JP6527058B2 - Hazard information processing method and server device - Google Patents

Description

  The present disclosure relates to the technology of hazard information processing, and more particularly to management of collected hazard information for each point.

  In recent years, various services via networks have been provided to mobiles including automobiles. As one of the conventional services, a dangerous information processing system is known which determines and warns a point where an accident is likely to occur while the vehicle is traveling (see, for example, Patent Document 1).

JP, 2014-154004, A

  In the above-mentioned conventional hazard information processing system, further improvement has been required.

In order to solve the above-mentioned conventional problems, the danger information processing method according to an aspect of the present invention is a danger information processing method executed by a computer that manages the degree of danger to a point where a moving object is located, the computer stored, and the point that hazardous event occurs, the risk of the dangerous event is input to the combination of a situation when generated in the location, in a storage unit in which the danger information processing system comprises as the danger information By using the three or more pieces of danger information stored in the storage unit as a processing target for the first combination in which the degree of danger is not input, by calculating the degree of danger for the first combination , The first risk information having the risk level is estimated, and the three or more pieces of risk information are the same as the first combination and the second combination is different in the situation. The second danger information, which is the degree of danger to the user, the third danger information, which is the degree of danger for the third combination whose situation is the same as that of the first combination but differs in point, and the situation is the same as the second combination And fourth risk information, which is the degree of risk for the same fourth combination as the third combination and the point.

  Note that these general or specific aspects may be realized by a system, device, integrated circuit, computer program, or recording medium, and any of the system, device, method, integrated circuit, computer program, and recording medium It may be realized by any combination.

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

BRIEF DESCRIPTION OF THE DRAWINGS It is a figure which shows an example of the whole structure of the danger information processing system in embodiment. FIG. 1 is a block diagram showing an example of a configuration of a danger information processing system for realizing a danger information processing method according to a first embodiment. FIG. 8 is a flow chart showing an example of a processing procedure for recording an evaluation value in the entire observation table according to Embodiment 1. FIG. 7 is a flow chart showing an example of a processing procedure in which the risk information processing system according to Embodiment 1 records an estimated evaluation value in an overall estimation table. It is a flow figure showing an example of the processing procedure which a danger information processing system records an presumed evaluation value on a whole presumed table. It is a flow figure showing an example of the processing procedure which a danger information processing system records an presumed evaluation value on a whole presumed table. 5 is a diagram for describing a configuration of a general observation table according to Embodiment 1. FIG. 5 is a diagram for describing a configuration of a whole estimation table according to Embodiment 1. FIG. 5 is a diagram showing an example of a general observation table according to Embodiment 1. FIG. FIG. 7 is a diagram showing an example of an entire estimation table according to the first embodiment. FIG. 8 is a block diagram showing an example of the configuration of a danger information processing system for realizing the danger information processing method according to the second embodiment. It is a flowchart which shows an example of the process sequence which the danger information processing system which concerns on Embodiment records an evaluation value on a general observation table and an individual observation table. FIG. 14 is a flowchart showing an example of a processing procedure in which the danger information processing system according to the second embodiment records an estimated evaluation value in the entire estimation table and the individual estimation table. FIG. 14 is a flowchart showing an example of a processing procedure in which the danger information processing system according to the second embodiment records an estimated evaluation value in the entire estimation table and the individual estimation table. FIG. 14 is a flowchart showing an example of a processing procedure in which the danger information processing system according to the second embodiment records an estimated evaluation value in the entire estimation table and the individual estimation table. FIG. 16 is a diagram showing an example of an entire estimation table according to a second embodiment. FIG. 18 is a diagram showing an example of an individual observation table according to Embodiment 2. FIG. 16 is a diagram showing an example of an individual estimation table according to Embodiment 2. FIG. 18 is a block diagram showing an example of the configuration of a danger information processing system for realizing the danger information processing method according to the third embodiment. It is a flowchart which shows an example of the process sequence which the danger information processing system which concerns on Embodiment records on an event frequency table. FIG. 18 is a flow chart showing an example of a processing procedure in which the danger information processing system according to the third embodiment records the estimated evaluation value in the overall estimation table in accordance with the priority. FIG. 18 is a diagram showing an example of an event frequency table according to the third embodiment. FIG. 18 is a block diagram showing an example of a configuration of a danger information processing system for realizing the danger information processing method according to the fourth embodiment. FIG. 18 is a flow chart showing an example of the processing procedure of the danger information presentation apparatus according to the fourth embodiment. It is a general view showing an example of a danger information processing system. It is a general view showing an example of a danger information processing system. It is a general view showing an example of a danger information processing system. It is a general view showing an example of a danger information processing system. It is a general view showing an example of a danger information processing system.

(Findings that formed the basis of this disclosure)
The present inventor has found that the following problems occur in the danger information processing system described in the "Background Art" section.

  In recent years, safe driving support systems have been developed to support safe driving of automobiles. In such a safe driving support system, while traveling on a road, an automobile acquires information ahead of the automobile from a distance sensor or the like. Then, using the acquired information, the vehicle performs control such as automatically applying a brake when the distance to the obstacle ahead becomes smaller than a certain value so as not to cause a traffic accident.

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

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

  The hazard information processing system of Patent Document 1 generates a hazard statistically indicated in the hazard type based on each individual hazard degree of the same road section, the same time zone, and the same hazard type for each hazard type. Determine the statistical risk level that indicates ease. At this time, the statistical risk of one risk type is statistically determined based on each risk factor obtained from each image of the same road section, the same time zone, and each piece of behavior operation information. In other words, Patent Document 1 actually senses all risk factors and all types of risk for all road sections and all time zones included in the area when trying to calculate the degree of risk of a wide area where a car travels. Without it, there was a problem that the degree of risk could not be determined. In addition, there is a problem that even in the same road section, the same time zone, and the same risk type, the point that the degree of risk differs depending on the driver is not taken into consideration.

  Based on the above examination, the inventor examined the following improvement measures in order to solve the above-mentioned problems.

  A danger information processing method according to an aspect of the present invention is a danger information processing method used in a danger information processing system for managing a danger level to a point where a moving object is located, the point at which a danger event occurred, and the point The risk level input for the combination with the situation at the time of occurrence of the risk event is stored in the storage unit of the risk information processing system as the risk information, and the risk level is not input. The first risk information, which is the degree of risk, is calculated by calculating the degree of risk for the first combination using the three or more pieces of danger information stored in the storage unit with the combination as a processing target. Presuming that the three or more pieces of danger information are second danger information, which is a degree of danger to a second combination having the same point as the first combination but different in situation; Third hazard information, which is the degree of risk to the third combination having the same situation and situation but different points, and the fourth combination for which the situation is the same and the third combination and point are the same, with the second combination And fourth risk information which is the degree of risk.

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

  Also, for example, in the estimation, the degree of danger between the points obtained by calculating and calculating the correlation of the degree of danger between the points using the second danger information and the fourth danger information. From the degree of danger of the third danger information, the degree of danger for the first combination may be calculated using the correlation of

  Also, for example, in the estimation, the degree of danger between the situations obtained by calculating and calculating the correlation of the degree of danger between the situations using the third danger information and the fourth danger information. From the degree of danger of the second danger information, the degree of danger for the first combination may be calculated using the correlation of

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

  Therefore, the estimated degree of risk can be held in the storage unit.

  Also, for example, for the combination of the point at which the dangerous event occurred and the situation at the time of the dangerous event occurring at the point, the danger level inputted for each driver is It is stored in the storage unit as information and as overall danger information for all drivers, and is obtained by calculating and calculating the correlation of the degree of danger between the individual danger information and the overall danger information. The degree of risk of the individual danger information may be calculated from the degree of danger of the general danger information using the correlation of the degree of danger between the individual danger information and the general danger information.

  According to this, it is possible to identify the driver and obtain the individual hazard level for each identified driver, using the risk level for the combination of the point and the situation targeted 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 with respect to combinations of all points being managed and the situation.

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

  For this reason, without waiting for the process of estimating the risk to the combination for which the risk is not input among all the combinations, obtain an estimated evaluation value of a point where the frequency of traffic events is high, such as a point with high traffic. Can.

  Further, for example, frequency information in which the driving frequency for each driver and the driver are associated is further stored in the storage unit, and the processing order of a plurality of drivers is determined using the frequency information, and the individual In the calculation of the risk level of the risk information, the individual risk information to be processed may be determined according to the processing order.

  For this reason, it is possible to obtain individual danger information of the driver with a high driving frequency without waiting for estimation processing of the danger degree to the combination in which the danger degree among the individual danger information of all the drivers is not input. .

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

  For this reason, it is possible to notify, in real time, the degree of danger of the point at which the user is driving the moving body.

  Further, for example, when the acquired degree of risk exceeds a threshold held in advance, it is determined that a warning is presented, and information based on the acquired degree of danger is transmitted to the mobile unit, and the acquisition is performed. If the determined degree of risk is equal to or less than the threshold value held in advance, it is determined that the warning is not presented, and information based on the acquired degree of risk may not be transmitted to the mobile body.

  Note that these general or specific aspects may be realized by a system, a device, an integrated circuit, a computer program, or a recording medium such as a computer readable CD-ROM, a system, a method, an integrated circuit, It may be realized by any combination of computer program or recording medium.

  The present disclosure discloses a method of calculating the degree of risk of a portion where sensing can not be performed by sensing some road sections, some time zones, and some risk factors.

  Based on the above consideration, the present inventors have arrived at each aspect of the present disclosure.

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

  The embodiments described below each show a preferable specific example of the present disclosure. That is, numerical values, shapes, materials, components, arrangement and connection of components, steps, order of steps, and the like shown in the following embodiments are an example of the present disclosure and are not the gist of the present disclosure. . The present disclosure is identified based on the description of the claims. Therefore, among the components in the following embodiments, components which are not described in the independent claim showing the top concept of the present disclosure are not necessarily required to achieve the problems of the present disclosure, but are more preferable. It is described as a component that constitutes a form.

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

  The group C 100 is, for example, a company, a group, a home, a roadside device, a vehicle, or the like, regardless of the size. In the group C100, a plurality of devices C101, device A, device B, and gateway C102 exist. If there are devices (for example, a smartphone, a PC, a TV, etc.) that can be connected to the Internet in the plurality of devices C101, devices that can not be connected to the Internet by themselves (eg, lighting, washing machine, refrigerator, car navigation, etc.) Also exist. Even devices that can not be connected to the Internet by themselves may be devices that can be connected to the Internet via the gateway C102. In group C100, there are users C10 who use a plurality of devices C101.

  A cloud server C111 exists in the data center operating company C110. The cloud server C111 is a virtualization server that cooperates with various devices via the Internet. It mainly manages huge data (big data) that are difficult to handle with ordinary database management tools etc. The data center operating company C110 performs data management, management of the cloud server C111, and management of a data center that performs those. Details of 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 performs only data management, management of the cloud server C111, and the like. For example, if a device maker developing and manufacturing one of the plurality of devices C101 simultaneously performs data management, management of the cloud server C111, etc., the device maker corresponds to the data center operating company C110. (FIG. 25 (B)). Also, the data center operating company C110 is not limited to one company. For example, when the device maker and another management company jointly or share the data management and the cloud server C111, either or both of them correspond to the data center operating company C110 (FIG. 25). (C)).

  The service provider C120 has a server C121. The server C 121 referred to here includes, for example, a memory in a personal PC, etc. regardless of its size. Also, there are cases where the service provider does not possess the server C121.

  The gateway C 102 is not essential in the above service. For example, when the cloud server C111 performs all data management, the gateway C102 becomes unnecessary. Also, there may be no device that can not connect to the Internet by itself, as in the case where 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 C 100 transmits each piece of log information to the cloud server C 111 of the data center operating company C 110. The cloud server C 111 accumulates log information of the device A or the device B ((a) in FIG. 25). Here, the log information is information indicating, for example, the operating condition, the operation date and time, and the like of the 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, number of times of opening, etc. Say information. The log information may be provided to the cloud server C111 directly from the plurality of devices C101 itself via the Internet. Also, 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, it may be a unit capable of organizing the information accumulated by the data center operating company and providing it to the service provider C 120, or a unit requested by the service provider C 120. Although the unit is described as a fixed unit, it may not be fixed, and the amount of information to be provided may change depending on the situation. The log information is stored in the server C 121 held by the service provider C 120 as necessary ((b) in FIG. 25). Then, the service provider C 120 organizes the log information into information compatible with the service to be provided to the user, and provides the information to the user. The user to be provided may be a user C10 using a plurality of devices C101 or an external user C20. The service providing method to the user may be provided, for example, directly from the service provider to the user ((b) and (e) in FIG. 25). Also, the service providing method to the user may be provided to the user, for example, via the cloud server C111 of the data center operating company C110 again ((c) and (d) in FIG. 25). Also, the cloud server C 111 of the data center operating company C 110 may organize log information into information compatible with the service provided to the user, and provide the information to the service provider C 120.

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

  Hereinafter, an embodiment 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 entire configuration of the danger information processing system 100 according to the first embodiment. The danger information processing system 100 illustrated in FIG. 1 includes a server apparatus 101, a mobile unit 102, a mobile unit 103, an observation apparatus 104, and an observation apparatus 105.

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

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

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

  In FIG. 1, although only two moving bodies 102 and 103 are shown, the number of moving bodies may be one or three or more. Further, although only two observation devices 104 and observation devices 105 are shown in FIG. 1, one or three or more observation devices may be provided.

  FIG. 2 is a block diagram showing an example of a configuration of the danger information processing system 100 for realizing the danger information processing method according to the first embodiment. In the danger information processing system of FIG. 2, the mobile unit 102 for acquiring and transmitting position information and other sensor data such as the surrounding situation, and the server apparatus 101 having the function of the danger information processing apparatus are connected via a network. Shows the configuration to be performed. The server apparatus 101 is configured by, for example, a computer including a central processing unit (CPU), a read only memory (ROM), a random access memory (RAM), and the like.

  Each of the mobile unit 102 and the mobile unit 103 includes a data acquisition unit 201. The observation device 104 and the observation device 105 also include a data acquisition unit 201. Although the mobile unit 102 will be described below, the mobile unit 102 may be replaced with the mobile unit 103, the observation device 104, or the observation device 105.

  The data acquisition unit 201 senses the surrounding situation of the moving body 102, and acquires position information indicating the current position, and sensor data indicating the situation of the moving body 102 itself, the surrounding situation of the moving body 102, and the like. 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, position acquisition means such as GPS (Global Positioning System) that acquires position information, and vehicle data that acquires sensor data, vehicle data, laser data, millimeter wave radar, sonar, infrared camera, road surface condition It is equipped with sensor devices such as sensors, drowsiness sensors, weather sensors and the like. Then, the data acquisition unit 201 acquires position information acquired by the position acquisition unit and sensor data sensed by each sensor device. In addition, the data acquisition unit 201 includes CAN (Controller Area Network) information acquisition means, inter-vehicle communication information means, road-to-vehicle communication information means, vehicle peripheral information recognition means, etc., and the position acquisition means The acquired position information and sensor data sensed by each sensor device are acquired.

  For example, the data acquisition unit 201 includes an on-vehicle camera, and the presence or absence of a front vehicle as a result obtained by performing image recognition processing on a captured image, and the distance to the front vehicle measured using the captured image , And may be acquired as sensor data. In addition, the data acquisition unit 201 may include an on-vehicle camera or a millimeter wave radar, measure how many pedestrians are within a predetermined distance, and acquire as sensor data. Further, the data acquisition unit 201 may include a road surface state 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 weather, and acquire it as sensor data. There is no restriction on the type of sensor data.

  Moreover, the sensor data acquired by the data acquisition unit 201 may be different or the same depending on each of the moving bodies 102 and 103 and each of the observation devices 104 and 105.

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

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

  The control unit 200 includes a management unit 202 and an operation unit 204.

  The management unit 202 generates an overall observation table in which an evaluation value indicating the presence or absence of a dangerous event is associated with a combination of a point and a situation, and stores the whole observation table in the storage unit 205. The general observation table has, for example, a point indicated by position information, a situation indicated by sensor data as a column, and each combination of point and situation is constituted by danger information which is the degree of danger to the combination. Although the plurality of danger information in which the degree of danger is associated with each of the plurality of combinations is held in the storage unit 205 as the entire observation table, it may not be held as a table, and each of the plurality of combinations is As long as the information and the risk are associated with each other, the information may be information represented in a form other than the table.

  The management unit 202 uses the position information and sensor data acquired from the reception unit 206, or the presence or absence of the occurrence of the dangerous event acquired from the input unit 203, to evaluate the combination of the point indicated by the position information and the condition indicated by the sensor data. A value (an evaluation value (risk) indicating the presence or absence of occurrence of a dangerous event) is recorded in a cell indicating a corresponding combination of the general observation table stored in the storage unit 205. The occurrence of a dangerous event is, for example, the presence or absence of an accident. The occurrence of the dangerous event may be, for example, the presence or absence of a near incident.

  Further, the management unit 202 generates an overall estimation table in which the risk (estimated evaluation value indicating the presence or absence of a dangerous event) estimated for each combination of a point and a situation is associated using the overall observation table. And store it in the storage unit 205. The overall estimation table has, for example, a point indicated by position information, a situation indicated by sensor data as a column, and for each combination of point and situation, a danger indicating an estimated evaluation value as the danger for the combination Composed of information. For the cell (hereinafter referred to as “unevaluated cell”) corresponding to the combination of the point where the risk is not held in the overall observation table and the situation (hereinafter referred to as “unevaluated cell”), the management unit 202 The estimated evaluation value is calculated using the degree of risk held in the cell corresponding to the combination of. Specifically, when calculating the estimated evaluation value, the management unit 202 instructs the calculation unit 204 to perform calculation, and performs calculation processing of the estimated evaluation value using the calculation result of the calculation unit 204. The calculation procedure of the estimated evaluation value will be described in detail later using the drawings. In addition, the management unit 202 directly applies the evaluation value of the cell (hereinafter referred to as “the evaluated cell”) corresponding to the combination of the point where the evaluation value is held in the overall observation table and the situation, Store in a cell Furthermore, the management unit 202 stores the estimated evaluation value calculated using the operation unit 204 for the unevaluated cell in the corresponding cell of the overall estimation table.

  Arithmetic unit 204 has a function of specifying a point from the position information received from reception unit 206, and a function of determining what condition it is from the sensor data received from reception unit 206 based on a predetermined condition. . In the case where there are a plurality of situations, a plurality of conditions respectively corresponding to the plurality of situations are associated. In addition, the calculation unit 204 has a function of calculating the correlation coefficient of the 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. In addition, the operation unit 204 determines whether or not an accident has occurred from, for example, operation data of an Antilock Brake System (ABS) device among sensor data received from the reception unit 206, and inputs it as the presence or absence of occurrence of a dangerous event. May be included.

  The input unit 203 inputs the presence or absence of occurrence of a dangerous event. The input unit 203 transmits the input presence or absence of the occurrence of the dangerous event to the control unit 200. For example, the input unit 203 is a device such as a keyboard or a mouse, and is operated by the operator to input the presence or absence of occurrence of a dangerous event.

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

  The above is the description of the configuration of the danger 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 whole observation table.

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

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

  When the management unit 202 determines that the point registered in the whole observation table is passed (Yes in step S302), the sensor data is acquired using the sensor data input together with the position information indicating the point It is determined whether the situation of the mobile unit or the situation around the observation apparatus is a situation registered in the general observation table (step S303). Specifically, management unit 202 causes operation unit 204 to determine whether or not the input sensor data satisfies a predetermined condition for determining whether or not the situation is registered in the general observation table. . Then, when the determination result acquired from the calculation unit 204 is a result determined to satisfy the predetermined condition, the management unit 202 obtains the input sensor data and the situation of the mobile object or the situation around the observation device. It is determined that the condition is registered in the whole observation table. That is, the input sensor data indicates the situation in which the situation of the mobile that has acquired the sensor data or the situation around the observation apparatus is registered in the general observation table.

  On the other hand, when the management unit 202 acquires from the calculation unit 204 a result determined that the input sensor data does not satisfy the predetermined condition, the management unit 202 acquires the condition of the mobile object from which the sensor data is acquired or the surroundings of the observation device. It is determined that the situation is not a situation registered in the general observation table (No in step S303), and the process ends.

  If the management unit 202 determines that the condition of the mobile body from which the input sensor data has been acquired or the condition around the observation apparatus is a condition registered in the general observation table (Yes in step S303), the input unit 203 An evaluation value (an evaluation value indicating the presence or absence of 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 presence or absence of the occurrence of the dangerous event acquired (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 dangerous information processing system 100 records the evaluation value in the whole observation table. That is, the danger information processing system 100 stores 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 in the storage unit 205 as danger information. .

  4 to 6 are flowcharts showing an example of a processing procedure in which the danger information processing system 100 according to the present embodiment records the 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 among all the points registered in the row of the general observation table from the cell in which the evaluation value of the general observation table is input. (Step S401).

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

  FIG. 5 corresponds to step S401 in FIG. 4 and 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 danger degree between each point from the general observation table.

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

  Next, for each of all the points registered in the general observation table, the management unit 202 determines whether or not the correlation coefficient of the risk with other points has been calculated (step S502). Specifically, the management unit 202 determines, for a point whose correlation coefficient is currently calculated, whether i at the point 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 whole observation table. When the management unit 202 determines that i at the point for which 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 for all points. Is determined to have been calculated. On the other hand, when the management unit 202 determines that i at the point for which 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 It is determined that the correlation coefficient of has not been calculated.

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

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

  Next, with respect to the point i registered in the general observation table, the management unit 202 determines whether the correlation coefficient of the degree of risk with all other points j has been calculated (step S504). Specifically, the management unit 202 determines whether j at another point is equal to the number of registered points. When it is determined that j of another point is equal to the number of registered points, the management unit 202 calculates the correlation coefficient of the risk with all other points j for the point i registered in the general observation table. It is judged that it is 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 phase of the degree of risk with respect to all other points j with respect to point i registered in the general observation table. It is determined that the relation number has not been calculated.

  When the management unit 202 determines that the correlation coefficient of the risk with all other points j is not calculated for the point i registered in the overall observation table (No in step S504), the calculation unit At 204, calculation of the correlation coefficient of the degree of risk between the point i and the point j is instructed using the evaluation value for the situation having the evaluation value in common between the point i and the point j. The calculation unit 204 calculates the correlation coefficient of the risk degree 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 it is determined that the correlation coefficient of the risk with all other points j is calculated for the point i registered in the general observation table (Yes in step S504), the management unit 202 increments i. (Step S507), the process proceeds to step S502, and the process is repeated.

  The above is the processing procedure for the danger information processing system 100 to calculate the correlation coefficient of the danger degree between each point from the whole observation table.

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

  As shown in FIG. 6, the management unit 202 first determines whether there is an unevaluated cell in the overall observation table (step S601). When the management unit 202 determines that there is no unevaluated cell in the whole observation table (No in step S601), the management unit 202 stores the evaluation value of the whole observation table as it is in the corresponding cell of the whole estimation table and completes the process. .

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

  The above is the processing procedure in which the dangerous information processing system 100 calculates the estimated evaluation value for the unevaluated cell of each point from the whole observation table and the correlation coefficient.

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

  The equation of the relative risk to the situation y at the point x is expressed as the following equation 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 x represents the average value of the evaluation value (presence or absence of occurrence of a dangerous event) of the point x.

  The equation of the similarity of the evaluation value (presence or absence of occurrence of a dangerous event) to the situation k of the point i and the point j is expressed as the following equation 2. In the case where the value of this expression is negative, they are dissimilar, and in the case of positive, they are similar.

  The equation of the correlation coefficient of the risk between the point a and the point x is expressed as the following equation 3. At this time, only those that are evaluating the evaluation value in common between the point a and the point x are the targets of the calculation.

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

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

  From the evaluation value (presence or absence of occurrence of a dangerous event) of all the points x other than the point a, the equation of the degree of risk for the combination of the point a and the situation y is expressed as the following equation 4.

  In order to correct the problem that the evaluation value (presence or absence of occurrence of a dangerous event) is determined more at many points than Expression 4, the following Expression 5 can be obtained by performing normalization.

  The equation of the estimated evaluation value for the situation y at the point a is expressed as Equation 6 below.

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

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

  The whole observation table 701 holds “2” which means unevaluated in each cell as an initial value. For example, when an accident occurs when sensor data indicating the situation a is acquired at the point A, the management unit 202 determines that a dangerous event has occurred in the cell of the point A and the situation a in the general observation table 701. Record the meaning "3" as the evaluation value. For example, when an accident does not occur when sensor data indicating the situation m is acquired at the point N, the management unit 202 causes a dangerous event to occur in the point N in the general observation table 701 and the cell of the situation m Record "1" as an evaluation value, which means that there was no. In the example of the general observation table 701 shown in FIG. 7, only the cells of the point B and the situation b are unevaluated cells, and the other cells are already evaluated. In addition, the sensor data which shows a condition are sensor data which satisfy | fill the predetermined | prescribed condition for determining whether it is the said condition.

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

  The whole estimation table 801 has the same value of the evaluated cell as the whole observation table 701 shown in FIG. 7. The difference between the two is that the estimated evaluation value “2.75” is input to the cells of the point B and the situation b that have not been evaluated in the general observation table 701. The estimated evaluation value being input is calculated by calculating the correlation coefficient of the risk between the point B and each of the other points, the calculated correlation coefficient, and the evaluation value of each cell of the situation b at each point It is the result of computing an estimated evaluation value using Formula 6, and.

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

  FIG. 9 is a diagram showing an example of the general 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 more specifically disclosed about the whole observation table. As in the case of FIG. 7, the general observation table 901 holds “2” which means unevaluated for each cell as an initial value. For example, when an accident occurs when sensor data indicating that there is a vehicle ahead within a certain distance at point B is acquired, the management unit 202 makes a point A of the whole observation table 901 and a cell with a vehicle ahead within a certain distance. , "3" which means that a dangerous event has occurred is recorded as an evaluation value. Also, for example, when an accident does not occur when sensor data indicating that the road surface is frozen is acquired at the point N, the management unit 202 causes a danger to the point N in the general observation table 901 and the cell on the road surface frozen. Record "1" as an evaluation value, which means that the event did not occur. In the example of the general observation table 901 shown in FIG. 9, a cell with a point B and a certain distance behind the vehicle, a cell with the point B and a certain number of pedestrians> a threshold a, a point O and a certain distance with an oncoming vehicle Is an example of an unevaluated cell and the other cells are already evaluated.

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

  The difference between FIG. 10 and FIG. 8 is that each situation is more specifically disclosed for the entire estimation table. As in the case of FIG. 8, the cell B at a point B and a rear vehicle within a certain distance not evaluated in the general observation table 901 of FIG. 9, and the cell of the point B and the number of pedestrians within a certain distance> the threshold a. The estimated evaluation values “2.98”, “1.05”, and “1.12” are input to the point O and the cell having an oncoming car within a certain distance, respectively. The estimated evaluation value “2.98” that has been input calculates the correlation coefficient of the risk between the point B and the other points, and the calculated correlation coefficient and the vehicle behind the fixed distance at each point It is the result of calculating the estimated evaluation value using the evaluation value of each cell of the column and the equation 6. In addition, the estimated evaluation value “1.05” that is input is the correlation coefficient of the risk between the point B and the other points, and the calculated correlation coefficient and a certain distance at each point It is the result of calculating the estimated evaluation value using the evaluation value of each cell of the column of the number of pedestrians> threshold value a and Equation 6. Also, the estimated evaluation value “1.12” that is input is the correlation coefficient of the risk degree between the point O and other points, and the calculated correlation coefficient, and within a certain distance at each point It is the result of calculating the estimated evaluation value using the evaluation value of each cell of the oncoming vehicle included column and the equation (6).

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

(effect)
The danger information processing system 100 according to the present embodiment is a point (that is, a point at which sensor data indicating a situation can not be obtained from a combination of the point at which the evaluation value has already been obtained and the situation (that is, evaluated cells). It is possible to obtain the degree of risk (an evaluation value indicating the presence or absence of a dangerous event) of the unevaluated cell). As a result, the risk information processing system 100 determines the degree of risk (an evaluation value indicating the presence or absence of occurrence of a dangerous event) without actually acquiring all sensing data with respect to combinations of all managed points and conditions. You can get

Second Embodiment
FIG. 11 is a block diagram showing an example of the configuration of a danger information processing system 110 for realizing the danger information processing method according to the second embodiment.

  In the danger information processing system 110 shown in FIG. 11, mobile units 112 and 113 and observation devices 114 and 115 for acquiring and transmitting position information and sensor data, and a server apparatus 111 having a function of the danger information processing apparatus, It shows a configuration for connection via a network. Below, only the difference in the function of each component from Embodiment 1 is explained. The description of the components having no difference in function from the first embodiment will be omitted.

  Each of the mobile unit 112 and the mobile unit 113 includes a data acquisition unit 211. The observation device 114 and the observation device 115 further include a data acquisition unit 211. Although the mobile unit 112 will be described below, the mobile unit 112 may be replaced with the mobile unit 113, the observation device 114, or the observation device 115.

  In addition to the function of the data acquisition unit 201 of the first embodiment, the data acquisition unit 211 also acquires sensor data for specifying a driver. The data acquisition unit 211 may specify the driver by matching the driver information registered in advance with the smartphone owned by the driver, the key of the car, the driver's license, and the like. Also, the driver may be identified from the driver's own physical characteristics, such as fingerprints, palm prints, pupils, voice prints, 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 receiving 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 an arithmetic unit 214.

  The management unit 212 generates a general observation table and an individual observation table in which evaluation values indicating presence or absence of a dangerous event are associated with a combination of a point and a situation, and stores the general observation table in the storage unit 215. The whole observation table is, for example, the same as the whole observation table described in the first embodiment. Here, the general observation table can also be said to be general 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 separately managed as an individual table for each driver. That is, the individual observation table is an individual hazard level for each driver, which is the hazard level input for each driver with respect to the combination of the point where the hazard event occurred and the situation when the hazard event occurred at the point. Composed of danger information.

  The management unit 212 identifies the driver using the position information acquired from the reception unit 206 and the sensor data including sensor data for identifying the driver, and the individual observation table associated with the identified driver. Choose The management unit 212 uses the position information and sensor data acquired from the reception unit 206, or the presence or absence of the dangerous event occurrence acquired from the input unit 203, to the danger of the combination of the point indicated by the position information and the condition indicated by the sensor data. The degree (an evaluation value indicating the presence or absence of a dangerous event) is recorded in the corresponding cell of each of the general observation table and the individual observation table stored in the storage unit 215. The occurrence of a dangerous event is, for example, the presence or absence of an accident. The occurrence of the dangerous event may be, for example, the presence or absence of a near incident.

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

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

  In addition to the function of the operation unit 204 of the first embodiment, the operation unit 214 has a function of calculating the correlation coefficient of the risk between the entire estimation table and the individual observation table, and individualized from the estimated evaluation value of the entire estimation table. And a function of calculating estimated evaluation values for unevaluated cells of the observation table.

  In addition to the general observation table and the general estimation table held by the memory unit 205 of the first embodiment, the storage unit 215 holds one or more individual observation tables provided with the same rows and columns as the general observation table for each driver. Also, for each driver, one or more separate estimation tables are provided, with the same rows and columns as the overall estimation table.

  The above is the description of the configuration of the danger 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 flowchart showing an example of a processing procedure in which the danger information processing system 110 according to the second embodiment records an evaluation value in the entire observation table and the individual observation table.

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

  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 identified driver (step S1202).

  Next, the management unit 212 performs the same processing as steps S302 to S305 in FIG. 3 of the first embodiment, using the input position information and 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 dangerous information processing system 100 records the evaluation value in the general observation table and the individual observation table. In other words, the danger information processing system 100 determines, for each driver, the degree of danger input for each driver with respect to the combination of the point where the danger event occurred and the situation when the danger event occurred at the point. It is stored in the storage unit 215 as individual danger information and as whole danger information for all drivers.

  FIGS. 13-15 is a flowchart which shows an example of the process sequence which the danger information processing system 110 which concerns on Embodiment records an estimated evaluation value on a whole estimation table and an individual estimation table.

  As shown in FIG. 13, the management unit 212 first performs the same process as step S401 and step S402 in 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 an estimated evaluation value for an unevaluated cell of the individual observation table from the estimated evaluation value of the entire 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. Furthermore, the management unit 212 stores the estimated evaluation value, which is an estimated evaluation value for an unevaluated cell among the plurality of cells of the individual observation table, calculated in the calculation unit 214 in the corresponding cell of the individual estimation table. Do.

  FIG. 14 corresponds to step S1301 of FIG. 13 and is a flowchart showing an example of a processing procedure in which the risk information processing system 110 calculates the correlation coefficient of the risk between the entire estimation table and the individual observation table. It is.

  As shown in FIG. 14, the management unit 212 first defines 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 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 in 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 being calculated. The number of individual observation tables is the number of all the individual observation tables stored in the storage unit 215. When the management unit 212 determines that i of the individual observation table currently calculating the correlation coefficient is equal to the number of individual observation tables, the correlation coefficient of the risk with the entire estimation table for all the individual observation tables Is determined to have been calculated. On the other hand, when the management unit 212 determines that i of the individual observation table currently calculating the correlation coefficient is not equal to the number of individual observation tables, the degree of risk between all individual observation tables and the overall estimation table It is determined that the correlation coefficient of has not been calculated.

  If 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 management unit 212 compares the overall estimation table with the individual observation table. The process of calculating the correlation coefficient of the risk degree between the processes is ended.

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

  The management unit 212 instructs the calculation unit 214 to calculate the correlation coefficient of the risk degree between the individual observation table selected in step S1403 and the overall estimation table. Arithmetic 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 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 entire estimation table is not calculated. , I is incremented (step S1405), and the process proceeds to step S1402 to repeat the process.

  The above is the processing procedure for the risk information processing system 110 to calculate the correlation coefficient of the 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 overall danger information.

  FIG. 15 corresponds to step S1302 in FIG. 13, and the dangerous 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 entire estimation table. It is a flow figure showing an example of processing procedure.

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

  Next, the management unit 212 determines whether 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 i in the individual observation table is equal to the number of individual observation tables for the individual observation table for which the currently estimated evaluation value is calculated. The number of individual observation tables is the number of all the individual observation tables stored in the storage unit 215. When it is determined that i of the individual observation table currently calculating the estimated evaluation value is equal to the number of individual observation tables, the management unit 212 determines that the estimated evaluation value has been calculated for all the individual observation tables. On the other hand, when it is determined that i of the individual observation table currently calculating the estimated evaluation value is not equal to the number of individual observation tables, the management unit 212 determines that the estimated evaluation value has not been calculated for all individual observation tables. Do.

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

  When it is determined that the estimated evaluation value has not been calculated for all the individual observation tables (No in step S1502), the management unit 212 selects an individual observation table for which the estimated evaluation value is not calculated yet as a processing target (step S1503).

  The management unit 212 determines whether there is an unevaluated cell in the individual observation table selected in step S1503 (step S1504). When the management unit 212 determines that there is no unevaluated cell in the individual observation table (N in step S1504), the process transitions to step S1506.

  When the management unit 212 determines that there is an unevaluated cell in the individual observation table (Yes in step S1504), the management unit 212 instructs the calculation unit 214 to calculate an estimated evaluation value for the unevaluated cell. According to the instruction from management unit 212, operation unit 214 calculates the correlation coefficient of the risk between the entire estimation table and the individual observation table, and the evaluation value (including the estimated evaluation value) of the evaluated cells of the entire estimation table. An estimated evaluation value for the unevaluated cell of the individual observation table is calculated using (step S1505).

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

  The above is the processing procedure for the danger information processing system 110 to calculate 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 entire estimation table. That is, the danger information processing system 110 uses the correlation of the degree of danger between the individual observation table as individual danger information and the whole estimation table as the whole danger information, which is obtained by calculation, to obtain the whole estimation table. Calculate the estimated evaluation value of the individual observation table from the evaluation value of.

  Next, processing of the risk information processing system will be described using specific examples of the entire estimation table, the individual observation table, and the individual estimation table.

  FIG. 16 is a diagram showing an example of the entire 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 entire estimation table 1601 is a table generated by the completion of step S402.

  FIG. 17 is a diagram illustrating 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, evaluation values are recorded only at the point A and a cell having a front vehicle within a certain distance. Other cells are not evaluated. Here, when the correlation coefficient is calculated using Equation 6 between the evaluated cell of the individual observation table 1701 and the evaluated cell of the entire 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 the entire of the cells not evaluated in the individual observation table 1701. The same evaluation value as the estimation table 1601 is input.

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

(effect)
The danger information processing system 110 according to the present embodiment is a point (that is, an unevaluated cell) in which sensor data can not be acquired from a combination of the point where an evaluation value has already been acquired and a situation (that is, evaluated cells). ) (A rating value indicating the presence or absence of a dangerous event) can be obtained. Further, in the danger information processing system 110, it is possible to identify the driver and obtain the individual danger level for each identified driver, using the risk level for the combination of the point and the situation for the whole. As a result, the danger information processing system 110 determines the degree of risk for each driver without actually acquiring all the sensing data individually for each driver with respect to combinations of all points and situations managed. It becomes possible to obtain an evaluation value indicating the presence or absence of the occurrence of a dangerous event.

Third Embodiment
FIG. 19 is a block diagram showing an example of the configuration of a danger information processing system 120 for realizing 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 for acquiring and transmitting position information and sensor data, and the server device 121 having the function of the danger information processing device, It shows a configuration for connection via a network. Below, only the difference in the function of each component from Embodiment 1 is explained. The description of the components having no difference in function from the first embodiment will be omitted.

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

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

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

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

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

  The management unit 222 uses the position information and sensor data acquired from the reception unit 206, or the presence or absence of the occurrence of a dangerous event acquired from the input unit 203, to evaluate the combination of the point indicated by the position information and the condition indicated by the sensor data. A value (an evaluation value indicating the presence or absence of a dangerous event (risk)) is recorded in a cell indicating a corresponding combination of the general observation table stored in the storage unit 225. The occurrence of a dangerous event is, for example, the presence or absence of an accident. The occurrence of the dangerous event may be, for example, the presence or absence of a near incident.

  In addition, the management unit 222 acquires position information and sensor data acquired from the reception unit 206, and extracts information (for example, traffic volume for each point) included in the sensor data to be used for priority determination, The event frequency table stored in the storage unit 225 is recorded.

  Further, the processing of the management unit 222 to generate the whole estimation table using the whole observation table is almost the same as the processing of the management unit 202 of the first embodiment. In the processing of generating the entire 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 in the processing of the management unit 202 according to the first embodiment. It is different from

  The priority determination unit 227 refers to the event frequency table stored in the storage unit 225 to determine the order of calculating the estimated evaluation value. 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 risk between each point and the process of calculating the estimated evaluation value (for example, , In order of increasing traffic per point).

  In addition to the general observation table and the general estimation table held by the memory unit 205 of the first embodiment, the storage unit 225 uses 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) as an event frequency table.

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

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

  FIG. 20 is a flowchart showing an example of a processing procedure for the danger information processing system 120 according to the third embodiment to record in the event frequency table.

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

  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 that the dangerous information processing system 120 records in the event frequency table.

  FIG. 21 is a flow chart showing an example of a processing procedure in which the risk information processing system 120 according to the third embodiment records the estimated evaluation value in the overall estimation table in accordance with 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 calculates the estimated evaluation value for the unevaluated cell at each point of the general observation table. Determine the priority order. The priority determining unit 227 rearranges, in the order of the determined priorities, the processing order in which the estimated evaluation values for the unevaluated cells of the points in the general observation table are calculated (step S2101). Here, the priority determination unit 227 determines to calculate the estimated evaluation value of the unevaluated cell at each point in the order in which the traffic volume at each point is high. Specifically, the priority determination unit 227 arranges the order of calculating the estimated evaluation value for the unevaluated cells of each point by the priority order by assigning points with more traffic to those with small values of i. Change.

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

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

  When the management unit 222 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 general observation table in step S504 (Yes in step S504), The process transitions to step S2103.

  Then, the management unit 222 determines whether there is an unevaluated cell in each cell of each situation of the point i (step S2103).

  When the management unit 222 determines that there is an unevaluated cell in each cell of each situation of the point i (Yes in step S2103), the management unit 222 instructs the calculation unit 204 to calculate an 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 degree of risk with another point and the evaluation value of the other point in the column of the unrated cell status according to the instruction from the management unit 222. Then, the estimated evaluation value for the unevaluated cell is calculated (step S2104). The management unit 222 stores the evaluation value of the whole observation table as it is in the corresponding cell of the whole estimation table, and further stores the estimated evaluation value calculated by the calculation unit 204 in the corresponding cell of the whole 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 risk information processing system 120 records the estimated evaluation value in the overall estimation table in accordance with the determined priority order. That is, the dangerous information processing system 120 determines the processing order of the plurality of points using the event frequency table as the frequency information, and the first evaluation value of the non-evaluated cell to be processed according to the determined processing order. Determine hazard 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 processing is to be performed in order from a point where the number of accidents is large.

  Further, in combination with the second embodiment, the number of driving times may be set for each driver. In this case, for example, it is determined to generate an individual estimation table in order from the driver with the largest number of driving times. That is, in this case, the storage unit holds frequency information in which the driving frequency of each driver and the driver are associated, and the risk information processing system determines the processing order of a 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 in accordance with the determined processing order.

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

(effect)
The danger information processing system 120 according to the present embodiment is a point (that is, an unevaluated cell) in which sensor data can not be acquired from a combination of the point where an evaluation value has already been acquired and a situation (that is, evaluated cells). ) (A rating value indicating the presence or absence of a dangerous event) can be obtained. As a result, the risk information processing system 120 determines the degree of risk (an evaluation value indicating the presence or absence of the occurrence of a dangerous event) without actually acquiring all the sensing data with respect to combinations of all the points and situations managed. It is possible to obtain

  In addition, the risk information processing system 120 can obtain an estimated evaluation value of a point with high priority, such as a point with a large traffic volume, without waiting for the update of all the unevaluated values of the entire estimation table.

Embodiment 4
FIG. 23 is a block diagram showing an example of the configuration of a danger information processing system for realizing the danger information processing method according to the fourth embodiment. In the danger information processing system 130 shown in FIG. 23, the server apparatus 101 (111, 121) having the function of the danger information processing apparatus described in the first to third embodiments and the movement having the function of the danger information presentation apparatus A configuration in which the bodies 102 (112, 122) are connected via a network is shown. That is, in the risk information processing system 130, even if it is realized by a combination of any of the server apparatuses 101, 111 and 121 and any of the mobile objects 102, 112 and 122 described in the first to third embodiments. Good.

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

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

  The data acquisition unit 201 and the storage unit 205 (215, 225) are the same as the constituent elements of the first to third embodiments, and there is no difference in the functions, so the description will be omitted.

  The danger information processing system 130 will be described below in combination with the server apparatus 101 and the mobile object 102.

  The determination unit 2301 receives position information and sensor data from the data acquisition unit 201. The determination unit 2301 is a whole stored in the storage unit 205 of the server apparatus 101 with an evaluation value (including an estimated evaluation value) which is a degree of risk for a combination of the point indicated by the received position information and the condition indicated by the sensor data. Obtain from the estimation table. The determination unit 2301 associates the point indicated by the received position information with the row of the point of the overall estimation table, and further associates the situation indicated by the received sensor data with the column of the situation 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 to present information such as a warning based on the comparison result. Specifically, the determination unit 2301 determines whether the acquired evaluation value exceeds a threshold held in advance. Then, the determining unit 2301 determines to present a warning when it determines that the acquired evaluation value exceeds the threshold held in advance. On the other hand, when it is determined that the acquired evaluation value does not exceed the threshold held in advance, the determination unit 2301 determines not to present a warning.

  The presentation unit 2302 presents information to the user. For example, lighting and blinking of lights such as a display, a speaker, and an LED are presented, and information such as a warning is presented to the driver of the moving body 102. Specifically, when the determining unit 2301 determines that a warning is to be presented, the presenting unit 2302 presents the driver of the mobile object 102 with information based on the evaluation value used for the determination.

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

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

  FIG. 24 is a flowchart showing an example of a processing procedure of the danger information presentation apparatus 2300 according to the fourth embodiment.

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

  Next, from the point indicated by the received position information and the condition indicated by the sensor data, the determination unit 2301 evaluates the row of the point and the evaluation recorded in the cell of the overall estimation table corresponding to the column of the sensor data. The value is referred to from the storage unit 205 of the server apparatus 101, and it is determined whether the referred evaluation value exceeds a threshold (step S2402).

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

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

  The threshold may be determined in advance, or may be an average of evaluation values.

  In addition, the presentation unit 2302 is not limited to only directly presenting information to the user, but indirectly outputs information to the user by outputting operation information of the mobile (for example, a car) as a control signal of the mobile. May be presented. For example, when 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. Also, 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 car, or if 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.

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

(effect)
The danger information processing system 130 according to the present embodiment is a point (that is, a point at which the estimated sensor data can not be obtained from the combination of the point at which the evaluation value has already been obtained and the situation (that is, the evaluated cell). It is possible to obtain the degree of risk (evaluation value indicating the presence or absence of a dangerous event) of the evaluation cell). As a result, the risk information processing system 130 determines the degree of risk (an evaluation value indicating the presence or absence of the occurrence of a dangerous event) without actually acquiring all the sensing data with respect to combinations of all the managed points and conditions. It is possible to present information based on As a result, it is possible to notify, in real time, the degree of danger of the point in which the user is driving.

<Modification>
Although the present invention has been described based on the above-described embodiment and the modification thereof, it is a matter of course 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 techniques described in the above aspects may be implemented, for example, in the following cloud service types. However, the type in which the technology 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). The present type is a type in which the service provider C 120 acquires information from the group C 100 and provides a service to the user. In this type, the service provider C 120 has the function of a data center operating company. That is, the service provider has a cloud server C111 that manages big data. Therefore, there is no data center operating company.

  In this type, the service provider C 120 operates and manages the data center (cloud server C 111) (C 203). The service provider C 120 also manages the OS (C 202) and the application (C 201). The service provider C 120 performs service provision using the OS (C 202) and the application (C 201) managed by the service provider C 120 (C 204).

(Service type 2: IaaS usage type)
FIG. 26 shows service type 2 (IaaS utilization type). Here, IaaS is an abbreviation of infrastructure as a service, and is a cloud service providing model that provides a platform itself for building 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). The service provider C 120 also manages the OS (C 202) and the application (C 201). The service provider C 120 performs service provision using the OS (C 202) and the application (C 201) managed by the service provider C 120 (C 204).

(Service Type 3: PaaS Type)
FIG. 27 shows service type 3 (PaaS using type). Here, PaaS is an abbreviation of platform as a service, and is a cloud service providing model that provides a platform serving as a foundation 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). In addition, the service provider C120 manages an application (C201). The service provider C 120 performs service provision using the OS (C 202) managed by the data center operating company and the application (C 201) managed by the service provider C 120 (C 204).

(Service type 4: SaaS usage type)
FIG. 28 shows service type 4 (SaaS usage type). Here, SaaS is an abbreviation of software as a service. For example, an application provided by a platform provider having a data center (cloud server) can be used by a company / individual (user) who does not have a data center (cloud server) via a network such as the Internet. Cloud service provision model.

  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). The service provider C120 also provides a 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 C 120 has performed a service providing act. Further, for example, the service provider or the data center operating company may develop an OS, an application, a database of big data, etc. by itself, or may outsource it to a third party.

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

(3)
The data acquisition unit may be any device, sensor or the like having a function of sensing the inside or the periphery of the mobile unit, or 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, sleepiness sensor, weather sensor, CAN (Controller Area Network) information acquisition means, time Information acquisition means, inter-vehicle communication information means, road-vehicle communication information means, communication means, etc. may be used. Further, the position information may be acquired not only by GPS but also by estimation from received electric field strength of Wi-Fi, cellular phone communication, etc., for example. Moreover, a sensor provided with a vehicle periphery and vehicle internal information recognition means etc. may be sufficient. For example, the on-vehicle camera may determine the presence or absence of a preceding vehicle or measure the distance to the preceding vehicle. In addition, it may be measured how many pedestrians are within a certain distance from the on-vehicle camera or the millimeter wave radar. Also, it may be measured from the road surface condition sensor whether the road surface is frozen. Also, whether it is rainy or not may be measured from the weather sensor. Alternatively, the driver may be specified by sensing a smartphone owned by the driver, a car key, a driver's license, and the like. The driver may be identified from the driver's own physical features, such as fingerprints, palm prints, pupils, voice prints, and the like. It may be one that senses the method of driving operation, or one that senses the operation of another device or the content of the operation, and is not limited to the type of sensor data.

(4)
After calculating the correlation coefficient of the degree of risk between the points and then calculating the estimated evaluation value using the evaluation value of each situation column of other points, the correlation coefficient of the degree of danger between the situations is conversely Then, the estimated evaluation value may be calculated using the evaluation value of the row of each point in the other situation. That is, in the estimation of the evaluation value of the first cell not evaluated, the evaluation value input to the second cell and the evaluation value input to the fourth cell are used to calculate the degree of risk between the situations. The degree of risk for the first cell may be calculated from the evaluation value input to the second cell using the degree of risk correlation between situations obtained by calculating and calculating the correlation. Here, the second cell is a cell corresponding to a combination having the same point as the first cell and a different situation. The third cell is a cell corresponding to a combination having the same situation as the first cell but different points. Further, the fourth cell is a cell corresponding to a combination of the third cell and the point in the same situation as the second cell.

(5)
The term "point" in the above embodiment does not limit the scope in the sense of a specific position on the earth. The way 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)
The evaluation value is, for example, "3" when there is an accident, "1" when there is no accident, "2" otherwise, but this is a mere example. There is no limitation on the invention. The evaluation value may be a result of evaluating a dangerous event other than an accident, for example, the presence or absence of a near incident, traffic violation, sudden braking, change in acceleration sensor, change in steering device, driver's avoidance Whether or not an action is taken may be used, and any evaluation value may be adopted as long as it represents a dangerous event. Moreover, although it was set as the numerical value of "3", "2", and "1" also about an evaluation value, a value may be other than this. Also, although evaluation is made based on the presence or absence of a dangerous event, the case where there is a dangerous event and other cases may be adopted.

(7)
Although the event frequency is traffic volume, it may be the number of accidents for each point, may be the traffic volume of children, may be determined according to the lane width, or may be determined according to the intersection It may be any event that can be prioritized, or anything that represents a property. Further, in combination with the second embodiment, the number of driving times may be set for each driver.

(8)
Specifically, each of the above-described devices is a computer system including 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. Each device achieves its function by the microprocessor operating according to the computer program. Here, the computer program is configured by combining a plurality of instruction codes indicating instructions to the computer in order to achieve a predetermined function.

(9)
Some or all of the components constituting each of the above-described devices may be configured from one system LSI (Large Scale Integration: large scale integrated circuit). The system LSI is a super-multifunctional LSI manufactured by integrating a plurality of components on one chip, and more specifically, a computer system including a microprocessor, a ROM, a RAM, and the like. . A computer program is stored in the RAM. The system LSI achieves its functions as the microprocessor operates in accordance with the computer program.

(10)
Some or all of the components that make up each of the above-described devices may be configured from an IC card or a single module that can be attached to or detached from each device. The IC card or the module is a computer system including a microprocessor, a ROM, a RAM, and the like. The IC card or the module may include the super multifunctional LSI described above. The IC card or the module achieves its function by the microprocessor operating according to the computer program. This IC card or this module may be tamper resistant.

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

  Furthermore, the present invention is a computer readable recording medium that can read 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, a BD (Blu-ray It may be recorded in a registered trademark) Disc), a semiconductor memory or the like. Further, the present invention may be the digital signal recorded on these recording media.

  In the present invention, the computer program or the digital signal may be transmitted via a telecommunication line, a wireless or wired communication line, a network represented by the Internet, data broadcasting, and the like.

  The present invention may be a computer system comprising a microprocessor and a memory, wherein the memory stores the computer program, and the microprocessor operates according to the computer program.

  In addition, it is implemented by another independent computer system by recording and transferring the program or the digital signal on the recording medium, or transferring the program or the digital signal via the network or the like. It may be

(12)
The above embodiment and the above modification may be combined respectively.

  The present invention is applicable to, for example, a danger information processing apparatus or system that determines and warns a point where an accident is likely to occur while the vehicle is traveling.

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

Claims (10)

A risk information processing method executed by a computer that manages the degree of risk for a point where a mobile body is located, comprising:
The computer is
The risk degree input for the combination of the point at which the dangerous event occurred and the situation at the time of the dangerous event occurring at the point is stored as the danger information in the storage unit of the computer .
By calculating the degree of risk for the first combination, using the three or more pieces of danger information stored in the storage unit, with the first combination for which the degree of danger is not input as a processing target Estimate the first danger information, which is the degree of danger,
The three or more pieces of danger information have the same situation as the first combination and a second danger information that is the degree of danger to a second combination that is the same as the first combination but different in situation. Third danger information, which is the degree of danger to a different third combination, and fourth danger information, which is the same as the second combination and the same as the fourth combination of the third combination and the point. And risk information processing methods. In the above estimation,
Using the second danger information and the fourth danger information, calculate the correlation of the degree of danger between the points,
The danger information processing according to claim 1, wherein the danger degree for the first combination is calculated from the danger degree of the third danger information using the correlation of the danger degree between the points obtained by the calculation. Method. In the above estimation,
Using the third hazard information and the fourth hazard information, calculate the correlation of hazards between situations,
The danger information processing according to claim 1, wherein the danger degree for the first combination is calculated from the danger degree of the second danger information using the correlation of the danger degree between the situations obtained by the calculation. Method. The danger information processing method according to any one of claims 1 to 3, wherein the first danger information obtained in the estimation is stored in the storage unit. further,
For the combination of the point at which the dangerous event occurred and the situation when the dangerous event occurred at the point, the hazard level input for each driver is used as individual hazard information for each driver, and all Stored in the storage unit as overall hazard information for drivers of
Calculate the correlation of the risk between the individual danger information and the overall danger information,
The risk degree of the individual danger information is calculated from the danger degree of the overall danger information using the correlation of the danger degree between the individual danger information and the overall danger information obtained by the calculation. The danger information processing method according to any one of 4. further,
Storing frequency information in which the frequency of traffic events and points are associated in the storage unit;
Determine the processing order of the plurality of points using the frequency information;
The danger information processing method according to claim 2, wherein in the estimation, the first danger information to be processed is determined according to the processing order. further,
Holding frequency information in which the driving frequency for each driver and the driver are associated in the storage unit;
The processing order of a plurality of drivers is determined using the frequency information,
The danger information processing method according to claim 5, wherein in the calculation of the degree of danger of the individual danger information, the individual danger information to be processed is determined according to the processing order. further,
Receiving from the mobile body position information indicating the current position and sensor data indicating the current state;
The degree of risk for the combination of the point indicated by the received position information and the condition indicated by the sensor data is acquired from the storage unit,
The danger information processing method according to claim 1, wherein the information based on the acquired degree of danger is transmitted to the mobile body. further,
When the acquired degree of risk exceeds a threshold held in advance, it is determined that a warning is presented, and information based on the acquired degree of danger is transmitted to the mobile unit,
It is determined that the warning is not presented when the acquired risk is equal to or less than the previously held threshold, and information based on the acquired risk is not transmitted to the mobile unit. Information processing method. A server device that manages the degree of risk to a point where a mobile body is located,
The server device is
A storage unit for storing risk information indicating the degree of risk for the combination of each of the point and the situation;
A receiving unit that receives a degree of risk input for a combination of a point at which a dangerous event has occurred and a situation when the dangerous event has occurred at the point;
A control unit that stores the degree of risk received by the receiving unit as the danger information in the storage unit;
The control unit calculates the degree of risk for the first combination, using the at least three or more pieces of danger information for which the degree of risk is input, for the first combination for which the degree of risk is not input. To estimate the first danger information indicating the degree of danger,
The three or more pieces of danger information have the same points as the first combination and a second danger information indicating the degree of danger for the second combination in which the points are the same as in the first combination and the situation is different. Third danger information indicating the degree of danger to a different third combination, and fourth danger information indicating the degree of danger to a fourth combination having the same situation as the second combination and the same third combination and point And server devices.

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