JP2020052905A - Difficulty estimating apparatus, difficulty estimating method, and computer program - Google Patents

Abstract

To estimate difficulty in driving on a road.SOLUTION: A difficulty estimating apparatus has a portable terminal movement information acquiring unit, a road usage tendency information generating unit, and a driving difficulty estimating unit. The portable terminal movement information acquiring unit acquires portable terminal movement information including a movement history of the portable terminal. The road usage tendency information generating unit generates, based on the portable terminal movement information, road usage tendency information indicating tendency of usage of a road. The driving difficulty estimating unit estimates, based on the road usage tendency information, difficulty in driving on the road.SELECTED DRAWING: Figure 1

Description

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

  In the modern era, vehicles and other vehicles are indispensable in conducting daily life and economic activities, and devices and systems used for operating automobile insurance are becoming increasingly important. For example, Patent Literature 1 discloses a setting device that searches for a traveling route from a departure point to an arrival point, and sets an automobile insurance premium when traveling on the traveling route based on the traveling distance, driving time, and the like. ing.

JP 2015-215775 A

  However, since the setting device described above grasps the use situation of the road by vehicles and pedestrians and does not set an insurance premium based on the difficulty of driving on the road, it may not be possible to set an appropriate insurance premium. Was.

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

  (1) One embodiment of the present invention is a portable terminal movement information acquisition unit that acquires portable terminal movement information including a movement history of a portable terminal, and a road indicating a tendency to use a road based on the portable terminal movement information. A difficulty level estimating apparatus comprising: a road usage tendency information generating unit that generates usage trend information; and a driving difficulty level estimating unit that estimates a driving difficulty level on the road based on the road usage trend information.

  (2) One aspect of the present invention further includes a user information acquisition unit that acquires user information of the mobile terminal, wherein the road use tendency information generation unit is configured to perform a process based on the user information in addition to the mobile terminal movement information. The difficulty estimating apparatus according to the above (1), which generates the road use tendency information.

  (3) One aspect of the present invention further includes a vehicle state acquiring unit that acquires information indicating a vehicle state of the user information providing vehicle from the user information providing vehicle, wherein the driving difficulty estimating unit includes the road use tendency information. In addition, in the difficulty estimating apparatus according to (1) or (2), the difficulty is estimated based on information indicating the vehicle state.

  (4) In one aspect of the present invention, the driving difficulty estimating unit may be configured to associate the vehicle state of the user information providing vehicle with a factor of a situation encountered by the user information providing vehicle on the road by a factor. The difficulty estimating device according to (3) above, wherein the driving difficulty estimating unit is estimated based on information indicating a vehicle state.

  (5) In one aspect of the present invention, the driving difficulty estimating unit estimates the difficulty by applying a multivariate analysis to the road use tendency information. Three difficulty level estimation devices.

  (6) One aspect of the present invention is an evaluation value calculation unit that calculates an evaluation value of a candidate of a travel route on which the user information providing vehicle moves from a departure place to a destination based on the difficulty level. Any of the above (1) to (5), further comprising: a route information distribution unit that distributes, to the user information providing vehicle, route information indicating the travel route whose evaluation value satisfies a predetermined condition among the candidates. One of the difficulty level estimating devices.

  (7) One aspect of the present invention is that, for a vehicle that does not provide the vehicle state to the difficulty estimating device, the vehicle does not provide the user information, the candidate of the movement route that moves from the departure point to the destination of the vehicle that does not provide the user information. An evaluation value calculation unit that calculates an evaluation value of the travel route based on the degree of difficulty, and among the travel route candidates, route information indicating the travel route whose evaluation value satisfies a predetermined condition is provided to the user information non-providing vehicle. And a route information distribution unit for distributing the information. The apparatus according to any one of (1) to (5), further comprising:

  (8) One aspect of the present invention is a mobile terminal movement information obtaining step of obtaining mobile terminal movement information including a movement history of a mobile terminal, and a road indicating a tendency to use a road based on the mobile terminal movement information. A difficulty estimating method including a road use tendency information generating step of generating use tendency information, and a driving difficulty estimation step of estimating a driving difficulty of the road based on the road use tendency information.

  (9) One embodiment of the present invention provides a computer with a mobile terminal movement information acquisition function of acquiring mobile terminal movement information including a movement history of a mobile terminal, and a tendency to use a road based on the mobile terminal movement information. Computer program for realizing a road use tendency information generating function of generating road use tendency information indicating the following, and a driving difficulty estimation function of estimating a driving difficulty on the road based on the road use tendency information. It is.

  According to the present invention, it is possible to estimate the difficulty of driving on a road.

It is a figure showing an example of functional composition of a difficulty level estimation system concerning an embodiment of the present invention. FIG. 7 is a diagram illustrating an example of a display mode of a moving route whose evaluation value satisfies a predetermined condition in the embodiment of the present invention. It is a flowchart which shows an example of the process which the difficulty level estimation apparatus which concerns on embodiment of this invention performs. 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 a user information provision vehicle from a vehicle mounting apparatus.

[Embodiment]
The difficulty level estimation system according to the embodiment will be described with reference to FIGS.
FIG. 1 is a diagram illustrating 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 illustrating an example of a display mode of a moving 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 level estimation system 1 will be described.

[(Function 1) Estimation of driving difficulty of road]
The difficulty level estimation system 1 estimates a road driving difficulty level. Here, the driving difficulty is an index indicating the difficulty (or ease of driving) of a vehicle traveling on a road. For example, driving difficulty is high (driving is difficult) in a place where a road is depressed or in a place where pedestrians easily jump out. The driving difficulty changes depending on static factors caused by the structure of the road and the like and dynamic factors caused by the situation of the user using the road. As an example, the static factors include a road structure, a feature related to the road, a maintenance state of a white line, a slope of a road surface, and the like. The dynamic factors include a sudden jump of a vehicle or a pedestrian, a sudden braking of a preceding vehicle, a game on a road, and the like. In this example, the dynamic factor has a property that its occurrence frequency changes depending on a temporal element such as a day of the week or a time zone.
The difficulty level estimation system 1 collects various information from a vehicle running on a road, a driver of the vehicle, a pedestrian walking on the road, and the like, and estimates a driving difficulty level of the road based on the collected information. The difficulty level estimation system 1 analyzes the factors of the driving difficulty level and estimates the driving difficulty level of the road.
Note that the difficulty level estimation system 1 may estimate the driving difficulty level of the road by classifying each road by day of the week or time zone.

[(Function 1-1) Estimation of Road Driving Difficulty Based on Vehicle Driving State]
The difficulty level estimating system 1 is based on the position information of the mobile terminal possessed by the user of the road and the attribute information (user information) of the user associated with the mobile terminal, for each road and for each time zone. Calculate road usage trends. In addition, the difficulty level estimation system 1 uses the user information providing vehicle 50 as a probe car and acquires a risk factor of a road.
The difficulty level estimating system 1 compares the calculated road usage tendency with the risk factor of the road obtained from the user information providing vehicle 50, and determines the traffic on the road for each road and time zone in which the risk factor has occurred. The number of people and the attribute distribution of passers-by are extracted.
The difficulty level estimation system 1 accumulates, as a data set, the number of passers-by and the attribute distribution of passers-by for each road and each time zone extracted as described above.
The difficulty level estimation system 1 calculates the danger level of the road (that is, the driving difficulty level) 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) Estimation of Road Driving Difficulty Based on Road User Attributes]
The difficulty level estimating system 1 is provided for each road and for each time zone based on the position information of the portable terminal possessed by the user of the road and the attribute information (user information) of the user associated with the portable terminal. The degree of danger of the road (that is, the degree of difficulty of driving) is estimated based on the calculated road use tendency.

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

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

[Functional Configuration of Difficulty Estimation System 1]
An example of a functional configuration of the difficulty level estimating system 1 for realizing the above-described 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 movement information storage unit 30 (for example, a position information database). , A road use 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.

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

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

[(Function 1-1) Estimation of Road Driving Difficulty Based on Vehicle Driving State]
The types of vehicles according to 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 level estimation system 1 and acquires information from the difficulty level estimation system 1. The user information non-providing vehicle 60 is a vehicle that acquires information from the difficulty level estimation system 1 without providing information to the difficulty level estimation system 1. In this function, the driving difficulty of the road is estimated based on information provided from the user information providing vehicle 50.

The difficulty level estimation system 1 calculates a road use tendency for each road and for 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 level estimation system 1 compares the road use tendency calculated from the position information and the attribute information of the portable terminal with the risk factor of the road obtained from the user information providing vehicle 50, and for each road where the risk factor occurs. Calculate the usage tendency of the road (for example, the number of passers and the attribute distribution of passers) for each time zone.
The difficulty level estimation system 1 accumulates, as a data set, the number of road passers and the attribute distribution of passers for each road and each time zone calculated as described above.
The difficulty level estimation system 1 calculates the danger level of the road (that is, the driving difficulty level) 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 function of estimating the driving difficulty of the road based on the driving state of the vehicle by the difficulty estimation system 1 will be described.

[Acquisition of mobile terminal movement information]
The portable terminal (for example, portable terminals 31-1, 31-2, ..., 31-m, 32-1, 32-2, ..., 32-n) is stored in the portable terminal movement information storage unit 30 (position information database). The mobile terminal movement information indicating the movement history of the position of ()) is stored.
Specifically, the mobile terminal transmits position information indicating the position of the terminal itself 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 portable terminals 31-1, 31-2,..., 31-m are examples of portable terminals that are carried by the driver of the vehicle and move with the vehicle. The portable terminals 32-1, 32-2,..., 32-n are examples of portable terminals carried by pedestrians.

The mobile terminal movement information obtaining unit 12 obtains 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 use tendency information generation unit 13.
The road use tendency information generation unit 13 has a use road estimation function for estimating a road (use road) corresponding to the position of the mobile terminal, and a road use tendency grasp function for grasping the use tendency of the road.

[Estimation of use road]
The road use tendency information generation unit 13 determines which road the mobile terminal position 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. Is estimated (that is, the use road).

  Specifically, the road use tendency information generation unit 13 performs map matching for comparing 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. I 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 from the above-described road usage determination target. In this case, the road use tendency information generation unit 13 determines that, for example, the user of the mobile terminal whose travel distance per unit time is equal to or less than a predetermined threshold does not use the road.

  Further, the road usage 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 fast, the road usage tendency information generation unit 13 determines that the mobile terminal is being carried into a 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 use tendency information generation unit 13 indicates the use tendency of the road by grasping the road use tendency based on the use road of the user of the portable terminal estimated as described above and the user information of the portable terminal. Generate road usage tendency information.

Specifically, the user information storage unit 20 stores various user information including contract information of the mobile terminal user. Here, the user information is information on the users of these portable terminals. The user information includes, for example, the age and gender of the user of the mobile terminal, the contracted years of the mobile terminal, the payment status of the communication fee of the mobile terminal, and the like.
The user information may include information that is not related to the contract information of the mobile terminal. For example, the user information includes the type of application installed on the mobile terminal, the number of years a driver's license has been retained, the history of driver's license updates, the history of traffic violations and traffic accidents, and the results of personality tests performed when a driver's license is obtained. Etc. may be included. Further, the user information may include a behavior characteristic of the user, a driving skill level, a traffic manner level, and the like estimated from the information.

  The road usage tendency information generation unit 13 acquires the user information of the mobile terminal for which the use road has been estimated as described above from the user information storage unit 20 via the user information acquisition unit 11. The road use tendency information generation unit 13 associates the estimated use road with the acquired user information. The road usage tendency information generation unit 13 statistically processes a result obtained by linking the estimated user road and the acquired user information for a plurality of mobile terminals, and thereby the user tendency on the road (that is, the road usage tendency). Is generated.

  For example, the road use tendency information may include the number of users who use the road for each time zone. Specifically, the road use tendency information generation unit 13 generates the mobile terminal (for example, the 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 and each time zone is generated as road use tendency information.

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

  The road use tendency information generation unit 13 causes the generated road use tendency information storage unit 40 to store the generated road use tendency information.

[Acquisition of road hazards by vehicle]
Next, the function of acquiring the danger factors of the road by the user information providing vehicle 50 will be described.
The user information providing vehicle 50 includes a vehicle mounted 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 an abrupt steering wheel, an abrupt brake, or vibration from a road surface, using an acceleration sensor.
When detecting a rapid change in the acceleration of the vehicle, the vehicle-mounted device 51 determines the cause of the rapid change in the acceleration based on information such as images and sounds inside and outside the vehicle by the drive recorder.

The factors of the rapid change in the acceleration of the vehicle include static factors caused by the structure of the road and the like, and dynamic factors caused by the situation of the user using the road. As described above, the static factors include the structure of the road, features on the road, the maintenance status of the white line, the inclination of the road surface, and the like. The dynamic factors include a sudden jump of a vehicle or a pedestrian, a sudden braking of a preceding vehicle, a game on a road, and the like.
The on-vehicle device 51 determines whether a 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 on the road pavement is photographed in an image outside the vehicle by the drive recorder, the vehicle-mounted device 51 determines that the rapid change in acceleration is due to a static factor. When a sudden jump of a pedestrian is photographed in an image outside the vehicle by the live recorder, the vehicle-mounted device 51 determines that a sudden change in acceleration is due to a dynamic factor.
If the rapid change in acceleration is due to a static factor, the vehicle-mounted device 51 determines the vehicle state in which the position on the road where the rapid change in acceleration has occurred is associated with the type of danger factor (static factor). Generate information. If the rapid change in acceleration is due to a dynamic factor, the vehicle-mounted device 51 determines the position on the road where the rapid change in acceleration has occurred, the time at which the risk factor occurred, and the type of the risk factor (dynamic ) Is generated in association with the factor.
The vehicle-mounted device 51 transmits the generated vehicle state information to the difficulty level estimating device 10.

  In this example, the vehicle-mounted device 51 is described as determining the occurrence of a danger factor based on the acceleration of the vehicle, but is not limited thereto. The on-vehicle device 51 may determine the occurrence of a risk factor based on the speed of the vehicle, the engine speed, the brake pedal pressure, the inclination angle, or the brightness or sound volume of images inside and outside the vehicle by a drive recorder or the like. .

[Dataset accumulation]
The vehicle state acquisition unit 15 of the difficulty level estimation device 10 acquires vehicle state information transmitted by the vehicle mounted device 51 of the user information providing vehicle 50. The vehicle state obtaining unit 15 obtains vehicle state information from each of the plurality of user information providing vehicles 50, and generates a data set based on the obtained vehicle state information.
When the acquired vehicle state information is “static factors”, the vehicle state acquiring unit 15 aggregates the number of occurrences of the static factors for each road, and stores the totaled result 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 acquiring unit 15 uses the position on the road where the dynamic factor has occurred and the occurrence time as a search key, and uses the road use tendency information storage unit. The road use tendency information stored in 40 is searched. The vehicle state acquisition unit 15 determines the number of road users (for example, the number of pedestrians passing through the road) indicated by the road usage tendency information obtained as a result of the search, and the attributes of the road users (for example, gender, age, The type of behavior characteristic) is associated with the road / time zone indicated by the vehicle state information and stored in the data set storage unit 80 as a data set. As a result, the distribution of the number of road users and the distribution of the attributes of the road users are stored in the data set storage unit 80 for each road and time zone in which the risk factor has occurred.

[Estimation of driving difficulty]
The driving difficulty estimating unit 14 estimates the driving difficulty of the road in the use condition of the road indicated by the road use 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 tally result for the “static factor” and the tally result for the “dynamic factor”.

The driving difficulty estimating unit 14 calculates the driving difficulty of the road based on the number of occurrences of the static factor for the data set of “static factors”.
The driving difficulty estimating unit 14 calculates the driving difficulty of the road by applying a multivariate analysis method such as a multiple regression analysis to the data set of the “dynamic factors”.
An example of the road use tendency information is information obtained by measuring the number of users in each time zone of each road according to the age of the user. Even on a road where the user information providing vehicle 50 does not travel and data for some or all time zones does not exist, multivariate analysis is applied to estimate the number of occurrences of a risk factor in a part where no data exists. , Estimate the difficulty.
Next, a specific example in which the driving difficulty estimating unit 14 estimates the difficulty by applying the multivariate analysis will be described.

The driving difficulty estimating unit 14 acquires the number of pedestrians by age and the number of vehicles by driver age from the road use tendency information generating unit 13.
The number of pedestrians aged 75 and over is x1, the number of vehicles driven by aged 75 and over is x2, the number of pedestrians aged 65 and under is x3, and the number of vehicles driven by a person over 65 and under x4 is 10 Let the number of pedestrians less than x5 be x5. x1, x2, x3, x4 and x5 are explanatory variables. The number of occurrences of a dynamic risk factor is defined as y. This y is an objective variable. The driving difficulty estimating unit 14 solves these and derives a multiple regression equation “y = b0 + b1x1 + b2x2 + b3x3 + b4x4 + b5x5”. The driving difficulty estimating unit 14 substitutes the values of x1 to x5 in an arbitrary time zone of the road into the multiple regression equation, so that data measurement of risk factor occurrence in a predetermined time zone of the road is lacking. However, it is possible to estimate the difficulty of driving on the road.
Further, the difficulty y3 is expressed by an expression “y3 = c1y1 + c2y2” using the difficulty y1 due to a static risk factor and the difficulty y2 due to a dynamic risk factor. Here, c1 and c2 are weights, and may be uniformly set to 1, or may be appropriately adjusted according to the probability of occurrence of a risk factor.
Note that the driving difficulty estimating unit 14 may estimate the difficulty as a value normalized by the unit length of the road.
As the multivariate analysis, in addition to the multiple regression analysis illustrated above, a principal component analysis, an independent component analysis, a factor analysis, a discriminant analysis, a cluster analysis, a conjoint analysis, or a multidimensional scaling method may be applied.

[(Function 1-2) Estimation of Road Driving Difficulty Based on Road User Attributes]
Next, estimation of the driving difficulty of the road based on the attribute of the road user will be described. As described above, the difficulty level estimating system 1 uses the road information based on the location information of the mobile terminal possessed by the user of the road and the attribute information (user information) of the user associated with the mobile terminal. Estimate the driving difficulty. Hereinafter, a specific example will be described.

The driving difficulty estimating unit 14 estimates the driving difficulty of the road based on the road use tendency information generated by the road use tendency information generation unit 13.
For example, in some cases, a statistical result is obtained in which a person aged 6 to 12 years old frequently jumps out of a roadway while walking during elementary school lower school hours.
In this case, between 15 and 17 o'clock on a road where an elementary school is nearby, 30 people between the ages of 6 and 12 use the road usage tendency information and indicate that 30 people are using the road. It is considered that this road has a higher degree of danger (that is, driving difficulty) than other roads. In this case, the driving difficulty estimating unit 14 estimates the driving difficulty of the road as “high” based on the road use tendency information.

Also, for example, in a case where a statistical result is obtained that, during the nighttime of a downtown area, a person aged between 30 and 39 years old frequently acts against traffic manners such as protruding into a roadway and walking. There is.
In this case, in the downtown area, between 22:00 and 24:00, 100 people aged 30 to 39 use the road. Is considered to be highly dangerous. In addition, the risk of this road is considered to be lower than the risk of a road in which the frequency of occurrence of jumping out of a child returning from school is high. In this case, the driving difficulty estimating unit 14 estimates the driving difficulty of the road as “medium” based on the road use tendency information.

In addition, for example, in the daytime, a statistical result may be obtained in which people aged between 40 and 49 are less likely to take actions that violate traffic manners.
In this case, between 12 o'clock and 13 o'clock, if the road usage tendency information indicates that 50 people aged 40 to 49 are using the road, this road is not used for other roads. It is considered that the degree of risk is lower than that of roads. In this case, the driving difficulty estimating unit 14 estimates the driving difficulty of the road as “low” based on the road use tendency information.

  The driving difficulty is not limited to 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 estimating 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 estimating 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 route with low accident risk]
Next, the function of presenting a traveling 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, 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. Only 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 a candidate for a route on which the user information providing vehicle 50 runs.
In this example, the input unit 52 includes a button used to input information indicating an instruction to a car navigation system (not shown), and a touch panel. In this example, target information indicating a destination of the movement of the vehicle is input to the input unit 52.
The input unit 52 outputs the input destination information to a car navigation system (not shown).
The car navigation system (not shown) calculates a plurality of travel route candidates from the departure point S to the destination G based on the destination G indicated by the destination information and the current position (departure point S) of the vehicle. The display unit 53 displays a plurality of candidates for the calculated moving route. Further, the car navigation system (not shown) outputs the calculated plurality of candidates for the movement route to the evaluation value calculation unit 17.
FIG. 2 shows an example of a plurality of calculated travel route candidates. It should be noted that the candidates for the travel route may include candidates other than the travel routes R1, R2, and R3 shown in FIG.

The evaluation value calculating unit 17 causes the driving difficulty estimating unit 14 to estimate the driving difficulty level of each road included in the travel route candidates for each road.
The evaluation value calculation unit 17 calculates an evaluation value of each of the plurality of candidates for the travel 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 period, day of the week, or attribute information of the user, and is calculated in consideration of the driving difficulty of the entire travel route. For example, the evaluation value may be a value obtained by summing the degree of difficulty per unit length of the moving route or for each predetermined section over the entire travel of the moving route.

  The route information distribution unit 18 distributes, to the user information providing vehicle 50, route information indicating a travel route whose evaluation value satisfies a predetermined condition among the travel route candidates. In response, for example, as shown in FIG. 2, the display unit 53 displays the travel routes R1, R2, and R3 along with a dashed line, a dashed line, and a solid line together with the map. The evaluation values “20”, “15” and “10” are displayed.

  The evaluation value calculation unit 17 determines, based on the difficulty level, not only the user information providing vehicle 50 equipped with the vehicle-mounted device 51 but also the user information non-providing vehicle 60 not equipped with the vehicle-mounted device 51 in the departure point S. The evaluation value of the candidate of the moving route moving from to the destination G may be calculated. Here, the vehicle 60 that does not provide user information is a vehicle that does not transmit information indicating the state of its own vehicle to the difficulty level estimating 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 travel route whose evaluation value satisfies the predetermined condition among the travel route candidates to the user information non-providing vehicle 60. In this case, the method of inputting the destination G and the method of calculating the evaluation value are the same as those of the user information providing vehicle 50.

[(Function 3) Calculation of insurance premium based on travel route]
Next, the function of calculating the insurance premium 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 on which the user information providing vehicle 50 and the user information non-providing vehicle 60 have actually traveled from the starting point S to the destination G. For example, when the evaluation value increases as the driving on the moving route becomes more difficult, the insurance premium calculation device 70 calculates a higher insurance premium as the vehicle travels on a moving route having a higher evaluation value.

[Operation of the difficulty level estimation system]
Next, an example of the operation of the difficulty level estimation system according to the embodiment will be described with reference to FIGS.
FIG. 3 is a flowchart illustrating an example of a process executed by the difficulty level estimation device according to the embodiment of the present invention.
FIG. 4 is a flowchart illustrating 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 on-board device. 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 a tendency to use a road based on road user information acquired from a plurality of mobile terminals.

  In step S20, the difficulty level estimation system 1 acquires information indicating the state of the user information providing vehicle from the user information providing vehicle. For example, the difficulty level 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 with the acceleration sensor, and captures a surrounding moving image with the drive recorder.

  In step S202, the vehicle state acquisition unit 15 determines whether at least one of the two types of acceleration measured in step S201 exceeds a predetermined threshold. The threshold value uses, for example, a near-miss incident that is a potential danger 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 determining that at least one of the two types of acceleration measured in step S201 exceeds a predetermined threshold (step S202: Yes), the vehicle state acquisition unit 15 advances the process to step S203, and measures in step S201. If it is determined that none of the two types of acceleration has exceeded the predetermined threshold (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 a risk factor of the user information providing vehicle 50.

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

  In step S205, the vehicle state acquisition unit 15 determines whether 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 driving violently or an outlier of the acceleration sensor, which is substantially dangerous. This is not 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 returns to step S201, and the process is extracted in step S203. If 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 the risk factor extracted in step S203 is a dynamic risk factor. When determining that the risk factor extracted in step S203 is a dynamic risk factor (that is, a dynamic factor) (step S206: Yes), the vehicle state acquisition unit 15 advances the process to step S207, and proceeds to step S203. If it is determined that the danger factors extracted in (2) are not dynamic danger factors (that is, they are static factors) (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 occurrence time, and proceeds to step S30 illustrated 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 advances the process to step S30 illustrated in FIG.

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

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

  In step S50, the evaluation value calculation unit 17 calculates an evaluation value of a candidate route on which the user information providing vehicle moves from the departure place to the destination based on the difficulty level estimated in step S30.

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

  The difficulty level estimation system 1 according to the embodiment has been described above. The difficulty level estimating apparatus 10 generates road use tendency information indicating a tendency to use a road based on road user information acquired from a plurality of mobile terminals, and performs driving on a road based on the road use tendency information. Estimate the difficulty of. Thereby, the difficulty level estimating apparatus 10 can grasp the difficulty level of driving on the road on which the vehicle insurance member drives the vehicle.

  Further, the difficulty level estimation device 10 acquires information indicating the state of the vehicle of the user information providing vehicle 50 from the user information providing vehicle 50, and calculates the degree of difficulty based on the information indicating the state of the vehicle in addition to the road use tendency information. presume. Thereby, the difficulty level estimation device 10 can more accurately estimate the difficulty level based on the state of the vehicle.

  Further, the difficulty level estimating apparatus 10 determines whether the danger factor is the road based on the danger factor-specific vehicle state data in which the vehicle state of the user information providing vehicle 50 is associated with the danger factor encountered by the user information providing vehicle 50 on the road. Of the vehicle or pedestrian present in the vehicle, and the degree of difficulty is estimated based on the information. Thereby, the difficulty level estimating apparatus 10 can obtain a vehicle existing on the road as compared with a case where the difficulty level is estimated by acquiring only the static danger factors such as the road gradient, the road maintenance state, and the visibility of the road sign. The difficulty level can be estimated more accurately because dynamic risk factors such as the time zone of the pedestrian and the number of each attribute information are considered.

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

  Further, the difficulty level estimating apparatus 10 calculates an evaluation value of a candidate for a moving route in which the user information providing vehicle 50 moves from the departure place S to the destination G based on the difficulty level. Route information indicating travel routes R1, R2, and R3 whose values satisfy a predetermined condition is distributed to the user information providing vehicle 50. Thereby, the difficulty level estimation device 10 can present the driver of the user information providing vehicle 50 with a plurality of routes and the evaluation value of each route. Further, the same applies to the vehicle 60 that does not provide user information.

  Each of the above-described devices has a computer inside. The processes of the above-described processes are stored in a computer-readable recording medium in the form of a program, and are executed when the computer reads and executes the program. Here, the recording medium readable by the 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 a computer via a communication line, and the computer may execute the program.

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

  The embodiment of the present invention has been described above, but the embodiment is merely an example and is not intended to limit the scope of the invention. The present embodiment can be implemented in other various forms, and various omissions, replacements, and changes can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope and gist of the invention, and are also included in the invention described in the claims and their equivalents.

  DESCRIPTION OF SYMBOLS 1 ... Difficulty estimation system, 10 ... Difficulty estimation apparatus, 11 ... User information acquisition part, 12 ... Mobile terminal movement information acquisition part, 13 ... Road use tendency information generation part, 14 ... Driving difficulty estimation part, 15 ... Vehicle State 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 ... portable terminal, 40 ... road use tendency information storage unit, 50 ... user information providing vehicle, 51 ... vehicle mounted device, 52 ... input unit, 53 display unit, 60 user information non-providing vehicle, 62 input unit, 63 display unit, 70 insurance premium calculation device, C symbol, G destination, R1, R2, R3 travel route, S Point of departure

Claims (9)

A mobile terminal movement information obtaining unit that obtains mobile terminal movement information including a mobile terminal movement history;
Based on the mobile terminal movement information, a road use tendency information generation unit that generates road use tendency information indicating a tendency of road use,
A driving difficulty estimating unit that estimates a driving difficulty of the road based on the road use tendency information;
A difficulty level estimating device comprising: Further comprising a user information acquisition unit for acquiring user information of the mobile terminal,
The road use tendency information generating unit generates the road use tendency information based on the user information, in addition to the mobile terminal movement information,
The difficulty level estimation device according to claim 1. The apparatus further includes a vehicle state obtaining unit that obtains information indicating a vehicle state of the user information providing vehicle from the user information providing vehicle,
The driving difficulty estimation unit estimates the difficulty based on information indicating the vehicle state, in addition to the road use tendency information,
The difficulty level estimation device according to claim 1 or 2. The driving difficulty estimating unit, based on the information indicating the vehicle state of each factor associated with 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, Estimating the driving difficulty estimating unit,
The difficulty level estimation device according to claim 3. The driving difficulty estimation unit estimates the difficulty by applying a multivariate analysis to the road use tendency information,
The difficulty level estimating device according to any one of claims 1 to 4. An evaluation value calculation unit configured to calculate an evaluation value of a candidate of a movement route on which the user information providing vehicle moves from a departure place to a destination based on the difficulty level;
A route information distribution unit that distributes, to the user information providing vehicle, route information indicating the travel route that satisfies a predetermined condition, among the travel route candidates;
The difficulty estimation device according to any one of claims 1 to 5, further comprising: For a user information non-providing vehicle that does not provide the vehicle state to the difficulty estimating device, an evaluation value of a candidate for a movement route that moves from the departure point to the destination of the user information non-providing vehicle is determined based on the difficulty level. An evaluation value calculation unit to calculate;
A route information distribution unit that distributes route information indicating the travel route that satisfies a predetermined condition to the user information non-providing vehicle, among the travel route candidates,
The difficulty estimation device according to any one of claims 1 to 5, further comprising: A mobile terminal movement information obtaining step of obtaining mobile terminal movement information including a mobile terminal movement history;
A road use tendency information generating step of generating road use tendency information indicating a tendency to use a road based on the mobile terminal movement information;
A driving difficulty estimation step of estimating a driving difficulty on the road based on the road use tendency information;
Difficulty level estimation method. On the computer,
A mobile device movement information acquisition function for obtaining mobile device movement information including a movement history of the mobile device;
A road use tendency information generating function of generating road use tendency information indicating a tendency of road use based on the mobile terminal movement information;
A driving difficulty estimation function for estimating a driving difficulty on the road based on the road use tendency information;
A computer program for realizing.

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