Calculation method, calculation program, and calculation system

Abstract

A calculation method according to an embodiment of the present disclosure includes a CPU acquiring the movement route of an autonomous moving body, acquiring information on a sensor device capable of monitoring the autonomous moving body located in the surrounding of the movement route, and calculating an insurance premium to be applied to the autonomous moving body in accordance with the state of whether or not monitoring of the autonomous moving body by the sensor device is possible when moving along the movement route.

Description

Calculation method, calculation program, and calculation system

[0001] The present disclosure relates to a calculation method, a calculation program, and a calculation system.

[0002] Since various risks are anticipated in the operation of autonomous vehicles such as self-driving cars and robots, insurance contracts with reasonable premiums for autonomous vehicles are required.

[0003] For example, it is known that various information is collected from vehicles in operation using vehicle telematics technology and used for risk analysis by insurance companies to set insurance premium prices (for example, Patent Document 1).

[0004] Japanese Patent Application Laid-Open No. 2021-168193

[0005] However, autonomous vehicles do not necessarily have passengers, and there is a possibility that the status of the autonomous vehicle may not be known when an accident or incident involving the autonomous vehicle occurs. As a result, insurance premiums applied to autonomous vehicles tend to be high. It is desirable to reduce the compensation burden on insurance companies and enable operators of autonomous vehicles to set reasonable insurance premiums.

[0006] Therefore, the present disclosure proposes a calculation method, calculation program, and calculation system that can reduce the compensation burden on insurance companies and set reasonable insurance premiums for operators of autonomous mobile bodies.

[0007] In order to solve the above problem, one embodiment of a calculation method according to the present disclosure involves a CPU acquiring the movement route of an autonomous moving body, acquiring information on sensor devices located in the vicinity of the movement route that can monitor the autonomous moving body, and calculating an insurance premium to be applied to the autonomous moving body depending on whether the sensor devices are able to monitor the autonomous moving body as it moves along the movement route.

[0008] 1 is a diagram illustrating an overview of a calculation system according to an embodiment. FIG. 2 is an explanatory diagram illustrating a situation in which monitoring by a sensor device according to an embodiment is possible. FIG. 3 is a diagram illustrating a procedure related to calculation of insurance premiums. FIG. 4 is a diagram illustrating an example of movement risk of an autonomous moving body. FIG. 5 is a schematic diagram for illustrating a risk map according to an embodiment. FIG. 6 is a schematic diagram for illustrating selection of a movement route of an autonomous moving body. FIG. 7 is a diagram illustrating an example of a configuration of an autonomous moving body according to an embodiment. FIG. 8 is a diagram illustrating an example of a configuration of a fixed device according to an embodiment. FIG. 9 is a diagram illustrating an example of a configuration of a management device according to an embodiment. FIG. 10 is a flowchart illustrating information processing of an autonomous moving body. FIG. 11 is a flowchart illustrating information processing of a management device. FIG. 12 is a flowchart illustrating movement route determination processing. FIG. 13 is a flowchart illustrating monitoring request processing. FIG. 14 is a flowchart illustrating information processing of a fixed device. FIG. 15 is a diagram illustrating an example of a management device including a route generation unit. FIG. 16 is a diagram illustrating an example of a management device including an image processing unit. FIG. 17 is a diagram illustrating a calculation system according to another embodiment. FIG. 18 is a hardware configuration diagram illustrating an example of a computer that realizes the functions of the management device.

[0009] Hereinafter, embodiments will be described in detail with reference to the drawings. In the following embodiments, the same components are designated by the same reference numerals, and redundant description will be omitted.

[0010] The present disclosure will be described in the following order: 1. Embodiment 1-1. Overview of a calculation system according to an embodiment 1-2. Description of a calculation method according to an embodiment 1-3. Configuration of the calculation system 1-4. Information processing by the calculation system 1-5. Modified example of the calculation system 2. Other embodiments 3. Effects of an information processing device according to the present disclosure 4. Hardware configuration

[0011] (1. Embodiment) (1-1. Overview of Calculation System According to Embodiment) First, an overview of information processing according to an embodiment of the present disclosure will be described using FIG. 1. FIG. 1 is a diagram showing an overview of a calculation system according to an embodiment. In the example shown in FIG. 1, the calculation system has both a function of calculating insurance premiums to be applied to an autonomous moving body and a function of managing the operation of the autonomous moving body.

[0012] The calculation system 1 includes an autonomous mobile body 10, a sensor device 20, and a management device 30. The calculation system 1 cooperates with an insurance company system 2 operated by an insurance company, and performs processing related to insurance premium calculation. The calculation system 1 cooperates with a user system 3, which is a user's system that provides services using the autonomous mobile body 10, and performs processing related to the operation of the autonomous mobile body 10.

[0013] The calculation system 1 according to the embodiment includes one or more autonomous moving bodies 10. In the example of Fig. 1, the number of the autonomous moving bodies 10 is assumed to be n (n is a natural number). Each of the autonomous moving bodies 10 is capable of communicating with the management device 30 via a network.

[0014] The autonomous mobile body 10 is a mobile body that can move autonomously. The autonomous mobile body 10 has the function of recognizing its own position and surrounding environment using sensors mounted thereon and automatically moving to a destination. The type of mobile body is not particularly limited. In one example, the autonomous mobile body 10 is a transport robot. A transport robot is a robot that loads luggage and transports it to a destination. In this example, the user, who is a service provider, is a transportation company that provides a service to transport luggage entrusted to it by customers, or a sales company that delivers products it sells in response to orders from customers.

[0015] The user makes a delivery request to the management device 30 in accordance with the content of the order placed on the user system 3. The management device 30 sets a delivery task for the autonomous mobile body 10 in accordance with the delivery request. The autonomous mobile body 10 moves in accordance with the delivery task. The autonomous mobile body 10 receives a package from the user at a receiving location and delivers the package by automatically moving to the delivery location, which is the destination.

[0016] The management device 30 calculates the insurance premium to be applied to the autonomous mobile body 10 according to the delivery task and notifies the insurance company system 2 of the calculated insurance premium and various information related to the calculation of the insurance premium. The insurance company accepts payment of the insurance premium from the user. Based on the insurance contract, the insurance company undertakes to compensate for losses incurred during the execution of a delivery task by the autonomous mobile body 10. The insurance company is liable for compensation for damages (i.e., payment of insurance money) if the autonomous mobile body 10 encounters an accident or incident during the execution of a delivery task and a loss occurs. Compensation for damages includes compensation for personal injury and property damage, compensation for damage caused by damage to the autonomous mobile body 10, and compensation for damage or theft of packages being delivered by the autonomous mobile body 10. In this specification, the term "insurance premium" refers to money (premium) paid by the policyholder (user) to the insurance company. The term "insurance money" refers to money (compensation) paid by the insurance company to the parties specified in the insurance contract when an insured event occurs.

[0017] The calculation system 1 includes one or more sensor devices 20. The sensor device 20 detects information about the surroundings of the sensor device 20. Specifically, the sensor device 20 detects information that can be used for object recognition around the sensor device 20. The sensor device 20 detects information within a predetermined detection area. The detection area is determined depending on the type and specifications of the sensor device 20. The sensor device 20 includes, for example, an image sensor, a distance sensor, etc. The sensor device 20 may include multiple sensors. It is preferable that the sensor device 20 includes at least an image sensor that generates image information.

[0018] The sensor device 20 is provided in the autonomous moving body 10 to recognize the surrounding environment. The sensor device 20 is provided in a fixed device 40 installed on a road, a building, or the like.

[0019] The fixed device 40 is a device that does not have a mobile function and is installed in a specific location. There are no particular limitations on the fixed device 40 as long as it can acquire information about the surrounding environment using the sensor device 20. The fixed device 40 can collect information in the detection area of ​​the sensor device 20 at the installation location. The fixed device 40 is, for example, an imaging device such as a surveillance camera or a camera for fixed-point observation. The fixed device 40 may also be a signage device or a vending machine that changes the advertising information it outputs depending on the information acquired by the sensor device 20.

[0020] In the present disclosure, the sensor device 20 mounted on the fixed device 40 or the autonomous moving body 10 can acquire information representing other autonomous moving bodies 10 present within the detection area and the situation around the autonomous moving body 10.

[0021] The management device 30 is a computer that executes the calculation method according to the present disclosure. The management device 30 is, for example, a server device or an information processing device such as a PC (Personal Computer). In the example of FIG. 1 , the management device 30 is a computer independent of the autonomous mobile body 10, the insurance company system 2, and the user system 3. However, the management device 30 may be provided in the autonomous mobile body 10, the insurance company system 2, or the user system 3.

[0022] The management device 30 is capable of communicating with the user system 3, the insurance company system 2, the autonomous mobile body 10, and the fixed device 40. The management device 30 cooperates with the user system 3 and performs processing related to the provision of delivery services using the autonomous mobile body 10. The management device 30 receives a package delivery request from the user system 3. The management device 30 sets a delivery task based on the delivery request and transmits it to the autonomous mobile body 10. The delivery task can include information such as a pick-up location, a destination (delivery location), a pick-up time and an arrival time at the destination, and a task priority.

[0023] The autonomous mobile body 10 accepts a delivery task transmitted from the management device 30. If there is no delivery task, the autonomous mobile body 10 waits for the assignment of a delivery task at a standby position where charging facilities and the like are present. When the autonomous mobile body 10 receives a delivery task from the management device 30, it acquires a travel route to the destination. The autonomous mobile body 10 receives the package at a receiving position and moves to the destination. The standby position and the receiving position may be substantially the same position. The autonomous mobile body 10 moves to the destination along the travel route. The autonomous mobile body 10 recognizes its own position and surrounding obstacles based on information acquired by the sensor device 20, and automatically moves to the destination.

[0024] The management device 30 acquires information detected by each sensor device 20. The management device 30 collects information acquired by the sensor devices 20 while the autonomous mobile body 10 is moving and information acquired by the sensor devices 20 of the fixed devices 40. The management device 30 collects this information at predetermined time intervals. The management device 30 collects the information detected by the sensor devices 20 by associating it with position information of the sensor devices 20 (i.e., position information of the autonomous mobile body 10 and the fixed devices 40). The information collected by the management device 30 may be the information itself acquired by the sensor devices 20 (primary information), or may be secondary information generated by arithmetic processing or the like from the primary information acquired by the sensor devices 20. Primary information may be image data acquired by an image sensor or point cloud data acquired by a ranging sensor. Secondary information may be, for example, information on traffic conditions, weather, congestion, etc. calculated based on the primary information.

[0025] The management device 30 according to the present disclosure calculates an insurance premium to be applied to the autonomous mobile body 10 based on information acquired from each sensor device 20. The management device 30 acquires a movement route 50 (see FIG. 2 ) of the autonomous mobile body 10, and calculates an insurance premium to be applied to the autonomous mobile body 10 when it moves along the movement route 50. The management device 30 according to the present disclosure calculates an insurance premium to be applied to the autonomous mobile body 10 depending on whether or not the sensor device 20 can monitor the autonomous mobile body 10 when it moves along the movement route 50.

[0026] (1-2. Description of Calculation Method According to Embodiment) A calculation method according to an embodiment will be described. FIG. 2 is an explanatory diagram showing a situation in which monitoring by a sensor device according to an embodiment is possible. FIG. 2 is a schematic diagram showing a movement route 50 of an autonomous moving body 10 in a plan view.

[0027] In FIG. 2 , the autonomous mobile body 10 that is the subject of insurance premium calculation is designated as autonomous mobile body 10A. The other autonomous mobile bodies 10 other than the autonomous mobile body 10A are designated as autonomous mobile bodies 10B, 10C, and 10D. The travel route 50 of the autonomous mobile body 10A shown in FIG. 2 is a route from a waiting position in the southeast toward a destination 90 in the northwest, and is a route that makes left and right turns along a road 91 in sequence. In the example of FIG. 2 , three fixed devices 40 are installed along a north-south axis near the road 91 on the west side. The three fixed devices 40 are designated as fixed device 40A, fixed device 40B, and fixed device 40C, in order from south to south. In FIG. 2 , the detection areas AR of the sensor devices 20 mounted on each device are indicated by dotted lines.

[0028] (Status of Monitoring Possibility) The management device 30 according to the present disclosure acquires information on sensor devices 20 capable of monitoring the autonomous moving body 10A that are located in the vicinity of the movement route 50. Here, "sensor devices capable of monitoring" refers to sensor devices that have the function of monitoring the autonomous moving body by photographing and measuring distances. The management device 30 identifies sensor devices 20 located in the vicinity of the movement route 50 based on the position information of each autonomous moving body 10 and each fixed device 40. "In the vicinity of the movement route 50" means, for example, that the minimum distance between the current position of the sensor device 20 and the movement route 50 is equal to or less than a predetermined threshold. The sensor devices 20 located in the vicinity of the movement route 50 may be able to monitor the autonomous moving body 10A as it moves along the movement route 50.

[0029] 2, the management device 30 identifies the sensor devices 20 included in each of the autonomous mobile bodies 10B, 10C, 10D, the fixed device 40B, and the fixed device 40C as sensor devices 20 located in the vicinity of the movement route 50. The sensor device 20 of the fixed device 40A is excluded because the minimum distance from the movement route 50 is greater than a predetermined threshold.

[0030] Here, if the autonomous mobile body 10A is in a situation where it can be monitored by another sensor device 20 while moving, even if an accident or the like occurs, data observing the situation at the time of the accident or the like from a third-party perspective can be obtained from the sensor device 20. The observation data at the time of the accident or the like serves as objective evidence showing the situation at the time of the accident or theft. In other words, if the autonomous mobile body 10A is in a situation where it can be monitored by the sensor device 20 while moving, the risk of not being able to determine who is liable for damages because objective evidence showing the situation of the accident or the like cannot be obtained is reduced. As a result, it is possible to reduce the insurance premiums collected by insurance companies. Therefore, the management device 30 calculates the insurance premium depending on whether the autonomous mobile body 10 can be monitored by the sensor device 20.

[0031] The management device 30 according to the present disclosure reduces the insurance premium when the autonomous moving body 10 can be monitored by the sensor device 20 while moving along the movement route 50, compared to when monitoring is not possible. Conversely, the management device 30 may set the insurance premium to a standard amount when the autonomous moving body 10 can be monitored by the sensor device 20 while moving along the movement route 50, and may increase the insurance premium from the standard amount when the autonomous moving body 10 cannot be monitored by the sensor device 20 while moving along the movement route 50.

[0032] In the embodiment, monitoring of the autonomous mobile body 10 by the sensor device 20 includes monitoring by the fixed device 40 and monitoring by other mobile bodies MB. In the present disclosure, "other mobile bodies" refers to other mobile bodies other than the autonomous mobile body 10 for which the insurance premium is calculated. Therefore, in the example of FIG. 2 , the other mobile bodies MB correspond to the autonomous mobile bodies 10B, 10C, and 10D other than the autonomous mobile body 10A for which the insurance premium is calculated. If an insurance premium to be applied to the autonomous mobile body 10B is to be calculated, the autonomous mobile bodies 10A, 10C, and 10D correspond to the other mobile bodies MB.

[0033] First, monitoring by the fixed device 40 will be described. As shown in FIG. 2 , the fixed device 40 can monitor the range of a detection area AR that can be detected by the sensor device 20 mounted on the fixed device 40. The fixed device 40 may have functions such as panning and tilting to change the range that can be monitored. In this case, the detection area AR that can be detected by the sensor device 20 of the fixed device 40 is the entire range that can be monitored (imaged). Therefore, in the first monitoring mode, the information on the sensor device 20 acquired by the management device 30 includes the detection areas AR of the sensor devices 20 mounted on the fixed devices 40 installed in the vicinity of the movement route 50. The management device 30 acquires information on the position and range of the detection area AR.

[0034] When the movement route 50 of the autonomous moving body 10A passes inside the detection area AR of the sensor device 20 mounted on the fixed device 40, the sensor device 20 of the fixed device 40 can monitor the autonomous moving body 10 as it moves along the movement route 50. When the movement route 50 passes outside the detection area AR, the sensor device 20 of the fixed device 40 cannot monitor the autonomous moving body 10 as it moves along the movement route 50. Therefore, the status of whether monitoring by the sensor device 20 is possible includes whether or not there is a detection area AR through which the movement route 50 passes. When the movement route 50 passes through the detection area AR of the sensor device 20, the management device 30 lowers the insurance premium compared to when the movement route 50 does not pass through the detection area AR.

[0035] Next, monitoring by other moving objects MB will be described. The information of the sensor device 20 acquired by the management device 30 includes information of other moving objects MB that are located in the vicinity of the movement route 50 and have the sensor device 20 mounted thereon.

[0036] When the movement route 50 of the autonomous moving body 10A passes inside the detection area AR of the sensor device 20 mounted on another moving body MB that is stopped, the autonomous moving body 10 can be monitored by the sensor device 20 while moving along the movement route 50. Furthermore, the other moving body MB can change the position of the detection area AR by moving itself. When the other moving body MB can move while keeping the moving autonomous moving body 10A within the detection area AR, the sensor device 20 can monitor the autonomous moving body 10 while moving along the movement route 50. Therefore, the status of whether monitoring by the sensor device 20 is possible includes whether the other moving body MB can monitor the autonomous moving body 10 while moving along the movement route 50. When the autonomous moving body 10 can be monitored by the sensor device 20 of the other moving body MB while moving along the movement route 50, the management device 30 lowers the insurance premium compared to when monitoring is not performed by the sensor device 20 of the other moving body MB.

[0037] The management device 30 performs a monitoring process in which the autonomous moving body 10 is monitored by the other moving bodies MB as it moves along the movement route 50, based on information about the other moving bodies MB and the movement route 50 of the autonomous moving body 10.

[0038] When the autonomous moving body 10A moves along the movement route 50, the status of the other moving bodies MB can be roughly divided into three cases: stopped, in a standby state waiting for task allocation, and moving.

[0039] The other moving body MB that is stopped is not moving for various reasons, such as charging or refueling. In the example of FIG. 2 , the autonomous moving body 10C is charging and is in a stopped state. Like the fixed device 40, such other moving body MB that is stopped can monitor the range of the detection area AR of the sensor device 20 from a certain point. Therefore, in one example according to the present disclosure, in the monitoring process, the sensor device 20 of the other moving body MB that is stopped monitors the autonomous moving body 10 that is moving along the movement route 50.

[0040] The other mobile body MB in the standby state has no delivery task assigned by the management device 30 and can move without restrictions. In the example of FIG. 2 , the autonomous mobile body 10B is in a standby state. The autonomous mobile body 10B in the standby state can move for the purpose of monitoring the autonomous mobile body 10A moving along the movement route 50. Therefore, in one example according to the present disclosure, in the monitoring process, the other mobile body MB in the standby state is made to move along the same movement route 50 as the autonomous mobile body 10, thereby monitoring the autonomous mobile body 10A. The other mobile body MB performs monitoring movement accompanying the autonomous mobile body 10A while keeping the autonomous mobile body 10A within the detection area AR of the sensor device 20. This allows the autonomous mobile body 10A to be monitored even outside the area that can be monitored by the fixed device 40 or the other mobile body MB that is stopped, thereby reducing insurance premiums.

[0041] When the autonomous mobile body 10A moves along the travel route 50, another moving body MB moves along a travel route set for the autonomous mobile body. In the example of FIG. 2 , the autonomous mobile body 10D is moving. The travel route of such another moving body MB may share part or all of the travel route with the travel route 50 of the autonomous mobile body 10A. For example, in FIG. 2 , the autonomous mobile body 10D moves along a travel route 55. The travel route 55 is a route that moves north from a waiting position in the southeast along a road 91 and then turns right. The path portion of the travel route 55 that moves north from the waiting position (the portion surrounded by a dashed dotted line) is shared with the travel route 50 of the autonomous mobile body 10A. Therefore, in an example according to the present disclosure, in the monitoring process, the autonomous mobile body 10 traveling along the shared portion of the travel route 50 is monitored by another moving body MB that moves along a route that is at least partially shared with the travel route 50 of the autonomous mobile body 10. The autonomous mobile body 10D, which is another mobile body MB, performs monitoring movement along its own movement route 55 while keeping the autonomous mobile body 10A within the detection area AR of the sensor device 20 for the common portion of the movement route. By performing monitoring while the other mobile body MB is executing its own task, it is possible to monitor the autonomous mobile body 10A and reduce the insurance premium without reducing the task processing efficiency of the entire system.

[0042] The monitoring process by the other moving body MB in FIG. 2 has been described in the case where the other moving body MB is the autonomous moving body 10. In the present disclosure, the other moving body MB is not limited to an autonomous moving body as long as it can monitor the autonomous moving body 10. For example, even a manned vehicle without an autonomous driving function can be used as the other moving body MB if it is equipped with a sensor device 20 capable of communicating with the management device 30. Such a moving body may be, for example, a vehicle equipped with a sensor device 20 equipped with a drive recorder (image sensor) or radar (ranging sensor), and a mobile communication system capable of communicating with the management device 30.

[0043] If the other moving body MB is a non-autonomous moving body and cannot perform monitoring processing, the information of the sensor device 20 acquired by the management device 30 may include information on the current position and orientation of the other moving body MB. From the information on the current position and orientation of the other moving body MB, the detection area AR of the sensor device 20 of the other moving body MB at the time of information acquisition can be identified. If the current position of the autonomous moving body 10 is within the identified detection area AR, the sensor device 20 can monitor the autonomous moving body 10 as it moves along the movement route 50.

[0044] The management device 30 determines (judges) whether or not the sensor device 20 can monitor the autonomous mobile body 10 while it is moving along the travel route 50, using one or a combination of the various monitoring modes described above, and calculates an insurance premium based on the monitoring status.

[0045] When calculating the insurance premium according to whether monitoring is possible or not, the degree of reduction in the insurance premium (discount rate) may be adjusted, for example, according to the proportion of the entire travel route 50 that can be monitored. For example, when the entire travel route 50 can be monitored, the management device 30 applies the maximum discount rate to the base amount of the insurance premium. The management device 30 increases the discount rate the greater the proportion of the route that can be monitored. The management device 30 decreases the discount rate the smaller the proportion of the route that can be monitored. Conversely, the management device 30 may set the base amount of the insurance premium as the minimum amount, and apply a surcharge rate that increases the smaller the proportion of the route that can be monitored.

[0046] (Risk Score) Next, the risk score, which is one of the elements in calculating insurance premiums, will be described.

[0047] When the autonomous mobile body 10 travels from its current location to its destination, multiple travel route 50 candidates are considered. From among the multiple candidates, the candidate that best suits the operational purpose of the autonomous mobile body 10 is selected as the travel route 50. In the case of a delivery task, the travel route 50 is generally selected based on criteria such as the shortest travel distance, taking into account energy consumption and the time required for task processing (processing efficiency). From the perspective of property insurance for the operation of the autonomous mobile body 10, the risk associated with the movement of the autonomous mobile body 10 may vary depending on the area through which the travel route 50 passes. Here, "risk" refers to the probability of an event that is the subject of insurance benefits occurring. Therefore, in the present disclosure, the management device 30 calculates a risk score Rs (see FIG. 3 ) for the travel route 50 when the autonomous mobile body 10 travels, and calculates an insurance premium based on the risk score Rs of the travel route 50 in addition to the availability of monitoring by the sensor device 20. The risk score Rs is an index value that indicates the degree of risk involved when the autonomous mobile body 10 travels.

[0048] 3 is a diagram showing the procedure for calculating the insurance premium. In order to calculate the risk score Rs of the travel route 50, parameters 60 related to the travel risk of the autonomous mobile body 10 are determined in advance. A model (calculation formula) for calculating the risk score Rs is constructed using the determined parameters 60. When the calculation system 1 is in operation, the risk score Rs is calculated based on the information on the parameters 60 for the specific travel route 50 and the calculation formula.

[0049] 4 is a diagram showing an example of movement risks of an autonomous mobile body. The movement risks of the autonomous mobile body 10 include, as types of risks, an accident risk in which the autonomous mobile body 10 encounters an accident, and a theft risk of the autonomous mobile body 10 or property mounted on the autonomous mobile body 10. The accident risk is the risk that the autonomous mobile body 10 will be involved in a traffic accident as either the assailant or the victim. The theft risk is the risk that packages delivered by the autonomous mobile body 10 or equipment equipped on the autonomous mobile body 10 will be taken away or damaged.

[0050] The travel risk is calculated based on a calculation formula including at least one of the parameters 60 of the degree of congestion at the location coordinates of interest, visibility, the history of accident occurrences, travel speed, and the presence or absence of a monitoring device. Note that the correlation between each parameter 60 and the risk type shown in Figure 4 is an example shown to explain the concept of travel risk according to the present disclosure, and is not necessarily limited to that shown in Figure 4.

[0051] The congestion degree is a parameter 60 that represents the degree of congestion on the roadway (area where vehicles travel) or the sidewalk (area where people travel) at a point of interest. One or both of roadway congestion and sidewalk congestion may be considered as the congestion degree. When considering either the roadway or the sidewalk, the congestion degree of the area where the autonomous mobile body 10 mainly travels is considered. In the example of FIG. 4 , the congestion degree parameter 60 has a positive correlation in which the higher the congestion degree, the higher the risk of an accident, and a negative correlation in which the higher the congestion degree, the lower the risk of theft.

[0052] Visibility is a parameter 60 that represents the degree of visibility of the surroundings at a point of interest. Visibility is affected by weather conditions such as sunny, rainy, snowy, and foggy weather, and by the time of day, day, and night, and is evaluated as being better the farther one can see. Visibility is also affected by the presence or absence of buildings and terrain that may act as obstacles. In the example of Figure 4, the visibility parameter 60 has a negative correlation in which the better the visibility, the lower the risk of an accident, and a negative correlation in which the better the visibility, the lower the risk of theft. The risk of theft increases in rainy weather because fingerprints and other traces are less likely to be left behind and the theft is less likely to be witnessed.

[0053] The accident record is a parameter 60 that indicates the number of accidents that have occurred in the past at a location of interest. The accident record may be the number of accidents or the frequency of accidents. In the example of FIG. 4, the parameter 60 of the accident record has a positive correlation in which the greater the number of accidents, the higher the accident risk. The accident record is considered to have no correlation with the theft risk.

[0054] The moving speed is a parameter 60 that indicates the moving speed of an object (such as a person or a vehicle) moving through a point of interest. The moving speed may be the average value or the mode of the moving speed of the object under consideration at the point of interest. In the example of FIG. 4 , the moving speed parameter 60 has a positive correlation such that the higher the moving speed of the object, the higher the accident risk. The moving speed of the object is considered to have no correlation with the risk of theft.

[0055] The presence or absence of a monitoring device is a parameter 60 that indicates the presence or absence of a monitoring device at a location of interest. If the fixed device 40 equipped with the sensor device 20 is a monitoring device such as a surveillance camera, that monitoring device is taken into consideration by this parameter. The monitoring device taken into consideration by the movement risk parameter 60 does not necessarily have to be a fixed device 40 that can communicate with the management device 30, and also includes general monitoring devices installed to monitor the perimeter of a building or the like. If a monitoring device is present, the location has a deterrent effect against crimes such as theft, and therefore the risk of theft is lower. If a monitoring device is not present, the deterrent effect is not present, and therefore the risk of theft is higher. On the other hand, the presence or absence of a monitoring device is thought to have no correlation with accident risk.

[0056] The parameters 60 are not limited to those described above, and any of the parameters may be changed to another parameter, or other parameters may be added.

[0057] The risk score Rs is calculated based on the above-described various parameters 60. The risk score Rs may be calculated based on each of the accident risk and the theft risk, or separate risk scores Rs may be calculated for the accident risk and the theft risk.

[0058] Once the parameters 60 are determined, modeling is performed to construct a model (calculation formula) that represents the relationship between the risk score Rs and each parameter 60 in order to calculate the risk score Rs. As shown in Equation (1), the risk score Rs can be calculated using the calculation formula f(a, b, c, d, e, ...) obtained by modeling. Rs = f(a, b, c, d, e, ...) (1) Here, a is a parameter that represents the degree of congestion. b is a parameter that represents visibility. c is a parameter that represents the history of accident occurrences. d is a parameter that represents the movement speed of objects (people, vehicles, etc.). e is a parameter that represents the presence or absence of surveillance cameras. Calculation formula f is a function of each parameter 60, such as a, b, c, d, and e. The specific content of calculation formula f is not particularly limited. Each parameter 60 may be assigned a weighting coefficient according to the degree of influence it has on the risk score. Furthermore, machine learning may be used to learn the strength of the influence that the value of each parameter 60 has on the risk score, and the value of the weighting coefficient may be changed over time. For example, the weighting coefficient value may be changed according to the time of day or the season of the year.

[0059] FIG. 5 is a schematic diagram illustrating a risk map according to an embodiment. The management device 30 acquires a risk map 70 to calculate a risk score Rs of a travel route 50 when the autonomous mobile body 10 travels. The risk map 70 is map data that associates position coordinates in the map data with the travel risk of the autonomous mobile body 10. In the example shown in FIG. 5, the risk map 70 is illustrated by superimposing a risk score Rs for each position coordinate of the map data on map data that includes roads 91 (roadways or sidewalks) that may be travel routes of the autonomous mobile body 10. Note that FIG. 5 illustrates the risk map 70 in map format for the purpose of explaining the concept of the risk map 70, but the data format of the risk map 70 processed by the management device 30 is not limited to that illustrated. The risk map 70 may be any data set in which a data group in which position coordinates are associated with the risk scores Rs for the coordinates is specified for each position coordinate. In other words, the risk map 70 may be any data set that can represent a risk distribution on a map such as that shown in FIG. 5. Note that the map referred to here is not limited to a two-dimensional map, but may be a three-dimensional map. In the example of Fig. 5, for convenience, the risk score Rs is classified into three levels, and each level is displayed in a different display format. In the example of Fig. 5, the risk map 70 displays the positions of monitoring devices 71 related to the parameter 60.

[0060] The management device 30 acquires the risk map 70. The risk map 70 may be created in advance and stored in the management device 30. The management device 30 may calculate a risk score Rs at each position coordinate on the map based on data collected from the fixed device 40 and each sensor device 20 of the autonomous mobile body 10, and generate or update the risk map 70.

[0061] The management device 30 calculates a risk score Rs for the travel route 50 from the risk map 70. The management device 30 calculates a risk score Rs for the entire travel route 50 based on the risk score Rs for each position coordinate passed by the travel route 50. The method for calculating the risk score Rs for the entire travel route 50 is not particularly limited, and may be, for example, the sum of the risk scores Rs for each position coordinate passed by the travel route 50.

[0062] As shown in FIG. 3 , the management device 30 calculates an insurance premium according to the monitoring availability status and the risk score Rs based on the monitoring availability status by the sensor device 20 and the calculated risk score Rs for the entire travel route 50. The management device 30 is not particularly limited in its method of calculating the insurance premium. The higher the risk score Rs of the travel route 50, the higher the insurance premium. The management device 30 lowers the insurance premium according to the risk score Rs of the travel route 50. As an example, the management device 30 may calculate a base amount of the insurance premium based on the risk score Rs of the travel route 50, and calculate the insurance premium by applying a discount rate or a surcharge rate according to the monitoring availability status by the sensor device 20 to the calculated base amount. In addition to the monitoring availability status and the risk score Rs, various risk factors that have traditionally been used to calculate insurance premiums can be taken into account when calculating the insurance premium.

[0063] The management device 30 transmits the calculated insurance premium and information on the basis of the insurance premium to the insurance company system 2.

[0064] (Selection of Travel Route) As described above, the risk score Rs differs depending on the travel route 50 taken by the autonomous mobile body 10. Therefore, in the embodiment, the calculation system 1 determines the travel route 50 based on the risk score Rs. The entity that determines the travel route 50 of each autonomous mobile body 10 may be the autonomous mobile body 10 itself or the management device 30.

[0065] 6 is a schematic diagram illustrating selection of a travel route of an autonomous moving body. In the present disclosure, a travel route 50 can be selected from a plurality of travel route candidates 51. Specifically, one of the plurality of travel route candidates 51 is selected as the travel route 50 based on the risk scores Rs of the plurality of travel route candidates 51.

[0066] 6 illustrates a first movement route candidate 51A and a second movement route candidate 51B. The first movement route candidate 51A and the second movement route candidate 51B are routes from the current location to the destination 90, and are at least partially different from each other. The movement distance of the first movement route candidate 51A is denoted by LA, and the risk score Rs of the first movement route candidate 51A is denoted by RsA. The movement distance of the second movement route candidate 51B is denoted by LB, and the risk score Rs of the second movement route candidate 51B is denoted by RsB.

[0067] An example of a method for selecting a travel route 50 is shown below. In one example, the travel route 50 is selected based on the risk score Rs and the shortness of the travel distance. Specifically, among the travel route candidates 51 whose travel distance satisfies the travel distance condition, the travel route candidate 51 with the lowest risk score Rs is selected as the travel route 50.

[0068] The travel distance condition is the upper limit of the travel distance allowed for the autonomous mobile body 10 to reach the destination. For example, in a delivery task, the arrival time of the package is set. Also, the average travel speed of the autonomous mobile body 10 is set in advance. Therefore, the upper limit of the travel distance that allows the autonomous mobile body 10 to reach the destination by the arrival time is calculated as the travel distance condition. A travel route candidate 51 whose travel distance is shorter than the distance calculated by the travel distance condition is selected as the travel route 50. In other words, the travel route 50 does not have to be the shortest route as long as it satisfies (is shorter than) the travel distance condition, and may be a detour route. Therefore, in the embodiment, a candidate that satisfies the travel distance condition and has the lowest risk score Rs is selected as the travel route 50.

[0069] For example, the travel distance LA of the first travel route candidate 51A and the travel distance LB of the second travel route candidate 51B both satisfy the travel distance condition, but the travel distance LA is shorter than the travel distance LB. However, the risk score RsA of the first travel route candidate 51A is higher than the risk score RsB of the second travel route candidate 51B. In this case, even if a candidate with a shorter travel distance (the first travel route candidate 51A) exists, the second travel route candidate 51B, which has the lowest risk score Rs, is selected as the travel route 50 of the autonomous moving body 10.

[0070] This reduces the risk score Rs of the travel route 50, and therefore reduces the insurance premium according to the risk score Rs.

[0071] As described above with reference to FIGS. 1 to 6 , the calculation method implemented by the calculation system 1 calculates insurance premiums based on whether the sensor device 20 is monitored, thereby reducing the insurance company's compensation burden in the event of an accident or other incident and setting a reasonable insurance premium for the operator of the autonomous mobile body. Furthermore, by calculating the risk score Rs of the movement route 50 of the autonomous mobile body 10 using the risk map 70 and calculating the insurance premium based on the risk score Rs, the risks associated with the movement of the autonomous mobile body 10 can be appropriately estimated and reflected in the insurance premium. If the user uses a lower-risk movement route 50, the insurance premium applied to the autonomous mobile body 10 can be reduced. Even if the user prioritizes task processing efficiency and uses a high-risk but short-distance movement route 50, a reasonable insurance premium commensurate with the risk can be determined. Furthermore, by selecting a candidate that satisfies the movement distance condition and has a low risk score Rs as the movement route 50 from among multiple movement route candidates 51, the autonomous mobile body 10 can be operated with lower risk and lower insurance premiums.

[0072] (1-3. Configuration of Calculation System) Next, the configuration of the calculation system 1 will be described.

[0073] 1 , the calculation system 1 includes an autonomous moving body 10, a sensor device 20, and a management device 30. The sensor device 20 is mounted on each of the autonomous moving body 10 and a fixed device 40.

[0074] (Autonomous Mobile Body) Fig. 7 is a diagram showing an example of the configuration of an autonomous mobile body according to an embodiment. The autonomous mobile body 10 is a mobile body that can detect the surrounding environment using a sensor device 20 and automatically move to a set destination. In the example shown in Fig. 7, the autonomous mobile body 10 is a transport robot that transports luggage.

[0075] The autonomous moving body 10 includes a sensor device 20 , a position information acquisition unit 11 , a control device 12 , a power supply device 13 , and a communication device 14 .

[0076] The sensor device 20 mounted on the autonomous mobile body 10 includes an image sensor 21. The image sensor 21 is, for example, a complementary metal oxide semiconductor (CMOS) image sensor configured with a chip. It receives incident light from an optical system, performs photoelectric conversion, and outputs image data corresponding to the incident light. As a result, the sensor device 20 generates and outputs a captured image of the detection area AR. The image sensor 21 may be a time-of-flight (ToF) distance image sensor, which is a distance measurement sensor. The ToF method is a distance measurement technique that measures the distance to an object by irradiating light from a light source onto the object and utilizing the time difference until the reflected light is detected by the image sensor. The sensor device 20 may also include a distance measurement sensor separate from the image sensor 21. Examples of distance measurement sensors that can be used include optical sensors such as a stereo camera or LiDAR (Laser Imaging Detection and Ranging), radio wave sensors such as millimeter-wave radar, and ultrasonic sensors. The sensor device 20, which has a distance measurement function, generates and outputs three-dimensional data of the detection area AR.

[0077] The position information acquisition unit 11 includes a positioning sensor 11A and an inertial sensor 11B. The positioning sensor 11A is a positioning module that receives a positioning signal from a positioning system such as a GPS (Global Positioning System) and outputs information on the current position coordinates of the autonomous mobile body 10. The inertial sensor 11B is a so-called IMU (inertial measurement unit) that detects angles or angular velocities around three orthogonal coordinate axes and accelerations in the directions of the respective coordinate axes, and outputs information on the detected angles and accelerations.

[0078] The control device 12 is a computer that performs control processing of each part of the autonomous mobile body 10. The control device 12 includes a storage device 15. The control device 12 executes various control processes of the autonomous mobile body 10 by using a central processing unit (CPU), an MPU, a graphics processing unit (GPU), or the like to execute programs stored in the storage device 15 using a RAM or the like as a work area. The control device 12 is a controller, and may be configured with an integrated circuit such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or an MCU, for example.

[0079] The storage device 15 is realized by a storage device such as a semiconductor memory element such as a flash memory, a solid state drive, or a hard disk drive.

[0080] The control device 12 includes an image processing unit 12A, a self-position recognition unit 12B, a path generation unit 12C, and a drive control unit 12D.

[0081] The image processing unit 12A acquires information output from the sensor device 20. The image processing unit 12A acquires images captured by the image sensor 21. The image processing unit 12A acquires three-dimensional data obtained by distance measurement using a distance measuring sensor. The three-dimensional data is, for example, point cloud data including position information of a large number of measurement points. The image processing unit 12A recognizes the surrounding environment of the autonomous mobile body 10 based on the information acquired from the sensor device 20. Specifically, the image processing unit 12A generates an environmental map of the detection area AR of the sensor device 20. The image processing unit 12A generates information on movement risk parameters 60 in the detection area AR of the sensor device 20. That is, based on the information acquired from the sensor device 20, the image processing unit 12A generates information such as the degree of congestion at the current location of the autonomous mobile body 10, visibility, and the movement speed of moving objects (people, vehicles, etc.). The generation of the environmental map and the information of the movement risk parameters 60 by the image processing unit 12A may be realized by rule-based information processing or by information processing using a pre-trained AI (Artificial Intelligence) model. The image processing unit 12A outputs the output data of the image sensor 21 (captured images and three-dimensional data) and each of the generated information to the storage device 15.

[0082] The self-position recognition unit 12B recognizes the self-position of the autonomous mobile body 10 based on the information output from the position information acquisition unit 11. The self-position recognition unit 12B acquires information on current position coordinates from the positioning sensor 11A. The self-position recognition unit 12B acquires information on the angle around each coordinate axis of the autonomous mobile body 10 and acceleration information in the direction of each coordinate axis from the inertial sensor 11B. The self-position recognition unit 12B recognizes the position and attitude (orientation) of the autonomous mobile body 10 based on the acquired information. From the recognized position and attitude of the autonomous mobile body 10, it is possible to determine from which position coordinates and in which direction the detection area AR of the sensor device 20 mounted on the autonomous mobile body 10 is facing. The self-position recognition unit 12B outputs information on the position and attitude of the autonomous mobile body 10 and the position and orientation of the detection area AR to the storage device 15.

[0083] The route generation unit 12C generates travel route candidates 51 to the destination. The route generation unit 12C acquires a delivery task from the storage device 15. The delivery task includes information such as the package pickup location, the destination, the pickup time, the arrival time at the destination, and the task priority. The route generation unit 12C calculates a travel distance condition to the destination from the acquired delivery task. The route generation unit 12C acquires map data including the destination and pickup location from the storage device 15. The route generation unit 12C acquires travel route candidates 51 to the destination based on the acquired information.

[0084] The selection of the travel route 50 from the plurality of travel route candidates 51 described above may be performed by the autonomous moving body 10 (route generation unit 12C) or the management device 30. In one example, the route generation unit 12C generates the plurality of travel route candidates 51 based on the delivery task and map data. The route generation unit 12C transmits the generated plurality of travel route candidates 51 to the management device 30 via the communication device 14. Then, the management device 30 selects one travel route 50 from the received plurality of travel route candidates 51 and transmits the selected travel route 50 to the autonomous moving body 10. The communication device 14 records the travel route 50 received from the management device 30 in the storage device 15.

[0085] The path generation unit 12C performs control to move to the destination based on the determined movement route 50. The path generation unit 12C also acquires the environmental map generated by the image processing unit 12A from the storage device 15 at predetermined time intervals. The path generation unit 12C calculates the amount of movement in each direction until the next time point based on the determined movement route 50 and the self-position of the autonomous mobile body 10 and the surrounding environmental map, and calculates a control command to the drive control unit 12D. The path generation unit 12C outputs the calculated control command to the drive control unit 12D.

[0086] The drive control unit 12D controls the drive device provided in the autonomous mobile body 10. The drive control unit 12D controls various actuators that make up the drive device based on control commands supplied from the path generation unit 12C. The actuators include, for example, a drive motor and a steering motor. The control commands include, for example, rotation angle commands for the motors.

[0087] The power supply device 13 controls the supply of power to each component from a power supply unit included in the autonomous mobile body 10. The power supply unit includes, for example, a rechargeable secondary battery. The power supply device 13 calculates the remaining battery capacity of the power supply unit and outputs the calculated remaining battery capacity to the storage device 15. For example, the power supply device 13 acquires the discharge current value of the power supply unit over time and calculates the remaining battery capacity based on the integrated amount of discharge current. Based on the calculated remaining battery capacity, for example, the path generation unit 12C determines whether charging is necessary and decides whether to perform charging operation at charging equipment.

[0088] The communication device 14 communicates between the autonomous mobile body 10 and external devices via a network. The communication device 14 is realized, for example, by a network interface card (NIC) or a network interface controller. The communication device 14 is wirelessly connected to the network and transmits and receives information to and from the management device 30 via the network. The network is realized by one or a combination of wireless communication standards or methods, such as Bluetooth (registered trademark), the Internet, Wi-Fi (registered trademark), UWB (Ultra Wide Band), LPWA (Low Power Wide Area), and ELTRES (registered trademark).

[0089] The communication device 14 receives information such as task information such as delivery tasks, map data, and a risk map 70 from the management device 30. The communication device 14 outputs the information received from the management device 30 to the storage device 15. The communication device 14 acquires each piece of information generated in the autonomous mobile body 10 from the storage device 15 and transmits it to the management device 30. Each piece of information generated in the autonomous mobile body 10 includes images captured by the image sensor 21, three-dimensional data from the ranging sensor, an environmental map, and information on movement risk parameters 60. Each piece of information generated in the autonomous mobile body 10 includes information on the position and attitude of the autonomous mobile body 10, and the position and orientation of the detection area AR. Each piece of information generated in the autonomous mobile body 10 includes a movement route candidate 51 and a risk score Rs acquired during movement.

[0090] (Fixed Device) FIG. 8 is a diagram illustrating an example of the configuration of a fixed device according to an embodiment. The fixed device 40 is installed as fixed equipment at a predetermined location and is capable of detecting the surrounding environment using the sensor device 20. The fixed device 40 is, for example, a photographing device such as a surveillance camera or a fixed-point observation camera. However, as described above, the fixed device 40 may also be a signage device or a vending machine equipped with the sensor device 20. In the present disclosure, the term "fixed device" refers to a device that cannot move to an arbitrary location like an autonomous mobile object. In other words, the fixed device may be movable along a fixed trajectory on a fixed rail, or may be movable within a certain range using a movable arm or the like. The fixed device may also be capable of changing its orientation, such as panning (changing its orientation in the horizontal direction) or tilting (changing its orientation in the vertical direction).

[0091] The fixing device 40 includes the sensor device 20 , a position information acquisition unit 41 , a control device 42 , a power supply device 43 , and a communication device 44 .

[0092] In the example of Fig. 8, the sensor device 20 mounted on the fixed device 40 includes an image sensor 21. The sensor device 20 generates and outputs a captured image of the detection area AR using the image sensor 21. The image sensor 21 may be a ToF distance image sensor that is a distance measurement sensor. The sensor device 20 may also include a distance measurement sensor in addition to the image sensor 21. The sensor device 20, which has a distance measurement function, generates and outputs three-dimensional data of the detection area AR.

[0093] The position information acquisition unit 41 includes a positioning sensor 41A and an inertial sensor 41B. The positioning sensor 41A outputs information on the current position coordinates of the fixing device 40. The inertial sensor 41B detects angles or angular velocities around three orthogonal coordinate axes and accelerations in the directions of each coordinate axis, and outputs information on the detected angles and accelerations. If the fixing device 40 is fixed in a predetermined position and does not move, the current position coordinates will be constant values, so the fixing device 40 does not need to be equipped with the positioning sensor 41A. If the fixing device 40 does not have an attitude changing function, the attitude of the fixing device 40 will be constant values, so the fixing device 40 does not need to be equipped with the inertial sensor 41B.

[0094] The control device 42 is a computer that performs control processing of each part of the fixing device 40. The control device 42 includes a storage device 45. The control device 42 executes various control processes of the fixing device 40 by executing programs stored in the storage device 45 using a CPU, MPU, GPU, or the like, with a RAM or the like as a work area. The control device 42 is a controller, and may be configured with an integrated circuit such as an ASIC, FPGA, or MCU, for example.

[0095] The storage device 45 is realized by a storage device such as a semiconductor memory element such as a flash memory, a solid state drive, or a hard disk drive.

[0096] The control device 42 includes an image processing unit 42A and a self-position recognition unit 42B.

[0097] The image processing unit 42A acquires information output from the sensor device 20. The image processing unit 42A acquires images captured by the image sensor 21. The image processing unit 42A acquires three-dimensional data obtained by distance measurement using a ranging sensor. The image processing unit 42A generates an environmental map of the detection area AR of the sensor device 20 based on the information acquired from the sensor device 20. The image processing unit 42A generates information on movement risk parameters 60 in the detection area AR of the sensor device 20. That is, based on the information acquired from the sensor device 20, the image processing unit 42A generates information such as the degree of congestion at the installation location of the fixed device 40, visibility, and the movement speed of moving objects (people, vehicles, etc.). The image processing unit 42A calculates a risk score Rs for the detection area AR based on the generated information on movement risk parameters 60. The image processing unit 42A outputs the images captured by the image sensor 21 and each generated information to the storage device 45.

[0098] The self-position recognition unit 42B estimates the self-position of the fixing device 40 based on the information output from the position information acquisition unit 41. The self-position recognition unit 42B acquires information on current position coordinates from the positioning sensor 41A. The self-position recognition unit 42B acquires information on the angle around each coordinate axis of the fixing device 40 and the acceleration in each coordinate axis direction from the inertial sensor 41B. The self-position recognition unit 42B recognizes the position and attitude (orientation) of the fixing device 40 based on the acquired information. The self-position recognition unit 42B outputs information on the position and attitude of the fixing device 40 and the position and orientation of the detection area AR to the storage device 45. Note that if the position and attitude of the fixing device 40 and the position and orientation of the detection area AR are all fixed values, the control device 42 does not need to be equipped with the self-position recognition unit 42B.

[0099] The power supply device 43 controls the power supply to each part of the fixing device 40. The power supply device 43 includes, for example, an adapter connected to a power supply facility such as a commercial power source, or a socket into which a battery (a primary battery or a secondary battery) is attached.

[0100] The communication device 44 communicates between the fixed device 40 and external devices via a network. The communication device 44 is realized by, for example, a NIC or a network interface controller. The communication device 44 is connected to the network via a wired or wireless connection, and transmits and receives information to and from the management device 30 via the network.

[0101] The communication device 44 acquires each piece of information generated in the fixed device 40 from the storage device 45 and transmits it to the management device 30. Each piece of information generated in the fixed device 40 includes images captured by the image sensor 21, three-dimensional data from the distance measurement sensor, an environmental map, information on movement risk parameters (i.e., information on the degree of congestion at the current location, visibility, movement speed, etc.), and a risk score Rs in the detection area AR. Each piece of information generated in the fixed device 40 includes information on the position and attitude of the fixed device 40 and the position and orientation of the detection area AR.

[0102] In this way, the fixing device 40 according to the embodiment is capable of generating information on movement risk parameters by processing information acquired by the sensor device 20 with the control device 42 (image processing unit 42A) and transmitting the generated information to the management device 30. The fixing device 40 according to the embodiment is configured as an edge device capable of so-called edge computing processing, which processes information output by the sensor device 20 at the end (edge) of the system and transmits the information to the management device 30.

[0103] (Management Device) Fig. 9 is a diagram showing an example of the configuration of a management device according to an embodiment. The management device 30 is communicatively connected to the autonomous mobile body 10 and the fixed device 40 via a network, collects data from the sensor devices 20 mounted on each device, and manages delivery tasks assigned to the autonomous mobile body 10. The management device 30 is connected to the user system 3 via the network, and receives requests for delivery by the autonomous mobile body 10 from the user system 3. The management device 30 is connected to the insurance company system 2 via the network, calculates insurance premiums to be applied to the autonomous mobile body 10, and transmits the calculated insurance premiums and information on a risk map 70 to the insurance company system 2.

[0104] The management device 30 includes a communication device 31, a map update unit 32, an insurance premium calculation unit 33, and a delivery instruction unit 34. The management device 30 according to the embodiment is realized by one or more computers. The management device 30 can also be realized by, for example, a cloud server made up of one or more server devices (computers). The server device provides hardware resources for realizing information processing as the map update unit 32, the insurance premium calculation unit 33, and the delivery instruction unit 34.

[0105] The communication device 31 communicates between the management device 30 and external devices via a network. The communication device 31 is realized by, for example, a NIC or a network interface controller. The communication device 31 is connected to the network via a wired or wireless connection and transmits and receives information to and from the autonomous mobile body 10, the fixed device 40, the insurance company system 2, and the user system 3 via the network. The management device 30 acquires, from the autonomous mobile body 10, various pieces of information generated in the autonomous mobile body 10 via the communication device 31. The management device 30 transmits various pieces of information, such as map data, a risk map 70, delivery task information, and monitoring requests, to the autonomous mobile body 10 via the communication device 31. The management device 30 acquires, from the fixed device 40, various pieces of information generated in the fixed device 40 via the communication device 31.

[0106] The map update unit 32 collects information generated by the sensor device 20 and updates the map data and the risk map 70. The map update unit 32 collects, from each autonomous mobile body 10, information on captured images and three-dimensional data at each point along the travel route 50, the environmental map and travel risk parameters 60, the travel route candidates 51, and the risk score Rs for the travel route 50, via the communication device 31. The map update unit 32 acquires, from each fixed device 40, information on captured images and three-dimensional data of the monitoring area, the environmental map and travel risk parameters 60, and the risk score Rs for the monitoring area, via the communication device 31. The map update unit 32 updates the map data and the risk map 70 using the collected information on each point. This keeps the map data and the risk map 70 up to date. The map update unit 32 provides the updated map data and risk map 70 to the autonomous mobile body 10 via the communication device 31. The map update unit 32 outputs the updated map data and risk map 70 to the insurance premium calculation unit 33 .

[0107] The insurance premium calculation unit 33 calculates an insurance premium to be applied to the autonomous mobile body 10 when it moves along the travel route 50. The insurance premium calculation unit 33 acquires the risk score Rs of the travel route 50 transmitted from the autonomous mobile body 10. The insurance premium calculation unit 33 determines whether the sensor device 20 can monitor the autonomous mobile body 10 when it moves along the travel route 50. The insurance premium calculation unit 33 calculates an insurance premium according to the status of whether monitoring by the sensor device 20 can be performed and the risk score Rs of the travel route 50. The insurance premium calculation unit 33 transmits the calculated insurance premium and data on the risk score Rs and risk map 70, which are the basis for the calculation, to the insurance company system 2 via the communication device 31.

[0108] The delivery instruction unit 34 receives a delivery request from the user system 3 via the communication device 31 and generates a delivery task. The delivery instruction unit 34 assigns the delivery task to the autonomous mobile body 10 and transmits information about the assigned delivery task to the autonomous mobile body 10 via the communication device 31.

[0109] The delivery instruction unit 34 acquires candidate travel routes 51 for executing a delivery task from the autonomous moving body 10 to which the delivery task has been assigned, via the communication device 31. The delivery instruction unit 34 selects one travel route 50 from the received plurality of candidate travel routes 51.

[0110] The delivery instruction unit 34 determines whether the determined travel route 50 can be monitored by the sensor devices 20. In the embodiment, the delivery instruction unit 34 acquires information about the sensor devices 20 located in the vicinity of the travel route 50 and capable of monitoring the autonomous moving body 10 based on map data. The delivery instruction unit 34 determines whether monitoring by the sensor devices 20 is possible along the entire travel route 50 based on the acquired information about the sensor devices 20. When monitoring by the fixed device 40 or another moving body MB that is stopped is not possible along at least a portion of the travel route 50, the delivery instruction unit 34 determines whether monitoring movement by another moving body MB is necessary. If the delivery instruction unit 34 determines that monitoring movement is necessary, it searches for other autonomous moving bodies 10 that share at least a portion of the travel route 50 or other autonomous moving bodies 10 that are in a standby state, and identifies autonomous moving bodies 10 that can perform monitoring movement. The delivery instruction unit 34 assigns a monitoring task for performing monitoring movement to the identified autonomous moving body 10, and transmits a monitoring request to execute the monitoring task via the communication device 31.

[0111] 7 to 9 conceptually show the functions of the calculation system 1, and may take various forms depending on the embodiment. Furthermore, the number of devices included in the calculation system 1 is not limited to the number shown in the figures.

[0112] (1-4. Information Processing by Calculation System) A description will now be given of information processing by the calculation system 1. The calculation system 1 having the above configuration executes the calculation method according to the present disclosure.

[0113] (Processing of Autonomous Moving Body) FIG. 10 is a flowchart showing information processing of the autonomous moving body.

[0114] The autonomous mobile body 10 activates the sensor device 20 and the position information acquisition unit 11 (step S10). Next, the autonomous mobile body 10 acquires sensor data 80 (step S11). That is, the image processing unit 12A generates an environmental map based on information output from the sensor device 20, generates information on movement risk parameters 60 (such as congestion level, visibility, and movement speed of people and vehicles), and calculates a risk score Rs for the area indicated by the environmental map. The self-position recognition unit 12B recognizes the self-position (position and orientation) of the autonomous mobile body 10 based on information output from the position information acquisition unit 11 (positioning sensor 11A and inertial sensor 11B). The sensor data 80 includes the environmental map, information on the movement risk parameters 60, the risk score Rs, and the self-position of the autonomous mobile body 10. The sensor data 80 is stored in the storage device 15.

[0115] The autonomous mobile body 10 transmits sensor data 80 to the management device 30 (step S12). The communication device 14 reads the sensor data 80 stored in the storage device 15 and transmits the read sensor data 80 to the management device 30. The communication device 14 receives the latest risk map 70 and map data from the management device 30 (step S13). If there is a delivery task assigned to the autonomous mobile body 10, the communication device 14 receives the delivery task from the management device 30. Note that if there is no delivery task, the autonomous mobile body 10 enters a standby state until it receives a delivery task or another task (such as a monitoring task).

[0116] Upon receiving the delivery task, the route generation unit 12C generates travel route candidates 51 and transmits them to the management device 30 (step S14). The route generation unit 12C acquires the delivery task, map data, and risk map 70 from the storage device 15, and generates multiple travel route candidates 51 based on the acquired information. The route generation unit 12C transmits the generated multiple travel route candidates 51 to the management device 30 via the communication device 14.

[0117] After transmitting the travel route candidate 51, the communication device 14 receives an instruction for the travel route 50 from the management device 30 (step S15). The communication device 14 records the received travel route 50 in the storage device 15. If there is a monitoring task, the communication device 14 receives a monitoring request 81 from the management device 30 to execute the monitoring task.

[0118] The autonomous mobile body 10 moves along the received movement route 50 (step S16). The path generation unit 12C acquires the movement route 50 from the storage device 15, calculates a control command to the drive control unit 12D based on the movement route 50, its own position, and an environmental map around the autonomous mobile body 10, and outputs the calculated control command to the drive control unit 12D. The drive control unit 12D drives the actuators in accordance with the control command, causing the autonomous mobile body 10 to move to the next point in time.

[0119] The autonomous moving body 10 moves for the set time, and when the next control time arrives, it again executes the processes from step S11 to step S16. The autonomous moving body 10 acquires sensor data 80 at every set time, transmits it to the management device 30, and moves to the destination while updating the movement route 50 according to its own position at the current time.

[0120] When the destination is reached, the route generation unit 12C executes a delivery completion process, for example, to execute a process for handing over the package and notify the management device 30 of the completion of the delivery task.

[0121] (Processing of Management Apparatus) FIG. 11 is a flowchart showing information processing of the management apparatus.

[0122] The management device 30 receives the sensor data 80 from the autonomous moving body 10 and the fixed device 40 on which the sensor device 20 is mounted via the communication device 31 (step S20).

[0123] The map update unit 32 generates a risk map 70 based on the received sensor data 80. If a risk map 70 has already been generated, the map update unit 32 updates the risk map 70 (step S21). The map update unit 32 updates the map data based on the environmental map and three-dimensional data. The map update unit 32 transmits the latest risk map 70 and map data to each autonomous mobile body 10 via the communication device 31. As a result, each autonomous mobile body 10 shares the latest risk map 70 updated by the management device 30.

[0124] The delivery instruction unit 34 receives delivery requests from the user system 3 at any timing independent of each step, and generates delivery tasks. The delivery instruction unit 34 assigns the generated delivery tasks to the autonomous moving bodies 10 and transmits them via the communication device 31. The delivery instruction unit 34 then performs a movement route determination process for the assigned delivery tasks (step S22).

[0125] FIG. 12 is a flowchart showing the travel route determination process.

[0126] The delivery instruction unit 34 acquires travel distance conditions based on the map data and the delivery task (step S220). The travel distance conditions include a threshold Lmax, which is the maximum distance that can be traveled until the arrival time.

[0127] The delivery instruction unit 34 receives a plurality of travel route candidates 51 from the autonomous mobile body 10 via the communication device 31. The delivery instruction unit 34 receives, as the travel route candidate 51, a first route A that has the shortest travel distance to the destination (step S221). The delivery instruction unit 34 acquires the travel distance of the first route A (referred to as "LA") and an overall risk score of the first route A (referred to as "RsA"). Next, the delivery instruction unit 34 receives, as the travel route candidate 51, a second route B that has the second shortest travel distance to the destination (step S222). The delivery instruction unit 34 acquires the travel distance of the second route B (referred to as "LB") and an overall risk score of the second route B (referred to as "RsB").

[0128] The delivery instruction unit 34 determines whether the travel distance LB of the second route B is smaller than the threshold Lmax, which is the travel distance condition (step S223). If the travel distance LB of the second route B is smaller than the threshold Lmax (step S223; Yes), the delivery instruction unit 34 determines whether the risk score RsB of the second route B is lower than the risk score RsA of the first route A (step S224).

[0129] If the travel distance LB of the second route B is smaller than the threshold Lmax (step S223; Yes) and the risk score RsB of the second route B is lower than the risk score RsA of the first route A (step S224; Yes), the delivery instruction unit 34 selects the second route B as the current travel route 50 (step S225). The delivery instruction unit 34 transmits an instruction to adopt the second route B as the travel route 50 to the autonomous moving body 10 via the communication device 31.

[0130] On the other hand, if the travel distance LB of the second route B is equal to or greater than the threshold Lmax (step S223; No), or if the risk score RsB of the second route B is equal to or greater than the risk score RsA of the first route A (step S224; No), the delivery instruction unit 34 selects the first route A as the current travel route 50 (step S226). The delivery instruction unit 34 transmits an instruction to the autonomous moving body 10 via the communication device 31 to adopt the first route A as the travel route 50.

[0131] In this way, even if the travel distance is not the shortest, if there is a travel route candidate 51 that satisfies the travel distance condition (the travel distance is smaller than the threshold Lmax) and has a risk score Rs lower than other candidates, the delivery instruction unit 34 selects the travel route candidate 51 as the current travel route 50.

[0132] As shown in FIG. 11, after determining the travel route 50 in step S22, the delivery instruction unit 34 executes a monitoring request process (step S23).

[0133] FIG. 13 is a flowchart showing the monitoring request process.

[0134] The delivery instruction unit 34 searches for sensor devices 20 in the vicinity of the determined travel route 50 (step S230). From the map data, the delivery instruction unit 34 identifies the sensor devices 20 mounted on the autonomous mobile bodies 10 and the fixed devices 40 that are located in the vicinity of the travel route 50 and the positions of the detection areas AR of those sensor devices 20.

[0135] The delivery instruction unit 34 determines whether the travel route 50 can be placed within the detection area AR of the identified sensor device 20 (step S231). For example, the delivery instruction unit 34 determines that the travel route 50 can be placed within the detection area AR if the proportion of the travel route 50 that falls within the detection area AR is equal to or greater than a predetermined proportion. The delivery instruction unit 34 determines that the travel route 50 cannot be placed within the detection area AR if the proportion of the travel route 50 that falls within the detection area AR is less than a predetermined proportion. The delivery instruction unit 34 determines whether or not a predetermined proportion or more of the travel route 50 can be monitored based on the possibility of monitoring by the fixed device 40, monitoring by other moving objects MB that are stopped, monitoring movement by other moving objects MB that share at least a portion of the travel route, and monitoring movement using other moving objects MB that are in a standby state. The predetermined proportion is not particularly limited, and may be, for example, 50%, 80%, or 90%.

[0136] In addition, when determining whether the travel route 50 can be placed within the detection area AR of the sensor device 20, even if there is an autonomous mobile body 10 equipped with the sensor device 20 in the vicinity of the travel route 50, the delivery instruction unit 34 will consider that monitoring by the autonomous mobile body 10 is not possible if, for example, the autonomous mobile body 10 has a low battery level or is currently performing another task with a higher priority and is therefore unable to move for monitoring.

[0137] When the delivery instruction unit 34 determines that the movement route 50 can be placed within the detection area AR (step S231; Yes), it determines whether monitoring movement by another mobile object MB in a standby state is necessary (step S232). If monitoring movement by another mobile object MB that shares at least a part of the movement route with the detection area AR or monitoring movement using another mobile object MB in a standby state is necessary in order to make the proportion of the portion that fits within the detection area AR equal to or greater than a predetermined proportion (step S232; Yes), the delivery instruction unit 34 generates a monitoring request 81 including a monitoring task by the other mobile object MB and transmits the monitoring request 81 via the communication device 31 (step S233).

[0138] If monitoring movement by other moving bodies MB is not required to make the proportion of the portion that fits within the detection area AR equal to or greater than a predetermined proportion (step S232; No), the delivery instruction unit 34 generates a monitoring request 81 including a monitoring task by the sensor device 20 that can monitor the fixed device 40, other moving bodies MB that are stopped, etc., and transmits the monitoring request 81 via the communication device 31 (step S234). Each device that receives the monitoring request 81 detects the autonomous moving body 10 to be monitored using the sensor device 20 when the autonomous moving body 10 passes through the detection area AR, records the captured video and three-dimensional data, and transmits them to the management device 30.

[0139] On the other hand, if the delivery instruction unit 34 determines that the travel route 50 cannot be placed within the detection area AR (step S231; No), it generates flag information to increase the insurance premium and outputs it to the insurance premium calculation unit 33 (step S235).

[0140] 12 shows an example in which, depending on whether monitoring by the sensor device 20 is possible, the insurance premium is increased when a predetermined percentage or more of the travel route cannot be monitored, and the insurance premium is not increased when a predetermined percentage or more of the travel route can be monitored. As a result, when monitoring is possible, the insurance premium is reduced compared to when monitoring is not possible. In the present disclosure, instead of increasing the insurance premium when monitoring is not possible, the insurance premium may be discounted when a predetermined percentage or more of the travel route can be monitored.

[0141] As shown in Figure 11, after executing the monitoring request processing in step S23, the insurance premium calculation unit 33 calculates the insurance premium to be applied when the autonomous mobile body 10 that is the subject of insurance premium calculation moves along the movement route 50 based on the risk score Rs of the determined movement route 50 and the presence or absence of flag information that increases the insurance premium depending on whether monitoring is possible or not (step S24).

[0142] When the set time has elapsed and the next control time arrives, the management device 30 again executes the processes from step S20 to step S24. The management device 30 collects sensor data 80 from the autonomous mobile bodies 10 and fixed devices 40 at every set time, updates the risk map 70, determines (updates) the movement route 50 according to the current position of each autonomous mobile body 10, processes monitoring requests, and calculates (updates) insurance premiums.

[0143] That is, the management device 30 collects sensor data 80 from the sensor devices 20 and updates the travel risk in the risk map 70 based on the collected information, so the risk score Rs of the travel route 50 may change even while the delivery task is being performed. Therefore, by updating the travel route 50, a low-risk travel route 50 that corresponds to the latest situation is determined. As a result, it becomes possible to select a route that corresponds to changes in the situation, such as changes in weather or the occurrence of traffic obstructions in the surrounding area, and the insurance premium applied to the autonomous mobile body 10 can also be reduced.

[0144] Note that the travel route 50 does not have to be updated at every set time. In this case, the autonomous mobile body 10 travels to the destination along the travel route 50 determined in the initial travel route determination process at the start of the delivery task. In this case, the risk score Rs of the travel route 50 and the status of whether monitoring by the sensor device 20 is possible are determined at the start of the delivery task, so the insurance premium to be applied when the autonomous mobile body 10 travels along the travel route 50 can be determined at the start of the delivery task.

[0145] (Processing of the Fixed Device) FIG. 14 is a flowchart showing information processing of the fixed device.

[0146] The fixing device 40 activates the sensor device 20 (step S30). The sensor device 20 captures an image using the image sensor 21 and measures three-dimensional data using the distance measurement sensor, and outputs the captured image data and three-dimensional data.

[0147] The self-position recognition unit 42B recognizes the current position of the fixing device 40 based on the information output from the position information acquisition unit 41 (the positioning sensor 41A and the inertial sensor 41B) (step S31).

[0148] The image processing unit 42A calculates a risk score Rs for the current location based on the data acquired by the sensor device 20 (step S32). The image processing unit 42A analyzes the image captured by the image sensor 21 and the three-dimensional data acquired by the distance measurement sensor to generate information on movement risk parameters 60. The image processing unit 42A calculates the risk score Rs for the detection area AR from the values ​​of the generated parameters 60 based on the calculation formula shown in Equation (1). The image processing unit 42A outputs the captured image data, the three-dimensional data, the information on the movement risk parameters 60, and the calculated risk score Rs to the storage device 45.

[0149] The communication device 44 reads the captured image data, three-dimensional data, movement risk parameter information, and calculated risk score Rs from the storage device 45, and transmits them to the management device 30 as sensor data 80 (step S33). The fixed device 40 repeats the above process at predetermined time intervals to transmit the latest data to the management device 30.

[0150] When the fixed device 40 receives a monitoring task from the management device 30 via the communication device 44, it records captured video and three-dimensional data of the autonomous mobile body 10 as monitoring data while the autonomous mobile body 10 to be monitored is entering the detection area AR of the sensor device 20. The fixed device 40 transmits the recorded monitoring data to the management device 30 via the communication device 44.

[0151] In this way, information processing by each device constituting the calculation system 1 is realized.

[0152] (1-5. Variations of the calculation system) (Variations of the determination of the travel route) In the above, an example has been shown in which the route generation unit 12C of the autonomous mobile body 10 generates travel route candidates 51, and the delivery instruction unit 34 of the management device 30 selects (determines) the travel route 50 from the travel route candidates 51, but the autonomous mobile body 10 may also select (determine) the travel route 50.

[0153] In this case, the route generation unit 12C performs a process of selecting a travel route 50 from a plurality of travel route candidates 51. For example, after the route generation unit 12C generates the travel route candidates 51 in step S14 of Fig. 10, it executes the travel route determination process shown in Fig. 12. Instead of step S15, the route generation unit 12C performs a process of transmitting the determined travel route 50 and the risk score Rs of the travel route 50 to the management device 30.

[0154] In this case, the management device 30 performs monitoring request processing (step S23) and insurance premium calculation (step S24) based on the travel route 50 received from the autonomous moving body 10.

[0155] (Modification of Monitoring Request Processing) In the above, an example has been shown in which the delivery instruction unit 34 of the management device 30 performs the monitoring request processing (step S23), but the autonomous moving body 10 may also perform the monitoring request processing.

[0156] In this case, for example, the route generation unit 12C executes the monitoring request process shown in Fig. 13 based on the determined travel route 50. The route generation unit 12C transmits the monitoring requests in steps S233 and S234 to the management device 30, and the management device 30, having received the monitoring requests, transmits (transfers) the monitoring requests to the other relevant mobile bodies MBs and fixed devices 40. In addition, the route generation unit 12C transmits information on the insurance premium increase flag in step S235 to the management device 30, and the insurance premium calculation unit 33 of the management device 30 executes the insurance premium calculation process (step S24) based on the received information on the insurance premium increase flag.

[0157] (Variation of Generation of Travel Route Candidates) In the above, an example has been shown in which the autonomous moving body 10 includes a route generation unit 12C that generates the travel route candidates 51, but the management device 30 may also generate the travel route candidates 51. Fig. 15 is a diagram showing an example of a management device including a route generation unit.

[0158] In the example shown in FIG. 15 , the autonomous mobile body 10 does not have a route generation unit 12C, and the management device 30 has a route generation unit 110. The route generation unit 110 acquires delivery task information from the delivery instruction unit 34. The route generation unit 110 acquires current location coordinates transmitted from the autonomous mobile body 10. The route generation unit 110 acquires the latest map data and risk map 70 from the map update unit 32. The route generation unit 110 generates multiple travel route candidates 51 based on the acquired information. The route generation unit 110 outputs the generated multiple travel route candidates 51 to the delivery instruction unit 34. The delivery instruction unit 34 selects one travel route 50 from the multiple travel route candidates 51 by a travel route determination process (step S22). The delivery instruction unit 34 transmits the determined travel route 50 together with the delivery task to the autonomous mobile body 10 via the communication device 31.

[0159] The communication device 14 of the autonomous mobile body 10 receives the travel route 50 and the delivery task from the management device 30 and outputs them to the storage device 15 .

[0160] 15, the autonomous mobile body 10 includes a command generation unit 111. The command generation unit 111 reads out the travel route 50 from the storage device 15, calculates a control command to be sent to the drive control unit 12D based on the determined travel route 50 and an environmental map of the surroundings of the autonomous mobile body 10, and outputs the control command to the drive control unit 12D.

[0161] (Variations of Generation of Movement Risk Parameters and Risk Score) In the above, an example has been shown in which the fixed device 40 is equipped with an image processing unit 42A that calculates movement risk parameter information and the risk score Rs, but the fixed device 40 does not have to be equipped with the image processing unit 42A. Furthermore, the management device 30 may perform image processing. Fig. 16 is a diagram showing an example of a management device equipped with an image processing unit.

[0162] 16, the fixed device 40 does not have an image processing unit 42A, and the management device 30 has an image processing unit 120. Also, in Fig. 16, the fixed device 40 does not have a position information acquisition unit 41 or a self-position recognition unit 42B. The position and orientation of the fixed device 40 and the position of the detection area AR of the sensor device 20 are registered in the management device 30 together with identification information when the fixed device 40 is installed, and are therefore known.

[0163] The fixing device 40 transmits three-dimensional data such as captured images and point cloud data acquired by the sensor device 20 to the management device 30 .

[0164] The communication device 31 of the management device 30 outputs the captured images and three-dimensional data received from the fixed device 40 to the image processing unit 120. The image processing unit 120 generates environmental information for the detection area AR based on the received captured images and three-dimensional data and known position information for the detection area AR. Specifically, the image processing unit 120 generates an environmental map for the detection area AR. The image processing unit 120 generates information on movement risk parameters 60 for the detection area AR. The image processing unit 120 calculates a risk score Rs for the detection area AR based on the generated information on the movement risk parameters 60. The image processing unit 120 outputs the generated environmental map and risk score Rs to the map update unit 32. The map update unit 32 updates the map data based on the environmental map acquired from the image processing unit 120, and updates the risk map 70 based on the risk score Rs acquired from the image processing unit 120.

[0165] (2. Other Embodiments) The processing according to each of the above-described embodiments may be implemented in various different forms other than the above-described embodiments.

[0166] Fig. 17 is a diagram showing a calculation system according to another embodiment. In the calculation system 1A shown in Fig. 17, an autonomous moving body 10 is a serving robot that serves food and drinks in a restaurant or the like.

[0167] The autonomous mobile body 10 transports food and drink between a food and drink receiving position, such as a kitchen, and a destination 90, such as a table where a customer is located, inside a building such as a restaurant. A fixed device 40, such as a surveillance camera, is installed at a predetermined location inside the restaurant. A management device 30 acquires order information from an ordering terminal or the like inside the restaurant and transmits the acquired order information to kitchen area equipment. The management device 30 generates a food delivery task according to the order information and transmits the generated food delivery task to the autonomous mobile body 10. When the ordered food and drink is prepared in the kitchen area, the autonomous mobile body 10 that receives the food delivery task loads the prepared food and drink at the receiving position, travels along a travel route 50 to a destination 90 specified in the order information (e.g., the table where the order was accepted), and delivers the food and drink at the destination 90.

[0168] For example, an insurance contract is concluded between an insurance company and a restaurant that is a user of the autonomous mobile body 10, providing compensation for damages or the like involving the autonomous mobile body 10. The management device 30 calculates an insurance premium to be applied to the autonomous mobile body 10 depending on whether the sensor device 20 is able to monitor the autonomous mobile body 10 while it is moving along the travel route 50.

[0169] When a travel route 50 to a destination is included in the detection area AR of a fixed device 40 or a sensor device 20 mounted on another mobile body MB, the management device 30 discounts the insurance premium compared to when the route is not included in the detection area AR.

[0170] There may be areas within the store that are blind spots for the fixed device 40. When the travel route 50 passes through an area that is a blind spot, the management device 30 can assign a monitoring movement task to the autonomous mobile body 10 so that another mobile body (autonomous mobile body 10) performs monitoring movement. This allows monitoring by the sensor device 20 even when passing through an area that is a blind spot, thereby reducing the insurance premium applied to the autonomous mobile body 10.

[0171] The management device 30 requests each autonomous mobile body 10 to perform monitoring movement so that each autonomous mobile body 10 is positioned within the detection area AR of the sensor device 20 in the common portion of the movement route 50 of each autonomous mobile body 10, even if the destinations 90 of each autonomous mobile body 10 are different. This increases the number of situations in which the autonomous mobile body 10 can be monitored by the sensor device 20, thereby reducing the insurance premiums applied to the autonomous mobile body 10. In a food delivery service using the autonomous mobile body 10, for example, there is a possibility that food and drink may be stolen during delivery or that the food and drink may be soiled by vandals, etc. Monitoring the autonomous mobile body 10 while moving using the fixing device 40 or another autonomous mobile body 10 can prevent damage such as theft of food and drink and make it easier to obtain objective evidence when damage occurs.

[0172] In addition to the examples shown in the above-described embodiments, the autonomous moving body 10 according to the present disclosure may be any service robot other than a transport robot. The autonomous moving body 10 may be, for example, a cleaning robot or a security robot. The autonomous moving body 10 is not limited to a moving body that transports cargo.

[0173] Furthermore, the autonomous mobile body 10 according to the present disclosure may be a mobile body that moves autonomously (by automatic driving) in any environment, such as on land, on water, in the air, or underwater. The mobile body may be, for example, a ground vehicle such as an automobile, an unmanned aerial vehicle, an unmanned underwater vehicle, etc. The autonomous mobile body 10 may be capable of moving with a human on board, or may not be capable of moving with a human on board.

[0174] Therefore, in one example, the autonomous mobile body 10 may be an automobile that performs autonomous driving. The autonomous mobile body 10 moves from its current location to its destination according to a travel route 50 set using a car navigation system. The travel route 50 may be determined not only by the autonomous mobile body 10 or the management device 30, but also based on operational input from the passenger. When the calculation system according to the present disclosure acquires the travel route 50 of the autonomous mobile body 10, it acquires information on sensor devices 20 that are located in the vicinity of the travel route 50 and can monitor the autonomous mobile body 10. The calculation system calculates an insurance premium to be applied to the autonomous mobile body 10 depending on whether the sensor devices 20 are able to monitor the autonomous mobile body 10 when it moves along the travel route 50.

[0175] Furthermore, among the processes described in the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using known methods. Furthermore, the information, including the processing procedures, specific names, various data, and parameters shown in the above documents and drawings, can be changed as desired unless otherwise specified. For example, the various information shown in each drawing is not limited to the information shown in the drawings.

[0176] Furthermore, the components of each device shown in the figure are functional concepts and do not necessarily have to be physically configured as shown. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc. For example, the processing executed by the insurance premium calculation unit 33 of the management device 30 may be realized by each autonomous mobile body 10.

[0177] Furthermore, the above-described embodiments and modifications can be combined as appropriate within the scope of not causing any contradiction in the processing content.

[0178] Furthermore, the effects described in this specification are merely examples and are not limiting, and other effects may also be present.

[0179] (3. Effects of the calculation method according to the present disclosure) As described above, in the calculation method according to the present disclosure, a CPU (in an embodiment, the CPU of the management device 30, CPU 1100 (see Figure 18) described below) acquires the movement route of an autonomous moving body, acquires information about sensor devices located in the vicinity of the movement route that can monitor the autonomous moving body, and calculates an insurance premium to be applied to the autonomous moving body depending on whether the sensor devices can monitor the autonomous moving body as it moves along the movement route.

[0180] In this way, the calculation method according to the present disclosure calculates insurance premiums depending on whether an autonomous mobile object can be monitored using sensor devices located in the vicinity of its travel route. When a moving autonomous mobile object can be monitored by surrounding sensor devices, objective evidence showing the situation when an accident involving the autonomous mobile object or a theft occurs can be obtained. In other words, depending on whether monitoring by the sensor devices is possible, it is possible to determine the magnitude of the risk that objective evidence will not be obtained in the event of an accident or other incident, making it impossible to determine who is liable for damages, and insurance premiums can be calculated based on the magnitude of this risk. This reduces the compensation burden on insurance companies and allows insurance premiums to be set at reasonable levels for operators of autonomous mobile objects.

[0181] Furthermore, the information on the sensor device includes the detection area of ​​the sensor device mounted on a fixed device installed around the travel route, and the monitoring availability status includes whether or not there is a detection area through which the travel route passes. In this case, for example, if the travel route passes through a detection area, the insurance premium is reduced compared to when the travel route does not pass through the detection area.

[0182] In this way, the calculation method can determine whether or not the autonomous mobile object can be monitored by using fixed devices such as fixed cameras and signage devices equipped with sensor devices installed around the movement route. When the autonomous mobile object passes through the detection area of ​​the sensor device mounted on the fixed device, there is a high possibility that objective evidence will be obtained in the event of an accident, etc., and therefore the insurance premium burden on the operator of the autonomous mobile object can be reduced.

[0183] Furthermore, the information about the sensor device includes information about other moving bodies that are located in the vicinity of the travel route and are equipped with a sensor device, and the monitoring availability status includes whether the other moving bodies are able to monitor the autonomous moving body while it is traveling along the travel route. In this case, for example, if the autonomous moving body while traveling along the travel route can be monitored by the sensor device of the other moving body, the insurance premium is reduced compared to when it is not monitored by the sensor device of the other moving body.

[0184] In this way, the calculation method can use other moving bodies located in the vicinity of the movement route to determine whether or not the autonomous moving body that is the subject of insurance premium payments can be monitored. If the autonomous moving body can be monitored by a sensor device mounted on another moving body, there is a high possibility that objective evidence will be obtained in the event of an accident, etc., and therefore the insurance premium burden on the operator of the autonomous moving body can be reduced.

[0185] In addition, the calculation method involves the CPU performing a monitoring process in which the other moving bodies monitor the autonomous moving body as it moves along the movement route, based on information about the other moving bodies and the movement route of the autonomous moving body.

[0186] In this way, the calculation method allows other moving bodies in the vicinity of the autonomous moving body to perform monitoring processing, thereby actively monitoring the autonomous moving body as it moves along its movement route. This effectively reduces the risk of not being able to obtain objective evidence in the event of an accident, etc., and as a result, effectively reduces the insurance premiums borne by operators of the autonomous moving body.

[0187] In addition, the calculation method includes, in the monitoring process, having a sensor device of another moving body that is stopped monitor the autonomous moving body moving along the movement route.

[0188] In this way, the calculation method can monitor the autonomous moving object by utilizing other moving objects that are stopped, for example, for charging or refueling, and can increase the availability of objective evidence when an accident or the like occurs.

[0189] In addition, the calculation method involves having another mobile object in a standby state monitor the autonomous mobile object by moving the other mobile object along the same movement route as the autonomous mobile object in the monitoring process.

[0190] In this way, the calculation method allows other moving bodies that have no tasks to perform to accompany the autonomous moving body to be monitored, thereby making it possible to monitor the autonomous moving body, thereby effectively increasing the availability of objective evidence in the event of an accident or the like.

[0191] In addition, the calculation method includes, in the monitoring process, having another moving body moving on a route that is at least partially common to the moving route of the autonomous moving body monitor the autonomous moving body traveling on the common portion of the moving route.

[0192] In this way, the calculation method allows other moving objects that share at least a portion of the same route as the autonomous moving object to be monitored to monitor the autonomous moving object, thereby effectively increasing the availability of objective evidence in the event of an accident or the like.

[0193] The CPU also calculates a risk score of the travel route when the autonomous mobile body travels, and calculates the insurance premium according to the risk score of the travel route in addition to the status of whether monitoring by the sensor device is possible.

[0194] In this way, the degree of risk according to the travel route (i.e., the risk score) can be estimated, and the insurance premium can be calculated according to the risk score of the travel route. This makes it possible to select an appropriate insurance premium according to the operator's objectives, such as prioritizing efficiency by using a route that is relatively risky but has a short travel distance, or prioritizing risk reduction.

[0195] In addition, the calculation method involves the CPU acquiring a risk map that associates position coordinates of map data with movement risks of autonomous moving bodies, and calculating a risk score of the movement route from the risk map.

[0196] In this way, the risk map can appropriately calculate the risk score for each travel route.

[0197] In addition, the calculation method involves the CPU acquiring a plurality of travel route candidates and selecting one of the plurality of travel route candidates as the travel route based on the risk scores of the plurality of travel route candidates.

[0198] In this way, the calculation method not only calculates insurance premiums but also determines an appropriate travel route from the perspective of travel risk based on the risk scores of multiple travel route candidates. For example, by determining the travel route candidate that can most reduce the risk score as the travel route, the insurance premium burden of the operator of the autonomous mobile body can be effectively reduced.

[0199] In the calculation method, the CPU selects a travel route based on the risk score and the shortness of the travel distance. For example, the CPU selects the travel route candidate with the lowest risk score from among travel route candidates whose travel distance satisfies the travel distance condition.

[0200] In this way, the calculation method can determine the travel route from the perspective of the risk score and the shortness of the travel distance, which is related to the operational efficiency of the autonomous mobile unit. For example, it is possible to select the travel route that can reduce risk the most within a range where the travel distance satisfies the travel distance condition, thereby contributing to the realization of an operational form of the autonomous mobile unit that can reduce insurance premiums with lower risk while ensuring operational efficiency.

[0201] Furthermore, the movement risk of an autonomous moving body includes the risk of an accident in which the autonomous moving body encounters an accident, and the risk of theft of the autonomous moving body or property mounted on the autonomous moving body.

[0202] In this way, the calculation method can calculate an appropriate insurance premium that takes into consideration both the risk of an accident involving the autonomous moving body and the risk of theft of the autonomous moving body or property mounted on the autonomous moving body.

[0203] In addition, the travel risk is calculated based on a calculation formula that includes at least one of the parameters of the degree of congestion at the location coordinates, visibility, the history of accidents, travel speed, and the presence or absence of a monitoring device.

[0204] In this way, the calculation method can calculate an appropriate movement risk based on parameters related to at least one of the accident risk and theft risk of the autonomous moving body.

[0205] The CPU also collects information from the sensor devices and updates the movement risk in the risk map based on the collected information.

[0206] In this way, by updating the movement risk in the risk map based on information from the sensor device, insurance premiums can be calculated using real-time movement risk.

[0207] In addition, the calculation method updates the travel route in accordance with the current position of the autonomous moving body while the autonomous moving body is moving along the travel route.

[0208] In this way, the calculation method can update the movement route according to the current position of the autonomous moving body using the movement risk updated based on the information from the sensor device, which makes it possible to flexibly determine the movement route, such as changing to a movement route that can further reduce the movement risk in response to changes in the real-time situation.

[0209] In addition, the calculation system according to the present disclosure includes an autonomous moving body that moves automatically along a movement route, a sensor device capable of monitoring the autonomous moving body, and a management device that communicates with the autonomous moving body and the sensor device, and the management device acquires information about the sensor devices located in the vicinity of the movement route of the autonomous moving body, and calculates an insurance premium to be applied to the autonomous moving body depending on whether the sensor device can monitor the autonomous moving body as it moves along the movement route.

[0210] In this way, in the calculation system according to the present disclosure, the management device calculates insurance premiums based on whether or not the autonomous mobile object can be monitored using sensor devices located in the vicinity of the autonomous mobile object's travel route. When a moving autonomous mobile object can be monitored by surrounding sensor devices, objective evidence showing the situation when an accident involving the autonomous mobile object or a theft occurs can be obtained. In other words, depending on whether or not monitoring by the sensor devices is possible, the degree of risk that objective evidence cannot be obtained in the event of an accident or other incident, making it impossible to determine who is liable for damages, can be determined, and insurance premiums can be calculated based on the degree of this risk. This reduces the compensation burden on insurance companies and allows insurance premiums to be set at reasonable levels for operators of autonomous mobile objects.

[0211] (4. Hardware Configuration) Information devices such as the management device 30 and the control device 12 according to each of the above-described embodiments are realized by a computer 1000 having a configuration such as that shown in FIG. 18 . The following description will be given using the management device 30 according to the present disclosure as an example. FIG. 18 is a hardware configuration diagram showing an example of a computer 1000 that realizes the functions of the management device. The computer 1000 has a CPU 1100, a RAM 1200, a ROM (Read Only Memory) 1300, a HDD (Hard Disk Drive) 1400, a communication interface 1500, and an input / output interface 1600. The various components of the computer 1000 are connected by a bus 1050.

[0212] The CPU 1100 operates and controls each component based on programs stored in the ROM 1300 or the HDD 1400. For example, the CPU 1100 loads the programs stored in the ROM 1300 or the HDD 1400 into the RAM 1200 and executes processing corresponding to the various programs.

[0213] The ROM 1300 stores boot programs such as a Basic Input Output System (BIOS) that is executed by the CPU 1100 when the computer 1000 is started, and programs that depend on the hardware of the computer 1000 .

[0214] HDD 1400 is a computer-readable recording medium that non-temporarily records programs executed by CPU 1100 and data used by such programs. Specifically, HDD 1400 is a recording medium that records program data 1450, which is an example of a calculation program according to the present disclosure.

[0215] The communication interface 1500 is an interface for connecting the computer 1000 to an external network 1550 (e.g., the Internet). For example, the CPU 1100 receives data from other devices and transmits data generated by the CPU 1100 to other devices via the communication interface 1500.

[0216] The input / output interface 1600 is an interface for connecting the input / output device 1650 and the computer 1000. For example, the CPU 1100 receives data from input devices such as a keyboard or a mouse via the input / output interface 1600. The CPU 1100 also transmits data to output devices such as a display, a speaker, or a printer via the input / output interface 1600. The input / output interface 1600 may also function as a media interface for reading programs and the like recorded on a predetermined recording medium. Examples of media include optical recording media such as DVDs (Digital Versatile Discs) and PDs (Phase Change Rewritable Disks), magneto-optical recording media such as MOs (Magneto-Optical Disks), tape media, magnetic recording media, and semiconductor memories.

[0217] For example, when the computer 1000 functions as the management device 30 according to the embodiment, the CPU 1100 of the computer 1000 executes a program loaded onto the RAM 1200 to realize functions such as the insurance premium calculation unit 33 and the delivery instruction unit 34. The HDD 1400 stores data such as sensor data, map data, and a risk map 70. The CPU 1100 reads and executes program data 1450 from the HDD 1400, but as another example, the CPU 1100 may obtain these programs from another device via an external network 1550.

[0218] The present technology may also be configured as follows. (1) A calculation method in which a CPU acquires a movement route of an autonomous moving body, acquires information about sensor devices located in the vicinity of the movement route and capable of monitoring the autonomous moving body, and calculates an insurance premium to be applied to the autonomous moving body depending on a status of whether the sensor devices can monitor the autonomous moving body while the autonomous moving body is moving along the movement route. (2) The calculation method described in (1), in which the information about the sensor devices includes a detection area of ​​the sensor device mounted on a fixed device installed in the vicinity of the movement route, and the status of whether monitoring can be performed includes whether the detection area passes through the movement route. (3) The calculation method described in (2), in which the insurance premium is reduced when the movement route passes through the detection area compared to when the movement route does not pass through the detection area. (4) The calculation method described in any of (1) to (3), in which the information about the sensor devices includes information about other moving bodies located in the vicinity of the movement route and equipped with the sensor devices, and the status of whether monitoring can be performed includes whether the other moving bodies can monitor the autonomous moving body while the autonomous moving body is moving along the movement route. (5) The calculation method according to (4), wherein, when the autonomous moving body can be monitored by the sensor device of the other moving body while moving along the movement route, the insurance premium is reduced compared to when the autonomous moving body is not monitored by the sensor device of the other moving body. (6) The calculation method according to (4) or (5), wherein the CPU performs a monitoring process in which the other moving body monitors the autonomous moving body while moving along the movement route, based on information about the other moving body and the movement route of the autonomous moving body. (7) The calculation method according to (6), wherein, in the monitoring process, the sensor device of the other moving body that is stopped monitoring the autonomous moving body moving along the movement route. (8) The calculation method according to (6) or (7), wherein, in the monitoring process, the other moving body in a standby state is made to monitor the autonomous moving body by moving along the same movement route as the autonomous moving body.(9) The calculation method according to any one of (6) to (8), wherein, in the monitoring process, the other moving body moving on a route at least partially common to the movement route of the autonomous moving body monitors the autonomous moving body traveling on a common portion of the movement route. (10) The calculation method according to any one of (1) to (9), wherein the CPU calculates a risk score of the movement route when the autonomous moving body moves, and calculates the insurance premium according to the risk score of the movement route in addition to a status of whether monitoring by the sensor device is possible. (11) The calculation method according to (10), wherein the CPU acquires a risk map that associates position coordinates of map data with movement risks of the autonomous moving body, and calculates the risk score of the movement route from the risk map. (12) The calculation method according to (11), wherein the CPU acquires a plurality of movement route candidates, and selects one of the plurality of movement route candidates as the movement route based on the risk scores of the plurality of movement route candidates. (13) The calculation method according to (12), wherein the CPU selects the travel route based on the risk score and the shortness of the travel distance. (14) The calculation method according to (13), wherein the CPU selects the travel route candidate with the lowest risk score as the travel route from among the travel route candidates whose travel distance satisfies a travel distance condition. (15) The calculation method according to any of (11) to (14), wherein the travel risk of the autonomous moving body includes an accident risk of the autonomous moving body being involved in an accident and a theft risk of the autonomous moving body or property mounted on the autonomous moving body. (16) The calculation method according to any of (11) to (15), wherein the travel risk is calculated based on a calculation formula including at least any one of parameters of congestion level at the position coordinates, visibility, accident history, travel speed, and presence or absence of a monitoring device. (17) The calculation method according to (16), wherein the CPU collects information from the sensor device and updates the travel risk in the risk map based on the collected information.(18) The calculation method according to (17), wherein the movement route is updated according to a current position of the autonomous moving body while moving along the movement route. (19) A calculation program causing a CPU to execute the following steps: acquiring a movement route of an autonomous moving body; acquiring information about sensor devices located in the vicinity of the movement route and capable of monitoring the autonomous moving body; and calculating an insurance premium to be applied to the autonomous moving body according to a status of whether the sensor devices can monitor the autonomous moving body when it moves along the movement route. (20) A calculation system comprising: an autonomous moving body that moves automatically along a movement route; a sensor device capable of monitoring the autonomous moving body; and a management device that communicates with the autonomous moving body and the sensor device, wherein the management device acquires information about the sensor devices located in the vicinity of the movement route of the autonomous moving body, and calculates an insurance premium to be applied to the autonomous moving body according to a status of whether the sensor devices can monitor the autonomous moving body when it moves along the movement route.

[0219] 1, 1A Calculation system 10, 10A, 10B, 10C, 10D Autonomous moving body 20 Sensor device 30 Management device 40, 40A, 40B, 40C Fixed device 50 Movement route 51 Movement route candidate 60 Parameter 70 Risk map 1000 Computer 1100 CPU Rs Risk score

Claims

1. A calculation method in which a CPU acquires the movement route of an autonomous moving body, acquires information on sensor devices located in the vicinity of the movement route that can monitor the autonomous moving body, and calculates an insurance premium to be applied to the autonomous moving body depending on whether the sensor devices can monitor the autonomous moving body as it moves along the movement route.

2. The calculation method according to claim 1, wherein the information on the sensor device includes the detection area of ​​the sensor device mounted on a fixed device installed in the vicinity of the movement route, and the monitoring possibility status includes the presence or absence of the detection area through which the movement route passes.

3. The calculation method according to claim 2, wherein the insurance premium is reduced when the travel route passes through the detection area compared to when the travel route does not pass through the detection area.

4. The calculation method according to claim 1, wherein the information about the sensor device includes information about other moving bodies that are located in the vicinity of the movement route and are equipped with the sensor device, and the monitoring availability status includes whether the other moving bodies are able to monitor the autonomous moving body when it moves along the movement route.

5. The calculation method according to claim 4, wherein, when the autonomous moving body can be monitored by the sensor device of the other moving body while moving along the movement route, the insurance premium is reduced compared to when the autonomous moving body is not monitored by the sensor device of the other moving body.

6. The calculation method according to claim 4, wherein the CPU performs a monitoring process in which the other moving body monitors the autonomous moving body as it moves along the movement route, based on information about the other moving body and the movement route of the autonomous moving body.

7. The calculation method according to claim 6, wherein the monitoring process involves having the sensor device of the other moving body that is stopped monitor the autonomous moving body that is moving along the movement route.

8. The calculation method according to claim 6, wherein in the monitoring process, the other mobile body in a standby state is made to monitor the autonomous mobile body by moving along the same movement route as the autonomous mobile body.

9. The calculation method according to claim 6, wherein, in the monitoring process, the other moving body moving on a route that is at least partially common to the movement route of the autonomous moving body is made to monitor the autonomous moving body traveling on the common portion of the movement route.

10. The calculation method according to claim 1, wherein the CPU calculates a risk score of the movement route when the autonomous moving body moves, and calculates the insurance premium according to the risk score of the movement route in addition to the status of whether monitoring by the sensor device is possible.

11. The calculation method according to claim 10, wherein the CPU acquires a risk map that associates position coordinates of map data with movement risks of the autonomous moving body, and calculates the risk score of the movement route from the risk map.

12. The calculation method according to claim 11, wherein the CPU acquires a plurality of travel route candidates, and selects one of the plurality of travel route candidates as the travel route based on the risk scores of the plurality of travel route candidates.

13. The calculation method according to claim 12, wherein the CPU selects the travel route based on the risk score and a short travel distance.

14. The calculation method according to claim 13, wherein the CPU selects, as the travel route, the travel route candidate with the lowest risk score from among the travel route candidates whose travel distance satisfies a travel distance condition.

15. The calculation method according to claim 11, wherein the movement risk of the autonomous mobile body includes an accident risk of the autonomous mobile body being involved in an accident and a theft risk of the autonomous mobile body or property mounted on the autonomous mobile body.

16. The calculation method according to claim 11, wherein the movement risk is calculated based on a calculation formula including at least one of the parameters of the degree of congestion at the location coordinates, visibility, accident history, movement speed, and the presence or absence of a monitoring device.

17. The calculation method according to claim 16, wherein the CPU collects information from the sensor device, and updates the movement risk in the risk map based on the collected information.

18. The calculation method according to claim 17, wherein the travel route is updated in accordance with a current position of the autonomous moving body while the autonomous moving body is moving along the travel route.

19. A calculation program that causes a CPU to execute the following steps: acquiring the movement route of an autonomous mobile body; acquiring information about sensor devices located in the vicinity of the movement route that can monitor the autonomous mobile body; and calculating an insurance premium to be applied to the autonomous mobile body depending on whether the sensor devices can monitor the autonomous mobile body when it moves along the movement route.

20. A calculation system comprising: an autonomous mobile body that moves automatically along a movement route; a sensor device capable of monitoring the autonomous mobile body; and a management device that communicates with the autonomous mobile body and the sensor device, wherein the management device acquires information about the sensor devices located in the vicinity of the movement route of the autonomous mobile body, and calculates an insurance premium to be applied to the autonomous mobile body depending on whether the sensor device can monitor the autonomous mobile body as it moves along the movement route.

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