JP2024039964A - Information processing device, information processing method, and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a new technique for calculating collection degree information regarding travel information and determining the viability of a prescribed business on the basis of the collection degree information.

SOLUTION: An information processing device comprises: an existing roadside device position information acquisition unit that acquires existing roadside device position information that represents the position information of an already installed existing roadside device; a first installation candidate facility position information acquisition unit that acquires first installation candidate facility position information that represents the position information of at least one facility in which a roadside device can be newly installed; a travel information acquisition unit that acquires travel information that was collected based on a different scheme than the scheme conforming to ETC 2.0, and that includes information pertaining at least to the position of a mobile entity; and a calculation unit that calculates first collection degree information that pertains to the degree of collection for the case where it is assumed that travel information was collected, based on the scheme conforming to ETC 2.0, by a roadside device that corresponds to the existing roadside device position information and the first installation candidate facility position information.

SELECTED DRAWING: Figure 16

COPYRIGHT: (C)2024,JPO&INPIT

Description

The present invention relates to an information processing device, an information processing method, a program, and the like.

BACKGROUND ART As a device (system) for measuring a certain measurement target, there is, for example, one that can measure driving information such as position information and acceleration information of a vehicle (for example, Patent Document 1).

JP2022-58421A

This is just an example, but when thinking about developing a new service (business) using measurement data measured by a device that measures a predetermined measurement target as described above, it is necessary to actually use that device to make measurements. Normally, a huge amount of measurement data is acquired and evaluated, which incurs time and financial costs.
Furthermore, currently, there are only a limited number of locations where measuring devices are installed, and it is desired to install more measuring devices. However, due to cost issues, it is necessary to install it in a location where data can be measured efficiently.

According to a first aspect of the present invention, the information processing device is a device that measures information regarding a moving object based on first measurement data measured by a first device that measures information regarding the moving object. A data generation unit that generates estimated data of second measurement data measured by a second device different from the device.
According to a second aspect of the present invention, an information processing method includes a device that measures information about a moving object based on first measurement data measured by a first device that measures information about the moving object. The method includes generating estimated data of second measurement data measured by a second device different from the device.
According to the third aspect of the present invention, the program to be executed by a computer is a device that measures information about a moving object based on first measurement data measured by a first device that measures information about a moving object. The computer is caused to generate estimated data of second measurement data measured by a second device that is different from the first device.
According to a fourth aspect of the present invention, the information processing device is a device that measures information about a moving object based on first measurement data measured by a first device that measures information about the moving object. A first determination unit that determines a measurement period of first measurement data necessary for evaluation of driving based on second measurement data measured by a second device different from the device.
According to a fifth aspect of the present invention, an information processing method includes a device that measures information about a moving object based on first measurement data measured by a first device that measures information about the moving object. The method includes determining a measurement period of first measurement data necessary for evaluation of driving based on second measurement data measured by a second device different from the device.
According to the sixth aspect of the present invention, the program to be executed by a computer is a device that measures information about a moving object based on first measurement data measured by a first device that measures information about a moving object. The computer is caused to determine a measurement period of first measurement data necessary for evaluation of driving based on second measurement data measured by a second device different from the first device.
According to a seventh aspect of the present invention, the information processing device is a device that measures information regarding a moving object based on first measurement data measured by a first device that measures information regarding the moving object. a generation unit that pseudo-generates second measurement data measured by a second device different from the device for a predetermined period; an acquisition unit that obtains data loss ratio information in the generated pseudo second measurement data; A determining unit that determines the validity of the pseudo second measurement data for a predetermined period based on the loss ratio information.
According to an eighth aspect of the present invention, an information processing method includes a device that measures information about a moving object based on first measurement data measured by a first device that measures information about the moving object. pseudo-generating second measurement data measured by a second device different from the device for a predetermined period; obtaining data loss rate information in the generated pseudo second measurement data; and data loss rate. and determining the validity of the pseudo second measurement data for a predetermined period based on the information.
According to the ninth aspect of the present invention, the program to be executed by a computer is a device that measures information about a moving object based on first measurement data measured by a first device that measures information about a moving object. pseudo-generating second measurement data measured by a second device different from the first device for a predetermined period; and obtaining data loss rate information in the generated pseudo second measurement data. , and determining the validity of the pseudo second measurement data for a predetermined period based on the data loss rate information.
According to the tenth aspect of the present invention, the information processing device is a roadside device that collects information regarding the traveling of a mobile object based on a method compatible with ETC 2.0, and the information processing device is a roadside device that collects information regarding traveling of a mobile object based on a method compatible with ETC 2.0, and the information processing device an existing roadside unit position information acquisition unit that acquires existing roadside unit position information, which is information; and a first installation candidate facility position information that acquires first installation candidate facility position information, which is position information of at least one facility in which the roadside unit can be newly installed. an installation candidate facility location information acquisition unit; and a travel information acquisition unit that acquires travel information that is collected based on a method different from the method compatible with ETC 2.0 and that includes at least information regarding the position of a mobile object. and a first method regarding the degree of collection based on the method compatible with ETC 2.0, assuming that the traveling information is collected by the roadside device corresponding to the existing roadside device location information and the first installation candidate facility location information. A calculation unit that calculates collection degree information.
According to the eleventh aspect of the present invention, the information processing method is a roadside machine that acquires travel data of a mobile body based on a method compatible with ETC 2.0, and the information processing method is a roadside machine that acquires traveling data of a mobile object based on a method compatible with ETC 2.0, and the information processing method is a roadside machine that acquires travel data of a mobile object based on a method compatible with ETC 2.0, and the information processing method is a acquiring first installation candidate facility location information that is location information of at least one facility where the roadside unit can be newly installed; and corresponding to ETC 2.0. Acquisition of traveling information that is measured based on a method different from the above-mentioned method and that includes at least information regarding the position of the moving object; and based on the method corresponding to ETC 2.0.
According to the twelfth aspect of the present invention, the program to be executed by a computer is a roadside machine that acquires travel data of a mobile body based on a method compatible with ETC 2.0, and is installed on an existing roadside machine that is already installed. ETC2. 0, the traveling information is measured based on a method different from the method corresponding to 0, and includes at least information regarding the position of the moving body, and the existing roadside unit position information and the first installation candidate are obtained. The computer is caused to calculate, based on the facility location information, first collection degree information regarding the collection degree of the traveling information when measurement is virtually performed based on a method compatible with ETC 2.0.
According to the tenth aspect of the present invention, the information processing device is a mobile body that acquires mobile body position information from a first device provided in the mobile body based on a method different from a method compatible with ETC 2.0. a position information acquisition unit; a density information acquisition unit that acquires density information of the mobile body position information based on the acquired mobile body position information; and a method compatible with ETC 2.0 based on the acquired density information. and a specifying unit that specifies a position for installing a new roadside unit that acquires travel information based on.
According to the eleventh aspect of the present invention, the information processing device includes a roadside unit position information acquisition unit that acquires existing roadside unit position information that is position information of at least one existing roadside unit, and outputs the roadside unit position information. The roadside device includes an output unit, and a specifying unit that specifies a position for installing a new roadside unit based on a user input performed in response to the output.
According to the twelfth aspect of the present invention, the information processing device acquires existing roadside machine position information that is the position information of at least one existing roadside machine that acquires traveling information based on a method compatible with ETC 2.0. The present invention includes a roadside unit position information acquisition unit, and a specification unit that specifies a position for installing a new roadside unit based on the existing roadside unit position information.
According to the thirteenth aspect of the present invention, the information processing device acquires facility location information that is location information of a facility where a roadside unit that acquires traveling information can be newly installed based on a method compatible with ETC 2.0. and a specifying unit that specifies a position for installing the new roadside unit based on the facility position information.
According to the fourteenth aspect of the present invention, the information processing method includes acquiring mobile body position information from a first device provided in the mobile body based on a method different from a method compatible with ETC 2.0. , acquiring density information of the mobile body position information based on the acquired mobile body position information, and acquiring traveling information based on a method compatible with ETC 2.0 based on the acquired density information. identifying a location for installing a new roadside unit;
According to the fifteenth aspect of the present invention, the information processing method includes the steps of: acquiring existing roadside unit position information that is position information of at least one existing roadside unit; outputting the roadside unit position information; and identifying a location for installing a new roadside unit based on user input performed in response to.
According to the sixteenth aspect of the present invention, the information processing method includes acquiring existing roadside unit position information that is position information of at least one existing roadside unit that acquires travel information based on a method compatible with ETC 2.0. and specifying a position for installing a new roadside unit based on the existing roadside unit position information.
According to the seventeenth aspect of the present invention, the information processing method acquires facility location information that is location information of a facility where a roadside unit that acquires traveling information can be newly installed based on a method compatible with ETC 2.0. and identifying a location for installing the new roadside unit based on the facility location information.
According to the eighteenth aspect of the present invention, a program for causing a computer to execute is configured to obtain mobile body position information from a first device provided in a mobile body based on a method different from a method compatible with ETC 2.0. acquiring density information of the mobile body position information based on the acquired mobile body position information; and driving based on the method compatible with ETC 2.0 based on the acquired density information. A computer is caused to specify a position for installing a new roadside unit from which information is to be acquired.
According to the nineteenth aspect of the present invention, a program to be executed by a computer acquires existing roadside unit position information that is position information of at least one existing roadside unit, and outputs the roadside unit position information. and specifying a position for installing a new roadside unit based on user input performed in response to the output.
According to the 20th aspect of the present invention, the program for causing the computer to execute the existing roadside machine position information, which is the position information of at least one existing roadside machine that acquires traveling information based on a method compatible with ETC 2.0. and specifying a position for installing a new roadside unit based on the existing roadside unit position information.
According to the twenty-first aspect of the present invention, a program to be executed by a computer is a facility that is a facility where a roadside device that acquires travel information based on a method compatible with ETC 2.0 can be newly installed. A computer is caused to acquire position information and to specify a position for installing the new roadside unit based on the facility position information.

An explanatory diagram of the principle according to the embodiment. An explanatory diagram of the principle according to the embodiment. 5 is a flowchart illustrating an example of the flow of processing according to the embodiment. The figure which shows an example of the functional structure of the server based on 1st Example. FIG. 3 is an explanatory diagram of the principle according to the first embodiment. FIG. 3 is an explanatory diagram of the principle according to the first embodiment. FIG. 3 is an explanatory diagram of the principle according to the first embodiment. 5 is a flowchart showing an example of the flow of processing according to the first embodiment. FIG. 7 is a diagram illustrating an example of information stored in a storage unit of a server according to a second embodiment. 7 is a flowchart illustrating an example of the flow of processing according to the second embodiment. FIG. 7 is a diagram showing an example of a histogram according to the second embodiment. FIG. 7 is a diagram showing an example of a histogram according to the second embodiment. FIG. 7 is a diagram illustrating an example of information stored in a storage unit of a server according to a third embodiment. FIG. 7 is an explanatory diagram of the principle according to the third embodiment. FIG. 7 is an explanatory diagram of the principle according to the third embodiment. FIG. 7 is an explanatory diagram of the principle according to the third embodiment. Flowchart showing an example of the flow of processing according to the third embodiment A diagram showing an example of information stored in a storage unit of a server according to a fourth embodiment FIG. 7 is an explanatory diagram of the principle according to the fourth embodiment. Flowchart showing an example of the flow of processing according to the fourth embodiment FIG. 7 is an explanatory diagram of the principle according to the fourth embodiment. FIG. 13 is a diagram illustrating the principle according to the fourth embodiment. FIG. 7 is an explanatory diagram of the principle according to the fourth embodiment.

EMBODIMENT OF THE INVENTION Hereinafter, an example of the form for implementing this invention is demonstrated with reference to drawings.
In addition, in the description of the drawings, the same elements may be designated by the same reference numerals, and redundant description may be omitted.
However, the components described in this embodiment are merely examples, and the scope of the present invention is not intended to be limited thereto.

<Embodiment>
An example of an embodiment for realizing the present invention will be described below.

<Principle>
1.1. Principle of Data Estimation FIG. 1 is a diagram showing the flow of processing performed by an information processing apparatus according to one aspect of the present embodiment.
For example, the information processing device may detect a second device whose measurement target is different from the first device based on data measured by the first device (hereinafter referred to as “first device measurement data”). It has a second device measurement data estimation unit 11 as a functional unit that generates (estimates) data when measured. Note that measurement may be detection or measurement. The same can be said below. Furthermore, the data generated by the second device measurement data estimating section 11 is referred to as "estimated second device measurement data."

The first device may be, for example, a device that measures the measurement target at predetermined time intervals (hereinafter referred to as “predetermined time intervals”). In this case, the first device measurement data is data obtained by measuring the measurement target by the first device at predetermined time intervals.

The second device may be, for example, a device whose sampling unit (which may also be referred to as a measurement unit) when measuring the measurement target is different from that of the first device. For example, if the first device is a device that measures the measurement target at predetermined time intervals using a sampling unit of time as described above, the second device is a device that measures the measurement target using a sampling unit other than time. It can be done.
In addition, in performing processing such as learning described below, it is of course preferable to use measurement data from the second device as the teacher data, but it is also possible to use data generated from measurement data from the first device. This is a feature of the present invention. Transfer learning is an example of an approach that uses other data to process target data, but transfer learning is a method that uses a model learned for a certain task as a starting point for a model that executes a similar task. This is fundamentally different from the present invention, which uses data generated from other data on a zero basis.

For example, if the measurement target is the position (position information) of a device, the first device can be a device that measures the position at predetermined time intervals.
Further, in this case, the second device may be a device that measures the position using, for example, a “distance” as a sampling unit, and a device that measures the position at predetermined distance intervals (hereinafter referred to as “predetermined distance intervals”). I can do it.

In this example, if the predetermined time interval is "1 second interval" and the predetermined distance interval is "100 m", the second device measurement data estimating unit 11 calculates, for example, 1 second included in the first device measurement data. Estimated second device measurement data can be generated by thinning (resampling) the measurement data of each position every "100 m" based on the position information.

When setting a plurality of sets of first device measurement data as a “first device measurement data set,” the second device measurement data estimating unit 11 performs the above processing on each first device measurement data set included in the first device measurement data set. By performing this on the device measurement data, estimated second device measurement data corresponding to each first device measurement data can be generated. The set of estimated second device measurement data generated in this way may be referred to as an "estimated second device measurement data set."

Note that at least one of the first device and the second device is a device that measures a measurement target based on a plurality of sampling units (for example, distance and acceleration; It may also be used as a device that acquires the distance when
Other devices may also be used.

1.2. Machine learning using estimated measurement data FIG. 2 is a diagram for explaining the principle of learning and inference using estimated measurement data in this embodiment.

(1) During learning As shown in FIG. 2(1), the information processing device includes, for example, the aforementioned second device measurement data estimation section 11, first model processing section 13, and second model generation section 15. It has as a functional part.

The first model processing unit 13 is, for example, a functional unit that uses a predetermined model (an existing model) as a first model, inputs certain measurement data to the first model, and calculates (computes) predetermined quantities. be able to. Note that various quantities may be regarded as calculation targets. The same can be said below.
In this embodiment, the first model processing unit 13 calculates predetermined quantities by inputting the first device measurement data described above to the first model.

The second model generation unit 15 can be, for example, a functional unit that generates a second model that can calculate the same quantities as the first model.
In the present embodiment, the second model generation unit 15 uses, for example, estimated second device measurement data generated by the second device measurement data estimation unit 11 described above and various quantities calculated by the first model processing unit 13. A second model is generated by machine learning using as a learning data set. The second model generated in this way is referred to as a "trained second model."
Note that the second model is not limited to a machine learning model. For example, the second model generation unit 15 may be a mathematical statistical model such as a linear or nonlinear regression model, or a connectionist model such as a DNN (Deep Neural Network).
The various quantities calculated by the first model processing unit 13 are calculated using the existing first model, and can be said to be desirable data (so to speak, correct data). ).

(2) At the time of inference As shown in FIG. 2(2), the information processing device includes, for example, a trained second model processing unit 17 as a functional unit.

The learned second model processing unit 17 uses, for example, data obtained by measuring the measurement target by the second device (hereinafter referred to as "second device measurement data") as input to the learned second model, and performs a predetermined process as input to the learned second model. It can be a functional unit that calculates various quantities.
In other words, the trained second model processing unit 17 infers predetermined quantities using the trained second model by inputting data obtained by actually measuring the measurement target by the second device, and infers the inference results (hereinafter referred to as , referred to as "various quantity inference results").

In addition to the above, for example, the estimated second device measurement data may be used as input data to the first model, and some data held in the past that corresponds to the first device measurement data accumulated from the past (for example, , data on elements that can affect various quantities, data on elements that have a relationship with various quantities) may be used as training data to generate the second model by machine learning or the like.

<Processing>
FIG. 3 is a flowchart showing an example of the flow of the process shown in FIG.
First, the information processing device performs a learning data set generation process.
In the learning data set generation process, the information processing apparatus uses the first device measurement data to be processed (in the example of FIG. 2, each first device measurement data included in the first device measurement data set) as the target. Processing of loop A is performed (A1 to A7).
The first device measurement data to be processed may be data having the same measurement period, for example.

In the process of loop A, the second device measurement data estimation unit 11 generates estimated second device measurement data based on the first device measurement data (A3).
Further, the first model processing unit 13 calculates teacher quantities based on the first device measurement data and the first model (A5).
The information processing device then moves on to the next first device measurement data.

For example, after performing the processes A3 and A5 on all the first device measurement data to be processed, the information processing device ends the process of loop A (A7).

After that, the information processing device performs learning processing (A9). Specifically, the second model generation unit 15 generates, for example, a supervised model based on the estimated second device measurement data generated in the process of loop A and the teacher quantities generated in the process of loop A. A learned second model is generated by learning.

Next, the information processing device performs inference processing (A11). Specifically, the trained second model processing unit 17 uses the trained second model generated in A9 and the second device measurement data of each processing target (in the example of FIG. 2, the second device measurement data set). The various quantities are calculated based on the included second device measurement data). Then, the calculation results are taken as various quantity inference results.
The information processing device then ends the process.

Note that step A11 may be omitted and the processing up to the generation of the model may be performed.
Alternatively, only the step A3 may be used, and the process may be performed up to generating the estimated second device measurement data based on the first device measurement data.

Although the principle has been explained above, the measurement target can be, for example, information regarding a moving object.

The mobile object may include at least one of the following, for example.
・Four-wheeled vehicles ・Two-wheeled vehicles ・Personal mobility ・Ships ・Railroads ・Aerial vehicles (at least one of a drone and an aircraft)

Further, the information regarding the mobile object may include, for example, at least one of the following.
・Position information of the moving object ・Speed information of the moving object ・Acceleration information of the moving object ・Direction information of the moving object (direction information)
・Angular velocity information of a moving object ・Time information of a moving object (time information, etc.)
Note that information including at least one of these pieces of information may be used as traveling information of a moving object.

Furthermore, the measurement target is not limited to information related to a moving body (driving information, etc.), and may be other information.
For example, the same process as above may be performed by setting the measurement target to "sound", setting the sampling unit of the first device to "time", and setting the sampling unit of the second device to "frequency".
Further, for example, processing similar to the above may be performed using information regarding a living body (blood pressure, pulse rate, heart rate, respiratory rate, body temperature, brain waves, etc.) as a measurement target. In this case as well, the same processing as above may be performed, for example, by setting the sampling unit of the first device to "time" and setting the sampling unit of the second device to "frequency."

<Example>
Next, an example of a server that is an example of the above information processing device will be described.
In the following, an embodiment will be described in which a vehicle such as a four-wheeled vehicle is used as the moving body.
Note that the information processing device is not limited to a server, and may be an electronic device (also referred to as a terminal) such as a personal computer, a mobile phone including a smartphone, a PDA, and various tablet terminals. Furthermore, these devices including servers may also be referred to as computer devices.
However, the embodiments to which the present invention can be applied are not limited to the embodiments described below.

<First example>
ETC2.0 is becoming popular these days. For example, it is conceivable to develop insurance products such as telematics insurance as a service (business) based on this ETC 2.0.
However, this requires measurement data obtained by having a driver actually drive a vehicle equipped with an ETC 2.0 compatible device (for example, an ETC 2.0 compatible onboard device or a DSRC (Dedicated Short Range Communications) compatible onboard device). In addition, data such as accident data and corresponding accident data are required, which incurs time and monetary costs.

In this case, for example, by estimating (reproducing) data measured by a device compatible with ETC 2.0 from data already measured (obtained data) with a device different from the device compatible with ETC 2.0, It is conceivable that insurance products and the like can be developed without collecting data using devices compatible with ETC 2.0.

In developing insurance products for telematics insurance, for example, we created a model (hereinafter referred to as the "driving score model") of a score related to the driver's driving (driving quality) (hereinafter referred to as the "driving score model" (driving quality score model)). ) can be considered.
Generally speaking, a driving score model should be constructed as a model in which at least one of the following elements (including elements standardized per unit time or unit distance, etc.) influences the score value. I can do it.
・Sudden steering frequency (or sudden steering frequency or distribution of steering wheel operation degree including sudden steering)
・Number of sudden decelerations (or sudden deceleration frequency or distribution of deceleration rate including sudden deceleration)
・Number of sudden accelerations (or sudden acceleration frequency or distribution of acceleration including sudden acceleration)
・Number of times of overspeeding (or frequency of overspeeding or distribution of speeds including overspeeding)

For example, a driving score model can be constructed in which the higher the number of these elements (or the higher the frequency or sudden maneuver/excessive speed), the higher the accident risk and the higher the driving score (or conversely, the lower the driving score). can do.
One of the uses (methods of utilization) of this driving score is, for example, to reflect the driving score in the insurance premium of telematics insurance (determine the insurance premium based on the driving score).

In this example, although the above factors are not necessarily strictly reflected, estimated ETC2.0 measurement data that reflects at least the general driving tendency of the driver is generated, and the generated estimated A method of generating a driving score model based on ETC2.0 measurement data will be described.

Note that the content described in the first embodiment is applicable to any of the other embodiments and other modifications.

<Functional configuration>
FIG. 4 is a diagram showing an example of the functional configuration of the server 100 in this embodiment.
The server 100 includes, for example, a processing section (control section) 110, an operation section 120, a display section 130, a sound output section 140, a communication section 150, a clock section 160, and a storage section 190. It may be a computer system connected by bus B.

The processing unit 110 is, for example, a processing device or a control device that centrally controls each part of the server 100 or performs various processes according to various programs such as a system program stored in the storage unit 190. It is configured with processors such as a GPU (Graphics Processing Unit), a DSP (Digital Signal Processor), and an integrated circuit such as an ASIC (Application Specific Integrated Circuit) and an FPGA (Field Programmable Gate Array).

The operation unit 120 includes input devices such as operation buttons and operation switches for a user to input various operations to the server 100 . Further, the operation unit 120 may include a touch panel configured integrally with the display unit 130, and this touch panel may function as an input interface between the user and the server 100. The operation unit 120 outputs an operation signal according to the user's operation to the processing unit.

The display unit 130 is a display device including an LCD or the like, and performs various displays based on display signals output from the processing unit 110. The display unit 130 may be integrally configured with a touch panel to form a touch screen.

The sound output unit 140 is a sound output device including a speaker and the like, and outputs various sounds based on the sound output signal output from the processing unit 110.

The communication unit 150 is a communication device for transmitting and receiving information used within the device to and from an external device. The communication method of the communication unit 150 includes a wired connection via a cable that complies with a predetermined communication standard, a connection via an intermediate device called a cradle that also serves as a charger, and a wireless connection using wireless communication. Various methods such as connection formats can be applied.

The clock unit 160 is a built-in clock of the server 100, and outputs time information (timekeeping information). The clock unit 160 includes, for example, a clock using a crystal oscillator.
Note that the clock unit 160 may be configured with a clock that conforms to the NITZ (Network Identity and Time Zone) standard or the like.

The storage unit 190 is a storage device that includes volatile or nonvolatile memory such as ROM, EEPROM, flash memory, and RAM, a hard disk device, and the like.

In the present embodiment, the storage unit 190 includes, for example, a driving score model processing program 191, a cigarette socket type device measurement database 192, an ETC2.0 measurement database 193, an estimated ETC2.0 measurement database 194, and a first driving Score model data 195 and learned second driving score model data 196 are stored.

The driving score model processing program 191 is a program read out by the processing unit 110 and executed as driving score model processing to be described later.

The cigarette socket type device measurement database 192 is, for example, a database in which cigarette socket type device measurement data including travel information measured by a cigarette socket type device mounted on a vehicle is stored.
The cigarette socket type device is a type of first device, and the cigarette socket type device measurement data can be a type of first measurement data (first device measurement data).

Note that the processing unit 110 may process the first device measurement data by adding, for example, at least one of the following information (attribute information) in addition to the information regarding the vehicle (moving object).
・Vehicle information (car model, year, model, etc.)
・Driver information (gender, age, address, purpose, etc.)

A cigarette socket type device is a device that can be used by inserting it into a vehicle's accessory socket. Traveling information (information regarding the moving body) can be measured.

Note that the first device is not limited to a cigarette socket type device. The first device may be any device that is directly or indirectly provided on the moving body and is capable of acquiring information regarding the moving body. For example, the first device may be at least one of the following devices.
・Drive recorder ・Digital tachograph (digital tachograph)
・Some kind of on-board equipment installed or built into a vehicle (may include connected cars)
・In-vehicle devices and their terminals that have a communication function (e.g. Bluetooth (registered trademark) communication function) that collects data in cooperation with terminals such as smartphones ・Single devices including terminals such as smartphones ・In-vehicle devices including these Devices including retrofitted in-vehicle equipment or terminals such as smartphones

The travel information can include, for example, information such as position, speed, acceleration, and angular velocity, and any sensor or the like that can measure this information can be provided in the cigarette socket type device. The device may be configured to be able to measure at least one of these pieces of information.

The travel information measured by the cigarette socket type device can be transmitted to the server 100 or the cloud server, for example, at any time or at regular timing.

In addition, the position is calculated using a satellite positioning unit (satellite positioning sensor), which is a unit (sensor) that detects position and speed using a satellite positioning system such as GPS (Global Positioning System), or UWB (Ultra Wide Band). A UWB positioning unit (UWB positioning sensor), which is a unit (sensor) for this purpose, may be provided in a cigarette socket type device.

The satellite positioning unit is, for example, an RF receiving circuit that converts an RF (Radio Frequency) signal including a positioning satellite signal transmitted from a positioning satellite received by an antenna (not shown) into a digital signal, or an RF receiving circuit A baseband processing circuit that performs correlation calculation processing etc. on the output digital signal, captures the positioning satellite signal, and outputs information such as satellite orbit data and time data extracted from the positioning satellite signal as positioning information. etc. Note that information such as not only the position but also the speed may be detected by Doppler positioning or the like.

The inertial measurement unit (IMU) can include, for example, an inertial sensor that detects information necessary for calculating driving information by inertial navigation calculation. The inertial sensor may include, for example, a 3-axis acceleration sensor or a 3-axis gyro sensor, and can output the acceleration detected by the acceleration sensor or the angular velocity detected by the gyro sensor.

The UWB positioning unit includes, for example, an ultra-wideband RF receiving circuit that converts an ultra-wideband RF signal including an ultra-wideband pulse signal for positioning transmitted from a positioning beacon received by an antenna (not shown) into a digital signal, The device may include a relative position calculation processing circuit that calculates the relative position between the device itself and the positioning beacon based on the digital signal output from the RF receiving circuit.
Note that the UWB positioning unit may function as a positioning beacon by transmitting an ultra-wideband RF signal including an ultra-wideband pulse signal for positioning from an antenna (not shown), or it may not do so. It's okay.

For example, the cigarette socket type device measurement database 192 can store measurement data of a plurality of drivers for each measurement period. That is, measurement data of a plurality of drivers can be stored in association with a measurement period.
The measurement period may be, for example, a monthly period (1 month, 2 months, . . . , 6 months, etc.) or a period of other units. Furthermore, for example, the measurement data may be classified and stored by road type, region, or the like.

The ETC 2.0 measurement database 193 includes, for example, measurements taken by an ETC 2.0 on-vehicle device (ETC 2.0-compatible on-vehicle device, DSRC-compatible on-vehicle device) installed in a vehicle or an ETC 2.0-compatible car navigation device that can be linked with this. This is a database in which ETC2.0 measurement data including driving information is accumulated and stored.
The ETC2.0 onboard device is a type of first device, and the ETC2.0 measurement data can be a type of second measurement data (second device measurement data).

ETC2.0 measurement data is based on the probe information recorded in the ETC2.0 on-vehicle device from devices such as ITS spots and route information collection devices installed (deployed) on roads (hereinafter collectively referred to as "roadside devices"). ) may be obtained by the server 100 based on the data collected by the server 100.

Roadside units are installed at several thousand locations on expressways and national highways nationwide.
The roadside device and the ETC 2.0 onboard device are configured to enable two-way communication, for example, and the roadside device collects probe information from the ETC onboard device, as well as route guidance information based on road conditions, obstacle locations in front, etc. It is possible to provide information that supports safe driving, such as information on traffic conditions and information on accident-prone locations, from the roadside device to the ETC 2.0 onboard device.

The ETC 2.0 on-vehicle device also includes, for example, the various sensors and units described above, and can measure and record various driving information.
Then, when a vehicle equipped with the ETC 2.0 on-vehicle device passes near the roadside device, probe information can be transmitted to the server 100 and the probe server via the roadside device.
In addition, various sensors and units are installed in the ETC 2.0 compatible car navigation device, etc., and various driving information is sent to the ETC 2.0 onboard device based on the information output (transmitted) from the ETC 2.0 compatible car navigation device. It may also be recorded.

Here, the probe information can include, for example, the following information.
・Basic information ・Driving history information ・Behavior history information

The basic information can include, for example, information regarding the ETC 2.0 onboard device, information regarding the ETC 2.0 compatible car navigation device, information regarding the vehicle, and the like.

The travel history information can include, for example, time information, position information, speed information, and the like.
This travel history information can be recorded (accumulated) each time it is detected that the vehicle has traveled "200 m", for example. In addition to this, for example, the information may be recorded when the traveling direction of the vehicle changes by "45 degrees" or more.
Note that the value "200 m" is a value based on the specifications of ETC 2.0, but is not limited to this, and may be, for example, "100 m". Similarly, "45 degrees" may be set to, for example, "22.5 degrees". A distance such as "200 m" based on this ETC 2.0 specification is conveniently referred to as a "unit distance".
Note that, as will be described later, it is also possible to change the unit distance depending on the road type or speed zone.

The behavior history information can include, for example, information such as time information, position information, azimuth information, acceleration information (information such as longitudinal acceleration and lateral acceleration), and angular velocity information (for example, yaw angular velocity information).
In this behavior history information, for example, any one of longitudinal acceleration, lateral acceleration, and yaw angular velocity has a threshold (for example, longitudinal acceleration threshold "-0.25G", lateral acceleration threshold "±0.25G", yaw angular velocity threshold It is possible to record (accumulate) the peak value when it exceeds a threshold value such as "±8.5 deg/sec" (these threshold values are just an example). For example, longitudinal acceleration of "0.25G" or more indicates sudden behavior, which may indicate that an action such as crisis avoidance has been taken.

Note that the driving history information and behavior history information may include road type information (information such as expressway, urban expressway, general road, etc., information on speed zone, etc.).

Further, among the above information, for example, at least one of the driving history information and the behavior history information may be used as the driving information.

In addition, in ETC 2.0, from the perspective of personal information protection, for example, the travel start point (for example, within a range of 500 m from the travel start point) and the travel end point (for example, within a range of 500 m from the travel end point). Information (for example, at least driving history information) can be prevented from being acquired.
Note that not being acquired may mean not being recorded in the ETC 2.0 onboard device or not being transmitted to the roadside device.

The estimated ETC 2.0 measurement database 194 is a database in which estimated ETC 2.0 measurement data generated based on cigarette socket type device measurement data is stored in an accumulative manner.
The estimated ETC2.0 measurement data can be a type of estimation data (estimated second device measurement data).

The first driving score model data 195 is data of a first driving score model that is the basis of a driving score used to determine vehicle insurance premiums and the like.

The learned second driving score model data 196 is data of a driving score model generated by learning using estimated ETC 2.0 measurement data and the like.

Here, the driving score model can be, for example, a mathematical model for calculating a driving score for telematics insurance.
For example, with telematics insurance, an insurance company obtains driving information such as travel information, driving speed, and braking method measured by a device installed in a vehicle (automobile, etc.), and uses that information to analyze the driver's accident risk. insurance premiums may be calculated based on

The driving score model is, for example, a model that can estimate the driver's accident risk (accident risk), and the driving score is set as a value in a numerical range such as "0 to 1,""0 to 10," or "0 to 100." It can be a calculated model. For example, a model may be used in which it is estimated that the higher the driving score, the higher the accident risk (the lower the driving score, the lower the accident risk). In this case, the insurance company can make the insurance premium higher as the driving score is higher, and lower the insurance premium as the driving score is lower.
Alternatively, a model may be used in which it is estimated that the higher the driving score, the lower the accident risk.

Moreover, the input to the driving score model can be the measured data of driving information, and the driving score can be calculated based on the measured data of driving information.
In this embodiment, for example, a learned second driving score model is generated using the existing first driving score model as a base and using the method described below.
Then, by inputting the driver's actual ETC2.0 measurement data into the learned second driving score model, the driver's driving score can be calculated.

Note that the server 100 may be a cloud server.
Furthermore, when the server 100 is separate from the cloud server, the server 100 may acquire the cigarette lighter socket type device measurement data from the cloud server.
The server that performs the processing described below may acquire and store data similar to the data stored in the storage unit 190 of the server 100.

Similarly, the probe server may be the server 100.
Furthermore, when the server 100 is separate from the probe server, the server 100 may acquire ETC2.0 measurement data from the probe server.
The server that performs the processing described below may acquire and store data similar to the data stored in the storage unit 190 of the server 100.

<Principle>
FIG. 5 is a diagram for explaining the principle of learning and inference using estimated ETC2.0 measurement data in this embodiment, and corresponds to FIG. 2.

(1) During learning As shown in FIG. 5(1), the processing unit 110 of the server 100 includes, for example, an ETC2.0 measurement data estimation unit 111, a first driving score model processing unit 113, and a second driving score model. It has a generation unit 115 as a functional unit.
The ETC2.0 measurement data estimation unit 111 can be a type of the second device measurement data estimation unit 11 in FIG. 2(1).
The first driving score model processing unit 113 can be a type of the first model processing unit 13 in FIG. 2(1).
The second driving score model generation unit 115 can be a type of the second model generation unit 15 in FIG. 2(1).

6 and 7 are diagrams showing an example of a method for generating estimated ETC2.0 measurement data.
In FIG. 6, the left side of the drawing shows the time change of the measured position of the vehicle (hereinafter referred to as "cigar socket type device measurement position") measured every second by the cigarette socket type device. In addition, it shows the time change of the measurement position included in one cigarette socket type device measurement data.
Further, on the right side of the drawing, time changes in estimated ETC2.0 measurement positions selected from the cigarette socket type device measurement positions and serving as data elements of estimated ETC2.0 measurement data are shown. Each hatched rectangle is an estimated ETC2.0 measurement position.
Here, as an example, a method of generating estimated ETC2.0 measurement data based on position information included in travel information will be exemplified.

Also, this cigarette socket type device measurement data is referred to as "D1", and the cigarette socket type device measurement position included in the cigarette socket type device measurement data "D1" is expressed as "D1(p(t))" (t: time). .
Furthermore, the estimated ETC2.0 measurement data is expressed as "D2", and the estimated ETC2.0 measurement position included in this estimated ETC2.0 measurement data is expressed as "D2(p(t))" (t: time).

Here, as an example, the measurement position is shown as two-dimensional position information excluding the altitude direction (height direction) (an overhead view of the measurement position). Note that the same can be applied to three-dimensional position information.
Furthermore, the illustrated measurement positions are merely for convenience of explanation and are not necessarily accurate.

From the viewpoint of personal information protection of ETC 2.0 mentioned above, the processing unit 110, for example, collects travel information within a range of "500m" from the travel start point and "500m" from the travel end point among the measurement positions of the cigarette socket type device. ” will be excluded from processing. Alternatively, if the measurement data includes travel information within these ranges, it is deleted.

Next, the processing unit 110 selects, for example, the first cigarette socket type device measurement position D1 (p(1)) outside the range of 500 m from the travel start point, and selects this as the first estimated ETC2.0 measurement position D2. (p(1)). Also, for example, information such as speed, acceleration, angular velocity, etc. of the cigarette socket type device measurement data corresponding to the time is acquired.

Next, the processing unit 110 first deviates on the time axis from a circle with a radius of 200 m based on the unit distance (hereinafter referred to as "unit circle" for convenience) centered on the cigarette socket type device measurement position P11. The measured cigarette socket type device measurement position D1 (p(q)) is selected and is set as the second estimated ETC2.0 measurement position D2 (p(2)). Also, for example, information such as speed, acceleration, angular velocity, etc. of the cigarette socket type device measurement data corresponding to the time is acquired.

Thereafter, the processing unit 110 repeats this process, and determines the cigarette socket type device measurement position [D1(p(1)), D1(p(q)), D1(p(r)), D1(p(s)), . ] is selected, the estimated ETC2.0 measurement position [D2(p(1)), D2(p(2)), D2(p(3)), D2(p(4)),...] is selected. ...] is obtained. Information such as velocity, acceleration, angular velocity, etc. can be similarly acquired.
Then, data in which these driving information are associated with time can be used as estimated ETC2.0 measurement data.

In this way, basically, travel information to be used as estimated ETC 2.0 measurement data can be acquired from the cigarette socket type device measurement data based on the unit distance based on the ETC 2.0 specifications.

Note that here, an example has been shown in which the processing unit 110 performs processing with the unit distance as "200 m", but in accordance with the specifications of ETC 2.0, the processing may be performed with the unit distance as the aforementioned "100 m", etc. It's okay.

Further, the processing unit 110 may perform processing by changing the unit distance depending on the road type or speed zone. For example, if the road type is "expressway", the unit distance may be longer than if the road type is "general road" because there is a possibility of relatively stable driving even though the vehicle is traveling at high speed. Conversely, considering the risks of high-speed driving, the unit distance may be shorter than that for the road type "general road."
Similarly, regarding the speed zone, the unit distance may be made longer (or shorter) as the speed zone is higher.
The road type and speed zone can be specified, for example, from road type information.

Furthermore, here, the estimated ETC2.0 measurement data is generated by selecting travel information included in the cigarette socket type device measurement data based on the distance (unit distance), but the present invention is not limited thereto.
Alternatively or in addition to this, the estimated ETC2.0 measurement data may be generated by selecting travel information included in the cigarette socket type device measurement data based on time. Specifically, for example, the time required to travel a unit distance is calculated based on the speed information included in the data measured by the cigarette socket type device, and the travel information at the corresponding time is selected based on the calculated time. Estimated ETC2.0 measurement data may be generated by performing the following steps.
Alternatively, assuming that the vehicle travels at a constant speed (for example, 60 km/h), estimated ETC2.0 measurement data may be generated in the same way by calculating the time required to travel a unit distance. good.

Here, in ETC 2.0, as mentioned above, there is a specification that, for example, when the traveling direction of the vehicle changes by 45 degrees or more, driving information is recorded. Therefore, travel information may be acquired by additionally applying at least one of the conditions exemplified below.
However, it is not necessary to apply these conditions.

FIG. 7(1) shows, as condition A, a condition regarding a change in the traveling direction (traveling azimuth) of the vehicle.
In this example, the cigarette socket type device measurement position D1 (p(u)) is greater than or equal to the threshold angle with respect to the most recent cigarette socket type device measurement position or the average of multiple past most recent cigarette socket type device measurement positions. (or exceeding the threshold angle), this indicates that the cigarette socket type device measurement position D1 (p(u)) is selected as the estimated ETC2.0 measurement position. This corresponds to a case where the traveling direction of the vehicle changes significantly. The threshold angle can be set to a value such as "45 degrees".
That is, a measurement position in the vicinity of a position where the change in the traveling direction has been greater than or equal to the threshold angle may be appropriately selected.

Note that here, an example was shown in which the processing unit 110 performs processing with the threshold angle set to "45 degrees," but in accordance with the specifications of ETC 2.0, the processing unit 110 may perform processing with the threshold angle set to "22.5 degrees" as described above. You may also do this.

FIG. 7(2) shows, as condition B, a condition based on the traveling distance of the vehicle.
In this example, the cigarette socket type device measurement position D1(p(n)) that first deviates from the unit circle is the cigarette socket type device measurement position D1(p(n)) which was previously selected as the estimated ETC2.0 measurement position. m)), this indicates that the cigarette socket type device measurement position D1 (p(n)) is not selected. The threshold distance is determined based on the vehicle speed calculated based on data measured by a cigarette socket type device or a predetermined vehicle speed (for example, 60 km/h), in which the vehicle advances from the previous selected point. It is possible to set a value that is difficult to assume as a distance.

In other words, it is possible to set a distance margin and select travel information if the distance falls within the distance margin, but not select the travel information if it does not fall within the distance margin.

In this case, for example, the measurement position D1 (p(n-1)) immediately before the measurement position D1 (p(n)) may be selected as the estimated ETC2.0 measurement position.
Further, for example, the measurement position D1 (p(n+1)) next to the measurement position D1 (p(n)) may be selected as the estimated ETC2.0 measurement position.
That is, a measurement position near the end of the unit circle may be appropriately selected. Further, the range serving as the selection reference is not limited to the unit circle, but may be any range (for example, an ellipse, a rectangle, etc.) based on the position of the reference data.

Further, for example, the average (or an arithmetic average or a weighted average) of the measurement position D1 (p(n)) and the measurement position D1 (p(m)) is calculated, and the calculated position is estimated by the ETC2. For example, the measurement position may be set to .0 measurement position. In addition, the calculation is not limited to calculating the average, and for example, a position obtained by dividing the measurement position D1 (p(n)) and the measurement position D1 (p(m)) at an arbitrary ratio (for example, 2:1, etc.) may be used as the estimated ETC2.0 measurement position, or if the value between them is obtained using measurement position D1 (p(n)) and measurement position D1 (p(m)), the contents are particularly limited. Not done.

Moreover, regarding the condition of FIG. 7(2), a temporal margin may be provided instead of a distance margin, and similar processing may be performed.

Furthermore, in the ETC 2.0 specification, as described above, a peak value is recorded when any one of longitudinal acceleration, lateral acceleration, and yaw angular velocity exceeds a threshold value. Conditions for reproducing this may be set, and travel information may be selected based on acceleration information and angular velocity information included in the data measured by the cigarette socket type device.

In addition, although the method of generating estimated ETC2.0 measurement data based on position information has been exemplified above, instead of this, or in addition to this, an estimated ETC2.0 measurement data can be generated based on speed information and time information. Measurement data may be generated in the same way.

Mathematically, it can be said that the estimated ETC2.0 measurement data generated as described above is included in the cigarette socket type device measurement data.
By generating a driving score model using the estimated ETC 2.0 measurement data generated in this way, a highly valid driving score model can be generated.

Returning to FIG. 5, the first driving score model processing unit 113 calculates a driving score by using, for example, the cigarette socket type device measurement data as input to the first driving score model.

The second driving score model generation unit 115 uses, for example, the estimated ETC2.0 measurement data estimated by the ETC2.0 measurement data estimation unit 111 and the driving score (teacher driving score) calculated by the first driving score model processing unit 113. The second driving score model is generated by, for example, performing learning (for example, supervised learning) on the first driving score model using the first driving score model as a learning data set. The model for which learning has been completed becomes the learned second driving score model.

(2) At the time of inference As shown in FIG. 5(2), the processing unit 110 of the server 100 includes, for example, a learned second driving score model processing unit 117 as a functional unit.
The learned second driving score model processing section 117 can be a type of the learned second model processing section 17 shown in FIG. 2(2).

The learned second driving score model processing unit 117 calculates a driving score by using, for example, the ETC2.0 measurement data stored in the ECT2.0 measurement database 194 as input to the learned second driving score model.
In other words, the learned second driving score model processing unit 117 receives ETC2.0 measurement data measured by the ETC2.0 onboard device actually installed in the vehicle and uses the learned second driving score model to drive the vehicle. The score is inferred and a driving score inference result, which is the inference result, is output.

The first driving score model can be, for example, an existing driving score model.
Here, the cigarette socket type device measurement data inputted to the first driving score model processing unit 113 includes driving information such as vehicle position, acceleration (velocity when integrated over time), and angular velocity (orientation when integrated over time). Contains information about Therefore, the driving situation of the driver is reflected in the data measured by the cigarette socket type device, and the driving score calculated by the first driving score model processing unit 113 can be a value that reflects the driving situation of the driver. There is sex. This can be used for learning as teacher data.

In addition, as explained in the principle of the embodiment, in addition to this, for example, estimated ETC2.0 measurement data is used as input data to the first driving score model, and various types including cigarette socket type devices accumulated from the past are used. The second driving score model may be generated by machine learning or the like using some data held in the past corresponding to the first device measurement data measured by the first device as the teacher data. .
Although the teacher data in this case is just an example, it may also be data related to vehicle accidents (data such as the presence or absence of accidents, the number of accidents (frequency, ratio), etc.).

<Processing>
FIG. 8 is a flowchart showing an example of the flow of driving score model processing executed by the processing unit 110 of the server 100 in this embodiment.
First, the processing unit 110 of the server 100 performs a learning data set generation process.
In the learning data set generation process, the processing unit 110 performs loop B processing on the cigarette socket type device measurement data to be processed (S1 to S7).
The cigarette socket type device measurement data to be processed is, for example, among the cigarette socket type device measurement data stored in the cigarette socket type device measurement database 192 under similar conditions (for example, the same measurement period, the same road type, the same (e.g. those measured locally). However, it is not limited to this.

In the process of loop B, the processing unit 110 generates estimated ETC2.0 measurement data based on the cigarette socket type device measurement data (S3). Then, the processing unit 110 associates the generated estimated ETC 2.0 measurement data with the cigarette socket type device measurement data (for example, associates it with identification information (data number, etc.) of the cigarette socket type device measurement data), It is stored in the estimated ETC2.0 measurement database 194.
Furthermore, the processing unit 110 calculates a teacher driving score based on the cigarette socket type device measurement data and the first driving score model (S5). Then, the processing unit 110 stores the calculated teacher driving score in the storage unit 190 in association with the cigarette socket type device measurement data.
The processing unit 110 then moves on to the next cigarette socket type device measurement data.

After performing the processes of S3 and S5 for all the cigarette socket type device measurement data to be processed, the processing unit 110 ends the process of loop B (S7).

After that, the processing unit 110 performs a learning process (S9). Specifically, each estimated ETC 2.0 measurement data stored in the estimated ETC 2.0 measurement database 194 and the corresponding teacher driving score (identification information of the cigarette socket type device measurement data is the same) The model is trained based on the above, and a learned second driving score model is generated.

Next, the processing unit 110 performs inference processing (S11). Specifically, the learned second driving score model generated in S9 and the ETC2.0 measurement data of each processing target (for example, each ETC2.0 measurement data stored in the ETC2.0 measurement data) A second driving score is calculated based on. Then, the calculation results are taken as various quantity inference results.
Then, the processing unit 110 ends the process.

Note that the step S11 may be omitted and the process may be performed until the second driving score model is generated.
Alternatively, only the step S3 may be used, and the process may be performed until the estimated ETC2.0 measurement data is generated based on the cigarette socket type device measurement data.

Further, the processing unit 110 may cause the display unit 130 to display the calculated value of the second driving score (inference result).
Further, instead of displaying the calculated second driving score value itself on the display unit 130, or in addition to this, the processing unit 110 may classify drivers based on the calculated second driving score value. Information such as rank (information related to order) may be displayed on the display unit 130. Specifically, for example, the following rank information may be displayed on the display unit 130.
・In descending order of rank: 5 stars, 4 stars, 3 stars, 2 stars, 1 star
・In descending order of rank: “◎, 〇, △, ×”
・In descending order of rank: "A (A rating), B (B rating), C (C rating)"
・Gold, Silver, Bronze in descending order of rank

For example, when the insurance company that has acquired this information from the server 100 presents the calculated insurance premium to the customer, it also presents the customer's second driving score value and rank information. Good too.

Furthermore, the processing unit 110 estimates how much G is applied in which direction due to the driver's acceleration/deceleration, left/right handling operations, etc., based on the data measured by the cigarette socket type device, and converts it into a graphic. The information may also be displayed on the display unit 130.

<Effects of the first embodiment>
In this example, as described above, estimated ETC2.0 measurement data was generated by selecting travel information from the cigarette socket type device measurement data, for example, based on unit distance or unit time.
In this case, it is considered that the information such as sudden deceleration or sudden acceleration described above is not always accurately reflected in the estimated ETC2.0 measurement data (so-called near-misses, etc. are reflected).
However, the inventor of the present application believes that by using the estimated ETC2.0 measurement data generated as described above, the general driving tendencies of the driver (such as a tendency toward safe driving or a tendency toward dangerous driving) can be reflected. We were able to obtain estimated ETC2.0 measurement data, and as a result, we obtained knowledge that it is possible to generate a highly valid driving score model.

In the present embodiment, the server 100 (an example of an information processing device) stores cigarette socket type device measurement data (first measurement data) including driving information measured by a device such as a cigarette socket type device (an example of a first device). Estimation that is estimated data of ETC2.0 measurement data (an example of second measurement data) measured by a device such as an ETC2.0 onboard device (an example of a second device different from the first device) based on It includes a processing unit 110 that generates ETC2.0 measurement data.
Thereby, based on the first measurement data measured by the first device that measures information about the moving object, the second measurement data that is measured by the second device that measures information about the moving object and which is different from the first device. Estimated data of two measurement data can be generated.

In this case, even if the sampling unit when measuring data is different between a device such as a cigarette socket type device (an example of the first device) and an ETC2.0 onboard device (an example of the second device). good.
As a result, the second measurement is performed by the second device that measures information regarding the moving object based on the first measurement data in which information regarding the moving object is measured by the first device of the two devices having different sampling units. It becomes possible to generate estimated data.

In this case, even if the sampling unit of the device such as a cigarette socket type device (an example of the first device) is time, and the sampling unit of the ETC2.0 onboard device (an example of the second device) is distance. good.
As a result, based on the first measurement data in which information regarding the moving object is measured by the first device whose sampling unit is time, the second measurement data measured by the second device whose sampling unit is distance for measuring information regarding the moving object is determined. It becomes possible to generate estimated data of two measurement data.

Furthermore, in this case, the device such as a cigarette socket type device (an example of the first device) may measure at predetermined time intervals, and the ETC2.0 onboard device (an example of a second device) may measure at predetermined distance intervals. good.
Thereby, based on the first measurement data measured by the first device that measures information about the moving object at predetermined time intervals, the second measurement data measured by the second device that measures the information about the moving object at predetermined distance intervals. Estimated data of measured data can be generated.

Further, a device such as a cigarette socket type device (an example of a first device) measures at predetermined time intervals, an ETC 2.0 onboard device (an example of a second device) measures at predetermined distance intervals, and the processing unit 110 of the server 100 measures is estimated ETC 2.0 measurement data (an example of estimated data) based on cigarette socket type device measurement data (an example of first measurement data), a predetermined distance interval measured by the ETC 2.0 onboard device, and a predetermined condition. may be generated.
Thereby, it becomes possible to appropriately generate estimated data based on the first measurement data, the predetermined distance interval, and the predetermined condition.

Further, in this case, the predetermined conditions may include at least a condition regarding a change in the direction of the moving body based on the position included in the cigarette socket type device measurement data (an example of the first measurement data).
This makes it possible to generate estimated data that takes into account (reflects) a change in the direction of the moving object based on the position included in the first measurement data.

Further, the processing unit 110 of the server 100 may generate a model for calculating various quantities based on estimated ETC2.0 measurement data (an example of estimated data).
Thereby, a model for calculating various quantities can be appropriately generated based on the estimated data.

Further, in this case, the processing unit 110 of the server 100 may generate a model such as a driving score model by machine learning using at least estimated ETC 2.0 measurement data (an example of estimated data) as learning data. .
Thereby, a model that can appropriately calculate various quantities can be generated based on the estimated data.

Further, in this case, the processing unit 110 of the server 100 may calculate various quantities such as the driving score based on the ETC 2.0 measurement data and the generated model such as the driving score model.
Thereby, various quantities can be appropriately calculated based on the second measurement data and the generated model.

In addition, in this case, the cigarette socket type device (an example of the first device) and the ETC2.0 on-vehicle device (an example of the second device) measure driving information, and the various quantities are of the mobile object whose driving information is measured. The driving score is related to insurance, and the model may be a second driving score model for calculating the driving score based on ETC 2.0 measurement data.
Thereby, a model for calculating the driving score can be generated based on the second measurement data.

In addition, in this case, the processing unit 110 of the server 100 generates a driving score (first measurement data) based on the estimated ETC2.0 measurement data (an example of estimation data) and the cigarette socket type device measurement data (an example of first measurement data). The second driving score model may be generated by machine learning using as learning data an example of information based on .
Thereby, it becomes possible to generate a model with high validity for calculating the driving score based on the second measurement data.

<Modification (1)>
In the above embodiment, the first device and the second device may be reversed to perform similar processing.

Specifically, for example,
- First device: ETC 2.0 on-vehicle device - Second device: Cigarette socket type device The cigarette socket type device measurement data may be estimated based on the ETC 2.0 measurement data.

In other words, including the above embodiment, the sampling unit of one of the first device and the second device may be time, and the sampling unit of the other may be distance. Further, one of the first device and the second device may measure at predetermined time intervals, and the other may measure at predetermined distance intervals.

<Modification (2)>
In the above embodiment, the estimated ETC2.0 measurement data is generated based on the driving information of the time-series discrete cigarette socket type device measurement data, but the present invention is not limited thereto.
A continuous function may be calculated that approximates the driving information included in the cigarette socket type device measurement data, and estimated ETC2.0 measurement data may be generated based on the calculated function.

For example, based on time-series two-dimensional position information included in cigarette socket type device measurement data, a function of two-dimensional position (X, Y) with time “t” as a parameter “(X, Y) = f (t)".
Then, for example, the time required to travel a unit distance is calculated from the speed information included in the cigarette socket type device measurement data, and based on the calculated time, the position information at the corresponding time "t" is converted to the function "(X , Y)=f(t)", the estimated ETC2.0 measurement data may be generated.
Note that the same can be applied to three-dimensional position information.

Assuming that the vehicle travels at a constant speed (for example, 60 km/h), by calculating the time required to travel a unit distance, the function "(X, Y) = f(t)" can be similarly calculated. The estimated ETC2.0 measurement data may be generated using the following.

<Modification (3)>
In the above embodiment, the second driving score model generation unit 115 generates the driving score model through learning using at least the estimated ETC2.0 measurement data and the ETC2.0 measurement data as learning data. Good too.

Specifically, for example, by performing supervised learning in which the teacher driving score and ETC2.0 measurement data are used as the teacher data, and in addition to these, the estimated ETC2.0 measurement data is set as the learning data, driving A score model may also be generated.

In this modification, the processing unit 110 of the server 100 uses at least estimated ETC2.0 measurement data (for example, an example of estimated data) and ETC2.0 measurement data (for example, an example of second measurement data) for learning. A model is generated by machine learning using data.
This makes it possible to generate a more accurate model.

<Modification (4)>
The validity of the estimation of the ETC2.0 measurement data described in the above embodiment may be verified.
Specifically, for example, ETC2.0 measurement data obtained by having a driver drive a vehicle equipped with an ETC2.0 onboard device under similar conditions, and estimated ETC2.0 generated as in the above example. Obtain measurement data. Then, the processing unit 110 of the server 100 calculates a value (correlation value) indicating the correlation between these measurement data, and, for example, when it is determined that the correlation value is equal to or higher than a predetermined threshold (or exceeds the threshold) (there is a correlation between the two). ), it may be determined that the estimation of the ETC2.0 measurement data is appropriate.
In this case, by performing the above processing on a certain number of data, validity can be verified more appropriately.

Further, the validity of the driving score model generated as in the above embodiment may be verified.
Specifically, in the same way as above, for example, under similar conditions, ETC2.0 measurement data obtained by having a driver drive a vehicle equipped with an ETC2.0 onboard device, and generated as in the above example. Obtain the estimated ETC2.0 measurement data. The processing unit 110 of the server 100 then generates a driving score obtained by inputting the ETC2.0 measurement data into the driving score model and a driving score obtained by inputting the estimated ETC2.0 measurement data into the driving score model. If the difference between these driving scores is less than a predetermined threshold (or below a threshold), the driving score model may be determined to be appropriate.
In this case, by performing the above processing on a certain number of data, validity can be verified more appropriately.

<Modification (5)>
Although the user has entered into a car insurance contract with an insurance company, during the contract period, the user changes the device installed in the car from the first device (e.g., cigarette socket type device) to the second device (e.g., There may be cases where the vehicle needs to be changed (switched) to ETC 2.0 on-board device).

Therefore, for example, the processing unit 110 of the server 100 determines whether a device installed in a car (used to measure measurement data) is When changing from the first device to the second device, the measurement data of the first device during at least the first period from the start of the contract period to the device change (for example, the first device measured from the insurance start date to the device change date) Estimated data based on the device measurement data) and measurement data of the second device in the second period from the device change to the end of the contract period (for example, the data measured from the device change date to the driving score calculation date (continuation procedure date)) The driving score for continuing automobile insurance may be calculated based on the second device measurement data).

In this case, the driving score may be calculated using any of the following methods, for example.
・Learn the first score value calculated by inputting the estimated data (e.g., estimated ETC2.0 measurement data) into the learned second driving score model and the second device measurement data (e.g., ETC2.0 measurement data) It is calculated by averaging the second score value calculated by inputting it into the second driving score model.
- Calculate by inputting data obtained by integrating estimated data (for example, estimated ETC2.0 measurement data) and second device measurement data (for example, ETC2.0 measurement data) into the learned second driving score model.

Note that instead of the estimated data based on the first device measurement data in the first period from the start of the contract period to the device change, the first device measurement data in the first period from the start of the contract period to the device change is used. It's okay. In this case, the driving score may be calculated using the following method, for example.
・The first score value calculated by inputting the first device measurement data (for example, cigarette socket type device measurement data) into the first driving score model and the second device measurement data (for example, ETC2.0 measurement data) It is calculated by averaging the second score value calculated by inputting it into the learned second driving score model.

<Second example>
In the first embodiment, an example related to an insurance product such as telematics insurance has been described. However, when developing services based on ETC 2.0, including services for such telematics insurance products, there are many issues such as how many people can use the service, the size of the market, and whether it can be provided equitably across the country. It is necessary to evaluate whether the service is suitable or not. The second example is an example related to this evaluation.
Note that the content described in the second embodiment is applicable to any of the other embodiments and other modifications.

<Data structure>
FIG. 9 is a diagram showing an example of information stored in the storage unit 190 of the server 100 in this embodiment.
The storage unit 190 includes, for example, a first ETC 2.0 service evaluation support processing program 197 that is read out by the processing unit 110 and executed as an ETC 2.0 service evaluation support process, and the aforementioned cigarette socket type device measurement database 192. , a roadside machine information database 198, and a creation histogram database 199 are stored.

As described above, the cigarette socket type device measurement database 192 can store measurement data of a plurality of drivers for each measurement period, for example. The measurement period may be, for example, a monthly period (1 month, 2 months, . . . , 6 months, etc.) or a period of other units. Further, for example, the information may be classified and stored by road type or region.

The roadside machine information database 198 is, for example, a database that stores information including location information of roadside machines nationwide.

The creation histogram database 199 is a database that stores histogram data created by the processing unit 110 in order to perform evaluations and the like regarding services based on ETC 2.0, which will be described later.

Note that the estimated ETC2.0 measurement database 194 and the like described above may be stored in the storage unit 190.

<Specifications of ETC2.0 onboard equipment>
In ETC 2.0, as described above, probe information including traveling information is collected by a roadside device from an ETC 2.0 onboard device, for example.
On the other hand, the ETC2.0 on-vehicle device may only be able to record travel information for a predetermined distance, such as a maximum of 80 km, and travel information exceeding the predetermined distance will not be recorded. It may be discarded. For this reason, if a vehicle equipped with an ETC 2.0 onboard device does not pass near the roadside device for a long time, the roadside device may not collect the travel information that has been recorded.

The ETC 2.0 specifications may be updated in the future.
For example, the aforementioned unit distance (e.g., 200 m) may be changed, and in some cases, it may become necessary to increase the memory of the ETC 2.0 vehicle-mounted unit.
However, the technique of the present invention can also accommodate such changes in the ETC 2.0 specifications, and it is only necessary to adjust the values of various parameters.

<Processing>
FIG. 10 is a diagram showing an example of the flow of the first ETC 2.0 service evaluation support process performed by the processing unit 110 of the server 100 in this embodiment.
For example, the processing unit 110 performs the following processing on each piece of cigarette socket type device measurement data included in the cigarette socket type device measurement database 192.

First, the processing unit 110 performs the following processing on the travel information included in the cigarette socket type device measurement data, for example, in order from the oldest travel information in chronological order.
The processing unit 110 determines whether the measured position of the vehicle included in the travel information is included in the area of the roadside machine (new roadside machine) (S21).

Here, the area near the roadside machine is referred to as a "roadside machine area." The roadside machine area can be set, for example, as an area within a radius of 5 m or an area within a radius of 10 m from the position of the roadside machine.
Specifically, the processing unit 110 determines whether the measured position of the vehicle included in the travel information is included in the roadside device area of the roadside device based on the location information of the roadside device stored in the roadside device information database 198. Determine whether or not.

If it is determined that the vehicle is included (S21: YES), the processing unit 110 determines whether the measured position of the vehicle exceeds "80 km" from the position of the previous roadside device (S23).

If it is determined that it exceeds the range (S23), the processing unit 110 selects a set of travel information within 80 km from the location of the roadside device, from among the travel information included in the cigarette socket type device measurement data. The transmission flag (collection flag) is set to "ON" (S25).

The transmission flag can be a flag that simulates transmission of travel information to the roadside device when a vehicle equipped with the ETC 2.0 onboard device passes near the roadside device.
In this example, in order to evaluate services based on ETC 2.0, we will use data measured by a cigarette socket type device (obtained cigarette socket type device measurement data) to virtually create an ETC 2.0 in-vehicle service. This reproduces (simulates) the transmission of travel information (collection of travel information by the roadside device) when a vehicle equipped with the device passes near the roadside device.

For example, if the measured position of the vehicle included in the travel information is "100 km" away from the position of the previous roadside unit, then according to the ETC 2.0 specifications mentioned above, Travel information for ``20 km'' beyond ``80 km'' from the location will be discarded from the ETC 2.0 onboard device. In order to reproduce this, in step S25, a transmission flag "ON" is set for the set of travel information within "80 km" from the position of the roadside device, excluding the travel information exceeding "80 km".

On the other hand, if it is determined that the distance has not exceeded "80 km" from the position of the previous roadside device (S23: NO), the processing unit 110 selects one of the driving information included in the cigarette socket type device measurement data. A transmission flag "ON" is set for the travel information set from the previous roadside device position (S27).

After S25 or S27, the processing unit 110 determines whether or not to end the process (S29), and if it is determined to continue the process (S29: NO), returns the process to S1.
On the other hand, if it is determined to end the process (S29: YES), the processing unit 110 ends the first ETC2.0 service evaluation support process.

<Measurement period settings>
FIG. 11 is a diagram showing an example of a histogram for ETC 2.0 service evaluation generated by the processing unit 110 of the server 100 based on the processing result of the first ETC 2.0 service evaluation support process in this embodiment. be.

Based on the above processing results, for example, the processing unit 110 sets the horizontal axis (class) to the estimated collection time (h) at the roadside device and the vertical axis (frequency) for each measurement period of the cigarette socket type device measurement data. Create a histogram for the number of vehicles (units).
The expected collection time at the roadside device can be derived, for example, from the travel information (set of travel information) for which the transmission flag is set to "ON," so it is virtually impossible for the expected collection time to be collected at the roadside device. It can also be said that it is synonymous with the number of data created. Therefore, the horizontal axis may be used as the estimated number of collected data.
Alternatively, the vertical axis may be expressed as the number of times the vehicle travels (times).

This figure shows an example of a histogram for a measurement period of "1 month", and as an example, the horizontal axis shows sections in which the estimated collection time at the roadside device is divided into "1 h" as classes. Furthermore, the number of vehicles included in each section is shown as a frequency on the vertical axis. The total number of vehicles is the sum of the number of vehicles in all sections. Note that the values on the horizontal axis and the vertical axis are only examples, and are not limited to these.

FIG. 12 shows an example of a histogram similarly created for the measurement period "2 months". The view of the figure is the same as that of FIG.

For example, the processing unit 110 sets the threshold value of the estimated collection time to "3h", and the ratio of the number of vehicles to the total number of vehicles for which the estimated collection time exceeds the threshold value "3h" (hereinafter referred to as "number of vehicles ratio"). ) is calculated.
As a result of calculating the vehicle number ratio based on the histogram for the measurement period "1 month" in FIG. Suppose that the result of calculating is, for example, "80%".

For example, if the target value of the number of vehicles is set to 75% when the estimated collection time threshold is 3 hours, the ratio of vehicles will reach the target value during the measurement period of "1 month" in Figure 11. However, the ratio of the number of vehicles reached the target value during the measurement period “2 months” in FIG. 12.
Therefore, in this example, it can be determined that there is no problem even if the measurement data of ETC 2.0 is reproduced by using the cigarette socket type device measurement data whose measurement period is "2 months".

In actual processing, the processing unit 110 of the server 100 creates a histogram by updating the measurement periods in order of shortest measurement period until the vehicle number ratio reaches a target value, for example. Then, when the ratio of the number of vehicles reaches the target value, that measurement period is set as the measurement period to be operated.
In this case, for example, among the sets of cigarette socket type device measurement data for each measurement period stored in the cigarette socket type device measurement database 192 of the storage unit 190, only the set of cigarette socket type device measurement data for the set measurement period is used. may be retained, and cigarette socket type device measurement data sets for other measurement periods may be deleted.

Then, the processing unit 110 generates the estimated ETC2.0 measurement data described in the first embodiment using, for example, the set of cigarette socket type device measurement data of the set measurement period, and generates the driving score model. It can be done.
By doing this, it becomes possible to create a telematics insurance product with guaranteed reliability.

Note that the following processing may be performed.
For example, when the processing unit 110 sets a period of "2 months" as the measurement period of the cigarette socket type device measurement data, the driving score model is created using the cigarette socket type device measurement data of this "2 months" measurement period. generate. Similarly, the processing unit 110 similarly generates a driving score model using the estimated ETC2.0 measurement data for this "two month" measurement period.
Then, if the processing unit 110 inputs predetermined measurement data into these generated driving score models, it determines whether similar results can be obtained (for example, the difference between the models is less than or equal to a predetermined threshold (or less than a threshold)). If similar results are obtained, the measurement period of ETC2.0 measurement data used to calculate driving scores (used for evaluation of driving) will be changed to 2 months. Set.

On the other hand, if similar results are not obtained, the measurement period of the ETC 2.0 measurement data used to calculate the driving score (used for evaluation of driving) may not be enough for 2 months, so the process will be processed. The unit 110 sets the measurement period of the ETC2.0 measurement data used for calculating the driving score to a measurement period longer than "2 months" (for example, any period from "3 months" to "6 months"). You may also set it to .

This is because in the method of the present invention, estimated ETC2.0 measurement data is generated by thinning out (resampling) the travel information of the cigarette socket type device measurement data. This is because it may not be appropriate to apply the measurement period of the measurement data as is.
Therefore, as described above, the two driving score models are compared and verified, and the measurement period of ETC2.0 measurement data used for calculating the driving score is set (determined) based on the results of the comparison and verification. good.

<Business evaluation of insurance products>
Using the histogram created by the server 100 as described above, it is also possible to evaluate the success or failure of an insurance product based on ETC 2.0 as a business.

When considering creating insurance products based on ETC 2.0 driving information, it is necessary to have users actually drive a vehicle equipped with an ETC 2.0 onboard device and collect measurement data. This has the problem of being costly as mentioned above, and if it becomes clear that the ETC 2.0 mechanism cannot collect sufficient driving information, it may not be viable as an insurance product business in the first place. There is. In other words, as described above, data loss may occur if a vehicle equipped with an ETC 2.0 onboard device travels, for example, 80 km or more without passing near the roadside device. This will not be known until we actually conduct a demonstration experiment using a vehicle equipped with the ETC 2.0 on-board device.

Therefore, for example, data collected by other means not based on ETC 2.0 (for example, cigarette socket type device measurement data) and roadside equipment included in the area covered by telematics insurance (for example, the metropolitan area, etc.) An estimated collection time is calculated for each vehicle based on the location information. Then, for all target vehicles, how much driving information is virtually collected by the roadside device (or conversely, how much driving information is discarded) is calculated using the above-mentioned histogram, etc. It can be evaluated.

Specifically, the processing unit 110 of the server 100 generates, for example, a histogram of how much driving information is collected per month based on measurement data of the cigarette socket type device during a predetermined measurement period. 11 and FIG. 12.
Then, the processing unit 110 of the server 100 sets a threshold value (for example, 7 hours, 10 hours, etc.) for the estimated collection time at the roadside device, and sets the number of vehicles (or the number of vehicles) for which the estimated collection time is equal to or greater than the threshold (or exceeds the threshold). If the ratio) is equal to or greater than a set value (or exceeds a set value), it can be determined that the business of a general insurance product is viable.

In this case, for example, processing may be performed using a period of "1 month" as a predetermined measurement period, or processing may be performed using a period of "2 months" or "3 months" as a predetermined measurement period. It's okay. In this case, the estimated collection time in a predetermined measurement period may be used as is, or the estimated collection time per month may be used.
Alternatively, the time calculated by creating a histogram from the cigarette socket type device measurement data of a plurality of different measurement periods and averaging the estimated collection time of each measurement period may be used.

Furthermore, instead of the estimated collection time, the above-mentioned estimated number of collected data may be used.
Further, similar processing may be performed based on the number of data (estimated number of discarded data) or time (estimated discarded time) indicating how much driving information is virtually discarded without being collected by the roadside device. This is the rate (degree) of data loss in estimated ETC2.0 measurement data generated based on cigarette socket type device measurement data (pseudo ETC2.0 measurement data generated based on cigarette socket type device measurement data). ) (data loss rate information (data loss degree information)).

In addition, determining the success or failure of the above insurance product as a business is based on the above data loss rate information, estimated ETC 2.0 measurement data generated based on cigarette socket type device measurement data (cigarette socket type device measurement data) It can also be said that the validity of pseudo ETC 2.0 measurement data generated based on measurement data is determined.

Note that in this case, the server 100 may or may not actually generate the estimated ETC2.0 measurement data. In other words, the estimated ETC2.0 measurement data may be generated based on the cigarette socket type device measurement data, and then the validity of the generated estimated ETC2.0 measurement data may be determined by the above method. In the case where estimated ETC 2.0 measurement data is generated based on cigarette socket type device measurement data without generating .0 measurement data, the validity of the generated estimated ETC 2.0 measurement data is determined by the above method. You can do it like this.

Note that if it is determined that the business of a general insurance product is viable, the processing unit 110 of the server 100 sets the measurement period based on the method described in <Measurement period setting> above, and then performs the set measurement period. The estimated ETC2.0 measurement data described in the first embodiment may be generated using a set of cigarette socket type device measurement data for the period, and the driving score model may be generated.

Conversely, if the processing unit 110 of the server 100 determines that the general insurance product is not viable as a business, it may perform any of the following.
・Do not generate estimated ETC2.0 measurement data ・Generate estimated ETC2.0 measurement data but do not use it

Furthermore, in the above <Measurement period setting> and <Business evaluation of insurance products>, the processing unit 110 of the server 100 uses estimated ETC2.0 measurement data instead of the cigarette socket type device measurement data, and similarly You may also perform an evaluation.

<Effects of the second embodiment>
In this embodiment, the server 100 transmits ETC2.0 measurement data (an example of second measurement data) to a roadside device (an example of a third device) installed at a predetermined location. Based on the cigarette socket type device measurement data (an example of the first measurement data) and the position information of the roadside device, it is determined whether estimated ETC 2.0 measurement data is to be generated.
Thereby, it can be appropriately determined whether or not to generate estimated data of the second measurement data based on the first measurement data and the position information of the third device installed at a predetermined position.

Further, in this embodiment, the server 100 transmits ETC2.0 measurement data (an example of second measurement data) to a roadside device (an example of a third device) installed at a predetermined position. Based on the cigarette socket type device measurement data (an example of the first measurement data) and the location information of the roadside device, it is determined whether or not to use the estimated ETC 2.0 measurement data transmitted from the device (an example of the device). judge.
Thereby, it is possible to appropriately determine whether or not to use the estimated data of the second measurement data based on the first measurement data and the position information of the third device installed at a predetermined position.

Further, in this embodiment, the server 100 transmits ETC2.0 measurement data (an example of second measurement data) to a roadside device (an example of a third device) installed at a predetermined position. Cigarette socket type device measurement data used to generate ETC2.0 measurement data, based on the cigarette socket type device measurement data (an example of the first measurement data) and the position information of the roadside device. Determine the measurement period.
Thereby, the measurement period of the first measurement data used to generate the estimated data of the second measurement data can be appropriately determined based on the first measurement data and the position information of the third device installed at a predetermined position. I can do it.

In addition, in this embodiment, the server 100 is necessary for driving evaluation based on ETC 2.0 measurement data measured by an ETC 2.0 onboard device, based on cigarette socket type device measurement data measured by a cigarette socket type device. The device includes a processing unit 110 that determines a measurement period of cigarette socket type device measurement data.
Thereby, based on the first measurement data measured by the first device that measures information about the moving object, the second measurement data that is measured by the second device that measures information about the moving object and which is different from the first device. The measurement period of the first measurement data necessary for evaluation of driving based on the second measurement data can be appropriately determined.

Furthermore, in this case, the server 100 measures ETC2.0 measurement data necessary for driving evaluation based on the ETC2.0 measurement data, based on the cigarette socket type device measurement data of the measurement period determined as described above. Determine the period.
This makes it possible to appropriately determine the measurement period of the second measurement data, which is necessary for the evaluation of driving based on the second measurement data, based on the first measurement data of the measurement period determined as described above. can.

Furthermore, the server 100 generates pseudo ETC 2.0 measurement data for a predetermined period based on the cigarette socket type device measurement data, and acquires data loss rate information in this pseudo ETC 2.0 measurement data. Then, the server 100 determines the validity of this pseudo ETC 2.0 measurement data based on the acquired data loss rate information.
Thereby, the validity of the pseudo second measurement data generated based on the first measurement data for a predetermined period can be appropriately determined based on the data loss rate information.

<Modified example>
In the above embodiment, the server 100 uses cigarette socket type device data and estimated ETC 2.0 measurement data that are measured with a leisure area etc. as the destination (for example, when the user is located within the range of the leisure area for a predetermined time). Based on this, it may be determined whether or not business for insurance products for leisure (leisure use) is viable using the same method as described in the above embodiment. If it is determined that this is true, the insurance company may provide a special insurance product for leisure use.

Additionally, in this case, as explained in the above embodiment, the server 100 determines that it can be established as a general insurance product based on evaluation based on cigarette socket type device measurement data and estimated ETC 2.0 measurement data in the metropolitan area etc. If this is the case, insurance companies will uniformly provide general insurance products that are determined to be valid, regardless of whether they are for leisure use or not.

On the other hand, if the server 100 determines that it is not viable as a general insurance product based on the evaluation based on the cigarette socket type device measurement data and estimated ETC 2.0 measurement data in the metropolitan area, etc., the server 100 performs the above-mentioned In this way, it may be determined whether or not business for leisure insurance products is viable. If it is determined that this is true, the insurance company may provide a special insurance product for leisure use.

<Third Example>
In collecting driving information under a system based on ETC 2.0, we are installing new roadside devices (ITS spots) that acquire driving information from devices compatible with ETC 2.0 placed on vehicles. It can be expected to collect driving information. Therefore, under the system based on ETC2.0, we measured the degree of collection of driving information that would be collected assuming that a new roadside unit was installed in addition to the existing roadside unit, and based on the measured degree of collection. An example of determining the feasibility of a service related to ETC 2.0 will be described as a third embodiment.
Note that the content described in the third embodiment is applicable to any of the other embodiments and other modifications.

<Data structure>
FIG. 13 is a diagram showing an example of information stored in the storage unit 190 of the server 100 in this embodiment.
The storage unit 190 includes, for example, a second ETC 2.0 service evaluation support processing program 201 that is read out by the processing unit 110 and executed as the ETC 2.0 service evaluation support process, map information 202, and a roadside machine information database 198. , a cigarette socket type device measurement database 192 , and a facility information database 203 where new roadside devices can be installed are stored.

The map information database 201 is, for example, a database that stores information including road information, topography, etc. nationwide.

As described above, the roadside machine information database 198 is a database that stores information including, for example, location information of roadside machines nationwide.

As described above, the cigarette socket type device measurement database 192 is, for example, a database in which cigarette socket type device measurement data including driving information measured by a cigarette socket type device mounted on a vehicle is stored.
In this embodiment, it is assumed that the travel information includes at least position information.

The roadside unit new installation facility information database 203 stores, for example, location information of facilities where new roadside units can be installed. Preferably, each piece of location information is stored in association with facility property information (including chain store names, for example, shopping mall names, gas station names, convenience store names, etc.). It is now possible to extract a set of industries (gas stations, etc.) operated by the same company based on the property information obtained.

FIG. 14 is a diagram showing an example of the distribution of already placed ETC 2.0 roadside units on a map, generated with reference to information stored in the map information database 201 and the roadside unit database 198. . In addition, in this embodiment, for each roadside machine, area information that is a guideline for the acquisition position range of travel information that is likely to be collected is also displayed. Note that such area information is preferably stored in advance or calculated based on the position information of each roadside unit, surrounding road information, etc. in a new roadside unit installation position derivation process to be described later. Details of the area information will be described later.
As shown in FIG. 14, the position of the roadside machine is indicated by a diamond in a manner superimposed on the map information. As described above, in the roadside device, when a vehicle equipped with a device compatible with ETC 2.0 is located nearby, travel information stored in the device is acquired. Further, with reference to the area information, a circle with a predetermined length r as a radius and centered on the position where the roadside machine is placed is drawn for each roadside machine.
Note that the predetermined length r, which is the radius in the guideline range, may be 80 km, which is the maximum amount of travel information that can be recorded in the ETC 2.0 onboard device, or the distance of 80 km including curves, etc. In consideration of this, the range may be a circle with a radius of less than 80 km (for example, 50 km) from the roadside machine, instead of a circle of 80 km assuming a straight line distance from the roadside machine. Furthermore, a plurality of reference ranges may be shown, such as a circle with a radius of 80 km and a circle with a radius of less than 80 km. Further, the range of the guideline may not be a circle centered on the roadside machine position, but may be an arbitrary range based on the roadside machine position. For example, the calculated range may be set as a distance of 80 km from the roadside device position and reflect the actual road distance in consideration of road curves, etc., and this range may be indicated.

Ultimately, if roadside units are installed in all locations, it will be possible to collect all travel information, but this is difficult due to cost considerations, and the number of roadside units that can be installed is limited.
In addition, when installing a new roadside unit, for example, it is possible to cooperate with a private company and have it installed at a private business facility. For example, it is possible to cooperate through an arrangement in which a private company pays for data to be provided from the installed roadside equipment in exchange for having it installed at their own business facilities at a private company's expense.

Consider the case where new roadside units are installed at multiple business facilities managed by such private companies based on such cooperation.
FIG. 15 shows information on installation candidate facilities superimposed on the diagram shown in FIG. 14. Specifically, in FIG. 15, for the situation in FIG. 14, by referring to the roadside unit new installable facility information database 203, for example, shopping mall X, gas station Y, and convenience store Z are selected as installation candidate facilities. Extracted and shown. Of course, the types of installation candidate facilities are not limited to these, and any type can be set.

The example shown in FIG. 16 shows a situation where a roadside unit is newly installed at gas station Y among these candidate facilities. As shown in Figure 16, a hollow diamond is placed at each gas station Y to indicate the installation location of a new roadside unit, and based on the location of each gas station Y, a guideline for obtaining driving information is placed. The range is indicated by a dotted line.

Regarding the situation shown in Figure 16, based on the traveling information collected by a method different from the method corresponding to ETC 2.0, we have hypothetically assumed the traveling information collection situation when providing services related to ETC 2.0. By evaluating it, it is possible to judge the feasibility, effectiveness, etc. of services related to ETC 2.0. These processes will be specifically explained below.

<Processing>
Assuming that a new roadside unit is installed, we will explain below the process of acquiring the degree of collection of driving information under that assumed situation, and the feasibility of services related to ETC 2.0 by evaluating the degree of collection. Processing for determining the following will be explained.
FIG. 17 is a diagram showing an example of the flow of the second ETC 2.0 service evaluation support process performed by the processing unit 110 of the server 100 in this embodiment. Note that the second ETC 2.0 service evaluation support process has some of the same content as the first ETC 2.0 service evaluation support process, so a description of the similar content will be omitted.
Similarly to the first ETC 2.0 service evaluation support process, the processing unit 110 performs the following process for each cigarette socket type device measurement data included in the cigarette socket type device measurement database 192, for example.

First, the processing unit 110 processes the driving information included in the cigarette socket type device measurement data, for example, in order from the oldest driving information in chronological order (of course, it may start from the newest driving information, and the order does not matter), as follows: Process.
The processing unit 110 determines whether any of the travel information for a predetermined distance (for example, 80 km) after the travel information is included in the roadside device area of an existing roadside device or a newly installed roadside device. A determination is made (S31). In other words, as mentioned above, the ETC 2.0 on-vehicle device may only be able to record travel information for a predetermined distance, such as a maximum of 80 km, and travel information for a distance exceeding the predetermined distance may not be recorded. This is to determine whether such traveling information will ultimately be acquired by the roadside device or whether it will be discarded without being recorded.

If it is determined that the travel information is included in the roadside device area (S31: YES), the processing unit 110 sets a transmission flag (collection flag) “ON” to the travel information that is determined to be included in the roadside device area. (S32).

On the other hand, if it is determined that the vehicle does not pass through the roadside machine area (S31: NO), no flag is set in the travel information (the transmission flag is "OFF").

After S31 or S32, the processing unit 110 determines whether to end the process (S33). That is, it is determined whether there is any remaining travel information to be processed, and if so, the process is continued; if not, the process is ended. (S33: NO), the process returns to S31.
On the other hand, if it is determined to end the process (S33: YES), the processing unit 110 ends the second ETC2.0 service evaluation support process.

In addition, in this embodiment, in each travel information, the determination criterion is whether or not the roadside device area is passed in the subsequent travel information for a predetermined distance, but the determination criterion is not limited to this, and the determination criterion is not limited to this. Any criteria based on the above criteria can be applied. For example, the determination criterion may be whether each piece of travel information is included in an area that is a guideline for the travel information acquisition position range described above. Alternatively, for example, the determination criterion may be whether the range is within a predetermined distance (for example, 80 km) from the roadside device position, and is included in a range that reflects the actual road distance in consideration of road curves and the like.

Similarly to the second embodiment, in the third embodiment, information indicating the degree of collection (hereinafter referred to as "collection degree information ”) is calculated. For example, a histogram similar to that shown in FIGS. 11 and 12 in the second embodiment is created. Then, for example, the processing unit 110 appropriately sets a threshold value for the estimated collection time (for example, "3 hours"), and calculates the proportion of the number of vehicles whose estimated collection time is equal to or greater than this threshold value.

Note that the method for evaluating the collection degree information is not limited to the method using the threshold value of the assumed collection time mentioned above, and any method can be applied. For example, collection degree information may be obtained for each region, and an evaluation may be performed such as whether the collection degree is greater than or equal to a predetermined threshold in an area.
Furthermore, the collection degree information may be evaluated by, for example, visually representing the locations where travel information was acquired on map information, and having the user visually confirm the distribution of these locations. Thereby, it is possible to evaluate whether travel information is collected in a specific area, whether travel information is collected uniformly in a specific area (or all areas), etc.

<Determination of feasibility of specified business>
Then, using the collection degree information such as the histogram created by the server 100 as described above, it is also possible to determine the feasibility of a predetermined business based on ETC 2.0, as in the second embodiment. .

In determining the viability of a given business, the following determination method can be applied, by way of example and not limitation.
The processing unit 110 sets, for example, a period of "2 months" as the measurement period of the cigarette lighter socket type device measurement data, and the collection degree information calculated based on the measurement data during the measurement period is determined based on some condition related to the realization of the predetermined business. (For example, if the estimated collection time threshold is set to 3 hours, and the ratio of the number of vehicles to the total number of vehicles for which the estimated collection time exceeds 3 hours is 75%, and the specified business is being carried out) 70%), it is determined that the predetermined business can be established.
On the other hand, if the conditions are not met, it is determined that it is difficult to establish the predetermined business.

For example, prepare multiple installation patterns for new roadside units (for example, pattern 1: install at "gas station Y", pattern 2: install at "shopping mall X" and "gas station Y", pattern 3: install at "gas station Y", : Installed at "Shopping Mall That is, for example, even if it is determined that it is difficult to establish a predetermined business in pattern 1, if it is determined that the predetermined business can be established in pattern 2 or pattern 3, then based on pattern 2 or pattern 3, A decision can be made to install a new roadside unit.

<Modified example>
In the above example, the collection degree information is calculated under the assumption that a new roadside unit is installed in the first pattern, which is a combination of roadside units, and the combination of roadside units that is different from the first pattern. The second pattern may be compared with the collection degree information under an assumed situation in which a new roadside unit is installed, and the difference may be determined. For example, in a situation where a period of "2 months" is set as the measurement period, the ratio of the number of vehicles to the total number of vehicles for which the expected collection time in the former case exceeds "3 hours" is "75%", and the expected collection time in the latter case is 75%. If the ratio of the number of vehicles to the total number of vehicles for which the time exceeds "3 hours" is "70%", it can be seen that the difference is 5 points. The 5 points of this difference can be said to be the difference in effect when a new roadside unit is installed in the first pattern and the second pattern.

Furthermore, in the above embodiment, the collection degree information calculated under the assumed situation where a new roadside unit is installed is compared with the collection degree information under a situation where no new roadside unit is installed, It is also possible to calculate the difference. For example, in a situation where a period of "2 months" is set as the measurement period, the ratio of the number of vehicles to the total number of vehicles for which the expected collection time in the former case exceeds "3 hours" is "75%", and the expected collection time in the latter case is 75%. It can be seen that when the ratio of the number of vehicles to the total number of vehicles for which the time exceeds "3 hours" is "65%", the difference is 10 points. This difference of 10 points can be said to be the effect of installing a new roadside unit.

Furthermore, for example, an optimization method can be used to identify a new roadside unit from among the candidate facilities where new roadside units can be installed, so that the collection level is as high as possible while installing as few new roadside units as possible. may be used to specify a new combination of roadside units to be installed. In other words, based on the location information of existing roadside units, the location information of candidate facilities where new roadside units can be installed, and travel information, an optimization method such as the steepest descent method is used, as an example and not a limitation. Identify candidate combinations of facilities where new roadside units can be installed.

Furthermore, for example, when selecting a new roadside machine to be processed, the new roadside machine to be installed may be differentiated by business type, region, etc., as described above. In area A, the search is limited to gas station Y and shopping mall X, and in area B, all candidates for gas station Y, shopping mall X, and convenience store Z are extracted.'' Furthermore, any one (or any plurality) may be specified as a new roadside machine installation target. Note that the collection degree information may be calculated for patterns of combinations of a plurality of candidates, and a new combination of roadside machine candidates for which the collection degree information is optimal may be determined.

Further, there may be cases where, for example, the location where a new roadside unit can be installed is almost the same as an existing roadside unit, and even if it is installed, no significant additional effect can be expected. Taking such a case into consideration, the collection degree information may be calculated in a manner that does not include any new roadside devices. In other words, based on the location information of existing roadside units, user input, etc., those that are not included in the newly installed roadside units are identified (for example, out of the set of "gas station Y", those that are not included are identified based on some criteria). It is also possible to calculate the collection degree information in a manner that does not include such roadside machines. Alternatively, collection degree information may be calculated for a plurality of patterns including patterns that do not include such specific roadside machines, and a pattern with optimal collection degree information may be determined.

<Effects of the third embodiment>
In this embodiment, the server 100 transmits the cigarette socket type device measurement data based on the location of the existing roadside device and the location of the facility where the new roadside device can be installed, based on the method compatible with ETC 2.0. The degree of collection of the measurement data is calculated by imitating the manner in which the measurement data is collected by each roadside machine. Furthermore, based on the degree of collection, the feasibility of a predetermined service related to ETC 2.0 is determined. In addition, the difference in the degree of collection when a new roadside unit is installed in different patterns, and the difference in the degree of collection between when a new roadside unit is installed and when no roadside unit is installed are determined, and the effect thereof is determined. Furthermore, a combination of facility candidates for installing new roadside units is determined using an optimization method, taking into consideration the installation cost of roadside units and the level of collection.
As a result, it is possible to evaluate the collection level under the assumption that a new roadside device is installed without installing a new roadside device.

<Fourth example>
In collecting driving information under a system based on ETC 2.0, we are installing new roadside devices (ITS spots) that acquire driving information from devices compatible with ETC 2.0 placed on vehicles. It can be expected to collect driving information. Therefore, an example of deriving the installation position of a new roadside device that can effectively collect travel information under a mechanism based on ETC 2.0 will be described as a fourth embodiment.
Note that the content described in the fourth embodiment is applicable to any of the other embodiments and other modifications.

<Data structure>
FIG. 18 is a diagram showing an example of information stored in the storage unit 190 of the server 100 in this embodiment.
The storage unit 190 includes, for example, a new roadside unit installation position derivation processing program 204 that is read by the processing unit 110 and executed as a process for deriving a new roadside unit installation position, a map information database 202, and a roadside unit information database. 198, a cigarette socket type device measurement database 192, and a facility information database 203 where new roadside devices can be installed are stored.
These data structures are the same as those in the storage unit 190 in the third embodiment except that the second ETC 2.0 service evaluation support processing program 201 has become the new roadside unit installation position derivation processing program 204, so the details will be explained below. Further explanation will be omitted.

FIG. 14 shows an example of the distribution of already installed ETC 2.0 roadside units on a map, which is generated with reference to the information stored in the map information database 201 and the roadside unit database 198, as described above. FIG.
Ultimately, if roadside units are installed in all locations, it will be possible to collect all driving information, but this is difficult due to cost considerations, and the number of roadside units that can be installed is limited. Therefore, it is necessary to install roadside machines in locations where collection efficiency is as high as possible.
In the example shown in FIG. 14, four roadside units are installed, but there is an area near the center of the map that is outside the approximate range of travel information acquisition by each roadside unit. This indicates that there may be many omissions in information acquisition.

FIG. 19 is a diagram showing a situation when a fifth roadside unit is newly installed compared to the situation in FIG. 14. As shown in FIG. 19, the position of the newly installed roadside unit is indicated by a hollow diamond, and the circle, which is a guideline for the traveling information position covered by the new roadside unit, is indicated by a dotted line. In this way, more effective collection of travel information can be expected by installing new roadside devices in such a manner as to fill in areas that cannot be covered by travel information acquisition using existing roadside devices.

<New roadside unit installation position derivation process>
Hereinafter, when installing a new roadside unit, a process for deriving its appropriate position will be described.
FIG. 20 is a flowchart illustrating an example of the flow of a new roadside device installation position derivation process executed by the processing unit 110 of the server 100 in this embodiment.

First, the processing unit 110 of the server 100 identifies an area where a new roadside unit is to be installed (S41). The region is specified, for example, by receiving a region designation input by a user via the operation unit 120 or the communication unit 150. In addition, specifying an area through user input may be, for example, specifying a range using a mouse operation or pinch operation, or specifying an address by inputting characters such as "3-chome, C-cho, B-city, A-prefecture." It may be something that does.
Note that the identification of the area where a new roadside unit is to be installed, which is performed in step S41, is not limited to the above-mentioned method, and any method may be applied. For example, based on the location information of the existing roadside unit, an area containing a group of areas that cannot be covered by the acquisition of travel information by the existing roadside unit is extracted, and the area is set as one of the extracted areas. It may also be a thing.

Next, with reference to the map information database 202 and the roadside device information database 198, information as shown in FIG. 19 corresponding to the area specified in S31 is displayed on the display unit 130 (S42). At this time, it is preferable to also display information on the approximate range of travel information acquisition by each roadside device.

Next, the location of the newly installed roadside unit is specified (S43). When specifying the location of a newly installed roadside unit, the user specifies the location by looking at the content presented via the display unit 130, etc. Alternatively, multiple recommended locations may be specified based on existing roadside unit location information, etc., and the user may be asked to specify one of the multiple recommended locations. It may also be a thing.
The latter case will be explained below using several examples.

<Identifies the location of the new roadside unit based on the location information of the existing roadside unit>
An example of a method for specifying the installation position of a new roadside unit is a method based on position information of existing roadside units existing in the vicinity.

As a specific method, by way of example and not limitation, the farthest position from each existing roadside unit within the designated area may be specified as the installation position of the new roadside unit, as shown in FIG. 21. . Alternatively, set a score that decreases as the distance from each existing roadside device increases (e.g., inversely proportional to the length of the distance), and based on the aggregate value of the scores (e.g., the total value is the lowest) It may also be specified as the installation position of a new roadside unit. Furthermore, by deriving a plurality of candidate positions using such a method, presenting the plurality of candidate positions to the user via the display unit 130, etc., and having the user select one of them, a new roadside device can be installed. It may also be specified as the installation position.
Furthermore, based on the approximate range of travel information acquisition by each roadside unit, the location farthest from the range may be specified as the installation position of the new roadside unit. Furthermore, the approximate range of driving information acquisition by each roadside device and the approximate range of driving information acquisition by a new roadside device (for example, a circular area with radius s; s=r or s≠r) The installation position of a new roadside unit may be specified by comparing the above and taking into consideration the smallness of the overlapping area.

<Identifies the location of the new roadside unit based on the density of location information of multiple mobile objects obtained separately>
Another example of a method for specifying the installation position of a new roadside device is a method based on the density of position information of a plurality of moving bodies that is separately acquired.

FIG. 22 further shows a superimposed display based on the density of positional information included in travel information measured by the cigarette socket type device with reference to the cigarette socket type device measurement database 192 for the situation in FIG. 14. This is a diagram. A circle indicates a position where position information is observed at a predetermined density (for example, 20 times/day) or more, and the larger the circle, the greater the density.
Based on the density of location information as shown in FIG. 22, for example, the location with the highest density may be specified as the installation location of a new roadside device, and the new roadside device may acquire traveling information. The roadside machine position where the density is highest in the range of the guideline may be specified as the installation position of the new roadside machine.
Furthermore, similar to the method described above, a plurality of candidate positions are derived using the method described above, the plurality of candidate positions are presented to the user via the display unit 130, etc., and the user selects one of them. This may be used to specify the installation position of a new roadside unit. In addition, we compared the approximate range of travel information acquisition by each existing roadside unit with the approximate range of travel information acquisition by the new roadside unit, and determined the installation location of the new roadside unit, taking into account the lack of overlapping areas. It may also be possible to specify.

Note that the position information used in this embodiment is included in the travel information stored in the cigarette socket type device measurement database 192, but is not limited thereto. For example, location information collected by other means may be used. This other means may be a device compatible with ETC 2.0.

<Identification based on the location of facilities where new roadside units can be installed>
Furthermore, as yet another example of a method for specifying the installation location of a new roadside unit, facilities where a new roadside unit can be newly installed are extracted in advance as installation candidate sites, and the new roadside unit is installed from the candidate installation site. It is also possible to specify the installation position of the.

FIG. 23 shows installation candidate locations superimposed near the center of the map shown in FIG. 14. In FIG. 23, for example, a shopping mall, a gas station, and a convenience store are extracted and shown as installation candidate locations with reference to the roadside unit new installable facility information database 203 for the situation in FIG. 14. Of course, the types of installation candidate sites are not limited to these, and any type can be set.
In the example shown in FIG. 23, among these candidate locations, a shopping mall is identified as the location of a new roadside unit to be installed, and there is a hollow mark in the shopping mall indicating that it is a new installation location. Diamond shapes are arranged, and dotted lines indicate the approximate range of travel information acquisition based on the location of the shopping mall.

In other words, as shown in Figure 23, the map information of the area where a new roadside unit is to be installed includes the existing roadside unit and the approximate range of travel information positions covered by the existing roadside unit, as well as installation candidates for the roadside unit. It is also possible to specify the installation position of a new roadside unit by presenting a superimposed image of the roadside unit to the user via the display unit 130 or the like, and having the user select one of the installation candidate sites. good.
Furthermore, similar to the method described above, the range of the range of travel information positions covered by each existing roadside unit is compared with the range of range of travel information positions expected to be covered by the new roadside unit, and the overlap is determined. The installation position of a new roadside unit may be specified by taking into account the small area where the roadside unit is to be installed.

Although the method of identifying the installation position of a new roadside unit has been described above, the method is not limited to these, and any method may be used. For example, a combination of multiple methods among the three methods described above may be used. That is, for example, in a state where the density information of the location where the traveling information is acquired and the installation candidate site information of the facility where the roadside unit can be installed are known in advance and are shown superimposed on the map information, such density information and installation candidate A plurality of new roadside unit installation candidate positions are derived based on the location information and shown to the user via the display unit 130 etc., and by selecting one of them, the user can select the new roadside unit installation position. It may also be specified.

In addition, in the above explanation, it is assumed that a certain amount of roadside units have already been installed, but this is not limited to this. good.
Furthermore, instead of only specifying the position of one new roadside machine, the positions of a plurality of new roadside machines may be specified. In other words, after identifying one location of a new roadside unit, the location of the next new roadside unit may be identified assuming that it has already been installed, or the locations of multiple new roadside units may be specified as described above. They may be specified simultaneously based on the specified elements.

As in the second embodiment, the expected degree of acquisition of travel information before and after installation of the new roadside unit is compared, and how much increase in the amount of travel information acquisition is expected is determined. By comparing the amount of acquired driving information after installation with a threshold value, it is possible to evaluate whether the acquisition status of driving information is sufficient or not. In turn, for example, it can be determined whether an insurance product business or the like is viable based on the amount of travel information expected to be acquired due to the installation of a new roadside device.

<Effects of the fourth embodiment>
In this embodiment, the server 100 creates new information based on user input, location information of existing roadside devices, density of location information obtained from multiple moving objects, location information of facilities where roadside devices can be installed, etc. Identify the location where the roadside unit will be installed.
As a result, it is possible to know in advance an effective position to install a roadside device that acquires driving information using a method compatible with ETC 2.0, without installing a new roadside device.

<Others>
The server 100 in the above embodiment is configured as a plurality of physically separated servers, and the first server performs some of the processing described in the above embodiment, and the second server performs other processing. You may also do something like this. A server system may be considered to be composed of a plurality of servers.

Further, the moving object is not limited to a four-wheeled vehicle, but may be a two-wheeled vehicle, a personal mobility vehicle, a ship, a railway, an aircraft, etc., as described above. In this case, the same processing as in the above embodiment can be performed by using various measuring devices as the first device and second device mounted or built in the moving body.

Furthermore, as mentioned above, for example, the measurement target is "sound", the sampling unit of the first device is "time", and the sampling unit of the second device is "frequency", and the same processing as above is performed. You may do so.
Further, for example, processing similar to the above may be performed using information regarding a living body (blood pressure, pulse rate, heart rate, respiratory rate, body temperature, brain waves, etc.) as a measurement target. In this case as well, the same processing as above may be performed, for example, by setting the sampling unit of the first device to "time" and setting the sampling unit of the second device to "frequency."

As an example of this application, when a user to be insured is driving a vehicle, information regarding the living body of the user is measured by the first device. Then, the server 100 generates estimated second device measurement data when information related to the living body of the user is measured by the second device, based on the first device measurement data in which information related to the living body is measured by the first device. You may also generate one.
Further, in this case, the travel information of the vehicle being driven by this user may be measured by a cigarette socket type device or the like, and the server 100 may generate the estimated ETC 2.0 measurement data described above. That is, the user to be insured and the vehicle may be linked, and the server 100 may generate estimated data of information regarding the living body in addition to generating estimated ETC 2.0 measurement data. In addition to evaluating the user's driving (driving quality), the user's health condition may also be evaluated.
Note that the estimated data of information regarding the user's biological body may be generated not only when driving but also at times other than driving.

In addition, cigarette socket type device measurement data and ETC 2.0 measurement data are acquired from a vehicle equipped with the cigarette socket type device measurement data and the ETC 2.0 onboard device. Then, in the estimated ETC2.0 measurement data generated based on the cigarette socket type device measurement data, the ratio of data loss to the actual ETC2.0 measurement data is calculated. Good too.
In addition, the score calculated by inputting the estimated ETC2.0 measurement data into the second driving score model is compared with the score calculated by inputting the actual ETC2.0 measurement data into the second driving score model. However, if the difference is less than or equal to a threshold (or less than a threshold), it may be determined that the estimated ETC2.0 measurement data is valid.

Furthermore, in the above embodiments, various programs and data related to various processes are stored in the storage unit, and the processing unit reads and executes these programs to perform the processes in each of the above embodiments. Realized. In this case, the storage section of each device includes internal storage devices such as ROM, EEPROM, flash memory, hard disk, and RAM, as well as memory cards (SD cards), compact flash (registered trademark) cards, memory sticks, USB memories, and CDs. - It may have a recording medium (recording medium, external storage device, storage medium) such as RW (optical disk) or MO (magneto-optical disk), and the various programs and data mentioned above are stored in these recording media. It may also be a thing.

100 server 110 processing section 120 operation section 130 display section 140 sound output section 150 communication section 160 clock section 190 storage section

Claims (28)

An information processing device,
An existing roadside unit position that is a roadside unit that collects information regarding the travel of a mobile object based on a method compatible with ETC 2.0, and that acquires existing roadside unit position information that is position information of an existing roadside unit that has already been installed. Information acquisition department;
a first installation candidate facility location information acquisition unit that acquires first installation candidate facility location information that is location information of at least one facility in which the roadside unit can be newly installed;
a travel information acquisition unit that acquires travel information that is collected based on a method different from the method compatible with ETC 2.0 and that includes at least information regarding the position of the moving object;
A first collection degree regarding the collection degree when it is assumed that the travel information is collected by the roadside device corresponding to the existing roadside device location information and the first installation candidate facility location information based on a method compatible with ETC 2.0. a calculation unit that calculates information;
An information processing device comprising: The information processing device according to claim 1,
a determination unit that determines the feasibility of a predetermined business based on the collection degree information;
An information processing device further comprising: The information processing device according to claim 1,
a second installation candidate facility location information acquisition unit that acquires second installation candidate facility location information that is location information about at least one facility different from the first installation candidate facility location information;
Equipped with
The calculation unit is
calculating second collection degree information regarding the degree of collection when the traveling information is collected by the roadside machine corresponding to the existing roadside machine position information and the second installation candidate facility position information;
calculating first difference information regarding a difference between the first collection degree information and the second collection degree information;
Information processing device. The information processing device according to claim 1,
The calculation unit is
Calculating third collection degree information regarding the collection degree assuming that the traveling information is collected by the roadside device corresponding to the existing roadside device position information,
calculating second difference information regarding a difference between the first collection degree information and the third collection degree information;
Information processing device. The information processing device according to claim 1,
Based on the existing roadside unit location information, the first installation candidate facility location information, and the travel information, an optimization method is used to determine the new location to be installed, taking into account the small number of installations and the high degree of collection. a identification unit that identifies a combination of roadside units;
An information processing device comprising: An information processing method,
A roadside unit that collects information regarding the travel of a mobile object based on a method compatible with ETC 2.0, and acquiring existing roadside unit position information that is position information of an existing roadside unit that has already been installed;
acquiring first installation candidate facility location information that is location information of at least one facility where the roadside unit can be newly installed;
Obtaining travel information that is collected based on a method different from a method compatible with ETC 2.0 and that includes at least information regarding the position of a mobile object;
A first collection degree regarding the collection degree when it is assumed that the travel information is collected by the roadside device corresponding to the existing roadside device location information and the first installation candidate facility location information based on a method compatible with ETC 2.0. calculating information;
information processing methods, including to the computer,
A roadside unit that collects information regarding the travel of a mobile object based on a method compatible with ETC 2.0, and acquiring existing roadside unit position information that is position information of an existing roadside unit that has already been installed;
acquiring first installation candidate facility location information that is location information of at least one facility where the roadside unit can be newly installed;
Obtaining travel information that is collected based on a method different from a method compatible with ETC 2.0 and that includes at least information regarding the position of a mobile object;
A first collection degree regarding the collection degree when it is assumed that the travel information is collected by the roadside device corresponding to the existing roadside device location information and the first installation candidate facility location information based on a method compatible with ETC 2.0. calculating information;
A program to run. An information processing device,
a mobile body position information acquisition unit that acquires mobile body position information from a first device provided in the mobile body based on a method different from a method compatible with ETC 2.0;
a density information acquisition unit that acquires density information of the mobile body position information based on the acquired mobile body position information;
an identification unit that identifies a position for installing a new roadside unit that acquires traveling information based on a method compatible with ETC 2.0 based on the acquired density information;
An information processing device comprising: The information processing device according to claim 8,
a roadside unit position information acquisition unit that acquires existing roadside unit position information that is position information of at least one existing roadside unit;
Equipped with
The identifying unit identifies a position for installing the new roadside unit based on the density information and the existing roadside unit position information.
Information processing device. The information processing device according to claim 8 or 9,
a facility location information acquisition unit that acquires facility location information that is location information of a facility where the new roadside unit can be installed;
Equipped with
The identifying unit identifies a location for installing the new roadside unit based on the density information, the existing roadside unit location information, and the facility location information.
Information processing device. An information processing device,
a roadside unit position information acquisition unit that acquires existing roadside unit position information that is position information of at least one existing roadside unit;
an output unit that outputs the roadside machine position information;
an identification unit that identifies a location for installing a new roadside unit based on a user input performed in response to the output;
An information processing device comprising: The information processing device according to claim 11,
a mobile body position information acquisition unit that acquires mobile body position information from a first device provided in the mobile body;
a density information acquisition unit that acquires density information of the mobile body position information based on the acquired mobile body position information;
further comprising;
The output unit outputs the roadside machine position information and the density information,
The identification unit identifies a location for installing a new roadside unit based on a user input to the output.
Information processing device. The information processing device according to claim 11 or 12,
a guideline range information acquisition unit that acquires guideline range information regarding a guideline range for travel information acquisition by at least one roadside machine based on the roadside machine position information;
Equipped with
The output unit outputs the roadside machine position information, the density information, and the guide range information,
The identification unit identifies a position for installing a new roadside unit based on a user input to the output.
Information processing device. An information processing device,
an existing roadside machine position information acquisition unit that acquires existing roadside machine position information that is position information of at least one existing roadside machine that acquires travel information based on a method compatible with ETC 2.0;
an identification unit that identifies a position for installing a new roadside unit based on the existing roadside unit position information;
An information processing device comprising: The information processing device according to claim 14,
The identifying unit identifies a position for installing the new roadside machine based on a distance from the at least one existing roadside machine.
Information processing device. The information processing device according to claim 14,
The specifying unit specifies a position for installing the new roadside unit based on a first range based on the position of the at least one existing roadside unit.
Information processing device. The information processing device according to claim 14,
The identifying unit determines a position for installing the new roadside unit based on an overlapping range between a first range based on the position of the at least one existing roadside unit and a second range based on the position of the new roadside unit. identify,
Information processing device. An information processing device,
a facility location information acquisition unit that acquires facility location information that is location information of a facility where a roadside unit that acquires travel information based on a method compatible with ETC 2.0 can be newly installed;
an identification unit that identifies a location for installing the new roadside unit based on the facility location information;
An information processing device comprising: The information processing device according to claim 18,
a roadside unit position information acquisition unit that acquires existing roadside unit position information that is position information of at least one existing roadside unit;
Equipped with
The identifying unit identifies a location for installing the new roadside unit based on the facility location information and the existing roadside unit location information.
Information processing device. The information processing device according to any one of claims 8, 11, 14 and 18,
Traveling information acquisition degree acquisition for acquiring acquisition degree information regarding the acquisition degree when traveling information is acquired based on a method compatible with ETC 2.0, assuming that the new roadside unit is installed at the specified location. Department and
a determination unit that determines whether a predetermined business is established based on the acquired acquisition degree information;
An information processing device comprising: An information processing method,
Obtaining mobile body position information from a first device provided in the mobile body based on a method different from a method compatible with ETC 2.0;
Obtaining density information of the mobile body position information based on the acquired mobile body position information;
Based on the acquired density information, specifying a position for installing a new roadside device that acquires traveling information based on a method compatible with ETC 2.0;
information processing methods, including An information processing method,
Obtaining existing roadside unit position information that is position information of at least one existing roadside unit;
outputting the roadside machine position information;
identifying a location for installing a new roadside unit based on user input performed in response to the output;
information processing methods, including An information processing method,
acquiring existing roadside unit position information that is position information of at least one existing roadside unit that acquires travel information based on a method compatible with ETC 2.0;
Identifying a position for installing a new roadside unit based on the existing roadside unit position information;
Information processing equipment, including. An information processing method,
Obtaining facility location information that is location information of a facility where a roadside unit that obtains travel information based on a method compatible with ETC 2.0 can be newly installed;
Identifying a location for installing the new roadside unit based on the facility location information;
information processing methods, including to the computer,
Obtaining mobile body position information from a first device provided in the mobile body based on a method different from a method compatible with ETC 2.0;
Obtaining density information of the mobile body position information based on the acquired mobile body position information;
Based on the acquired density information, specifying a position for installing a new roadside device that acquires traveling information based on a method compatible with ETC 2.0;
A program to run. to the computer,
Obtaining existing roadside unit position information that is position information of at least one existing roadside unit;
outputting the roadside machine position information;
identifying a location for installing a new roadside unit based on user input performed in response to the output;
A program to run. to the computer,
acquiring existing roadside unit position information that is position information of at least one existing roadside unit that acquires travel information based on a method compatible with ETC 2.0;
Identifying a position for installing a new roadside unit based on the existing roadside unit position information;
A program to run. to the computer,
Obtaining facility location information that is location information of a facility where a roadside unit that obtains travel information based on a method compatible with ETC 2.0 can be newly installed;
Identifying a location for installing the new roadside unit based on the facility location information;
A program to run.

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