environment risk data, and the virtual point data are transmitted from the server 3 to the vehicle 5 and then stored in the storage device of the vehicle 5.

[0184] As shown in FIG. 24, in step S30, the vehicle control unit of the vehicle 5 acquires current position information indicating the current position of the vehicle 5 from the GNSS receiver. Next, the vehicle control unit extracts map information around the current position of the vehicle 5 from the map data and extracts road links around the current position from the road network data (step S31). In step S32, the vehicle control unit specifies the virtual point K associated with the extracted road link and the position of the virtual point K based on the virtual point data.

[0185] In step S33, the vehicle control unit specifies the visibility risk at each virtual point K based on the visibility risk data and specifies the surrounding environment risk at each virtual point K based on the surrounding environment risk data and the visibility risk data. Here, the visibility risk and the surrounding environment risk at each virtual point K on the planned travel route of the vehicle 5 may be specified. That is, the visibility risk and the surrounding environment risk of the virtual point K outside the planned travel route of the vehicle 5 may not be specified.

[0186] In step S34, the vehicle control unit presents information regarding the visibility risk and the surrounding environment risk visually and / or audibly to the occupants of the vehicle 5 through the HMI (for example, a navigation device). For example, when a virtual point K having a visibility risk and / or a surrounding environment risk exists within a predetermined distance from the vehicle 5, a message indicating that there is a visibility risk and / or a surrounding environment risk Xm ahead from the vehicle 5 may be presented visually and / or audibly. In particular, when the vehicle 5 approaches an intersection with poor visibility, such a message is beneficial from the viewpoint of alerting the occupants of the vehicle.

[0187] According to the present embodiment, by utilizing the visibility risk data and the surrounding environment risk data, it is possible to provide information regarding the visibility risk and the surrounding environment risk around the vehicle 5 to the vehicle 5 (particularly the occupants of the vehicle 5). In this way, it is possible to prompt the driver and the like to drive safely considering the visibility risk and the surrounding environment risk, and it is possible to contribute to reducing the occurrence of traffic accidents caused by the visibility risk and the surrounding environment risk.

[0188] Furthermore, in this example, information regarding the visibility risk and the surrounding environment risk around the vehicle 5 is provided to the occupants of the vehicle 5, but either one of the information regarding the visibility risk and the surrounding environment risk may be provided to the occupants of the vehicle 5.

[0189] Also, when the vehicle 5 is traveling in the fully autonomous driving mode, the vehicle control unit identifies information regarding the visibility risk and the surrounding environment risk around the vehicle 5 (for example, there is a visibility risk and / or a surrounding environment risk several meters ahead of the vehicle 5, etc.) through steps S30 to S33. Thereafter, the vehicle control unit may execute driving control of the vehicle 5 in consideration of the visibility risk and the surrounding environment risk based on the identified information. For example, it can be assumed that the vehicle 5 decelerates or stops before the point where the visibility risk or the surrounding environment risk exists. Still, also in this case, the driving control of the vehicle may be executed based on information regarding either one of the visibility risk and the surrounding environment risk around the vehicle 5.

[0190] As described above, the embodiments of the present invention have been explained, but the technical scope of the present invention should not be construed in a limited manner by the description of this embodiment. This embodiment is an example, and it is understood by those skilled in the art that various modifications of the embodiments are possible within the scope of the invention described in the claims. The technical scope of the present invention should be determined based on the scope of the invention described in the claims and the equivalent scope thereof.

Explanation of Reference Numerals

[0191] 1: Information processing system 2, 5: Vehicle 3: Server 4: Communication network 6: User terminal 20: Vehicle control unit 21: Camera 22: Wireless communication unit 23: GNSS receiver 25: Storage device 26: Drive system 27: Azimuth sensor 30: Control unit 31: Memory device 32: Input / output interface 33: Communication unit 34: Input operation unit 35: Display unit 36: Communication bus 70: Building 72: Traffic signal 500: Map 520: Plot point K, K0~K7: Virtual point L: Road link M: Configuration point O1: Feature object O2: Virtual point object O3: Road surface object S: Geographic coordinate system T: End point F: Start point U: User V: Traveling vehicle

Claims

1. 1. An information processing method for generating visibility risk data indicative of visibility risk at each point on a road, comprising: acquiring surrounding environment data indicating a surrounding environment of a first virtual point among a plurality of virtual points set on a plurality of road links; automatically determining a visibility risk at the first virtual point based on the surrounding environment data; generating the visibility risk data in which the visibility risk at the first virtual point and at least one road link are associated with each other; Including, A computer-implemented information processing method.

2. The step of automatically determining a visibility risk at the first virtual point comprises: generating a three-dimensional object corresponding to an object existing in the vicinity of the first virtual point based on the surrounding environment data; placing the three-dimensional object in a geographic coordinate system; placing a plurality of virtual point objects corresponding to a plurality of virtual points existing around the first virtual point in the geographic coordinate system; acquiring a field of view image captured by a virtual camera corresponding to the first virtual point; determining a visibility risk at the first virtual point by evaluating the visibility of at least some of the virtual point objects shown in the field of view image; Including, The information processing method according to claim 1 .

3. The at least some of the virtual point objects are virtual point objects corresponding to N virtual points (N is a natural number) existing in the vicinity of the first virtual point, determining a visibility risk at the first virtual point by evaluating the visibility of the N virtual point objects shown in the field of view image; The visibility of a predetermined virtual point object among the N virtual point objects is evaluated based on a ratio of a visible area of ​​the predetermined virtual point object to a total area of ​​the predetermined virtual point object in the field of view image. The information processing method according to claim 2 .

4. The surrounding environment data is image data. The information processing method according to claim 1 .

5. The image data is captured by a camera mounted on a vehicle traveling around the position of the first virtual point. The information processing method according to claim 4.

6. The three-dimensional object is A feature object corresponding to a feature existing in the vicinity of the position of the first virtual point; a road surface object corresponding to a road surface existing around the position of the first virtual point; At least one of The information processing method according to claim 2 .

7. The surrounding environment data is image data captured by a camera mounted on a vehicle traveling around the position of the first virtual point, The step of placing the three-dimensional object in the geographic coordinate system comprises: placing the three-dimensional object in the geographic coordinate system based on a shooting direction, a shooting position, and an angle of view of the camera; The information processing method according to claim 2 .

8. a plurality of virtual points associated with a predetermined road link among the plurality of road links are disposed at equal intervals on the predetermined road link; The information processing method according to claim 1 .

9. the three-dimensional object includes a road surface object corresponding to a road surface existing in the vicinity of the position of the first virtual point; the plurality of virtual point objects are arranged on the road surface object; the virtual camera is disposed directly above a road surface object that exists at the position of the first virtual point; The information processing method according to claim 2 .

10. When visibility risk data indicating a visibility risk associated with a first viewer of a first attribute is generated, a height position of the virtual camera with respect to the road surface object is set according to a line of sight of the first viewer, When visibility risk data indicating a visibility risk associated with a second viewer having a second attribute different from the first attribute is generated, a height position of the virtual camera with respect to the road surface object is set according to a line of sight of the second viewer. The information processing method according to claim 9.

11. The one or more road links include a road link associated with the first virtual point; road links associated with virtual points corresponding to at least some of the virtual point objects whose visibility has been evaluated; having The information processing method according to claim 2 .

12. An information processing program that causes a computer to execute the information processing method according to any one of claims 1 to 11.

13. At least one processor; At least one memory storing computer readable instructions, When the computer-readable instructions are executed by the processor, the information processing device performs the information processing method according to any one of claims 1 to 11. Information processing device.

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