JP7655881B2 - DETECTION DEVICE, DETECTION METHOD, AND PROGRAM - Google Patents

Description

The present invention relates to a detection device, a detection method, and a program.

Currently, in order to ensure the safety of drivers who drive vehicles, ADAS (Advanced Driver Assistance System) is being developed.
Development of a function called Autonomous Driver Assistance Systems (ADAS) is underway. ADAS includes various functions, such as warning of an obstacle ahead, automatic braking when it is determined that a collision is unavoidable, and warning when the vehicle deviates from its lane. Patent Document 1 also discloses a device that predicts the driver's drowsiness level based on information acquired from the vehicle and the driver.

JP 2019-68935 A

There are various factors that cause accidents, but they are not necessarily limited to factors that occur while driving. In some cases, an accident may occur as a result of a driver's reckless driving due to accumulated fatigue or mental stress, etc., and other factors that cause an accident may be a state that is different from normal. Therefore, it is considered necessary to understand the driving tendencies of drivers in order to prevent accidents from occurring.

ADAS can detect a moment of inattention on the part of the driver while driving a car and automatically apply the brakes, issue a warning, etc. However, ADAS cannot detect changes in the driver's driving tendencies, such as a gradual deterioration in the driver's mental state, which results in the driver's driving becoming increasingly aggressive.

Therefore, an object of the present invention is to provide a technique that makes it possible to detect a change in a driver's driving tendency.

A detection device according to one aspect of the present invention provides a detection device having an acquisition unit that acquires driving data of a vehicle, a determination unit that determines, based on the driving data, a driving time model related to the driver's driving time, a driving range model indicating the range in which the driver drives the vehicle, and a driving method model related to the method in which the driver drives the vehicle, and a detection unit that detects a change in the driver's driving tendency based on at least one of a change in the driving time model, a change in the driving range model, and a change in the driving method model.

According to the present invention, it is possible to provide a technique that makes it possible to detect a change in a driver's driving tendency.

FIG. 1 illustrates an example of a detection system according to an embodiment of the present invention. FIG. 2 illustrates an example of a hardware configuration of a detection device. FIG. 2 is a diagram illustrating an example of a functional block configuration of a detection device. 11 is a flowchart illustrating an example of a processing procedure for determining and updating a driving model of a driver. FIG. 2 illustrates an example of a driving time cluster. FIG. 11 is a diagram illustrating an example of the frequency of driving time. FIG. 2 illustrates an example of a driving range cluster. FIG. 11 is a diagram illustrating an example of the frequency of travel locations. FIG. 2 illustrates an example of a driving method cluster. 10 is a flowchart showing an example of a processing procedure for detecting a change in a driving model of a driver. FIG. 11 is a diagram showing an example of a predetermined control according to the content of a change in a driver's driving tendency.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described with reference to the accompanying drawings, in which the same reference numerals denote the same or similar configurations.

<System Configuration>
FIG. 1 is a diagram illustrating an example of a detection system 1 according to the present embodiment.
This system detects changes in the driver's driving habits and takes action according to the nature of the change. Actions according to the nature of the change include alerting the driver to prevent accidents (i.e., detecting accident signs). Detection system 1
The system includes a detection device 10 and one or more vehicles 20. The detection device 10 and the vehicles 20 are connected via a wireless communication network and can communicate with each other.

The detection device 10 acquires driving data from the vehicle 20, and determines a driving model of the driver based on the acquired driving data. Here, the driving model is an index or pattern that indicates the tendency of the driver when driving a vehicle. The driving model in this embodiment includes at least three types of models: a driving time model, a driving range model, and a driving method model.

The "driving time model" represents the tendency of drivers to drive their cars at certain times of the day (for example, they tend to drive in the morning, or they tend to drive in the morning and at night).
The "driving style model" represents the tendency of the driver's driving style (for example, whether the driver mostly drives short distances or long distances, etc.). The "driving style model" represents the tendency of the driver's driving style (what is the average vehicle speed, and whether the accelerator and steering are operated carefully or roughly, etc.).

For example, in the case of a driver who often drives in the morning to go shopping in the neighborhood and drives carefully, the driving time model is "driving in the morning," the driving range model is "driving within a 10 km radius from the home," and the driving method model is "careful accelerator and steering operation."

The vehicle 20 is equipped with an on-board device that transmits and receives data to and from the detection device 10. The on-board device transmits various data acquired by various sensors equipped in the vehicle 20 or the on-board device to the detection device 10. The on-board device also transmits various data acquired by various sensors equipped in the vehicle 20 or the on-board device to the detection device 10.
It has a function for changing the settings of the AS function and displaying messages received from the detection device 10, etc.

When the detection device 10 detects that the driver's driving model has changed, it performs control such as, for example, sending a specified notification to the vehicle 20 driven by the driver or a terminal owned by the driver, or changing the settings of the ADAS function installed in the vehicle 20, based on the content of the change in the driving model.

<Hardware Configuration>
FIG. 2 is a diagram illustrating an example of a hardware configuration of the detection device 10. The detection device 10 includes a CPU
Processor 1 such as a Central Processing Unit (Central Processing Unit) or a Graphical Processing Unit (GPU)
The input device 1 includes a storage device 12 such as a memory, a hard disk drive (HDD) and/or a solid state drive (SSD), a communication interface (IF) 13 for wired or wireless communication, an input device 14 for accepting input operations, and an output device 15 for outputting information.
The input device 4 is, for example, a keyboard, a touch panel, a mouse, and/or a microphone, etc. The output device 15 is, for example, a display, a touch panel, and/or a speaker, etc.

The detection device 10 may be configured using one or more physical servers, or may be configured using a virtual server operating on a hypervisor, or may be configured using a cloud server.

<Function block configuration>
3 is a diagram showing an example of a functional block configuration of the detection device 10. The detection device 10 includes an acquisition unit 101, a determination unit 102, a detection unit 103, a control unit 104, and a storage unit 105.
The storage unit 105 can be realized by using the storage device 12 included in the detection device 10. The acquisition unit 101, the determination unit 102, the detection unit 103, and the control unit 104 are implemented by the detection device 10.
The above-mentioned program can be realized by the processor 11 of the computer 10 executing a program stored in the storage device 12. The program can be stored in a storage medium. The storage medium storing the program is a computer-readable non-transitory storage medium (Non-volatile storage medium).
The non-transient storage medium is not particularly limited, and may be, for example, a storage medium such as a USB memory or a CD-ROM.

The acquisition unit 101 acquires driving data of the vehicles 20 from a plurality of vehicles 20 and stores the data in the storage unit 105.
The operation data is stored in a database (DB).

The determination unit 102 determines a driving model based on the driving data of the driver of the detection target, and stores the determined driving model of the driver of the detection target in a driving model history DB. More specifically, the determination unit 102 determines a driving time model related to the driving time of the driver, a driving range model indicating the range in which the driver drives the vehicle 20, and a driving method model related to the method in which the driver drives the vehicle 20, based on the driving data of the driver of the detection target, and stores the determined driving time model, driving range model, and driving method model in a driving model history DB.
Store it in B.

The determination unit 102 may determine the driving model in any manner, but may also analyze driving data for a predetermined period (e.g., from one week ago to the present) and determine the driving model according to data that appears a predetermined number of times or more during the predetermined period (e.g., data that appears frequently).

The detection unit 103 detects a change in the driving tendency of the driver based on the presence or absence of a change in the driving model of the driver. More specifically, the detection unit 103 detects a change in the driving tendency of the driver based on at least one of a change in the driving time model, a change in the driving range model, and a change in the driving method model.

When the detection unit 103 detects that the driver's driving tendency has changed, the control unit 104 performs a predetermined control according to the change in the driver's driving tendency. The predetermined control is as follows:
The driver may be notified that their driving habits are changing.
The notification indicating that the driving tendency has changed may be, for example, a message displayed on a display, an alarm sound, or a vibrator. In addition, the predetermined control may be a change in the setting of an ADAS (driving support device) mounted on the vehicle 20.

The memory unit 105 stores a driving data DB and a driving model history DB.

<Processing Procedure>
(Decision of driving model)
FIG. 4 is a flowchart showing an example of a processing procedure for determining and updating a driving model of a driver.

In step S10, the acquisition unit 101 acquires driving data from a plurality of vehicles 20 and stores the data in the driving data DB.

The driving data may be any data, but for example, the position information of the vehicle 20 (G
The driving data may include the output information of the PS, speed, acceleration, traveling direction, accelerator opening, braking force, steering angle, etc. The driving data may also include the driver's heart rate, a facial image showing the driver's expression, etc. The driving data includes time data indicating when the data was measured.

In step S11, the determination unit 102 determines, based on the driving data of the driver to be detected,
The driving method model, the driving range model, and the driving method model are determined.
2. The determined driving method model, driving range model, and driving method model of the driver to be detected are recorded in a driving model history DB in association with date information indicating the point in time at which the driving model was determined (for example, a driving model determined using driving data for one week from January 20 to January 26, 2020).

The driver to be detected may be any driver selected, but may be, for example, a driver selected by a company that provides an accident warning service using the detection system 1 (for example, a driver who is subscribed to the accident warning service).

The determination unit 102 may determine the driving method model, the driving range model, and the driving method model in any manner, but may also determine the driving model by analyzing driving data for a predetermined period (e.g., one month) and determining to which of predetermined clusters data that appears a predetermined number of times or more during the predetermined period (e.g., data that appears frequently) belongs.

5 is a diagram showing an example of a driving time cluster. In the example of FIG. 5, the driving time clusters are “1. Night travel (18:00 to 3:00)”, “2. Afternoon travel (13:00 to 18:00)”, “3.
Morning travel (6:00-12:00)", "4. Early morning (3:00-6:00) and evening (15:00-18:00)
Five clusters have been defined: “5. Morning (6:00-9:00) and nighttime (18:00-3:00) movements.”

FIG. 6 is a diagram showing an example of the frequency of driving time. FIG. 6 shows data obtained by tallying up the driving data of five drivers for a certain month on a weekly basis. The bright parts in the graph indicate time periods with high driving frequency. For example, it can be seen that driver A drives a lot at night during a certain month. Therefore, the determination unit 102 determines the driving time model of driver A to be "1. Nighttime travel". Similarly, it can be seen that driver D drives a lot in the early morning and evening. Therefore, the determination unit 102 determines the driving model of driver D to be "4. Early morning and evening travel".

Fig. 7 is a diagram showing an example of a driving range cluster. In the example of Fig. 7, three clusters are defined as the driving range cluster: "1. Short distance (less than 4 km)", "2. Medium distance (4 km or more to less than 50 km)", and "3. Long distance (4 km or more to less than 50 km)".

FIG. 8 is a diagram showing an example of the frequency of driving locations. FIG. 8 shows the frequency with which the driver has driven each mesh on the map. The larger the driving frequency number in the legend, the higher the driving frequency. The determination unit 102 determines the driving frequency based on the size of the range including all meshes with a driving frequency equal to or greater than a predetermined value (e.g., a driving frequency of 4 or greater),
A driving range model may be determined. For example, the determination unit 102 may determine the radius of the smallest circle that includes all meshes as the driving range of the driver. For example, assume that the radius of the circle is 10 km. In this case, the determination unit 102 may determine that the driving range model of the driver is a model for driving "2. Medium distance".

FIG. 9 is a diagram showing an example of a driving style cluster. In the example of FIG. 9, the driving style clusters are classified into "1. Low speed range (less than 30 km/h)", "2. Medium speed range (30 km/h to 70 km/h)", and "3.
/h), "3. High speed range (70 km/h or more)", ..., "7. Medium speed range + sudden deceleration and stopping + sudden acceleration", etc. are defined.

For example, the determination unit 102 classifies the driving data for a predetermined period into a constant speed driving state, an accelerating state, and a decelerating state. In addition, the determination unit 102 classifies the constant speed driving state into a low speed range (less than 30 km/h), a medium speed range (30 km/h or more and less than 70 km/h), and a high speed range (70 km/h or more).
1/h or more). In addition, the determination unit 102 classifies acceleration into normal acceleration or rapid acceleration, and deceleration into normal deceleration, rapid deceleration (sudden braking), or further rapid deceleration followed by a stop. Next, the determination unit 102
A driving style model for the driver is determined by determining which driving style cluster the scene that appears most frequently among the classified scenes belongs to.

Returning to FIG. 4, the description will be continued. The determination unit 102 repeats the processing procedures of steps S10 and S11 every predetermined period to determine the driving model of the driver for every predetermined period (S12-NO). In addition, the determination unit 102 ends the processing for the driver for whom it is no longer necessary to detect a change in the driving model (S12-YES). That is, the driving model of the driver is determined in units of a predetermined period. The predetermined period may be, for example, a one-week unit or a one-month unit. As a result, the driving model of the driver to be detected is recorded in the driving model history DB in order for each predetermined period. For example, if the predetermined period is a one-month unit, the driving models will be recorded as follows: a driving model for January 2020 (more precisely, a driving model based on driving data from January 1 to January 31), a driving model for February 2020 (more precisely, a driving model based on driving data from February 1 to February 29), a driving model for March 2020, ..., a driving model for December 2020.

The processing procedure shown in FIG. 4 may be executed as a batch process on the accumulated past operating data, or may be executed in real time every time new operating data is accumulated.

For example, the determination unit 102 may determine a driving model for each of the driving data divided into predetermined periods of driving data for the past few months, thereby determining a driving model for each predetermined period of driving data for the past few months collectively.

Alternatively, the determination unit 102 may determine a driving model every time new driving data is accumulated. For example, when a predetermined period of time has elapsed since the determination of the driving model and driving data for the predetermined period has been newly accumulated, the determination unit 102 may determine a driving model for the newly accumulated driving data for the predetermined period of time.

(Detection of changes in driving model)
FIG. 10 is a flowchart showing an example of a processing procedure for detecting a change in a driving model of a driver.

In step S20, the detection unit 103 acquires data on the driving model of the driver to be detected from the driving model history DB.

In step S21, the detection unit 103 determines whether or not there is a short-term change in the driving method model. Specifically, the detection unit 103 detects a driving time model, a driving range model, a driving method model, a driving time model, a driving range model, and a driving method model at a first time.
By comparing the driving method model with the driving method model, it is determined whether there is a short-term change in the driving model. If there is a short-term change in the driving model (S21-YES), the detection unit 103 detects that the driver's short-term driving tendency has changed (S22).

In step S23, the detection unit 103 determines whether or not there is a long-term change in the driving method model. Specifically, the detection unit 103 determines whether or not there is a long-term change in the driving model by comparing the driving time model, the driving range model, and the driving method model at the first time with the driving time model, the driving range model, and the driving method model at the third time, which is a time earlier than the second time. If there is a long-term change in the driving model (S23-Y
ES), the detection unit 103 detects that the driver's long-term driving tendency has changed (S
24).

Here, it is assumed that the driving model of the driver to be detected is recorded in the driving model history DB every month. For example, the driving model history DB includes the driving model for January 2020, the driving model for February 2020, the driving model for March 2020, ..., the driving model for January 20 ...
Assume that a driving model for February has been recorded. Short-term changes may mean, for example, comparing the latest driving model with a driving model from one month ago. That is, the driving model at a first time is the latest driving model (i.e., the driving model from December 2020), and the driving model at a second time is the driving model from one month ago (i.e., the driving model from December 2020) of the latest driving model.
The long-term change may be, for example, a comparison of the latest operating model with an operating model from six months ago.
The operation model at time is, for example, the operation model six months before the latest operation model (2020
In addition, regarding short-term and long-term changes,
The timing of the driving models to be compared is not limited to the above example.

When the detection unit 103 detects that the driver's short-term or long-term driving tendency has changed (S22, S24), the control unit 104 performs predetermined control according to the content of the change in the driver's driving tendency.

FIG. 11 is a diagram showing an example of a predetermined control according to the content of the change in the driver's driving tendency. In FIG. 11, "no change" means that there is no change, and "-" means that there is no change. In addition, in FIG. 11, "worsening performance" means that the driving method model has changed to a more dangerous driving, and "improving performance" means that the driving method model has been improved. In the example of FIG. 9, which cluster is determined to be "worsening performance" when moving from which cluster, and which cluster is determined to be "improving performance" when moving from which cluster may be stored in advance as setting information in the storage unit 105. The detection unit 103 may determine whether the driving method model has "worsened performance" or "improved performance" by referring to the setting information.

For example, assume that the driver's driving time model has changed in the short term from "2. Afternoon travel" to "1. Night travel", and the driving method model has not changed. In this case, it is considered that the driving model has changed because the driver returned home late due to short-term overtime work, etc. Therefore, as a predetermined control, the control unit 104 strengthens the ADAS settings (for example, increases the sensitivity of the automatic brake) and sends a message to the vehicle 20 driven by the driver to be careful when driving at night.
The information may be displayed on an in-vehicle device or a mobile device owned by the driver.

It is also assumed that the driver's driving time model and driving range model do not change, and the driving method model has changed over the long term from "1. Low speed range" to "5. Low speed range + sudden deceleration stop + starting". In this case, it is considered that the driving model has changed due to distraction caused by stress or the like. Therefore, as a predetermined control, the control unit 104 may strengthen the setting of the ADAS (for example, increase the sensitivity of the automatic brake), or display comments that encourage stress relief or comments that praise the mind and body on an in-vehicle device of the vehicle 20 driven by the driver or a mobile terminal owned by the driver.

<Additional Information>
The driving method clusters, driving range clusters, and driving method clusters shown in Figures 5, 7, and 9 are merely examples, and may be classified into clusters other than those shown. In addition, the detection device 10 analyzes a large amount of driving data stored in the driving data DB using machine learning technology (e.g., k-
The driving method cluster, the travel range cluster, and the driving method cluster may be defined by clustering using a method such as a mean mean method.

The determination unit 102 may determine the driving time model taking into consideration the day of the week. For example, "1. Night travel (weekday) + no travel (holiday)" and "2. Night travel (weekday) +
Night travel (holidays)", "3. Night travel (weekdays) + afternoon travel (holidays)", "4. Night travel (
"5. Night travel (weekdays) + early morning and evening travel (holidays)"
The categories may be further classified, such as "6. Night travel (weekdays) + morning and night travel (holidays)", "7. Afternoon travel (weekdays) + no travel (holidays)", ..., "XX. Morning and night travel (weekdays) + morning and night travel (holidays)", etc.

The determination unit 102 may determine the driving range model by taking into consideration the viewpoint of the starting point. For example, "1. Short distance with address XX as the center", "2. Medium distance with address XX as the center",
3. A driving range model may be determined such as "long distance with address XX at the center." This makes it possible to detect short-term or long-term changes in the driving model, such as changes due to moving.

In addition, the control unit 104 may determine the content of the predetermined control based on the driver's age in addition to the change in the driving model. For example, if the driving method model has changed in a long-term direction corresponding to "worsening performance" and the driver is elderly, the control unit 1
04 may be configured to transmit a notification message to a terminal or the like owned by a third party other than the driver (e.g., a relative or the like).

The driving method model may also be subdivided by road type, area and/or time period. For example, when the driving method model is subdivided by road type, the determination unit 102 may determine the driver's driving method model for each road type, such as a driving method model for driving on an expressway and a driving method model for driving on a general road. When the driving method model is subdivided by area, the determination unit 102 may determine a driving method model for driving in an area where the driver drives daily (for example, an area determined by a driving range model),
The driver's driving model may be determined for each predetermined area, such as a driving method model for driving in an area other than the area where the driver drives daily (for example, an area other than the area determined by the driving range model). In addition, when the driving method model is subdivided by time period, the determination unit 102 may determine the driver's driving model for each predetermined time period, such as early morning, morning, mid-morning, afternoon, evening, night, and late night. Note that the time period may be divided in any manner. There may be two time periods (such as a first time period and a second time period), or three or more time periods (such as a first time period, a second time period, and a third time period). Note that, when the driving method model is subdivided by road type, area, and/or time period, the detection unit 103 may detect a change in the driving method model by comparing driving method models for the same road type, the same area, and/or the same time period. It is considered that there are drivers whose driving method is different on general roads and expressways, and drivers whose driving method is different when going to work and when returning home. By subdividing the driving style model by road type, area and/or time period, it becomes possible to capture changes in the driver's driving style in more detail.

In the above description, the determination unit 102 classifies the driving data for a predetermined period into during constant speed driving, during acceleration, and during deceleration, and determines which driving method cluster the most frequently occurring scene among the classified scenes belongs to, thereby determining the driving method model of the driver, but this is not limited to this. For example, the determination unit 102 may determine the driving method model of the driver based on the scene whose occurrence frequency is equal to or greater than a predetermined threshold among the classified scenes. In this case, if there are multiple scenes whose occurrence frequency is equal to or greater than a predetermined threshold, the determination unit 102 may determine the driving method model based on which driving method cluster each of the multiple scenes belongs to. For example, if the driving method clusters corresponding to the scenes whose occurrence frequency is equal to or greater than a predetermined threshold among the driving data belong to "1. Low speed range" and "11. High speed range + rapid deceleration + acceleration", the determination unit 102 may determine the driving method model of the driver based on "
It is possible to determine that the driving method model is both "1. Low speed area" and "11. High speed area + rapid deceleration + acceleration". In the case of a driving method model expressed by a plurality of driving method clusters, the detection unit 103 may determine that the driving method model has changed when any one of the driving method clusters has changed. This makes it possible to grasp the change in the driver's driving method in more detail.

<Summary>
According to the embodiment described above, a driving model is determined based on the driving data of the driver, and a change in the driving model is detected. This makes it possible to detect a change in the driver's driving tendency. In addition, short-term and long-term changes in the driving model are detected. This makes it possible to detect changes in the driver's driving model in detail, and makes it possible to take more appropriate actions in response to the changes.

The above-described embodiments are intended to facilitate understanding of the present invention, and are not intended to limit the present invention. The flow charts, sequences, elements included in the embodiments, and their arrangements, materials, conditions, shapes, sizes, etc., described in the embodiments are not limited to those shown as examples, and can be changed as appropriate. In addition, configurations shown in different embodiments can be partially replaced or combined with each other.

For example, all or part of the functions of the detection device 10 may be installed in an in-vehicle device. As an example, the functions of the detection device 10 may be installed in an in-vehicle device such as a drive recorder. It becomes possible to provide a drive recorder or the like that has a function of detecting and notifying changes in the driving tendency of a driver.

1 ... detection system, 10 ... detection device, 11 ... processor, 12 ... storage device, 13 ... communication IF, 14 ... input device, 15 ... output device, 20 ... vehicle, 101 ... acquisition unit, 102
...determination unit, 103...detection unit, 104...control unit, 105...storage unit

Claims (7)

An acquisition unit that acquires driving data of a vehicle;
A determination unit that determines, based on the driving data , a driving time model related to a driving time of a driver, a driving range model indicating a range in which the driver drives the vehicle, and a driving method model related to a method in which the driver drives the vehicle;
A detection unit that detects a change in a driving tendency of the driver based on at least one of a change in the driving time model, a change in the driving range model, and a change in the driving method model;
having
The determination unit determines the driving method model by dividing the driving method model into a driving method model corresponding to a range defined by the driving range model and a driving method model corresponding to a range outside the range defined by the driving range model.
Detection device. The determination unit records the driving time model, the driving range model, and the driving method model in association with time,
The detection unit is
Detecting a change in the driver's short-term driving tendency by comparing the driving time model, the driving range model, and the driving method model at a first time with the driving time model, the driving range model, and the driving method model at a second time;
By comparing the driving time model, the driving range model, and the driving method model at a first time with the driving time model, the driving range model, and the driving method model at a third time that is a time in the past than the second time, a change in the long-term driving tendency of the driver is detected.
The detection device of claim 1 . A control unit that performs a predetermined control according to the change in the driving tendency of the driver when a change in the driving tendency of the driver is detected.
3. The detection device according to claim 1 or 2. The control unit performs a predetermined control by notifying the driver of a predetermined event.
The detection device of claim 3. The control unit changes a setting of a driving support device mounted on the vehicle as the predetermined control.
5. A detection device according to claim 3 or 4. A detection method performed by a detection device, comprising:
acquiring driving data of a vehicle;
determining, based on the driving data , a driving time model relating to a driver's driving time , a driving range model indicating a range in which the driver drives the vehicle, and a driving style model relating to a manner in which the driver drives the vehicle;
Detecting a change in the driver's driving tendency based on at least one of a change in the driving time model, a change in the driving range model, and a change in the driving style model;
Including ,
The determining step determines the driving method model by dividing the driving method model into a driving method model corresponding to a range defined by the driving range model and a driving method model corresponding to a range outside the range defined by the driving range model.
Detection method. acquiring driving data of a vehicle;
determining, based on the driving data , a driving time model relating to a driver's driving time , a driving range model indicating a range in which the driver drives the vehicle, and a driving style model relating to a manner in which the driver drives the vehicle;
Detecting a change in the driver's driving tendency based on at least one of a change in the driving time model, a change in the driving range model, and a change in the driving style model;
on the computer ,
The determining step determines the driving method model by dividing the driving method model into a driving method model corresponding to a range defined by the driving range model and a driving method model corresponding to a range outside the range defined by the driving range model.
program.

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