JP6857211B2 - Information processing method and server equipment - Google Patents

Description

The present disclosure relates to a technique for processing dangerous information, and particularly to the management of dangerous information at each collected point.

In recent years, various services have been provided to mobile objects such as automobiles via a network. As one of the conventional services, there is known a danger information processing system that determines and warns a point where an accident is likely to occur while a car is running (see, for example, Patent Document 1).

Japanese Unexamined Patent Publication No. 2014-154004

In the above-mentioned conventional danger information processing system, further improvement is required.

In order to solve the above-mentioned conventional problems, the information processing method according to one aspect of the present invention is an information processing method executed by a computer that manages a degree of risk in which the presence or absence of a dangerous event is expressed numerically. The storage unit included in the computer is configured so that the risk level can be stored for each of the plurality of points and each combination of the plurality of situations, and the computer is the first unit in which the risk level is not stored in the storage unit. The risk of the combination is estimated using the risk stored in the storage unit.

In order to solve the above-mentioned conventional problems, the danger information processing method according to one aspect of the present invention is a danger information processing method used in a danger information processing system that manages the degree of danger with respect to a point where a moving body is located. The degree of danger input for the combination of the point where the dangerous event occurred and the situation when the dangerous event occurred at the point is stored as the danger information in the storage unit provided in the danger information processing system. By using the first combination in which the risk level is not input as the processing target and calculating the risk level for the first combination using three or more of the danger information stored in the storage unit, the said The first danger information, which is the degree of danger, is estimated, and the three or more danger information are the second danger information, which is the degree of danger for the second combination having the same point as the first combination but different situations. , The third danger information, which is the degree of danger for the third combination having the same situation as the first combination but different points, and the third combination having the same situation as the second combination and the same point as the third combination. It includes a fourth danger information which is a degree of danger for the combination of four.

It should be noted that these comprehensive or specific embodiments may be realized by a system, an apparatus, an integrated circuit, a computer program, or a recording medium, and any of the system, an apparatus, a method, an integrated circuit, a computer program, and a recording medium. It may be realized by various combinations.

According to the present disclosure, further improvement can be realized.

It is a figure which shows an example of the whole structure of the dangerous information processing system in embodiment. It is a block diagram which shows an example of the structure of the danger information processing system which realizes the danger information processing method which concerns on Embodiment 1. FIG. It is a flow chart which shows an example of the processing procedure which records the evaluation value in the whole observation table which concerns on Embodiment 1. FIG. FIG. 5 is a flow chart showing an example of a processing procedure in which the danger information processing system according to the first embodiment records an estimated evaluation value in an overall estimation table. It is a flow chart which shows an example of the processing procedure which a dangerous information processing system records an estimated evaluation value in an overall estimation table. It is a flow chart which shows an example of the processing procedure which a dangerous information processing system records an estimated evaluation value in an overall estimation table. It is a figure for demonstrating the structure of the whole observation table which concerns on Embodiment 1. FIG. It is a figure for demonstrating the structure of the whole estimation table which concerns on Embodiment 1. FIG. It is a figure which shows an example of the whole observation table which concerns on Embodiment 1. FIG. It is a figure which shows an example of the whole estimation table which concerns on Embodiment 1. FIG. It is a block diagram which shows an example of the structure of the danger information processing system which realizes the danger information processing method which concerns on Embodiment 2. FIG. 5 is a flow chart showing an example of a processing procedure in which the danger information processing system according to the second embodiment records evaluation values in an overall observation table and an individual observation table. FIG. 5 is a flow chart showing an example of a processing procedure in which the danger information processing system according to the second embodiment records estimated evaluation values in an overall estimation table and an individual estimation table. FIG. 5 is a flow chart showing an example of a processing procedure in which the danger information processing system according to the second embodiment records estimated evaluation values in an overall estimation table and an individual estimation table. FIG. 5 is a flow chart showing an example of a processing procedure in which the danger information processing system according to the second embodiment records estimated evaluation values in an overall estimation table and an individual estimation table. It is a figure which shows an example of the whole estimation table which concerns on Embodiment 2. FIG. It is a figure which shows an example of the individual observation table which concerns on Embodiment 2. FIG. It is a figure which shows an example of the individual estimation table which concerns on Embodiment 2. FIG. It is a block diagram which shows an example of the structure of the danger information processing system which realizes the danger information processing method which concerns on Embodiment 3. It is a flow chart which shows an example of the processing procedure which the dangerous information processing system which concerns on Embodiment 3 records in an event frequency table. FIG. 5 is a flow chart showing an example of a processing procedure in which the danger information processing system according to the third embodiment records an estimated evaluation value in an overall estimation table according to a priority. It is a figure which shows an example of the event frequency table which concerns on Embodiment 3. It is a block diagram which shows an example of the structure of the danger information processing system which realizes the danger information processing method which concerns on Embodiment 4. It is a flow chart which shows an example of the processing procedure of the danger information presenting apparatus which concerns on Embodiment 4. FIG. It is an overall view which shows an example of a dangerous information processing system. It is an overall view which shows an example of a dangerous information processing system. It is an overall view which shows an example of a dangerous information processing system. It is an overall view which shows an example of a dangerous information processing system. It is an overall view which shows an example of a dangerous information processing system.

(Knowledge on which this disclosure was based)
The present inventor has found that the following problems arise with respect to the dangerous information processing system described in the "Background Technology" column.

In recent years, a safe driving support system has been developed to support safe driving of automobiles. In such a safe driving support system, the automobile acquires information in front of the automobile from a distance sensor or the like while traveling on the road. Then, using the acquired information, the automobile automatically applies a brake or the like when the distance to the obstacle in front becomes less than a certain level so as not to cause a traffic accident.

However, when a vehicle targeted for safe driving support controls driving only according to information sensed by a sensor mounted on the vehicle, it is difficult to warn the driver in advance before a dangerous situation occurs. There was a problem that there was.

For example, the danger information processing system of Patent Document 1 discloses the following technology. In this technology, while the vehicle is driving, the camera of the tablet terminal captures and records images at regular intervals. In addition, the operation information and the behavior information, which are the results of detecting the driving operation and the behavior of the vehicle during driving, are recorded. From each image, operation information, and behavior information acquired in this way, risk factors that can be factors that cause a risk such as an accident at a target point are acquired. Then, the danger type to be statistically warned is determined based on each acquired risk factor, and the danger information of the target point is registered in the map data. As a result, potential danger information can be obtained in advance.

In the danger information processing system of Patent Document 1, the dangers indicated in the danger types statistically occur based on the individual risk levels of the same road section, the same time zone, and the same danger type for each danger type. Find the statistical risk that indicates ease. At this time, the statistical risk of one danger type is statistically obtained based on each risk factor obtained from each of the same road section, each image in the same time zone, and each behavior operation information. That is, in Patent Document 1, when trying to calculate the risk level in a wide area where a car travels, all risk factors and all risk types are actually sensed for all road sections and all time zones included in the area. Without it, there was a problem that the degree of risk could not be determined. Further, there is a problem that the fact that the degree of danger differs depending on the driver even if the road section is the same, the time zone is the same, and the danger type is the same is not taken into consideration.

Based on the above studies, the present inventor has examined the following improvement measures in order to solve the above problems.

In order to solve the above-mentioned conventional problems, the information processing method according to one aspect of the present invention is an information processing method executed by a computer that manages a degree of risk in which the presence or absence of a dangerous event is expressed numerically. The storage unit included in the computer is configured so that the risk level can be stored for each of the plurality of points and each combination of the plurality of situations, and the computer is the first unit in which the risk level is not stored in the storage unit. The risk of the combination is estimated using the risk stored in the storage unit.

Further, for example, the position information indicating the current position of the moving body and the sensor data indicating the current situation are received, and the estimation determines the degree of risk for the combination of the received point indicated by the position information and the situation indicated by the sensor data. When the first combination is not stored in the storage unit, the degree of risk for the first combination may be estimated.

Further, for example, in the storage unit, there is a second risk level for the second combination having the same point as the first combination but different situations, and a third having the same situation as the first combination but different points. Includes a third risk for the combination of, and a fourth risk for a fourth combination that is in the same situation as the second combination and has the same location as the third combination. In the estimation of the degree of risk, the correlation of the degree of risk between the points is calculated using the second degree of risk and the fourth degree of risk, and the correlation of the degree of risk between the points obtained by the calculation is calculated. May be used to estimate the risk for the first combination from the third risk.

Further, for example, in the storage unit, there is a second risk level for the second combination having the same point as the first combination but different situations, and a third having the same situation as the first combination but different points. Includes a third risk for the combination of, and a fourth risk for a fourth combination that is in the same situation as the second combination and has the same location as the third combination. In the estimation of the degree of risk, the correlation of the degree of risk between the situations is calculated using the third degree of risk and the fourth degree of risk, and the correlation of the degree of risk between the situations obtained by the calculation is performed. May be used to estimate the risk for the first combination from the second risk.

Further, for example, the risk level obtained in the estimation may be stored in the storage unit.

Further, for example, in the storage unit, the risk level for each driver for each combination of the plurality of points and the plurality of situations for each of the plurality of points is stored as individual danger information for each driver. , The risk level for all drivers is configured to be storable as overall danger information, and the risk level for a combination in which the risk level is not stored in the individual danger information for each driver is described in the individual danger information and the above. Using the correlation between the individual danger information and the total danger information obtained by calculating the correlation between the total danger information and the risk level, the risk level of the total danger information can be calculated from the risk level. The risk level of the individual danger information may be estimated.

Further, for example, in the storage unit, frequency information in which the frequency of traffic events and each of the plurality of points are associated with each other is stored in the storage unit, and the frequency information is used to process the processing order of the plurality of points. In the estimation, the risk level for the first combination to be processed may be estimated according to the processing order.

Further, for example, the storage unit holds frequency information in which the driving frequency of each driver is associated with the driver, and the frequency information is used to determine the processing order of a plurality of drivers. However, in estimating the risk of the individual danger information, the risk of the individual danger information to be processed may be estimated according to the processing order.

Further, for example, further, the position information indicating the current position and the sensor data indicating the current situation are received from the moving body, and the degree of risk for the combination of the received point indicated by the position information and the situation indicated by the sensor data is determined. Information acquired from the storage unit and based on the acquired risk level may be transmitted to the moving body.

Further, for example, the information based on the risk level may be a control signal for operating the moving body.

Further, for example, the operation of the moving body is any one of an operation of changing the moving speed of the moving body, an operation of stopping the moving body, and an operation of changing the steering of the moving body. It may be an operation.

The danger information processing method according to one aspect of the present invention is a danger information processing method used in a danger information processing system that manages the degree of danger with respect to a point where a moving object is located, and is a point where a danger event occurs and the above-mentioned point. In the first method, the risk level input for the combination with the situation when the danger event occurs is stored as the danger information in the storage unit provided in the danger information processing system, and the risk level is not input. By using the combination as a processing target and calculating the risk level for the first combination using three or more of the danger information stored in the storage unit, the first danger information which is the risk level can be obtained. Estimated, the three or more danger information has the same situation as the first combination as the second danger information which is the degree of danger for the second combination having the same point as the first combination but different situations. The third danger information, which is the risk level for the third combination with different points, and the fourth combination, which is the same situation as the second combination but the same point as the third combination. Includes danger information.

According to this, it can be obtained by estimating the risk level of the first combination in which the risk level is not input from the combination of the point where the risk level has already been acquired and the situation. As a result, it is possible to acquire the degree of risk for the combination of all the managed points and the situation without actually acquiring the sensed data.

Further, for example, in the estimation, the correlation between the points is calculated by using the second danger information and the fourth danger information, and the risk between the points obtained by the calculation is calculated. The risk level for the first combination may be calculated from the risk level of the third danger information using the correlation of.

Further, for example, in the estimation, the risk degree between the situations is calculated by using the third danger information and the fourth danger information, and the risk degree between the situations is calculated. The risk level for the first combination may be calculated from the risk level of the second danger information using the correlation of.

Further, for example, the first danger information obtained in the estimation may be stored in the storage unit.

Therefore, the estimated risk level can be retained in the storage unit.

Further, for example, for the combination of the point where the dangerous event occurs and the situation when the dangerous event occurs at the point, the degree of danger input for each driver is set to the individual danger for each driver. It is obtained by storing it in the storage unit as information and as overall danger information for all drivers, and calculating the correlation of the degree of danger between the individual danger information and the overall danger information. The risk level of the individual danger information may be calculated from the risk level of the overall danger information by using the correlation of the risk level between the individual danger information and the overall danger information.

According to this, it is possible to identify a driver and obtain an individual risk level for each specified driver by using the risk level for a combination of a point and a situation for the whole. As a result, it is possible to acquire the degree of risk for each driver without actually acquiring all the sensing data individually for each driver for the combination of all the managed points and the situation.

Further, for example, frequency information in which the frequency of traffic events and points are associated with each other is stored in the storage unit, and the processing order of a plurality of points is determined using the frequency information. In the estimation, the processing is performed. The first danger information to be processed may be determined according to the order.

For this reason, it is possible to obtain an estimated evaluation value of a point where the frequency of traffic events is high, such as a point with a large amount of traffic, without waiting for the risk estimation process for the combination in which the risk level is not input among all the combinations. Can be done.

Further, for example, the frequency information in which the driving frequency of each driver is associated with the driver is stored in the storage unit, and the frequency information is used to determine the processing order of a plurality of drivers, and the individual drivers are determined. In the calculation of the degree of danger of the danger information, the individual danger information to be processed may be determined according to the processing order.

Therefore, it is possible to obtain the individual danger information of the driver who frequently drives without waiting for the risk estimation process for the combination in which the risk level is not input among the individual danger information of all the drivers. ..

Further, for example, further, the position information indicating the current position and the sensor data indicating the current situation are received from the moving body, and the degree of risk for the combination of the received point indicated by the position information and the situation indicated by the sensor data is determined. , The information acquired from the storage unit and based on the acquired risk level may be transmitted to the moving body.

Therefore, it is possible to notify the user who is driving the moving body of the degree of danger of the traveling point in real time.

Further, for example, it is further determined that a warning is presented when the acquired risk level exceeds a threshold value held in advance, and information based on the acquired risk level is transmitted to the moving body to obtain the acquisition. When the risk level is equal to or lower than the threshold value held in advance, it is determined that the warning is not presented, and the information based on the acquired risk level does not have to be transmitted to the moving body.

It should be noted that these general or specific embodiments may be implemented in a recording medium such as a system, device, integrated circuit, computer program or computer readable CD-ROM, system, method, integrated circuit, etc. It may be realized by any combination of computer programs or recording media.

This disclosure discloses a method of calculating the degree of risk of a part that cannot be sensed by sensing some road sections, some time zones, and some risk factors.

Based on the above considerations, the inventors have come up with the respective aspects of the present disclosure.

Hereinafter, the dangerous information processing system according to the embodiment of the present disclosure will be described with reference to the drawings.

It should be noted that all of the embodiments described below show a preferred specific example of the present disclosure. That is, the numerical values, shapes, materials, components, arrangement and connection forms of components, steps, order of steps, etc. shown in the following embodiments are examples of the present disclosure, and are not intended to limit the present disclosure. .. The present disclosure is specified based on the statements of the claims. Therefore, among the components in the following embodiments, the components not described in the independent claims indicating the highest level concept of the present disclosure are not necessarily necessary for achieving the tasks of the present disclosure, but are more preferable. It is described as a component constituting the form.

(Overview of services provided)
FIG. 25A shows an overall picture of the information providing system according to the present embodiment. As one of the services of the information providing system shown in FIG. 25, a danger information processing system that manages the collected danger information for each point is provided.

Group C100 is, for example, a company, a group, a household, a roadside device, a vehicle, or the like, and the scale thereof does not matter. In group C100, there are a plurality of devices C101, device A, device B, and gateway C102. The plurality of devices C101 include devices that can connect to the Internet (for example, smartphones, PCs, TVs, etc.) and devices that cannot connect to the Internet by themselves (for example, lighting, washing machines, refrigerators, car navigation systems, etc.). Also exists. Even if the device itself cannot connect to the Internet, there may be a device that can connect to the Internet via the gateway C102. Further, in the group C100, there is a user C10 who uses a plurality of devices C101.

The data center operating company C110 has a cloud server C111. The cloud server C111 is a virtualization server that cooperates with various devices via the Internet. It mainly manages huge data (big data) that is difficult to handle with ordinary database management tools. The data center operating company C110 manages data, manages the cloud server C111, and operates a data center that performs these operations. Details of the services performed by the data center operating company C110 will be described later. Here, the data center operating company C110 is not limited to a company that only manages data and operates the cloud server C111. For example, if a device maker that develops and manufactures one of a plurality of devices C101 also manages data and cloud server C111, the device maker corresponds to the data center operating company C110. ((B) in FIG. 25). Further, the data center operating company C110 is not limited to one company. For example, when a device maker and another management company jointly or share data management and operation of the cloud server C111, both or one of them shall correspond to the data center operating company C110 (FIG. 25). (C)).

The service provider C120 owns the server C121. The server C121 referred to here includes, for example, a memory in a personal computer regardless of its scale. In addition, the service provider may not have the server C121.

The gateway C102 is not indispensable in the above service. For example, when the cloud server C111 manages all the data, the gateway C102 becomes unnecessary. Also, there may be no device that cannot connect to the Internet by itself, such as when all devices in the home are connected to the Internet.

Next, the flow of information in the above service will be described.

First, the device A or the device B of the group C100 transmits each log information to the cloud server C111 of the data center operating company C110. The cloud server C111 collects the log information of the device A or the device B ((a) in FIG. 25). Here, the log information is information indicating, for example, an operation status, an operation date and time, and the like of a plurality of devices C101. For example, TV viewing history, recorder recording reservation information, washing machine operation date / time / amount of laundry, refrigerator opening / closing date / time / opening / closing frequency, etc., but not limited to these, all that can be obtained from any device. Information. The log information may be provided directly to the cloud server C111 from the plurality of devices C101 itself via the Internet. Further, log information may be temporarily accumulated in the gateway C102 from the plurality of devices C101 and provided to the cloud server C111 from the gateway C102.

Next, the cloud server C111 of the data center operating company C110 provides the accumulated log information to the service provider C120 in a fixed unit. Here, the unit may be a unit capable of organizing the collected information by the data center operating company and providing it to the service provider C120, or may be a unit requested by the service provider C120. Although it is described as a fixed unit, it does not have to be constant, and the amount of information to be provided may change depending on the situation. The log information is stored in the server C121 owned by the service provider C120 as needed ((b) in FIG. 25). Then, the service provider C120 organizes the log information into information suitable for the service provided to the user and provides the log information to the user. The providing user may be a user C10 who uses a plurality of devices C101, or an external user C20. The service providing method to the user may be provided to the user directly from the service provider, for example ((b) and (e) in FIG. 25). Further, the method of providing the service to the user may be provided to the user via the cloud server C111 of the data center operating company C110 again ((c) and (d) of FIG. 25). Further, the cloud server C111 of the data center operating company C110 may organize the log information into information suitable for the service provided to the user and provide the log information to the service provider C120.

The user C10 and the user C20 may be different or the same.

Hereinafter, embodiments according to one aspect of the present invention will be described with reference to the drawings.

(Embodiment 1)
FIG. 1 is a diagram showing an example of the overall configuration of the danger information processing system 100 according to the first embodiment. The danger information processing system 100 shown in FIG. 1 includes a server device 101, a mobile body 102, a mobile body 103, an observation device 104, and an observation device 105.

The server device 101, the mobile body 102, the mobile body 103, the observation device 104, and the observation device 105 are connected to each other so as to be able to communicate with each other via a communication network. As the communication network, for example, the Internet can be adopted. Therefore, the server device 101, the mobile body 102, the mobile body 103, the observation device 104, and the observation device 105 each transmit and receive various data using a communication protocol such as TCP / IP.

In the present embodiment, the server device 101 receives the position information and the sensor data from the mobile body 102, the mobile body 103, the observation device 104, or the observation device 105. The server device 101 records an overall observation table in which an evaluation value indicating the presence or absence of a dangerous event is associated as a risk level with respect to a combination of a point indicated by the received position information and a situation indicated by the sensor data. The server device 101 estimates the degree of risk (estimated evaluation value) for the combination of the point and the situation not recorded in the overall observation table by using the overall observation table.

Each of the mobile body 102 and the mobile body 103 is, for example, an automobile, a two-wheeled vehicle, a mobile terminal, or the like. In the present embodiment, each of the moving body 102 and the moving body 103 acquires position information indicating the current position of the moving body and other sensor data such as the state of the moving body itself and the surrounding situation. Each of the mobile body 102 and the mobile body 103 transmits the acquired position information and sensor data to the server device 101.

The observation device 104 and the observation device 105 are, for example, an ETC (Electronic Toll Collection System) roadside device, a DSRC (Dedicated Short Range Communication) roadside device, and the like. In the present embodiment, position information indicating the current position of the observation device and other sensor data such as the state of the observation device itself and the surrounding conditions are acquired. The observation devices 104 and 105 transmit the acquired position information and sensor data to the server device 101.

Although only two moving bodies, a moving body 102 and a moving body 103, are shown in FIG. 1, one moving body or three or more moving bodies may be provided. Further, in FIG. 1, only two observation devices, the observation device 104 and the observation device 105, are shown, but one or three or more observation devices may be provided.

FIG. 2 is a block diagram showing an example of the configuration of the danger information processing system 100 that realizes the danger information processing method according to the first embodiment. In the danger information processing system of FIG. 2, a mobile body 102 that acquires and transmits position information and other sensor data such as surrounding conditions and a server device 101 having a function of a danger information processing device are connected via a network. The configuration to be processed is shown. The server device 101 is composed of, for example, a computer including a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), and the like.

Each of the mobile body 102 and the mobile body 103 includes a data acquisition unit 201. Further, the observation device 104 and the observation device 105 include a data acquisition unit 201. Hereinafter, the moving body 102 will be described, but the moving body 102 may be replaced with the moving body 103, the observation device 104, or the observation device 105.

The data acquisition unit 201 senses the peripheral situation of the moving body 102, and acquires the position information indicating the current position and the sensor data indicating the state of the moving body 102 itself and the surrounding situation of the moving body 102. The data acquisition unit 201 transmits the acquired position information and sensor data to the server device 101. The data acquisition unit 201 acquires, for example, a position acquisition means such as GPS (Global Positioning System) for acquiring position information and sensor data, an in-vehicle camera, laser data, a millimeter wave radar, a sonar, an infrared camera, and a road surface condition. It is equipped with sensor devices such as sensors, drowsiness sensors, and weather sensors. Then, the data acquisition unit 201 acquires the position information acquired by the position acquisition means and the sensor data sensed by each sensor device. Further, the data acquisition unit 201 includes CAN (Control Area Network) information acquisition means, vehicle-to-vehicle communication information means, road-to-vehicle communication information means, vehicle peripheral information recognition means, and the like, and the position acquisition means can be obtained by communicating or recognizing. The acquired position information and the sensor data sensed by each sensor device are acquired.

For example, the data acquisition unit 201 is provided with an in-vehicle camera, and determines the presence or absence of a vehicle in front, which is the result obtained by performing image recognition processing on the captured image, and the distance to the vehicle in front measured using the captured image. , May be acquired as sensor data. Further, the data acquisition unit 201 may include an in-vehicle camera or a millimeter-wave radar, measure the number of pedestrians within a certain distance, and acquire it as sensor data. Further, the data acquisition unit 201 may include a road surface condition sensor, measure whether or not the road surface is frozen, and acquire it as sensor data. Further, the data acquisition unit 201 may include a weather sensor, measure whether it is rainy or not, and acquire it as sensor data. There are no restrictions on the type of sensor data.

Further, the sensor data acquired by the data acquisition unit 201 may be different or the same depending on the individual mobile bodies 102 and 103 and the individual observation devices 104 and 105.

The server device 101 includes a control unit 200, an input unit 203, a storage unit 205, and a reception unit 206. The server device 101 has a function as a danger information processing device.

The receiving unit 206 receives the position information and the sensor data from the mobile bodies 102 and 103 or the observation devices 104 and 105. The receiving unit 206 notifies (transmits) the received position information and the sensor data to the control unit 200.

The control unit 200 includes a management unit 202 and a calculation unit 204.

The management unit 202 generates an overall observation table in which evaluation values indicating the presence or absence of a dangerous event are associated with a combination of a point and a situation, and stores it in the storage unit 205. The overall observation table is composed of, for example, a row of points indicated by position information and a column of situations indicated by sensor data, and for each combination of points and situations, danger information which is a degree of danger for the combination. The plurality of danger information whose risk levels are associated with each of the plurality of combinations is stored in the storage unit 205 as an overall observation table, but it does not have to be stored as a table, and each of the plurality of combinations As long as the information is associated with the degree of risk, the information may be expressed in a form other than the table.

The management unit 202 evaluates the combination of the point indicated by the position information and the situation indicated by the sensor data by using the position information and the sensor data acquired from the receiving unit 206 or the presence / absence of the occurrence of a dangerous event acquired from the input unit 203. The value (evaluation value (risk degree) indicating the presence or absence of the occurrence of a dangerous event) is recorded in the cell indicating the corresponding combination of the overall observation table held in the storage unit 205. The occurrence of a dangerous event is, for example, the presence or absence of an accident. The occurrence of a dangerous event may be, for example, the presence or absence of a hiyari hat.

In addition, the management unit 202 uses the overall observation table to generate an overall estimation table in which the estimated risk levels (estimated evaluation values indicating the presence or absence of occurrence of dangerous events) are associated with each combination of points and situations. Then, it is stored in the storage unit 205. In the overall estimation table, for example, the points indicated by the position information are rowed, the situations indicated by the sensor data are arranged in columns, and for each combination of the points and the situations, the risk indicating the risk including the estimated evaluation value as the risk for the combination is indicated. Consists of information. The management unit 202 sets the cell (hereinafter referred to as “unevaluated cell”) corresponding to the combination of the point and the situation where the risk level is not held in the general observation table with other points and the situation in the general observation table. Estimated evaluation values are calculated using the risk levels held in the cells corresponding to the combination of. Specifically, when calculating the estimated evaluation value, the management unit 202 instructs the calculation unit 204 to perform the calculation, and performs the calculation processing of the estimated evaluation value using the calculation result by the calculation unit 204. The calculation procedure of the estimated evaluation value will be described in detail later with reference to the drawings. In addition, the management unit 202 directly applies the evaluation value of the cell (hereinafter, referred to as “evaluated cell”) corresponding to the combination of the point where the evaluation value is held in the overall observation table and the situation to the overall estimation table. Store in cell. Further, the management unit 202 stores the estimated evaluation value calculated by the calculation unit 204 for the unevaluated cell in the corresponding cell of the overall estimation table.

The calculation unit 204 has a function of identifying a point from the position information received from the reception unit 206 and a function of determining what kind of situation the situation is from the sensor data received from the reception unit 206 based on a predetermined condition. .. When there are a plurality of situations, a plurality of conditions corresponding to the plurality of situations are associated with each other. Further, the calculation unit 204 has a function of calculating the correlation coefficient of the degree of risk between points according to an instruction from the management unit 202 and a function of calculating an estimated evaluation value for an unevaluated cell. The function of calculating the correlation coefficient and the function of calculating the estimated evaluation value will be described in detail later with reference to the drawings. Further, the calculation unit 204 has a function of determining whether or not an accident has occurred from the operation data of, for example, an ABS (Antilock Brake System) device among the sensor data received from the reception unit 206, and inputting it as the presence or absence of a dangerous event. May have.

The input unit 203 inputs whether or not a dangerous event has occurred. The input unit 203 transmits to the control unit 200 whether or not the input dangerous event has occurred. For example, the input unit 203 is a device such as a keyboard or a mouse, and the operator operates to input whether or not a dangerous event has occurred.

The storage unit 205 holds as an overall observation table the danger information associated with the evaluation value indicating the presence or absence of the occurrence of the danger event for the combination of the point and the situation. Further, the storage unit 205 holds the danger information in which the estimated evaluation value, which is the estimated evaluation value, is associated with the combination of the point and the situation as an overall estimation table.

The above is the description of the configuration of the dangerous information processing system 100 according to the present embodiment.

Next, the operation of the danger information processing system 100 according to the present embodiment will be described.

FIG. 3 is a flow chart showing an example of a processing procedure in which the danger information processing system 100 according to the first embodiment records an evaluation value in the overall observation table.

As shown in FIG. 3, the receiving unit 206 receives the position information and the sensor data from the mobile bodies 102 and 103 and the observation devices 104 and 105, and transmits the received position information and the sensor data to the control unit 200. The control unit 200 receives the input of the position information and the sensor data (step S301).

Next, the management unit 202 of the control unit 200 determines whether or not the moving bodies 103 and 104 have passed the points registered in the overall observation table using the input position information (step S302). When the management unit 202 determines that the point indicated by the input position information is in the row of the point registered in the general observation table, it determines that the point has passed the registered point. If the point indicated by the input position information is not in the row of the point registered in the general observation table (No in step S302), the process ends.

When it is determined that the control unit 202 has passed the point registered in the overall observation table (Yes in step S302), the management unit 202 acquires the sensor data using the sensor data input together with the position information indicating the point. It is determined whether or not the situation of the moving body or the situation around the observation device is the situation registered in the general observation table (step S303). Specifically, the management unit 202 causes the calculation unit 204 to determine whether or not the input sensor data satisfies a predetermined condition for determining whether or not the input sensor data is registered in the overall observation table. .. Then, when the determination result acquired from the calculation unit 204 is the result of determining that the predetermined condition is satisfied, the management unit 202 obtains the input sensor data and obtains the status of the moving body or the status around the observation device. Is determined to be the status registered in the overall observation table. That is, the input sensor data indicates the situation in which the situation of the moving body that acquired the sensor data or the situation around the observation device is registered in the overall observation table.

On the other hand, when the management unit 202 acquires the result of determining that the input sensor data does not satisfy the predetermined condition from the calculation unit 204, the management unit 202 obtains the sensor data from the state of the moving body or the surroundings of the observation device. It is determined that the status is not the status registered in the overall observation table (No in step S303), and the process ends.

When the management unit 202 determines that the status of the moving body that has acquired the input sensor data or the status around the observation device is the status registered in the overall observation table (Yes in step S303), the management unit 202 starts from the input unit 203. An evaluation value (evaluation value indicating the presence or absence of the occurrence of a dangerous event) for the combination of the point indicated by the position information and the situation indicated by the sensor data is determined according to the acquired presence or absence of the occurrence of the dangerous event (step S304).

Then, the management unit 202 records the determined evaluation value in the cell corresponding to the combination in the overall observation table (step S305), and ends the process.

The above is the processing procedure in which the danger information processing system 100 records the evaluation value in the overall observation table. That is, the danger information processing system 100 stores in the storage unit 205 the degree of danger input for the combination of the point where the danger event occurs and the situation when the danger event occurs at the point. ..

4 to 6 are flow charts showing an example of a processing procedure in which the danger information processing system 100 according to the present embodiment records an estimated evaluation value in the overall estimation table.

As shown in FIG. 4, the management unit 202 first calculates the correlation coefficient of the degree of risk between all the points registered in the row of the overall observation table from the cell in which the evaluation value of the overall observation table is input. (Step S401).

Next, the management unit 202 determines the overall observation table from the correlation coefficient of the risk between all the points registered in the row of the overall observation table calculated in step S401 and the evaluation value of the cell already entered in the overall observation table. The estimated evaluation value for the unevaluated cell of is calculated (step S402). Specifically, the management unit 202 stores the evaluation value of the evaluated cell among the plurality of cells corresponding to the combination of the point and the situation of the overall observation table as it is in the corresponding cell of the overall estimation table. Further, the management unit 202 stores the estimated evaluation values for the unevaluated cells among the plurality of cells in the overall observation table, and the estimated evaluation values calculated by using the calculation unit 204, in the corresponding cells of the overall estimation table. To do. That is, the management unit 202 reads the danger information, which is the degree of danger input to the cell corresponding to each combination, from the storage unit 205 with the first combination in which the evaluation value is not input as the processing target, and reads the danger. By calculating the risk level for the first combination using the information, the first risk information indicating the risk level is estimated.

FIG. 5 is a flow chart showing an example of a processing procedure in which the danger information processing system 100 calculates the correlation coefficient of the degree of risk between each point from the overall observation table, which corresponds to step S401 in FIG.

As shown in FIG. 5, the management unit 202 first determines one of the rows of points registered in the overall observation table as point i = 0 (step S501).

Next, the management unit 202 determines whether or not the correlation coefficient of the degree of risk with other points has been calculated for each of all the points registered in the overall observation table (step S502). Specifically, the management unit 202 determines whether or not i at the point where the correlation coefficient is currently calculated is equal to the number obtained by subtracting 1 from the number of registered points. The number of registered points is the number of all points registered in the overall observation table. When the management unit 202 determines that i at the point where the correlation coefficient is currently calculated is equal to the number obtained by subtracting 1 from the number of registered points, the correlation coefficient of the degree of risk with other points at all points. Is determined to have been calculated. On the other hand, if the management unit 202 determines that i at the point where the correlation coefficient is currently calculated is not equal to the number obtained by subtracting 1 from the number of registered points, the degree of risk between all points and other points is high. It is determined that the correlation coefficient of is not calculated.

When the management unit 202 determines that the correlation coefficient of the degree of risk with other points has been calculated for each of all the points registered in the overall observation table (Yes in step S502), the overall observation The process of calculating the correlation coefficient of the degree of risk between each point from the table is completed.

When the management unit 202 determines that the correlation coefficient of the degree of risk with other points has not been calculated for each of all the points registered in the overall observation table (No in step S502), the point i = A point j = i + 1, which is different from 0 and the correlation coefficient has not been calculated yet, is determined (step S503).

Next, the management unit 202 determines whether or not the correlation coefficient of the degree of risk with all the other points j has been calculated for the point i registered in the overall observation table (step S504). Specifically, the management unit 202 determines whether or not j at another point is equal to the number of registered points. When the management unit 202 determines that j at another point is equal to the number of registered points, the management unit 202 calculates the correlation coefficient of the degree of risk with all other points j for the point i registered in the overall observation table. Judge that it has been completed. On the other hand, when the management unit 202 determines that j at another point is not equal to the number of registered points, the risk phase of the point i registered in the overall observation table with all the other points j. Determine that the number of relations has not been calculated.

When the management unit 202 determines that the correlation coefficient of the degree of risk with all the other points j has not been calculated for the point i registered in the overall observation table (No in step S504), the calculation unit 202 Instruct 204 to calculate the correlation coefficient of the degree of risk between the point i and the point j by using the evaluation value for the situation where the point i and the point j have a common evaluation value. The calculation unit 204 calculates the correlation coefficient of the degree of risk between the point i and the point j according to the instruction from the management unit 202 (step S505).

The management unit 202 increments j (step S506), transitions to step S504, and repeats the process.

When the management unit 202 determines that the correlation coefficient of the degree of risk with all other points j has been calculated for the point i registered in the overall observation table (Yes in step S504), i is incremented. (Step S507), the process proceeds to step S502, and the process is repeated.

The above is the processing procedure in which the danger information processing system 100 calculates the correlation coefficient of the degree of risk between each point from the overall observation table.

FIG. 6 corresponds to step S402 of FIG. 4, and shows an example of a processing procedure in which the danger information processing system 100 calculates an estimated evaluation value for an unevaluated cell at each point from the overall observation table and the correlation coefficient. It is a flow chart.

As shown in FIG. 6, the management unit 202 first determines whether or not there are unevaluated cells in the overall observation table (step S601). When the management unit 202 determines that there are no unrated cells in the overall observation table (No in step S601), the management unit 202 stores the evaluation value of the overall observation table as it is in the corresponding cell of the overall estimation table, and completes the process. ..

When the management unit 202 determines that there are unevaluated cells in the overall observation table (Yes in step S601), the management unit 202 instructs the calculation unit 204 to calculate the estimated evaluation value for the unevaluated cells. In accordance with the instruction from the management unit 202, the calculation unit 204 uses the correlation coefficient of the degree of risk between each point in the column and the evaluated evaluation value for each status column registered in the overall observation table. The estimated evaluation value for the unevaluated cell is calculated (step S602). The management unit 202 stores the evaluation value of the overall observation table as it is in the corresponding cell of the overall estimation table, and further stores the estimated evaluation value calculated by the calculation unit 204 in the corresponding cell of the overall estimation table.

The above is the processing procedure in which the danger information processing system 100 calculates the estimated evaluation value for the unevaluated cells at each point from the overall observation table and the correlation coefficient.

Next, in step S505, an example of a calculation formula for calculating the correlation coefficient of the degree of risk between points using the evaluation value for a situation in which the point i and the point j have an evaluation value in common will be described.

The formula of the relative risk with respect to the situation y of the point x is expressed as the following formula 1.

Here, r _xy indicates an evaluation value (presence or absence of occurrence of a dangerous event) for the situation y at the point x. Further, r￣x represents the average value of the evaluation values (presence or absence of occurrence of dangerous events) at the point x.

The formula of the degree of similarity of the evaluation value (presence or absence of occurrence of dangerous event) to the situation k of the point i and the point j is shown as the following formula 2. If the value of this equation is negative, it can be said to be dissimilar, and if it is positive, it can be said to be similar.

The formula of the correlation coefficient of the degree of risk between the point a and the point x is expressed as the following formula 3. At this time, only those whose evaluation values are evaluated in common between the point a and the point x are the targets of the calculation.

Here, r'￣x represents the average value of the evaluation values (presence or absence of occurrence of dangerous events) of the points x with respect to the situation when a common dangerous event occurs at the points a and x.

Next, in step S602, an example of a calculation formula for calculating the correlation coefficient of the degree of risk between each point and the unevaluated evaluation value from the evaluated evaluation value will be described for each sensor data.

From the evaluation values (presence or absence of occurrence of dangerous events) of all points x other than the point a, the formula of the degree of danger for the combination of the point a and the situation y is shown as the following formula 4.

The following equation 5 can be obtained by normalizing the equation 4 in order to correct the problem that the evaluation value (whether or not a dangerous event occurs) is determined at many points, the easier it is to be evaluated.

The formula of the estimated evaluation value for the situation y at the point a is expressed as the following formula 6.

Next, the processing of the danger information processing system 100 will be described using a specific example of the overall observation table and the overall estimation table.

FIG. 7 is a diagram for explaining the configuration of the overall observation table stored in the storage unit 205 according to the first embodiment of the invention.

The overall observation table 701 holds "2", which means unevaluated, in each cell as an initial value. For example, if an accident occurs when sensor data indicating the situation a is acquired at the point A, the management unit 202 indicates that a dangerous event has occurred in the cells of the point A and the situation a of the overall observation table 701. The meaning "3" is recorded as an evaluation value. For example, if an accident does not occur when the sensor data indicating the situation m is acquired at the point N, the management unit 202 causes a dangerous event to occur in the cell of the point N and the situation m of the overall observation table 701. Record "1", which means that there was no such thing, as the evaluation value. In the example of the overall observation table 701 shown in FIG. 7, only the cells at the point B and the situation b are unevaluated cells, and the other cells are evaluated. The sensor data indicating the situation is sensor data that satisfies a predetermined condition for determining whether or not the situation is present.

FIG. 8 is a diagram for explaining the configuration of the overall estimation table stored in the storage unit 205 according to the first embodiment.

The global estimation table 801 has the same cell values as the global observation table 701 shown in FIG. The difference between the two is that the estimated evaluation value "2.75" is entered in the cells of point B and situation b, which were not evaluated in the overall observation table 701. The input estimated evaluation value is calculated by calculating the correlation coefficient of the degree of risk between the point B and each of the other points, and the calculated correlation coefficient and the evaluation value of each cell of the situation b at each point. , Is the result of calculating the estimated evaluation value using Equation 6.

That is, in order to estimate the first danger information which is the evaluation value of the first cell (first combination) that has not been evaluated, the management unit 202 has the same point as the first cell but different situations. The second danger information, which is the risk (evaluation value) for the cell (second combination), and the risk (third combination) for the third cell (third combination), which has the same situation as the first cell but different points. The third danger information (evaluation value) and the fourth cell (evaluation value) which is the risk (evaluation value) for the fourth cell (fourth combination) which has the same situation as the second cell and the same point as the third cell. The first danger information is estimated using the evaluation values (danger information) of the three evaluated cells with the danger information of. Specifically, the management unit 202 first calculates the correlation of the degree of risk between points using the evaluation value input in the second cell and the evaluation value input in the fourth cell. , The risk degree for the first cell is calculated from the evaluation value entered in the third cell by using the risk degree correlation between the points obtained by the calculation.

FIG. 9 is a diagram showing an example of an overall observation table stored in the storage unit 205 according to the first embodiment, as in FIG. 7.

The difference between FIG. 9 and FIG. 7 is that each situation is disclosed more specifically in the overall observation table. As in the case of FIG. 7, the overall observation table 901 holds "2", which means unevaluated, in each cell as an initial value. For example, if an accident occurs when sensor data indicating that there is a vehicle in front within a certain distance is acquired at point B, the management unit 202 moves the cell to point A in the overall observation table 901 and the cell that has a vehicle in front within a certain distance. , Record "3", which means that a dangerous event has occurred, as an evaluation value. Further, for example, if an accident does not occur when the sensor data indicating that the road surface is frozen is acquired at the point N, the management unit 202 is dangerous to the point N of the general observation table 901 and the cell with the road surface frozen. Record "1", which means that the event did not occur, as the evaluation value. In the example of the overall observation table 901 shown in FIG. 9, the cell with the point B and the rear vehicle within a certain distance, the cell with the point B and the number of pedestrians within a certain distance> the threshold value a, the point O and the oncoming vehicle within a certain distance Is an example in which the cell of is an unevaluated cell, and the other cells are evaluated.

FIG. 10 is a diagram showing an example of an overall estimation table stored in the storage unit 205 according to the first embodiment, as in FIG. 8.

The difference between FIG. 10 and FIG. 8 is that each situation is disclosed more specifically with respect to the overall estimation table. Similar to the case of FIG. 8, the cell with the point B and the rear vehicle within a certain distance, which was not evaluated in the general observation table 901 of FIG. 9, and the cell with the point B and the number of pedestrians within a certain distance> threshold value a. Estimated evaluation values "2.98", "1.05", and "1.12" are input to the point O and the cell with an oncoming vehicle within a certain distance, respectively. The input estimated evaluation value "2.98" is calculated by calculating the correlation coefficient of the degree of risk between point B and other points, and the calculated correlation coefficient and the vehicle behind within a certain distance at each point. This is the result of calculating the estimated evaluation value using the evaluation value of each cell in the existence column and the formula 6. Further, the input estimated evaluation value "1.05" is calculated by calculating the correlation coefficient of the degree of risk between the point B and other points, and the calculated correlation coefficient is within a certain distance at each point. This is the result of calculating the estimated evaluation value using the evaluation value of each cell in the column of pedestrian number> threshold value a and the formula 6. In addition, the input estimated evaluation value "1.12" is calculated by calculating the correlation coefficient of the degree of risk between the point O and other points, and the calculated correlation coefficient is within a certain distance at each point. This is the result of calculating the estimated evaluation value using the evaluation value of each cell in the row with oncoming vehicles and the equation 6.

The above is the description of the processing of the dangerous information processing system 100 according to the present embodiment.

(effect)
The danger information processing system 100 according to the present embodiment has not acquired the sensor data indicating the situation from the combination of the point where the evaluation value has already been acquired and the situation (that is, the evaluated cell) (that is, the point). The risk level (evaluation value indicating the presence or absence of a dangerous event) of the unrated cell) can be obtained. As a result, the danger information processing system 100 does not actually acquire all the sensing data for the combination of all the managed points and the situation, and the danger level (evaluation value indicating the presence or absence of the occurrence of a dangerous event). Can be obtained.

(Embodiment 2)
FIG. 11 is a block diagram showing an example of the configuration of the danger information processing system 110 that realizes the danger information processing method according to the second embodiment.

In the danger information processing system 110 shown in FIG. 11, the moving bodies 112 and 113 and the observation devices 114 and 115 that acquire and transmit the position information and the sensor data, and the server device 111 having the function of the danger information processing device are used. Shows the configuration to connect via the network. In the following, only the difference in the function of each component from the first embodiment will be described. The description of the components whose functions are the same as those of the first embodiment will be omitted.

Each of the moving body 112 and the moving body 113 includes a data acquisition unit 211. Further, the observation device 114 and the observation device 115 include a data acquisition unit 211. Hereinafter, the moving body 112 will be described, but the moving body 112 may be replaced with the moving body 113, the observation device 114, or the observation device 115.

The data acquisition unit 211 acquires sensor data that identifies the driver in addition to the function of the data acquisition unit 201 of the first embodiment. The data acquisition unit 211 may identify the driver by matching the driver information registered in advance with the smartphone owned by the driver, the car key, the driver's license, and the like. In addition, the driver may be identified from the physical characteristics of the driver himself, for example, a fingerprint, a palm print, a pupil, a voice print, and the like. The type of sensor data is not limited.

The server device 111 includes a control unit 210, an input unit 203, a storage unit 215, and a reception unit 206. The server device 111 has a function as a danger information processing device.

The control unit 210 includes a management unit 212 and a calculation unit 214.

The management unit 212 generates an overall observation table and an individual observation table in which evaluation values indicating the presence or absence of a dangerous event are associated with the combination of the point and the situation, and stores them in the storage unit 215. The global observation table is, for example, the same as the global observation table described in the first embodiment. Here, the overall observation table can be said to be overall danger information for all drivers regardless of the driver. The individual observation table has the same rows and columns as the general observation table, and is managed separately as an individual table for each driver. That is, the individual observation table is an individual for each driver, which is the degree of danger input for each driver with respect to the combination of the point where the dangerous event occurred and the situation when the dangerous event occurred at the point. Consists of danger information.

The management unit 212 identifies the driver by using the position information acquired from the reception unit 206 and the sensor data including the sensor data that identifies the driver, and the individual observation table associated with the specified driver. Select. The management unit 212 uses the position information and sensor data acquired from the receiving unit 206, or the presence / absence of a dangerous event acquired from the input unit 203, to pose a danger to the combination of the point indicated by the position information and the situation indicated by the sensor data. The degree (evaluation value indicating the presence or absence of the occurrence of a dangerous event) is recorded in each corresponding cell of the general observation table and the individual observation table held in the storage unit 215. The occurrence of a dangerous event is, for example, the presence or absence of an accident. The occurrence of a dangerous event may be, for example, the presence or absence of a hiyari hat.

Further, the process of generating the overall estimation table using the overall observation table of the control unit 212 is the same as the process of the control unit 202 of the first embodiment, and thus the description thereof will be omitted. The management unit 212 stores the generated overall estimation table in the storage unit 215.

In addition, the management unit 212 calculates the correlation coefficient of the degree of risk between the overall estimation table and the individual observation table. Then, the management unit 212 calculates the estimated evaluation value for the unevaluated cell of the individual observation table from the estimated evaluation value of the overall estimation table using the correlation coefficient obtained by the calculation, and generates the individual estimation table. And store it in the storage unit 215.

In addition to the functions of the calculation unit 204 of the first embodiment, the calculation unit 214 has a function of calculating the correlation coefficient of the degree of risk between the overall estimation table and the individual observation table, and the calculation unit 214 individually from the estimated evaluation value of the overall estimation table. It has a function to calculate the estimated evaluation value for the unrated cells of the observation table.

The storage unit 215 holds one or more individual observation tables having the same rows and columns as the general observation table for each driver, in addition to the total observation table and the total estimation table held by the storage unit 205 of the first embodiment. Also, each driver holds one or more individual estimation tables with the same rows and columns as the overall estimation table.

The above is the description of the configuration of the dangerous information processing system 110 according to the present embodiment.

Next, the operation of the danger information processing system 110 according to the present embodiment will be described.

FIG. 12 is a flow chart showing an example of a processing procedure in which the danger information processing system 110 according to the second embodiment records evaluation values in the overall observation table and the individual observation table.

As shown in FIG. 12, the receiving unit 206 receives the position information and the sensor data from the mobile bodies 112 and 113 and the observation devices 114 and 115, and transmits the received position information and the sensor data to the control unit 210. The control unit 210 receives the input of the position information and the sensor data (step S1201). Here, the sensor data received by the control unit 210 includes sensor data that identifies the driver, unlike the first embodiment.

Next, the management unit 212 of the control unit 210 identifies the driver from the input sensor data and selects the individual observation table associated with the specified driver (step S1202).

Next, the management unit 212 performs the same processing as steps S302 to S305 of FIG. 3 of the first embodiment by using the input position information and the sensor data.

After step S305, the management unit 212 records the evaluation value determined in step S305 in the corresponding cell of the individual observation table selected in step S1202 (step S1203), and ends the process.

The above is the processing procedure in which the danger information processing system 100 records the evaluation values in the overall observation table and the individual observation table. That is, the danger information processing system 100 sets the degree of danger input for each driver for each driver for the combination of the point where the danger event occurs and the situation when the danger event occurs at the point. It is stored in the storage unit 215 as individual danger information and as overall danger information for all drivers.

13 to 15 are flow charts showing an example of a processing procedure in which the danger information processing system 110 according to the second embodiment records estimated evaluation values in an overall estimation table and an individual estimation table.

As shown in FIG. 13, the management unit 212 first performs the same processing as in steps S401 and S402 of FIG. 4 of the first embodiment.

Next, the management unit 212 calculates the correlation coefficient of the degree of risk between the overall estimation table and the individual observation table (step S1301).

The management unit 212 calculates the estimated evaluation value for the unevaluated cell of the individual observation table from the estimated evaluation value of the overall estimation table (step S1302). The management unit 212 stores the evaluation value of the evaluated cell among the plurality of cells of the individual observation table as it is in the corresponding cell of the individual estimation table. Further, the management unit 212 stores the estimated evaluation values for the unevaluated cells among the plurality of cells in the individual observation table, which are calculated by using the calculation unit 214, in the corresponding cells of the individual estimation table. To do.

FIG. 14 corresponds to step S1301 of FIG. 13, and is a flow chart showing an example of a processing procedure in which the danger information processing system 110 calculates the correlation coefficient of the degree of risk between the overall estimation table and the individual observation table. Is.

As shown in FIG. 14, the management unit 212 first determines one of the individual observation tables as i = 0 (step S1401).

Next, the management unit 212 determines whether or not the correlation coefficient of the degree of risk with the overall estimation table has been calculated for all the individual observation tables (step S1402). Specifically, the management unit 212 determines whether or not i of the individual observation table is equal to the number of individual observation tables for the individual observation table for which the correlation coefficient is currently calculated. The number of individual observation tables is the number of all individual observation tables held in the storage unit 215. When the management unit 212 determines that i of the individual observation table for which the correlation coefficient is currently calculated is equal to the number of individual observation tables, the correlation coefficient of the degree of risk with the overall estimation table for all individual observation tables. Is determined to have been calculated. On the other hand, if the management unit 212 determines that i of the individual observation table for which the correlation coefficient is currently calculated is not equal to the number of individual observation tables, the degree of risk between the individual observation tables and the overall estimation table is high. It is determined that the correlation coefficient of is not calculated.

When the management unit 212 determines that the correlation coefficient of the degree of risk with the overall estimation table has been calculated for all the individual observation tables (Yes in step S1402), the overall estimation table and the individual observation table are combined. The process of calculating the correlation coefficient of the degree of risk between the two ends.

When the management unit 212 determines that the correlation coefficient of the degree of risk with the overall estimation table has not been calculated for all the individual observation tables (No in step S1402), the individual has not yet calculated the correlation coefficient. The observation table is selected as the processing target (step S1403).

The management unit 212 instructs the calculation unit 214 to calculate the correlation coefficient of the degree of risk between the individual observation table selected in step S1403 and the overall estimation table. The calculation unit 214 calculates the correlation coefficient using the evaluation value of the evaluated cell in common between the cell of the overall estimation table and the cell of the individual observation table according to the instruction from the management unit 212 (step S1404).

The management unit 212 calculates the correlation coefficient of the risk between the overall estimation table and the individual observation table for the individual observation table for which the correlation coefficient of the risk with the overall estimation table has not been calculated. , I is incremented (step S1405), the transition to step S1402 is performed, and the process is repeated.

The above is the processing procedure in which the danger information processing system 110 calculates the correlation coefficient of the degree of risk between the overall estimation table and the individual observation table. That is, the danger information processing system 110 calculates the correlation of the degree of danger between the individual observation table as the individual danger information and the overall estimation table as the total danger information.

FIG. 15 corresponds to step S1302 of FIG. 13, and the danger information processing system 110 calculates the estimated evaluation value for the unevaluated cell of the individual observation table from the evaluation value (including the estimated evaluation value) of the overall estimation table. It is a flow chart which shows an example of a processing procedure.

As shown in FIG. 15, the management unit 212 first determines one of the individual observation tables as i = 0 (step S1501).

Next, the management unit 212 determines whether or not the estimated evaluation values have been calculated for all the individual observation tables held in the storage unit 215 (step S1502). Specifically, the management unit 212 determines whether or not i of the individual observation table is equal to the number of individual observation tables for the individual observation table for which the estimated evaluation value is currently calculated. The number of individual observation tables is the number of all individual observation tables held in the storage unit 215. When the management unit 212 determines that i of the individual observation table for which the estimated evaluation value is currently calculated is equal to the number of individual observation tables, it determines that the estimated evaluation value has been calculated for all the individual observation tables. On the other hand, when the management unit 212 determines that i of the individual observation table for which the estimated evaluation value is currently calculated is not equal to the number of individual observation tables, it determines that the estimated evaluation value has not been calculated for all the individual observation tables. To do.

When it is determined that the estimated evaluation values have been calculated for all the individual observation tables (Yes in step S1502), the process ends.

When the management unit 212 determines that the estimated evaluation values have not been calculated for all the individual observation tables (No in step S1502), the management unit 212 selects the individual observation tables for which the estimated evaluation values have not been calculated as the processing target (step). S1503).

The management unit 212 determines whether or not there are unevaluated cells in the individual observation table selected in step S1503 (step S1504). When the management unit 212 determines that there are no unevaluated cells in the individual observation table (N in step S1504), the management unit 212 transitions to step S1506.

When the management unit 212 determines that there are unevaluated cells in the individual observation table (Yes in step S1504), the management unit 212 instructs the calculation unit 214 to calculate the estimated evaluation value for the unevaluated cells. According to the instruction from the management unit 212, the calculation unit 214 sets the correlation coefficient of the degree of risk between the overall estimation table and the individual observation table, and the evaluation value (including the estimated evaluation value) of the evaluated cell of the overall estimation table. Is used to calculate the estimated evaluation value for the unevaluated cells in the individual observation table (step S1505).

The management unit 212 increments i (step S1506), transitions to step S1502, and repeats the process.

The above is the processing procedure in which the danger information processing system 110 calculates the estimated evaluation value for the unevaluated cell of the individual observation table for each driver from the evaluation value (including the estimated evaluation value) of the overall estimation table. That is, the danger information processing system 110 uses the correlation of the degree of risk between the individual observation table as the individual danger information and the overall estimation table as the overall danger information obtained by calculation, and uses the overall estimation table. Calculate the estimated evaluation value of the individual observation table from the evaluation value of.

Next, the processing of the danger information processing system will be described using a general estimation table, an individual observation table, and specific examples of the individual estimation table.

FIG. 16 is a diagram showing an example of an overall estimation table held by the storage unit 215 according to the second embodiment. Evaluation values or estimated evaluation values are recorded in all cells of the overall estimation table 1601. That is, the overall estimation table 1601 is a table generated by the end of step S402.

FIG. 17 is a diagram showing an example of an individual observation table held by the storage unit 215 according to the second embodiment. In this example, in the individual observation table 1701, the evaluation value is recorded only in the point A and the cell with the vehicle in front within a certain distance. Other cells have not been evaluated. Here, when the correlation coefficient is calculated using the equation 6 between the evaluated cell of the individual observation table 1701 and the evaluated cell of the overall estimation table 1601, the correlation coefficient becomes “1”.

FIG. 18 is a diagram showing an example of an individual estimation table held by the storage unit 215 according to the second embodiment. In this example, since the correlation coefficient of the degree of risk between the overall estimation table 1601 and the individual observation table 1701 is "1", the overall estimation table 1601 is placed in a cell that has not been evaluated in the individual observation table 1701. The same evaluation value as that of the estimation table 1601 is input.

The above is a description of the operation of the dangerous information processing system 110 according to the present embodiment.

(effect)
The danger information processing system 110 according to the present embodiment is a point where sensor data cannot be acquired (that is, an unevaluated cell) from a combination of a point where an evaluation value has already been acquired and a situation (that is, an evaluated cell). ) Risk level (evaluation value indicating the presence or absence of a dangerous event) can be obtained. Further, in the danger information processing system 110, the driver can be specified by using the degree of danger for the combination of the point and the situation for the whole, and the individual degree of danger for each specified driver can be obtained. As a result, the danger information processing system 110 does not actually acquire all the sensing data individually for each driver for the combination of all the managed points and the situation, and the risk level for each driver ( It is possible to obtain an evaluation value) indicating the presence or absence of a dangerous event.

(Embodiment 3)
FIG. 19 is a block diagram showing an example of the configuration of the danger information processing system 120 that realizes the danger information processing method according to the third embodiment.

In the danger information processing system 110 shown in FIG. 19, the moving bodies 122 and 123 and the observation devices 124 and 125 that acquire and transmit the position information and the sensor data, and the server device 121 having the function of the danger information processing device are arranged. Shows the configuration to connect via the network. In the following, only the difference in the function of each component from the first embodiment will be described. The description of the components whose functions are the same as those of the first embodiment will be omitted.

The mobile body 122 includes a data acquisition unit 221. Further, the observation device 124 and the observation device 125 include a data acquisition unit 221. Hereinafter, the moving body 122 will be described, but the moving body 122 may be replaced with the moving body 123, the observation device 124, or the observation device 125.

In addition to the function of the data acquisition unit 201 of the first embodiment, the data acquisition unit 221 also acquires sensor data used for priority determination such as traffic volume at each point.

The server device 121 includes a control unit 220, an input unit 203, a storage unit 225, and a reception unit 206. The server device 121 has a function as a danger information processing device.

The control unit 220 includes a management unit 222, a calculation unit 204, and a priority determination unit 227.

The management unit 222 generates an overall observation table in which evaluation values indicating the presence or absence of a dangerous event are associated with the combination of the point and the sensor data, and stores it in the storage unit 225. The global observation table is the same as the global observation table described in the first embodiment.

The management unit 222 evaluates the combination of the point indicated by the position information and the situation indicated by the sensor data by using the position information and the sensor data acquired from the receiving unit 206 or the presence / absence of the occurrence of a dangerous event acquired from the input unit 203. The value (evaluation value (risk degree) indicating the presence or absence of the occurrence of a dangerous event) is recorded in a cell indicating the corresponding combination of the overall observation table held in the storage unit 225. The occurrence of a dangerous event is, for example, the presence or absence of an accident. The occurrence of a dangerous event may be, for example, the presence or absence of a hiyari hat.

Further, the management unit 222 acquires the position information and the sensor data acquired from the receiving unit 206, extracts the information used for the priority determination (for example, the traffic volume for each point) included in the sensor data, and then obtains the position information and the sensor data. Record in the event frequency table held in the storage unit 225.

Further, the process of generating the overall estimation table using the overall observation table by the management unit 222 is almost the same as the process of the management unit 202 of the first embodiment. In the process of generating the overall estimation table, the management unit 222 instructs the calculation unit 204 to calculate the estimated evaluation value according to the order determined by the priority determination unit 227, which is the process of the management unit 202 of the first embodiment. Different from.

The priority determination unit 227 determines the order in which the estimated evaluation values are calculated with reference to the event frequency table held in the storage unit 225. In the process of generating the overall estimation table from the overall observation table, the priority determination unit 227 determines the order of the process of calculating the correlation function of the degree of risk between each point and the process of calculating the estimated evaluation value (for example,). , In descending order of traffic volume at each point).

In addition to the overall observation table and the overall estimation table held by the storage unit 205 of the first embodiment, the storage unit 225 includes information used for priority determination (for example, the frequency and location of traffic events such as traffic volume at each location). (Frequency information associated with) is held as an event frequency table.

The above is the description of the configuration of the dangerous information processing system 120 according to the present embodiment.

Next, the operation of the dangerous information processing system according to the present embodiment will be described.

FIG. 20 is a flow chart showing an example of a processing procedure recorded in the event frequency table by the danger information processing system 120 according to the third embodiment.

As shown in FIG. 20, the receiving unit 206 receives the position information and the sensor data from the mobile bodies 122 and 123 and the observation devices 124 and 125, and transmits the received position information and the sensor data to the control unit 220. The control unit 220 receives the input of the position information and the sensor data (step S2001). Here, the sensor data received by the control unit 220 includes sensor data used for determining the priority, such as the traffic volume for each point, unlike the first embodiment.

Next, the management unit 222 of the control unit 220 extracts the traffic volume for each point from the input sensor data, and records the extracted traffic volume for each point in the event frequency table stored in the storage unit 225 ( Step S2002).

The above is the description of the processing procedure recorded in the event frequency table by the danger information processing system 120.

FIG. 21 is a flow chart showing an example of a processing procedure in which the danger information processing system 120 according to the third embodiment records an estimated evaluation value in the overall estimation table according to the priority.

As shown in FIG. 21, the priority determination unit 227 refers to the event frequency table stored in the storage unit 225, and is a processing order for calculating the estimated evaluation value for the unevaluated cells at each point in the overall observation table. Determine the priority order. The priority determination unit 227 rearranges the processing order for calculating the estimated evaluation values for the unevaluated cells at each point in the overall observation table in the determined priority order (step S2101). Here, the priority determination unit 227 determines that the estimated evaluation values of the unevaluated cells at each point are calculated in the order of the traffic volume at each point. Specifically, the priority determination unit 227 arranges the order of calculating the estimated evaluation values for the unevaluated cells at each point in the order of priority by allocating points with a larger traffic volume to those with a smaller value of i. Change.

Next, the management unit 222 starts the process from the point i = 0 in the row of the points registered in the overall observation table (step S2102).

Next, the management unit 222 performs the same processing as in steps S502 to S506 of FIG. 5 of the first embodiment.

When the management unit 222 determines in step S504 that the correlation coefficient of the degree of risk with all other points j has been calculated for the point i registered in the overall observation table (Yes in step S504). The transition to step S2103.

Then, the management unit 222 determines whether or not there is an unevaluated cell in each cell of each situation at the point i (step S2103).

When the management unit 222 determines that there is an unevaluated cell in each cell of each situation at the point i (Yes in step S2103), the management unit 222 instructs the calculation unit 204 to calculate the estimated evaluation value for the unevaluated cell. In accordance with the instruction from the management unit 222, the calculation unit 204 uses the correlation coefficient of the risk with other points for the unevaluated cell and the evaluation value of the other point in the status column of the unevaluated cell. Then, the estimated evaluation value for the unevaluated cell is calculated (step S2104). The management unit 222 stores the evaluation value of the overall observation table as it is in the corresponding cell of the overall estimation table, and further stores the estimated evaluation value calculated by the calculation unit 204 in the corresponding cell of the overall estimation table.

The management unit 222 increments i (step S2105), transitions to step S502, and repeats the process.

The above is the processing procedure in which the danger information processing system 120 records the estimated evaluation value in the overall estimation table according to the determined priority order. That is, the danger information processing system 120 determines the processing order of a plurality of points using the event frequency table as the frequency information, and according to the determined processing order, the first evaluation value of the unevaluated cell to be processed. Determine danger information.

FIG. 22 is a diagram showing an example of an event frequency table held by the storage unit 225 according to the third embodiment.

In the description of the present embodiment, the traffic volume for each point is used as the event for recording the frequency, but the number of accidents for each point may be used. In this case, for example, it is determined that the processing is performed in order from the point where the number of accidents is large.

Further, in combination with the second embodiment, the number of operations may be set for each driver. In this case, for example, it is determined that the individual estimation table is generated in order from the driver with the highest number of operations. That is, in this case, the storage unit holds the frequency information in which the driving frequency for each driver is associated with the driver, and the danger information processing system determines the processing order of the plurality of drivers using the frequency information. Then, in the calculation of the evaluation value of the unevaluated cell of the individual estimation table as the individual risk information, the individual estimation table to be processed is determined according to the determined processing order.

The above is a description of the operation of the dangerous information processing system 120 according to the present embodiment.

(effect)
The danger information processing system 120 according to the present embodiment is a point where sensor data cannot be acquired (that is, an unevaluated cell) from a combination of a point where an evaluation value has already been acquired and a situation (that is, an evaluated cell). ) Risk level (evaluation value indicating the presence or absence of a dangerous event) can be obtained. As a result, the danger information processing system 120 does not actually acquire all the sensing data for the combination of all the managed points and the situation, and the danger level (evaluation value indicating the presence or absence of the occurrence of a dangerous event). Can be obtained.

Further, the danger information processing system 120 can obtain the estimated evaluation values of high-priority points such as points with heavy traffic without waiting for the update of all the unrated values in the overall estimation table.

(Embodiment 4)
FIG. 23 is a block diagram showing an example of the configuration of a danger information processing system that realizes the danger information processing method according to the fourth embodiment. In the danger information processing system 130 shown in FIG. 23, the server devices 101 (111, 121) having the functions of the danger information processing devices described in the first to third embodiments and the movement having the functions of the danger information presenting device. The body 102 (112, 122) shows a configuration in which the body 102 (112, 122) is connected via a network. That is, in the danger information processing system 130, even if it is realized by a combination of any of the server devices 101, 111, 121 and any of the mobile bodies 102, 112, 122 described in the first to third embodiments. Good.

The storage unit 205 (215, 225) of the server device 101 (111, 121) stores the overall estimation table described in the first to third embodiments.

The danger information presenting device 2300 includes a data acquisition unit 201 (211 and 221), a determination unit 2301, and a presentation unit 2302. In FIG. 23, the danger information presenting device 2300 is configured as a part of the moving body 102 (112, 122).

Since the data acquisition unit 201 and the storage unit 205 (215, 225) are the same as the components of the first to third embodiments and have no functional difference, the description thereof will be omitted.

The dangerous information processing system 130 will be described below with a combination of the server device 101 and the mobile body 102.

The determination unit 2301 receives the position information and the sensor data from the data acquisition unit 201. The determination unit 2301 stores the evaluation value (including the estimated evaluation value), which is the degree of risk for the combination of the point indicated by the received position information and the situation indicated by the sensor data, in the storage unit 205 of the server device 101 as a whole. Get from the estimation table. The determination unit 2301 associates the row of the point indicated by the received position information with the row of the point of the overall estimation table, and further associates the status indicated by the received sensor data with the status column of the overall estimation table.

Next, the determination unit 2301 compares the acquired evaluation value with the threshold value held in advance, and determines whether or not to present information such as a warning based on the result of the comparison. Specifically, the determination unit 2301 determines whether or not the acquired evaluation value exceeds the threshold value held in advance. Then, the determination unit 2301 determines that a warning is presented when it is determined that the acquired evaluation value exceeds the threshold value held in advance. On the other hand, the determination unit 2301 determines that the warning is not presented when it is determined that the acquired evaluation value does not exceed the threshold value held in advance.

The presentation unit 2302 presents information to the user. For example, lighting such as a display, a speaker, or an LED is turned on or blinks, and information such as a warning is presented to the driver of the moving body 102. Specifically, when the determination unit 2301 determines that the determination unit 2301 presents a warning, the presentation unit 2302 presents information based on the evaluation value used for the determination to the driver of the moving body 102.

The above is the description of the configuration of the danger information presenting device 2300 according to the present embodiment.

The operation of the danger information presenting device according to the present embodiment will be described below.

FIG. 24 is a flow chart showing an example of the processing procedure of the danger information presenting device 2300 according to the fourth embodiment.

As shown in FIG. 24, the determination unit 2301 first receives the position information and the sensor data from the data acquisition unit 201 (step S2401).

Next, the determination unit 2301 evaluates recorded in the row of the point and the cell of the overall estimation table corresponding to the column of the sensor data from the point indicated by the received position information and the situation indicated by the sensor data. The value is referred to from the storage unit 205 of the server device 101, and it is determined whether or not the referred evaluation value exceeds the threshold value (step S2402).

When the determination unit 2301 determines that the evaluation value exceeds the threshold value (Yes in step S2402), the determination unit 2301 presents information such as a warning based on the evaluation value via the presentation unit 2302 (step S2403). When the determination unit 2301 determines that the estimated evaluation value does not exceed the threshold value (No in step S2402), the determination unit 2301 ends the process.

The above is a description of the operation of the danger information processing system 130 according to the present embodiment.

The threshold value may be predetermined or may be the average of the evaluation values.

The presentation unit 2302 is not limited to presenting information directly to the user, but indirectly informs the user by outputting operation information of the moving body (for example, an automobile) as a control signal of the moving body. May be presented. For example, if the evaluation value is large when the vehicle speed of the vehicle is high at a certain point, the presentation unit 2302 may reduce the speed of the vehicle or may stop the vehicle. If the evaluation value is large when the vehicle speed is below a certain value at a certain point, the presentation unit 2302 may accelerate the speed of the vehicle, or when there is a bicycle on the left side of the own vehicle at a certain point. If the evaluation value is large, the presentation unit 2302 may steer the vehicle to the right.

Further, the determination unit 2301 may be included in the server device. In this case, depending on the result of the determination, information such as a warning based on the evaluation value may or may not be transmitted to the presentation unit of the moving body.

(effect)
In the danger information processing system 130 according to the present embodiment, the point where the sensor data estimated from the combination of the point where the evaluation value has already been acquired and the situation (that is, the evaluated cell) cannot be acquired (that is, not yet). The risk level (evaluation value indicating the presence or absence of a dangerous event) of the evaluation cell) can be obtained. As a result, the danger information processing system 130 does not actually acquire all the sensing data for the combination of all the managed points and the situation, and the danger level (evaluation value indicating the presence or absence of the occurrence of a dangerous event). It is possible to present information based on. As a result, it is possible to notify the driving user of the degree of danger at the driving point in real time.

<Modification example>
Although the present invention has been described based on the above-described embodiment and its modifications, it goes without saying that the present invention is not limited to the above-described embodiment and the like. The following cases are also included in the present invention.

(1)
The technique described in the above aspect can be realized, for example, in the following cloud service types. However, the type in which the technique described in the above aspect is realized is not limited to this.

(Service type 1: In-house data center type)
FIG. 25 shows service type 1 (in-house data center type). This type is a type in which the service provider C120 acquires information from the group C100 and provides a service to the user. In this type, the service provider C120 has the function of a data center operating company. That is, the service provider owns a cloud server C111 that manages big data. Therefore, there is no data center operating company.

In this type, the service provider C120 operates and manages the data center (cloud server C111) (C203). Further, the service provider C120 manages the OS (C202) and the application (C201). The service provider C120 provides the service using the OS (C202) and the application (C201) managed by the service provider C120 (C204).

(Service type 2: IaaS usage type)
FIG. 26 shows service type 2 (IaaS utilization type). Here, IaaS is an abbreviation for Infrastructure as a Service, and is a cloud service provision model that provides the infrastructure itself for constructing and operating a computer system as a service via the Internet.

In this type, the data center operating company operates and manages the data center (cloud server C111) (C203). Further, the service provider C120 manages the OS (C202) and the application (C201). The service provider C120 provides the service using the OS (C202) and the application (C201) managed by the service provider C120 (C204).

(Service type 3: PaaS usage type)
FIG. 27 shows service type 3 (PaaS utilization type). Here, PaaS is an abbreviation for Platform as a Service, and is a cloud service provision model that provides a platform as a base for building and operating software as a service via the Internet.

In this type, the data center operating company C110 manages the OS (C202) and operates and manages the data center (cloud server C111) (C203). The service provider C120 also manages the application (C201). The service provider C120 provides the service using the OS (C202) managed by the data center operating company and the application (C201) managed by the service provider C120 (C204).

(Service type 4: SaaS usage type)
FIG. 28 shows service type 4 (SaaS utilization type). Here, SaaS is an abbreviation for Software as a Service. For example, a function that allows companies / individuals (users) who do not have a data center (cloud server) to use applications provided by a platform provider who owns a data center (cloud server) via a network such as the Internet. It is a cloud service provision model that it has.

In this type, the data center operating company C110 manages the application (C201), manages the OS (C202), and operates and manages the data center (cloud server C111) (C203). Further, the service provider C120 provides the service using the OS (C202) and the application (C201) managed by the data center operating company C110 (C204).

In any of the above types, it is assumed that the service provider C120 has performed the service provision act. Further, for example, a service provider or a data center operating company may develop an OS, an application, a database of big data, or the like by itself, or may outsource it to a third party.

(2)
The moving body may be a car (automobile), a motorcycle, a bicycle, a train, an airplane, a mobile phone, a tablet, a laptop computer, or a wearable. It may be a device, or any device as long as it is mobile and has a data acquisition unit.

(3)
The data acquisition unit may be any device, sensor, or the like that has a function of sensing the inside or the periphery of the moving body, the inside or the periphery of the observation device, or the situation. For example, GPS (Global Positioning System) for acquiring position information, in-vehicle camera, laser data, millimeter-wave radar, sonar, infrared camera, road surface condition sensor, drowsiness sensor, weather sensor, CAN (Control Area Network) information acquisition means, time. Information acquisition means, vehicle-to-vehicle communication information means, road-to-vehicle communication information means, communication means, and the like may be used. Further, the position information may be acquired not only by GPS but also by estimating from the received electric field strength of, for example, Wi-Fi or mobile phone communication. Further, a sensor provided with means for recognizing information around the vehicle or inside the vehicle may be used. For example, the presence or absence of a vehicle in front may be determined from the in-vehicle camera, or the distance to the vehicle in front may be measured. In addition, the number of pedestrians may be measured within a certain distance from an in-vehicle camera, millimeter-wave radar, or the like. Further, it may be measured from the road surface condition sensor whether or not the road surface is frozen. In addition, the weather sensor may be used to measure whether or not it is raining. In addition, the driver may be identified by sensing a smartphone owned by the driver, a car key, a driver's license, or the like. The driver may be identified by his or her own physical characteristics, such as fingerprints, palm prints, pupils, voiceprints, and the like. It may be one that senses the method of driving operation, or it may be one that senses the operation of other devices or the operation content thereof, and is not limited to the type of sensor data.

(4)
After calculating the correlation coefficient of risk between points, the estimated evaluation value was calculated using the evaluation value of each situation column at other points, but on the contrary, the correlation coefficient of risk between situations was calculated. And then the estimated evaluation value may be calculated using the evaluation value of the row at each point in other situations. That is, in the estimation of the evaluation value of the first cell that has not been evaluated, the evaluation value input in the second cell and the evaluation value input in the fourth cell are used to determine the degree of risk between situations. The risk level for the first cell may be calculated from the evaluation value entered in the second cell by calculating the correlation and using the risk level correlation between the situations obtained by the calculation. Here, the second cell is a cell corresponding to a combination having the same point as the first cell but different situations. Further, the third cell is a cell corresponding to a combination in which the situation is the same as that of the first cell but the points are different. Further, the fourth cell is a cell corresponding to a combination having the same situation as the second cell and the same point as the third cell.

(5)
The point in the above embodiment means a specific position on the earth, and the word does not limit the size. The method of expressing the point may be latitude / longitude information, an address, a road name, an intersection name, or anything that indicates a specific position on the earth.

(6)
Regarding the evaluation value, as an example, if there was an accident, it was evaluated with "3", if there was no accident, it was evaluated with "1", and in other cases it was evaluated as "2", but this is just an example. It does not limit the invention. The evaluation value may be the result of evaluating a dangerous event other than an accident. For example, the presence or absence of a hilarious hat event, the presence or absence of a traffic violation, the presence or absence of sudden braking, the degree of change in the acceleration sensor, the degree of change in the steering device, and the driver avoid It may be whether or not an action has been taken, and any evaluation value may be adopted as long as it represents a dangerous event. Further, the evaluation value is also set to the numerical value of "3", "2", and "1", but the value may be other than this. In addition, although the evaluation is based on the presence or absence of dangerous events, it may be the case where there is a dangerous event or not.

(7)
The frequency of events is the traffic volume, but it may be the number of accidents at each point, the traffic volume of children, the lane width, or the intersection. Any event or property that can be prioritized is acceptable. Further, in combination with the second embodiment, the number of operations may be set for each driver.

(8)
Specifically, each of the above devices is a computer system composed of a microprocessor, a ROM, a RAM, a hard disk unit, a display unit, and the like. A computer program is stored in the RAM or the hard disk unit. When the microprocessor operates according to the computer program, each device achieves its function. Here, a computer program is configured by combining a plurality of instruction codes indicating commands to a computer in order to achieve a predetermined function.

(9)
A part or all of the components constituting each of the above devices may be composed of one system LSI (Large Scale Integration: large-scale integrated circuit). A system LSI is an ultra-multifunctional LSI manufactured by integrating a plurality of components on a single chip, and specifically, is a computer system including a microprocessor, a ROM, a RAM, and the like. .. A computer program is stored in the RAM. When the microprocessor operates according to the computer program, the system LSI achieves its function.

(10)
Some or all of the components constituting each of the above devices may be composed of an IC card or a single module that can be attached to and detached from each device. The IC card or the module is a computer system composed of a microprocessor, a ROM, a RAM, and the like. The IC card or the module may include the above-mentioned super multifunctional LSI. When the microprocessor operates according to a computer program, the IC card or the module achieves its function. This IC card or this module may have tamper resistance.

(11)
The present invention may be the method shown above. Further, it may be a computer program that realizes these methods by a computer, or it may be a digital signal composed of the computer program.

The present invention also relates to a computer-readable recording medium such as the computer program or the digital signal, such as a flexible disk, a hard disk, a CD-ROM, an MO, a DVD, a DVD-ROM, a DVD-RAM, or a BD (Blu-ray (Blu-ray). It may be recorded on a registered trademark) Disc), a semiconductor memory, or the like. Further, it may be the digital signal recorded on these recording media.

Further, the present invention may transmit the computer program or the digital signal via a telecommunication line, a wireless or wired communication line, a network typified by the Internet, data broadcasting, or the like.

Further, the present invention is a computer system including a microprocessor and a memory, in which the memory stores the computer program, and the microprocessor may operate according to the computer program.

Further, it is carried out by another independent computer system by recording and transferring the program or the digital signal on the recording medium, or by transferring the program or the digital signal via the network or the like. May be.

(12)
The above-described embodiment and the above-mentioned modification may be combined.

The present invention can be used, for example, in an information processing device or system that determines and warns a point at which an accident is likely to occur while the vehicle is running.

100, 110, 120, 130 Danger information processing system 101, 111, 121 Server device 102, 103, 112, 113, 122, 123 Mobile object 104, 105, 114, 115, 124, 125 Observation device 200, 210, 220 Control Units 201, 211, 221 Data acquisition unit 202, 212, 222 Management unit 203 Input unit 204, 214 Calculation unit 205, 215, 225 Storage unit 206 Reception unit 227 Priority determination unit 701, 901 Overall observation table 801, 1601 Overall estimation Table 1701 Individual observation table 2300 Danger information presentation device 2301 Judgment unit 2302 Presentation unit

Claims (13)

It is an information processing method executed by a computer that manages the degree of danger that numerically expresses the presence or absence of a dangerous event.
The storage unit included in the computer is
The risk level is configured to be storable for each of multiple points and each combination of multiple situations.
The computer
The risk of the first combination risk in the storage unit is not stored, estimated using the risk stored in the storage unit,
In the storage unit, a second risk level for a second combination having the same point as the first combination but a different situation, and a third combination for a third combination having the same situation as the first combination but a different point. Includes a risk of 3 and a fourth risk for a fourth combination that is in the same situation as the second combination and has the same location as the third combination.
The risk estimation for the first combination uses the second risk and the fourth risk to calculate the risk correlation between points.
An information processing method for estimating the degree of risk for the first combination from the third degree of risk by using the correlation of the degree of risk between the points obtained by calculation. It is an information processing method executed by a computer that manages the degree of danger that numerically expresses the presence or absence of a dangerous event.
The storage unit included in the computer is
The risk level is configured to be storable for each of multiple points and each combination of multiple situations.
The computer
The risk level of the first combination in which the risk level is not stored in the storage unit is estimated using the risk level stored in the storage unit.
In the storage unit, a second risk level for a second combination having the same point as the first combination but a different situation, and a third combination for a third combination having the same situation as the first combination but a different point. Includes a risk of 3 and a fourth risk for a fourth combination that is in the same situation as the second combination and has the same location as the third combination.
The estimation of the degree of risk for the first combination is
Using the third risk and the fourth risk, the correlation of risk between situations was calculated.
An information processing method for estimating the degree of risk for the first combination from the second degree of risk by using the correlation of the degree of risk between the above situations obtained by calculation. It is an information processing method executed by a computer that manages the degree of danger that numerically expresses the presence or absence of a dangerous event.
The storage unit included in the computer is
The risk level is configured to be storable for each of multiple points and each combination of multiple situations.
The computer
The risk level of the first combination in which the risk level is not stored in the storage unit is estimated using the risk level stored in the storage unit.
In the storage unit, the degree of danger for each driver for each combination of the plurality of points and the plurality of situations for each of the plurality of points is used as individual danger information for each driver and for all driving. It is configured so that the degree of danger for people can be stored as overall danger information.
Calculate the correlation of the degree of danger between the individual danger information and the overall danger information,
Using the correlation of the degree of danger between the individual danger information and the total danger information obtained by calculation, the degree of danger is stored in the individual danger information for each driver from the danger level of the total danger information. An information processing method that estimates the degree of risk for unmatched combinations. In the storage unit, frequency information in which the driving frequency of each driver is associated with the driver is stored in the storage unit.
Using the frequency information, the processing order of a plurality of drivers is determined, and the processing order is determined.
The information processing method according to claim 3, wherein in estimating the risk of the individual danger information, the risk of the individual danger information to be processed is estimated according to the processing order. It is an information processing method executed by a computer that manages the degree of danger that numerically expresses the presence or absence of a dangerous event.
The storage unit included in the computer is
The risk level is configured to be storable for each of multiple points and each combination of multiple situations.
The computer
The risk level of the first combination in which the risk level is not stored in the storage unit is estimated using the risk level stored in the storage unit.
In the storage unit, frequency information in which the frequency of traffic events indicating the traffic volume or the number of accidents is associated with each of the plurality of points is stored in the storage unit.
Using the frequency information, the processing order of the plurality of points is determined, and the processing order is determined.
In the estimation, an information processing method for estimating the degree of risk for the first combination of processing targets according to the processing order. Et al. Is,
Receives position information indicating the current position and sensor data indicating the current status from the moving body,
The degree of risk for the combination of the received point indicated by the position information and the situation indicated by the sensor data is acquired from the storage unit.
A control signal for operating the moving body based on the acquired risk level is transmitted to the moving body.
The information processing method according to any one of claims 1 to 5. further,
When the acquired risk level exceeds the threshold value held in advance, it is determined that a warning is presented, and the control signal based on the acquired risk level is transmitted to the moving body.
According to claim 6, when the acquired risk level is equal to or lower than the threshold value held in advance, it is determined that the warning is not presented, and the control signal based on the acquired risk level is not transmitted to the moving body. The information processing method described. The operation of the moving body is
The operation of changing the moving speed of the moving body and
The operation to stop the moving body and
The operation of changing the steering of the moving body and
Any one of the operations,
The information processing method according to claim 6. The information processing method according to any one of claims 1 to 8, wherein the risk level obtained in the estimation is stored in the storage unit. It is a server device that manages the degree of danger that numerically expresses the presence or absence of a dangerous event.
The server device
A storage unit that can store the degree of risk for each of multiple points and each combination of multiple situations,
A control unit that estimates the risk level of the first combination in which the risk level is not stored in the storage unit by using the risk level stored in the storage unit is provided.
In the storage unit, a second risk level for a second combination having the same point as the first combination but a different situation, and a third combination for a third combination having the same situation as the first combination but a different point. Includes a risk of 3 and a fourth risk for a fourth combination that is in the same situation as the second combination and has the same location as the third combination.
The risk estimation for the first combination uses the second risk and the fourth risk to calculate the risk correlation between points.
A server device that estimates the degree of risk for the first combination from the third degree of risk by using the correlation of the degree of risk between the points obtained by calculation. It is a server device that manages the degree of danger that numerically expresses the presence or absence of a dangerous event.
The server device
A storage unit that can store the degree of risk for each of multiple points and each combination of multiple situations,
A control unit that estimates the risk level of the first combination in which the risk level is not stored in the storage unit by using the risk level stored in the storage unit is provided.
In the storage unit, a second risk level for a second combination having the same point as the first combination but a different situation, and a third combination for a third combination having the same situation as the first combination but a different point. Includes a risk of 3 and a fourth risk for a fourth combination that is in the same situation as the second combination and has the same location as the third combination.
The estimation of the degree of risk for the first combination is
Using the third risk and the fourth risk, the correlation of risk between situations was calculated.
A server device that estimates the risk level for the first combination from the second risk level using the correlation of risk levels between the situations obtained by calculation. It is a server device that manages the degree of danger that numerically expresses the presence or absence of a dangerous event.
The server device
A storage unit that can store the degree of risk for each of multiple points and each combination of multiple situations,
A control unit that estimates the risk level of the first combination in which the risk level is not stored in the storage unit by using the risk level stored in the storage unit is provided.
In the storage unit, the degree of danger for each driver for each combination of the plurality of points and the plurality of situations for each of the plurality of points is used as individual danger information for each driver and for all driving. It is configured so that the degree of danger for people can be stored as overall danger information.
Calculate the correlation of the degree of danger between the individual danger information and the overall danger information,
Using the correlation of the degree of danger between the individual danger information and the total danger information obtained by calculation, the degree of danger is stored in the individual danger information for each driver from the danger level of the total danger information. A server device that estimates the risk for uncombined combinations. It is a server device that manages the degree of danger that numerically expresses the presence or absence of a dangerous event.
The server device
A storage unit that can store the degree of risk for each of multiple points and each combination of multiple situations,
A control unit that estimates the risk level of the first combination in which the risk level is not stored in the storage unit by using the risk level stored in the storage unit is provided.
In the storage unit, frequency information in which the frequency of traffic events indicating the traffic volume or the number of accidents is associated with each of the plurality of points is stored in the storage unit.
Using the frequency information, the processing order of the plurality of points is determined, and the processing order is determined.
In the estimation, a server device that estimates the degree of risk for the first combination to be processed according to the processing order.

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