JP2023074415A - Operator specifying device, operator specifying method, and operator specifying program - Google Patents

Abstract

To provide an operator specifying device capable of suppressing cost and weight when specifying an operator, and to provide an operator specifying method and an operator specifying program therefor.SOLUTION: An operator specifying device comprises an acquisition unit for acquiring a feature amount indicating a feature when operating a vehicle for each time series; a determination unit for determining an operator for every divided data, using the divided data obtained by dividing the acquired feature amount for every predetermined period; and a specification unit for specifying the operator from a determined result for each of the divided data.SELECTED DRAWING: Figure 5

Description

The present invention relates to a driver identification device, a driver identification method, and a driver identification program for identifying a driver who is driving a vehicle.

In Patent Document 1, the number of times the driver's driving deviates from at least one of safe driving and fuel-efficient driving is counted as the number of violations, and when the number of violations exceeds a threshold value, driving diagnosis management that transmits to a management terminal. A system is disclosed.

JP-A-09-011857

When one vehicle is used by a plurality of drivers, it is necessary to specify the drivers in order to perform driving evaluation for each driver. However, when a driver is identified by mounting a dedicated authentication device equipped with a camera or the like, it is necessary to provide the camera and the authentication device, which may increase cost and weight.

SUMMARY OF THE INVENTION It is an object of the present invention to provide a driver identification device, a driver identification method, and a driver identification program that can reduce costs and weight when identifying a driver.

The driver identification device according to claim 1 comprises an acquisition unit that acquires, for each time series, a feature amount indicating characteristics when driving a vehicle, and a division that divides the acquired feature amount for each predetermined period. A determination unit that uses data to determine a driver for each of the divided data, and an identification unit that identifies the driver based on the determination result for each of the divided data.

A driver identification device according to claim 1 identifies a driver who drives a vehicle from characteristics of a driving operation for driving the vehicle. The driver identification device acquires a feature amount indicating a characteristic of driving a vehicle for each time series, and uses divided data obtained by dividing the feature amount for each predetermined period to determine driving for each of the divided data. The driver is determined using the determination result determined for each divided data. In other words, according to the driver identification device, the driver is identified using the characteristic amount of driving indicated by the driving information obtained in advance from the sensor connected to the vehicle-mounted device. As a result, cost and weight can be suppressed when identifying the driver.

The driver identification device according to claim 2 is the driver identification device according to claim 1, wherein the determination unit is a machine for determining the driver from past divided data relating to past driving by the driver. A driver is determined using the trained model that has been trained.

According to the driver identification device of claim 2, it is possible to identify the driver by utilizing divided data relating to driving acquired in the past.

The driver identification device according to claim 3 is the driver identification device according to claim 1 or 2, wherein the identification unit aggregates the drivers indicated by the determination results for each of the divided data, and the aggregate number is Identify the driver with the most number of drivers as the driver of the vehicle.

According to the driver identification device of claim 3, the driver can be identified even if there is an error in the determination of one piece of divided data. That is, the accuracy of specifying the driver is improved.

The driver identification device according to claim 4 is the driver identification device according to claim 3, wherein the data accuracy of the divided data is set for each period, and the identification unit has the same number of totals. In such a case, the driver indicated by the determination result in the divided data with high data accuracy is identified as the driver of the vehicle.

According to the driver identification device of claim 4, even if the drivers have similar features in each divided data, the driver can be identified.

The driver identification device according to claim 5 is the driver identification device according to any one of claims 1 to 4, wherein the divided data overlap in the predetermined period. is divided as follows:

According to the driver identification device of claim 5, the feature quantity indicating the driver is included in each divided data, and the accuracy of identifying the driver is improved.

In the driver identification method according to claim 6, a feature amount indicating the characteristics of driving a vehicle is acquired for each time series, and divided data obtained by dividing the acquired feature amount for each predetermined period is used. Then, the driver is determined for each of the divided data, and the driver is specified from the determination result for each of the divided data.

The driver identification method according to claim 6 identifies the driver who drives the vehicle from the characteristics of the driving operation of driving the vehicle. The method for identifying a driver is to acquire a feature quantity indicating a characteristic of driving a vehicle for each time series, divide the feature quantity into predetermined periods, and use divided data to determine driving for each of the divided data. The driver is determined using the determination result determined for each divided data. In other words, according to the driver identification method, the driver is identified using the driving feature quantity indicated by the driving information previously acquired from the sensor connected to the vehicle-mounted device. As a result, cost and weight can be suppressed when identifying the driver.

A program for identifying a driver according to claim 7 acquires a feature amount indicating a characteristic of driving a vehicle for each time series, and uses divided data obtained by dividing the acquired feature amount for each predetermined period. Then, the computer determines the driver for each of the divided data and specifies the driver from the determination result for each of the divided data.

A computer on which the driver identification program according to claim 7 is executed identifies the driver who drives the vehicle from the characteristics of the driving operation for driving the vehicle. The computer acquires a feature amount indicating characteristics when driving a vehicle for each time series, and uses divided data obtained by dividing the feature amount for each predetermined period to determine the driver's performance for each divided data. A determination is made, and the driver is specified using the determination result determined for each divided data. In other words, according to the computer, the driver is identified using the driving feature quantity indicated by the driving information previously acquired from the sensor connected to the vehicle-mounted device. As a result, cost and weight can be suppressed when identifying the driver.

ADVANTAGE OF THE INVENTION According to this invention, when specifying a driver|operator, cost and weight can be suppressed.

1 is a diagram showing a schematic configuration of a driver identification system according to an embodiment; FIG. 1 is a block diagram showing the hardware configuration of a vehicle according to an embodiment; FIG. It is a block diagram which shows the functional structure of the onboard equipment of embodiment. It is a block diagram which shows the hardware constitutions of the center server of embodiment. It is a block diagram showing a functional configuration of a center server of the embodiment. It is a figure which shows an example of the driving|running information with which it uses for description of the division|segmentation of the data which concerns on embodiment. FIG. 5 is a data flow diagram showing an example of data flow of processing executed in the center server of the embodiment; 4 is a flowchart showing the flow of driver identification processing executed in the center server of the embodiment; 4 is a flow chart showing a flow of processing for generating a trained model executed in the center server of the embodiment;

A driver identification system including the driver identification device of the present invention will be described. A driver identification system is a system that identifies a driver who is driving a vehicle by using information related to driving operations (hereinafter referred to as "driving information") obtained from onboard equipment installed in the vehicle. be.

(overall structure)
As shown in FIG. 1, a driver identification system 10 according to an embodiment of the present invention includes a vehicle 12 and a center server 30 as a driver identification device. A vehicle-mounted device 20 is mounted on the vehicle 12 , and the vehicle-mounted devices 20 are mutually connected to a center server 30 through a network N.

Although FIG. 1 shows one vehicle 12 including the vehicle-mounted device 20 for one center server 30, the number of vehicles 12, vehicle-mounted device 20, and center server 30 is limited to do not have.

The vehicle-mounted device 20 is a device that acquires driving information regarding the operation of the vehicle 12 and transmits the driving information to the center server 30 . Here, the driving information according to the present embodiment is a feature amount related to driving operation detected from each device mounted on the vehicle 12 . For example, the driving information according to the present embodiment is a feature amount (data) related to the operation of the vehicle 12, such as the force applied to the accelerator and the brake, the steering angle in the steering, and switching of the turn signals.

The center server 30 is installed, for example, in a manufacturer that manufactures the vehicle 12 or a car dealer affiliated with the manufacturer. The center server 30 acquires the driving information from the vehicle-mounted device 20 and identifies the driver related to the driving information.

(vehicle)
As shown in FIG. 2 , the vehicle 12 according to this embodiment includes an on-board device 20 , a plurality of ECUs (Electronic Control Units) 22 , and a plurality of on-board devices 24 .

The vehicle-mounted device 20 includes a CPU (Central Processing Unit) 20A, a ROM (Read Only Memory) 20B, a RAM (Random Access Memory) 20C, an in-vehicle communication I/F (Interface) 20D, and a wireless communication I/F 20E. ing. The CPU 20A, ROM 20B, RAM 20C, in-vehicle communication I/F 20D, and wireless communication I/F 20E are communicably connected to each other via an internal bus 20G.

The CPU 20A is a central processing unit that executes various programs and controls each section. That is, the CPU 20A reads a program from the ROM 20B and executes the program using the RAM 20C as a work area.

The ROM 20B stores various programs and various data. The ROM 20</b>B of the present embodiment stores a collection program 100 for collecting driving information related to the driving operation of the vehicle 12 from the ECU 22 . Along with execution of the collection program 100 , the vehicle-mounted device 20 executes processing for transmitting driving information to the center server 30 . The ROM 20B also stores history information 110, which is backup data of the driving information. The RAM 20C temporarily stores programs or data as a work area.

In-vehicle communication I/F 20D is an interface for connecting with each ECU 22 . A communication standard based on the CAN protocol is used for the interface. In-vehicle communication I/F 20D is connected to external bus 20F.

Wireless communication I/F 20E is a wireless communication module for communicating with center server 30 . The wireless communication module uses communication standards such as 5G, LTE, Wi-Fi (registered trademark), for example. A wireless communication I/F 20E is connected to the network N.

The ECU 22 includes at least an ADAS (Advanced Driver Assistance System)-ECU 22A, a steering ECU 22B, a brake ECU 22C and an engine ECU 22D.

The ADAS-ECU 22A centrally controls the advanced driving support system. A vehicle speed sensor 24A, a yaw rate sensor 24B, and an external sensor 24C, which constitute the in-vehicle device 24, are connected to the ADAS-ECU 22A. The external sensor 24</b>C is a sensor group used for detecting the surrounding environment of the vehicle 12 . The external sensor 24C includes, for example, a camera that images the surroundings of the vehicle 12, a millimeter wave radar that transmits search waves and receives reflected waves, and a lidar (Laser Imaging Detection and Ranging) that scans ahead of the vehicle 12. included.

The steering ECU 22B controls power steering. A steering angle sensor 24D and a winker switch 24E, which constitute the in-vehicle device 24, are connected to the steering ECU 22B. The steering angle sensor 24D is a sensor that detects the steering angle of the steering wheel, and the winker switch 24E is a switch for operating the winkers.

Brake ECU 22C controls the brake system of vehicle 12 . The brake ECU 22C is connected to a brake sensor 24F that constitutes the vehicle-mounted device 24 and detects the depression force of the brake pedal.

Engine ECU 22</b>D controls the engine of vehicle 12 . The engine ECU 22D is connected to an accelerator sensor 24G and sensors 24H that constitute the in-vehicle device 24 . The accelerator sensor 24G detects the accelerator opening. The sensors 24H include an oil temperature sensor for measuring the oil temperature of the engine oil, a hydraulic pressure sensor for measuring the oil pressure of the engine oil, and a rotation sensor for detecting the number of revolutions of the engine.

As shown in FIG. 3 , in the vehicle-mounted device 20 of this embodiment, the CPU 20A functions as a collection unit 200 and an output unit 210 by executing the collection program 100 .

The collection unit 200 has a function of acquiring information detected by the in-vehicle device 24 from each ECU 22 of the vehicle 12, evaluating the driving operation using the information, and collecting driving information.

The output unit 210 has a function of outputting the driving information collected by the collection unit 200 to the center server 30 .

(Center server)
As shown in FIG. 4, the center server 30 includes a CPU 30A, a ROM 30B, a RAM 30C, a storage 30D and a communication I/F 30E. The CPU 30A, ROM 30B, RAM 30C, storage 30D and communication I/F 30E are communicably connected to each other via an internal bus 30F. The functions of the CPU 30A, ROM 30B, RAM 30C, and communication I/F 30E are the same as those of the CPU 20A, ROM 20B, RAM 20C, and wireless communication I/F 20E of the vehicle-mounted device 20 described above. Note that the communication I/F 30E may perform wired communication.

A storage 30D as a memory is configured by a HDD (Hard Disk Drive) or an SSD (Solid State Drive), and stores various programs and various data. A driver identification program 130, a driving information database (hereinafter referred to as "driving information DB") 140, a learned model 150, and learning data 160 are stored in the storage 30D of this embodiment. Note that the ROM 30B may store the driver identification program 130, the driving information DB 140, the learned model 150, and the learning data 160.

Driver identification program 130 as a program is a program for controlling center server 30 . Along with execution of the driver identification program 130, the center server 30 executes various processes including identification processing for identifying the driver from the driving information.

The driving information DB 140 stores driving information received from the vehicle-mounted device 20 .

The learned model 150 is a learned model that has undergone machine learning to identify the driver using the driving information acquired from the vehicle-mounted device 20 . For example, the learned model 150 learns a feature amount related to driving operation as input data, and a label indicating the driver who performed the driving operation related to the driving information as correct data. In addition, the learned model 150 outputs the driver related to the driving information when the driving information is input.

The learning data 160 is data for making the trained model 150 learn. The learning data 160 is data in which previously acquired and pre-stored driving information is used as input data, and a label indicating the driver corresponding to the driving information is stored as teacher data.

As shown in FIG. 5 , in the center server 30 of the present embodiment, the CPU 30A executes the driver identification program 130 to obtain an acquisition unit 300, a determination unit 310, an identification unit 320, an output unit 330, and a learning unit 340. , and the storage unit 350 .

The acquisition unit 300 has a function of acquiring driving information transmitted from the vehicle-mounted device 20 of the vehicle 12 . Here, the acquiring unit 300 according to the present embodiment acquires, as the driving information, time-series data related to the driving operation until four hours have passed since the start of driving.

The determination unit 310 uses the learned model to determine the driver related to the driving information. Specifically, the determination unit 310 determines the driving operation such as sudden acceleration, sudden braking, sudden steering, reverse operation, turn signal operation, brake operation, and both depressing the accelerator pedal and the brake pedal included in the acquired driving information. Evaluate features. That is, the determination unit 310 evaluates the acquired driving information, and quantifies the feature amount related to each driving operation as an evaluation result.

The determination unit 310 quantifies the evaluation results obtained by quantifying the feature values for 4 hours of data (hereinafter referred to as “4 hours data”), 3 hours of data (hereinafter referred to as “3 hours data”), and 2 hours of data. divided into minute data (hereinafter referred to as "two-hour data"). Using the trained model 150, the determination unit 310 determines the driver for each divided data from the quantified feature amount included in the data divided into each period (hereinafter referred to as "divided data"). do.

As an example, as shown in FIG. 6, the determination unit 310 divides the evaluation result into divided data of 4-hour data, 3-hour data, and 2-hour data. The determination unit 310 identifies the driver for each of the six pieces of divided data shown in FIG.

Here, when dividing the data into the 3-hour data and the 2-hour data, the determination unit 310 divides the divided data so that the periods overlap. For example, as shown in FIG. 6, two 3-hour data are divided such that 2 of the 3 hours overlap. Also, the three two-hour data are divided so that one hour of the two hours overlaps. By dividing the data so that each data overlaps, each data includes a characteristic amount indicating the driver, thereby improving the accuracy of identifying the driver.

The identification unit 320 identifies the driver related to the driving information using the determination result obtained by the determination unit 310 . Specifically, the identification unit 320 totalizes the drivers determined for each of the six pieces of divided data shown in FIG. 6, and identifies the driver with the highest total count as the driver related to the driving information.

Here, when the total numbers are the same, the identification unit 320 identifies the driver indicated by the 4-hour data with the highest data accuracy as the driver related to the driving information. In the present embodiment, it is assumed that the accuracy is higher in the order of 4-hour data, 3-hour data, and 2-hour data, which have a longer period in the data and contain more feature amounts.

The output unit 330 outputs the driver associated with the driving information identified by the identification unit 320 as the identification result. Here, the output unit 330 may transmit and output the identification result to the vehicle-mounted device 20 or the like, or display and output the identification result on a monitor (not shown) provided in the center server 30 .

Learning unit 340 uses learning data 160 stored in storage 30D to generate learned model 150 that has undergone machine learning for determining a driver. Specifically, the learning unit 340 quantifies and evaluates the feature amount of the driving information, which is included in the learning data 160 and which is acquired in advance from the vehicle-mounted device 20 . The learning unit 340 generates the trained model 150 by executing supervised learning using the driving information evaluation result as input data and the driver's label corresponding to the driving information as teacher data. Here, the learning unit 340 divides the evaluation result into divided data of 4-hour data, 3-hour data, and 2-hour data, and performs machine learning on each of the divided data.

The storage unit 350 stores the trained model 150 generated by the learning unit 340 . The storage unit 350 also stores, as learned data, evaluation results of driving information and the driver associated with the driving information identified by the identifying unit 320 .

Before explaining the operation of the driver identification system 10, the flow of data in the center server 30 as a driver identification device will be described with reference to FIG. FIG. 7 is a data flow diagram showing an example of data flow in the center server 30. As shown in FIG.

As an example, as shown in FIG. 7, the learning unit 340 uses learning data 160 stored in the storage 30D to generate a trained model 150, and the storage unit 350 stores the generated trained model 150. do.

The acquisition unit 300 acquires driving information from the vehicle-mounted device 20 and inputs the acquired driving information to the determination unit 310 .

The determination unit 310 evaluates the input driving information, divides the evaluation result into each divided data, and uses the stored learned model 150 to determine the driver for each divided data. . The determination unit 310 inputs the driver associated with each divided data to the identification unit 320 as a determination result.

The identification unit 320 aggregates the drivers determined by the determination unit 310 and inputs the driver with the largest total number to the output unit 330 as the driver related to the driving information.

The output unit 330 outputs the driver associated with the input driving information to the monitor as the identification result.

(control flow)
The flow of each process executed by the driver identification system 10 of this embodiment will be described with reference to the flowcharts of FIGS. 8 and 9. FIG. Each process in the center server 30 is executed by the CPU 30A of the center server 30 functioning as an acquisition unit 300, a determination unit 310, an identification unit 320, an output unit 330, a learning unit 340, and a storage unit 350. The identifying process shown in FIG. 8 is executed, for example, when an instruction to identify the driver is input.

In step S100, CPU 30A acquires learned model 150 stored.

In step S<b>101 , the CPU 30</b>A acquires driving information from the vehicle-mounted device 20 .

In step S102, the CPU 30A evaluates and quantifies the feature amount included in the acquired driving information as an evaluation result.

In step S103, the CPU 30A divides the evaluation result into divided data of 4-hour data, 3-hour data, and 2-hour data.

In step S104, CPU 30A determines the driver associated with each divided data.

In step S105, the CPU 30A tallies the determined drivers.

In step S106, the CPU 30A determines whether or not the driver with the highest count is one and the driver can be identified as one. If the driver can be identified as one (step S106: YES), the CPU 30A proceeds to step S107. On the other hand, if the driver cannot be identified as one (step S106: NO), the CPU 30A proceeds to step S108.

In step S107, the CPU 30A identifies the driver with the largest count as the driver related to the driving information.

In step S108, the CPU 30A identifies the driver indicated by the data with the highest accuracy (for example, 4-hour data) as the driver related to the driving information.

In step S109, CPU 30A outputs the specified driver.

Next, the process of generating the learned model 150 executed by the driver identification system 10 of this embodiment will be described using the flowchart of FIG. The learning process shown in FIG. 9 is executed, for example, when an instruction to execute the process of generating the trained model 150 is input.

In step S200, CPU 30A acquires learning data stored in advance. The learning data is information including driving information obtained in advance from the vehicle-mounted device 20 and a label indicating the driver related to the driving information.

In step S201, the CPU 30A evaluates the driving information included in the learning data and quantifies the feature quantity as the evaluation result.

In step S202, CPU 30A divides the evaluation result into divided data.

In step S203, CPU 30A generates trained model 150 using each divided data.

In step S204, CPU 30A determines whether or not to end the process of generating learned model 150. FIG. If the process of generating the learned model 150 is to end (step S204: YES), the process proceeds to step S205. On the other hand, if the process of generating learned model 150 is not finished (step S204: NO), CPU 30A proceeds to step S200 and acquires learning data.

In step S205, CPU 30A stores the generated learned model 150 in storage 30D and terminates the process of generating learned model 150. FIG.

As described above, according to the present embodiment, it is possible to reduce cost and weight when identifying a driver.

(summary)
The center server 30 as a driver identification device of the present embodiment acquires feature amounts indicating characteristics of driving a vehicle for each time series, and divides the feature amounts for each predetermined period to obtain divided data. is used to determine the driver for each divided data. The driver identification device identifies the driver using the determination result determined for each divided data. In other words, according to the driver identification device, the driver is identified using the driving characteristic quantity indicated by the driving information acquired from the sensor connected to the vehicle-mounted device. As a result, cost and weight can be suppressed when identifying the driver.

[remarks]
In the present embodiment, the center server 30 acquires the feature amount (data) related to the driving operation as the driving information. However, it is not limited to this. The center server may evaluate the driving information and acquire numerical evaluation results. For example, the vehicle-mounted device 20 may perform evaluation using data related to driving operation and digitize the feature amount, and the center server 30 may acquire the digitized feature amount from the vehicle-mounted device 20 .

In addition, in the present embodiment, a form in which four hours of time-series data relating to driving operations is acquired as driving information has been described. However, it is not limited to this. Any period may be used as long as it is time-series data. For example, as driving information, time-series data for 30 minutes may be acquired, or time-series data for one day may be acquired.

In addition, in the present embodiment, a form will be described in which the drivers determined for each piece of divided data are tallied, and when the tallied numbers are the same, the driver indicated by the data with the highest accuracy is specified as the driver related to the driving information. bottom. However, it is not limited to this. For example, for each piece of divided data, a set value is set such that the higher the accuracy of the data, the larger the value, the set values are aggregated for each driver, and the driver with the largest number of totals is regarded as the driver related to the driving information. may be specified.

Also, in the present embodiment, a mode of determining a driver using a learned model has been described. However, it is not limited to this. The driver may be determined by detecting a characteristic amount indicating the driver from the data related to the driving information by pattern matching. For example, if each driver has characteristics in the timing of taking a break without driving, the characteristics of the timing of taking a break for each driver are stored in advance, and the characteristics are detected from the driving information by pattern matching. If so, the driver indicated by the feature may be identified.

Further, in the present embodiment, a mode in which the center server 30 is equipped with a driver identification device has been described. However, it is not limited to this. The vehicle-mounted device 20 mounted on the vehicle 12 may be the driver identification device. For example, the vehicle-mounted device 20 uses the information acquired from the vehicle-mounted device 24 to collect driving information, identify the driver related to the driving information, and transmit the identified driver related to the driving information to the center server 30 or the like. may

The various processes executed by the CPU 20A and the CPU 30A by reading the software (programs) in the above embodiments may be executed by various processors other than the CPU. In this case, the processor is a PLD (Programmable Logic Device) whose circuit configuration can be changed after manufacturing, such as an FPGA (Field-Programmable Gate Array), and an ASIC (Application Specific Integrated Circuit) for executing specific processing. A dedicated electric circuit or the like, which is a processor having a specially designed circuit configuration, is exemplified. Further, each of the processes described above may be executed by one of these various processors, or by a combination of two or more processors of the same or different type (for example, multiple FPGAs, and a combination of a CPU and an FPGA). combination, etc.). More specifically, the hardware structure of these various processors is an electric circuit in which circuit elements such as semiconductor elements are combined.

Further, in the above embodiments, each program has been described as being pre-stored (installed) in a computer-readable non-temporary recording medium. For example, the collection program 100 in the vehicle-mounted device 20 is pre-stored in the ROM 20B, and the driver identification program 130 in the center server 30 is pre-stored in the storage 30D. However, not limited to this, each program is recorded on non-temporary recording media such as CD-ROM (Compact Disc Read Only Memory), DVD-ROM (Digital Versatile Disc Read Only Memory), and USB (Universal Serial Bus) memory. may be provided in any form. Also, the program may be downloaded from an external device via a network.

The flow of processing described in the above embodiment is an example, and unnecessary steps may be deleted, new steps added, or the order of processing may be changed without departing from the scope.

12 vehicle 20 vehicle-mounted device 30 center server 130 driver identification program 150 learned model 300 acquisition unit 310 determination unit 320 identification unit 340 learning unit

Claims (7)

an acquisition unit that acquires, for each time series, feature amounts indicating features when driving a vehicle;
a determination unit that determines a driver for each of the divided data by using the divided data obtained by dividing the acquired feature amount for each predetermined period;
an identification unit that identifies a driver from the determination result for each of the divided data;
a driver identification device. 2. The driver according to claim 1, wherein the determination unit determines the driver using a learned model that has undergone machine learning for determining the driver from past divided data relating to past driving by the driver. Specific device. 3. The driver identification according to claim 1 or 2, wherein the identifying unit aggregates the drivers indicated by the determination results for each of the divided data, and identifies the driver with the largest number of totals as the driver of the vehicle. Device. The divided data has data accuracy set for each period,
4. The driver identification device according to claim 3, wherein, when the total numbers are the same, the identification unit identifies the driver indicated by the determination result in the divided data with high data accuracy as the driver of the vehicle. 5. The driver identification device according to any one of claims 1 to 4, wherein the divided data are divided so that the periods overlap in the predetermined period. Acquisition of feature values indicating features when driving a vehicle for each time series,
determining a driver for each of the divided data by using divided data obtained by dividing the acquired feature amount for each predetermined period;
Identifying the driver from the determination result for each of the divided data;
A driver identification method in which processing is performed by a computer. Acquisition of feature values indicating features when driving a vehicle for each time series,
determining a driver for each of the divided data by using divided data obtained by dividing the acquired feature amount for each predetermined period;
Identifying the driver from the determination result for each of the divided data;
A driver specific program that causes a computer to perform processing.

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