Information processing device

Abstract

To provide an information processor capable of providing disaster information on a travel road of a vehicle independently of information sent from the vehicle.SOLUTION: An on-vehicle apparatus serving as an information processor includes: an acquisition unit 221 that acquires a latest satellite image of an area including a travel road of a vehicle at predetermined time intervals; a detection unit 222 that detects at least one of a shape change in the travel road and an obstacle on the travel road on the basis of the satellite image acquired by the acquisition unit 221; and an output unit 224 that outputs a result detected by the detection unit 222.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to an information processing device. [Background technology]

[0002] Patent Document 1 below discloses a technology relating to an information transmission and reception system that provides route guidance when a disaster occurs. This information transmission and reception system includes a mobile device and an information distribution agency. The mobile device is mounted on a vehicle and includes a passage information transmission unit. The passage information transmission unit transmits passage information indicating roads that the vehicle has passed through and size information of the vehicle to the information distribution agency. The information distribution agency updates and distributes disaster road information based on the received passage information and size information. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2010-176506 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the above prior art relies on information transmitted from the vehicle's mobile device to the information distribution agency, and if such information is insufficient, it may not be possible to provide disaster information on the vehicle's route, and there is room for improvement in this regard.

[0005] In consideration of the above, an object of the present invention is to provide an information processing device that can provide disaster information on a route along which a vehicle is traveling without relying on information from the vehicle itself. [Means for solving the problem]

[0006] The information processing device of the present invention described in claim 1 includes an acquisition unit that acquires the latest satellite images of an area including a vehicle's travel path at predetermined time intervals, and a detection unit that detects at least one of a change in the shape of the travel path and an obstacle on the travel path based on the satellite images acquired by the acquisition unit. a rank assigning unit that assigns a risk level rank to each section of the road based on the satellite image acquired by the acquisition unit; Outputting the detection result by the detection unit and outputting the risk level rank assigned by the rank assigning unit in association with the section of the road. an output unit; The rank assigning unit estimates the degree of danger of each section of the road using a machine learning model, and assigns the danger rank based on the estimated degree of danger.

[0007] According to the above configuration, the acquisition unit acquires the latest satellite images of the area including the vehicle's travel path at predetermined time intervals. Further, the detection unit detects at least one of a change in the shape of the travel path and an obstacle on the travel path based on the satellite images acquired by the acquisition unit. death, The output unit outputs the detection result from the detection unit. In addition, based on the satellite image acquired by the acquisition unit, the rank assignment unit assigns a risk rank to the sections into which the road is divided, and the output unit outputs the risk rank assigned by the rank assignment unit in correspondence with the section of the road. Therefore, disaster information regarding at least one of changes in the shape of the road and obstacles on the road can be obtained without relying on information from the vehicle. and information on the risk level of each section of the road. It will be possible to provide. Furthermore, the ranking unit uses a machine learning model to estimate the level of danger for each section of the road and assigns a danger rank based on the estimated level of danger, making it possible to present a more accurate danger rank. The information processing device of the present invention described in claim 2 comprises an acquisition unit that acquires the latest satellite images of an area including a vehicle's travel path at a predetermined time interval, a detection unit that detects changes in the shape of the travel path and obstacles on the travel path based on the satellite images acquired by the acquisition unit, and an output unit that outputs the detection results by the detection unit, wherein the detection unit detects points on the travel path where the vehicle has meandered based on the satellite images acquired by the acquisition unit, and detects changes in the shape of the travel path and obstacles on the travel path at the detected points with higher accuracy than at other points. According to the above configuration, the acquisition unit acquires the latest satellite images of the area including the vehicle's travel path at predetermined time intervals. The detection unit detects changes in the shape of the travel path and obstacles on the travel path based on the satellite images acquired by the acquisition unit, and the output unit outputs the detection results by the detection unit. This makes it possible to provide disaster information about changes in the shape of the travel path and obstacles on the travel path without relying on information from the vehicle. The detection unit also detects points on the travel path where the vehicle meandered based on the satellite images acquired by the acquisition unit, and detects changes in the shape of the travel path and obstacles on the travel path at those detected points with higher accuracy than at other points.

[0008] Claim 3 The information processing device of the present invention described in claim 1 or claim 2 In the configuration described in 1), the detection unit further detects embankments around the travel path based on the satellite image acquired by the acquisition unit.

[0009] According to the above configuration, the detection unit further detects embankments around the roadway based on the satellite image acquired by the acquisition unit, making it possible to provide information about the embankments around the roadway, thereby making it possible to grasp the risk of disasters caused by the embankments.

[0012] The information processing device of the present invention described in claim 4 is 1In the configuration described above, the device includes a reception unit that receives information on the current position of the vehicle and the destination, a route search unit that searches for a route from the current position of the vehicle to the destination based on the information received by the reception unit and the risk rank assigned by the rank assignment unit, the route having a risk rank lower than a predetermined rank, and a route presentation unit that outputs the route searched for by the route search unit.

[0013] According to the above configuration, the reception unit receives information on the current location of the vehicle and the destination. The route search unit searches for a route from the current location of the vehicle to the destination that has a risk level lower than a predetermined level based on the information received by the reception unit and the risk level assigned by the rank assignment unit, and the route presentation unit outputs the route searched by the route search unit. This makes it possible to provide information on low-risk routes without relying on information from the vehicle. [Effects of the Invention]

[0016] As described above, the information processing device of the present invention has the excellent effect of being able to provide disaster information on the route on which a vehicle is traveling without relying on information from the vehicle. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a diagram illustrating an example of a schematic configuration of an information system according to an embodiment. [Figure 2] FIG. 2 is a block diagram showing an example of a hardware configuration of the in-vehicle device according to the embodiment. [Figure 3] 2 is a block diagram showing an example of a functional configuration of a CPU in an ECU according to the embodiment; FIG. [Figure 4] 10 is a flowchart showing an example of the flow of a disaster information output process according to the embodiment. [Figure 5] 10 is a flowchart illustrating an example of the flow of a route guidance process according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0018] An information processing device according to one embodiment of the present invention will be described below with reference to FIGS.

[0019] (Configuration of the embodiment) FIG. 1 shows an example of a schematic configuration of an information system 100 according to this embodiment. As shown in FIG. 1, the information system 100 includes a satellite image server 10 and an on-board device 20. The on-board device 20 is an example of an information processing device, and is mounted on a vehicle 30. The on-board device 20 also functions as a navigation device. The number of vehicles 30 equipped with on-board devices 20 is not limited to the number shown in FIG. 1. The satellite image server 10 and the on-board devices 20 are connected to each other via a network N.

[0020] Satellite images, which are images of the ground periodically taken from the sky by artificial satellites, are sequentially stored in the satellite image server 10. The satellite image server 10 also stores satellite images, the date and time the satellite images were taken, and the location where the satellite images were taken, in association with each other, making it possible to provide the latest satellite images for each location.

[0021] Fig. 2 is a block diagram showing an example of the hardware configuration of the in-vehicle device 20 according to this embodiment. As shown in Fig. 2, the in-vehicle device 20 includes an ECU (Electrical Control Unit) 22, a GPS (Global Positioning System) device 24, and a user interface (abbreviated as "user I / F" in Fig. 2) 26.

[0022] The GPS device 24 includes an antenna (not shown) for receiving GPS signals and measures the current position of the vehicle 30 (see FIG. 1). The user interface 26 is an interface used when the user uses the in-vehicle device 20, and includes an input unit and a display unit. As an example, the user interface 26 is a liquid crystal display equipped with a touch panel that allows the user to perform touch operations.

[0023] The ECU 22 includes a CPU 22A (Central Processing Unit: processor), a ROM (Read Only Memory) 22B, a RAM (Random Access Memory) 22C, a storage 22D, a communication interface (abbreviated as "communication I / F" in FIG. 2) 22E, and an input / output interface (abbreviated as "input / output I / F" in FIG. 2) 22F. The CPU 22A, the ROM 22B, the RAM 22C, the storage 22D, the communication interface 22E, and the input / output interface 22F are connected to each other via a bus 22Z so as to be able to communicate with each other.

[0024] The CPU 22A is a central processing unit that executes various programs and controls each part. That is, the CPU 22A reads a program from the ROM 22B or the storage 22D and executes the program using the RAM 22C as a work area.

[0025] The ROM 22B stores various programs and various data. The RAM 22C temporarily stores programs or data as a working area. The storage 22D is configured with a storage device such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive), and stores various programs and various data. In this embodiment, the ROM 22B or the storage 22D stores a disaster information output program, a safe route guidance program, and a disaster information reference program.

[0026] The communication interface 22E is an interface for communicating with the satellite image server 10 (see FIG. 1) and the like (in other words, an interface for connecting to the network N). For this communication, a wireless communication standard such as 4G, 5G, or Wi-Fi (registered trademark) is used.

[0027] The input / output interface 22F is an interface for communicating with each device mounted on the vehicle 30 (see FIG. 1). In the in-vehicle device 20 of this embodiment, a GPS device 24 and a user interface 26, for example, are connected to the ECU 22 via the input / output interface 22F.

[0028] An example of the functional configuration of the CPU 22A is shown in a block diagram in Fig. 3. As shown in Fig. 3, the CPU 22A has, as its functional configuration, an acquisition unit 221, a detection unit 222, a rank assignment unit 223, an output unit 224, a reception unit 225, a route search unit 226, and a route presentation unit 227.

[0029] The acquisition unit 221, detection unit 222, rank assignment unit 223, and output unit 224 shown in Fig. 3 are realized by the CPU 22A shown in Fig. 2 reading and executing a disaster information output program stored in the ROM 22B or storage 22D. Also, the reception unit 225, route search unit 226, and route presentation unit 227 shown in Fig. 3 are realized by the CPU 22A shown in Fig. 2 reading and executing a safe route guidance program stored in the ROM 22B or storage 22D.

[0030] 3 acquires the latest satellite images of the area including the vehicle's travel path at predetermined time intervals. The latest satellite images are acquired from the satellite image server 10 (see FIG. 1).

[0031] The detection unit 222 detects changes in the shape of the road and obstacles on the road based on the satellite image acquired by the acquisition unit 221. As an example, the detection unit 222 uses a known image recognition technology to compare a pre-stored image of only the road with the latest satellite image acquired by the acquisition unit 221 to detect changes in the shape of the road and obstacles on the road. Note that changes in the shape of the road include, for example, a collapsed highway, and obstacles on the road include soil and sand caused by a landslide, falling rocks, flying objects, etc. Vehicles and people traveling on the road are not included in the obstacles on the road.

[0032] In this embodiment, the detection unit 222 further detects embankments around the travel path based on the satellite images acquired by the acquisition unit 221. As an example, the detection unit 222 uses a known image recognition technique to detect embankments around the travel path from images of the area around the travel path among the satellite images acquired by the acquisition unit 221.

[0033] The rank assigning unit 223 assigns a risk rank to each section of the roadway based on the satellite image acquired by the acquisition unit 221. The risk ranks can be classified into, for example, rank A, which indicates a dangerous state where the roadway is completely impassable or where there is a high possibility that the roadway will become impassable in the future, rank B, which indicates a state where the roadway is not classified as rank A and where the roadway is passable but where normal travel is not possible, and rank C, which indicates a state where normal travel is possible, but the ranks may be further subdivided.

[0034] In the present embodiment, as an example, the rank assigning unit 223 estimates the risk level of each section of the road using a machine learning model and assigns a risk level rank based on the estimated risk level. To further explain, the rank assigning unit 223 estimates the risk level of each section of the road by inputting the latest satellite image acquired by the acquisition unit 221 into a machine learning model that has been trained using the risk level of the road and satellite imagery as a data set.

[0035] The output unit 224 outputs the detection result obtained by the detection unit 222. Furthermore, the output unit 224 also outputs the risk level rank assigned by the rank assigning unit 223 in association with the section of the road.

[0036] The reception unit 225 also receives information on the current position and destination of the vehicle 30. As an example, the reception unit 225 receives the measurement value of the GPS device 24 as the current position of the vehicle 30, and receives information on the destination input using the user interface 26.

[0037] The route searching unit 226 searches for a route from the current position of the vehicle 30 to the destination, which has a risk level lower than a predetermined level, based on the information received by the receiving unit 225 and the risk level rank assigned by the rank assigning unit 223. The route presenting unit 227 outputs the route searched by the route searching unit 226.

[0038] (Operation of the embodiment) Next, the operation of the in-vehicle device 20 shown in FIG. 2 will be described.

[0039] An example of the flow of the disaster information output process is shown in a flowchart in Fig. 4. The CPU 22A reads a disaster information output program from the ROM 22B or the storage 22D, loads it into the RAM 22C, and executes it, thereby performing the disaster information output process. Note that the process shown in Fig. 4 is executed, for example, when an ignition switch (not shown) of the vehicle 30 is turned on.

[0040] First, the CPU 22A acquires the latest satellite image from the satellite image server 10 (step S101). Next, the CPU 22A performs a process of detecting changes in the shape of the road, obstacles on the road, and embankments around the road, based on the satellite image acquired in step S101 (step S102).

[0041] Next, the CPU 22A assigns a risk level rank to each section of the road based on the detection result of step S102 (step S103). In step S103, the CPU 22A, for example, estimates the risk level of each section of the road using a machine learning model, and assigns a risk level rank based on the estimated risk level.

[0042] Next, the CPU 22A outputs the detection result of step S102 and information on the risk level rank assigned in step S103 to the storage 22D (step S104). Here, the risk level rank is associated with the section of the road and output to the storage 22D. By the processing of step S104, the detection result of step S102 and information on the risk level rank assigned in step S103 are stored in the storage 22D.

[0043] Next, the CPU 22A determines whether the time that has elapsed since the execution of step S104 is less than a set time (a pre-set time) (step S105). If the time that has elapsed since the execution of step S104 has reached the set time (step S105: N), the CPU 22A repeats the process from step S101. If the time that has elapsed since the execution of step S104 is less than the set time (step S105: Y), the CPU 22A determines whether an end instruction has been issued based on whether an ignition switch (not shown) has been turned off or the like (step S106).

[0044] If there is no instruction to end (step S106: N), the CPU 22A returns to the process of step S 105. If there is an instruction to end (step S106: Y), the CPU 22A ends the process based on the disaster information output program.

[0045] An example of the flow of the route guidance process is shown in a flowchart in Fig. 5. The route guidance process is performed by the CPU 22A reading out a safe route guidance program from the ROM 22B or the storage 22D, expanding it in the RAM 22C, and executing it. When destination information is input from the user interface 26, execution of the route guidance process shown in Fig. 5 is started.

[0046] First, the CPU 22A receives information about the destination and receives information from the GPS device 24, i.e., information about the current position of the vehicle 30 (step S201). Next, the CPU 22A searches for a route from the current position of the vehicle 30 to the destination that has a risk level lower than a predetermined level (for example, the above-mentioned B level) based on the information about the current position of the vehicle 30 and the destination, and the risk level stored in the storage 22D (step S202). Next, the CPU 22A displays information about the route found in step S202 on a map on the liquid crystal display of the user interface 26 (step S203).

[0047] Next, the CPU 22A determines whether or not an instruction to end the display output of the route information has been received based on an operation of the user interface 26 or the like (step S204). If an instruction to end the display output of the route information has not been received (step S204: N), the CPU 22A waits until an instruction to end the display output of the route information is received. If an instruction to end the display output of the route information has been received (step S204: Y), the CPU 22A ends the display output of the route information and ends the processing based on the safe route guidance program.

[0048] 2, when an operation to refer to disaster information for a predetermined area (e.g., a surrounding area) is performed from the user interface 26, the CPU 22A reads a disaster information reference program from the ROM 22B or the storage 22D, expands it into the RAM 22C, and executes it, thereby performing a disaster information display process. Although not shown in a flowchart, when the disaster information reference program is executed, the CPU 22A reads data stored in the storage 22D and displays, on the liquid crystal display of the user interface 26, information on changes in the shape of the travel path and obstacles on the travel path for the relevant area, information on embankments around the travel path, and information on risk levels associated with sections of the travel path. When an operation to end the reference to disaster information is performed from the user interface 26, the CPU 22A ends the display process of disaster information on the liquid crystal display of the user interface 26.

[0049] As described above, according to this embodiment, it is possible to provide disaster information on the route on which a vehicle is traveling without relying on information from the vehicle.

[0050] Furthermore, in this embodiment, changes in the shape of the road and obstacles on the road are detected based on satellite images without relying on information from the vehicle, so it is possible to detect things that are difficult to detect from wheel speed data obtained by communication from the vehicle, for example. To further explain, since vehicle drivers generally drive in a way that avoids small changes in the shape of the road surface and small obstacles such as potholes, it is difficult to detect small changes in the shape of the road surface and small obstacles from wheel speed data. In contrast, in this embodiment, changes in the shape of the road and obstacles on the road are detected based on satellite images, so it is possible to detect small changes in the shape of the road surface and small obstacles.

[0051] In addition, in this embodiment, it is possible to provide information about embankments around the vehicle's driving path, allowing the driver of the vehicle 30 shown in Figure 1 to understand the risk of disasters caused by embankments.

[0052] Furthermore, in this embodiment, it is possible to provide information on the risk level of each section of the road, so the driver of the vehicle 30 can easily understand the risk level of each section. Furthermore, in this embodiment, the risk level is estimated using a machine learning model, so it is possible to present a more accurate risk level rank. Additionally, in this embodiment, the rank assigning unit 223 (see FIG. 3) can estimate the risk level by predicting, for example, landslides, the spread of flooding, etc., using a machine learning model, so it is possible to assign a more accurate risk level rank.

[0053] Furthermore, in this embodiment, it is possible to provide information on routes with low risk, so the driver of the vehicle 30 can easily know safer routes and drive safely.

[0054] (Supplementary explanation of the embodiment) In the above embodiment shown in Figures 1 to 5, an in-vehicle device 20 is applied as the information processing device, but the information processing device may also be, for example, a server that is not mounted on a vehicle and is capable of outputting information to an in-vehicle device or a user terminal.

[0055] Furthermore, in the above embodiment, satellite images taken by artificial satellites are stored sequentially in the satellite image server 10, and the in-vehicle device 20 acquires the latest satellite images from the satellite image server 10. However, for example, the configuration may be such that satellite images taken by artificial satellites are stored sequentially directly in the storage 22D of the in-vehicle device 20, and the CPU 22A acquires the latest satellite images from a database of these satellite images at predetermined time intervals.

[0056] Furthermore, in the above embodiment, the detection unit 222 detects both changes in the shape of the road and obstacles on the road based on the satellite image acquired by the acquisition unit 221, but as a variation of the above embodiment, the detection unit 222 may detect either changes in the shape of the road or obstacles on the road based on the satellite image acquired by the acquisition unit 221.

[0057] Furthermore, in the above embodiment, the detection unit 222 detects embankments around the driving path based on the satellite image acquired by the acquisition unit 221, but as a variation of the above embodiment, the detection unit 222 may be configured not to detect embankments around the driving path.

[0058] Furthermore, in the above embodiment, the vehicle-mounted device 20 has a rank assigning unit 223 that assigns a risk level rank to each section of the road based on the satellite image acquired by the acquisition unit 221. However, as a variation of the above embodiment, the vehicle-mounted device 20 may also be configured not to have the rank assigning unit 223.

[0059] Furthermore, in the above embodiment, the rank assigning unit 223 estimates the degree of danger of each section of the road using a machine learning model and assigns a danger rank based on the estimated degree of danger, but as a modification of the above embodiment, the rank assigning unit 223 may assign a danger rank based on the magnitude of a change in the shape of the road and the size of an obstacle on the road as determined from the satellite image acquired by the acquisition unit 221. Furthermore, as another modification, the rank assigning unit 223 may assign a danger rank based on the position of a change in the shape of the road and the position of an obstacle on the road as determined from the satellite image acquired by the acquisition unit 221.

[0060] Furthermore, in the above embodiment, the in-vehicle device 20 has a route search unit 226 that searches for a route from the current position of the vehicle 30 to the destination based on the information received by the reception unit 225 and the risk rank assigned by the rank assignment unit 223, and the route has a risk rank lower than a predetermined rank. However, as a variation of the above embodiment, the in-vehicle device 20 may also be configured not to have the route search unit 226.

[0061] Furthermore, as a variation of the above embodiment, the CPU 22A may be configured to detect points in the satellite image where the vehicle has meandered based on the latest satellite image obtained from the satellite image server 10, and to detect changes in the shape of the road and obstacles on the road at the detected points with higher accuracy than at other points.

[0062] In addition, various processors other than a CPU may execute the processes executed by the CPU 22A after reading software (programs) in the above-described embodiment. Examples of processors in this case include dedicated electrical circuits, such as programmable logic devices (PLDs) (such as field-programmable gate arrays (FPGAs)) whose circuit configuration can be changed after fabrication, and application-specific integrated circuits (ASICs) that are processors with circuit configurations specifically designed to execute specific processes. Each process may be executed by one of these various processors, or by a combination of two or more processors of the same or different types (e.g., multiple FPGAs, or a combination of a CPU and an FPGA). The hardware structure of these various processors is, more specifically, an electrical circuit that combines circuit elements such as semiconductor devices.

[0063] The disaster information output program, the safe route guidance program, and the disaster information reference program described in the above embodiments may be provided in a form recorded on a recording medium such as a CD-ROM (Compact Disc Read Only Memory), a DVD-ROM (Digital Versatile Disc Read Only Memory), a USB (Universal Serial Bus) memory, etc. The programs may also be downloaded from an external device via a network.

[0064] The above-described embodiment and the above-described modifications can be implemented in appropriate combinations.

[0065] The above describes one example of the present invention, but the present invention is not limited to the above, and it goes without saying that the present invention can be implemented in various modified forms within the scope of the gist of the present invention. [Explanation of symbols]

[0066] 20 In-vehicle device (information processing device) 30 vehicles 221 Acquisition Department 222 Detection unit 223 Ranking Department 224 Output Section 225 Reception 226 Route Search Unit 227 Route presentation section

Claims

1. an acquisition unit that acquires the latest satellite images of an area including a vehicle travel path at predetermined time intervals; a detection unit that detects at least one of a change in the shape of the travel path and an obstacle on the travel path based on the satellite image acquired by the acquisition unit; a rank assigning unit that assigns a risk level rank to each section of the road based on the satellite image acquired by the acquisition unit; an output unit that outputs the detection result by the detection unit and outputs the risk level rank assigned by the rank assigning unit in association with the section of the road; Equipped with The ranking unit estimates a degree of danger for each section of the road using a machine learning model, and assigns the degree of danger based on the estimated degree of danger.

2. an acquisition unit that acquires the latest satellite images of an area including a vehicle travel path at predetermined time intervals; a detection unit that detects a change in the shape of the travel path and an obstacle on the travel path based on the satellite image acquired by the acquisition unit; an output unit that outputs a detection result by the detection unit; Equipped with The detection unit detects points on the road where the vehicle has meandered based on the satellite image acquired by the acquisition unit, and detects changes in the shape of the road and obstacles on the road at the detected points with higher accuracy than at other points.

3. The information processing device according to claim 1 or 2, wherein the detection unit further detects embankments around the road based on the satellite image acquired by the acquisition unit.

4. a reception unit that receives information on the current location of the vehicle and the destination; a route search unit that searches for a route from the current position of the vehicle to the destination, the route having a risk level lower than a predetermined level, based on the information received by the reception unit and the risk level assigned by the rank assigning unit; a route presentation unit that outputs the route searched by the route search unit; The information processing device according to claim 1 , comprising:

Images