JP4995046B2 - Auto insurance premium setting system - Google Patents

Description

  The present invention relates to a safe driving diagnosis system and an automobile insurance premium setting system, and more particularly to a safe driving diagnosis system for diagnosing a safe driving level of a driver of an automobile, and an individual insurance premium according to the diagnosed safe driving level. The present invention relates to an automobile insurance premium setting system for calculating

  2. Description of the Related Art Conventionally, a system for measuring the behavior of a vehicle with a speedometer, an accelerometer, or the like and determining a driver's safe driving level and a driver's driving skill is known. There is also a known system that measures risk driving information (for example, rapid acceleration and inter-vehicle distance) of a driver and calculates an insurance premium for each driver based on the measured risk driving information.

  For example, in Patent Document 1, a vehicle behavior characteristic is detected from a vehicle speed pulse, a longitudinal accelerometer (an accelerometer that measures acceleration in the lateral and longitudinal directions of the vehicle), an angular accelerometer, and the like. The technique for judging is described.

  Patent Document 2 discloses rapid acceleration, inter-vehicle distance, response delay time (delay time until the driver recognizes an obstacle ahead and steps on the accelerator and brake, or operates the steering wheel), speed violation, etc. Describes a technique for calculating insurance premiums according to each driver's risk driving information based on the driver's risk driving information.

Patent Document 3 discloses physical data (for example, speed violation, sudden acceleration, sudden deceleration, sudden handle, number of near-misses, and lateral G) and labor data (for example, current time and current) related to the driving of the driver in a predetermined period. Technology for creating a safe driving form by taking in the data and statistically processing the taken-in data.
JP 2002-2111265 A JP 2004-30190 A Japanese Patent Laid-Open No. 2004-234260

  In the conventional system, information such as the speed generated as a result of the driver's driving behavior, the longitudinal acceleration of the vehicle, and the lateral acceleration of the vehicle is used to determine the safe driving level of the driver and the driving skill of the driver. . However, in order to strictly determine the safe driving level and the driving skill, information on not only the driver's action but also the driving environment of the vehicle and the state of the driver (for example, side-view driving and dozing driving) is necessary. In the conventional system, since the safe driving level and the driving skill are determined without considering the information on the driving environment of the vehicle and the state of the driver, there is a problem that an accurate determination cannot be made.

  Patent Document 2 shows an example in which the inter-vehicle distance and the driver response delay time are used as the driving environment information, but there is no strict risk index definition using the driving environment information, Similarly, accurate driving diagnosis cannot be performed.

  Further, in the conventional system, the insurance premium corresponding to the risk driving information of each driver is calculated based on the driver's risk driving information such as rapid acceleration, inter-vehicle distance, response delay time, and speed violation. If the average risk level of the driver is high, a predetermined additional premium is added so that the premium is higher, and a specific method for calculating the premium based on the driver's risk driving information is disclosed. Not. Therefore, even when the risk level of each driver can be grasped, there is a problem that an appropriate insurance premium corresponding to the risk level of the driver cannot be calculated.

  Therefore, the first problem is to determine an accurate safe driving level and driving skill of the driver.

  The second problem is to show a specific method for calculating the insurance premium according to the risk level of each driver.

A typical example of the present invention is as follows. That is, an automobile insurance premium setting system that acquires a driving state of a vehicle based on a driving operation of the driver and determines a safe driving level of the driver based on the acquired driving state, and acquires an identifier of the driver A driver identification unit; a device for obtaining first information relating to a driving state of the vehicle; and obtaining second information relating to a traveling environment of the vehicle; the obtained driver identifier; the first information; A terminal that transmits the information of 2 and the driver identifier, the first information, and the second information, and based on the first information and the second information, for each driver identifier A computer that determines a driving level and calculates the automobile insurance premium for each of the driver identifiers based on a determination result of the safe driving level. Of the drivers corresponding to the driver identifiers, the risk index database that stores information on the risk index that is the determination item of the safe driving level, the determination result database that stores the result of the determination of the safe driving level, and the driver identifier. An insurance premium database for storing information on the automobile insurance premium paid by the driver every predetermined period, and the received driver based on the received first information and the received second information For each identification, calculate the value of the risk index, determine the safe driving level for each risk index for the driver corresponding to the received driver identifier based on the calculated risk index value, and In the index database, the driver identifier, the calculated risk index value, and the list The entry information that associates the result of the determination of the safe driving level for each of the vehicle index is stored, and when the new first information and the new second information are received, the newly received first information Based on the information and the newly received second information, a new value of the risk index is calculated, and the value corresponding to the received driver identifier is calculated based on the newly calculated risk index value. The driver determines the safe driving level for each risk index, and updates the risk index database based on the newly calculated risk index value and the newly determined safe driving level for each risk index. And updating the determination result database by storing information stored in the risk index database after the predetermined period has elapsed, The result of determination of the safe driving level for each risk index is read from the determination result database, the insurance premiums of the plurality of drivers are read from the insurance premium database, and the risk drivers of the plurality of read drivers are read for each risk index. Based on the safe driving level determination result and the read automobile insurance premiums of the plurality of drivers, a calculation formula for the automobile insurance premium corresponding to the safe driving level is generated, and the generated calculation formula is And calculating the car insurance premium of the driver corresponding to the driver identifier.

  According to an embodiment of the present invention, the safe driving level and driving skill of a driver can be accurately determined. Moreover, the insurance premium according to the risk level of each driver can be calculated based on the determination result.

  The outline of the embodiment of the present invention is as follows.

  In the present invention, in order to solve the first problem, not only the behavior of the vehicle based on the behavior of the driver but also the driving environment of the vehicle and the state of the driver are considered, and the driver is based on a specific risk index. Determine the safe driving level and driving skill.

  In addition, in order to solve the second problem, a driver risk index and a database of insurance claims paid due to driver accidents are constructed, and data collected from vehicles is statistically processed to extract risk indices that affect insurance premiums. . The correlation between the extracted risk index and the insurance premium is calculated, and the insurance premium is calculated based on the calculated correlation and the value of the risk information.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings.

  In the embodiment of the present invention, an insurance company is a subject that performs a safe driving diagnosis service (determination of a driver's safe driving level and calculation of a driver's insurance fee according to the determination result). The driver's safe driving diagnosis service may be provided by, for example, an automobile manufacturer and other business companies other than the insurance company. In addition, in order to receive the safe driving diagnosis service, the driver shall make a contract with an insurance company in advance.

  The information flow of the safe driving diagnosis system will be described with reference to FIG.

  FIG. 1 shows a flow of information between devices constituting a safe driving diagnosis system according to an embodiment of the present invention.

  The driver makes a contract with an insurance company and purchases the in-vehicle terminal 10 compatible with the safe driving diagnosis service from the terminal provider. The in-vehicle terminal 10 may be a terminal dedicated to a safe driving diagnosis service or a terminal with an improved car navigation system. The terminal is equipped with a communication means, and can communicate with the telecenter center server 120 wirelessly. Note that a mobile phone owned by the driver may be used as the communication means.

  The in-vehicle terminal 10 transmits the driving information of each driver (the driving environment information of the vehicle 1 and the driving state information of the driver) used for the driver safe driving diagnosis service to the insurance company server 130 via the telecenter center server 120. . The insurance company server 130 determines the safe driving level of each driver based on the received travel information, and stores the determination result in the database.

  For example, in response to a request for sending a determination result of the safe driving level requested by the driver (contractor), the insurance company server 130 receives the in-vehicle terminal 10 or the driver (contractor) via the telemer center server 120. The determination result is transmitted to the contractor terminal (for example, home PC) 150 that is held.

  The traffic information providing server 140 transmits traffic information (for example, traffic jam information and accident information) to the in-vehicle terminal 10 and the insurance company server 130 via the telecenter center server 120. The traffic information transmitted by the traffic information providing server 140 is used as part of a trigger for the in-vehicle terminal 10 to acquire travel information.

  FIG. 2 is a configuration diagram of the safe driving diagnosis system according to the embodiment of the present invention.

  The safe driving diagnosis system includes an in-vehicle terminal 10, a traffic information providing server 140, a telema center server 120, an insurance company server 130, and a contractor terminal 150.

  The in-vehicle terminal 10 includes a telematics ECU 101, a communication unit 102, a man-machine interface 103, and a display 104.

  The communication means 102 transmits / receives the travel information and the determination result of the safe driving level to / from the telema center server 120.

  The man-machine interface 103 designates a driver who receives the safe driving diagnosis service or requests the insurance company server 130 for a determination result of the safe driving level.

  The display 104 is a device that displays the determination result of the safe driving level.

  The telematics ECU 101 is a device that appropriately acquires information necessary for safe driving based on information input from each ECU mounted on the vehicle 1.

  The telematics ECU 101 includes a clock 110, various sensor information units 11, an engine / transmission ECU 12, a brake control ECU 13, an inter-vehicle distance control ECU 14, a skid prevention control ECU 15, a traction control ECU 16, a lane keeping assist ECU 17, a face image measurement ECU 18, an information system Information is input from the ECU 19, the GPS receiver 20, and the seat adjuster 111.

  The watch 110 inputs information on the current time to the telematics ECU 101.

  The various sensor information units 11 acquire information such as vehicle speed and acceleration as information related to the driving state of the vehicle 1, and input the acquired information to the telematics ECU 101.

  The engine / transmission ECU 12 is a control device that changes the speed of the vehicle 1 by controlling the engine and the accelerator.

  The brake control ECU 13 is a device that adjusts the brake fluid pressure applied to the four wheels according to the depression amount of the brake pedal and controls the rotation of the wheels when the driver steps on the brake pedal. Further, when instructed by the inter-vehicle distance control ECU 14 to control the brake, the brake fluid pressure applied to the four wheels is regulated, and the rotation of the wheels is automatically controlled. The brake control ECU 13 inputs the operation state of the brake to the telematics ECU 101 as information relating to the driving state of the vehicle 1.

  The inter-vehicle distance control ECU 14 measures the inter-vehicle distance between the driver's driving vehicle and the preceding vehicle with a radar, and when the inter-vehicle distance is short and dangerous, issues an alarm to the driver and instructs the brake control ECU 13 to control the brake. This reduces the driving load on the driver. The inter-vehicle distance control ECU 14 inputs the inter-vehicle distance to the telematics ECU 101 as information related to the traveling environment of the vehicle 1.

  The side-slip prevention control ECU 15 is a control device that controls the braking force of the four wheels independently to prevent side-slip on a slippery road surface. The skid prevention control ECU 15 inputs information indicating that the control for preventing skidding has been activated to the telematics ECU 101 as information related to the traveling environment of the vehicle 1.

  The traction control ECU 16 detects a slip of the tire on a slippery road surface (tire slip ratio) and controls the engine (for example, lowers the output of the engine) to control the tire so as to capture the road surface as much as possible. It is. The traction control ECU 16 inputs information indicating that the engine control based on the idling of the tire has been activated to the telematics ECU 101 as information related to the traveling environment of the vehicle 1.

  The lane keeping assist control ECU 17 is a control device that automatically deviates from the traveling lane by using a vehicle-mounted image sensor or the like to issue a warning to the driver and automatically operate the steering. The deviation from the driving lane is determined by, for example, recognizing the white lines on the left and right of the front of the vehicle 1 with a camera such as an image sensor and the four wheels deviating from the recognized white lines (for example, the four wheels are on the white line It is to determine whether or not The lane keeping assist control ECU 17 inputs information indicating that the four wheels have deviated from the white line to the telematics ECU 101 as information related to the traveling environment of the vehicle 1.

  The face image measurement ECU 18 measures the driver's face image with a camera installed in front of the driver, and uses the image processing technology to detect the movement of the driver's face sideways and the degree of eyelid closure from the measured face image. To do. It is a device that determines a driver's side-by-side driving and dozing operation based on the detected state and issues a warning to the driver. The face image measurement ECU 18 inputs the side-view driving and dozing operation states to the telematics ECU 101 as information relating to the driving state of the vehicle 1.

  The information system ECU 19 is a device that controls a direction indicator (blinker) and a hazard lamp. The information system ECU 19 inputs information indicating that the direction indicator and the hazard lamp are operated to the telematics ECU 101 as information on the driving state of the vehicle 1.

  The GPS receiver 20 is a device that detects position information of the vehicle 1 by receiving radio waves transmitted from artificial satellites. Based on the detected position information of the vehicle 1, traffic information around the position of the vehicle 1 is acquired from the traffic information providing server 140 or the like. The GPS receiver 20 inputs the detected position information of the vehicle 1 to the telematics ECU 101 as information related to the traveling environment of the vehicle 1.

  The seat adjuster 111 is a device that automatically adjusts the position of the seat, the position of the steering, and the like according to the driver who drives the vehicle 1. Specifically, a driver identifier (driver ID), a seat position, a steering position, and the like are registered in advance. When each driver drives the vehicle 1, the driver's identifier is input from the man-machine interface 103, so that the position of the seat, the position of the steering, and the like are automatically adjusted according to each driver.

  The functions of each ECU described above can be used as a pre-crash safety system (a system that reduces collisions with preceding vehicles and obstacles ahead). Each ECU inputs to the telematics ECU 101 information on whether or not the driving support function has been activated, and information such as an inter-vehicle distance and a vehicle speed acquired by each ECU. The driving support function is a function that does not operate in a normal driving operation but operates when a predetermined driving condition is satisfied. The brake control ECU 13, the skid prevention control ECU 15, the traction control ECU 16, the lane keeping assist ECU 17, and the face image measurement ECU 18 are ECUs that provide driving assistance. The engine / transmission ECU 12, the inter-vehicle distance control ECU 14, and the information system ECU 19 are ECUs that do not provide driving assistance.

  The telematics ECU 101 transmits information (information related to the driving state of the vehicle 1 and information related to the traveling environment of the vehicle 1) input from each ECU to the telematic center server 120 via the communication unit 102.

  FIG. 3 shows a configuration diagram of the telematics ECU 101 according to the embodiment of the present invention.

  The telematics ECU 101 includes a CPU 21, a ROM 22, a RAM 23, and an I / O 24. Each component is connected by a bus.

  The ROM 22 stores a program for extracting driving information and a program for acquiring driving information used for determining the safe driving level. The CPU 21 executes a program stored in the ROM 22. The RAM 23 stores temporarily stored data. The I / O 24 is a device that converts an input analog signal into a digital signal or converts an input digital signal into an analog signal and outputs the analog signal.

  FIG. 4 is a configuration diagram of the telecenter center server 120 according to the embodiment of this invention.

  The telecenter center server 120 includes a CPU 31, a communication unit 34, a ROM 33, a RAM 32, and an operation diagnosis information DB 35. Each component is connected by a bus.

  The ROM 33 stores a program for periodically transmitting the information stored in the driving diagnosis information DB 35 to the insurance company server 130 via the communication means 34. The communication means 34 is a device that transmits and receives data to and from the outside connected to the network. The CPU 31 executes a program stored in the ROM 33. The RAM 32 stores temporarily stored data.

  The driving diagnosis information DB 35 temporarily stores the driving information corresponding to the identifier (in-vehicle terminal ID) of the in-vehicle terminal 10 received from the in-vehicle terminal 10 and the driver identifier (driver ID) input by the driver to identify the driver. To store. The vehicle-mounted terminal ID is an ID that is set in advance when the driver purchases the vehicle-mounted terminal 10 and is stored in the telematics ECU 101.

  The telecenter center server 120 may include a man-machine interface, and an administrator of the telema center server 120 may add, change, and delete information stored in the driving diagnosis information DB 35.

  FIG. 5 is a configuration diagram of the insurance company server 130 according to the embodiment of this invention.

  The insurance company server 130 includes a CPU 41, a man-machine interface 42, a communication means 43, a ROM 44, a RAM 45, a contractor information DB 401, an in-vehicle terminal information DB 402, a risk index DB 403, a diagnosis result DB 404, an insurance premium calculation basic data DB 405, and individual insurance premiums. DB 406 and payment insurance DB 407 are provided. Each component is connected by a bus.

  The ROM 44 stores a program for receiving information from the telema center server 120 and a program for transmitting the diagnosis result of the safe driving diagnosis service to the in-vehicle terminal 10 via the communication unit 43. The communication means 43 is a device that transmits and receives data to and from the outside connected to the network. The CPU 41 executes a program stored in the ROM 44. The RAM 45 stores temporarily stored data. The man-machine interface 42 is used by an administrator of the insurance company server 130 to add, change, and delete information stored in each DB.

  The contractor information DB 401 stores information (service subscriber information) of a driver who receives a safe driving diagnosis service. Details of the contractor information DB 401 will be described later with reference to FIG.

  The in-vehicle terminal information DB 402 stores service subscriber information (driver ID) corresponding to the information (in-vehicle terminal ID) of the in-vehicle terminal 10. Details of the in-vehicle terminal information DB 402 will be described later with reference to FIG.

  The risk index DB 403 stores information on the in-vehicle terminal 10 and the driver received from the telecenter center server 120 and travel information used for determining the driver's safe driving level. Details of the risk index DB 403 will be described later with reference to FIG.

  The diagnosis result DB 404 stores the determination result of the safe driving level of each driver determined based on the information stored in the risk index DB 403. Details of the diagnosis result DB 404 will be described later with reference to FIG.

  The insurance premium calculation basic data DB 405 stores basic data used for calculating insurance premiums based on the safe driving level determination result stored in the diagnosis result DB 404. Details of the insurance premium calculation basic data DB 405 will be described later with reference to FIG.

  The individual insurance premium DB 406 stores the monthly appropriate insurance premium calculated using the basic data stored in the insurance premium calculation basic data DB 405 corresponding to the driver ID. Details of the individual insurance premium DB 406 will be described later with reference to FIG.

  The payment insurance DB 407 stores a past accident payment insurance amount corresponding to the driver ID. Details of the payment insurance DB 407 will be described later with reference to FIG.

  FIG. 6 shows an example of information stored in the contractor information DB 401 according to the embodiment of this invention.

  The contractor information DB 401 stores a driver ID, name, date of birth, address, and other information.

  The driver ID is a unique ID that identifies the contractor. The name is information on the name of the contractor. The date of birth is information on the date of birth of the contractor. The address is information on the address of the contractor. Others are, for example, information such as the sex and age of the contractor.

  FIG. 7 shows an example of information stored in the in-vehicle terminal information DB 402 according to the embodiment of the present invention.

  The in-vehicle terminal information DB 402 stores an in-vehicle terminal ID and a driver ID.

  The in-vehicle terminal ID is a unique ID that identifies the in-vehicle terminal 10. The driver ID is a unique ID that identifies the contractor.

  Next, before describing details of the safe driving diagnosis system process executed by the in-vehicle terminal 10, the telecenter center server 120, and the insurance company server 130, and the details of the DB provided in the insurance company server 130, an embodiment of the present invention will be described. The risk index used for determining the safe driving level of the driver will be described.

  FIG. 8 shows an example of the risk index according to the embodiment of the present invention.

  The insurance company server 130 receives the driving information of each driver from the in-vehicle terminal 10 and calculates the risk index value (risk information) shown in FIG. 8 based on the received driving information. The calculated risk information is used to determine the safe driving level.

  Item 1 (No. 1) is a risk index based on the operation status of a driving support function called pre-crash safety (ABS, traction control, skid prevention device, lane keeping assist, automatic brake, etc.).

  In general, it is considered that the risk of accidents is high when the operation of the driving support function frequently occurs in a short period of time. Note that, as measurement information for detecting the operation of the driving support function, a flag for notifying that the control of the driving support function has been operated, the time of operation, and the like are used.

  As one of the risk indices of item 1, the number of control support operations per unit travel distance (number of control support operations / travel distance) and the number of control support operations per unit travel time (control support operation count / travel time) are used. . The control support operation count is the number of times that the driving support function is activated. As another index, when the number of control support operations in a unit travel distance (for example, 100 m) exceeds a threshold value (for example, 3 times), the number N of times the threshold is exceeded is counted, and the number N is counted as the travel distance and the travel distance. The risk index may be divided by time.

  Item 2 (No. 2) is a risk index based on the deviation of the driving lane. Driving lane departure is a risk that a novice or elderly person commits without knowing it, and is considered as one of the causes of accidents. Note that, as measurement information for detecting a departure from a traveling lane, an event that generates a departure warning from the traveling lane, a state that is likely to deviate from the traveling lane before the alarm is generated, and the like are used.

  The departure warning from the driving lane occurs when it is recognized by the lane keeping assist control that the four wheels of the vehicle have stepped on the white line. Moreover, the state that is likely to deviate from the traveling lane is a state in which the four wheels of the vehicle approach within a predetermined distance (for example, within 20 cm) from the white line.

  As one of the risk indexes of item 2, a value obtained by dividing the number of warnings of the lane keeping assist control by the travel distance and the travel time is used. Alternatively, the number of times that the vehicle is likely to deviate from the traveling lane before the warning is generated may be counted, and the counted number divided by the traveling distance may be used.

  Item 3 (No. 3) is a risk index based on the brake response time. In general, when a time from when a front obstacle (for example, a car traveling ahead) is found to when the driver steps on the brake is short, an accident such as a rear-end collision is likely to occur. Note that as the measurement information for detecting the brake response time, the vehicle speed v and the inter-vehicle distance L between the host vehicle and the preceding vehicle (the vehicle traveling ahead) are used.

Further, an appropriate inter-vehicle distance L ₀ is calculated from the speed v and the inter-vehicle distance L. The calculation of the proper vehicle distance L _0, graph of proper inter-vehicle distance L ₀ and velocity v shown in Figure 9 is used. In the example shown in FIG. 9, the appropriate inter-vehicle distance represented by L ₀ = 0.83 × v is calculated with the appropriate inter-vehicle time being 3 seconds. The appropriate inter-vehicle time is an appropriate time until the host vehicle reaches the position where the preceding vehicle is traveling at a certain time (for example, when the preceding vehicle suddenly steps on the brake, The time when the host vehicle can avoid a collision with the preceding vehicle) Further, the characteristic shown in FIG. 9 (a straight line indicated by L ₀ = 0.83 × v) may be corrected according to the relative speed between the preceding vehicle and the host vehicle.

As one of the risk indexes of item 3, the time when the inter-vehicle distance L is smaller than the appropriate inter-vehicle distance L ₀ and the relative speed Δv becomes equal to or less than a predetermined threshold is t0 (the preceding vehicle has decelerated for some reason) The time when the driver starts to brake is t1, and the average value of (t1-t0) (for example, the average value when the inter-vehicle distance L becomes less than the appropriate inter-vehicle distance L ₀ ) is the brake response. Used as time. When the value of the brake response time is large, the probability of causing an accident due to the driver's cognitive delay and operation delay increases.

Item 4 (No. 4) is a risk index based on a determination as to whether or not the driver is driving while maintaining an appropriate inter-vehicle distance L ₀ . The risk index of item 4 uses information on the appropriate inter-vehicle distance L ₀ shown in FIG. As measurement information for detecting an appropriate inter-vehicle distance, a vehicle speed v, an inter-vehicle distance L with respect to a vehicle traveling ahead, and the like are used.

  Whether or not the vehicle has deviated from the appropriate inter-vehicle distance when the vehicle is traveling at a substantially constant speed, that is, the inter-vehicle distance between the preceding vehicle and the host vehicle is less than a predetermined threshold (appropriate inter-vehicle distance x coefficient β) Is measured at a predetermined cycle (for example, every minute), and the number of times N deviating from the appropriate inter-vehicle distance is counted. The coefficient β is changed according to the degree of determination of the safe driving level when setting the threshold value. For example, when the safe driving level is strictly determined, the coefficient β is set to a value larger than 1.0 in order to set the appropriate inter-vehicle distance long. Further, when the safe driving level determination is loosened, the coefficient β is made shorter than 1.0 in order to set the appropriate inter-vehicle distance short. Further, the degree of determination of the safe driving level corresponds to the traffic volume of the road on which the vehicle 1 travels. Specifically, based on the traffic information acquired from the traffic information provider server 140, when the road on which the vehicle 1 is traveling is congested, the safe driving level is strictly determined, and the vehicle 1 When the road on which the vehicle is traveling is not congested, the safe driving level determination is relaxed. Thus, the threshold value can be corrected according to the traffic volume.

  As one of the risk indices of item 4, a value obtained by dividing the number N of deviations from the appropriate inter-vehicle distance by the travel distance or travel time is used. The coefficient multiplied by the appropriate inter-vehicle distance is a constant smaller than 1.

The inter-vehicle distance is L, and the appropriate inter-vehicle distance is L ₀ . L ₀ / L when deviating from the appropriate inter-vehicle distance is calculated in a predetermined cycle, and the accumulated addition value obtained by adding L ₀ / L in the predetermined cycle is Lc. Then, as one of the risk indexes, a value obtained by dividing the cumulative added value Lc by the travel distance or travel time is used. Therefore, since the case where the distance between vehicles becomes short is also considered, a safe driving level can be determined appropriately.

  Item 5 (No. 5) is a risk index based on the number of side-by-side driving or dozing driving by the driver when the preceding vehicle is traveling. As the measurement information for detecting the number of side-by-side driving and dozing driving, the vehicle speed v, the distance L between the vehicle traveling ahead, the side-by-side driving occurrence information, the dozing driving occurrence information, and the like are used. As described above, the side-view driving is detected by measuring the movement of the driver's face sideways using the face image measurement ECU 18. The snooze driving is detected by measuring the degree of closing the driver's eyelid using the face image measurement ECU 18.

  As one of the risk indicators of item 5, the number N of sidewalks or snoozing when the inter-vehicle distance is smaller than a predetermined threshold (appropriate inter-vehicle distance × coefficient γ) is counted, The value divided by the travel time is used. The coefficient γ is changed according to the degree of determination of the safe driving level when the threshold is set. For example, when the safe driving level is strictly determined, the coefficient γ is set to a value larger than 1.0 in order to set the appropriate inter-vehicle distance long. Further, when the safe driving level is loosely determined, the coefficient γ is made shorter than 1.0 in order to set the appropriate inter-vehicle distance short. Further, the degree of determination of the safe driving level corresponds to the traffic volume of the road on which the vehicle 1 travels. Specifically, based on the traffic information acquired from the traffic information provider server 140, when the road on which the vehicle 1 is traveling is congested, the safe driving level is strictly determined, and the vehicle 1 When the road on which the vehicle is traveling is not congested, the safe driving level determination is relaxed. Thus, the threshold value can be corrected according to the traffic volume.

  Item 6 (No. 6) is a risk index based on driving accompanied by dangerous operation of the driver. In particular, driving on roads with heavy traffic increases the risk of accidents. For example, driving with dangerous motion is (a) lane change without blinker (direction indicator) operation, and (b) sudden stop without hazard ramp. As measurement information for detecting a driving operation involving a dangerous operation of the driver, (a) a winker operation signal, a steering angle Θ, and the like are used for lane change without a winker operation. Further, (b) a hazard lamp operation signal, an acceleration α in the front-rear direction of the vehicle 1, and a large amount of traffic m are used for sudden stop without a hazard lamp.

  The traffic volume m is defined by the number of vehicles 1 passing through a point with one lane per unit time (for example, 1 minute) (the number of passing vehicles / unit time). When m> m0 (constant), it is determined that the route has a lot of traffic, and N1 is the number of times the lane is changed without operating the turn signal in a state where there is a lot of traffic, and N2 is the number of times of sudden stop without operating the hazard lamp. And Here, the sudden stop means that the vehicle 1 is stopped in a state where the acceleration of the vehicle 1 is a negative value and the acceleration of the vehicle 1 is equal to or less than a predetermined threshold.

  The traffic volume m uses the operation time when the driving support function operates and the traffic information from the traffic information providing server 140 corresponding to the operation time. Also, when the traffic information m at the point where the driver's vehicle is traveling is not measured by the traffic information providing server 140, the driver's vehicle out of the points where the traffic information providing server 140 is measuring the traffic amount m. Information on the closest point on the route on which the vehicle 1 is traveling is used.

  As one of the risk indices of item 6, a value obtained by dividing the number N1 of lane changes without blinker operation and the number N2 of sudden stops without hazard lamps by the travel distance is used.

  Here, FIG. 10 shows the timing of blinker (direction indicator) operation at the time of lane change and the timing of hazard lamp lighting at the time of sudden stop. At the timing shown in FIG. 10, when there is no operation of the blinker and the hazard lamp, the number N1 of the lane change without the blinker operation or the number N2 of the sudden stop without the hazard lamp is counted.

  Item 7 (No. 7) is a risk index based on sudden acceleration / deceleration and sudden steering on a road with a large amount of traffic. These operations increase the risk of accidents on high-traffic roads.

  The amount of traffic is defined using the traffic volume m described above, and when the traffic volume m exceeds a predetermined threshold (when it is determined that the traffic volume is large), the number of sudden acceleration / deceleration N1 and the sudden handle Count the number N2. As the measurement information for detecting sudden acceleration / deceleration and sudden steering, the amount of traffic m and the acceleration of the vehicle are used. Note that the rapid acceleration / deceleration is a state where the absolute value of the longitudinal acceleration of the vehicle 1 exceeds a predetermined threshold value. The sudden steering wheel is in a state where the absolute value of the steering angular acceleration exceeds a predetermined threshold value.

  As one of the risk indexes of item 7, a value obtained by dividing the number of times of rapid acceleration / deceleration N1 and the number of times of sudden steering N2 by the travel distance is used.

  Next, the process of the safe driving diagnosis system executed by the in-vehicle terminal 10, the telema center server 120, and the insurance company server 130 will be described with reference to FIG.

  FIG. 11 shows a flowchart of processing for generating a driving diagnosis result and processing for displaying the driving diagnosis result according to the embodiment of the present invention.

  In the example shown in FIG. 11, while the driver is driving the vehicle 1, the insurance company server 130 collects the driver's risk data (running information), and generates the driving diagnosis result by analyzing the collected risk data. When the driver requests a driving diagnosis result, the insurance company server 130 transmits the generated driving diagnosis result to the in-vehicle terminal 10, and the in-vehicle terminal 10 that has received the driving diagnosis result displays the driving diagnosis result on the display 104. .

  First, in step 901, the driver inputs his driver identifier (driver ID) via the man-machine interface 103 of the in-vehicle terminal 10. When there are a plurality of people who drive the same car, each driver ID is set in advance, and the set driver ID is used. For example, an ID (for example, “1” for one's ID, “2” for a spouse's ID, etc.) for each driver that is determined in advance at the time of contract is input. The driver may be automatically determined by a face image measurement sensor and a fingerprint authentication sensor.

  Next, in step 902, the in-vehicle terminal 10 receives the information input from the driver in step 901. The in-vehicle terminal 10 may automatically acquire the driver identifier (driver ID) from the seat adjuster 111.

  Next, in step 903, the in-vehicle terminal 10 acquires information (running information) from each ECU, speed sensor, and acceleration sensor and traffic information from the insurance company server 130.

  Next, in step 904, the in-vehicle terminal 10 determines whether or not it is the timing to transmit the travel information necessary for determining the safe driving level of the driver to the telecenter center server 120 from the information acquired in step 903. For example, based on the information acquired in step 903, it is determined that it is time to transmit travel information when the value of the inter-vehicle distance between the host vehicle and the preceding vehicle falls within a predetermined threshold. Alternatively, it may be determined that it is time to transmit travel information when the driving support function is activated.

  If it is time to transmit the travel information, the process proceeds to step 905. On the other hand, if it is not time to transmit the travel information, the process returns to step 903.

  Next, in step 905, the in-vehicle terminal 10 transmits the travel information to which the in-vehicle terminal ID, the driver ID, and the time information are added to the telematic center server 120 using the communication unit 102. The travel information includes information indicating which risk index item is the travel information.

  Next, in step 906, the telecenter center server 120 receives the information (in-vehicle terminal ID, driver ID, and travel information) transmitted from the in-vehicle terminal 10 in step 905. Then, the received traveling information is stored in the driving diagnosis information DB 35 (step 907). The information stored in the driving diagnosis information DB 35 is traveling information used for calculating risk information such as speed, acceleration, inter-vehicle distance, and a flag indicating the operation of the driving support function.

  Next, in step 908, the telecenter center server 120 transmits the driving diagnosis information stored in the driving diagnosis information DB 35 to the insurance company server 130 at a predetermined cycle (for example, every other day) using the communication means 34. To do.

  Next, in step 909, the insurance company server 130 receives the driving diagnosis information transmitted from the telecenter center server 120 in step 908. Based on the received driving diagnosis information, risk information and driving evaluation of each risk index are calculated, and the calculation result is stored in the risk index DB 403 (step 910). Details of step 910 will be described later with reference to FIG.

  Next, in step 911, the insurance company server 130 generates a driving diagnosis result based on the information in the risk index DB 403 stored in step 910, and stores the generated driving diagnosis result in the diagnosis result DB 404.

  In step 912, the driver designates the year and month and the driver ID, and requests the insurance company server 130 to display the driving diagnosis result of the designated year and month.

  Next, the in-vehicle terminal 10 transmits the in-vehicle terminal ID, the driver ID, and the date information to the insurance company server 130 via the telecenter center server 120. (Step 913 to Step 915).

  Next, the insurance company server 130 acquires the driving diagnosis result corresponding to the received in-vehicle terminal ID, driver ID, and year from the diagnosis result DB 404, and transmits the acquired driving diagnosis result to the in-vehicle terminal 10 (step 916). And step 917).

  Next, in step 918, the in-vehicle terminal 10 receives the driving diagnosis result from the insurance company server 130 and displays the driving diagnosis result of the corresponding year shown in FIG.

  FIG. 12 shows an example of information stored in the risk index DB 403 according to the embodiment of this invention.

  The risk index DB 403 stores data generation date, terminal ID, driver ID, cumulative mileage, risk index, risk driving frequency, risk information, and evaluation). Information stored in the risk index DB 403 is generated for each year, in-vehicle terminal ID, and driver ID when the information is acquired.

  The data generation date is the date when the information stored in the risk index DB 403 is acquired. The terminal ID is an ID that uniquely identifies the in-vehicle terminal 10. The driver ID is an ID that uniquely identifies the contractor. The cumulative travel distance is the cumulative travel distance of the year and month when data is generated. The risk index is an item of each risk index. The risk driving number is a value of basic data used for determining each item of the risk index. The risk information is a value of a risk index calculated based on the risk driving number. Evaluation is evaluation of a risk index determined based on risk information.

  In the example illustrated in FIG. 12, data is generated in May 2007, and the driver with the driver ID “1” driving the car with the in-vehicle terminal ID “203000” travels 500 km in May 2007.

  Further, the number of times of risk driving (number of times of control support operation) is 1, the value of risk information calculated based on the risk index of item 1 shown in FIG. 8 is 2 (times / 1000 km), and the evaluation is “D ". Specifically, since the cumulative travel distance is 500 km and the number of control support operations is one, the number of control support operations per unit travel distance (1000 km) is two when calculated based on the risk index of item 1. It is. When the evaluation of the driver having the control support operation frequency of 2 to 4 is “D”, the evaluation is “D”.

  The value corresponding to the risk index of each item is stored in the risk driving number, for example, the value of the control support operation number is stored in the risk driving number of item 1.

  The evaluation has five levels, A (good), B (slightly good), C (normal), D (slightly bad), and E (bad). This evaluation is performed based on the calculated risk information value. Specifically, the average value a and standard deviation σ of the risk index of the entire contractor are calculated for each risk information value. Generally, the higher the risk information value, the higher the accident risk. Therefore, when the risk information value is less than a-2σ, the evaluation is A, the evaluation is a-2σ or more and less than a−σ, the evaluation is B, the evaluation is a−σ or more and a + σ or less, the evaluation is C, the evaluation is greater than a + σ and a + 2σ is less than D, Is evaluated as E. In these determinations, the contractor may be divided into segments by age and mileage, and evaluation may be determined for each segment.

  The information stored in the risk index DB 403 is generated at the beginning of each month. In the initial state, the number of risk operations and the value of risk information are set to 0, and the evaluation is set to A. As long as the number of times of risk driving is the generated year and month, the number of risk driving increases based on the information transmitted from the in-vehicle terminal 10, and the value of the risk information is updated accordingly.

  FIG. 13 shows an example of information stored in the diagnosis result DB 404 according to the embodiment of this invention. A comment about driving is added instead of the value of risk information stored in the risk index DB 403.

  The comment is a diagnostic result of evaluation related to the driving of the driver. The comment is a form in which information stored corresponding to the evaluation is called and attached. The information stored in the diagnosis result DB 404 is generated when the month changes and the information stored in the risk index DB 403 shown in FIG. 10 is confirmed.

  FIG. 14 is a flowchart of processing for updating information stored in the risk index DB 403 according to the embodiment of this invention.

  First, in step 1801, the insurance company server 130 calculates the risk driving frequency based on the risk index of each item shown in FIG. 8, and updates the risk driving frequency of each risk index.

  In step 1802, the insurance company server 130 updates the value of risk information based on the number of risk driving operations updated in step 1801.

  Next, in 1803, the insurance company server 130 updates the evaluation based on the calculated risk information value.

  The cumulative mileage information is calculated by calculating the difference between the previous update time and the current update time for the total mileage from the time when the driver started driving to the time when driving ended. The information is updated by adding to the cumulative travel distance of the year and month when the information is updated.

  Next, an insurance premium calculation system for calculating an insurance premium based on the determination result of the driver's safe driving level will be described.

  The configuration of the insurance premium calculation system is the same as that in FIGS. 1 and 2, and a process for calculating the insurance premium based on the driving diagnosis result of each driver (contractor) is added.

  The insurance fee calculation process will be described with reference to the flowchart shown in FIG.

  FIG. 15 shows a flowchart of insurance premium calculation processing according to the embodiment of the present invention.

  Here, it is assumed that the contractor has an automobile insurance policy (personal compensation, objective compensation, vehicle insurance, etc.) with an insurance company in addition to the safe driving diagnosis service. Insurance premiums are collected monthly from the policyholder. The insurance premium is calculated based on the driving diagnosis result. For example, the insurance premium for June is calculated based on the driving diagnosis result in May, and the calculated insurance premium is charged in June.

  First, in step 1301, the insurance company server 130 calculates the risk information value for each contractor as described above, determines the safe driving level, and displays the information on the paid insurance money of the contractor who has caused the accident. accumulate. The accumulation period is a period (for example, one year or more) in which the amount of data that can be used to construct the insurance premium calculation model according to the risk is obtained by statistical processing. The information on the payment insurance money is stored in the payment insurance money DB 407 shown in FIG. 5 for each contractor.

  FIG. 16 shows an example of information stored in the payment insurance DB 407 according to the embodiment of this invention.

  The payment insurance DB 407 stores a driver ID and payment insurance information. The driver ID is a unique identifier that identifies the driver. Insurance claims paid are insurance claims paid by insurance companies to policyholders each year.

  Next, in step 1302 of FIG. 15, the insurance company server 130 extracts a risk index effective for calculating the insurance premium by the method described below.

  First, policyholders are divided into segments according to age, mileage, and insurance contract details. For example, the age is divided into segments every 10 years. Also, the mileage for one month is divided into segments of every 500 km. For example, a segment of a contractor has an age of 30 years old or more and less than 40 years old, a monthly mileage is 1000 km or more and less than 1500 km, and the insurance contract content is unlimited objective compensation and interpersonal compensation unlimited.

Next, the contractor i (i = 1 to n) belonging to a certain segment is extracted, and the value of the risk index j (j = 1 to 7) of each contractor for the year and month t is set to R _ijt and Let the amount be _Hit . The amount of the insurance money paid is acquired by referring to the payment insurance DB 407 shown in FIG. Next, an average value and a standard deviation of R _ijt values are calculated, and the contractor is evaluated in five stages as described above. Calculate the sum of policyholders' insurance claims for each evaluation, divide the calculated sum of each insurance premium paid by the number of policyholders belonging to each evaluation, and pay insurance per capita y _kt for each evaluation in each segment (Where k = 1 represents evaluation A, k = 2 represents evaluation B, k = 3 represents evaluation C, k = 4 represents evaluation D, and k = 5 represents evaluation E). calculate.

  FIG. 17 shows a graph in which the insurance premium per person for each evaluation of the safe driving level is plotted.

  In the example shown in FIG. 17, the age of the category (segment) is 30 years old or older and less than 40 years old, the monthly running distance is 500 km or more and less than 1000 km, and interpersonal compensation is unlimited. Moreover, it is a graph regarding the item 1 of the risk index shown in FIG.

The horizontal axis of the graph shown in FIG. 17 indicates evaluation k (takes discrete values of k = 1, 2, 3, 4, and 5). The vertical axis shows the actual y _kt of payment insurance per capita each evaluation. As shown in FIG. 17, when the plotted value can be approximated by a straight line that rises to the right, this risk index (item 1) indicates that it is an effective index for insurance premium calculation.

  Whether or not the plotted values can be linearly approximated can be determined by performing regression analysis using an assumed regression equation described below, and calculating estimated values of parameters a and b and their statistics.

y _kt = a · k + b + ε (1)
Here, the evaluation k is k = 1, 2, 3, 4, 5, the parameters a and b are regression coefficients, and ε is an error term.

  As a result of the regression analysis, when the absolute value of the t statistic of the parameters a and b is 2 or more, it indicates that the expression is meaningful. Further, when the estimated value of the parameter a is positive, it is a straight line rising upward. That is, when the absolute value of the t statistic of the parameters a and b is 2 or more and the parameter a is positive, it is determined that the risk index subjected to the regression analysis is an effective index for insurance premium calculation. The t statistic is a statistic set for estimating the variance of a population to be subjected to regression analysis and testing whether a certain regression coefficient is equal to a specific value. Then, the insurance company server 130 extracts a risk index determined to be an effective index for insurance premium calculation.

  In the example shown in FIG. 17, only data for a certain year and month is handled, but the reliability of the analysis is improved by performing pool regression analysis using data for a plurality of years simultaneously (for example, using panel data). can do. Pool regression analysis is to accumulate (pool) a plurality of data to be analyzed, and to perform regression analysis of the accumulated data. For example, the payment insurance value for a plurality of months is accumulated for each of five evaluations, and the accumulated value is subjected to regression analysis.

  FIG. 18 shows an example in which regression analysis is performed on the regression equation shown in Equation (1) using data for a plurality of months (from March 2007 to May 2007). The above-described calculations are performed on the above-mentioned seven items of risk indicators, and risk indicators determined to be effective indicators for insurance premium calculation are extracted. This process is performed for each segment of the contractor. Accordingly, the number of effective risk indicators or the items of effective risk indicators may be different for each segment.

  Next, in step 1303 of FIG. 15, the insurance company server 130 constructs a category-specific insurance fee calculation model based on the risk index extracted in step 1302.

  For example, assume that risk indicators for three items are extracted in a certain category. Over a period of time, each contractor in that category has an assessment of three items of risk indicators. Here, the evaluation is defined as evaluation A = 1, evaluation B = 2, evaluation C = 3, evaluation D = 4, and evaluation E = 5. Further, assuming that the contractor i (i = 1... N) has three evaluations (l, m, n), the payment per person of the corresponding year of the contractor having the same evaluation (l, m, n). The insurance money is R (l, m, n). Assuming the following regression equations with variables l, m, and n as explanatory variables and R as an explained variable, the estimated values of coefficient parameters a1, a2, a3, and b are calculated by regression analysis.

R (l, m, n) = a1 · l + a2 · m + a3 · n + b + ε (2)
Here, the variables i, m, and n are integers of 1 to 5, and ε is an error term.

  Assuming that the estimated values of the coefficient parameters a1, a2, a3, and b are ah1, ah2, ah3, and bh, a reasonable calculation model of the insurance premium Rh according to the risk is expressed as follows.

Rh = ah1 · l + ah2 · m + ah3 · n + bh (3)
This calculation model is calculated for each segment of the contractor by the method described above. The calculated model (model parameter) is stored in the insurance premium calculation basic data DB 405 shown in FIG.

  FIG. 19 illustrates an example of information stored in the insurance premium calculation basic data DB 405 according to the embodiment of this invention. The insurance premium calculation basic data DB 405 stores model parameters (ah1, ah2, ah3, bh) for each category.

  The categories are divided into segments according to age, mileage, compensation details, and the like. The parameter is a value of a coefficient parameter calculated by regression analysis.

  Therefore, a reasonable insurance premium can be calculated from the evaluation (l, m, n) of the previous month's driving diagnosis result using Equation (3). As described above, the reliability of the model can be improved by using the panel data extending over a plurality of years as the data applied to Equation (2). When a plurality of drivers drive a vehicle 1, the appropriate insurance premium for each driver is calculated using Equation (3), and the insurance premium obtained by adding the calculated appropriate insurance premiums is calculated for the vehicle 1. Insurance premium.

  Next, in Step 1304 of FIG. 15, the insurance company server 130 calculates a reasonable insurance premium based on the risk for each vehicle 1 (contractor). Specifically, it is calculated using a premium calculation model (calculation formula) of the category to which the contractor belongs. For example, model parameters corresponding to the contractor's category are obtained by referring to the insurance premium calculation basic data DB 405 shown in FIG. 5, a premium calculation formula is constructed, and the premium is calculated using the calculated formula. To do. In the current insurance system, since discounts are implemented according to the grade for each vehicle 1 (contractor), it is necessary to add a correction corresponding to the grade to the above-mentioned reasonable insurance premium.

  Next, a specific method for correcting the appropriate insurance premium according to the grade for each vehicle 1 (contractor) will be described with reference to FIG.

  FIG. 20 shows a table in which the reasonable insurance premium according to the embodiment of the present invention is corrected according to the grade.

  The table shown in FIG. 20 includes a driver, a risk, a base reasonable premium, a correction coefficient based on a grade, a revised valid premium (1), and a revised valid premium (2).

  For example, five contractors, drivers A to E, belong to a certain category, and their respective risks and appropriate insurance premiums calculated by the calculation method described above are shown in FIG.

  For example, in the case of driver A, the corrected appropriate insurance premium (1) is calculated as 3 × 0.5 / 0.58 = 2.58. Note that 0.58 is an average value of correction coefficients based on grades. However, if the calculated reasonable premium is left as it is, the average of the corrected reasonable premium (1) does not match the average of the base reasonable premium. Therefore, the corrected appropriate insurance premium (2) is obtained by multiplying the corrected effective insurance premium (1) by a uniform constant so that the average of the two coincides. In other words, the final reasonable insurance premium is set as the revised reasonable insurance premium (2).

  The insurance premium that is actually claimed is the revised reasonable insurance premium (2) plus personnel expenses and profit equivalents. The corrected appropriate insurance premium (2) is stored in the individual insurance premium DB 406 shown in FIG.

  FIG. 21 shows an example of information stored in the individual insurance premium DB 406 according to the embodiment of this invention. The individual insurance premium DB 406 stores information on insurance premiums for each year and month for each contractor (driver).

  The driver ID is a unique ID that identifies the contractor. The insurance premium is the value of the corrected reasonable insurance premium (2) for each year.

  In this way, based on the safe driving level of the determined driver, the insurance premium calculation model is constructed using regression analysis, and the insurance premium is calculated using the calculation model. Can be calculated automatically. In addition, by extracting a risk index effective for calculating the insurance premium and constructing a calculation model from the extracted risk index, it is possible to appropriately calculate the insurance premium paid by each driver.

  According to the embodiment of the present invention, the safe driving level of the driver can be accurately determined. In addition, it is possible to calculate a reasonable insurance premium corresponding to the driver's safe driving level (risk level).

The flow of the information between the apparatuses which comprise the safe driving | operation diagnostic system of embodiment of this invention is shown. The block diagram of the safe driving | operation diagnostic system of embodiment of this invention is shown. The block diagram of the telematics ECU of embodiment of this invention is shown. The block diagram of the telecenter center server of embodiment of this invention is shown. The block diagram of the insurance company server of embodiment of this invention is shown. The example of the information stored in the contractor information DB of embodiment of this invention is shown. The example of the information stored in the vehicle-mounted terminal information DB of embodiment of this invention is shown. The example of the risk parameter | index of embodiment of this invention is shown. It shows a graph of the relationship between the proper vehicle-to-vehicle distance L ₀ and velocity v. The timing of blinker (direction indicator) operation at the time of lane change and the timing of hazard lamp lighting at the time of sudden stop are shown. The flowchart of the process which produces | generates the driving diagnosis result of the embodiment of this invention and the process which displays a driving diagnosis result is shown. The example of the information stored in risk index DB of embodiment of this invention is shown. The example of the information stored in diagnostic result DB of embodiment of this invention is shown. The flowchart of the process which updates the information stored in risk index DB of embodiment of this invention is shown. The flowchart of the process of insurance premium calculation of embodiment of this invention is shown. The example of the information stored in payment insurance money DB of embodiment of this invention is shown. The graph which plotted the premium per person for every evaluation of safe driving level is shown. An example of regression analysis using multi-year data is shown. The example of the information stored in insurance premium calculation basic data DB of embodiment of this invention is shown. The table | surface which correct | amended the reasonable insurance premium of embodiment of this invention according to the grade is shown. The example of the information stored in personal insurance premium DB of embodiment of this invention is shown.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Vehicle 10 Vehicle-mounted terminal 120 Telema center server 130 Insurance company server 140 Traffic information provision server 150 Contractor terminal

Claims (6)

  A vehicle insurance premium setting system that acquires a driving state of a vehicle based on a driving operation of a driver and determines a safe driving level of the driver based on the acquired driving state,
  A driver identification unit for obtaining an identifier of the driver;
  An apparatus for acquiring first information relating to a driving state of the vehicle and acquiring second information relating to a traveling environment of the vehicle;
  A terminal that transmits the acquired driver identifier, the first information, and the second information;
  The driver identifier, the first information, and the second information are received, a safe driving level is determined for each driver identifier based on the first information and the second information, and the safe driving is performed A computer for calculating the automobile insurance premium for each driver identifier based on a level determination result,
  The calculator is
  Among the drivers corresponding to the driver identifier, a risk index database that stores information related to a risk index that is a determination item of the safe driving level, a determination result database that stores a result of determination of the safe driving level, and an accident A premium database for storing information on the automobile premium paid by the driver who has woken up every predetermined period; and
  Based on the received first information and the received second information, the value of the risk index is calculated for each received driver identification,
  Based on the calculated value of the risk index, determine the safe driving level for each risk index for the driver corresponding to the received driver identifier,
  In the risk index database, the driver identifier, the calculated risk index value, and entry information that associates the result of determination of the safe driving level for each risk index,
  When the new first information and the new second information are received, the new risk index is generated based on the newly received first information and the newly received second information. The value of
  Based on the value of the newly calculated risk index, determine the safe driving level for each risk index for the driver corresponding to the received driver identifier,
  Updating the risk index database based on the newly calculated risk index value and the safe driving level for each newly determined risk index;
  After the predetermined period, update the determination result database by storing information stored in the risk index database,
  The determination result of the safe driving level for each risk index of the plurality of drivers is read from the determination result database, the insurance premiums of the plurality of drivers are read from the insurance premium database, and the plurality of drivers read Based on the result of the determination of the safe driving level for each risk index and the automobile insurance premiums of the plurality of read out drivers, a formula for calculating the automobile insurance premium corresponding to the safe driving level is generated,
  The automobile insurance premium setting system, wherein the automobile insurance premium of the driver corresponding to the driver identifier is calculated using the generated calculation formula.   The calculator is
  When generating the formula for calculating the automobile insurance premium, the risk index effective for calculating the automobile insurance premium is extracted from the risk index,
  2. The automobile insurance premium setting system according to claim 1, wherein a calculation formula of the automobile insurance premium is generated using a result of the determination of the safe driving level for each extracted risk index.   The calculator is
  The number of times that the driving state of the vehicle has exceeded a predetermined condition from the first information and the number of times that the driving environment of the vehicle has exceeded a predetermined condition from the second information are calculated as the value of the risk index,
  2. The automobile insurance premium setting system according to claim 1, wherein the safe driving level is determined for each risk index based on a result of comparing a value of each risk index and a preset threshold value. 3. .   The device is
  Generating a first alarm when the driving state of the vehicle exceeds a predetermined condition;
  Generating a second warning when the driving environment exceeds a predetermined condition;
  The terminal
  Collecting the first alarm as the first information;
  Collecting the second alarm as the second information;
  The calculator calculates the number of the first alarm per unit mileage from the first information, and the number of the second alarm per unit mileage from the second information. The automobile insurance premium setting system according to claim 3, wherein the system is calculated as follows.   The said apparatus acquires the value which shows the operation | movement of a vehicle as the driving | running state of the said vehicle, or the value which shows the operation | movement of the said driver as said 1st information, The automobile insurance premium setting of Claim 3 characterized by the above-mentioned. system.   The value indicating the operation of the vehicle includes at least one of a steering angle, an accelerator operation amount, a brake operation state, a vehicle speed, an acceleration, and a wheel slip ratio,
  6. The automobile insurance premium setting system according to claim 5, wherein the value indicating the operation of the driver includes at least one of the number of times the driver falls asleep and the number of times the driver sees.

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