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

Abstract

To provide a technology that can contribute to reducing accidents involving work vehicles by appropriately setting insurance premiums based on the driving conditions of the work vehicles. [Solution] The information processing device disclosed herein is an information processing device for setting automobile insurance premiums for work vehicles 500. This information processing device includes an acquisition unit that acquires detection information, which is information detected by a detection device 510 installed on the work vehicle 500, an extraction unit that extracts driving conditions for the work vehicle 500 based on the detection information acquired by the acquisition unit, and a setting unit that sets insurance premiums based on the driving conditions extracted by the extraction unit. The setting unit sets insurance premiums by analyzing driving conditions for specified work performed using the work vehicle 500.

Description

[Technical Field]

[0001] The present invention relates to an information processing device, an information processing method, and an information processing program for setting automobile insurance premiums for work vehicles. [Background technology]

[0002] In recent years, it has become known that in vehicles such as automobiles, driving skills are evaluated and diagnosed by acquiring data on the driver while driving the vehicle and the driver's operational behavior while driving the vehicle.

[0003] Furthermore, a method has been proposed for setting automobile insurance premiums using such evaluation and diagnosis of driving skills, in order to raise awareness of safe driving and reduce traffic accidents.

[0004] For example, Patent Document 1 discloses a technology that acquires location information from a mobile terminal carried by a driver and derives a discount rate for the driver when signing up for automobile insurance based on the results of a diagnosis of the driver's driving based on that information. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Publication No. 2018-147211 Summary of the Invention [Problem to be solved by the invention]

[0006] For work vehicles such as excavators and forklifts, it is important to raise the safety awareness of the drivers who operate and drive these vehicles, and to improve the safety equipment installed on these vehicles. Therefore, it may be possible to offer an incentive in the form of a discount on the insurance premiums of the insured for such work vehicles based on the driving conditions of the vehicle.

[0007] According to the technology described in Patent Document 1, a driving diagnosis is performed based on acquired location information, such as driving operations during acceleration and deceleration, driving operations during right and left turns, vehicle travel data and vehicle behavior during acceleration and deceleration, and vehicle travel data and vehicle behavior during right and left turns, and an insurance premium discount is introduced based on the results. Therefore, it seems possible to encourage safe driving by encouraging drivers to obtain more favorable discount rates. However, with work vehicles such as excavators and forklifts, work is performed according to the vehicle's purpose. Therefore, setting insurance premiums based on driving diagnosis based on vehicle location information may not fully reflect the safety awareness of the driver operating the work vehicle or the impact of safety equipment installed on the work vehicle, which may result in the insurance premium discount not being fully effective in reducing work vehicle accidents.

[0008] An object of the present disclosure is to provide a technology that can contribute to reducing accidents involving work vehicles by appropriately setting insurance premiums based on the driving conditions of the work vehicles. [Means for solving the problem]

[0009] The information processing device disclosed herein is an information processing device for setting automobile insurance premiums for work vehicles. The information processing device includes an acquisition unit that acquires detection information detected by a predetermined detection device installed on the work vehicle, an extraction unit that extracts driving conditions for the work vehicle based on the detection information acquired by the acquisition unit, and a setting unit that sets the insurance premium based on the driving conditions extracted by the extraction unit. The setting unit sets the insurance premium by analyzing the driving conditions for predetermined work performed using the work vehicle.

[0010] Here, the above-mentioned work vehicle is equipped with safety equipment using a detection device to reduce accidents. For work vehicles equipped with such safety equipment, the above-mentioned information processing device sets insurance premiums based on the driving status of the vehicle, allowing insurance premiums to be set appropriately for specific work performed using the work vehicle, thereby contributing to a reduction in accidents involving the work vehicle.

[0011] In the above information processing device, the detection device may include an imaging device and / or a distance sensor that captures images of the work vehicle's surroundings. The extraction unit extracts, as the driving state, the presence or absence of obstacles, including people, around the work vehicle that may be detected by the detection device in a monitored area monitored by the detection device during the work by the work vehicle. The setting unit may set the insurance premium based on the number of times the obstacle appears around the work vehicle, so that the insurance premium decreases the fewer the number of times the obstacle appears. This fully utilizes the incentive of an insurance premium discount to reduce work vehicle accidents. In this case, the monitored area may be set in advance to include the work vehicle's travel area and turning area. By setting the monitored area in advance by the work vehicle user in this way, excessive detection and erroneous detection of obstacles can be minimized when the area around the work vehicle is monitored at a work site where the work vehicle is used. Furthermore, in this case, the monitored area may be updated and set while the work vehicle is traveling based on a predetermined identification that defines a predetermined non-target area detected by the detection device, while excluding an area defined by the identification. Note that the above-mentioned identification may be, for example, a gate or a marker displayed on the floor. In this way, by dynamically setting the monitored area while the work vehicle is traveling, excessive detection and erroneous detection of obstacles can be more effectively suppressed.

[0012] In the information processing device disclosed herein, the detection device may include a photographing device that photographs an occupant of the work vehicle, the extraction unit may extract, as the driving state, driving behavior of the occupant of the work vehicle from photographed image data representing an image of the occupant, and the setting unit may set the insurance premium by comparing the occupant's driving behavior of the work vehicle with a predetermined safe driving behavior pattern. This fully utilizes the incentive of a discount on insurance premiums to reduce work vehicle accidents and also reduces the recurrence of risky behavior. In this case, the safe driving behavior pattern may be pointing and calling that the occupant may perform before starting work with the work vehicle, the extraction unit may extract, as the driving behavior, the pointing behavior and facial direction of the occupant, and the setting unit may set the insurance premium based on the degree of match between the occupant's pointing and facial direction and the pointing and calling pattern.

[0013] In the information processing device of the present disclosure, the detection device may be configured to include a photographing device that photographs an occupant of the work vehicle, the extraction unit may extract, as the driving state, driving behavior of the work vehicle by the occupant and personal identification information of the occupant from photographed image data representing an image of the occupant, and the setting unit may set the insurance premium based on the driving behavior of the occupant of the work vehicle and the personal identification information of the occupant. In this way, by automatically extracting the personal identification information of the occupant from the photographed image data, it is possible to set the insurance premium while easily reflecting the past performance of each occupant.

[0014] The information processing device of the present disclosure may further include a providing unit that provides the driver of the work vehicle with information on the insurance premium fluctuation factors based on the driving condition. In this case, the providing unit notifies the driver of the insurance premium fluctuation factors using a predetermined output device installed in the work vehicle each time the fluctuation factors occur. This makes it possible to raise safety awareness among the driver of the work vehicle in real time, from the perspective of insurance premiums, about reducing accidents involving the work vehicle.

[0015] The present disclosure can also be viewed as an information processing method by a computer. That is, the information processing method of the present disclosure is an information processing method for setting automobile insurance premiums for work vehicles, in which a computer executes an acquisition step of acquiring detection information detected by a predetermined detection device installed on the work vehicle, an extraction step of extracting driving conditions for the work vehicle based on the detection information acquired in the acquisition step, and a setting step of setting the insurance premium based on the driving conditions extracted in the extraction step. Then, in the setting step, the computer sets the insurance premium by analyzing the driving conditions for predetermined work performed using the work vehicle.

[0016] In the above information processing method, the detection device may be configured to include an imaging device that captures images of the surroundings of the work vehicle, and / or a distance measurement sensor, and in the extraction step, the computer may extract, as the driving state, the presence or absence of obstacles, including people, around the work vehicle that may be detected by the detection device during the work by the work vehicle in the monitored area monitored by the detection device, and in the setting step, set the insurance premium based on the number of times the obstacle appears around the work vehicle, so that the lower the number of times the obstacle appears, the lower the insurance premium.

[0017] Furthermore, in the information processing method disclosed herein, the detection device may be configured to include a photographing device that photographs the occupant of the work vehicle, and in the extraction step, the computer may extract the driving behavior of the occupant of the work vehicle from the photographed image data representing an image of the occupant as the driving state, and in the setting step, set the insurance premium by comparing the driving behavior of the occupant of the work vehicle with a predetermined, specified safe driving behavior pattern.

[0018] The present disclosure can also be understood from the perspective of an information processing program. That is, the information processing program of the present disclosure is an information processing program for setting automobile insurance premiums for work vehicles, and causes a computer to execute an acquisition step of acquiring detection information detected by a predetermined detection device installed on the work vehicle, an extraction step of extracting driving conditions for the work vehicle based on the detection information acquired in the acquisition step, and a setting step of setting the insurance premium based on the driving conditions extracted in the extraction step. Then, in the setting step, the computer sets the insurance premium by analyzing the driving conditions for predetermined work performed using the work vehicle.

[0019] In the above information processing program, the detection device is configured to include an imaging device that captures images of the surroundings of the work vehicle and / or a distance measurement sensor, and the computer is caused to extract, as the driving state, the presence or absence of obstacles, including people, around the work vehicle that may be detected by the detection device while the work vehicle is working in a monitoring area monitored by the detection device in the extraction step, and to set, based on the number of times the obstacle appears around the work vehicle, the insurance premium is set so that the fewer the number of times the obstacle appears, the lower the insurance premium. Let me Good too.

[0020] In the information processing program of the present disclosure, the detection device is configured to include a photographing device that photographs an occupant of the work vehicle, and the computer is caused to extract, as the driving state, driving behavior of the work vehicle by the occupant from photographed image data representing an image of the occupant in the extraction step, and to set the insurance premium by comparing the driving behavior of the occupant of the work vehicle with a predetermined safe driving behavior pattern in the setting step. Let me Good too. [Effects of the Invention]

[0021] According to the present disclosure, by appropriately setting insurance premiums based on the driving conditions of work vehicles, it is possible to contribute to reducing accidents involving the work vehicles. [Brief explanation of the drawings]

[0022] [Figure 1] 1 is a diagram illustrating a schematic configuration of an information processing system according to a first embodiment. [Figure 2] FIG. 2 is a diagram showing in more detail the components of a server included in the information processing system according to the first embodiment, as well as the components of a user terminal that communicates with the server and a work vehicle. [Figure 3] 1 is a diagram showing a schematic configuration of a work vehicle according to a first embodiment. [Figure 4] FIG. 1 is a first diagram illustrating an example of the flow of operations of the information processing system according to the first embodiment. [Figure 5] FIG. 10 is a diagram for explaining a monitoring target area that can be set in advance. [Figure 6] FIG. 3 is a diagram illustrating an example of an insurance premium setting table in the first embodiment. [Figure 7] FIG. 2 is a second diagram illustrating the flow of operations of the information processing system in the first embodiment. [Figure 8] FIG. 3 is a diagram illustrating an example of a screen displayed on a display device of a work vehicle regarding fluctuations in insurance premiums based on driving conditions in the first embodiment. [Figure 9] FIG. 10 is a diagram showing in more detail the components of a server included in an information processing system in a modified example of the first embodiment, as well as the components of a user terminal that communicates with the server and a work vehicle. [Figure 10] FIG. 10 is a diagram showing in more detail the components of a server included in an information processing system according to a second embodiment, as well as the components of a user terminal that communicates with the server and a work vehicle. [Figure 11] FIG. 10 is a first diagram illustrating an example of the flow of operations of the information processing system according to the second embodiment. [Figure 12]FIG. 10 is a diagram for explaining the classification result obtained from an input to a pre-training model in the second embodiment, and the neural network that constitutes the pre-training model. [Figure 13] FIG. 10 is a diagram illustrating the positions of skeletal parts of a working vehicle's occupant user identified by a pre-learning model in the second embodiment. [Figure 14] FIG. 10 is a diagram illustrating an example of an insurance premium setting table in the second embodiment. [Figure 15] FIG. 10 is a second diagram illustrating the flow of operations of the information processing system according to the second embodiment. [Figure 16] FIG. 10 is a diagram illustrating an example of a screen displayed on a display device of a work vehicle, showing fluctuations in insurance premiums based on driving conditions, in the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0023] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. The configurations of the following embodiments are examples, and the present disclosure is not limited to the configurations of the embodiments.

[0024] First Embodiment An overview of the information processing system in the first embodiment will be described with reference to FIG. 1. FIG. 1 is a diagram showing the schematic configuration of the information processing system in this embodiment. The information processing system 100 according to this embodiment is configured to include a network 200, a server 300, a user terminal 400, and a work vehicle 500. The information processing system of the present disclosure is a system for setting automobile insurance premiums for the work vehicle 500, and the setting of the premiums is executed by the server 300. In the following description, among the users who use the information processing system 100, a user who drives the work vehicle 500 will be referred to as a passenger user, and a user who manages the work vehicle 500 as a business operator will be referred to as a business user. In the example shown in this embodiment, the business user may own the user terminal 400.

[0025] Network 200 is, for example, an IP network. As long as network 200 is an IP network, it may be wireless, wired, or a combination of wireless and wired. For example, in the case of wireless communication, user terminal 400 and work vehicle 500 may access a wireless LAN access point (not shown) and communicate with server 300 via a LAN or WAN. Furthermore, network 200 is not limited to these examples and may be, for example, a public switched telephone network, an optical fiber line, an ADSL line, a satellite communication network, etc.

[0026] Server 300 is connected to user terminal 400 and work vehicle 500 via network 200. Note that for simplicity of explanation, one server 300, one user terminal 400, and one work vehicle 500 are shown in FIG. 1, but it goes without saying that this is not limited to these.

[0027] Server 300 may be any electronic device having the processing power for arithmetic and processing operations such as data acquisition, generation, and updating, including personal computers, servers, mainframes, and other electronic devices. That is, server 300 may be configured as a computer having a processor such as a CPU or GPU, a main memory such as RAM or ROM, and an auxiliary memory such as an EPROM, a hard disk drive, or removable media. The removable media may be, for example, a USB memory or a disk recording medium such as a CD or DVD. The auxiliary memory stores an operating system (OS), various programs, various tables, and the like.

[0028] In addition, the server 300 may appropriately use SaaS (Software as a Service), Paas (Platform as a Service), or IaaS (Infrastructure as a Service) using a cloud server, without providing software, hardware, an OS, etc. dedicated to the information processing system 100 of this embodiment.

[0029] The user terminal 400 may be any electronic device such as a mobile terminal owned by a user (business user) who uses the information processing system 100, and may be, for example, a mobile terminal, a tablet terminal, a smartphone, a wearable terminal, a personal computer, or other terminal device.

[0030] Next, the components of the server 300 will be mainly described in detail with reference to Fig. 2. Fig. 2 shows in more detail the components of the server 300 included in the information processing system 100 in the first embodiment, as well as the components of the user terminal 400 that communicates with the server 300 and the work vehicle 500.

[0031] The server 300 has, as functional units, a communication unit 301, a storage unit 302, and a control unit 303. The server 300 loads a program stored in an auxiliary storage device into a working area of ​​a main storage device and executes it. The execution of the program controls each functional unit, thereby realizing each function that matches the predetermined purpose of each functional unit. However, some or all of the functions may be realized by hardware circuits such as ASICs and FPGAs.

[0032] Here, communication unit 301 is a communication interface for connecting server 300 to network 200. Communication unit 301 is configured to include, for example, a network interface board and a wireless communication circuit for wireless communication. Server 300 is connected via communication unit 301 to be able to communicate with user terminal 400, work vehicle 500, and other external devices.

[0033] The memory unit 302 is configured to include a main memory device and an auxiliary memory device. The main memory device is a memory in which the programs executed by the control unit 303 and the data used by the control programs are expanded. The auxiliary memory device is a device in which the programs executed by the control unit 303 and the data used by the control programs are stored. The memory unit 302 pre-stores an insurance premium setting table, which will be described later. The memory unit 302 also stores photographed image data and the like transmitted from the work vehicle 500. The server 300 can acquire data transmitted from the work vehicle 500 and the like via the communication unit 301. Furthermore, in this embodiment, the number of times an obstacle has appeared, which will be described later, is stored in the memory unit 302.

[0034] The control unit 303 is a functional unit that controls the server 300. The control unit 303 can be realized by an arithmetic processing unit such as a CPU. The control unit 303 further includes four functional units: an acquisition unit 3031, an extraction unit 3032, a setting unit 3033, and a provision unit 3034. Each functional unit may be realized by the CPU executing a stored program.

[0035] The acquisition unit 3031 acquires captured image data (detection information of the present disclosure) captured by an imaging device 510 that is installed on the work vehicle 500 and captures images of the surroundings of the work vehicle 500.

[0036] 3 is a diagram showing a schematic configuration of a work vehicle 500 in this embodiment. The work vehicle 500 in this embodiment is a forklift, and is equipped with an imaging device 510, a display device 520, and a communication device 530.

[0037] The photographing device 510 is a device that is installed on the work vehicle 500 and photographs the surroundings of the work vehicle 500, and has the function of accepting input of images such as still images and videos. Specifically, it is realized by a camera that uses an image sensor such as a Charged-Coupled Device (CCD), a Metal-oxide-semiconductor (MOS), or a Complementary Metal-Oxide-Semiconductor (CMOS).

[0038] Display device 520 is a device configured to be able to display image data captured by imaging device 510 and an insurance premium setting notice, which will be described later. Such display device 520 is provided, for example, in the driver's cab of a forklift. This allows the forklift user to check the situation around work vehicle 500 and information on insurance premiums that may be set in real time via display device 520.

[0039] The communication device 530 is a communication interface for connecting the work vehicle 500 to the network 200, and is configured to include, for example, a network interface board and a wireless communication circuit for wireless communication.

[0040] In this way, the work vehicle 500 of this embodiment is designed to reduce accidents by using safety equipment that uses the imaging device 510.

[0041] 2, the extraction unit 3032 extracts the driving state of the work vehicle 500 based on the captured image data (detection information of the present disclosure) acquired by the acquisition unit 3031. In particular, in this embodiment, the extraction unit 3032 extracts the state regarding the presence or absence of obstacles around the work vehicle 500 as the driving state.

[0042] Here, the above-mentioned obstacles are obstacles including people that can be detected by the image capture device 510 while the work vehicle 500 is working in the monitored area, and the extraction unit 3032 can detect obstacles around the work vehicle 500 by, for example, comparing the road surface on which the work vehicle 500 is traveling with the height coordinate value of the detected object. Alternatively, the extraction unit 3032 may detect obstacles around the work vehicle 500 based on well-known technology that uses captured image data from the image capture device 510.

[0043] In this embodiment, the work vehicle 500 may be configured to include a distance measurement sensor as the detection device of the present disclosure. Here, the distance measurement sensor is, for example, a so-called LiDAR (Light Detection and Ranging) sensor that measures the distance to an object in a target space by irradiating the object with laser light and receiving the light reflected from the object, and is capable of detecting obstacles that are relatively far away. In this case, the extraction unit 3032 can extract the presence or absence of obstacles around the work vehicle 500 based on the output from such a distance measurement sensor.

[0044] The above-mentioned monitored area is an area monitored by the imaging device 510 and the distance measuring sensor, and can be set in advance to include the travel area and turning area of ​​the work vehicle 500. The setting of such a monitored area will be described later with reference to FIG.

[0045] The setting unit 3033 sets the automobile insurance premium for the work vehicle 500 based on the driving conditions for a predetermined task performed using the work vehicle 500. In this embodiment, the setting unit 3033 sets the insurance premium based on the number of times an obstacle appears around the work vehicle 500, so that the insurance premium becomes lower the fewer the number of times the obstacle appears. Details of the processing performed by the setting unit 3033 will be explained later with reference to FIG. 6.

[0046] The providing unit 3034 notifies the business user who manages the work vehicle 500 of the automobile insurance premium for the work vehicle 500 that has been set by the setting unit 3033. At this time, the providing unit 3034 can notify the business user of the insurance premium by transmitting the insurance premium to the user terminal 400 of the business user. The providing unit 3034 also notifies the passenger user who drives the work vehicle 500 in real time of the automobile insurance premium for the work vehicle 500 that can be set by the setting unit 3033. At this time, the providing unit 3034 can notify the passenger user of the insurance premium via the display device 520 of the work vehicle 500 by transmitting the insurance premium to the work vehicle 500. Details of the processing performed by the providing unit 3034 will be described later with reference to FIG. 7.

[0047] Here, the user terminal 400 in this embodiment has, as functional units, a communication unit 401, an input / output unit 402, and a storage unit 403. The communication unit 401 is a communication interface for connecting the user terminal 400 to the network 200, and is configured to include, for example, a network interface board and a wireless communication circuit for wireless communication. The input / output unit 402 is a functional unit for displaying information transmitted from the outside via the communication unit 401, and for inputting information when transmitting the information to the outside via the communication unit 401. The storage unit 403 is configured to include a main storage device and an auxiliary storage device, similar to the storage unit 302 of the server 300.

[0048] The input / output unit 402 further includes a display unit 4021, an operation input unit 4022, and an image / audio input / output unit 4023. The display unit 4021 has a function of displaying various information and is realized, for example, by an LCD (Liquid Crystal Display) display, an LED (Light Emitting Diode) display, an OLED (Organic Light Emitting Diode) display, or the like. The operation input unit 4022 has a function of accepting operation input from a user and is realized, specifically, by soft keys or hard keys on a touch panel or the like. The image / audio input / output unit 4023 has a function of accepting input of images such as still images and videos and is realized, specifically, by a camera using an image sensor such as a Charged-Coupled Device (CCD), a Metal-Oxide-Semiconductor (MOS), or a Complementary Metal-Oxide-Semiconductor (CMOS). The image / audio input / output unit 4023 has a function of accepting input and output of audio and is realized, specifically, by a microphone or speaker.

[0049] Next, the flow of operation of the information processing system 100 in this embodiment will be described. Fig. 4 is a first diagram illustrating the flow of operation of the information processing system 100 in this embodiment. Fig. 4 explains the flow of operation between the server 300, user terminal 400, and work vehicle 500 in the information processing system 100 in this embodiment, and the processing executed by the server 300, user terminal 400, and work vehicle 500. The flow shown in Fig. 4 is repeatedly executed at a predetermined cycle while the work vehicle 500 is in operation, during a set period predetermined for setting insurance premiums using the information processing system 100.

[0050] In this embodiment, first, photographed image data, which is a photographed image of the surroundings of the work vehicle 500 photographed by the photographing device 510 of the work vehicle 500, is transmitted to the server 300 (S101).

[0051] In response to this, the server 300 acquires the captured image data transmitted from the work vehicle 500 as detection information (S102).

[0052] Then, server 300 executes an extraction process (S103) to extract the driving state of work vehicle 500. As described above, in this embodiment, the state regarding the presence or absence of obstacles around work vehicle 500 is extracted as the driving state.

[0053] In the processing of S103, the server 300 extracts from the captured image data the status of the presence or absence of obstacles in a predetermined monitoring target area. Note that, as will be explained in a modified example of the first embodiment described later, if the extraction processing is executed by the control device 540 of the work vehicle 500, the setting and updating of the monitoring target area can be executed by the control device 540.

[0054] 5 is a diagram for explaining a monitoring target area that can be set in advance. The monitoring target area includes an area that can be set by selection by the occupant user from among areas detectable by the image capture device 510, and the occupant user can input the selection of the monitoring target area based on, for example, distance information around the work vehicle 500 (for example, generated as a mesh map every 1 meter).

[0055] For example, as shown in FIG. 5 , the occupant user can select the monitoring target area by excluding installations at the work site, such as shelves, included in the area detectable by the image capture device 510 (such installations are objects whose existence the occupant user is fully aware of in advance, and therefore can be excluded), specifically, by excluding the area on the map that includes the installations at the work site. The grayed-out portion in FIG. 5 is the area selected by the user as the monitoring target area. By setting the monitoring target area in advance by the occupant user in this manner, excessive detection and erroneous detection of obstacles can be minimized when the periphery of the work vehicle 500 is monitored at the work site where the work vehicle 500 is used. In other words, when the number of appearances of obstacles around the work vehicle 500 is counted in the process described below, it is possible to prevent installations whose existence the occupant user is fully aware of at the work site from being excessively counted as a major cause of an accident in the work vehicle 500.

[0056] Furthermore, the monitored area may be updated and set while the work vehicle 500 is traveling based on a predetermined identification that defines a predetermined non-target area, while excluding areas defined by the identification. Note that the above-mentioned non-target area is, for example, a pedestrian-only area. Furthermore, the above-mentioned identification is, for example, a gate or a marker displayed on the floor. In this way, by dynamically setting the monitored area while the work vehicle 500 is traveling, it is possible to more effectively suppress excessive detection and erroneous detection of obstacles.

[0057] The process of extracting the presence or absence of obstacles in the monitored area from the captured image data, and the process of extracting the above identification from the captured image data while the work vehicle 500 is traveling, can be performed based on well-known technology.

[0058] 4, in the process of S104, the server 300 determines whether or not an obstacle is present in the monitoring target area based on the driving state extracted in the process of S103. If the determination in the process of S104 is affirmative, the server 300 proceeds to the process of S105, and if the determination in the process of S104 is negative, the server 300 proceeds to the process of S108.

[0059] If the determination in the process of S104 is affirmative, the server 300 then counts the affirmative determination as the number of times the obstacle has appeared (S105). As described above, the flow shown in Fig. 4 is repeatedly executed at a predetermined cycle during a set period that is predetermined for setting insurance premiums using the information processing system 100. Therefore, the server 300 initializes the current number of times the obstacle has appeared to 0 when the flow shown in Fig. 4 is executed for the first time, and updates the current number of times the obstacle has appeared by adding 1 to the current number of times the obstacle has appeared in the process of S105 each time a positive determination is made in the process of S104 during the set period.

[0060] The server 300 then provides the current occurrence count, which was counted and updated in the processing of S105, as a fluctuation factor of the insurance premium (S106). Here, the server 300 transmits the fluctuation factor of the insurance premium to the work vehicle 500.

[0061] In response, the work vehicle 500 acquires the information transmitted from the server 300 and displays it on the display device 520 (corresponding to the output device of the present disclosure) (S107). In other words, the occupant user of the work vehicle 500 is notified of the factors that cause fluctuations in the insurance premium based on the driving state. Note that the output device of the present disclosure is not limited to the display device 520 described above, and may be, for example, an audio device that notifies the occupant user of the work vehicle 500 of the information by voice, as long as it can notify the factors that cause fluctuations in the insurance premium based on the driving state.

[0062] In this way, by notifying the occupant users of the work vehicle 500 of the factors that affect insurance premium fluctuations in real time, it becomes possible to raise the occupant users' safety awareness in real time about reducing accidents on the work vehicle 500 from the perspective of insurance premiums.

[0063] Next, server 300 determines whether the end of the set period has arrived (S108). Here, the set period is a period determined in advance for setting insurance premiums using information processing system 100, and is, for example, a period equivalent to three days of operating time of work vehicle 500 (for example, 18 hours if work vehicle 500 operates for six hours per day). If the determination in S108 is affirmative, server 300 proceeds to S109, and if the determination in S108 is negative, one execution of this flow is terminated.

[0064] If the determination in S108 is affirmative, then server 300 sets the insurance premium based on the cumulative number of appearances during the set period (S109). Here, server 300 calculates the insurance premium based on the number of appearances of the obstacle around work vehicle 500, so that the insurance premium becomes lower the fewer the number of appearances. This will be explained with reference to FIG. 6.

[0065] FIG. 6 is a diagram illustrating an example of an insurance premium setting table in this embodiment. In this embodiment, the insurance premium is set so that the insurance premium at the time of the next contract (for example, the next month, six months later, one year later, three years later, etc.) varies depending on the average number of occurrences per hour. In the example shown in FIG. 6, the insurance premium can be set so that if the average number of occurrences per hour is 0 to 3, the insurance premium at the time of the next contract will be -30%; if the average number of occurrences per hour is 3 to 5, the insurance premium at the time of the next contract will be -20%; and if the average number of occurrences per hour is 5 to 10, the insurance premium at the time of the next contract will be -10%. Note that if the average number of occurrences per hour is 10 to 20, the insurance premium can be set so that there is no change in the insurance premium at the time of the next contract. Furthermore, the insurance premium can be set so that if the average number of occurrences per hour is 20 to 25, the insurance premium at the time of the next contract will be +10%; if the average number of occurrences per hour is 25 to 40, the insurance premium at the time of the next contract will be +20%; and if the average number of occurrences per hour is 40 or more, the insurance premium at the time of the next contract will be +30%.

[0066] In this way, by calculating the insurance premium so that the fewer the number of obstacles that appear around the work vehicle 500, the insurance premium becomes lower, and the incentive of insurance premium discounts is fully effective in reducing accidents involving the work vehicle 500.

[0067] In the process of S109, the insurance premium is set based on the insurance premium setting table shown in Fig. 6 above so that the insurance premium at the time of the next contract (for example, the next month, six months later, one year later, three years later, etc.) varies depending on the average number of occurrences per hour during the set period. For example, if the cumulative number of occurrences during the set 18-hour period is 126, the average number of occurrences per hour during the set period will be 7, so the insurance premium is set so that the insurance premium at the time of the next contract will be -10%. Then, the server 300 transmits the insurance premium set in this way to the user terminal 400 of the business user.

[0068] Then, the user terminal 400 of the business user acquires the information transmitted from the server 300 and displays it on the display unit 4021 (S110). In other words, the business user is provided with an insurance premium based on the driving state.

[0069] Server 300 may also calculate fluctuations in the insurance premium in real time based on the number of times an obstacle appears around work vehicle 500, so that the insurance premium becomes lower the fewer the number of times the obstacle appears. This will be described with reference to Figures 7 and 8.

[0070] FIG. 7 is a second diagram illustrating the operational flow of the information processing system 100 in this embodiment. FIG. 7 explains the operational flow between the server 300, the user terminal 400, and the work vehicle 500 in the information processing system 100 in this embodiment, and the processing executed by the server 300, the user terminal 400, and the work vehicle 500. The flow shown in FIG. 7 is repeatedly executed at a predetermined cycle while the work vehicle 500 is in operation, during a predetermined set period for setting insurance premiums using the information processing system 100. Furthermore, in each process shown in FIG. 7, processes that are substantially the same as those shown in FIG. 4 above are assigned the same reference numerals, and detailed description thereof will be omitted.

[0071] In the flow shown in Figure 7, server 300 calculates the fluctuation in insurance premium in real time based on the current number of appearances counted and updated in the processing of S105 (S1061). Here, server 300 calculates the fluctuation in insurance premium based on the number of times an obstacle has appeared around work vehicle 500 so that the insurance premium becomes lower the fewer the number of appearances, as shown in the insurance premium setting table shown in Figure 6 above.

[0072] Then, the server 300 transmits the insurance premium fluctuation calculated as described above to the work vehicle 500.

[0073] Then, work vehicle 500 acquires the information transmitted from server 300 and displays it on display device 520 (S1071). In other words, the fluctuation in insurance premiums based on the driving state is provided to the occupant user of work vehicle 500. This will be described with reference to FIG. 8.

[0074] FIG. 8 is a diagram illustrating an example of a screen displayed on display device 520 of working vehicle 500 regarding fluctuations in insurance premiums based on driving conditions in this embodiment.

[0075] The screen SC1 shown in Fig. 7(a) displays a message field SC11 regarding fluctuations in insurance premiums based on driving conditions, and in the example shown in Fig. 7(a), a message is displayed indicating that an obstacle has been detected around the work vehicle 500, and further displays the cumulative number of occurrences to date and the current average number of occurrences per hour. According to the insurance premium setting table shown in Fig. 6 above, if the average number of occurrences per hour is 5 to 10, the insurance premium at the time of the next contract will be -10%, so the message field SC11 displays a message indicating that the insurance premium at the time of the next contract will be -10%.

[0076] Similarly, in the example shown in Fig. 7(b), a message is displayed indicating that an obstacle has been detected around the work vehicle 500, and furthermore, the cumulative number of occurrences up to now is displayed. Here, in the example shown in Fig. 7(b), the operation time of the work vehicle 500 since the start of the flow shown in Fig. 4 is less than one hour, so the predicted number of occurrences per hour calculated based on the cumulative number of occurrences up to now is displayed. In the example shown in Fig. 7(b), if the current situation continues, it is predicted that the average number of occurrences per hour will be between 20 and 25, so a message is displayed in message field SC11 indicating that the insurance premium for the next contract will be increased by 10%.

[0077] In this way, by notifying the passenger users of the work vehicle 500 in real time of fluctuations in insurance premiums calculated in real time, it becomes possible to raise the passenger users' safety awareness in real time about reducing accidents on the work vehicle 500 by providing monetary value such as discounts or surcharges on insurance premiums.

[0078] As described above, according to this embodiment, by appropriately setting insurance premiums based on the driving conditions of work vehicles, it is possible to contribute to reducing accidents involving these work vehicles.

[0079] <Modification of the first embodiment> A modified example of the first embodiment will be described with reference to Fig. 9. In the above first embodiment, an example has been described in which the server 300 extracts the driving state of the work vehicle 500 based on the captured image data (detection information of the present disclosure) acquired by the acquisition unit 3031. In contrast, in this modified example, the extraction of the driving state is performed by the work vehicle 500.

[0080] Figure 9 shows in more detail the components of the server 300 included in the information processing system 100 in a modified example of the first embodiment, as well as the components of the user terminal 400 that communicates with the server 300 and the work vehicle 500.

[0081] In this modification, the control unit 303 of the server 300 is configured to have two functional units: a setting unit 3033 and a providing unit 3034. The functions of the acquisition unit 3031 and extraction unit 3032 described in the first embodiment above are provided in the work vehicle 500.

[0082] More specifically, the work vehicle 500 in this modification is equipped with a control device 540. The control device 540 is a functional unit that controls the processing performed by the work vehicle 500. The control device 540 can be realized by an arithmetic processing device such as a CPU. The control device 540 is further configured to have two functional units: an acquisition unit 5041 and an extraction unit 5042. Each functional unit may be realized by the CPU executing a stored program.

[0083] The acquisition unit 5041 then acquires photographed image data (detection information of the present disclosure) captured by an imaging device 510 that is installed on the work vehicle 500 and captures images of the surroundings of the work vehicle 500. The extraction unit 5042 also extracts the driving state of the work vehicle 500 based on the photographed image data (detection information of the present disclosure) acquired by the acquisition unit 5041.

[0084] In this manner, when the extraction of the driving state is performed by the work vehicle 500, the processing of S101 to S103 in the flow shown in Figures 4 and 7 above is performed by the work vehicle 500, and the server 300 sets the insurance premium by obtaining the above driving state extracted by the work vehicle 500.

[0085] Second Embodiment The second embodiment will be described with reference to Figures 10 to 16. In the first embodiment described above, an example was described in which the presence or absence of obstacles, including people, that may be detected in the monitored area around the work vehicle 500 was extracted as the driving state, and the insurance premium was set based on the number of times the obstacle appeared around the work vehicle 500. In contrast, in the present embodiment, an example will be described in which the driving behavior of the occupant of the work vehicle 500 is extracted from captured image data representing an image of the occupant as the driving state, and the insurance premium is set by comparing the occupant's driving behavior of the work vehicle 500 with a predetermined safe driving behavior pattern.

[0086] Here, Figure 10 shows in more detail the components of the server 300 included in the information processing system 100 in the second embodiment, as well as the components of the user terminal 400 that communicates with the server 300 and the work vehicle 500.

[0087] In this embodiment, as shown in FIG. 10, safe driving behavior patterns, which will be described later, are stored in advance in the storage unit 302 of the server 300.

[0088] In this embodiment, the image capturing device 510 of the work vehicle 500 is configured to be able to capture an image of the occupant user.

[0089] 11 is a first diagram illustrating the flow of operation of the information processing system 100 in this embodiment. FIG. 11 explains the flow of operation between the server 300, user terminal 400, and work vehicle 500 in the information processing system 100 in this embodiment, and the processing executed by the server 300, user terminal 400, and work vehicle 500. The flow shown in FIG. 11 is repeatedly executed at a predetermined cycle during a set period that is predetermined for setting insurance premiums using the information processing system 100.

[0090] 4 described in the first embodiment above, first, captured image data captured by the imaging device 510 of the work vehicle 500 is transmitted to the server 300 (S201). Here, the captured image data in this embodiment is image data representing an image of the occupant user of the work vehicle 500.

[0091] In response to this, the server 300 acquires the captured image data transmitted from the work vehicle 500 as detection information (S202).

[0092] Then, server 300 executes an extraction process (S203) to extract the driving state of work vehicle 500. Here, in this embodiment, the driving behavior of the occupant user of work vehicle 500 is extracted as the driving state of work vehicle 500.

[0093] (Extraction process) Here, the extraction process in this embodiment will be described in detail. In the extraction process in this embodiment, captured image data is input into a pre-learning model, and driving behavior of the work vehicle 500 by the occupant user is extracted. The pre-learning model is constructed by learning using data including images of people.

[0094] FIG. 12 illustrates a classification result obtained from an input to a pre-training model in this embodiment and a neural network constituting the pre-training model. In this embodiment, a neural network model generated by deep learning is used as the pre-training model. The pre-training model 30 in this embodiment includes an input layer 31 that receives input of predetermined image data, an intermediate layer (hidden layer) 32 that extracts features representing a person's skeletal information from the image data input to the input layer 31, and an output layer 33 that outputs a classification result based on the features. In the example of FIG. 12, the pre-training model 30 includes one intermediate layer 32, with the output of the input layer 31 input to the intermediate layer 32 and the output of the intermediate layer 32 input to the output layer 33. However, the number of intermediate layers 32 does not need to be limited to one; the pre-training model 30 may include two or more intermediate layers 32.

[0095] 12, each of the layers 31 to 33 includes one or more neurons. For example, the number of neurons in the input layer 31 can be set according to the input image data. The number of neurons in the output layer 33 can be set according to the driving behavior that is the classification result.

[0096] Then, neurons in adjacent layers are connected to each other as appropriate, and weights (connection loads) are set for each connection based on the results of machine learning. In the example of Figure 12, each neuron is connected to all neurons in the adjacent layer, but the neuron connections are not limited to this example and can be set as appropriate.

[0097] Such a pre-training model 30 is constructed by performing supervised learning using training data, which is a combination of image data including an image of a person and image labels indicating the positions of the person's skeletal features. Specifically, the combination of features and labels is provided to a neural network, and the weights of the connections between neurons are tuned so that the output of the neural network is the same as the label. In this way, the features of the training data are learned and a pre-training model for estimating results from inputs is inductively acquired.

[0098] In addition, the image data used to learn to construct the pre-learning model may be an image of a person in a motionless state, or an image of a person driving a work vehicle, etc.

[0099] The pre-trained model 30 may also be constructed by unsupervised learning. For example, domain adaptation, which is a type of transfer learning, can be used for unsupervised learning. This allows a pre-trained model to be acquired without preparing a large amount of labeled training data.

[0100] Then, by inputting captured image data into such a pre-learning model 30, the positions of the skeletal parts of the occupant user of the work vehicle 500 in the captured image data are identified, and based on this, the driving behavior of the occupant user of the work vehicle 500 is extracted.

[0101] 13 is a diagram illustrating the positions of skeletal parts of an occupant user of a work vehicle 500, identified by the pre-learning model 30 in this embodiment. In FIG. 13, the skeletal parts of the occupant user, such as the arms, torso, and face, are represented by points and line segments for the image of the occupant user included in the captured image data.

[0102] In this way, the server 300 can extract the orientation of the occupant user's face (whether the face is facing left, right, front, back, or diagonally) and pointing behavior (whether the arm is pointing left, right, front, back, or diagonally) as the occupant user's driving behavior of the work vehicle 500. In the example shown in Fig. 13, it is extracted that the occupant user's face is facing forward.

[0103] Thus, according to the extraction process of this embodiment, the pre-learning model 30 is used to identify the positions of the skeletal parts of the occupant user in the captured image data and the driving behavior of the occupant user based on this, thereby enabling the driving behavior to be accurately extracted from the captured image data.

[0104] In the above description of the extraction process, an example was described in which the driving behavior of the work vehicle 500 by the occupant user is extracted based on the positions of the skeletal parts of the occupant user. However, in the extraction process of this embodiment, captured image data may be input to the pre-learning model 30, and the driving behavior of the work vehicle 500 by the occupant user may be extracted by identifying the facial direction and arm direction of the occupant user in the captured image data. In this case, the pre-learning model 30 has an input layer 31 that accepts input of predetermined image data, an intermediate layer (hidden layer) 32 that extracts feature amounts representing the facial direction and / or arm direction of the occupant user from the image data input to the input layer 31, and an output layer 33 that outputs an identification result based on the feature amounts. Such a pre-learning model 30 may be constructed, for example, by performing supervised learning using training data that is a combination of image data including an image representing a person and an image label representing the facial direction and / or arm direction of the person.

[0105] Furthermore, the server 300 may extract a preliminary vehicle inspection action performed by the occupant user before getting into the work vehicle 500 as the driving action of the occupant user of the work vehicle 500. In this case, image data before the occupant user gets into the work vehicle 500 is acquired as the captured image data. Furthermore, the server 300 may extract the presence or absence of safety equipment (helmet, safety vest, safety shoes, etc.) worn by the occupant user together with the above driving action.

[0106] Then, returning to FIG. 11, in S204, the server 300 executes a determination process to determine whether the driving behavior of the occupant user of the work vehicle 500 belongs to a safe driving behavior pattern, based on the driving state extracted by the process of S203.

[0107] In the determination process in S204, by comparing the driving behavior of the occupant user extracted by the extraction process with predetermined safe driving behavior patterns stored in the memory unit 302 of the server 300, it can be determined whether or not the driving behavior of the occupant user for the work vehicle 500 belongs to a predetermined safe driving behavior pattern. Note that the above-mentioned safe driving behavior pattern is, for example, pointing and calling that can be performed by the occupant user before work using the work vehicle 500. The above-mentioned pointing and calling is a pointing and confirmation of the operation of the work vehicle 500, and is performed when the forklift, which is the work vehicle 500, starts, stops, or turns, or when the forklift's tines are inserted into a load.

[0108] (Determination process) Here, the details of the determination process in this embodiment will be described. In this embodiment, the pointing behavior and facial direction of the occupant user are extracted as driving behaviors. Then, the server 300 determines whether the extracted pointing behavior and facial direction of the occupant user belong to an implementation pattern of pointing and calling. The above-mentioned implementation patterns of pointing and calling are predetermined and stored in the storage unit 302 of the server 300, and are, for example, a forward pointing behavior when the work vehicle 500 starts, a backward pointing behavior when the work vehicle 500 is reversing, a pointing behavior toward the stop line when the work vehicle 500 is stopped, a pointing behavior to the left or right when the work vehicle 500 is turning, and a pointing behavior toward luggage when inserting the claws of the work vehicle 500 into the luggage.

[0109] Server 300 can then determine whether the pointing action of the occupant user is pointing forward, for example, when work vehicle 500 starts moving. Work vehicle 500 is equipped with vehicle sensors such as a speed sensor and an acceleration sensor, and server 300 can recognize the start, stop, turning, etc. of work vehicle 500 based on the detection values ​​of the vehicle sensors.

[0110] Furthermore, at this time, the server 300 may recognize information regarding the starting, stopping, turning, etc. of the work vehicle 500 based on image data representing the surroundings of the work vehicle 500 captured by the camera device 510 of the work vehicle 500. In this case, the camera device 510 may be positioned so that it can capture images of the surroundings of the work vehicle 500 as well as the occupant user of the work vehicle 500. The image data representing the surroundings of the work vehicle 500 that may be captured by the camera device 510 includes stop lines and their positions related to the starting and stopping of the work vehicle 500, as well as no entry signs and their text, and the server 300 can recognize these through image recognition processing. In this case, the server 300 can recognize stop lines, for example, based on the image data representing the surroundings of the work vehicle 500 captured by the camera device 510, and determine whether the pointing behavior of the occupant user is pointing forward when the work vehicle 500 reaches the stop line.

[0111] The above-mentioned pattern of pointing and naming involves turning the face in the direction in which the pointing action is being performed.

[0112] Therefore, the server 300 determines whether the face of the extracted occupant user is facing in the direction in which the pointing behavior is performed.

[0113] Returning to FIG. 11, if the determination in the process of S204 is affirmative, the server 300 proceeds to the process of S208, and if the determination in the process of S204 is negative, the server 300 proceeds to the process of S205.

[0114] If a negative determination is made in the process of S204, the server 300 then counts the number of times of misconduct, regarding the negative determination, as a driving behavior of the work vehicle 500 by the occupant user being a misconduct that does not belong to a safe driving behavior pattern (S205). As described above, the flow shown in Fig. 11 is repeatedly executed at a predetermined cycle during a set period that is predetermined for setting insurance premiums using the information processing system 100. Therefore, the server 300 initializes the current number of occurrences to 0 the first time the flow shown in Fig. 11 is executed, and each time a negative determination is made in the process of S204 during the set period, the server 300 updates the current number of times of misconduct by adding 1 to the current number of times of misconduct in the process of S205.

[0115] The server 300 then provides the current number of misconducts counted and updated in the process of S205 as a fluctuation factor of the insurance premium (S206). Here, the server 300 transmits the fluctuation factor of the insurance premium to the work vehicle 500.

[0116] In response, the work vehicle 500 acquires the information transmitted from the server 300 and displays it on the display device 520 (corresponding to the output device of the present disclosure) (S207). In other words, the occupant user of the work vehicle 500 is notified of the factors that cause fluctuations in the insurance premium based on the driving state. Note that the output device of the present disclosure is not limited to the display device 520 described above, and may be, for example, an audio device that notifies the occupant user of the work vehicle 500 of the information by voice, as long as it can notify the factors that cause fluctuations in the insurance premium based on the driving state.

[0117] In this way, by notifying the occupant users of the work vehicle 500 in real time of factors that affect insurance premium fluctuations based on the risky behavior of the occupant users, the occupant users will be more aware of safety in terms of reducing accidents on the work vehicle 500 from the perspective of insurance premiums, and the recurrence of subsequent risky behavior will also be reduced.

[0118] Next, server 300 determines whether the end of the set period has arrived (S208). Here, the set period is a period determined in advance for setting insurance premiums using information processing system 100, and is, for example, a period equivalent to three days of operating time of work vehicle 500 (for example, 18 hours if work vehicle 500 operates for six hours a day). If the determination in S208 is affirmative, server 300 proceeds to S209, and if the determination in S208 is negative, one execution of this flow is terminated.

[0119] If the determination in S208 is affirmative, the server 300 then sets the insurance premium based on the cumulative number of misconducts during the set period (S209). Here, the server 300 can calculate the insurance premium so that the lower the number of misconducts, the lower the insurance premium. This will be described with reference to FIG. 14.

[0120] FIG. 14 is a diagram illustrating an example of an insurance premium setting table in this embodiment. In the example shown in FIG. 14(a), the insurance premium is set so that the insurance premium at the time of the next contract (for example, the next month, six months later, one year later, three years later, etc.) varies depending on the average number of misconducts per hour. More specifically, in the example shown in FIG. 14(a), the insurance premium can be set so that if the average number of misconducts per hour is 0, the insurance premium at the time of the next contract will be -30%, if the average number of misconducts per hour is 1 to 2, the insurance premium at the time of the next contract will be -20%, and if the average number of misconducts per hour is 2 to 3, the insurance premium at the time of the next contract will be -10%. Note that if the average number of misconducts per hour is 3 to 5, the insurance premium can be set so that there is no change in the insurance premium at the time of the next contract. The insurance premiums can be set so that if the average number of bad behaviors per hour is 5 to 7, the insurance premium at the time of the next contract will be +10%, if the average number of bad behaviors per hour is 7 to 10, the insurance premium at the time of the next contract will be +20%, and if the average number of bad behaviors per hour is 10 or more, the insurance premium at the time of the next contract will be +30%.

[0121] In this way, by calculating the insurance premium so that the fewer the number of bad behaviors that the occupant user exhibits when driving the work vehicle 500 that do not belong to the safe driving behavior pattern, the insurance premium discount incentive is fully effective in reducing accidents on the work vehicle 500.

[0122] In the example shown in FIG. 14(b), the insurance premium is set based on the degree of match (degree of match) between the occupant user's pointing behavior and facial direction and the pointing and calling implementation pattern. The insurance premium is set so that the higher the safety rank based on the degree of match, the lower the insurance premium. More specifically, in the example shown in FIG. 14(b), if the safety rank based on the degree of match is S, the insurance premium may be set so that the insurance premium at the time of the next contract will be -20%. Here, a safety rank of S refers to a case where the degree of match is 95% or higher. The degree of match may be calculated by dividing the number of times the occupant user's driving behavior of the work vehicle 500 matches the safe driving behavior pattern by the determined total number. Note that, for example, if the occupant user performs a pointing behavior but does not turn their face in the direction of the pointing behavior, when the occupant user's driving behavior of the work vehicle 500 partially matches the safe driving behavior pattern, the number of times the above match may be counted as 0.5. If the safety rank based on the degree of match is A, the insurance premium may be set so that the insurance premium at the time of the next contract will be -10%. If the safety rank based on the degree of match is B, the insurance premium can be set so that there is no change in the insurance premium at the time of the next contract. If the safety rank based on the degree of match is C, the insurance premium at the time of the next contract will be +10%, and if the safety rank based on the degree of match is D, the insurance premium can be set so that the insurance premium at the time of the next contract will be +20%.

[0123] 14(b), in the process of S204, a positive determination is made when the driving behavior of the work vehicle 500 by the occupant user completely matches the safe driving behavior pattern, and a negative determination is made when the driving behavior of the work vehicle 500 by the occupant user partially matches the safe driving behavior pattern (for example, when the occupant user performs a pointing behavior but does not turn their face in the direction of the pointing behavior) or when there is no match. Then, in the process of S205, the degree of match can be calculated as described above.

[0124] In this case, the insurance premium may be set taking into consideration the personal identification information of the occupant user. Specifically, the storage unit 302 of the server 300 stores, for each occupant user, the number of times that the driving behavior up to that point has been determined to match the safe driving behavior pattern and the total number of times that determination has been made as the past performance of each occupant user, and in the process of S205 described above, these numbers may be updated to calculate the degree of match. This makes it possible to set the insurance premium by reflecting the past performance of each occupant user.

[0125] In this case, the extraction process of S203 may extract personal identification information of the occupant user from the captured image data. For example, the server 300 may extract facial recognition information of the occupant user as the personal identification information of the occupant user. In this case, the personal identification information of the occupant user is automatically extracted from the captured image data, making it possible to set insurance premiums that easily reflect the past performance of each occupant user.

[0126] Returning to FIG. 11, the server 300 then transmits the insurance premium set as described above to the user terminal 400 of the business user.

[0127] Then, the user terminal 400 of the business user acquires the information transmitted from the server 300 and displays it on the display unit 4021 (S210). In other words, the business user is provided with an insurance premium based on the driving state.

[0128] The server 300 may calculate the fluctuation of the insurance premium in real time based on the detected bad behavior so that the insurance premium becomes lower as the number of bad behaviors decreases. This will be described with reference to Figs. 15 and 16.

[0129] FIG. 15 is a second diagram illustrating the operational flow of the information processing system 100 in this embodiment. FIG. 15 explains the operational flow between the server 300, the user terminal 400, and the work vehicle 500 in the information processing system 100 in this embodiment, and the processing executed by the server 300, the user terminal 400, and the work vehicle 500. The flow shown in FIG. 15 is repeatedly executed at a predetermined cycle while the work vehicle 500 is in operation, during a predetermined set period for setting insurance premiums using the information processing system 100. Furthermore, in each process shown in FIG. 15, processes that are substantially the same as the processes shown in FIG. 11 above are assigned the same reference numerals, and detailed description thereof will be omitted.

[0130] In the flow shown in Fig. 15, the server 300 calculates the fluctuation of the insurance premium in real time based on the current number of misconducts counted and updated in the process of S205 (S2061). Here, the server 300 calculates the fluctuation of the insurance premium based on the detected misconduct so that the insurance premium becomes lower as the number of misconducts becomes lower, as shown in the insurance premium setting table shown in Fig. 14 above.

[0131] Then, the server 300 transmits the insurance premium fluctuation calculated as described above to the work vehicle 500.

[0132] In response, work vehicle 500 acquires the information transmitted from server 300 and displays it on display device 520 (S2071). In other words, the fluctuation in insurance premiums based on the driving state is provided to the occupant user of work vehicle 500. This will be described with reference to FIG. 16.

[0133] FIG. 16 is a diagram illustrating an example of a screen displayed on display device 520 of working vehicle 500 regarding fluctuations in insurance premiums based on driving conditions in this embodiment.

[0134] The screen SC1 shown in Fig. 16(a) displays a message field SC21 regarding fluctuations in insurance premiums based on driving conditions, and in the example shown in Fig. 16(a), it displays a message that bad behavior has been detected, and further displays the cumulative number of detections up to now and the current average number of detections per hour. According to the insurance premium setting table shown in Fig. 14 above, if the average number of detections per hour is 1, the insurance premium at the time of the next contract will be -20%, so the message field SC21 displays a message that the insurance premium at the time of the next contract will be -20%.

[0135] 16(b), the occupant user is pointing, but because the face is not facing the direction of the pointing, a message is displayed indicating that the occupant user's driving behavior of the work vehicle 500 does not partially match the safe driving behavior pattern, and further, the current safety rank is displayed. In the example shown in Fig. 16(b), the next time a notification is given that the driving behavior does not match the safe driving behavior pattern, a message is displayed indicating that the safety rank will be downgraded, along with the change in insurance premium at the time of the next contract if the rank is downgraded.

[0136] This allows for real-time notification of insurance premium fluctuations based on the risky behavior of occupant users. For example, with ex post facto determination based on driving operation history, notification is made after some time has passed since the risky behavior, which provides little motivation to correct behavior and makes the risky behavior more likely to recur. However, with such real-time notification, monetary value such as discounts or premium increases on insurance premiums can strongly instill safety awareness in occupant users regarding the reduction of accidents in work vehicle 500, and the recurrence of subsequent risky behavior can also be reduced.

[0137] In this embodiment, too, extraction of the driving state may be performed by work vehicle 500 in the same manner as described above in the description of the modified example of the first embodiment.

[0138] As described above, this embodiment also contributes to reducing accidents involving work vehicles by appropriately setting insurance premiums based on the driving conditions of the work vehicle.

[0139] <Other variations> The above-described embodiment is merely an example, and the present disclosure may be modified as appropriate within the scope of the present disclosure. For example, the processes and means described in the present disclosure may be freely combined and implemented as long as no technical contradiction occurs.

[0140] Furthermore, the processing described as being performed by one device may be shared and executed by multiple devices. For example, the acquisition unit 3031 may be formed in a separate arithmetic processing device. In this case, these arithmetic processing devices are configured to be able to cooperate with each other. Furthermore, the processing described as being performed by different devices may be executed by a single device. In a computer system, the hardware configuration (server configuration) by which each function is realized can be flexibly changed.

[0141] The present disclosure can also be realized by providing a computer program implementing the functions described in the above embodiments to a computer, and having one or more processors in the computer read and execute the program. Such a computer program may be provided to the computer via a non-transitory computer-readable storage medium connectable to the computer's system bus or via a network. Non-transitory computer-readable storage media include, for example, any type of disk, such as a magnetic disk (e.g., a floppy disk, a hard disk drive (HDD), etc.), an optical disk (e.g., a CD-ROM, a DVD disk, a Blu-ray disk), a read-only memory (ROM), a random access memory (RAM), an EPROM, an EEPROM, a magnetic card, a flash memory, an optical card, or any type of medium suitable for storing electronic instructions. [Explanation of symbols]

[0142] 100 Information Processing Systems 200···Network 300 Server 301···Communications Department 302...Storage section 303 Control section 400 User terminal 500···Work vehicle 510 Imaging device 520...Display device

Claims

1. An information processing device for setting automobile insurance premiums for work vehicles, an acquisition unit that acquires detection information that is information detected by a predetermined detection device installed in the work vehicle; an extraction unit that extracts a driving state of the work vehicle based on the detection information acquired by the acquisition unit; a setting unit that sets the insurance premium by analyzing the driving state for a predetermined task performed using the work vehicle based on the driving state extracted by the extraction unit; and Equipped with the detection device is configured to include an imaging device that images the surroundings of the work vehicle and / or a distance measuring sensor, the extraction unit extracts, as the driving state, a state regarding the presence or absence of obstacles, including people, around the work vehicle that may be detected by the detection device during the work by the work vehicle in a monitoring target area monitored by the detection device; the setting unit sets the insurance premium based on the number of times the obstacle appears around the work vehicle so that the insurance premium becomes lower the fewer the number of times the obstacle appears, the monitoring target area is a pre-set area including a travel area and a turning area of ​​the work vehicle, and is set in advance by selection by a user; Information processing device.

2. the monitoring target area is updated and set while the work vehicle is traveling based on a predetermined identification that defines a predetermined non-target area detected by the detection device, while excluding an area defined by the identification; The information processing device according to claim 1 .

3. a providing unit that provides the driver of the work vehicle with information on fluctuation factors of the insurance premium based on the driving state, The provision unit notifies the driver of a factor that causes a fluctuation in the insurance premium each time the factor occurs using a predetermined output device installed in the work vehicle. The information processing device according to claim 1 .

4. An information processing method for setting automobile insurance premiums for work vehicles, comprising: The computer an acquisition step of acquiring detection information, which is information detected by a predetermined detection device installed in the work vehicle; an extraction step of extracting a driving state of the work vehicle based on the detection information acquired by the acquisition step; a setting step of setting the insurance premium by analyzing the driving state for a predetermined task performed using the work vehicle based on the driving state extracted in the extraction step; the detection device is configured to include an imaging device that images the surroundings of the work vehicle and / or a distance measuring sensor, The computer In the extraction step, for obstacles including people that can be detected by the detection device during the work by the work vehicle in a monitoring target area monitored by the detection device, the presence or absence of the obstacles around the work vehicle is extracted as the driving state; In the setting step, the insurance premium is set based on the number of times the obstacle appears around the work vehicle so that the insurance premium becomes lower the fewer the number of times the obstacle appears; the monitoring target area is a pre-set area including a travel area and a turning area of ​​the work vehicle, and is set in advance by selection by a user; Information processing methods.

5. An information processing program for setting automobile insurance premiums for work vehicles, On the computer, an acquisition step of acquiring detection information, which is information detected by a predetermined detection device installed in the work vehicle; an extraction step of extracting a driving state of the work vehicle based on the detection information acquired by the acquisition step; a setting step of setting the insurance premium by analyzing the driving conditions for a predetermined task performed using the work vehicle based on the driving conditions extracted in the extraction step; the detection device is configured to include an imaging device that images the surroundings of the work vehicle and / or a distance measuring sensor, The computer, In the extraction step, for obstacles including people that can be detected by the detection device during the work by the work vehicle in a monitoring target area monitored by the detection device, the presence or absence of the obstacles around the work vehicle is extracted as the driving state, In the setting step, the insurance premium is set based on the number of times the obstacle appears around the work vehicle so that the insurance premium becomes lower the fewer the number of times the obstacle appears, the monitoring target area is a pre-set area including a travel area and a turning area of ​​the work vehicle, and is set in advance by selection by a user; Information processing program.

Images