JP6949799B2 - Difficulty estimation device, difficulty estimation method and computer program - Google Patents

Description

The present invention relates to a difficulty estimation device, a difficulty estimation method, and a computer program.

In modern times, vehicles such as passenger cars are indispensable for daily life and economic activities, so the devices and systems used for the operation of automobile insurance are becoming more and more important. For example, Patent Document 1 discloses a setting device that searches for a traveling route from a starting point to an arriving point and sets an automobile insurance premium when traveling on the traveling route based on the traveling distance, driving time, and the like. ing.

Japanese Unexamined Patent Publication No. 2015-215775

However, since the above-mentioned setting device grasps the usage status of the road by vehicles and pedestrians and does not set the insurance premium based on the difficulty of driving on the road, it may not be possible to set an appropriate insurance premium. rice field.

The present invention has been made in consideration of such circumstances, and an object of the present invention is to provide a difficulty level estimation device, a difficulty level estimation method, and a computer program capable of estimating the difficulty level of driving on a road. ..

(1) One aspect of the present invention is a mobile terminal movement information acquisition unit that acquires mobile terminal movement information including a movement history of the mobile terminal, and a road that shows a tendency for a road to be used based on the mobile terminal movement information. It is a difficulty estimation device including a road usage tendency information generation unit that generates usage tendency information and a driving difficulty estimation unit that estimates the driving difficulty level on the road based on the road usage tendency information.

(2) One aspect of the present invention further includes a user information acquisition unit that acquires user information of the mobile terminal, and the road usage tendency information generation unit is based on the user information in addition to the mobile terminal movement information. , The difficulty estimation device of the above (1) that generates the road use tendency information.

(3) One aspect of the present invention further includes a vehicle state acquisition unit that acquires information indicating the vehicle state of the user information providing vehicle from the user information providing vehicle, and the driving difficulty estimation unit is the road use tendency information. In addition, the difficulty level estimation device according to (1) or (2), which estimates the difficulty level based on the information indicating the vehicle state.

(4) In one aspect of the present invention, the driving difficulty estimation unit is based on factors in which the vehicle state of the user information providing vehicle is associated with the factors of the situation in which the user information providing vehicle encounters on the road. The difficulty estimation device according to (3) above, which estimates the driving difficulty estimation unit based on the information indicating the vehicle state.

(5) One aspect of the present invention includes an evaluation value calculation unit that calculates an evaluation value of a candidate for a movement route in which the user information providing vehicle moves from a departure point to a destination based on the difficulty level, and the movement route. The difficulty of (3) or (4) above, further comprising a route information distribution unit that distributes route information indicating the movement route whose evaluation value satisfies a predetermined condition to the user information providing vehicle. It is a degree estimation device.

(6) One aspect of the present invention is a candidate for a movement route for a vehicle that does not provide the vehicle state to the difficulty estimation device and that moves from the departure point to the destination of the vehicle that does not provide the user information. The evaluation value calculation unit that calculates the evaluation value of the above based on the difficulty level, and the route information indicating the movement route whose evaluation value satisfies a predetermined condition among the candidates for the movement route are transmitted to the vehicle that does not provide the user information. It is a difficulty level estimation device according to any one of ( 3 ) to (5) above, further comprising a route information distribution unit for distribution.

(7) In one aspect of the present invention, the driving difficulty estimation unit estimates the difficulty by applying multivariate analysis to the road usage tendency information, any one of (1) to ( 6) above. Two difficulty estimation devices.

(8) One aspect of the present invention is a mobile terminal movement information acquisition step for acquiring mobile terminal movement information including a mobile terminal movement history, and a road showing a tendency for a road to be used based on the mobile terminal movement information. This is a difficulty estimation method including a road usage tendency information generation step for generating usage tendency information and a driving difficulty estimation step for estimating the driving difficulty level on the road based on the road usage tendency information.

(9) One aspect of the present invention is a mobile terminal movement information acquisition function for acquiring mobile terminal movement information including a movement history of a mobile terminal on a computer, and a tendency for a road to be used based on the mobile terminal movement information. A computer program for realizing a road usage tendency information generation function that generates road usage tendency information indicating the above, and a driving difficulty estimation function that estimates the driving difficulty level on the road based on the road usage tendency information. Is.

According to the present invention, the difficulty of driving on the road can be estimated.

It is a figure which shows an example of the functional structure of the difficulty level estimation system which concerns on embodiment of this invention. It is a figure which shows an example of the display mode of the movement path whose evaluation value satisfies a predetermined condition in embodiment of this invention. It is a flowchart which shows an example of the process executed by the difficulty level estimation apparatus which concerns on embodiment of this invention. It is a flowchart which shows an example of the process which the vehicle state acquisition part which concerns on embodiment of this invention acquires the information which shows the vehicle state of the user information providing vehicle from a vehicle-mounted device.

[Embodiment]
The difficulty estimation system according to the embodiment will be described with reference to FIGS. 1 and 2.
FIG. 1 is a diagram showing an example of a functional configuration of a difficulty level estimation system according to an embodiment of the present invention.
FIG. 2 is a diagram showing an example of a display mode of a movement route whose evaluation value satisfies a predetermined condition in the embodiment of the present invention.
First, an outline of the functions provided by the difficulty estimation system 1 will be described.

[(Function 1) Estimating the difficulty of driving on the road]
The difficulty estimation system 1 estimates the driving difficulty of the road. Here, the driving difficulty level is an index indicating the difficulty (or ease of driving) of driving a vehicle traveling on a road. For example, driving is difficult (difficult to drive) in places where the road is depressed or where pedestrians can easily jump out. The difficulty of driving varies depending on a static factor caused by the structure of the road and a dynamic factor caused by the situation of the user who uses the road. As an example, static factors include road structure, road features, white line maintenance, and road slope. Dynamic factors include sudden jumps of vehicles and pedestrians, sudden braking of preceding vehicles, and play on the road. In this example, the dynamic factor has the property that the frequency of occurrence changes depending on temporal factors such as the day of the week and the time zone.
The difficulty estimation system 1 collects various information from a vehicle traveling on the road, a driver of the vehicle, a pedestrian walking on the road, and the like, and estimates the driving difficulty of the road based on the collected information. Further, the difficulty level estimation system 1 analyzes the factors of the driving difficulty level and estimates the driving difficulty level of the road.
The difficulty estimation system 1 may estimate the driving difficulty of roads by classifying each road by day of the week or time zone.

[(Function 1-1) Estimating the difficulty of driving on the road based on the driving condition of the vehicle]
The difficulty estimation system 1 is based on the position information of the mobile terminal possessed by the road user and the attribute information (user information) of the user associated with the mobile terminal, for each road and each time zone. Calculate road usage trends. Further, the difficulty estimation system 1 uses the user information providing vehicle 50 as a probe car to acquire a risk factor of the road.
The difficulty estimation system 1 collates the calculated road usage tendency with the road risk factors obtained from the user information providing vehicle 50, and road traffic for each road and time zone in which the risk factors occur. Extract the number of people and the attribute distribution of passersby.
The difficulty estimation system 1 accumulates the number of passersby on the road and the attribute distribution of passersby for each road / time zone extracted as described above as a data set.
The difficulty estimation system 1 determines the risk level (that is, driving difficulty level) of the road based on the data set accumulated for each road and each time zone, the position information of the mobile terminal, and the attribute information of the user. presume.

[(Function 1-2) Estimating the difficulty of driving a road based on the attributes of road users]
The difficulty estimation system 1 is based on the position information of the mobile terminal possessed by the road user and the attribute information (user information) of the user associated with the mobile terminal, for each road and each time zone. Based on the calculated road usage tendency, the road risk (that is, driving difficulty) is estimated.

[(Function 2) Presentation of travel routes with low accident risk]
The difficulty estimation system 1 presents the driver of the vehicle with a movement route having a lower accident risk when there are a plurality of movement route candidates calculated based on the current location and the destination of the vehicle.
More specifically, the difficulty level estimation system 1 has a driving difficulty level based on the road driving difficulty level estimated as described above and a plurality of movement route candidates calculated from the current location and destination of the vehicle. Is evaluated for each movement route candidate. The difficulty estimation system 1 selects a movement route having a lower accident risk based on the evaluation result, and presents the selected movement route to the driver of the vehicle.

[(Function 3) Calculation of insurance premiums based on travel routes]
The difficulty estimation system 1 calculates the insurance premium according to the driving difficulty of the road based on the movement route of the vehicle and the driving difficulty of the road. As an example, the difficulty estimation system 1 calculates a high insurance premium when a vehicle travels on a road with a high driving difficulty (difficult), and when the vehicle travels on a road with a low driving difficulty (easy). Calculates the premium low.

[Functional configuration of difficulty estimation system 1]
An example of the functional configuration of the difficulty estimation system 1 for realizing each of the above-mentioned functions will be described below.
As shown in FIG. 1, the difficulty level estimation system 1 includes a difficulty level estimation device 10, a user information storage unit 20 (for example, a personal information database), and a mobile terminal mobile information storage unit 30 (for example, a location information database). A road usage tendency information storage unit 40, a user information providing vehicle 50, a user information non-providing vehicle 60, an insurance premium calculation device 70, and a data set storage unit 80 are provided.

The difficulty estimation device 10 includes a user information acquisition unit 11, a mobile terminal movement information acquisition unit 12, a road usage tendency information generation unit 13, a driving difficulty estimation unit 14, a vehicle state acquisition unit 15, and a driving difficulty level. It includes an output unit 16, an evaluation value calculation unit 17, and a route information distribution unit 18.

[(Function 1) Estimating the difficulty of driving on the road]
The function of estimating the difficulty of driving on the road will be described. First, the road driving difficulty estimation function based on the driving state of the vehicle will be described, and then the road driving difficulty estimation function based on the attributes of the road user will be described.

[(Function 1-1) Estimating the difficulty of driving on the road based on the driving condition of the vehicle]
The vehicle types of the present embodiment include a user information providing vehicle 50 and a user information non-providing vehicle 60. The user information providing vehicle 50 is a vehicle that provides information to the difficulty estimation system 1 and acquires information from the difficulty estimation system 1. The user information non-providing vehicle 60 is a vehicle that acquires information from the difficulty estimation system 1 without providing information to the difficulty estimation system 1. In this function, the driving difficulty of the road is estimated based on the information provided by the user information providing vehicle 50.

The difficulty estimation system 1 calculates the road usage tendency for each road and each time zone based on the position information of the mobile terminal and the attribute information of the mobile terminal user, and uses the user information providing vehicle 50 as a probe car. Get road hazards.
The difficulty estimation system 1 collates the road usage tendency calculated from the position information and attribute information of the mobile terminal with the road risk factor obtained from the user information providing vehicle 50, and for each road in which the risk factor occurs. -Calculate the road usage tendency (for example, the number of passersby and the attribute distribution of passersby) for each time zone.
The difficulty estimation system 1 accumulates the number of passersby on the road and the attribute distribution of passersby for each road and time zone calculated as described above as a data set.
The difficulty estimation system 1 determines the risk level (that is, driving difficulty level) of the road based on the data set accumulated for each road and each time zone, the position information of the mobile terminal, and the attribute information of the user. presume.
Hereinafter, the details of the road driving difficulty estimation function based on the driving state of the vehicle by the difficulty estimation system 1 will be described.

[Acquisition of mobile terminal movement information]
The mobile terminal mobile information storage unit 30 (location information database) contains a mobile terminal (for example, mobile terminals 31-1, 31-2, ..., 31-m, 32-1, 32-2, ..., 32-n. The mobile terminal movement information indicating the movement history of the position of) is stored.
Specifically, the mobile terminal transmits the position information indicating the position of the own terminal obtained by GPS or the like to the mobile terminal movement information storage unit 30 at a predetermined timing. As a result, the mobile terminal movement information storage unit 30 stores the time-series data of the position information of each mobile terminal as the mobile terminal movement information.
The mobile terminals 31-1, 31-2, ..., 31-m are examples of mobile terminals carried by the driver of the vehicle and moving together with the vehicle. Further, mobile terminals 32-1, 32-2, ..., 32-n are examples of mobile terminals carried by pedestrians.

The mobile terminal movement information acquisition unit 12 acquires mobile terminal movement information for each mobile terminal from the mobile terminal movement information storage unit 30. The mobile terminal movement information acquisition unit 12 outputs the acquired mobile terminal movement information to the road usage tendency information generation unit 13.
The road use tendency information generation unit 13 has a use road estimation function for estimating the road (use road) corresponding to the position of the mobile terminal, and a road use tendency grasping function for grasping the road use tendency.

[Estimation of roads used]
The road usage tendency information generation unit 13 corresponds to which road the position of the mobile terminal corresponds to based on the mobile terminal movement information stored in the mobile terminal movement information storage unit 30 and the map information stored in advance. Estimate if there is (that is, the road used).

Specifically, the road usage tendency information generation unit 13 performs map matching that compares the map information stored in advance with the movement history of the mobile terminal, and estimates the position of the mobile terminal on the road for each mobile terminal. do.

The road usage tendency information generation unit 13 may exclude, for example, a mobile terminal whose travel distance per unit time is equal to or less than a predetermined threshold value from the target of the road usage determination described above. In this case, the road use tendency information generation unit 13 determines, for example, that the user of the mobile terminal whose travel distance per unit time is equal to or less than a predetermined threshold value is not using the road.

Further, the road use tendency information generation unit 13 may determine the moving means of the user of the mobile terminal by calculating the moving speed of the mobile terminal based on the movement history of the mobile terminal. For example, when the moving speed of the mobile terminal is relatively high, the road usage tendency information generation unit 13 determines that the mobile terminal has been brought into the moving vehicle. Further, for example, when the moving speed of the mobile terminal is relatively slow, the road use tendency information generation unit 13 determines that the mobile terminal is carried by a walking user.

[Understanding road usage trends]
The road usage tendency information generation unit 13 shows the road usage tendency by grasping the road usage tendency based on the road used by the user of the mobile terminal estimated as described above and the user information of the mobile terminal. Generate road usage trend information.

Specifically, the user information storage unit 20 stores various user information including contract information of the user of the mobile terminal. Here, the user information is information about the user of these mobile terminals. The user information includes, for example, the age and gender of the user of the mobile terminal, the contract years of the mobile terminal, the payment status of the communication charge of the mobile terminal, and the like.
The user information may include information not related to the contract information of the mobile terminal. For example, user information includes the type of application installed on the mobile terminal, the number of years the driver's license has been held, the history of driver's license renewal, the history of traffic violations and traffic accidents, the results of personality tests performed when a driver's license is obtained, etc. Etc. may be included. In addition, the user information may include a user's behavioral characteristics inferred from such information, a driving skill level, a traffic manner level, and the like.

The road use tendency information generation unit 13 acquires the user information of the mobile terminal whose usage road is estimated as described above from the user information storage unit 20 via the user information acquisition unit 11. The road usage tendency information generation unit 13 associates the estimated usage road with the acquired user information. The road usage tendency information generation unit 13 statistically processes the result of associating the estimated usage road with the acquired user information for a plurality of mobile terminals, thereby performing user tendency (that is, road usage tendency) on the road. Generates road usage tendency information indicating.

For example, the road usage tendency information may include the number of users for each time zone of users who use the road. Specifically, the road usage tendency information generation unit 13 is a mobile terminal (for example, mobile terminals 31-1, 31-2, ..., 31-m, 32-1, 32-2, ..., 32-n). Based on the mobile terminal movement information, the number of users for each road / time zone is generated as road usage tendency information.

Further, for example, the road use tendency information may include information for each time zone of the number of users by gender and age of users who use the road. Specifically, the road use tendency information generation unit 13 generates road use tendency information based on the user information of these mobile terminals in addition to the mobile terminal movement information of the mobile terminals. In this case, the road use tendency information generation unit 13 generates, for example, a counting result obtained by counting the number of users in each time zone of each road by gender, age group, or type of behavioral characteristic as road use tendency information.

The road use tendency information generation unit 13 stores the generated road use tendency information in the road use tendency information storage unit 40.

[Acquisition of road hazards by vehicle]
Next, the function of acquiring the risk factor of the road by the user information providing vehicle 50 will be described.
The user information providing vehicle 50 includes a vehicle mounting device 51. The vehicle-mounted device 51 includes, for example, an acceleration sensor and a drive recorder.
The vehicle-mounted device 51 detects a sudden change in the acceleration of the vehicle due to sudden steering, sudden braking, vibration from the road surface, or the like by the acceleration sensor.
When the vehicle-mounted device 51 detects a sudden change in the acceleration of the vehicle, the vehicle-mounted device 51 determines the cause of the sudden change in the acceleration based on information such as images and sounds inside and outside the vehicle by the drive recorder.

Factors of abrupt changes in vehicle acceleration include static factors caused by the structure of the road and dynamic factors caused by the situation of users using the road. As mentioned above, static factors include road structure, road features, white line maintenance, and road surface slope. Dynamic factors include sudden jumps of vehicles and pedestrians, sudden braking of preceding vehicles, and play on the road.
The vehicle-mounted device 51 determines whether the sudden change in acceleration is due to a static factor or a dynamic factor, based on information such as images and sounds inside and outside the vehicle by the drive recorder. For example, when a crack in the road pavement is photographed in the image of the outside of the vehicle by the drive recorder, the vehicle mounting device 51 determines that the sudden change in acceleration is due to a static factor. Further, when a sudden jump of a pedestrian is photographed in the image of the outside of the vehicle by the live recorder, the vehicle mounting device 51 determines that the sudden change in acceleration is due to a dynamic factor.
When the sudden change in acceleration is due to a static factor, the vehicle mounting device 51 links the position on the road where the sudden change in acceleration occurs with the type of danger factor (static factor). Generate information. When the sudden change in acceleration is due to a dynamic factor, the vehicle-mounted device 51 determines the position on the road where the sudden change in acceleration has occurred, the time when the risk factor occurs, and the type of risk factor (dynamic). Generate vehicle status information associated with factor).
The vehicle-mounted device 51 transmits the generated vehicle state information to the difficulty estimation device 10.

In this example, the vehicle-mounted device 51 will be described as determining the occurrence of a risk factor based on the acceleration of the vehicle, but the present invention is not limited to this. The vehicle-mounted device 51 may determine the occurrence of a risk factor based on the vehicle speed, engine speed, brake pressure, tilt angle, brightness of images inside and outside the vehicle, loudness of sound, etc. due to a drive recorder or the like. ..

[Data set accumulation]
The vehicle state acquisition unit 15 of the difficulty estimation device 10 acquires the vehicle state information transmitted by the vehicle mounting device 51 of the user information providing vehicle 50. The vehicle state acquisition unit 15 acquires vehicle state information from each of the plurality of user information providing vehicles 50, and generates a data set based on the acquired vehicle state information.
When the acquired vehicle state information is a "static factor", the vehicle state acquisition unit 15 aggregates the number of occurrences of the static factor for each road, and the aggregated result is stored in the data set storage unit 80 as a data set. Remember.
When the acquired vehicle state information is a "dynamic factor", the vehicle state acquisition unit 15 uses the position on the road where the dynamic factor has occurred and the time of occurrence as search keys to store the road usage tendency information. The road usage tendency information stored in 40 is searched. The vehicle state acquisition unit 15 determines the number of road users (for example, the number of passersby passing through the road) and the attributes of the road users (for example, gender, age, or) indicated by the road usage tendency information obtained as a result of the search. The type of behavioral characteristic) is associated with the road / time zone indicated by the vehicle state information, and is stored in the data set storage unit 80 as a data set. As a result, the data set storage unit 80 accumulates the distribution of the number of road users and the distribution of the attributes of the road users for each road and each time zone in which the risk factor occurs.

[Estimation of driving difficulty]
The driving difficulty estimation unit 14 estimates the driving difficulty of the road in the road usage status indicated by the road usage tendency information based on the data set stored in the data set storage unit 80.
As described above, the data set stored in the data set storage unit 80 stores the aggregation result of the "static factor" and the aggregation result of the "dynamic factor".

The driving difficulty estimation unit 14 calculates the driving difficulty of the road based on the number of occurrences of the static factor for the "static factor" data set.
Further, the driving difficulty estimation unit 14 calculates the driving difficulty of the road by applying a multivariate analysis method such as multiple regression analysis to the "dynamic factor" data set.
As an example of road use tendency information, it is information obtained by measuring the number of users in each time zone of each road according to the age of the user. User information provision Even on roads where the vehicle 50 is not running and data for some time zones or all time zones does not exist, multivariate analysis is applied to estimate the number of times that risk factors for the part where data does not exist occur. , Estimate the difficulty level.
Next, a specific example in which the driving difficulty estimation unit 14 applies multivariate analysis to estimate the difficulty will be described.

The driving difficulty estimation unit 14 acquires the number of pedestrians by age group and the number of vehicles by driver age from the road usage tendency information generation unit 13.
The number of pedestrians aged 75 and over is x1, the number of vehicles aged 75 and over is x2, the number of pedestrians aged 65 to under 75 is x3, and the number of vehicles aged 65 to under 75 is x4, 10 years old Let the number of pedestrians less than x5. x1, x2, x3, x4 and x5 are explanatory variables. Also, let y be the number of times a dynamic risk factor occurs. This y is the objective variable. The driving difficulty estimation unit 14 solves these problems and derives the multiple regression equation “y = b0 + b1x1 + b2x2 + b3x3 + b4x4 + b5x5”. When the driving difficulty estimation unit 14 substitutes the values of x1 to x5 in an arbitrary time zone of the road into this multiple regression equation, and thus lacks data measurement of the occurrence of a risk factor in a predetermined time zone of the road. However, the difficulty of driving on the road can be estimated.
Further, the difficulty level y3 is expressed by the formula “y3 = c1y1 + c2y2” using the difficulty level y1 due to the static risk factor and the difficulty level y2 due to the dynamic risk factor. Here, c1 and c2 are weights, and may be uniformly set to 1, or may be appropriately adjusted depending on the probability of occurrence of a risk factor or the like.
The driving difficulty estimation unit 14 may estimate the difficulty as a value normalized by the unit length of the road.
Further, as the multivariate analysis, in addition to the exemplified multiple regression analysis, a principal component analysis, an independent component analysis, a factor analysis, a discriminant analysis, a cluster analysis, a conjoint analysis, and a multidimensional scaling method may be applied.

[(Function 1-2) Estimating the difficulty of driving a road based on the attributes of road users]
Next, the estimation of the driving difficulty of the road based on the attributes of the road user will be described. As described above, the difficulty estimation system 1 is based on the position information of the mobile terminal possessed by the road user and the attribute information (user information) of the user associated with the mobile terminal. Estimate driving difficulty. A specific example will be described below.

The driving difficulty estimation unit 14 estimates the driving difficulty on the road based on the road usage tendency information generated by the road usage tendency information generation unit 13.
For example, statistical results may be obtained that people aged 6 to 12 frequently jump out onto the road while walking during the time when they leave elementary school.
In this case, on a road with an elementary school nearby, if the road usage tendency information indicates that 30 people aged 6 to 12 are using the road between 15:00 and 17:00. This road is considered to be more dangerous (ie, driving difficulty) than other roads. In this case, the driving difficulty estimation unit 14 estimates that the driving difficulty of the road is "high" based on the road usage tendency information.

In addition, for example, when statistical results are obtained that people aged 30 to 39 frequently take actions contrary to traffic etiquette, such as walking off the roadway during the night time in a downtown area. There is.
In this case, if the road usage tendency information indicates that 100 people aged 30 to 39 are using the road between 22:00 and 24:00 in the downtown area, this road. Is considered to be at high risk. In addition, the risk of this road is considered to be lower than the risk of the road where the frequency of children returning from school, such as jumping out, is high. In this case, the driving difficulty estimation unit 14 estimates that the driving difficulty of the road is "medium" based on the road usage tendency information.

In addition, for example, statistical results may be obtained that people aged 40 to 49 rarely behave in a manner contrary to traffic manners during the daytime.
In this case, if the road usage tendency information indicates that 50 people aged 40 to 49 are using the road between 12:00 and 13:00, this road is other than the other. It is considered to be less dangerous than the road. In this case, the driving difficulty estimation unit 14 estimates that the driving difficulty of the road is "low" based on the road usage tendency information.

The difficulty level of driving is not limited to the three stages of "high", "medium" and "low", but may be divided into two stages or four or more stages, and is represented by a continuous numerical value. You may.

The driving difficulty estimation unit 14 outputs the driving difficulty estimated as described above to the driving difficulty output unit 16.
The driving difficulty level output unit 16 outputs the driving difficulty level estimated by the driving difficulty level estimation unit 14 to the user information providing vehicle 50, the user information non-providing vehicle 60, or the insurance premium calculation device 70.

[(Function 2) Presentation of travel routes with low accident risk]
Next, the function of presenting a movement route with a low accident risk will be described.
The user information providing vehicle 50 and the user information non-providing vehicle 60 each include an input unit (input unit 52, input unit 62) and a display unit (display unit 53, display unit 63). In the description of this function, since the input unit 62 and the display unit 63 of the user information non-providing vehicle 60 have the same functions as the input unit 52 and the display unit 53 of the user information providing vehicle 50, the input unit Only the 52 and the display unit 53 will be described, and the description of the input unit 62 and the display unit 63 will be omitted.

The user information providing vehicle 50 includes an input unit 52 and a display unit 53.
In this example, the display unit 53 is a display of a car navigation system (not shown) included in the user information providing vehicle 50, and displays candidates for a route on which the user information providing vehicle 50 travels.
In this example, the input unit 52 includes a button and a touch panel used for inputting information indicating an instruction to a car navigation system (not shown). In this example, the input unit 52 is input with the purpose information indicating the destination of the movement of the vehicle.
The input unit 52 outputs the input destination information to the car navigation system (not shown).
The car navigation system (not shown) calculates a plurality of candidates for a movement route from the departure point S to the destination G based on the destination G indicated by the destination information and the current position of the vehicle (departure point S). , A plurality of calculated movement route candidates are displayed on the display unit 53. Further, the car navigation system (not shown) outputs a plurality of calculated candidates for the movement route to the evaluation value calculation unit 17.
FIG. 2 shows an example of a plurality of candidates for the calculated movement route. The movement route candidates may include candidates other than the movement routes R1, R2, and R3 shown in FIG.

The evaluation value calculation unit 17 causes the driving difficulty estimation unit 14 to estimate the driving difficulty of each road included in the movement route candidates for each road.
The evaluation value calculation unit 17 calculates the evaluation value of each of the plurality of candidates for the movement route calculated by the car navigation system (not shown) based on the driving difficulty of each road estimated by the driving difficulty estimation unit 14. .. The evaluation value calculated by the evaluation value calculation unit 17 may be calculated for each time zone, day of the week, or attribute information of the user, and is calculated by comprehensively considering the difficulty of driving in the entire stroke of the movement route. For example, the evaluation value may be a value obtained by totaling the difficulty levels per unit length of the movement route or for each predetermined section over the entire stroke of the movement route.

The route information distribution unit 18 distributes route information indicating a movement route whose evaluation value satisfies a predetermined condition among the movement route candidates to the user information providing vehicle 50. In response to this, for example, as shown in FIG. 2, the display unit 53 displays the movement paths R1, R2, and R3 as single-dot chain lines, broken lines, and solid lines together with the map, and the movement paths R1, R2, and R3, respectively. The evaluation values "20", "15" and "10" of are displayed.

In the evaluation value calculation unit 17, not only the user information providing vehicle 50 on which the vehicle mounting device 51 is mounted but also the user information non-providing vehicle 60 on which the vehicle mounting device 51 is not mounted is the departure point S based on the difficulty level. The evaluation value of the candidate of the movement route to move from to the destination G may be calculated. Here, the user information non-providing vehicle 60 is a vehicle that does not transmit information indicating the state of its own vehicle to the difficulty estimation device 10, and includes an input unit 62 and a display unit 63. The input unit 62 and the display unit 63 are the same as the input unit 52 and the display unit 53 included in the user information providing vehicle 50. In this case, the route information distribution unit 18 distributes the route information indicating the movement route whose evaluation value satisfies a predetermined condition among the movement route candidates to the user information non-providing vehicle 60. In this case, the input method of the destination G and the calculation method of the evaluation value are the same as in the case of the user information providing vehicle 50.

[(Function 3) Calculation of insurance premiums based on travel routes]
Next, the function of calculating insurance premiums based on the travel route will be described.
The insurance premium calculation device 70 calculates the insurance premium based on the evaluation value of the travel route actually traveled by the user information providing vehicle 50 and the user information non-providing vehicle 60 from the departure point S to the destination G. For example, when the evaluation value becomes larger as the driving on the moving route becomes more difficult, the insurance premium calculation device 70 calculates the higher insurance premium as the traveling route has a larger evaluation value.

[Operation of difficulty estimation system]
Next, an example of the operation of the difficulty estimation system according to the embodiment will be described with reference to FIGS. 3 and 4.
FIG. 3 is a flowchart showing an example of processing executed by the difficulty estimation device according to the embodiment of the present invention.
FIG. 4 is a flowchart showing an example of a process in which the vehicle state acquisition unit according to the embodiment of the present invention acquires information indicating the state of the user information providing vehicle from the vehicle-mounted device. Further, in the example described below, it is assumed that the road user information is big data acquired in advance.

In step S10, the road use tendency information generation unit 13 generates road use tendency information indicating the tendency of the road to be used based on the road user information acquired from a plurality of mobile terminals.

In step S20, the difficulty estimation system 1 acquires information indicating the state of the user information providing vehicle from the user information providing vehicle. For example, the difficulty estimation system 1 executes the method shown in FIG. 4 in step S20.

In step S201, the user information providing vehicle 50 measures the acceleration in the front-rear direction and the acceleration in the left-right direction of the vehicle by the acceleration sensor, and captures a moving image of the surroundings by the drive recorder.

In step S202, the vehicle state acquisition unit 15 determines whether or not at least one of the two types of accelerations measured in step S201 exceeds a predetermined threshold value. This threshold value uses, for example, a hiyari hat, which is a potential dangerous event, as an index, and 0.3 G is set for the acceleration in the front-rear direction and 0.22 G is set for the acceleration in the left-right direction. When the vehicle state acquisition unit 15 determines that at least one of the two types of accelerations measured in step S201 exceeds a predetermined threshold value (step S202: Yes), the process proceeds to step S203 and the measurement is performed in step S201. When it is determined that neither of the two types of accelerations exceeds a predetermined threshold value (step S202: No), the process returns to step S201.

In step S203, the vehicle state acquisition unit 15 performs image analysis on the moving image captured in step S201 and attempts to extract the risk factor of the user information providing vehicle 50.

In step S204, the vehicle state acquisition unit 15 determines whether or not the danger factor has been extracted in step S203. When the vehicle state acquisition unit 15 determines that the risk factor has been extracted in step S203 (step S204: Yes), the process proceeds to step S205, and when it determines that the risk factor has not been extracted in step S203 (step S204). : No), the process returns to step S201.

In step S205, the vehicle state acquisition unit 15 determines whether or not the risk factor extracted in step S203 is caused by the user information providing vehicle itself. Here, the danger factor caused by the user information providing vehicle 50 itself is, for example, a danger factor caused by rough driving and an outlier of the acceleration sensor, which is substantially dangerous. It is an event that cannot be said to be a factor. When the vehicle state acquisition unit 15 determines that the risk factor extracted in step S203 is caused by the user information providing vehicle itself (step S205: Yes), the process is returned to step S201 and extracted in step S203. When it is determined that the risk factor is not caused by the user information providing vehicle itself (step S205: No), the process proceeds to step S206.

In step S206, the vehicle state acquisition unit 15 determines whether or not the risk factor extracted in step S203 is a dynamic risk factor. When the vehicle state acquisition unit 15 determines that the risk factor extracted in step S203 is a dynamic risk factor (that is, a dynamic factor) (step S206: Yes), the process proceeds to step S207, and step S203 is performed. When it is determined that the risk factor extracted in step 2 is not a dynamic risk factor (that is, it is a static factor) (step S206: No), the process proceeds to step S208.

In step S207, the vehicle state acquisition unit 15 acquires the risk factor (dynamic factor) extracted in step S203 in association with the road and the time of occurrence, and proceeds to the process in step S30 shown in FIG.

In step S208, the vehicle state acquisition unit 15 acquires the risk factor (static factor) extracted in step S203 in association with the road, and proceeds to the process in step S30 shown in FIG.

Returning to FIG. 3, in step S30, the driving difficulty estimation unit 14 estimates the driving difficulty on the road based on the road usage tendency information and the information indicating the state of the vehicle.

In step S40, the driving difficulty output unit 16 outputs the difficulty estimated in step S30 to the evaluation value calculation unit 17.

In step S50, the evaluation value calculation unit 17 calculates the evaluation value of the candidate route for the user information providing vehicle to move from the departure point to the destination based on the difficulty level estimated in step S30.

In step S60, the route information distribution unit 18 distributes route information indicating a movement route whose evaluation value calculated in step S50 satisfies a predetermined condition among the candidates for the movement route to the user information providing vehicle.

The difficulty estimation system 1 according to the embodiment has been described above. The difficulty estimation device 10 generates road usage tendency information indicating the tendency of the road to be used based on the road user information acquired from a plurality of mobile terminals, and drives on the road based on the road usage tendency information. Estimate the difficulty level of. As a result, the difficulty level estimation device 10 can grasp the difficulty level of driving on the road on which the vehicle insurance subscriber drives the vehicle.

Further, the difficulty level estimation device 10 acquires information indicating the vehicle state of the user information providing vehicle 50 from the user information providing vehicle 50, and determines the difficulty level based on the information indicating the vehicle state in addition to the road usage tendency information. presume. As a result, the difficulty level estimation device 10 can more accurately estimate the difficulty level based on the state of the vehicle.

Further, the difficulty level estimation device 10 sets the risk factor on the road based on the vehicle state data for each risk factor in which the state of the vehicle of the user information providing vehicle 50 and the risk factor encountered by the user information providing vehicle 50 on the road are associated with each other. Obtain information indicating whether or not the information is caused by a vehicle or a pedestrian existing in the vehicle, and estimate the difficulty level based on the information. As a result, the difficulty level estimation device 10 is a vehicle existing on the road as compared with the case where the difficulty level is estimated by acquiring only static risk factors such as the slope of the road, the maintenance status of the road, and the visibility of the road sign. The difficulty level can be estimated more accurately by considering dynamic risk factors such as the time zone of pedestrians and the number of pedestrians for each attribute information.

Further, the difficulty estimation device 10 applies multivariate analysis to the road usage tendency information, so that even if a part of the road usage tendency information has a small count value or no information exists, the count value is small or the total. It is possible to estimate the difficulty level by estimating the movement information of the mobile terminal in the part where the numerical value does not exist.

Further, the difficulty estimation device 10 calculates an evaluation value of a movement route candidate in which the user information providing vehicle 50 moves from the departure point S to the destination G based on the difficulty level, and evaluates the movement route candidates among the movement route candidates. Route information indicating the movement routes R1, R2, and R3 whose values satisfy a predetermined condition is distributed to the user information providing vehicle 50. As a result, the difficulty estimation device 10 can present a plurality of routes and evaluation values of each route to the driver of the user information providing vehicle 50. Further, these are the same for the vehicle 60 that does not provide user information.

In addition, each of the above-mentioned devices has a computer inside. Each of the above-described processing processes is stored in a recording medium that can be read by a computer in a program format, and is executed by the computer reading and executing this program. Here, the recording medium that can be read by a computer is, for example, a magnetic disk, a magneto-optical disk, a CD-ROM, a DVD-ROM, or a semiconductor memory. Alternatively, the program may be distributed to the computer via a communication line, and the computer may execute the program.

Further, the above-mentioned program may be for realizing a part of the above-mentioned functions. Further, a so-called difference program may be used, which can realize the above-mentioned functions in combination with a program recorded in advance in the computer system.

Although the embodiments of the present invention have been described above, the embodiments are merely examples and are not intended to limit the scope of the invention. This embodiment can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the gist of the invention. These embodiments and modifications thereof are included in the scope and gist of the invention, and at the same time, are included in the scope of the invention described in the claims and the equivalent scope thereof.

1 ... Difficulty estimation system, 10 ... Difficulty estimation device, 11 ... User information acquisition unit, 12 ... Mobile terminal movement information acquisition unit, 13 ... Road usage tendency information generation unit, 14 ... Driving difficulty estimation unit, 15 ... Vehicle Status acquisition unit, 16 ... Driving difficulty output unit, 17 ... Evaluation value calculation unit, 18 ... Route information distribution unit, 20 ... User information storage unit, 30 ... Mobile terminal movement information storage unit, 31-1, 31-2, ..., 31-m, 32-1, 32-2, ..., 32-n ... Mobile terminal, 40 ... Road usage tendency information storage unit, 50 ... User information providing vehicle, 51 ... Vehicle mounting device, 52 ... Input unit, 53 ... Display unit, 60 ... Vehicles that do not provide user information, 62 ... Input unit, 63 ... Display unit, 70 ... Insurance premium calculation device, C ... Symbol, G ... Destination, R1, R2, R3 ... Movement route, S ... Point of departure

Claims (9)

A mobile terminal movement information acquisition unit that acquires mobile terminal movement information including the movement history of the mobile terminal,
A road use tendency information generation unit that generates road use tendency information indicating a tendency of road use based on the mobile terminal movement information, and a road use tendency information generation unit.
A driving difficulty estimation unit that estimates the difficulty of driving on the road based on the road usage tendency information, and a driving difficulty estimation unit.
Difficulty estimation device equipped with. A user information acquisition unit for acquiring user information of the mobile terminal is further provided.
The road use tendency information generation unit generates the road use tendency information based on the user information in addition to the mobile terminal movement information.
The difficulty estimation device according to claim 1. Further provided with a vehicle state acquisition unit that acquires information indicating the vehicle state of the user information providing vehicle from the user information providing vehicle.
The driving difficulty estimation unit estimates the difficulty based on the information indicating the vehicle state in addition to the road usage tendency information.
The difficulty estimation device according to claim 1 or 2. The driving difficulty estimation unit is based on information indicating a vehicle state for each factor in which the vehicle state of the user information providing vehicle and the factor of the situation encountered by the user information providing vehicle on the road are associated with each other. Estimate the driving difficulty estimation unit,
The difficulty estimation device according to claim 3. An evaluation value calculation unit that calculates an evaluation value of a candidate for a movement route in which the user information providing vehicle moves from a departure point to a destination based on the difficulty level.
Among the candidates for the movement route, a route information distribution unit that distributes route information indicating the movement route whose evaluation value satisfies a predetermined condition to the user information providing vehicle, and
The difficulty estimation device according to any one of claims 3 and 4 , further comprising. For a vehicle that does not provide the vehicle state to the difficulty estimation device, the evaluation value of a candidate for a movement route that moves from the departure point to the destination of the vehicle that does not provide the user information is evaluated based on the difficulty level. Evaluation value calculation unit to be calculated and
Among the candidates for the movement route, a route information distribution unit that distributes route information indicating the movement route whose evaluation value satisfies a predetermined condition to the vehicle that does not provide user information.
The difficulty estimation device according to any one of claims 3 to 5, further comprising. The driving difficulty estimation unit estimates the difficulty by applying multivariate analysis to the road usage tendency information.
The difficulty estimation device according to any one of claims 1 to 6. The mobile terminal movement information acquisition step for acquiring mobile terminal movement information including the movement history of the mobile terminal, and
A road usage tendency information generation step for generating road usage tendency information indicating a tendency for roads to be used based on the mobile terminal movement information, and a road usage tendency information generation step.
A driving difficulty estimation step for estimating the driving difficulty level on the road based on the road usage tendency information, and a driving difficulty estimation step.
Difficulty estimation method including. On the computer
Mobile terminal movement information acquisition function that acquires mobile terminal movement information including mobile terminal movement history,
A road usage tendency information generation function that generates road usage tendency information indicating a tendency for roads to be used based on the mobile terminal movement information, and a road usage tendency information generation function.
A driving difficulty estimation function that estimates the difficulty of driving on the road based on the road usage tendency information, and
A computer program to realize.

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