DRIVER IDENTIFICATION DEVICE, DRIVER IDENTIFICATION METHOD, AND DRIVER IDENTIFICATION PROGRAM

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

[Technical field]

[0001] 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. [Background technology]

[0002] Patent document 1 discloses a driving diagnosis management system that counts the number of times a driver's driving deviates from at least one of safe driving and fuel-efficient driving as the number of violations, and sends a message to a management terminal when the number of violations reaches or exceeds a threshold. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 09-011857 Summary of the Invention [Problem to be solved by the invention]

[0004] When multiple drivers use one vehicle, it is necessary to identify each driver in order to perform driving evaluation for each driver. However, when identifying the driver by installing a dedicated authentication device equipped with a camera, etc., it is necessary to install a camera and an authentication device, etc., which may increase costs and weight.

[0005] An object of the present invention is 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. [Means for solving the problem]

[0006] The driver identification device described in claim 1 includes an acquisition unit that acquires feature quantities indicative of characteristics when driving a vehicle for each time series, a determination unit that uses divided data obtained by dividing the acquired feature quantities for each predetermined period to determine a driver for each of the divided data, and an identification unit that identifies a driver from a determination result for each of the divided data.

[0007] The driver identification device described in claim 1 identifies a driver who drives a vehicle from the characteristics of the driving operation of the vehicle. The driver identification device acquires feature quantities indicating the characteristics when driving a vehicle for each time series, divides the feature quantities for each predetermined period, and uses divided data to determine the driver for each divided data, and identifies the driver using the determination result for each divided data. In other words, the driver identification device identifies the driver using the driving feature quantities indicated by driving information acquired in advance from a sensor connected to an on-board device. This makes it possible to reduce costs and weight when identifying the driver.

[0008] The driver identification device of claim 2 is the driver identification device of claim 1, wherein the determination unit determines the driver using a learned model that has undergone machine learning to determine the driver from past divided data regarding the driver's past driving.

[0009] According to the driver identification device of claim 2, it is possible to identify the driver by utilizing the divided data relating to driving acquired in the past.

[0010] The driver identification device of claim 3 is the driver identification device of claim 1 or 2, wherein the identification unit counts the drivers indicated by the judgment results for each of the divided data, and identifies the driver with the highest count as the driver of the vehicle.

[0011] According to the driver identification device of claim 3, even if an error occurs in the determination related to one of the divided data, the driver can be identified, that is, the accuracy of identifying the driver is improved.

[0012] The driver identification device of claim 4 is the driver identification device of claim 3, in which data accuracy is set for the divided data for each period, and when the aggregated numbers are the same, the identification unit identifies the driver indicated by the judgment result in the divided data with high data accuracy as the driver of the vehicle.

[0013] According to the driver identification device recited in claim 4, even if drivers have similar characteristics in each of the divided data, the driver can be identified.

[0014] A driver identification device according to a fifth aspect of the present invention is the driver identification device according to any one of the first to fourth aspects, wherein the divided data is divided so that periods overlap within the predetermined period.

[0015] According to the driver identification device of claim 5, a feature amount indicating a driver is included in each of the divided data, and the accuracy of identifying the driver is improved.

[0016] The driver identification method described in claim 6 acquires feature amounts indicating characteristics when driving a vehicle for each time series, divides the acquired feature amounts for each predetermined period of time into divided data, uses the divided data to determine a driver for each of the divided data, and identifies the driver from the determination result for each of the divided data.

[0017] The driver identification method described in claim 6 identifies a driver who drives a vehicle from the characteristics of the driving operation of the vehicle. The driver identification method acquires a feature amount indicating the characteristics when driving a vehicle for each time series, divides the feature amount for each predetermined period, uses divided data to determine the driver for each divided data, and identifies the driver using the determination result for each divided data. In other words, according to the driver identification method, the driver is identified using the driving feature amount indicated by driving information previously acquired from a sensor connected to an in-vehicle device. This makes it possible to reduce costs and weight when identifying the driver.

[0018] The driver identification program described in claim 7 causes a computer to execute a process of acquiring feature quantities indicating characteristics when driving a vehicle for each time series, determining a driver for each of the divided data using split data obtained by dividing the acquired feature quantities for each predetermined period, and identifying the driver from the determination result for each of the divided data.

[0019] A computer that executes the driver identification program according to claim 7 identifies a driver who drives a vehicle from the characteristics of the driving operation of the vehicle. The computer acquires a feature quantity that indicates the characteristics when driving a vehicle for each time series, and uses divided data obtained by dividing the feature quantity for each predetermined period to determine the driver for each divided data, and identifies the driver using the determination result obtained for each divided data. In other words, the computer identifies the driver using the driving feature quantity indicated by the driving information previously acquired from a sensor connected to the vehicle-mounted device. This makes it possible to reduce costs and weight when identifying the driver. Effect of the Invention

[0020] According to the present invention, it is possible to reduce costs and weight when identifying a driver. [Brief description of the drawings]

[0021] [Figure 1] 1 is a diagram showing a schematic configuration of a driver identification system according to an embodiment. [Diagram 2] 1 is a block diagram showing a hardware configuration of a vehicle according to an embodiment; [Diagram 3] FIG. 2 is a block diagram showing a functional configuration of the vehicle-mounted device according to the embodiment. [Figure 4] FIG. 2 is a block diagram showing a hardware configuration of a center server according to an embodiment. [Diagram 5] FIG. 2 is a block diagram showing a functional configuration of a center server according to an embodiment. [Figure 6] FIG. 11 is a diagram showing an example of driving information for explaining data division according to the embodiment. [Figure 7]4 is a data flow diagram showing an example of the data flow of processing executed in the center server of the embodiment. FIG. [Figure 8] 10 is a flowchart showing a flow of a driver identification process executed in the center server of the embodiment. [Figure 9] 13 is a flowchart showing the process flow for generating a trained model executed in the center server of an embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0022] A driver identification system including a driver identification device of the present invention will be described. The driver identification system is a system that identifies a driver who is driving a vehicle by using information related to driving operation (hereinafter referred to as "driving information") acquired from an on-board device mounted on the vehicle.

[0023] (Overall composition) 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. The vehicle 12 is equipped with an on-board device 20, and the on-board devices 20 are mutually connected to the center server 30 via a network N.

[0024] Although FIG. 1 illustrates one vehicle 12 including an on-board device 20 for one center server 30, the numbers of vehicles 12, on-board devices 20, and center servers 30 are not limited to this.

[0025] The in-vehicle device 20 is a device that acquires driving information related to the operation of the vehicle 12 and transmits it to the center server 30. Here, the driving information according to this embodiment is a feature amount related to the driving operation detected from each device mounted on the vehicle 12. For example, the driving information according to this embodiment is a feature amount (data) related to the operation of the vehicle 12, such as the accelerator and brake pedal forces, the steering angle, and switching of the turn signals.

[0026] The center server 30 is installed, for example, at a manufacturer that manufactures the vehicle 12 or at a car dealership affiliated with the manufacturer. The center server 30 acquires driving information from the vehicle-mounted device 20 and identifies a driver related to the driving information.

[0027] (vehicle) As shown in FIG. 2, the vehicle 12 according to this embodiment includes an in-vehicle device 20, a plurality of ECUs (Electronic Control Units) 22, and a plurality of in-vehicle devices 24.

[0028] The vehicle-mounted device 20 includes a central processing unit (CPU) 20A, a read only memory (ROM) 20B, a random access memory (RAM) 20C, an in-vehicle communication interface (I / F) 20D, and a wireless communication interface 20E. The CPU 20A, the ROM 20B, the RAM 20C, the in-vehicle communication interface 20D, and the wireless communication interface 20E are connected to each other so as to be able to communicate with each other via an internal bus 20G.

[0029] The CPU 20A is a central processing unit that executes various programs and controls each part. That is, the CPU 20A reads out the programs from the ROM 20B and executes the programs using the RAM 20C as a working area.

[0030] The ROM 20B stores various programs and various data. In the present embodiment, the ROM 20B stores a collection program 100 that collects driving information related to the driving operation of the vehicle 12 from the ECU 22. When the collection program 100 is executed, the vehicle-mounted device 20 executes a process of transmitting the 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 working area.

[0031] The in-vehicle communication I / F 20D is an interface for connecting to each ECU 22. The interface uses a communication standard based on the CAN protocol. The in-vehicle communication I / F 20D is connected to the external bus 20F.

[0032] The wireless communication I / F 20E is a wireless communication module for communicating with the center server 30. The wireless communication module uses communication standards such as 5G, LTE, and Wi-Fi (registered trademark). The wireless communication I / F 20E is connected to a network N.

[0033] 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.

[0034] The ADAS-ECU 22A controls the advanced driving assistance 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 24C is a group of sensors used to detect the surrounding environment of the vehicle 12. The external sensor 24C includes, for example, a camera that captures an image of the surroundings of the vehicle 12, a millimeter wave radar that transmits a detection wave and receives a reflected wave, and a lidar (Laser Imaging Detection and Ranging) that scans the area ahead of the vehicle 12.

[0035] The steering ECU 22B controls the power steering. A steering angle sensor 24D and a turn signal 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 turn signal switch 24E is a switch for operating a turn signal.

[0036] The brake ECU 22C controls the brake system of the vehicle 12. A brake sensor 24F constituting the in-vehicle device 24 is connected to the brake ECU 22C and detects the depression force of the brake pedal.

[0037] The engine ECU 22D controls the engine of the vehicle 12. An accelerator sensor 24G and sensors 24H constituting the in-vehicle device 24 are connected to the engine ECU 22D. The accelerator sensor 24G detects the accelerator opening. The sensors 24H include an oil temperature sensor for measuring the temperature of the engine oil, an oil pressure sensor for measuring the oil pressure of the engine oil, and a rotation sensor for detecting the engine speed.

[0038] As shown in FIG. 3, in the vehicle-mounted device 20 of this embodiment, a CPU 20A executes a collection program 100 to function as a collection unit 200 and an output unit 210.

[0039] The collection unit 200 has a function of acquiring information detected by the in-vehicle devices 24 from each ECU 22 of the vehicle 12, evaluating the driving operation using the acquired information, and collecting driving information.

[0040] The output unit 210 has a function of outputting the driving information collected by the collection unit 200 to the center server 30.

[0041] (Center server) 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, the ROM 30B, the RAM 30C, the storage 30D, and the communication I / F 30E are connected to each other via an internal bus 30F so as to be able to communicate with each other. The functions of the CPU 30A, the ROM 30B, the RAM 30C, and the communication I / F 30E are the same as those of the CPU 20A, the ROM 20B, the RAM 20C, and the wireless communication I / F 20E of the vehicle-mounted device 20 described above. The communication I / F 30E may perform wired communication.

[0042] The storage 30D as a memory is configured by a hard disk drive (HDD) or a solid state drive (SSD) and stores various programs and various data. In the present embodiment, the storage 30D stores a driver identification program 130, a driving information database (hereinafter referred to as "driving information DB") 140, a trained model 150, and trained data 160. The ROM 30B may store the driver identification program 130, the driving information DB 140, the trained model 150, and the trained data 160.

[0043] The driver identification program 130 as a program is a program for controlling the center server 30. With the execution of the driver identification program 130, the center server 30 executes each process including an identification process for identifying the driver from the driving information.

[0044] The driving information DB 140 stores driving information received from the vehicle-mounted device 20.

[0045] The trained model 150 is a trained model that has performed machine learning to identify a driver using driving information acquired from the in-vehicle device 20. For example, the trained model 150 learns a feature amount related to a driving operation as input data and a label indicating a driver who performed a driving operation related to the driving information as correct answer data. In addition, the trained model 150 outputs a driver related to the driving information by inputting driving information.

[0046] The learning data 160 is data for learning by the trained model 150. The learning data 160 is data in which driving information previously acquired and stored in advance is used as input data, and a label indicating a driver corresponding to the driving information is stored as teacher data.

[0047] As shown in FIG. 5, in the center server 30 of this embodiment, the CPU 30A executes the driver identification program 130 to function as an acquisition unit 300, a judgment unit 310, an identification unit 320, an output unit 330, a learning unit 340, and a memory unit 350.

[0048] The acquisition unit 300 has a function of acquiring driving information transmitted from the vehicle-mounted device 20 of the vehicle 12. Here, the acquisition unit 300 according to the present embodiment acquires, as the driving information, time-series data related to driving operations from the start of driving until four hours have elapsed.

[0049] The determination unit 310 uses the learned model to determine the driver related to the driving information. Specifically, the determination unit 310 evaluates the feature quantities of driving operations, such as sudden acceleration, sudden braking, sudden steering, backing operation, turn signal operation, braking operation, and depressing both the accelerator pedal and the brake pedal, included in the acquired driving information. That is, the determination unit 310 evaluates the acquired driving information and quantifies the feature quantities related to each driving operation as an evaluation result.

[0050] The determination unit 310 divides the evaluation results obtained by digitizing the features into four hours' worth of data (hereinafter referred to as "four-hour data"), three hours' worth of data (hereinafter referred to as "three-hour data"), and two hours' worth of data (hereinafter referred to as "two-hour data"). Using the trained model 150, the determination unit 310 determines the driver for each of the divided data based on the digitized features contained in the data divided into each period (hereinafter referred to as "divided data").

[0051] As an example, as shown in Fig. 6, the determination unit 310 divides the evaluation result into divided data of 4 hours data, 3 hours data, and 2 hours data. The determination unit 310 identifies the driver for each of the six divided data shown in Fig. 6.

[0052] Here, when dividing the data into 3-hour data and 2-hour data, the determination unit 310 divides the data so that the periods of the divided data overlap. For example, as shown in Fig. 6, two 3-hour data are divided so that two of the 3 hours overlap. Also, three 2-hour data are divided so that one of the 2 hours overlaps. By dividing the data so that each data overlaps, a feature indicating the driver is included in each data, and the accuracy of identifying the driver is improved.

[0053] The identification unit 320 identifies a driver related to the driving information by using the determination result obtained by the determination unit 310. Specifically, the identification unit 320 counts up the drivers determined for each of the six divided data shown in Fig. 6, and identifies the driver with the largest count as the driver related to the driving information.

[0054] Here, when the counts 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. Note that in this embodiment, the 4-hour data, 3-hour data, and 2-hour data, which have a long period in the data and contain many features, are described as having the highest accuracy in that order.

[0055] The output unit 330 outputs, as the identification result, the driver related to the driving information identified by the identification unit 320. Here, the output unit 330 may output the identification result by transmitting it to the in-vehicle device 20 or the like, or may output the identification result by displaying it on a monitor (not shown) provided in the center server 30.

[0056] The learning unit 340 generates a trained model 150 by performing machine learning for determining a driver, using the training data 160 stored in the storage 30D. Specifically, the learning unit 340 quantifies and evaluates the feature amount of the driving information included in the training data 160 and acquired in advance from the vehicle-mounted device 20. The learning unit 340 generates the trained model 150 by performing supervised learning using the evaluation result of the driving information as input data and the label of the driver 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 divided data.

[0057] The storage unit 350 stores the trained model 150 generated by the learning unit 340. The storage unit 350 also stores an evaluation result of the driving information and a driver related to the driving information identified by the identification unit 320 as learning data.

[0058] Before describing the operation of the driver identification system 10, a data flow in the center server 30 serving as a driver identification device will be described with reference to Fig. 7. Fig. 7 is a data flow diagram showing an example of a data flow in the center server 30.

[0059] As an example, as shown in FIG. 7, the learning unit 340 generates a trained model 150 using training data 160 stored in the storage 30D, and the memory unit 350 stores the generated trained model 150.

[0060] The acquisition unit 300 acquires driving information acquired from the vehicle-mounted device 20 , and inputs the acquired driving information to the determination unit 310 .

[0061] The determination unit 310 evaluates the input driving information, divides the evaluation result into each divided data, and determines the driver for each divided data by using the stored trained model 150. The determination unit 310 inputs the driver related to each divided data to the identification unit 320 as the determination result.

[0062] The identification unit 320 counts up the drivers determined by the determination unit 310, and inputs the driver with the highest count to the output unit 330 as the driver related to the driving information.

[0063] The output unit 330 outputs the driver related to the input driving information as an identification result to a monitor.

[0064] (Flow of Control) The flow of each process executed in the driver identification system 10 of this embodiment will be described with reference to the flowcharts of Figures 8 and 9. 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 identification process shown in Figure 8 is executed, for example, when an instruction to identify the driver is input.

[0065] In step S100, CPU 30A acquires trained model 150 that is stored.

[0066] In step S101, the CPU 30A acquires driving information from the vehicle-mounted device 20.

[0067] In step S102, CPU 30A evaluates and digitizes the feature amount included in the acquired driving information as an evaluation result.

[0068] In step S103, CPU 30A divides the evaluation result into divided data of 4-hour data, 3-hour data, and 2-hour data.

[0069] In step S104, CPU 30A determines the driver associated with each of the divided data.

[0070] In step S105, CPU 30A tallies the determined drivers.

[0071] In step S106, the CPU 30A determines whether the driver with the highest number of counts is one and whether 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.

[0072] In step S107, CPU 30A identifies the driver with the largest number of counts as the driver related to the driving information.

[0073] In step S108, CPU 30A identifies the driver indicated by the most accurate data (for example, the four-hour data) as the driver related to the driving information.

[0074] In step S109, CPU 30A outputs the identified driver.

[0075] Next, a process for generating the trained model 150 executed in the driver identification system 10 of the present embodiment will be described with reference to the flowchart of Fig. 9. The learning process shown in Fig. 9 is executed, for example, when an instruction to execute a process for generating the trained model 150 is input.

[0076] In step S200, the CPU 30A acquires pre-stored learning data. The learning data is information including driving information acquired in advance from the vehicle-mounted device 20 and a label indicating the driver related to the driving information.

[0077] In step S201, CPU 30A evaluates driving information included in the learning data, and quantifies the feature amount as the evaluation result.

[0078] In step S202, CPU 30A divides the evaluation result into divided data.

[0079] In step S203, CPU 30A generates trained model 150 using each of the divided data.

[0080] In step S204, CPU 30A determines whether or not to end the process of generating trained model 150. If the process of generating trained model 150 is to be ended (step S204: YES), the process proceeds to step S205. On the other hand, if the process of generating trained model 150 is not to be ended (step S204: NO), CPU 30A proceeds to step S200 and acquires training data.

[0081] In step S205, CPU 30A stores the generated trained model 150 in storage 30D, and ends the process of generating trained model 150.

[0082] As described above, according to this embodiment, it is possible to reduce costs and weight when identifying a driver.

[0083] (summary) The center server 30 as the driver identification device of this embodiment acquires feature quantities indicating characteristics when driving a vehicle for each time series, and judges the driver for each divided data by dividing the feature quantities for each predetermined period. The driver identification device identifies the driver using the judgment result judged for each divided data. In other words, the driver identification device identifies the driver using the driving feature quantities indicated by the driving information acquired from the sensor connected to the vehicle-mounted device. This makes it possible to reduce costs and weight when identifying the driver.

[0084] [remarks] In the present embodiment, the center server 30 acquires, as driving information, characteristic quantities (data) related to driving operation. However, the present invention is not limited to this. The center server may acquire an evaluation result obtained by evaluating and quantifying the driving information. For example, the vehicle-mounted device 20 may perform an evaluation using data related to driving operation to quantify the characteristic quantities, and the center server 30 may acquire the quantified characteristic quantities from the vehicle-mounted device 20.

[0085] In the present embodiment, the driving information is described as being acquired as time series data for four hours related to driving operations. However, the present invention is not limited to this. Any period of time series data may be acquired as long as the data is time series data. For example, the driving information may be acquired as time series data for 30 minutes or as time series data for one day.

[0086] In the present embodiment, the driver determined for each divided data is tallied, and if the total number is the same, the driver indicated by the data with the highest accuracy is identified as the driver related to the driving information. However, the present invention is not limited to this. For example, a set value that is larger as the data with higher accuracy is set for each divided data, the set value is tallied for each driver, and the driver with the largest total number is identified as the driver related to the driving information.

[0087] In the present embodiment, the driver is determined using a learned model. However, the present invention is not limited to this. A feature indicating the driver may be detected from the data related to the driving information by pattern matching to determine the driver. For example, if each driver has a feature in the timing at which the driver takes a break without performing driving operations, the feature of the timing at which each driver takes a break may be stored in advance, and when the feature is detected from the driving information by pattern matching, the driver indicated by the feature may be identified.

[0088] In the present embodiment, the center server 30 is described as being equipped with a driver identification device. However, the present invention 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 may collect driving information using information acquired from the vehicle-mounted device 24, 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.

[0089] In the above embodiment, various processes executed by the CPU 20A and the CPU 30A by reading the software (programs) may be executed by various processors other than the CPU. In this case, examples of the processor include a PLD (Programmable Logic Device) such as an FPGA (Field-Programmable Gate Array) whose circuit configuration can be changed after manufacture, and a dedicated electric circuit such as an ASIC (Application Specific Integrated Circuit) which is a processor having a circuit configuration designed exclusively for executing a specific process. In addition, each of the above-mentioned processes may be executed by one of these various processors, or may be executed by a combination of two or more processors of the same or different types (for example, a plurality of FPGAs, a combination of a CPU and an FPGA, etc.). In addition, the hardware structure of these various processors is, more specifically, an electric circuit that combines circuit elements such as semiconductor elements.

[0090] In the above embodiment, each program has been described as being prestored (installed) in a non-transitory computer-readable recording medium. For example, the collection program 100 in the vehicle-mounted device 20 is prestored in the ROM 20B, and the driver identification program 130 in the center server 30 is prestored in the storage 30D. However, the present invention is not limited to this, and each program may be provided in a form recorded in a non-transitory recording medium such as a CD-ROM (Compact Disc Read Only Memory), a DVD-ROM (Digital Versatile Disc Read Only Memory), or a USB (Universal Serial Bus) memory. Also, the program may be downloaded from an external device via a network.

[0091] The process flow described in the above embodiment is merely an example, and unnecessary steps may be deleted, new steps may be added, or the process order may be changed, without departing from the spirit of the invention. [Explanation of symbols]

[0092] 12 Vehicles 20 Onboard equipment 30 Center Server 130 Driver Specific Program 150 trained models 300 Acquisition Department 310 Judgment section 320 Specific section 340 Learning Department

Claims

1. an acquisition unit that acquires a feature quantity indicating a characteristic when driving a vehicle for each time series; a determination unit that determines a driver for each of the divided data by dividing the acquired feature amount for each predetermined period; An identification unit that identifies a driver based on a determination result for each of the divided data; Equipped with The divided data is divided so that the predetermined period overlaps with another period.

2. The driver identification device according to claim 1 , wherein the determination unit determines the driver using a learned model that has undergone machine learning to determine the driver from past divided data related to past driving by the driver.

3. The driver identification device according to claim 1 or 2, wherein the identification unit counts up the drivers indicated by the determination results for each of the divided data, and identifies the driver with the highest count as the driver of the vehicle.

4. The divided data has data precision set for each period, The driver identification device according to claim 3 , wherein when the counts 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. A feature quantity indicating the characteristics when driving a vehicle is acquired for each time series, Using divided data obtained by dividing the acquired feature amount for each predetermined period, a driver is determined for each of the divided data; Identifying the driver based on the determination result for each of the divided data. The computer executes the process, The divided data is divided so that the predetermined period overlaps with another period.

6. A feature quantity indicating the characteristics when driving a vehicle is acquired for each time series, Using divided data obtained by dividing the acquired feature amount for each predetermined period, a driver is determined for each of the divided data; Identifying the driver based on the determination result for each of the divided data. The process is executed by a computer, The divided data is a driver identification program in which the predetermined period is divided so that the periods overlap each other.

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