Information processing method, information processing device, and program

Abstract

To provide an information processing technique for discriminating whether a user is a driver.SOLUTION: An information processing method includes steps of: acquiring model curvature radius information concerning each one of at least one or more object curves based on position information related to a driver seat in a plurality of mobile body passage patterns for a mobile body passing at least one of the object curves; acquiring user curvature radius information concerning each one of the object curves based on position information on an information processing device associated with a user riding on a user mobile body which passes the at least one of the object curves; calculating a difference between the model curvature radius information and the user curvature radius information concerning each one of the object curves; and determining whether the user is a driver based on the difference.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an information processing technique for determining whether a user is a driver, for example. [Background technology]

[0002] Technological development is progressing in telematics technology, i.e., connecting devices installed in mobile vehicles to wide area networks such as the Internet to obtain various information about the mobile vehicles, manage the status and behavior of the mobile vehicles, and provide services based on the obtained information. Services using telematics technology are in demand, for example, for delivering advertisements tailored to user behavior and calculating insurance premiums for mobile vehicles.

[0003] One type of information that should be acquired about a moving object is information about whether or not the target person is a driver. For example, Patent Document 1 discloses a method for determining whether or not the user of a mobile device is the driver of a vehicle by analyzing an image captured by a camera. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 2018-166268 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the above-mentioned conventional technology only analyzes whether the user of the mobile device is a specific person, and is not able to determine whether the user is riding in a vehicle. In addition, even if it is possible to determine whether the user is riding in a vehicle, it is difficult to determine whether the user is riding in the vehicle as a driver or a non-driver. [Means for solving the problem]

[0006] According to one aspect of the present invention, an information processing method includes: acquiring model curvature radius information for each of at least one target curve based on position information related to a driver's seat in a plurality of moving body passing patterns when a moving body passes through the at least one target curve; acquiring user curvature radius information for each of the at least one target curve based on position information of an information processing device linked to a user riding in a user moving body passing through the at least one target curve; calculating a difference between the model curvature radius information and the user curvature radius information for each of the at least one target curve; and determining whether the user is a driver based on the difference. [Effects of the Invention]

[0007] The method, device, and program according to the present invention can provide information relating to whether a user is driving a mobile object. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a system configuration diagram of a driver discrimination system. [Figure 2] FIG. 2 is a block diagram showing an example of the functional configuration of the mobile information processing device 10. [Figure 3] FIG. 2 is a block diagram showing an example of the functional configuration of an on-board device 20. [Figure 4] FIG. 2 is a block diagram showing an example of the functional configuration of the server 30. [Figure 5] 10 is an example of a table in which target curve information and curvature radius information are linked together and stored in the storage unit 220. [Figure 6] A flowchart showing an example of a procedure for determining whether a user is a driver of a moving object 1. [Figure 7] FIG. 10 is a diagram showing a simplified example of the trajectory of each passenger seat when passing through a curve. [Figure 8] A diagram showing more detailed trajectories of each passenger seat when turning left. [Figure 9]10A and 10B are diagrams showing examples of the trajectory of a moving object when the angle of a curve is changed. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, an example of an embodiment of the present invention will be described with reference to the drawings. In the description of the drawings, the same elements are denoted by the same reference numerals, and duplicated descriptions may be omitted. Furthermore, the components described in these embodiments are merely examples and are not intended to limit the scope of the present invention.

[0010] [Embodiment] Hereinafter, an embodiment will be described as an example for realizing the information processing technology of the present invention. In this embodiment, it is determined whether the user is driving a car owned by the user. The contents described in this embodiment are applicable to any of the other embodiments, examples, and modified examples.

[0011] 1 is a system configuration diagram of a driver identification system according to one aspect of the present embodiment. In this system, various information such as location information is acquired by a mobile information processing device 10 carried by a user riding in a moving object 1, and the acquired information is transmitted to a server 20 via a network NW (for example, the Internet).

[0012] In this embodiment, the moving object 1 is a vehicle in which the user rides, but is not limited to such a vehicle. For example, the moving object 1 may be any moving object, such as a rental car or a friend's vehicle.

[0013] Fig. 2 is a block diagram showing the functional configuration of the mobile information processing device 10 of Fig. 1. The mobile information processing device 10 in this embodiment may be, for example and without limitation, a portable electronic device such as a smartphone or a tablet. The mobile information processing device 10 is configured to include, for example, a control unit 110, a storage unit 120, a communication unit 130, a display unit 140, an input unit 150, an audio output unit 160, a position information acquisition unit 170, a velocity information acquisition unit 180, and an angular velocity information acquisition unit 190. The mobile information processing device 10 is held by a user, and the user is associated with the mobile information processing device 10 by logging in to an application running on the device, etc. The mobile information processing device 10 is connected to a network such as the Internet via a communication unit 130, and transmits various pieces of information acquired by, for example, a position information acquisition unit 170, a velocity information acquisition unit 180, and an angular velocity information acquisition unit 190 to a server 20.

[0014] The control unit 110 is configured by a processing and arithmetic unit including, for example, a CPU (Central Processing Unit) and an MPU (Micro-Processing Unit). The control unit 110 performs various processes on each piece of data, and also reads and executes programs stored in the storage unit 120 to control each functional unit of the mobile information processing device 10, such as the communication unit 130, the display unit 140, the input unit 150, the audio output unit 160, the position information acquisition unit 170, the velocity information acquisition unit 180, and the angular velocity information acquisition unit 190.

[0015] The storage unit 120 includes, for example, a hard disk drive (HDD), a solid state drive (SSD), an electrically erasable programmable read-only memory (EEPROM), a read-only memory (ROM), a random access memory (RAM), etc., and stores control programs processed by the control unit 110 and various data, such as acquired location information. Note that the storage unit 120 is not limited to being built into the mobile information processing device 10, and may be an external storage device connected via a digital input / output port such as a universal serial bus (USB), etc.

[0016] The communication unit 130 is a module that can connect to a public network such as the Internet using, for example, mobile communications such as LTE (Long Term Evolution), 3G, 4G, or 5G, or narrowband communications such as DSRC (Dedicated Short Range Communication), and can communicate data with various devices such as the server 20 connected to the network. For example, the mobile information processing device 10 exchanges data with the server 20 via the communication unit 130 .

[0017] The display unit 140 is a display means for displaying various information, and is configured by, for example, a liquid crystal display or an organic EL (Electro-Luminescence) display. The display unit 140 displays, for example, various information acquired through the communication unit 130, processing results by the control unit 110, and the like.

[0018] The input unit 150 is an input means for the user to input various information to the mobile information processing device 10. For example, the input unit 150 is configured with buttons, a touch panel, switches, etc. The input unit 150 may also be configured as a touch screen that is integrated with the display unit 140. When a user performs an input operation on the input unit 150, a control signal corresponding to the input is generated and output to the control unit 110. Then, the control unit 110 performs arithmetic processing and control corresponding to the control signal.

[0019] The audio output unit 160 is, for example, an audio output terminal, and transmits an audio signal to output audio from a connected earphone, speaker, etc. Alternatively, it may be a speaker that outputs audio related to the audio signal. The audio output unit 160 outputs, for example, music data distributed over the Internet or the like and stored in the storage unit 120, which has been processed by the control unit 110 as an audio signal.

[0020] The location information acquisition unit 170 acquires location information (e.g., latitude and longitude information) of the mobile information processing device 10 at predetermined intervals, for example, based on radio waves arriving from GNSS satellites (e.g., GPS satellites). That is, it is possible to acquire location information of the user carrying the mobile information processing device 10. In other words, it is possible to acquire location information of the mobile object 1 by the user carrying the mobile information processing device 10 using the mobile object 1. The acquired location information is associated with the time (current time) at which the location information was acquired, and is stored in the storage unit 120. Here, the location information acquisition unit 170 may acquire the location information and also acquire an accuracy value (for example, a DOP value) indicating the accuracy of the location information. In this case, the acquired location information and accuracy value are associated with the current time and stored in the storage unit 120.

[0021] Note that the method of acquiring the location information by the location information acquiring unit 170 is not limited to the above, and any location information acquiring method may be applied. For example, the location information of the information processing device 10 may be acquired by the location information acquiring unit 170 receiving radio waves, which are emitted by a roadside device installed on the side of the road and contain location information specific to the roadside device, when the mobile object 1 carrying the mobile information processing device 10 approaches.

[0022] The speed acquisition unit 180 periodically acquires the traveling speed of the moving object 1 and supplies the traveling speed to the control unit 110. The speed acquisition unit 180 may have any configuration, and may, for example, calculate the speed information based on the position information acquired by the position information acquisition unit 170. Alternatively, the speed acquisition unit 180 may acquire, via the communication unit 130, vehicle speed pulse information acquired by a vehicle speed pulse acquisition unit mounted on the moving object 1 side, and calculate the speed of the moving object 1 based on the vehicle speed pulse information.

[0023] The angular velocity acquisition unit 190 is configured to be able to detect angular velocities around three axes in the mobile information processing device 10. Specifically, the angular velocity acquisition unit 190 can detect a roll angular velocity, which is the rate of change of the angle (roll angle) around the longitudinal axis of the mobile information processing device 10, a pitch angular velocity, which is the rate of change of the angle (pitch angle) around the lateral axis of the vehicle body, and a yaw angular velocity, which is the rate of change of the angle (yaw angle) around the vertical axis of the vehicle body. Note that, since the only angular velocity information required for calculating the radius of curvature is the yaw angle, it is possible to acquire only the yaw angle without acquiring information on the roll angle and pitch angle. Furthermore, the configuration of the angular velocity acquisition unit 190 is not limited thereto, and for example, the yaw angle may be calculated based on position information acquired by the position information acquisition unit 170. The angular velocity acquisition section 190 may have any configuration, and may calculate angular velocity information based on the position information acquired by the position information acquisition section 170, for example.

[0024] Fig. 3 is a block diagram showing the functional configuration of the server 20 in Fig. 1. The server 20 in this embodiment is configured to include a control unit 210, a storage unit 220, and a communication unit 230. The server 20 is connected to the mobile information processing device 10 etc. via a network such as the Internet, receives various information such as location information from the mobile information processing device 10 etc., and stores it in the storage unit 220. The server 20 also processes the stored information as appropriate and performs processing such as determining whether the user of the mobile information processing device 10 is the driver of the mobile object 1.

[0025] The control unit 210, like the control unit 110 of the mobile information processing device 10, is configured by a processing operation unit including, for example, a CPU (Central Processing Unit) and an MPU (Micro-Processing Unit). The control unit 210 performs various processes on each piece of data, and also reads and executes programs stored in the storage unit 220.

[0026] The storage unit 220, like the storage unit 120 of the mobile information processing device 10, includes, for example, a hard disk drive (HDD), a solid state drive (SSD), an electrically erasable programmable read-only memory (EEPROM), a read-only memory (ROM), a random access memory (RAM), etc., and stores the control program processed by the control unit 210 and various data, such as an on-board device table in which device identification information is registered. Note that the storage unit 220 is not limited to being built into the server 20, and may be an external storage device connected via a digital input / output port such as a universal serial bus (USB), etc.

[0027] The communication unit 340 is a module that can connect to a network such as the Internet using a wired communication interface or, similar to the communication unit 130 of the mobile information processing device 10, using, for example, mobile communications such as LTE (Long Term Evolution) or 3G, or narrowband communications such as DSRC (Dedicated Short Range Communication), and can communicate data with each device, such as the mobile information processing device 10, that is connected to the network.

[0028] [Information processing procedure: Obtaining the model curvature radius of the target curve] FIG. 4 is a flowchart showing an example of processing in this embodiment for obtaining, as a model, curvature radius information based on a group of driver trajectories when multiple moving bodies pass a curve (hereinafter referred to as a target curve) that serves as a reference for determining whether a user is riding in a moving body as a driver. The processing in the flowcharts of FIG. 4 and other figures is realized, for example, by the control unit 210 of the server 20 reading out the code of the information processing program stored in the storage unit 220 into a RAM (not shown) and executing it.

[0029] Each symbol S in the flowcharts of FIG. 4 and other figures means a step. Furthermore, the flowchart described below merely shows an example of the procedure for information processing in this embodiment, and other steps may be added or some steps may be deleted.

[0030] First, the server 20 identifies a curve (hereinafter referred to as a target curve) for which model curvature radius information is to be acquired (S1001). Here, it is assumed that model curvature radius information is associated with each of a plurality of curves as data, and the target curve is identified from the plurality of curves based on some selection condition. The selection condition will be described later. Here, when storing the target curve as data, for example, the position information of the intersection where the curve is located is identified and stored. However, the method of storing the target curve is not limited to this. For example, the curve may be identified by the position information of buildings near the curve, or any other information that can identify the curve is sufficient. It is preferable that the information on the identified target curve also includes information on the approach direction to the curve. In other words, the information is stored in association with the direction of approach to the curve. For example, the storage unit 220 stores information on the identified target curve in a format as shown in FIG. 5. As shown in FIG. 5, in this storage method, position information (x1, y1) on the target curve is associated with azimuth angle information (α) indicating the approach direction to the curve. The azimuth angle information means, for example, the magnitude of the horizontal angle measured in the direction clockwise from due north as the reference, but the definition of the azimuth is not limited to this, and it may be based on any other direction (for example, due south) or may be the magnitude of the horizontal angle measured in the direction counterclockwise.

[0031] Next, the server 20 acquires multiple patterns of information on the radius of curvature when the moving object passes through the target curve through a device or the like provided near the driver's seat in the moving object (S1003). In acquiring the information on the radius of curvature, for example, the following process is performed. First, the server 20 acquires speed information of the moving object when it passes through the target curve based on position information acquired from the moving object that passed through the target curve. Similarly, it acquires angular velocity (yaw angle) information of the moving object when it passed through the target curve. Then, it calculates curvature radius information for each pattern based on the following equation 1.

[0032]

number

[0033] The calculated curvature radius information may be composed of multiple curvature radius values, for example, curvature radius values ​​calculated for each piece of position information of the moving object when passing through the curve (for example, for each piece of position information obtained every second).

[0034] Furthermore, the period during which the vehicle is passing through a curve may be defined, for example, by setting the time when the acquired angular velocity becomes equal to or greater than a predetermined threshold as the curve passing start time, and the time when the acquired angular velocity becomes equal to or less than the predetermined threshold as the curve passing end time, but is not limited to this. For example, a range for passing through a curve may be registered in advance, and if the position information of the vehicle is within that range, it may be determined that the vehicle is passing through a curve. The method for identifying the range that is recognized as passing through a curve is not particularly limited, as long as the method can identify the start and end of the curve in a form that includes a certain range from the center of the curve, preferably in a form that includes a range four times the length of the moving body.

[0035] Preferably, the area through which the curve is traversed may be divided into multiple areas to acquire radius of curvature information. For example, two pieces of radius of curvature information are acquired: one for the first half of the curve traversal and one for the second half of the curve traversal. In particular, the radius of curvature information for the second half of the curve traversal is useful for determining whether the user is sitting in the front seat or the back seat of the moving object 1 in the driver identification process described below. The boundary between the first half of the curve and the second half of the curve may be set based on any criteria. For example, the boundary may be set based on half the time from when the moving object enters the curve to when it leaves the curve, or based on the point in time when the angular acceleration value of the moving object changes from positive to negative. Alternatively, the boundary may be set fixedly relative to the curve regardless of the way the moving object traverses the curve, for example, based on a position that physically divides the size of the curve in half.

[0036] Thereafter, the server 20 processes the acquired curvature radius information (S1005). Processing the curvature radius information means making the acquired multiple curvature radius values ​​easier to handle in subsequent processing. For example, in this embodiment, the multiple curvature radius values ​​are treated as a set, and the expected value and variance value of the set are calculated.

[0037] It is not necessary to perform step S1005. That is, the acquired values ​​of the plurality of curvature radii may be stored as they are and used in subsequent processing without processing.

[0038] Thereafter, the server 20 associates the processed curvature radius information with the target curve information and stores them in the storage unit 220 (S1007). As shown in Fig. 5, as the curvature radius information associated with the target curve information, for example, an expected value Ea1 and a variance Va1 of the set in the first half, and an expected value Ea2 and a variance Va2 of the set in the second half are stored corresponding to the position information (x1, y1) on the target curve. Then, the process of acquiring the model curvature radius information for the target curve ends.

[0039] Here, the method of acquiring the model curvature radius information is not particularly limited to the above, and any method can be applied. For example, a set of points plotted on a two-dimensional plane based on position information acquired when passing through a target curve may be fitted with a curve trajectory model with each curvature radius, and the information of the model that fits best (for example, the value of the curvature radius in the model) may be used as the model curvature radius information.

[0040] Furthermore, the velocity information and angular velocity information used in the above-described method for acquiring the radius of curvature are not limited to being calculated from the position information, but may be values ​​(observed values) acquired by the velocity information acquisition unit 180 and the angular velocity information acquisition unit 190 provided in the moving body, or may be values ​​obtained by applying a process to remove noise, etc., as appropriate, in consideration of the fact that these values ​​contain noise, etc.

[0041] Furthermore, the model curvature radius information does not have to be acquired for the entire period of passing through the curve, but may be acquired for a portion of the period of passing through the curve. For example, any portion, such as only the latter half of the period of passing through the curve, may be acquired. Alternatively, the curvature radius information for the entire period of passing through the curve may be acquired, and only the necessary portion may be cut out and processed in the processing process in step S1005.

[0042] [Information processing procedure: Determining whether the user is the driver] FIG. 6 is a flowchart showing an example of a procedure for determining whether or not the user is the driver of the moving object 1 in this embodiment.

[0043] First, based on the position information acquired from the mobile information processing device 10, the server 20 identifies the position information when the mobile information processing device 10 passed through the target curve (S2001). In this identification, for example, if the acquired position information is within a predetermined distance (for example, within 5 m) from the target curve position information for a predetermined period (for example, 3 seconds) or more and the mobile information processing device 10 entered from the same direction as the entry direction in the target curve information, the server 20 determines that the mobile information processing device 10 has passed through the target curve and identifies the corresponding position information. Of course, the method for determining whether the target curve has been passed is not limited to this method, and any method based on the acquired position information can be applied.

[0044] Next, the server 20 acquires curvature radius information when the mobile information processing device 10 passes through the target curve (S2003). As in S1003, the method of acquiring the curvature radius information may be to calculate the velocity and angular velocity based on the position information acquired by the position information acquisition unit 170 and acquire the curvature radius information based on these, or to acquire the curvature radius information based on the velocity information and angular velocity information acquired by the velocity acquisition unit 180 and the angular velocity acquisition unit 190, respectively, and is not particularly limited. In this embodiment, the curvature radius information is acquired for two separate periods: the first half of the curve and the second half of the curve.

[0045] The server 20 then compares the already acquired model curvature radius information with the curvature radius information acquired in S2003 for the first half of the curve and the second half of the curve, and determines whether the differences between the two are equal to or less than a predetermined threshold (S2005). If the differences between the two are equal to or less than the predetermined threshold (S2005; Y), the server 20 determines that the user linked to the mobile information processing device 10 is the driver of the moving object 1 (S2007). Conversely, if at least one of the differences exceeds the predetermined threshold (S2005; N), the server 20 determines that the user linked to the mobile information processing device 10 is not the driver of the moving object 1 (a non-driver) (S2009).

[0046] In this embodiment, for each of the first half and second half of the curve, the determination is made based on the difference between the acquired curvature radius value and the expected value in the model curvature radius, and a predetermined threshold value (which may be the same for the first half and second half). That is, for the first half of the curve, the difference between the expected value in the model curvature radius information and the acquired curvature radius value is calculated, and further, for the second half of the curve, the difference between the expected value in the model curvature radius information and the acquired curvature radius value is calculated, and these difference values ​​are compared with the predetermined threshold value. In addition, if one of the first and second half of the curve is below a predetermined threshold and the other exceeds the predetermined threshold, it is determined that the person is not the driver, but this determination is not limited to this, and for example, it is also possible to withhold determination as to whether the person is the driver.

[0047] Figure 7 shows an example of the trajectory of each passenger seat when passing through a curve. Figure 7(1) shows the case of a left turn, and Figure 7(2) shows the case of a right turn. As shown in Figure 7(1), when turning left, the driver's seat in the front right has the largest radius of curvature. Conversely, as shown in Figure 7(2), when turning right, the driver's seat (similar) has a larger radius of curvature than the rear right seat, but a smaller radius of curvature than the front left seat.

[0048] For this reason, it is preferable that the curve with the model curvature radius is a left turn, as this simplifies the calculation process. Also, in the case of a left turn, if the difference between the model curvature radius information and the acquired curvature radius information exceeds a predetermined threshold and the acquired curvature radius information is larger than the model curvature radius information, it is preferable to suspend the determination of whether or not the driver is the driver, assuming that the curve was not traversed in a manner normally expected (for example, the curvature radius was larger than normal due to detouring an obstacle, etc.). Furthermore, in consideration of such cases where judgment may be withheld, it is also possible to detect the passage of multiple target curves and determine whether or not the driver is the driver based on comprehensive information combining judgments at each target curve.

[0049] Furthermore, Figure 8 shows the detailed trajectories of each passenger seat when turning left. As shown in Figure 8, when comparing the first half of the curve and the second half of the curve, for the front seats in the moving body, the value of the radius of curvature is larger in the second half than in the first half (r2>r1). On the other hand, for the rear seats in the moving body, there is no significant difference in the radius of curvature between the first half and the second half. Therefore, by making a comparison in both the first half of the curve and the second half of the curve, it is possible to more appropriately determine whether the user is in the front seat or the back seat of the vehicle.

[0050] In addition, when the driver's seat is located at the front left of the moving body, the relationship is symmetrical to when it is located at the front right, and it can be said that, for example, it is preferable that the curve with the model curvature radius is a right turn.

[0051] Furthermore, when the model curvature radius information is stored in the storage unit 220 using an expected value and a variance value, as in this embodiment, the expected value is compared with the acquired curvature radius value based on a predetermined threshold value, but this predetermined threshold value may be changed depending on the variance value. For example, if the variance is small, the threshold value may be set small, and conversely, if the variance is large, the threshold value may be set large.

[0052] After step S2007, the server 20 identifies a range of the same driver that includes the time when the target curve was passed, and registers that the mobile object 1 was being driven by the user within this range of the same driver (S2011). Here, the range of the same driver is a range of time during which the same driver is assumed to be driving the mobile object 1. The range of the same driver may be determined, for example, based on location information acquired from the mobile information processing device 10. A period during which changes in the location information continue to occur is considered to be the range of the same driver. For example, if the speed value (e.g., calculated from the location information) observed over a certain period (e.g., 5 minutes) does not change beyond a predetermined speed (e.g., 10 km / h), the mobile object 1 is considered to be not in operation or not being driven. At that point, the range of the same driver is deemed to have ended, and the corresponding end time may be identified and recorded. Furthermore, the same can be applied to information regarding past driving. The start time of the range of the same driver may be identified and recorded based on the same process, going back to the time when the target curve was passed. Alternatively, for example, the mobile body 1 may be equipped with an operation information acquisition unit (not shown), which is configured to generate operation information of the mobile body 1 (information regarding engine on and off), and the server 20 may acquire such operation information, and the server 20 may identify the same driver range based on the operation information and the time of passing the target curve when it was determined that the person was the driver; the method for identifying the same driver range is not particularly limited. In this way, it is possible to determine when the user was driving the mobile body 1, which has the effect of making it possible to appropriately apply, for example, insurance linked to actual driving distance, in which the insurance premium is set according to the distance driven, or insurance linked to driving behavior, in which the insurance premium is set according to driving behavior.

[0053] Here, whether or not the person is the driver is immediately determined based on the result in step S2005, but this is not limited to this method. For example, similar processing may be performed for multiple target curves, and in step S2005, scores may be calculated according to the magnitude of the difference, and the scores calculated for all of the multiple target curves may be compiled (for example, summed up), and based on the result of comparing the compiled result with a predetermined threshold value (for example, whether the sum is equal to or less than the threshold value), whether or not the person is the driver may be determined.

[0054] [Variations] In the above-described embodiment, whether the user is the driver of the moving body 1 is determined based on model curvature radius information calculated from curvature radius information in multiple passing patterns for the target curve, but this is not limited to this. For example, it is preferable to use model curvature radius information calculated from curvature radius information of a group of moving bodies corresponding to the vertical length of the moving body 1 (meaning the length from front to back; hereinafter referred to as vehicle length). This is because the difference in curvature radius between the first half of the curve and the second half of the curve varies depending on the vehicle length of the moving body. For example, if the moving body 1 is a light vehicle, it is preferable to calculate the model curvature radius information from curvature radius information of multiple patterns at the target curve for a light vehicle.

[0055] Furthermore, in the above-described embodiment, the location information acquisition unit 170 acquires location information and also acquires an accuracy value (e.g., DOP value) indicating the accuracy of the location information. However, this accuracy value may be used so that if the accuracy value is lower than a predetermined threshold, the determination as to whether the user is the driver is not made, or the value may not be used as a basis for the determination (when the scores at multiple target curves are judged together).

[0056] In the above embodiment, the model curvature radius information is defined by an expected value and a variance value, but the present invention is not limited to this. The model curvature radius information may be any information that can express the acquired multiple pieces of curvature radius information in some form. Furthermore, if the variance value in the model curvature radius information is greater than a predetermined value, the reliability of the model may be deemed low, and the corresponding curve may not be adopted as a target curve.

[0057] In the above-described embodiment, the server 20 makes the determination in step S2007 or step S2009, but this is not limiting. For example, a device other than the server 20, including the mobile information processing device 10, may make these determinations by referring to the model curvature radius information stored in the server 20. Alternatively, all steps may be performed in the mobile information processing device 10.

[0058] In the above-described embodiment, the curvature radius information of the moving body 1 acquired for each of the first half of the curve and the second half of the curve is compared with the model curvature radius information. However, the method of determining whether or not a user is the driver using the curvature radius information is not limited to this method, and any method can be applied. For example, in consideration of the fact that the curvature radius is larger in the second half of the curve than in the first half of the curve for passengers in the front seats, the difference between the curvature radius information of the second half of the curve and the curvature radius information of the first half of the curve may be acquired for each of the curvature radius information of the moving body 1 and the model curvature radius information, and whether or not the user is the driver of the moving body 1 may be determined based on the difference. In this case as well, it is preferable to determine, for example, whether the comparison result (difference) is equal to or less than a predetermined threshold.

[0059] In addition, in the above-described embodiment, curvature radius information, i.e., the value of the curvature radius, is obtained for any range in the target curve, but this value may be the value of multiple curvature radii obtained in this range, or the value obtained by combining these multiple curvature radius values ​​(e.g., the average value) may be used as the curvature radius information.

[0060] Furthermore, in the above-described embodiment, the nature of the target curve is not particularly limited, but a curve with a smaller angle is preferable. Figures 9(1), (2), and (3) show examples of trajectories of a moving object when the curve angles are 120 degrees, 90 degrees, and 60 degrees, respectively. As shown in Figure 9, the smaller the angle, the greater the difference in curvature radius between the front and rear parts of the curve from the front passenger seat of the moving object. Therefore, the smaller the curve angle, the easier it is to clearly determine whether the user is the driver. Here, "curve angle" refers to, for example, the magnitude of the yaw angle that a moving object changes from when it enters a curve from a straight line to when it leaves the curve and returns to the straight line. In other words, it is not something that can be determined by focusing on one point on the curve, but rather indicates the amount of change in angle that occurs when the curve is viewed as a whole.

[0061] Therefore, for example, when making a comprehensive judgment based on the difference between the curvature radius when passing through multiple target curves and the model curvature radius, the judgment may be made by assigning a greater weight to the comparison results for target curves with smaller angles. Furthermore, when selecting a target curve, if the angle of the curve is not equal to or greater than a predetermined threshold, the curve may not be selected as the target curve.

[0062] Furthermore, the characteristics of the road on the approaching side of the target curve and the road on the exiting side of the curve that must be satisfied for the target curve are not particularly limited, but it is preferable that both the former and the latter have small widths. The term "width" here does not necessarily refer to the width of the entire road; for example, if the road on the approaching side of the target curve is a multi-lane road consisting of an approaching direction, a forward direction, and a reverse direction, it may refer to the width of only the forward lane. Similarly, if the road on the exiting side of the target curve is a multi-lane road consisting of an exiting direction, a forward direction, and a reverse direction, it may refer to the width of only the forward lane. Note that information relating to road width (hereinafter referred to as "road width information") may be acquired from, for example, map information that includes road width information.

[0063] In other words, the smaller the width of the road that a moving body can use when passing through the target curve, the smaller the dispersion of the trajectory of the moving body when passing through the curve is expected to be, and this property is utilized.

[0064] Furthermore, in the above-described embodiment, no particular mention was made of how the user's moving body passed through the target curve, but it is preferable that the characteristics when passing through the target curve satisfy, for example, the following conditions. - The speed when entering a curve is within a specified range -Speed ​​while passing through a curve is within a specified range - The speed when exiting a curve is within a specified range -Acceleration when entering a curve is within a specified range -Acceleration while passing through a curve is within a specified range -Acceleration when exiting a curve is within a specified range In other words, if the speed is too low or too high when entering, passing through, or leaving the curve, or if the acceleration does not behave as would normally be expected, it is likely that the curve was not passed through in a way that would normally be expected (for example, there was an obstacle, the vehicle was in a hurry more than usual, or a sudden event caused a sudden turn or sudden deceleration, etc.), and the trajectory in such cases is excluded from processing.

[0065] Furthermore, the server 20 may determine the target curve from among a plurality of target curve candidates based on some selection condition. For example, in light of the above explanation, it is preferable that the target curve candidate satisfies at least one of the following conditions. A large amount of information about past moving object trajectories has been accumulated (greater than a predetermined amount), and more preferably, a large amount of information about trajectories by moving objects corresponding to the user's moving object has been accumulated. The variance value of the curvature radius information of the target curve candidate is small (smaller than the specified value). · Turn left - Small angle (smaller than the specified angle) - The road width is small when entering a curve (shorter than the specified length) - The width of the road when exiting the curve is small (shorter than the specified length)

[0066] Similarly, for example, based on the above explanation, it is preferable that information about the user's moving body when the user's moving body passes through the target curve candidate satisfies at least one of the following conditions. - The accuracy of the position information acquired when passing through the target curve candidate is high (the accuracy value is higher than the specified value). - The speed when entering the target curve is within a specified range - The speed while passing through the target curve is within a specified range - The speed when leaving the target curve is within a specified range - The acceleration when entering the target curve is within a specified range - The acceleration while passing through the target curve is within a specified range - The acceleration when leaving the target curve is within a specified range

[0067] In addition, in the above-described embodiment, whether or not the user is a driver is determined by processing based on the radius of curvature, but by combining this with other processing, it may be possible to comprehensively determine whether or not the user is a driver. For example, the mobile information processing device 10 may be provided with an operation detection unit (not shown), which determines a period during which the user is likely to be driving based on information acquired from the position information acquisition unit 170, the speed information acquisition unit 180, the angular velocity information acquisition unit 190, etc., and the operation detection unit may determine whether any input has been made to the input unit 150 during that period, or whether input has been made with a frequency greater than a predetermined value, and the determination result may be used as one of the criteria for determining whether the user is a driver. This utilizes the background that if the user is operating the mobile information processing device 10 via the input unit 150 while driving, it is highly likely that the user is not a driver. Similarly, when the user of the portable information processing device 10 gets into the stopped moving body 1, which side the user gets into the moving body 1 from may be used as one of the factors for determining whether the user is the driver. This utilizes the fact that, since the driver's seat is usually on the right side, if the user gets into the moving body 1 from the right side, there is a high possibility that the user will sit in the driver's seat, but conversely, if the user gets into the moving body 1 from the left side, there is a high possibility that the user is not the driver.

[0068] Although the embodiments and modifications of the present invention have been described in detail above, the scope of the present invention is not limited to the above-described embodiments and modifications. Furthermore, the above-described embodiments and modifications can be improved or modified in various ways without departing from the spirit of the present invention. Furthermore, the above-described embodiments and modifications can be combined. [Explanation of symbols]

[0069] 1. Mobile 10 Portable information processing device 110 control section 120 Storage section 130 Communications Department 140 Display section 150 Input section 160 Audio output section 170 Location information acquisition unit 180 Speed ​​information acquisition section 190 Angular velocity information acquisition section 20 servers 210 Control Unit 220 Storage section 230 Communications Department

Claims

1. acquiring model curvature radius information for each of the at least one target curve based on position information related to a driver's seat in a plurality of moving object passing patterns when the moving object passes through the at least one target curve; acquiring user curvature radius information for each of the at least one target curve based on position information of an information processing device associated with a user who is riding in a user moving body passing through the at least one target curve; calculating a difference between the model curvature radius information and the user curvature radius information for each of the at least one target curve; determining whether the user is a driver based on the difference; An information processing method, including:

2. determining the at least one target curve from a plurality of target curve candidates based on a first condition; Further comprising: The first condition is at least one of the following: the amount of information about past moving body trajectories at the target curve candidate is greater than a predetermined amount; the amount of information about trajectories by a moving body corresponding to the user moving body at the target curve candidate is greater than a predetermined amount; the variance value of the curvature radius information of the target curve candidate is smaller than a predetermined value; the target curve candidate is a left turn; the angle of the target curve candidate is smaller than a predetermined angle; the width of the road on the side entering the target curve candidate is smaller than a predetermined length; and the width of the road on the side leaving the target curve candidate is smaller than a predetermined length. The information processing method according to claim 1 .

3. determining the at least one target curve from a plurality of target curve candidates based on a second condition; Further comprising: The second condition is at least one of the following: the accuracy of the position information acquired when the user moving body passes the target curve candidate is higher than a predetermined value; the speed of the user moving body when entering the target curve candidate is within a predetermined range; the speed of the user moving body while passing the target curve candidate is within a predetermined range; the speed of the user moving body when leaving the target curve candidate is within a predetermined range; the acceleration of the user moving body when entering the target curve candidate is within a predetermined range; the acceleration of the user moving body while passing the target curve candidate is within a predetermined range; and the acceleration of the user moving body when leaving the target curve candidate is within a predetermined range. The information processing method according to claim 1 .

4. In determining whether the user is a driver, if it is determined that the user is a driver, determining that the user was a driver in the same driving range, which is a time range from when the operation of the user moving body starts to when it ends and includes the time when it was determined that the user was a driver; Calculating insurance premiums based on the same driving range determined to be the driver; The information processing method according to claim 1 , further comprising:

5. The same operation range is determined based on a variation in the position information of the information processing device. The information processing method according to claim 4.

6. When the at least one target curve is a plurality of target curves, the determining is performed by giving a higher priority to a result due to a difference in the first target curve than to a result due to a difference in the second target curve when an angle of the first target curve is smaller than an angle of the second target curve in a first target curve and a second target curve included in the plurality of target curves.

6. The information processing method according to claim 1.

7. setting a threshold value based on a variance value of the model curvature radius information; Further comprising: The determining is performed based on the threshold value.

7. The information processing method according to claim 1.

8. An information processing device associated with a user who is riding in a user moving body passing through at least one target curve, a model curvature radius information acquisition unit that acquires model curvature radius information for each of the at least one target curve based on position information related to a driver's seat in a plurality of moving object passing patterns when a moving object passes through the at least one target curve; a location information acquisition unit that acquires location information; a user curvature radius information acquisition unit that acquires user curvature radius information for each of the at least one target curve based on the acquired position information; a calculation unit that calculates a difference between the model curvature radius information and the user curvature radius information for each of the at least one target curve; a determination unit that determines whether the user is a driver based on the difference; An information processing device comprising:

9. On the computer, acquiring model curvature radius information for each of the at least one target curve based on position information related to a driver's seat in a plurality of moving object passing patterns when the moving object passes through the at least one target curve; acquiring user curvature radius information for each of the at least one target curve based on position information of an information processing device associated with a user who is riding in a user moving body passing through the at least one target curve; calculating a difference between the model curvature radius information and the user curvature radius information for each of the at least one target curve; determining whether the user is a driver based on the difference; A program that executes.

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