Information processing method, storage medium, information processing apparatus, and data structure

Abstract

To provide an information processing method capable of generating highly reliable and consistent visibility risk data.SOLUTION: An information processing method for generating visibility risk data indicating a visibility risk at each point on a road, which is executed by a computer, includes acquiring surrounding environment data indicating a surrounding environment of a first virtual point among a plurality of virtual points set in a plurality of road links, automatically determining a 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.SELECTED DRAWING: Figure 11

Description

[Technical Field]

[0001] The present disclosure relates to an information processing method, an information processing program, an information processing device, and a data structure, and in particular to a method for generating visibility risk data indicating visibility risks at each point on a road. [Background technology]

[0002] Patent Document 1 discloses a route search method capable of searching for a route according to the priority of safety or drivability. In the route search method described in Patent Document 1, a weighting coefficient of a cost parameter for calculating the link cost of each road link is set according to the priority of safety or drivability. In this way, the link cost of each road link varies according to the priority of safety or drivability, making it possible to search for an optimal route according to the priority of safety or drivability. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2003-21525 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the route search method described in Patent Document 1 does not perform route search while taking into consideration the visibility risk of the surrounding environment at each point on the road (for example, an index indicating the quality of visibility at intersections and sharp curves). Meanwhile, statistical surveys of traffic accidents show that there are currently many collision accidents caused by poor visibility due to buildings and other factors (i.e., visibility risk). For this reason, route search that takes visibility risk into consideration may become an increasingly important technology in reducing collision accidents. In particular, the following three points may become increasingly important in a society in which automated driving (AD) and advanced driver assistance systems (ADAS) are widely adopted: 1) route search that takes visibility risk into consideration, 2) visualization of visibility risk on a map, and 3) notification of information regarding visibility risk to moving vehicles.

[0005] On the other hand, in order to achieve the above three technologies, visibility risk data showing the visibility risk at each point on the road is essential. In this regard, there are currently challenges in terms of cost in comprehensively acquiring visibility risk data. Furthermore, there are currently no fully established methods for ensuring the objectivity and consistency of visibility risk, making it difficult to provide highly reliable visibility risk data. As such, there is room for consideration of new methods for providing highly reliable and consistent visibility risk data.

[0006] In view of the above, 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 a route search that takes visibility risks into consideration.

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

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

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

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

[0012] An information processing method according to one aspect of the present disclosure is an information processing method executed by a computer for generating visibility risk data indicating visibility risks at each point on a road, and includes the steps of: acquiring 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.

[0013] An information processing method according to one aspect of the present disclosure is an information processing method executed by a computer for generating visibility risk data indicating the visibility risk at each point on a road, the information processing method including the steps of acquiring image data indicating the surrounding environment at each point on the road, automatically determining the 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 one aspect of the present disclosure is an information processing method executed by a computer and for performing a route search, the method including the steps of: acquiring information about a starting point and a destination; and performing a route search from the starting point to the destination, taking into account the visibility risk, based on road network data for route search and visibility risk data indicating the visibility risk at each point on a road. The visibility risk data is associated with the road network data via road links. The visibility risk data associates the visibility risk at virtual points set on the road links with the road links.

[0015] An information processing method according to one aspect of the present disclosure is executed by a computer and includes the steps of: displaying the map on a display unit; and 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, visibility risks at virtual points set on the road links and the road links are associated with each other.

[0016] According to one aspect of the present disclosure, there is provided an information processing method, which is executed by a computer and which presents predetermined information to a vehicle, and which includes the steps of: acquiring location information indicating a location of the vehicle; and presenting, to the vehicle, information regarding the visibility risk of a road around the vehicle based on the location information and visibility risk data indicating the visibility risk at each point on the road. In the visibility risk data, visibility risks at virtual points set on the road links are associated with the road links.

[0017] An information processing method according to one aspect of the present disclosure is an information processing method executed by a computer, and controls vehicle travel based on visibility risks at various points on a road, the method including the steps of: acquiring location information indicating a location of the vehicle; identifying information regarding the visibility risks around the vehicle based on the location information and visibility risk data indicating the visibility risks; and executing travel control of the vehicle taking the visibility risks into consideration based on the information regarding the visibility risks. In the visibility risk data, visibility risks at virtual points set on the road links are associated with the road links.

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

[0019] A data structure according to one aspect of the present disclosure is a data structure indicating a visibility risk at each point on a road, and includes identification information of a virtual point set on a road link, a 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. [Effects 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. Furthermore, according to the present disclosure, it is possible to provide an information processing method capable of performing a route search that takes visibility risk into consideration, an information processing method for visualizing visibility risk on a map, and an information processing method capable of providing information on visibility risk to a vehicle. Furthermore, the present disclosure is possible to provide an information processing program that causes a computer to execute the above information processing method, and an information processing device that executes the above information processing method. Furthermore, it is possible to provide a data structure that indicates visibility risk at each point on a road that is highly reliable and consistent. [Brief explanation of the drawings]

[0021] [Figure 1] 1 is a diagram illustrating an example of an information processing system according to an embodiment of the present disclosure (hereinafter, the present embodiment). [Figure 2] 1 is a diagram illustrating an example of a configuration of a vehicle according to an embodiment of the present invention. [Figure 3] FIG. 2 is a diagram illustrating an example of a configuration of a server according to the present embodiment. [Figure 4] FIG. 2 is a diagram for explaining road network data. [Figure 5] 1A is a diagram for explaining a road link, and FIG. 1B is a diagram for explaining a virtual point set on the road link. [Figure 6] FIG. 10 is a diagram showing imaginary points set on each road link at an intersection. [Figure 7] 1A is a diagram illustrating the visibility risk of a partial route A at a virtual point K0 (road link 100, virtual point ID: 0) set on a road link 100. FIG. 1B is a diagram illustrating the visibility risk of a partial route B at the virtual point K0. FIG. 1C is a diagram illustrating the visibility risk of a partial route C at the virtual point K0. FIG. 1D is a diagram illustrating the visibility risk of partial routes A to C. [Figure 8]1A is a diagram illustrating the visibility risk of a partial route T1 at a virtual point K0 (road link 100, virtual point ID: 0); FIG. 1B is a diagram illustrating the visibility risk of a partial route T2 at a virtual point K1 (road link 100, virtual point ID: 1); FIG. 1C is a diagram illustrating the visibility risk of a partial route T3 at a virtual point K2 (road link 100, virtual point ID: 2); and FIG. 1D is a diagram illustrating the visibility risk of a partial route T4 at a virtual point K1 (road link 102, virtual point ID: 1). [Figure 9] (a) is a table for explaining the visibility risk at each virtual point, and (b) is a table for explaining the surrounding environment risk. [Figure 10] (a) is a diagram for explaining the visibility risk on partial route D (sharp curve); (b) is a diagram for explaining the visibility risk on partial route E (crank); and (c) is a table for explaining the visibility risk on partial routes D and E. [Figure 11] 10 is a flowchart illustrating a series of processes for determining a visibility risk at a predetermined virtual point. [Figure 12] (a) is a diagram showing an original image, (b) is a diagram showing a depth image, (c) is a diagram showing a three-dimensional object placed in a geographic coordinate system, and (d) is a diagram showing a three-dimensional object placed in a geographic coordinate system and a virtual point object. [Figure 13] FIG. 10 is a diagram for explaining the placement position of a virtual camera corresponding to a predetermined virtual point. [Figure 14] 1A is a diagram illustrating the visibility of a virtual point object in a field of view image, in which (a) is a diagram illustrating a state in which the visibility of the virtual point object is 100%, (b) is a diagram illustrating a state in which the visibility of the virtual point object is 50%, and (c) is a diagram illustrating a state in which the visibility of the virtual point object is 25%. [Figure 15]1A is a diagram showing an example of virtual point data, FIG. 1B is a diagram showing an example of visibility risk data, and FIG. 1C is a diagram showing an example of surrounding environment risk data. [Figure 16] 10 is a flowchart illustrating an example of a process for executing a route search from a departure point to a destination taking into consideration visibility risks and surrounding environment risks. [Figure 17] (a) is a diagram showing each road link and imaginary point between the starting point and the destination, (b) is a diagram showing the entire route F from the starting point to the destination, and (c) is a diagram showing the entire route G from the starting point to the destination. [Figure 18] 18A and 18B are diagrams for explaining a series of steps for calculating the total visibility risk for route F shown in FIG. 17, in which (a) is a diagram explaining the visibility risk for route F in order (1). (b) is a diagram explaining the visibility risk for route F in order (2). (c) is a diagram explaining the visibility risk for route F in order (3). (d) is a diagram explaining the visibility risk for route F in order (4). (e) is a diagram explaining the visibility risk for route F in order (5). (f) is a diagram explaining the visibility risk for route F in order (6). [Figure 19] 10 is a table showing visibility risks and surrounding environment risks for each road link sequence at each virtual point on route F. [Figure 20] 18A and 18B are diagrams for explaining a series of steps for calculating the total visibility risk for route G shown in FIG. 17, in which (a) is a diagram explaining the visibility risk for route G in order (1). (b) is a diagram explaining the visibility risk for route G in order (2). (c) is a diagram explaining the visibility risk for route G in order (3). (d) is a diagram explaining the visibility risk for route G in order (8). (e) is a diagram explaining the visibility risk for route G in order (9). (f) is a diagram explaining the visibility risk for route G in order (10). [Figure 21] 10 is a table showing visibility risks and surrounding environment risks for each road link sequence at each virtual point on route G. [Figure 22] 10 is a flowchart illustrating a series of processes for visualizing visibility risks on a map. [Figure 23] FIG. 10 is a diagram illustrating an example of a map in which visibility risks are visualized. [Figure 24] 10 is a flowchart illustrating a series of processes for presenting information on visibility risks of roads around a vehicle and risks in the surrounding environment to a vehicle occupant. DETAILED DESCRIPTION OF THE INVENTION

[0022] (Outline of this embodiment) The outline of this embodiment will be described below.

[0023] An information processing method according to one aspect of the present disclosure is an information processing method executed by a computer for generating visibility risk data indicating visibility risks at each point on a road, and includes the steps of: acquiring 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, the visibility risk at the virtual point is automatically determined based on the surrounding environment data, and then visibility risk data indicating the visibility risk at the virtual point is generated, thereby enabling the generation of highly reliable and consistent visibility risk data.

[0025] Furthermore, the step of automatically determining the visibility risk at the first virtual point may include the steps of generating a three-dimensional object corresponding to an object present in the vicinity of the first virtual point based on the surrounding environment data, arranging the three-dimensional object in a geographic coordinate system, arranging a plurality of virtual point objects corresponding to a plurality of virtual points present in the vicinity of 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, 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 a first virtual point is automatically determined by evaluating the visibility of at least some of a plurality of virtual point objects shown in a field of view image captured by a virtual camera. In this way, the visibility of the virtual point objects is evaluated based on the influence of the arrangement of three-dimensional objects, making it possible to generate visibility risk data that is in line with the real world, which is difficult to evaluate using map data. Therefore, it is possible to generate highly reliable and consistent visibility risk data.

[0027] Furthermore, the at least some virtual point objects may be virtual point objects corresponding to N (N is a natural number) virtual points existing in the vicinity of the first virtual point. A 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 specific virtual point object among the N virtual point objects may be evaluated based on a ratio of a visible area of ​​the specific virtual point object to a total area of ​​the specific 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, thereby making it possible to generate consistent visibility risk data that ensures objectivity.

[0029] The surrounding environment data may be image data.

[0030] According to the above method, the visibility risk at a virtual point is automatically determined based on image data, and then visibility risk data indicating the visibility risk at the virtual point is generated. In this way, compared to point cloud data acquired 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] 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, the visibility risk at a virtual point is automatically determined based on image data captured by a camera mounted on a vehicle, and then visibility risk data indicating the visibility risk at the virtual point is generated. In this way, the visibility risk data can be updated more frequently through a moving vehicle equipped with a camera, making it possible to obtain highly up-to-date and comprehensive visibility risk data.

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

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

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

[0036] According to the above method, three-dimensional objects are placed in the geographic coordinate system based on the camera's shooting direction, shooting position, and field of view, making it possible to generate highly reliable and consistent visibility risk data.

[0037] Furthermore, the 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] The three-dimensional object may include a road surface object corresponding to a road surface existing around the position of the first virtual point. The plurality of virtual point objects may be disposed on the road surface object, and the virtual camera may be disposed directly above the road surface object existing at the position of the first virtual point.

[0039] According to the above method, the virtual camera is positioned directly above the road surface object located at the position of the first virtual point, making it possible to generate visibility risk data that is in line with the viewer's line of sight.

[0040] Furthermore, when visibility risk data indicating a visibility risk associated with a first viewer having a first attribute is generated, a height position of the virtual camera with respect to the road surface object may be 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 may be set according to a line of sight of the second viewer.

[0041] According to the above method, the height position of the virtual camera relative to the road surface object is set according to the line of sight of viewers with different attributes, so 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 uses the visibility risk data.

[0042] Furthermore, 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 whose visibility has been evaluated.

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

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

[0045] An information processing method according to one aspect of the present disclosure is an information processing method executed by a computer and for performing a route search, the method including the steps of: acquiring information about a starting point and a destination; and performing a route search from the starting point to the destination, taking into account the visibility risk, based on road network data for route search and visibility risk data indicating the visibility risk at each point on a road. The visibility risk data is associated with the road network data via road links. The visibility risk data associates the visibility risk at virtual points set on the road links with the road links.

[0046] According to the above method, by utilizing visibility risk data, it is possible to perform route searches that take into account the visibility risk at each point on the road. In this way, it is possible to perform safer route searches that take visibility risk into account.

[0047] The step of performing the route search may also include a step of performing a route search from the departure point to the destination taking into consideration the visibility risk and the surrounding environment risk based on the road network data, the visibility risk data, and surrounding environment risk data indicating surrounding environment risks at each point on a road. In the surrounding environment risk data, surrounding environment risk factors indicating risk factors around the virtual point, the virtual point, and the road link are associated with each other.

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

[0049] In addition, the step of performing the route search may include the steps of extracting a plurality of road links between the departure point and the destination based on the road network data, calculating a total visibility risk for a specified route consisting of at least some of the extracted plurality of road links based on the visibility risk data, calculating a total surrounding environment risk for the specified route based on the visibility risk data and the surrounding environment risk data, and calculating a total risk taking into account both the visibility risk and the surrounding environment risk for the specified route based on the total visibility risk and the total surrounding environment risk.

[0050] According to the above method, a total risk is calculated that takes into account both the visibility risk and the surrounding environment risk for a given route based on the total visibility risk and the total surrounding environment risk. In this way, it is possible to search for a safer route that takes into account the visibility risk and the surrounding environment risk, which can contribute to reducing traffic accidents caused by visibility risk.

[0051] An information processing method according to one aspect of the present disclosure is executed by a computer and includes the steps of: displaying the map on a display unit; and 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, visibility risks at virtual points set on the road links and the road links are associated with each other.

[0052] According to the above method, by utilizing visibility risk data, it is possible to visualize the visibility risk at each point on a map. In this way, through the information on visibility risk visualized on the map, it is possible to efficiently implement traffic safety measures (for example, installing convex mirrors and hazard warning signs) to reduce the visibility risk at each point, which can contribute to reducing traffic accidents caused by visibility risk.

[0053] An information processing method according to one aspect of the present disclosure is an information processing method executed by a computer and for presenting predetermined information to a vehicle, the method including the steps of: acquiring location information indicating a location of the vehicle; and presenting information regarding the visibility risk around the vehicle to the vehicle based on the location information and visibility risk data indicating the visibility risk at each point on a road. In the visibility risk data, visibility risks at virtual points set on the road links and the road links are associated with each other.

[0054] According to the above method, by utilizing visibility risk data, it is possible to provide information about the visibility risk of roads around the vehicle to the vehicle (especially the vehicle occupants). In this way, it is possible to encourage drivers and others to drive safely while taking visibility risk into consideration, which can contribute to reducing traffic accidents caused by visibility risk.

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

[0056] According to the above method, by utilizing visibility risk data and surrounding environment risk data, it is possible to provide information on visibility risks and surrounding environment risks around the vehicle to the vehicle (especially the vehicle occupants). In this way, it is possible to encourage drivers and others to drive safely while taking visibility risks and surrounding environment risks into consideration, which can contribute to reducing the occurrence of traffic accidents caused by visibility risks and surrounding environment risks.

[0057] An information processing method according to one aspect of the present disclosure is an information processing method executed by a computer, and controls vehicle travel based on visibility risks at various points on a road, the method including the steps of: acquiring location information indicating a location of the vehicle; identifying information regarding the visibility risks around the vehicle based on the location information and visibility risk data indicating the visibility risks; and executing travel control of the vehicle taking the visibility risks into consideration based on the information regarding the visibility risks. In the visibility risk data, visibility risks at virtual points set on the road links are associated with the road links.

[0058] According to the above method, by utilizing visibility risk data, it is possible to realize vehicle driving control that takes into account the visibility risk of the roads around the vehicle, thereby contributing to a reduction in traffic accidents caused by visibility risk.

[0059] Also provided is an information processing program that causes a computer to execute the information processing method. Furthermore, there is provided an information processing device comprising at least one processor and at least one memory that stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the information processing device executes the information processing method.

[0060] A data structure according to one aspect of the present disclosure is a data structure indicating a visibility risk at each point on a road, and includes identification information of a virtual point set on a road link, a 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.

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

[0062] The data structure may be associated with road network data for route search via the road link, and a route search taking the visibility risk into consideration may be performed based on the data structure and the road network data.

[0063] According to the above, by utilizing visibility risk data and road network data, it is possible to perform route searches that take visibility risks at each point on a road into consideration. In this way, it is possible to perform safer route searches that take visibility risks into consideration, which can contribute to reducing 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 in the vicinity of the predetermined virtual point based on the surrounding environment data, arranging the three-dimensional object in a geographic coordinate system, arranging a plurality of virtual point objects corresponding to a plurality of virtual points existing in the vicinity of the predetermined virtual point in the geographic coordinate system, acquiring a field of view image captured by a virtual camera corresponding to the predetermined virtual point, and evaluating the visibility of at least some of the plurality of virtual point objects shown in the field of view image.

[0065] According to the above, the visibility risk at a predetermined virtual point is automatically determined by evaluating the visibility of at least some of the virtual point objects shown in the field of view image captured by the virtual camera, thereby providing a highly reliable and consistent data structure regarding the visibility risk.

[0066] (Configuration of Information Processing System 1) An information processing system 1 according to this embodiment will be described below with reference to the drawings. FIG. 1 is a diagram showing an example of the information processing system 1 according to this 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 communicatively connected to a communication network 4. The vehicles 2 and 5 and the user terminal 6 are communicatively connected to the server 3 via the communication network 4. The communication network 4 is configured, for example, by the Internet. For ease of explanation, FIG. 1 shows a single vehicle 2 and 5 and a single user terminal 6, but multiple vehicles 2 and 5 and multiple user terminals 6 may be provided in the information processing system 1.

[0067] (Vehicle 2 configuration) 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 this 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 drivetrain system 26, and a direction sensor 27. The GNSS receiver 23 may be, for example, a GPS (Global Positioning System) receiver. In this embodiment, the vehicle 5 is assumed to have 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 drivetrain system, and a direction sensor.

[0068] The vehicle 2 may be a vehicle (for example, an autonomous vehicle) that can run in an autonomous driving mode. In this example, a four-wheeled vehicle is given as an example of a 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 configured 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 capture images of the environment surrounding the vehicle 2. The camera 21 is disposed at a predetermined position on the vehicle 2 so as to capture images of the environment surrounding the vehicle 2 through the windshield of the vehicle 2, for example. The images captured by the camera 21 may be still images or moving images. The camera 21 may also be detachably mounted on the vehicle 2. In this regard, the camera 21 may be portable by the 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 transmitting / receiving antenna and a wireless transmitting / receiving circuit. The wireless communication unit 22 may be a wireless communication module compatible with short-range wireless communication standards 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 or a fifth-generation mobile communication system such as LTE.

[0071] The GNSS receiver 23 is configured to acquire information about the position of the vehicle 2. In this example, the shooting position of the camera 21 is assumed to coincide with the position of the vehicle 2. Therefore, information about the position of the vehicle 2 detected by the GNSS receiver corresponds to information about the shooting position of the camera 21. The HMI 24 is configured to include an input unit that accepts input operations from the driver and an output unit that outputs information about the traveling 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). The storage device 25 may store map data and a vehicle control program. The drivetrain system 26 is configured to control the traveling state of the vehicle 2. For example, the drivetrain system 26 is configured to control the traveling of the vehicle 2 by controlling the accelerator, brake, and steering of the vehicle 2, respectively.

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

[0073] The photographing position data of the camera 21 includes photographing position information of a plurality of cameras 21 and a plurality of pieces of time information. The photographing position information is information indicating the position (longitude and latitude) of the 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 photographing 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 photographing position.

[0074] The shooting direction data includes shooting direction information of multiple cameras 21 and multiple pieces of time information. The shooting direction of the camera 21 is indicated within a range from 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 piece of shooting direction information is associated with one of multiple pieces of time information. Each piece of time information indicates the detection time of the corresponding shooting direction.

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

[0076] (Server 3 configuration) Next, the hardware configuration of the server 3 will be described below with reference to FIG. 3. FIG. 3 is a diagram showing an example of the configuration of the server 3 according to this embodiment. The server 3 may be configured with multiple servers that are physically separated from each other. The server 3 may be constructed on-premise or as a cloud server. As shown in FIG. 3, the 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 may include a read-only memory (ROM) storing various programs and a random access memory (RAM) having multiple work areas for storing various programs executed by the processor. The processor may include at least one of a central processing unit (CPU), a microprocessing unit (MPU), and a graphics processing unit (GPU). The CPU may include multiple CPU cores. The GPU may include multiple GPU cores. The processor may be configured to load a program specified by various programs stored 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 (the information processing method according to this embodiment) executed by the server 3.

[0078] The storage device 31 is, for example, a storage device (storage) such as an HDD or 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. The storage device 31 also 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, note 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). A road node is connected to a plurality of road links. Each road node is connected to other road nodes 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 streets, etc.). The road attribute information includes, for example, road type information, number of lanes information, and road width information. Each road link is also associated with link costs (e.g., distance cost, required time cost, etc.) used in route searches. Each road node is assigned unique identification information (ID). Each road node is associated with, for example, lane information, direction guidance information, traffic light information, and intersection name information. In this way, the road network data includes information related to road links and information related to road nodes. The server 3 can perform route searches from a departure point to a destination by using the road network data.

[0080] The background data includes illustration data related to the visual background of the map (e.g., map shapes such as buildings, ocean, forests, roads, etc.). The annotation data includes text information to be displayed on the map (e.g., names of buildings and mountains, etc.). The address data includes address information associated with each building on the map. The store information data includes information related to stores on the map.

[0081] The visibility risk data is data that indicates the visibility risk at each point on the road. The surrounding environment risk data is data that indicates the surrounding environment risk at each point on the road. The virtual point data is data that indicates 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 conforming 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 external terminals on the communication network 4. The input operation unit 34 is, for example, a touch panel, a mouse, and / or a keyboard, and is configured to accept input operations by an operator and to generate operation signals in response to the input operations by the operator. The display unit 35 is, for example, configured by 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, or a wearable device (for example, a smartwatch or AR glasses). 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 app or a web browser for displaying maps may be installed on the user terminal 6.

[0084] (Road link and virtual point configuration) Next, road links L and virtual points will be described below with reference to FIG. 5. FIG. 5(a) is a diagram illustrating road links L. FIG. 5(b) is a diagram illustrating virtual points K1 to K4 set on road links L. In the following description, virtual points K1 to K4 may be collectively referred to simply as virtual points K. As shown in FIG. 5(a), each road link L is set with a start point F, an end point T, and one or more component points M located between the start point F and the end point T. The shape of the road link L is formed by the plurality of component 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 this embodiment, the position of the first virtual point K0 corresponds to the position of the start 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 imaginary point K0 that overlaps with the starting point F is 0, and the imaginary point ID increases as it moves away from the starting point F.

[0085] (Visibility risk of each partial route A to C at a specific virtual point) Next, the visibility risk of each of the partial routes A to C at a specific virtual point K will be described below with reference to FIGS. 6 and 7. Here, the visibility risk at the virtual point K is an index for evaluating the visibility of the surrounding environment at the virtual point K (for example, objects such as other vehicles present around the virtual point K). FIG. 6 is a diagram showing the virtual points K set on the road links 100 to 103 at an intersection. FIG. 7(a) is a diagram for explaining the visibility risk of the partial route 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 route B at the virtual point K0. FIG. 7(c) is a diagram for explaining the visibility risk of the partial route C at the virtual point K0. FIG. 7(d) is a diagram for explaining the visibility risk of the partial routes A to C.

[0086] 6, a virtual point K is set on 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 the road links 101, 102, and 103, respectively.

[0087] In this example, the visibility risk at the virtual point K0 on the road link 100 corresponds to an evaluation index summarizing the visibility of the virtual point K0 and N virtual points K (more specifically, N virtual points K consecutive from the virtual point K0) existing in the vicinity of 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 summarizing the visibility of the virtual point K0 and each of the four virtual points K consecutive from the virtual point K0.

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

[0089] 7(a), the visibility of the five virtual points K of the partial route A (specifically, the virtual points K0 to K2 on the road link 100 and the virtual points K0 to K1 on the road link 103) at the virtual point K0 on the road link 100 is good, so the visibility risk is (5). Here, the partial route A is composed of a road link sequence consisting of the road links 100 and 103.

[0090] 7(b), the visibility of the five virtual points K (virtual points K0 to K2 on the road link 100 and virtual points K0 to K1 on the road link 101) of the partial route B at the virtual point K0 on the road link 100 is good, so the visibility risk is (5). Here, the partial route B is made up of a road link sequence consisting of the road links 100 and 101.

[0091] As shown in FIG. 7(c), among the five virtual points K of the partial path C, the visibility of virtual points K0 to K2 on the road link 100 at the virtual point K0 on the road link 100 is good. On the other hand, the visibility of virtual points K1 and K0 on the road link 102 at the virtual point K0 is obstructed by a building 80, so the visibility of virtual points K1 and K0 on the road link 102 at the virtual point K0 is poor. As a result, the visibility of 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 indicates that the visibility of the first to third three virtual points K, including the virtual point K0 itself, is good, but the visibility of the fourth and fifth two virtual points K is poor. The partial path C is composed of a series of road links consisting of the road links 100 and 102.

[0092] As shown in FIG. 7(d), the visibility risk at virtual point K0 on road link 100 has three visibility risks: the visibility risk of partial route A, the visibility risk of partial route B, and the visibility risk of partial route C. Here, each of partial routes A to C is configured by a road link sequence consisting of multiple road links. In this way, the visibility risk at virtual point K0 (road link 100, virtual point ID: 0) has a visibility risk for each road link sequence. In this example, the road link sequence (100, 103) corresponding to partial route 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 route that proceeds from road link 100 (road link ID: 100) to road link 103 (road link ID: 103).

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

[0094] Note that a unique value may be assigned to each virtual point ID. In this case, it is possible to identify the virtual point K only by the virtual point ID. In addition, in this example, the visibility risk at the virtual point K0 is defined as an evaluation index summarizing the visibility for the virtual point K0 and four virtual points K consecutive from the virtual point K0, but the number of consecutive virtual points K is not limited to four. The visibility risk at each virtual point K includes the visibility risk of each partial route. The visibility risk is associated with the series of road links that make up the partial route and the virtual point ID.

[0095] (Visibility risk at each virtual point) The visibility risk at each virtual point K will be described below with reference to FIGS. 8 and 9. In the example shown in FIG. 8, for simplicity of explanation, there is only one partial path at each virtual point. FIG. 8(a) is a diagram for explaining the visibility risk of a partial path T1 at a virtual point K0 (road link 100, virtual point ID: 0). FIG. 8(b) is a diagram for explaining the visibility risk of a partial path T2 at a virtual point K1 (road link 100, virtual point ID: 1). FIG. 8(c) is a diagram for explaining the visibility risk of a partial path T3 at a virtual point K2 (road link 100, virtual point ID: 2). FIG. 8(d) is a diagram for explaining the visibility risk of a partial path T4 at a 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, partial routes T1 to T4 are formed by a series of road links consisting of at least two of the road links 200 to 203. As shown in Fig. 8(a), when a traveling vehicle V is at a virtual point K0 on the road link 200, the visibility of five virtual points K on the partial route T1 formed by the series of road links consisting of the road links 200 and 201 is evaluated. In this case, the visibility of three virtual points K0 to K2 on the road link 200 among the five virtual points K on the partial route T1 from the virtual point K0 on the road link 200 is good. On the other hand, the visibility of two virtual points K0 and K1 on the road link 201 from the virtual point K0 is poor because they are obstructed by the building 70. As a result, the visibility of the five virtual points K on the partial route T1 from 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 is at a virtual point K1 on the road link 200, the visibility of five virtual points K on a partial path T2 formed by a series of road links consisting of the road links 200, 201, and 202 is evaluated. In this case, the visibility of two virtual points K1 and K2 on the road link 200 among the five virtual points K on the partial path T2 at the virtual point K1 on the road link 200 is good. On the other hand, the visibility of two virtual points K0 and K1 on the road link 201 at the virtual point K1 is poor because they are obstructed by the building 70. Furthermore, the visibility of the virtual point K1 on the road link 202 is also poor because it is obstructed by the building 70. As a result, the visibility of the five virtual points K on 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 is at virtual point K2 on road link 200, the visibility of five virtual points K on partial path T3 formed by the series of road links consisting of road links 200, 201, and 202 is evaluated. In this case, the visibility of virtual point K2 on road link 200, among the five virtual points K on partial path T3, is good at virtual point K2 on road link 200. On the other hand, the visibility of two virtual points K0 and K1 on road link 201 is poor because they are obstructed by building 70. Furthermore, the visibility of virtual points K1 and K0 on road link 202 is also poor because they are obstructed by building 70. As a result, the visibility of the five virtual points K on partial path T3 at virtual point K2 on road link 200 is (1, -4) (see FIG. 9(a)).

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

[0100] FIG. 9(a) shows the relationship between the visibility risk at each virtual point K, the road link sequence, and the virtual point ID. Also, as shown in FIG. 8, a traffic light 72, which is a surrounding environment risk factor, is present around the virtual point K0 of the road link 201. Surrounding environment risk factors such as traffic lights, intersections, crosswalks, railroad crossings, changes in the number of lanes, and changes in road width can also be risk factors for rear-end collisions and other accidents. In this embodiment, surrounding environment risk factors indicating risk factors around a virtual point are associated with road links and virtual points and stored in the server 3 as surrounding environment risk data. In the example shown in FIG. 9(b), the ID (0x0C2) of the surrounding environment risk factor indicating the traffic light, the ID (102) of the road link, and the ID (0) of the virtual point are associated with each other. In particular, in the surrounding environment risk data (see FIG. 15(c)) indicating the surrounding environment risk at each point on a road, which will be described later, the virtual point ID, the ID of the road link, and the ID of the surrounding environment risk factor are associated with each other.

[0101] Furthermore, 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 a surrounding environment risk factor such as a traffic light or intersection is identified, and the identified virtual point, the road link associated with the virtual point, and the surrounding environment risk factor are associated with each other. In this way, surrounding environment risk data is generated.

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

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

[0104] 7 and 8 in that partial routes D and E in this example are configured by a road link sequence consisting of a single road link. Similarly, in this example partial route, the visibility risk at a virtual point Kn (n is an integer) corresponds to an evaluation index that summarizes the visibility of the virtual point Kn and N virtual points K that exist in the vicinity of the virtual point Kn (more specifically, N virtual points K consecutive to the virtual point Kn).

[0105] As shown in FIG. 10(a), when a traveling vehicle V is located at a virtual point K2 on a road link 204, the visibility of five virtual points K on a partial route D (sharp curve) formed by the road link 204 is evaluated. In this case, the visibility of three virtual points K2 to K4 of the five virtual points K on the partial route D at the virtual point K2 is good. On the other hand, the visibility of two virtual points K5 and K6 is poor because they are obstructed by a building 74 located midway through the sharp curve. As a result, the visibility of the five virtual points K on the partial route D at the virtual point K2 is (3, -2). As shown in FIG. 10(c), 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, and this data is 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 is at virtual point K4 on road link 205, the visibility of five virtual points K of partial route E (crank) formed by road link 205 is evaluated. In this case, the visibility of three of the five virtual points K of partial route E at virtual point K4, namely, virtual points K4 to K2, is good. On the other hand, the visibility of two virtual points K1 and K0 is poor because they are obstructed by buildings 74a to 74c (mainly building 74c). As a result, the visibility of the five virtual points K of partial route E at virtual point K4 is (3, -2). As shown in FIG. 10(c), 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, and this data is stored in the server 3 as visibility risk data. In this example, since the direction of travel of partial route E is opposite to the forward direction from the start point F to the end point T of road link 205, the virtual point ID is stored as -4 instead of 4 in the visibility risk data.

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

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

[0109] As shown in FIG. 11, in step S1, the control unit 30 of the server 3 acquires from the storage device 31 a plurality of image data (an example of surrounding environment data) showing the surrounding environment of a predetermined virtual point Kn (an example of a first virtual point). Here, the image data shown in FIG. 12(a) is captured by a camera 21 mounted on a 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 from the storage device 31 a plurality of image data captured around the position of the predetermined virtual point Kn. The storage device 31 stores the image data transmitted from the vehicle 2 and metadata associated with the image data.

[0110] The metadata includes information regarding the shooting direction, shooting position, and angle of view 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 information regarding the shooting direction, shooting position, and angle of view 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 a predetermined virtual point Kn is identified, image data indicating the surrounding environment of the predetermined virtual point Kn can be extracted based on the identified 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 a depth estimation process for each image data by using a depth estimation model (trained 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 image data by estimating the depth of the subject in each image. Here, depth refers to 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 configured with polygons. In this embodiment, the three-dimensional object may include at least one of a feature object corresponding to the 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, the feature is an immovable object on the ground such as a building, a traffic sign, a traffic light, a tree, a guardrail, or a wall. On the other hand, moving objects such as a vehicle or a pedestrian may be excluded from the feature.

[0113] Furthermore, 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 shooting directions.

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

[0115] Next, in step S4, the control unit 30 places the generated three-dimensional object in a geographic coordinate system S based on the shooting direction, shooting position, and angle of view of the camera 21 in the multiple pieces of metadata associated with one or more pieces of image data (see FIG. 12(c)). The geographic coordinate system S is formed as a three-dimensional space, and the position of an object in the geographic coordinate system S is defined by latitude, longitude, and altitude. In this way, it is possible to place the three-dimensional object in the geographic coordinate system S with high accuracy based on the shooting direction, shooting position, and angle of view of the camera 21. In the geographic coordinate system S shown in FIG. 12(c), a feature object O1 and a three-dimensional road link L are placed among the three-dimensional objects. Although a road surface object O3 (see FIG. 13) among the three-dimensional objects is not displayed in the same figure, the road surface object O3 may also be placed in the geographic coordinate system S. The three-dimensional road link L is placed in the geographic coordinate system S along the road surface shape of the road surface object O3.

[0116] In step S5, the control unit 30 generates a plurality of virtual point objects O2 corresponding to a plurality of virtual points K present around a predetermined virtual point Kn, and then places the plurality of virtual point objects O2 in the geographic coordinate system S. As shown in Fig. 12(d) , the virtual point objects O2 are placed at equal intervals along the three-dimensional road link L or road surface object O3 placed in the geographic coordinate system S.

[0117] The planar position (latitude and longitude) of each virtual point object O2 is determined based on virtual point data (see FIG. 15(a)) indicating the planar position (latitude and longitude) of each virtual point K. That is, the planar position of each virtual point object O2 coincides with the planar position of the corresponding virtual point K. Meanwhile, as shown in FIG. 13, since each virtual point object O2 is placed on a road surface object O3 or a 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 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 a 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 placed in the geographic coordinate system S.

[0119] 13, each virtual point object O2 has a spherical shape. The diameter R of each virtual point object O2 may be set to, for example, 1.5 m, which is the average height of a standard vehicle. If the virtual point object O2 is spherical, this is preferable because it makes it easier to objectively evaluate the visibility of the virtual point object O2, which will be described later.

[0120] Returning to FIG. 11, in step S6, the control unit 30 places a virtual camera CAM corresponding to a predetermined virtual point Kn in the geographic coordinate system S. As shown in FIG. 13, the virtual camera CAM is placed directly above the road object O3 or the three-dimensional road link L located at the position of the predetermined virtual point Kn. In particular, the planar position (latitude and longitude) of the virtual camera CAM coincides with the planar position (latitude and longitude) of the predetermined virtual point Kn. Meanwhile, the height position of the virtual camera CAM is determined based on the height position of the road object O3 or the three-dimensional road link L at the planar position (latitude and longitude) of the predetermined virtual point Kn. More specifically, the height position D1 of the virtual camera CAM relative to the road object O3 at the planar position (latitude and longitude) of the predetermined virtual point Kn may be set to, for example, 1.2 m, which is the average height of the eye level of a driver of a standard vehicle.

[0121] In this regard, when visibility risk data indicating a visibility risk associated with a driver of an ordinary vehicle (an example of a first viewer of the first attribute) is generated, the height position D1 of the virtual camera CAM relative to the road 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 eye level of the driver of the ordinary vehicle. On the other hand, when visibility risk data indicating a 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 relative to the road 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 eye level of the driver of the large vehicle.

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

[0123] 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, and 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] 11, in step S7, the control unit 30 generates a field of view image captured by a virtual camera CAM placed in the geographic coordinate system S. Next, in step S8, the control unit 30 evaluates the visibility of 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 given virtual point Kn is evaluated by evaluating the visibility of the virtual point Kn and N virtual points K arranged consecutively in front of the virtual point Kn. Here, the visibility of the virtual point Kn itself is evaluated as good. Next, with regard to the visibility of the N virtual points K arranged consecutively in front of the virtual point Kn, the visibility of each of the virtual point objects O2 corresponding to the N virtual points K shown in the field of view image is evaluated.

[0126] In this regard, the visibility of a specific virtual point object O2 in the field of view image may be evaluated based on the percentage of the visible area of ​​the specific virtual point object O2 relative 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 at all by the feature object O1, and as a result, the ratio of the viewable area of ​​the specific virtual point object O2 to the entire area is evaluated as 100%. Also, as shown in Fig. 14(b), the visibility of a specific virtual point object O2 is affected by the feature object O1, and as a result, the ratio of the viewable area of ​​the specific virtual point object O2 to the entire area is evaluated as 50%. As shown in Fig. 14(c), the visibility of a specific virtual point object O2 is affected by the feature object O1, and as a result, the ratio of the viewable area of ​​the specific virtual point object O2 to the entire area is evaluated as 25%.

[0128] In this example, if the ratio of the viewable area of ​​a specific virtual point object O2 to the total area of ​​the specific virtual point object O2 is 50% or more, the visibility of the specific virtual point object O2 may be determined to be good. On the other hand, if the ratio of the viewable area of ​​a specific virtual point object O2 to the total area of ​​the specific virtual point object O2 is less than 50%, the visibility of the specific virtual point object O2 may be determined to be poor. For example, the ratio of the viewable 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 viewable 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 onto which the virtual point object O2 is two-dimensionally projected. The number of pixels of the viewable 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 entire area of ​​the virtual point object O2 may be determined based on the visible shape of the virtual point object O2 in the field of view image and a 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 an input layer, and the ratio of the visible area to the entire area of ​​the virtual point object may be output in an 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 objects O2 corresponding to N virtual points K arranged consecutively 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). Furthermore, if the visibility of the first to seventeenth virtual point objects O2 consecutive from the predetermined virtual point Kn is good, but the visibility of the eighteenth to twentieth virtual point objects O2 is poor, the visibility risk at the predetermined virtual point Kn is (18, −3). In this way, the server 3 can automatically determine the visibility risk at the predetermined virtual point Kn through the processes of steps S1 to S9.

[0131] Furthermore, when automatically determining the visibility risk at a virtual point Kn+1 adjacent to a predetermined virtual point Kn, the processes from steps S1 to S9 may be repeatedly executed. On the other hand, when there is no need to update the three-dimensional object located in the geographic coordinate system S (particularly when the visibility risk at the virtual point Kn+1 can be determined using an already generated three-dimensional object and a virtual point object), steps S6 to S9 may be repeatedly executed. In this way, the visibility risk of each partial route (series of road links) at each point (virtual points Kn, Kn+1, Kn+2...) can be automatically determined based on image data showing the surrounding environment at each point on the road, and visibility risk data indicating the visibility risk at each point can be generated.

[0132] Furthermore, in steps S1 to S5 shown in FIG. 11, three-dimensional objects and virtual point objects around a predetermined virtual point Kn are generated based on a plurality of image data representing the surrounding environment of the predetermined virtual point Kn. However, this embodiment is not limited to this. In this regard, a plurality of image data representing the surrounding environments of all virtual points K for which visibility risk is to be evaluated may be first prepared, and then three-dimensional objects and virtual point objects around each virtual point K may be generated simultaneously based on the plurality of image data. In this case, through the processing of steps S1 to S5, three-dimensional objects and virtual point objects existing in the placement area of ​​each virtual point K to be evaluated are placed on the geographic coordinate system S. Thereafter, the processing of steps S6 to S9 is repeatedly executed. That is, a virtual camera CAM is sequentially placed at the planar position of each virtual point K, and the visibility risk at each virtual point K is sequentially determined based on the field of view images captured by the virtual camera CAM.

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

[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 about each road link (each road link ID) and the position (latitude and longitude) of one or more virtual points, which are associated with each other. For example, information about a virtual point set on road link 100 (road link ID: 100) includes information about the latitude and longitude (x1, y1) of virtual point K0 (virtual point ID: 0), the latitude and longitude (x2, y2) of virtual point K1 (virtual point ID: 1), and the latitude and longitude (x3, y3) of virtual point K2 (virtual point ID: 2). Here, the information about the position of each virtual point indicates the position (x1, y1) of virtual point K0 (ID: 0) at the left end, and the position (x2, y2) of virtual point K1 (ID: 1) to its immediate right.

[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 related to a road link sequence (a combination of one or more road link IDs) consisting of one or more road links, a virtual point ID, and a visibility risk, which are associated with each other. For example, the visibility risk data includes information related to the visibility risk (3, -2) of a partial route consisting of a road link sequence (road link IDs: 100, 102) at 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) at each virtual point K. Because the visibility risk data includes road link IDs, the visibility risk data and the road network data for route search are associated with each other via the road link IDs. Therefore, by using visibility risk data and road network data, it is possible to perform a route search that takes visibility risk into consideration.

[0136] (surrounding environmental 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 on road links (road link IDs), virtual point IDs, and surrounding environment risk factors (surrounding environment risk factor IDs), which are associated with each other. For example, the surrounding environment risk data includes information on the surrounding environment risk factor ID associated with virtual point K0 (virtual point ID: 0) on the road link 102. The surrounding environment risk ID indicates the type of surrounding environment risk factor, such as an intersection or a traffic light. Since the surrounding environment risk data includes a road link ID, the surrounding environment risk data is associated with the visibility risk data and the road network data used for route search via the road link ID. Therefore, by using the visibility risk data, surrounding environment risk data, and road network data, it is possible to perform a route search that takes both visibility risk and surrounding environment risk into consideration.

[0137] According to this embodiment, the visibility risk at a predetermined virtual point Kn is automatically determined based on image data, and then visibility risk data including information about the visibility risk at the predetermined virtual point Kn is generated. In this way, it is possible to generate highly reliable and consistent visibility risk data. In particular, by evaluating the visibility of 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, the visibility of the virtual point object O2 is evaluated based on the influence of the arrangement of the feature object O1, so it is possible to efficiently and automatically generate visibility risk data that is in line with the real world, which is difficult to evaluate using map data. Therefore, it is 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 that ensures objectivity.

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

[0140] In this way, the visibility risk data can be updated more frequently through multiple vehicles 2 (multiple general vehicles, etc.) equipped with cameras 21, thereby making it possible to obtain highly fresh and comprehensive visibility risk data. In particular, in order to utilize the image data provided to multiple general vehicles, rewards such as tokens or points can be given to general vehicles that provide image data, thereby making it possible to further increase the visibility risk data update frequency.

[0141] (Route search taking into account visibility risks and surrounding environmental risks) Next, route search that takes visibility risk and surrounding environment risk into consideration will be described below with reference to Fig. 16. Fig. 16 is a flowchart for explaining an example of processing for executing a route search from a departure point to a destination that takes visibility risk and surrounding environment risk into consideration.

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

[0143] The road network data includes at least data indicating the connection relationships between road links via road nodes, and cost data indicating the link costs of each road link (for example, distance costs, required time costs, etc.). The road network data is also associated with the visibility risk data and surrounding environment risk data shown in FIG. 15 via the road links (road link IDs). Therefore, by using the road network data, visibility risk data, and surrounding environment risk data, it is possible to perform a route search from destination to destination, taking into account the link costs of each road link and the visibility risk and surrounding environment risk at each point (virtual point) on the road.

[0144] For example, the server 3 may extract multiple candidate routes with low route costs based on the link cost of each road link, and then determine, from among the extracted multiple routes, an optimal route with the lowest overall risk taking into account both visibility risk and surrounding environment risk, or an optimal route with the overall risk equal to or less than a predetermined threshold, based on the visibility risk data and surrounding environment risk data. Alternatively, the server 3 may extract multiple candidate routes with a low overall risk taking into account both visibility risk and surrounding environment risk (for example, an overall risk equal to or less than a predetermined threshold), based on the visibility risk data and surrounding environment risk data, and then determine, from among the extracted multiple routes, an optimal route with the lowest route cost, based on the link cost of each road link.

[0145] Note that there are various possible methods for route search based on the three data sets of road network data, visibility risk data, and surrounding environment risk data, and the route search is not limited to the above. Also, in this example, both visibility risk data and surrounding environment risk data are used, but if a route search from destination to destination is performed taking visibility risk only into consideration, the surrounding environment risk data does not need to be used.

[0146] Returning to FIG. 16 , in step S12, the server 3 transmits information regarding the route search results to the vehicle 5 via the communication network 4. The route search results are then displayed on the display unit of the vehicle 5. In this example, the server 3 performs a route search and then transmits the route search results to the vehicle 5 via the communication network 4; however, the vehicle 5 may also perform 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 a storage device of the vehicle 5. Thereafter, the vehicle control unit of the vehicle 5 can perform a route search from the departure point to the destination, taking visibility risk and surrounding environment risk into consideration, based on the 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 period.

[0147] (Example of comprehensive risk taking into account both visibility risk and surrounding environmental risk) Next, with reference to Figures 17 to 21, a specific example of the overall risk that takes into account both visibility risk and surrounding environment risk for two overall routes F and G will be described below. In a route search that takes into account both visibility risk and surrounding environment risk, the route with the lowest calculated overall risk may be selected as the optimal route. As described above, multiple candidate routes may first be extracted based on the link costs of each road link, and then the route with the lowest overall risk among the extracted candidate routes may be selected as the optimal route. Alternatively, a route with a route cost equal to or less than a first threshold and an overall risk equal to or less than a second threshold may be selected as the optimal route.

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

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

[0150] In this example, for ease of explanation, please note that the visibility risk at a specified virtual point K on a road link corresponds to an evaluation index that summarizes the visibility for the specified virtual point K and the three virtual points K consecutive to the specified virtual point K (i.e., four virtual points K).

[0151] (Method for calculating the overall risk of Route F) First, a method for calculating a total risk based on the total visibility risk of route F and the total surrounding environment risk will be described in detail below with reference to Figures 18 and 19. Figures 18(a) to 18(f) are diagrams for explaining a series of steps (particularly, the order (1) to (6)) for calculating the total visibility risk of route F shown in Figure 17. Figure 19 is a table showing the visibility risk and surrounding environment risk for each road link sequence at each virtual point on route F.

[0152] (Visibility risk of route F in order (1)) As shown in FIGS. 18(a) and 19, when a traveling vehicle V is located at a virtual point K0 (starting point, virtual point ID: 0) on a road link 100, the visibility risk of a series of road links (road link ID: 100, 102) and a series of road links (road link ID: 100, 400) is evaluated. The visibility risk of the series of road links (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 obstructed by a building 170. Furthermore, the visibility of the virtual point K0 on the road link 102 associated with a traffic light 174 is poor, and the traveling vehicle V is scheduled to pass through the virtual point K0 on route F. 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 order (2) of route F) As shown in FIGS. 18(b) and 19, when a traveling vehicle V is present at a virtual point K1 (virtual point ID: 1) on a road link 100, the visibility risks of the road link sequence (road link ID: 100, 102, 200), the road link sequence (road link ID: 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). In addition, the visibility of a virtual point K0 on the road link 102 associated with a traffic light 174 is poor, and the traveling vehicle V is scheduled to pass through the virtual point K0. On the other hand, the surrounding environment risk factor ID is 900 as in the above case, and therefore 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 order (3) of route F) As shown in Figures 18(c) and 19, when a traveling vehicle V is located at a virtual point K1 (virtual point ID: 1) on the road link 102, the visibility risk of a road link sequence (road link ID: 102,200) and a road link sequence (road link ID: 102,300) is evaluated. The visibility risk of the road link sequence (102,200) at the virtual point K1 on the road link 102 is (4). Furthermore, 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 order (4) of route F) As shown in Figures 18(d) and 19, when a traveling vehicle V is located at a virtual point K0 (virtual point ID: 0) on the road link 102, the visibility risk of a road link sequence (road link ID: 102,200) and a 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 order (5) of route F) 18(e) and 19, when a traveling vehicle V is present at a 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 order (6) of route F) 18(f) and 19, when a traveling vehicle V is at a 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 visibility risk for the entire route F is evaluated as 7. Note that the total visibility risk is the sum of the negative values ​​for poor visibility (2 + 2 + 2 + 1 = 7). Furthermore, since there are six virtual points K on route F, the average visibility risk is 7 / 6 = 1.16. Also, on route F, the visibility of virtual point K0 associated with traffic light 174 (surrounding environment risk factor ID: 900) as a surrounding environment risk factor is poor. Furthermore, on route F, a traveling vehicle V passes through this virtual point K0. Therefore, the total surrounding environment risk for route F is 1. At this point, if the visibility of N virtual points K associated with different types of surrounding environment risk factors is poor, and if a traveling vehicle passes through these N virtual points K on route F, the total surrounding environment risk is N. Thus, in this example, the overall risk of route F is 2.16 (= 1.16 + 1), which is obtained by adding the average visibility risk and the sum of the surrounding environment risk factors.

[0159] The total risk may be calculated using the following formula: Total risk R = (average visibility risk) + Σ (total of surrounding environmental risk factors by type T) × (weighting coefficient γ) Here, the weighting coefficient γ may be set according to the type of surrounding environment risk factor. For example, a weighting coefficient for an intersection may be prepared for the total T of surrounding environment risk factors indicating an intersection.

[0160] (Method for calculating the overall risk of Route G) Next, a method for calculating a total risk based on the total visibility risk of route G and the total surrounding environment risk will be described in detail below with reference to Figures 20 and 21. Figures 20(a) to 20(f) are diagrams for explaining a series of steps (particularly the order (1) to (10)) for calculating the total visibility risk of route G shown in Figure 17. Figure 21 is a table showing the visibility risk and surrounding environment risk for each road link sequence at each virtual point on route G.

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

[0162] (Visibility risk in order (2) of route G) As shown in Figures 20(b) and 21, when a traveling vehicle V is located at a virtual point K1 (virtual point ID: 1) on the road link 100, the visibility risks of the road link sequence (road link ID: 100, 102, 200), the road link sequence (road link ID: 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). 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 order (3) of route G) 20(c) and 21, when a traveling vehicle V is present at a 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 steps (4) to (7) of route G) As shown in Figure 21, when a traveling vehicle V is located at imaginary points K6 to K3 (imaginary point ID: 6 to 3) on the road link 400, the visibility risk of the road link sequence (400) at each of imaginary points K6 to K3 on the road link 400 is (4).

[0165] (Visibility risk in order (8) of route G) 20(d) and 21, when a traveling vehicle V is present at a 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 order (9) of route G) 20(e) and 21, when a traveling vehicle V is present at a virtual point K1 (virtual point ID: 1) on a 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 order (10) of route G) 20(f) and 21, when a traveling vehicle V is at a 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 for the entire route G is evaluated as 6 (6 = 2 + 2 + 2). Furthermore, since there are 10 virtual points K on route G, the average visibility risk is 6 / 10 = 0.6. Also, the total surrounding environment risk for route G is 0. Thus, in this example, the overall risk for route G is 0.6, which is calculated by adding the average visibility risk and the total surrounding environment risk factors.

[0169] Since the total risk of route G is smaller than the total risk of route F, route G is a more suitable route than route F from the perspective of visibility risk and surrounding environment risk. Note that the method for calculating the total risk is not limited to the above method, and various calculation methods are possible. Furthermore, when a route search is performed taking visibility risk into consideration only, the optimal route may be selected by comparing the average visibility risk of each route. For example, the average visibility risk of route G, 0.6, is smaller than the average visibility risk of route F, 1.16, so route G is a more suitable route than route F from the perspective of visibility risk.

[0170] When the control unit 30 of the server 3 calculates the total risk of each route, the control unit 30 extracts multiple road links between the departure point and the destination based on the road network data and information related to the departure point and the destination. Then, the control unit 30 calculates the total risk of each route made up of some of the extracted multiple road links based on the visibility risk data and surrounding environment risk data stored in the storage device 31.

[0171] According to this embodiment, by utilizing visibility risk data, it is possible to perform a route search that takes into account the visibility risk at each virtual point K on the road. In this way, it is possible to perform a safer route search that takes into account the visibility risk. In particular, according to this embodiment, an overall risk that takes into account both the visibility risk and the surrounding environment risk for each route is calculated based on the total visibility risk and the total surrounding environment risk. In this way, it is possible to perform a safer route search that takes into account the visibility risk and the surrounding environment risk, which can contribute to reducing traffic accidents caused by visibility risk and surrounding environment risk.

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

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

[0174] In step S22, the server 3 identifies the virtual points K associated with the extracted road links and the positions of the virtual points K 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 each virtual point K in accordance with the visibility risk of each virtual point K. Here, the visibility risk data stores information regarding the visibility risk of each road link sequence of each virtual point K, so the color scheme of the plot points corresponding to the positions of each virtual point K may be determined in accordance with the average visibility risk or the maximum visibility risk of each virtual point K.

[0175] For example, as shown in FIG. 21 , for virtual point K0 (virtual point ID: 0) on road link 100, the visibility risk data stores a visibility risk of (2, -2) associated with the road link sequence (100, 102) and a visibility risk of (4) associated with the road link sequence (100, 400). In this case, the average visibility risk of virtual point K0 is 1, while the maximum visibility risk is 2. In this way, the color scheme of the plot point corresponding to the position of 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 each plot point colored in the color scheme determined in step S24 on the map 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 risk at each point on the road is visualized is displayed on the display unit of the user terminal 6 (step S26).

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

[0178] According to this embodiment, by utilizing visibility risk data, it is possible to visualize the visibility risk at each point on a map. In this way, through the information on visibility risk visualized on the map, it is possible to efficiently implement traffic safety measures (for example, installation of convex mirrors, hazard warning signs, etc.) to reduce the visibility risk at each point, which can contribute to reducing traffic accidents caused by visibility risk.

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

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

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

[0182] (Processing that displays information about road visibility risks and surrounding environmental risks to vehicle occupants) Next, a series of processes for presenting information on visibility risks of roads around the vehicle and risks of the surrounding environment to the vehicle occupants will be described below with reference to Fig. 24. Fig. 24 is a flowchart for explaining a series of processes for presenting information on visibility risks of roads around the vehicle and risks of the surrounding environment to the vehicle occupants.

[0183] In this example, it is assumed that the vehicle 5 executes a series of processes to present information on visibility risks and surrounding environment risks to the vehicle occupants. In this case, it is assumed that 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 a storage device of the vehicle 5.

[0184] 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 about the area around the current position of the vehicle 5 from the map data, and extracts road links about the current position from the road network data (step S31). In step S32, the vehicle control unit identifies a 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 identifies the visibility risk at each virtual point K based on the visibility risk data, and also identifies 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 surrounding environment risk at each virtual point K on the planned travel route of the vehicle 5 may be identified. In other words, the visibility risk and surrounding environment risk of a virtual point K outside the planned travel route of the vehicle 5 do not need to be identified.

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

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

[0188] In this example, information regarding visibility risks and surrounding environmental risks around vehicle 5 is provided to the occupants of vehicle 5, but either information regarding visibility risks or surrounding environmental risks may be provided to the occupants of vehicle 5.

[0189] Furthermore, when the vehicle 5 is traveling in fully autonomous driving mode, the vehicle control unit identifies information regarding visibility risks and surrounding environment risks around the vehicle 5 through steps S30 to S33 (for example, the presence of visibility risks and / or surrounding environment risks several meters ahead of the vehicle 5). The vehicle control unit may then execute driving control of the vehicle 5 taking into consideration the visibility risks and surrounding environment risks based on the identified information. For example, it may be expected that the vehicle 5 will slow down or stop just before reaching a point where a visibility risk or surrounding environment risk exists. Note that in this case as well, vehicle driving control may be executed based on information regarding either the visibility risks or the surrounding environment risks around the vehicle 5.

[0190] Although the embodiments of the present invention have been described above, the technical scope of the present invention should not be construed as being limited by the description of the present embodiments. The present embodiments are merely examples, and it will be 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 its equivalents. [Explanation of symbols]

[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: Drivetrain system 27: Orientation sensor 30: Control section 31: Storage device 32: Input / output interface 33: Communications Department 34: Input operation section 35:Display section 36: Communication bus 70: Building 72: Traffic lights 500: Map 520: Plot points K, K0 to K7: virtual points L: Road link M: Constituent points O1: Feature object O2: Virtual point object O3: Road surface object S :Geographical coordinate system T: End point F:Start point U: User V: Moving vehicle

Claims

1. 1. An information processing method for generating visibility risk data indicating visibility risks 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 present 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 virtual point objects are virtual point objects corresponding to N (N is a natural number) virtual points 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 around 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; including 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 the shooting direction, shooting position, and 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 arranged 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 around the position of the first virtual point; the plurality of virtual point objects are arranged on the road surface object; the virtual camera is positioned directly above a road surface object that is located 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 having 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 the at least some of the virtual point objects whose visibility has been evaluated; having The information processing method according to claim 2 .

12. 1. An information processing method for generating visibility risk data indicating visibility risks at each point on a road, comprising: acquiring image data showing the surrounding environment at each point on the road; automatically determining a visibility risk at each of the locations based on the image data; generating visibility risk data indicative of visibility risk at each of the locations; Including, A computer-implemented information processing method.

13. 1. An information processing method executed by a computer for performing a route search, comprising: obtaining information about an origin and a destination; a step of executing a route search from the departure point to the destination taking into consideration the visibility risk based on road network data for route search and visibility risk data indicating the visibility risk at each point on a road; Including, the visibility risk data is associated with the road network data via road links; In the visibility risk data, visibility risks at virtual points set on the road links are associated with the road links. Information processing methods.

14. The step of performing a route search includes: a step of executing a route search from the departure point to the destination taking into consideration the visibility risk and the surrounding environment risk based on the road network data, the visibility risk data, and surrounding environment risk data indicating the surrounding environment risk at each point on a road; In the surrounding environment risk data, surrounding environment risk factors indicating risk factors around the virtual point, the virtual point, and the road link are associated with each other. The information processing method according to claim 13.

15. The step of performing a route search includes: extracting a plurality of road links between the departure point and the destination based on the road network data; calculating a total visibility risk for a predetermined route that is formed by at least some of the extracted road links based on the visibility risk data; Calculating a total of the surrounding environment risks along the predetermined route based on the visibility risk data and the surrounding environment risk data; calculating a total risk taking into account both the visibility risk and the surrounding environment risk along the predetermined route based on the total visibility risk and the total surrounding environment risk; Including, The information processing method according to claim 14.

16. 1. An information processing method executed by a computer for visualizing on a map a visibility risk at each point on a road, comprising: displaying the map on a display unit; a step of visualizing the visibility risk on a map displayed on the display unit based on visibility risk data indicating the visibility risk; Including, In the visibility risk data, visibility risks at virtual points set on the road links are associated with the road links. Information processing methods.

17. An information processing method that is executed by a computer and presents predetermined information to a vehicle, comprising: acquiring location information indicating a location of the vehicle; presenting information about the visibility risk around the vehicle to the vehicle based on the location information and visibility risk data indicating the visibility risk at each point on a road; Including, In the visibility risk data, visibility risks at virtual points set on the road links are associated with the road links. Information processing methods.

18. The method further includes a step of presenting information about the visibility risk and the surrounding environment risk around the vehicle to the vehicle based on the visibility risk data and surrounding environment risk data indicating the surrounding environment risk at each point on the road, In the surrounding environment risk data, surrounding environment risk factors indicating risk factors around the virtual point, the virtual point, and the road link are associated with each other.

18. The information processing method according to claim 17.

19. An information processing method executed by a computer for controlling vehicle travel based on visibility risks at various points on a road, comprising: acquiring location information indicating a location of the vehicle; Identifying information about the visibility risk around the vehicle based on the location information and visibility risk data indicating the visibility risk; executing a driving control of the vehicle taking the visibility risk into consideration based on the information regarding the visibility risk; Including, In the visibility risk data, visibility risks at virtual points set on the road links are associated with the road links. Information processing methods.

20. An information processing program that causes a computer to execute the information processing method according to any one of claims 1 to 19.

21. at least one processor; at least one memory that stores 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 19. Information processing device.

22. A data structure indicating visibility risk at each point on a road, Identification information of a virtual point set on a road link; a visibility risk at the virtual point; and Identification information of the road link; and Identification information of the virtual point, the visibility risk at the virtual point, and 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. Data structure.

23. the data structure is associated with road network data for route search via the road link; a predetermined process is executed based on the data structure and the road network data, taking the visibility risk into consideration; 23. The data structure of claim 22.

24. The automatic determination of the visibility risk at a predetermined one of the virtual points includes: generating a three-dimensional object existing around the predetermined virtual point based on the surrounding environment data; placing the three-dimensional object in a geographic coordinate system; arranging a plurality of virtual point objects corresponding to a plurality of virtual points existing around the predetermined virtual point in the geographic coordinate system; acquiring a field of view image captured by a virtual camera corresponding to the predetermined virtual point; assessing visibility of at least some of the virtual point objects shown in the field of view image; Including, 24. A data structure according to claim 22 or 23.

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