Individual characteristic management system and method and non-transitory storage medium

Abstract

The present invention relates to an individual characteristic management system and method and a non-transitory storage medium. An individual characteristic management system includes a first memory installed at a vehicle and storing first data related to an individual characteristic of a user, a second memory disposed outside the vehicle and storing second data related to the individual characteristic, and a controller that updates the first data and the second data based on a driving state of the user, respectively, wherein the controller updates the first data stored in the first memory during driving of the user and based on the driving state, and updates the second data based on the updated first data and the second data stored in the second memory.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to an individual characteristic management system, an individual characteristic management method, and a non-transitory storage medium storing a program. BACKGROUND

[0002] Japanese Patent Application Laid-Open (JP-A) No. 2007-327872 discloses a driver personality judging device that judges "impatience degree" based on the relative frequency of refueling timing and judges "compliance with regulations" based on the travel history of a vehicle.

[0003] The device disclosed in JP-A No. 2007-327872 can only estimate simple individual characteristics. On the other hand, a method is conceived in which detailed individual characteristics are estimated by performing calculation at a server or the like outside the vehicle based on the driving state of the user. However, because there is a large amount of communication between the vehicle and the server, the processing load on the vehicle side can increase. SUMMARY

[0004] The present disclosure provides an individual characteristic management system, an individual characteristic management method, and a non-transitory storage medium storing a program that can obtain detailed individual characteristics while reducing the processing load on the vehicle side.

[0005] A first aspect of the present disclosure is an individual characteristic management system including: a first storage that is installed at a vehicle and stores first data related to individual characteristics of a user; a second storage that is provided outside the vehicle and stores second data related to individual characteristics; and a controller that updates the first data and the second data based on a driving state of the user, respectively, wherein the controller updates the first data stored in the first storage based on the driving state during driving by the user, and updates the second data based on the updated first data and the second data stored in the second storage.

[0006] In the individual characteristic management system of the first aspect, the first storage is installed in the vehicle, and the second storage is provided outside the vehicle. The first data related to individual characteristics of the user is stored in the first storage, and the second data related to individual characteristics of the user is stored in the second storage. The controller updates the first data and the second data based on the driving state of the user, respectively. Here, during driving by the user, the controller updates the first data based on the driving state. In this way, by installing the first storage in the vehicle, the processing load on the vehicle when referring to individual characteristics can be reduced compared to a structure in which all data related to individual characteristics is stored outside the vehicle.

[0007] Further, the controller updates the second data based on the updated first data and second data stored in the second memory. Therefore, the second data related to the individual characteristics and stored in the second memory outside the vehicle can be periodically updated, and detailed individual characteristics can be obtained based on the second data. Note that the "driving state" referred to here is not limited to a driving operation of the vehicle such as acceleration, braking, steering, and the like. The "driving state" is a broad concept that includes a state of the vehicle during driving and a state of the driver (user) during driving.

[0008] In the individual characteristic management system of the second aspect, in the first aspect, the controller can update the second data in a case where the state of the vehicle becomes a predetermined state.

[0009] In the individual characteristic management system of the second aspect, the second data is updated in a case where the predetermined state is reached. Therefore, the first data can be transmitted to the second memory while avoiding a situation in which a large amount of communication exists with the vehicle.

[0010] In the individual characteristic management system of the third aspect, in the first aspect, the controller updates the second data in a case where the vehicle is stopped.

[0011] In the individual characteristic management system of the third aspect, because the second data is updated in a case where the vehicle is stopped, the first data can be transmitted to the second memory in a state in which communication for vehicle control is not performed. Note that the "when the vehicle is stopped" referred to here is not limited to a case in which the vehicle speed has become zero. For example, the "when the vehicle is stopped" includes a case in which power of the vehicle is turned off.

[0012] In the individual characteristic management system of the fourth aspect, in the third aspect, the controller transmits only data that is updated during driving, among the first data, to the second memory at the time of parking of the vehicle.

[0013] In the individual characteristic management system of the fourth aspect, only the first data that is updated during driving is transmitted to the second memory. Therefore, at the time when the vehicle is stopped, the amount of communication between the vehicle and the second memory can be kept to a minimum amount that is required.

[0014] In the individual characteristic management system of the fifth aspect, in the first aspect, in a case where the vehicle is started, the controller transmits some of the second data to the first memory and updates the first data.

[0015] In the individual characteristic management system of the fifth aspect, the first data is updated in a case where the vehicle is started. Therefore, at the time when the vehicle is started, the individual characteristics can be managed based on the latest first data at all times.

[0016] In the individual characteristic management system of the sixth aspect, in the fifth aspect, the controller transmits only data used during driving among the second data to the first memory in the case where the vehicle is started.

[0017] In the individual characteristic management system of the sixth aspect, in the case where the vehicle is started, the amount of communication between the vehicle and the second memory can be kept to a minimum required amount.

[0018] In the individual characteristic management system of the seventh aspect, in any one of the first aspect to the sixth aspect, the controller updates the first data by inputting driving data obtained from the sensor during driving to a learning model on which machine learning has been performed, and performing a calculation process of the learning model.

[0019] In the individual characteristic management system of the seventh aspect, by updating the first data using a learning model on which machine learning has been performed, individual characteristics can be obtained with high accuracy without complex calculations.

[0020] The individual characteristic management method of the eighth aspect includes a first memory installed at a vehicle and storing first data related to individual characteristics of a user, and a second memory provided outside the vehicle and storing second data related to the individual characteristics, the method including updating the first data stored in the first memory during driving of the user and based on a driving state, and updating the second data based on the updated first data and the second data stored in the second memory.

[0021] The non-transitory storage medium storing a program of the ninth aspect is a non-transitory storage medium storing a program executable by a computer to execute a process including updating first data stored in a first memory installed at a vehicle and related to individual characteristics of a user based on a driving state during driving of the user, and updating second data stored in a second memory provided outside the vehicle and related to the individual characteristics based on the updated first data and the second data.

[0022] The individual characteristic management system, the individual characteristic management method, and the non-transitory storage medium storing a program of the present disclosure can obtain detailed individual characteristics while reducing the processing load on the vehicle side. BRIEF DESCRIPTION OF DRAWINGS

[0023] Exemplary embodiments of the present disclosure will be described in detail based on the following drawings, in which:

[0024] Figure 1 is a schematic diagram showing the overall structure of an individual characteristic management system related to an embodiment;

[0025] Figure 2is a block diagram showing a hardware structure of an individual characteristic management device related to the embodiment;

[0026] Figure 3 is a block diagram showing a hardware structure of an in-vehicle device related to the embodiment;

[0027] Figure 4 is a block diagram showing a functional structure of an individual characteristic management device related to the embodiment;

[0028] Figure 5 is a block diagram for explaining a learning phase in the embodiment;

[0029] Figure 6 is a table showing an example of a driving evaluation item;

[0030] Figure 7 is a table showing an example of a driving characteristic and a cognitive characteristic; and

[0031] Figure 8 is a flowchart showing an example of an update processing flow in the embodiment. DETAILED DESCRIPTION

[0032] An individual characteristic control system S including an individual characteristic management device 10 related to the embodiment is described with reference to the drawings.

[0033] As Figure 1 shown, the individual characteristic control system S of the present embodiment is configured to include the individual characteristic management device 10, a vehicle V provided with an in-vehicle device 12, and a server 14. The individual characteristic management device 10, the vehicle V, and the server 14 are connected through a network N. Note that although a plurality of vehicles V are connected to the network N, only one vehicle V is shown in Figure 1 for ease of explanation.

[0034] A control device provided outside the vehicle V is an example of the individual characteristic management device 10 of the present embodiment. A memory 46 serving as a first memory is provided at the in-vehicle device 12 installed in the vehicle V, and first data 13 related to an individual characteristic is stored in the memory 46 (see Figure 3 ).

[0035] The server 14 is provided outside the vehicle V and is owned by a manager of a plurality of vehicles V. Further, second data 15 related to an individual characteristic is stored in the server 14. That is, the server 14 corresponds to a "second memory" of the present disclosure. The server 14 is owned by a manager who manages a plurality of vehicles V, and in the present embodiment, as an example, the vehicle V is a vehicle used as a taxi in which a user rides and travels. Further, the server 14 is owned by a taxi company.

[0036] Here, the individual characteristic management system S of this embodiment updates the first data 13, which is related to the user's individual characteristics and stored in the memory 46, during the user's driving and based on the driving state. Furthermore, the individual characteristic management system S of this embodiment updates the second data 15 based on the updated first data 13 and the second data 15 stored in the server 14.

[0037] (Hardware structure of the individual characteristic management device 10)

[0038] Figure 2 This is a block diagram illustrating the hardware structure of the individual characteristic management device 10. For example... Figure 2 As shown, the individual characteristic management device 10 has an ECU (Electronic Control Unit) 16 that acts as a controller. The ECU 16 is configured to include a CPU (Central Processing Unit): a processor 20, a ROM (Read-Only Memory) 22, a RAM (Random Access Memory) 24, a memory 26, a communication I / F (Communication Interface) 28, and an input / output I / F (Input / Output Interface) 30. These various structures are connected to enable communication with each other via a bus 32.

[0039] CPU 20 is the central computing processing unit, which executes various programs and controls various parts. That is, CPU 20 reads programs from ROM 22 or memory 26 and executes the programs by using RAM 24 as its workspace. CPU 20 performs control of the above-mentioned structures and various types of calculations according to the programs recorded in ROM 22 or memory 26.

[0040] ROM 22 stores various programs and data. RAM 24 serves as temporary storage for programs and data in the workspace. Memory 26 is composed of an HDD (Hard Disk Drive) or SSD (Solid State Drive) and stores various programs, including the operating system, and various data. In this embodiment, programs for performing update processing of individual characteristics and various data are stored in ROM 22 or memory 26.

[0041] Communication I / F 28 is the interface through which the individual characteristic management device 10 communicates with the server 14 and other devices, and uses, for example, CAN (Controller Area Network). LTE (Long Term Evolution), FDDI (Fiber Distributed Data Interface) Standards such as...

[0042] The input / output (I / F) 30 is electrically connected to the input device 34 and the display device 36. The input device 34 is a device for inputting predetermined instructions to the personal characteristic management device 10, and is configured to include, for example, a mouse and a keyboard. The display device 36 is a device such as a monitor for displaying information output from the personal characteristic management device 10.

[0043] (Hardware structure of in-vehicle device 12)

[0044] Figure 3 is a block diagram showing the hardware structure of the in-vehicle device 12. As shown in Figure 3 , the in-vehicle device 12 is configured to include a CPU (Central Processing Unit: processor) 40, a ROM (Read Only Memory) 42, a RAM (Random Access Memory) 44, a memory 46 that functions as a first memory, a communication I / F (Interface) 48, and an input / output I / F (Interface) 50. These individual structures are connected so as to be able to communicate with each other via a bus 52.

[0045] The CPU 40 is a central computing processing unit, and executes various programs and controls individual parts. That is, the CPU 40 reads out a program from the ROM 42 or the memory 46, and executes the program by using the RAM 44 as a work space. The CPU 40 executes the control of the individual structures and various types of computing processing according to the program recorded in the ROM 42 or the memory 46.

[0046] The ROM 42 stores various programs and various data. The RAM 44 temporarily stores programs and data as a work space. The memory 46 is constituted by an HDD (Hard Disk Drive) or an SSD (Solid State Drive), and stores various programs including an operating system and various data. In the present embodiment, programs and various data and the like for executing various processing are stored in the ROM 42 or the memory 46.

[0047] Further, the first data 13 related to the individual characteristics of the user is stored in the memory 46 of the present embodiment. Details of the first data 13 will be described below.

[0048] The communication I / F 48 is an interface through which the in-vehicle device 12 communicates with the server 14 and other devices, and there a standard such as, for example, CAN, LTE, FDDI, and the like is used.

[0049] The input / output I / F 50 is electrically connected to an acceleration sensor 54, a steering angle sensor 56, a peripheral camera 58, a driver camera 60, and a display 61.

[0050] The acceleration sensor 54 detects acceleration of the vehicle V. The steering angle sensor 56 detects a steering angle of the vehicle V. To judge the absence / presence of sudden acceleration of the vehicle V, for example, data of acceleration sensed by the acceleration sensor 54 is used. Further, to judge the absence / presence of sudden steering or U-turn, for example, data of the steering angle sensed by the steering angle sensor 56 is used. Note that, instead of the acceleration sensor 54, an operation amount of an accelerator pedal and a brake pedal or the like can be detected directly from a sensor such as an accelerator position sensor and a brake sensor.

[0051] The peripheral camera 58 is provided at a periphery of the vehicle V, and captures an image around the vehicle V. For example, the peripheral camera 58 is configured to include a front camera that captures an image of a front region of the vehicle V, a rear camera that captures an image of a rear region of the vehicle V, and a side camera that captures an image of a side region of the vehicle V, and the like. Further, to calculate an inter-vehicle distance between the vehicle V and a preceding vehicle, for example, image data captured by the peripheral camera 58 is used. Further, to detect a sign, for example, image data captured by the peripheral camera 58 is used.

[0052] The driver camera 60 is a camera for capturing an image of a driver, for example, is provided on an instrument panel, and is arranged to face the driver. To detect that the driver is driving while taking eyes off the road or the like, for example, image data captured by the driver camera 60 is used.

[0053] The display 61 is provided on an instrument panel of the vehicle V, and displays information to a user at a predetermined time. For example, a suggestion related to driving that is appropriate for the user or the like is displayed based on the first data 13.

[0054] (Functional configuration of individual characteristic management device 10)

[0055] The individual characteristic management device 10 realizes various functions by using the above-described hardware resources. Referring to Figure 4 The functional configuration realized by the individual characteristic management device 10 is described.

[0056] As Figure 4 indicated, the individual characteristic management device 10 is configured to include a data acquisition section 62, a score calculation section 64, a learning section 66, an individual characteristic estimation section 68, a first data update section 70, and a second data update section 72, as its functional configuration. Note that, since the CPU 20 reads out a program stored in the ROM 22 or the memory 26 and executes the program, these respective functional configurations are realized.

[0057] The data acquisition section 62 acquires driving data related to a plurality of driving evaluation items that are set in advance. In the present embodiment, as an example, the driving data is transmitted from the vehicle V to the server 14, and the driving data is accumulated in the server 14. Therefore, the data acquisition section 62 acquires the driving data from the server 14. The driving data acquired by the data acquisition section 62 is, for example, information related to acceleration and steering of the vehicle V, information captured by the peripheral camera 58, information captured by the driver camera 60, and the like. Furthermore, the data acquisition section 62 acquires data including a score calculated by the score calculation section 64 and a number of times of measurement as the driving data.

[0058] Based on the driving data acquired by the data acquisition section 62, the score calculation section 64 performs calculation of a score or measurement of a number of times for each of the driving evaluation items. Here, reference is made to Figure 6 Examples of the driving evaluation items are described.

[0059] As shown in Figure 6 , in the present embodiment, as an example, the diagnostic categories of the driving evaluation items are classified into safety, compliance with laws / regulations, and a driver state.

[0060] The diagnostic category of safety is classified into acceleration operation, braking operation, steering operation, and dangerous operation. Start and sudden acceleration are evaluated as the acceleration operation, respectively. Specifically, in the start item, acceleration operation from a stopped state of the vehicle V until a predetermined speed is reached is evaluated based on data sensed by the acceleration sensor 54 provided at the vehicle V. Furthermore, a score from 0 to 100 is calculated as evaluation of the start. For evaluation of sudden acceleration, sudden braking, stopping, right turn, and left turn, a score from 0 to 100 is similarly calculated.

[0061] For example, the score of the start item can be calculated based on a time from a stopped state of the vehicle V until a predetermined speed is reached and a variation amount of acceleration during a time until the predetermined speed is reached. Furthermore, a score of a most ideal start state can be set to 100, a score of a start state in which an accelerator is fully opened from a stopped state can be set to 0, and a score can be calculated by comparison with an actual start state.

[0062] The degree of execution of sudden acceleration is calculated as a score of the sudden acceleration item. For example, based on acceleration sensed by the acceleration sensor 54 provided at the vehicle V, in a case where an increase amount of acceleration is large in a relatively short time, the score can be calculated to be low.

[0063] An evaluation is performed for both sudden braking and stopping in response to a braking operation. For the sudden braking item, the degree of performing sudden braking is calculated as a score. For example, based on data sensed by the acceleration sensor 54 provided at the vehicle V, in a case where the amount of reduction in acceleration is large in a relatively short time, the score can be calculated to be low. Further, for the stopping item, a score can be calculated based on the time from a predetermined speed until the vehicle V stops and the amount of change in acceleration during the period until the vehicle V stops.

[0064] Both right and left turns are evaluated as steering operations. For example, for the right turn item, a score can be set based on the steering angle of the vehicle V sensed by the steering angle sensor 56, such that the closer the amount of change in the steering angle is to a preset ideal state during a predetermined period of time, the higher the score. Similarly for the left turn item, a score is calculated similarly to the right turn item.

[0065] U-turns, non-use of a turn signal, and inter-vehicle distance are evaluated as dangerous operations, respectively. These items have no score and are evaluated based on the number of detections.

[0066] For the U-turn item, the number of times a U-turn is performed is counted. For example, it can be judged that a U-turn has been performed based on the steering angle of the vehicle V sensed by the steering angle sensor 56 and the distance traveled. For the non-use of a turn signal item, the number of times the turn signal lamp is not turned on before a left or right turn is counted. For example, in a state where it is judged that a left or right turn has been performed based on the steering angle of the vehicle V sensed by the steering angle sensor 56, if an unillustrated turn signal switch installed in the vehicle V is not turned on in advance, it can be judged that there is non-use of a turn signal.

[0067] For the inter-vehicle distance item, the number of times the inter-vehicle distance to the preceding vehicle is shortened is counted. For example, in a case where the inter-vehicle distance between the vehicle V and the preceding vehicle calculated based on image data captured by the peripheral camera 58 is less than or equal to a predetermined inter-vehicle distance, it can be judged that the inter-vehicle distance to the preceding vehicle has been shortened.

[0068] The diagnosis category of compliance with laws / regulations is divided into signals and signs. For the signals, the number of times a signal is ignored is counted. For the signs, an evaluation is performed for each of a violation of a speed limit, a violation of a temporary stop regulation, and parking in a no-parking zone.

[0069] For the speed limit item, the number of times a speed limit is exceeded is counted. For example, based on image data captured by the peripheral camera 58, the speed limit displayed on a sign is detected, and if the vehicle speed detected by an unillustrated vehicle speed sensor installed in the vehicle V is greater than the speed limit, it can be judged that the speed limit has been exceeded.

[0070] For the item of violating a temporary parking regulation, the number of times that the vehicle V does not stop at a location where temporary parking is required is counted. For example, a sign that regulates temporary parking can be detected based on image data captured by the peripheral camera 58, and if the vehicle speed is not 0 at the location where temporary parking is required, it can be judged that there is a temporary parking violation.

[0071] For the item of parking in a no-parking zone, the number of times that parking is performed in a no-parking zone is counted. For example, a no-parking sign can be detected based on image data captured by the peripheral camera 58, and if the gear is set to park at the location where parking is prohibited, it can be judged that there is a no-parking violation. Note that, for the speed limit violation item, instead of the method of detecting a sign based on image data captured by the peripheral camera 58, speed limit information for the road on which the vehicle V is traveling can be acquired from the current position of the vehicle V and information registered in the navigation system. Similarly, the locations of temporary parking and no-parking can be sensed from the current position of the vehicle V and information registered in the navigation system. Note that the current position information of the vehicle V is acquired based on radio waves received by a not-shown GPS (Global Positioning System) device installed in the vehicle V.

[0072] In the diagnosis category of the driver state, both the item of low alertness and the item of driving while the driver has eyes off the road are evaluated. For the low alertness item, the number of times that the alertness of the driver is judged to be low is counted. For example, the degree of alertness of the driver can be calculated based on image data of the driver's face captured by the driver camera 60, and if the degree of alertness is lower than a preset threshold, it can be judged that the level of alertness is low.

[0073] For the item of driving while the driver has eyes off the road, the number of times that the driver has eyes off the road during driving is counted. For example, the direction of the driver's line of sight can be detected from image data of the driver's face captured by the driver camera 60, and if the direction of the driver's line of sight points in a direction different from the advancing direction for a predetermined period of time or more, it can be judged that driving while the driver has eyes off the road is being performed.

[0074] Figure 4 The illustrated learning section 66 has a function of generating a learning model M by machine learning. Specifically, as illustrated, Figure 5 The learning section 66 acquires teacher data in which driving data serving as information related to a plurality of driving evaluation items and correct answer values for the driving features and the cognitive features, respectively, are set corresponding to each other, as illustrated. Based on the acquired teacher data, when data related to the driving evaluation items is input, the learning section 66 generates a learning model M that estimates individual features including the driving features and the cognitive features. The driving features and the cognitive features are described below.

[0075] Note that, for example, a deep neural network is used as the learning model M. LSTM (Long Short Term Memory) is a type of RNN (Recurrent Neural Network) and is used as an example of the learning model M of the present embodiment. Further, for example, error backpropagation is used when generating the learning model M. When the driving data is input, the driving features and the cognitive features are output due to the machine training of the deep neural network model, and the learning model M is generated.

[0076] For example, a diagnosis result of a driver fitness diagnosis performed by an external organization such as the National Highway Traffic Safety Administration or the like can be used as the correct answer value of the driving features. Further, a test result of a test that evaluates cognitive functions such as MMSE (Mini Mental State Examination) or the like can be used as the correct answer value of the cognitive features.

[0077] The individual feature estimation section 68 estimates the individual features of the driver based on the driving data including scores and acquired by the data acquisition section 62. Specifically, the individual feature estimation section 68 inputs the driving data acquired by the data acquisition section 62 into the learning model M generated at the learning section 66 and performs a calculation process of the learning model M, thereby estimating the individual features. Further, in the present embodiment, as an example, the individual feature estimation section 68 estimates the driving features and the cognitive features as the individual features.

[0078] Here, reference is made to Figure 7 Examples of the driving features and the cognitive features that serve as the individual features are described. As Figure 7 indicated, in the present embodiment, the judgment timing, the operation correctness, the safe driving, the emotional stability, the danger sensitivity, and the civil driving are estimated as the driving features, respectively. The estimation results are calculated as scores from 0 to 100.

[0079] For the item of the judgment timing, a feature related to the degree to which the driver is able to quickly judge the situation around the vehicle V and the degree to which the driver is able to quickly perform operations is estimated. For the item of the operation correctness, a feature related to the degree of correctness of the operations of the vehicle V is estimated.

[0080] For the item of the safe driving, a feature related to the degree to which the driver pays attention to safe driving is estimated. Further, for the item of the emotional stability, a feature related to the degree of calmness in which the driver does not perform emotional driving is estimated. For the item of the danger sensitivity, a feature related to the degree to which the driver is able to quickly judge dangerous situations is estimated. For the item of the civil driving, a feature related to the degree to which the driver gives consideration to the vehicles and pedestrians around the vehicle is estimated.

[0081] As the cognitive features, the cognitive ability item is calculated as a score from 0 to 100.

[0082] In this embodiment, the individual characteristic management device 10 stores the individual characteristics estimated by the individual characteristic estimation section 68 in the server 14 as second data 15. Furthermore, in this second data 15, only data used during driving is transmitted to the memory 46, which serves as the first memory. For example, the individual characteristic management device 10 transmits data from the second data 15, such as judgment timing, operational correctness, and cognitive ability, to the memory 46.

[0083] Based on the user's driving status Figure 4 The first data update portion 70 shown updates the first data 13 stored in the memory 46. In this embodiment, as an example, the first data update portion 70 receives some of the second data 15 from the server 14 when the vehicle V starts, and updates the first data 13 based on the received data.

[0084] Furthermore, the first data update section 70 updates the first data 13 at predetermined intervals during driving. For example, the first data update section 70 may update the first data 13 every hour after the vehicle V starts driving. Alternatively, for example, the first data update section 70 may update the first data 13 every 10 kilometers after the vehicle V starts driving.

[0085] The first data update section 70 updates the first data 13 based on the individual characteristics estimated by the individual characteristic estimation section 68. That is, during driving, the first data update section 70 compares the individual characteristics estimated by the individual characteristic estimation section 68 with the first data 13 stored in the memory 46 and updates the differences.

[0086] When vehicle V stops, the second data update section 72 updates the second data 15 based on the updated first data 13 and the second data 15 stored in server 14.

[0087] Specifically, when the vehicle V enters a state where its power is turned off, the second data update section 72 only receives the data updated during driving from the first data 13, and updates the second data 15 based on the received first data 13.

[0088] (operate)

[0089] The operation of this embodiment is described below.

[0090] (Update processing)

[0091] By using Figure 8 The flowchart shown illustrates an example of the update process of the individual characteristic management device 10. Note that the update process is implemented by the CPU 20 reading and executing the program stored in the ROM 22 or memory 26. Furthermore, in this embodiment, as an example, the update process is performed when the vehicle V is started.

[0092] In step S102, the CPU 20 acquires the second data 15. Specifically, the CPU 20 acquires some of the second data 15 stored in the server 14. At this time, the CPU 20 acquires the second data 15 related to the individual characteristics of the user who drives the vehicle V. For example, if the identification number of the user is registered in advance in the key for starting the vehicle V, the user can be specified in accordance with the identification number registered in the key for starting the vehicle V. Or, if a biometric sensor is installed in the vehicle V, the user can be specified based on a signal acquired by the biometric sensor. Further, the user can be specified in accordance with an image of the driver detected by the driver camera 60 installed in the vehicle V.

[0093] In step S104, the CPU 20 determines whether the first data 13 must be updated. Specifically, if the second data 15 acquired from the server 14 matches the first data 13 stored in the memory 46, the CPU 20 determines that the update of the first data 13 is not necessary. If the second data 15 acquired from the server 14 is different from the first data 13 stored in the memory 46, the CPU 20 determines that the first data 13 needs to be updated. If it is determined that the first data 13 must be updated, the CPU 20 proceeds to the processing of step S106, and if it is determined that the first data 13 does not need to be updated, the CPU 10 proceeds to the processing of step S108.

[0094] In step S106, the CPU 20 updates the first data 13. Specifically, by the function of the first data update section 70, the CPU 20 updates the first data 13 based on the second data 15 received from the server 14.

[0095] In step S108, the CPU 20 determines whether it is time to update the first data 13. Specifically, the CPU 20 determines whether a predetermined update time has been received. For example, in the case where the first data 13 is updated every one hour after the vehicle V starts running, the CPU 20 counts by a timer from the time of the last update, and determines that it is the update time when one hour has elapsed. Or, for example, in the case where the first data 13 is updated every 10 kilometers of running of the vehicle V after starting running, the CPU 20 measures the running distance from the time of the last update, and determines that it is the update time when the vehicle V has run 10 kilometers.

[0096] If the CPU 20 determines in step S108 that it is time to update the first data 13, the CPU 20 proceeds to the processing of step S110. Further, if the CPU 20 determines in step S108 that it is not time to update the first data 13, the CPU 20 proceeds to the processing of step S114.

[0097] In step S110, the CPU 20 acquires driving data detected by various sensors of the vehicle V. In step S112, the CPU 20 updates the first data 13 based on the acquired driving data. The updated first data 13 is stored in the memory 46.

[0098] In step S114, the CPU 20 determines whether the vehicle V is stopped. In the present embodiment, as an example, in a case where an operation of turning off the power of the vehicle V is performed by the user, the CPU 20 determines that the vehicle V is stopped. If the CPU 20 determines that the vehicle V is stopped, the CPU 20 proceeds to the processing of step S116. Further, if the CPU 20 determines that the vehicle V is not stopped, that is, determines that there is a state in which the power of the vehicle V is turned on, the CPU 20 returns to the processing of step S108 and determines whether it is time to update the first data 13.

[0099] In step S116, the CPU 20 updates the second data 15. Specifically, by the function of the second data update portion 72, the CPU 20 receives only data among the first data 13 that is updated during driving, and updates the second data 15 based on the received first data 13.

[0100] As described above, according to the individual characteristic control system S provided with the individual characteristic management device 10 related to the present embodiment, the first data 13 related to the individual characteristics of the user is stored in the memory 46 as the first memory, and the second data 15 related to the individual characteristics of the user is stored in the server 14 as the second memory. Further, the first data 13 and the second data 15 are respectively updated by the ECU 16 based on the driving state of the user. Here, during driving of the user, the ECU 16 updates the first data 13 based on the driving state. By storing the first data 13 in the vehicle V in this way, compared to a structure in which all data related to the individual characteristics is stored outside the vehicle, it is possible to reduce the processing load of the vehicle V when referring to the individual characteristics.

[0101] Further, the ECU 16 updates the second data 15 based on the updated first data 13 and the second data 15 stored in the server 14. Therefore, it is possible to periodically update the second data 15 related to the individual characteristics and stored in the server 14 outside the vehicle V, and it is possible to obtain detailed individual characteristics based on the second data 15.

[0102] Further, because the individual characteristic management device 10 of the present embodiment updates the second data 15 when a predetermined state is reached, the first data 13 can be transmitted to the server 14 while avoiding a situation in which the vehicle V is engaged in a large amount of communication. Specifically, in the present embodiment, because the second data 15 is updated when the vehicle V is stopped, the first data 13 can be transmitted to the server 14 in a state in which communication for vehicle control is not performed.

[0103] Further, in the present embodiment, the individual characteristic management device 10 transmits only the first data 13 that is updated during driving to the server 14. Thus, when the vehicle V is parked, the amount of communication between the vehicle V and the server 14 can be kept to the minimum amount required.

[0104] Further, in the present embodiment, by updating the first data 13 when the vehicle V is started, the individual characteristic management device 10 can manage the individual characteristic based on the latest first data 13 at the start of driving. Further, because only data used during driving among the second data 15 is transmitted to the memory 46, the amount of communication between the vehicle V and the server 14 at the start of the vehicle V can be kept to the minimum amount required.

[0105] Further, in the present embodiment, by updating the first data 13 using a learning model that has performed machine learning, the individual characteristic can be obtained with high accuracy without complex calculation.

[0106] While the individual characteristic management system S and the individual characteristic management device 10 related to the embodiments have been described above, the technology of the present disclosure can of course be implemented in various forms without departing from the scope of the gist of the present disclosure. For example, while the individual characteristic management device 10 is provided outside the vehicle V in the above-described embodiments, the present disclosure is not limited thereto, and can be configured so that the individual characteristic management device 10 is installed in the vehicle V. In this case, the individual characteristic management device 10 can acquire driving data directly from the vehicle V without transmitting the driving data of the vehicle V to the server 14.

[0107] Further, the score calculation processing of the score calculation portion 64 can be performed within the individual characteristic management device 10, or necessary data can be transmitted to the server 14, and the score calculation processing can be performed on the server 14 side. Further, the individual characteristic estimation processing of the individual characteristic estimation portion 68 can be performed within the individual characteristic management device 10, or necessary data can be transmitted to the server 14, and the individual characteristic estimation processing can be performed on the server 14 side. In the case in which the score calculation processing and the individual characteristic estimation processing are performed on the server 14 side, data such as a formula required for score calculation and the like and the learning model M can be stored in the server 14.

[0108] Further, in the above-described embodiment, the individual characteristic estimation section 68 and the first data update section 70 estimate the individual characteristics by inputting the driving data and the like into the learning model M and performing the calculation processing of the learning model M. However, the present disclosure is not limited to this. For example, the individual characteristics can be estimated without using the learning model M. In this case, a method of storing a table in which the individual characteristics are associated with the scores and the number of measurements of the driving data in advance and estimating the individual characteristics by referring to the table can be used.

[0109] Further, although the above-described embodiment describes a structure of estimating the driving characteristics and the cognitive characteristics as the individual characteristics, the present disclosure is not limited to this. For example, in addition to the driving characteristics and the cognitive characteristics, a psychological characteristic, a visual function, and a health state can be estimated as the individual characteristics. Intelligence curiosity, responsibility, extroversion, cooperativeness, emotional instability, and the like are included as the psychological characteristic. Static vision, dynamic vision, night vision, horizontal visual field, and the like are included as the visual function.

[0110] Further, in the above-described embodiment, data including the acceleration operation, the brake operation, the steering operation, the dangerous operation, the signal following, the sign following, and the driver state are used as the driving data, but the present disclosure is not limited to this. For example, some of the above-described data can be used as the driving data. Alternatively, in addition to the above-described data, data including the absence / presence of road rage, the absence / presence of lane deviation, the absence / presence of a slight collision, and the like can be used as the driving data.

[0111] Further, although the above-described embodiment is configured so that the second data 15 is updated when the vehicle V is stopped, the present disclosure is not limited to this. For example, the second data 15 can be updated when the vehicle V is temporarily stopped. Alternatively, for example, the second data 15 can be updated when the gear position is a parking position. Even in this case, the travel of the vehicle V is not affected by the update of the second data 15. That is, the processing load of the ECU 16 can be reduced compared to a case where the second data 15 is updated during the travel of the vehicle V.

[0112] Further, the above-described embodiment is configured so that some of the second data 15 is transmitted to the ECU 16 of the vehicle V when the vehicle V is started, and the first data 13 is updated. However, the present disclosure is not limited to this. For example, after the second data 15 is updated by the second data update section 72 when the vehicle V is stopped, some of the updated second data 15 can be transmitted to the ECU 16 of the vehicle V, and the first data 13 can be updated. In this case, in a state before the vehicle V is started, the first data 13 is in a state of the latest data, and thus, the processing load at the time of starting the vehicle V can be reduced.

[0113] Further, in the above-described embodiments, any one of various types of processors other than the CPU 20 can execute the processing executed as a result of the CPU 20 reading in a program. In this case, examples of the processor include a PLD (programmable logic device) such as an FPGA (field programmable gate array) whose circuit structure can be changed after manufacture, and an application-specific circuit such as an ASIC (application-specific integrated circuit) that is a processor having a circuit structure designed for the sole purpose of executing a specific processing. Further, the above-described processing can be executed by any one of these various types of processors, or by a combination of two or more of the same type or different types of processors, for example, a plurality of FPGAs, or a combination of a CPU and an FPGA, and the like. Further, more specifically, the hardware structure of these various types of processors is a circuit that combines circuit elements such as semiconductor elements.

[0114] Further, although the above-described embodiments are configured so that various data are stored in the memory 26 and the memory 46, the present disclosure is not limited thereto. A non-transitory recording medium such as, for example, a CD (compact disc), a DVD (digital versatile disc), a USB (universal serial bus) memory, and the like can be used as the memory. In this case, various programs and data, and the like are stored on these recording media.

Claims

1. An individual characteristic management system (S) comprising: a first memory (46) installed at a vehicle and storing first data (13) related to an individual characteristic of a user; a second memory (14) provided outside the vehicle and storing second data (15) related to the individual characteristic; and a controller (16) that updates the first data (13) and the second data (15) respectively based on a driving state of the user, wherein the controller (16) updates the first data stored in the first memory during driving of the user and based on the driving state, and updates the second data based on the updated first data and the second data stored in the second memory, wherein the controller (16) updates the second data (15) in a case where the vehicle is stopped, and wherein the controller (16) transmits only data updated during driving among the first data (13) to the second memory (14) in a case where the vehicle is stopped. The controller (16) updates the second data (15) in a case where a vehicle state becomes a predetermined state.

2. The individual characteristic management system according to claim 1, wherein, The controller (16) transmits at least some of the second data (15) to the first memory (46) and updates the first data (13) in a case where the vehicle is started.

3. The individual characteristic management system according to claim 1, wherein, The controller (16) transmits only data used during driving among the second data (15) to the first memory (46) in a case where the vehicle is started.

4. The individual characteristics management system according to claim 3, wherein, The controller (16) updates the first data (13) by inputting driving data obtained from a sensor during driving to a learning model on which machine learning has been performed, and performing a calculation process of the learning model.

5. The individual characteristic management system according to any one of claims 1 to 4, wherein, 6. An individual characteristic management method for a system comprising: a first memory (46) installed at a vehicle and storing first data (13) related to an individual characteristic of a user; a second memory (14) provided outside the vehicle and storing second data (15) related to the individual characteristic, the method comprising: updating the first data (13) stored in the first memory (46) during driving of the user and based on a driving state, updating the second data (15) based on the updated first data (13) and the second data (15) stored in the second memory (14), the method characterized in that, the second data (15) is updated in a case where the vehicle is stopped, and in a case where the vehicle is stopped, only data updated during driving among the first data (13) is transmitted to the second memory (14).

7. A non-transitory storage medium storing a program executable by a computer to execute a process comprising: ​ during driving of the user, updating first data (13) based on the driving state, the first data (13) being stored in a first memory (46) installed at the vehicle and relating to individual characteristics of the user; and updating the second data (15) based on the updated first data (13) and the second data (15), the second data (15) being stored in a second memory (14) arranged outside the vehicle and relating to the individual characteristics, wherein the second data (15) is updated in the case where the vehicle is stopped, and wherein, in the case where the vehicle is stopped, only data updated during driving among the first data (13) is transmitted to the second memory (14).

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