Driving evaluation apparatus and driving evaluation method

Abstract

To provide a driving evaluation apparatus for appropriately evaluating the driving skill of a driver.SOLUTION: A driving evaluation apparatus includes: an error information generation section 12 that generates steering error information, which is information on an error of an actual measured value with respect to a predicted value of steering, on an evaluation target road identified by road map information on the basis of traveling information of a plurality of vehicles acquired by an information acquisition section 11; and a driving evaluation section 13 that evaluates a driving skill of a driver of an evaluation target vehicle on the basis of a difference between a reference error value determined by the generated steering error information of the plurality of vehicles for the evaluation target road within a predetermined period and a target error value determined by the steering error information, which is generated by the error information generation section, of the evaluation target vehicle for the evaluation target road within the predetermined period.SELECTED DRAWING: Figure 4

Description

Technical Field

[0001] The present invention relates to a driving evaluation device and a driving evaluation method for evaluating a driver's driving skill.

Background Art

[0002] Conventionally, as this type of device, there is known a device that determines the excellence level of a driver based on information from a G-sensor mounted on a vehicle and generates insurance information regarding the driver's insurance content according to the excellence level (see, for example, Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, in the configuration of the device described in Patent Document 1 above, the excellence level of a driver who frequently drives on a road surface that has an adverse effect on driving operations is likely to be low. If such an excellence level is used, it is difficult to appropriately evaluate the driver's driving skill.

Means for Solving the Problems

[0005] One aspect of the present invention, a driving evaluation device, includes driving information associated with time information within a predetermined period, the driving information including position information of a plurality of vehicles including an evaluation target vehicle and steering information which is operation information of a steering wheel, and road map information; an information acquisition unit that acquires the driving information and the road map information; an error information generation unit that generates steering error information which is information on an error between an actual measurement value and a predicted value of steering on an evaluation target road specified by the road map information based on the driving information of the plurality of vehicles acquired by the information acquisition unit; and a driving evaluation unit that evaluates the driving skill of a driver of the evaluation target vehicle based on a difference between a reference error value determined by the steering error information of the plurality of vehicles targeted at the evaluation target road within the predetermined period generated by the error information generation unit and a target error value determined by the steering error information of the evaluation target vehicle targeted at the evaluation target road within the predetermined period generated by the error information generation unit.

[0006] Another aspect of the present invention, a driving evaluation method, includes steps of: acquiring driving information associated with time information within a predetermined period, the driving information including position information of a plurality of vehicles including an evaluation target vehicle and steering information which is operation information of a steering wheel, and road map information; generating steering error information which is information on an error between an actual measurement value and a predicted value of steering on an evaluation target road specified by the road map information based on the driving information of the plurality of vehicles; and evaluating the driving skill of a driver of the evaluation target vehicle based on a difference between a reference error value determined by the steering error information of the plurality of vehicles targeted at the evaluation target road within the predetermined period and a target error value determined by the steering error information of the evaluation target vehicle targeted at the evaluation target road within the predetermined period, the steps being executed by a computer.

Effects of the Invention

[0007] According to the present invention, it is possible to appropriately evaluate the driving skill even for a driver who frequently drives on a road surface that has an adverse effect on driving operation.

Brief Description of the Drawings

[0008]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9A

Figure 9B

Embodiments for Carrying Out the Invention

[0009] Hereinafter, embodiments of the present invention will be described with reference to FIGS. 1 to 9B. The driving evaluation device according to the embodiment of the present invention is configured to evaluate the driving skill of a driver (referred to as a target driver) of a target vehicle (evaluation target vehicle) based on steering information, which is operation information of the steering wheel. When evaluating the driving skill, when the target vehicle travels on a road with a predetermined road surface condition such as an icy road, the steering of the target driver tends to become unstable, which may adversely affect the evaluation of the driving skill of the target driver. In the present embodiment, paying attention to this point, the driving evaluation device is configured so that the driving skill of the target driver can be appropriately evaluated in such a situation.

[0010] FIG. 1 is a diagram schematically showing the overall configuration of a driving evaluation system 1 including a driving evaluation device according to an embodiment of the present invention. As shown in FIG. 1, the driving evaluation system 1 includes a server device 10 that functions as a driving evaluation device and a plurality of in-vehicle devices 30 mounted on a plurality of vehicles 20. The server device 10 and the plurality of in-vehicle devices 30 can communicate with each other via a network 2.

[0011] The network 2 includes not only a public wireless communication network represented by the Internet or a mobile phone network but also a closed communication network provided for each predetermined management area, such as a wireless LAN, Wi-Fi (registered trademark), Bluetooth (registered trademark), etc. The server device 10 is configured, for example, as a single server or as a distributed server composed of separate servers for each function. The server device 10 can also be configured as a distributed virtual server created in a cloud environment called a cloud server.

[0012] The plurality of vehicles 20 include a target vehicle 20A driven by a target driver who is the subject of driving evaluation and other vehicles 20B and 20C driven by other drivers. Each vehicle 20 is assigned a unique identification ID (vehicle ID), and the vehicle type and class can be specified using the vehicle ID. The target vehicle 20A and the other vehicles 20B and 20C may have the same or different vehicle types and classes. A unique identification ID (user ID) of the driver is associated with the vehicle ID, and the driver of the vehicle 20 can be specified using the user ID.

[0013] FIG. 2 is a diagram showing the relationship between the target vehicle 20A and the other vehicles 20B and 20C. As shown in FIG. 2, when the horizontal axis is the time axis, the target vehicle 20A travels on a specific road within a predetermined area AR1, that is, a road RD1 (referred to as an evaluation target road) that is the subject of evaluation of driving skills, at time ta. The other vehicle 20B travels on the same evaluation target road RD1 at a time tb earlier than time ta. The other vehicle 20C travels on the same evaluation target road RD1 at a time tc later than time ta.

[0014] The time period from time point t1 to time point t2 is the target period ΔT set when evaluating the driving skill of the target driver. Time points ta, tb, and tc are included in the target period ΔT. As such, the other vehicles 20B and 20C in FIG. 1 are vehicles that travel at the same location as the target vehicle 20A within the target period ΔT. The number of other vehicles is not limited to two, and may be one or three or more. As long as it is included in the target period ΔT, either of the time points tb and tc when the other vehicles 20B and 20C travel may be a time point in the past or the future relative to the time point ta when the target vehicle 20A travels. The time points tb and tc may be the same as the time point ta.

[0015] The target period ΔT is a period during which a predetermined road surface condition of the evaluation target road RD1 is maintained. For example, the target period ΔT is a period during which snow accumulation or freezing occurs on the evaluation target road RD1, and the evaluation target road RD1 becomes an icy and snowy road (snowy road, frozen road). An icy and snowy road is a type of non-dry road, and compared with a dry road, the friction coefficient of the road surface is small. In addition, since snow accumulates on the road surface, the degree of unevenness of the road surface is large. For this reason, the steering operation (steering) of the driver is likely to become unstable. Thus, a period during which the steering is likely to become unstable due to the influence of the road surface can be set as the target period ΔT. The target period ΔT in this case is referred to as the icy and snowy period ΔTs.

[0016] The length of the icy and snowy period ΔTs varies by region. That is, in a region where the snow accumulation period is short, the icy and snowy period ΔTs is short, for example, about one to two months. On the other hand, in a region where the snow accumulation period is long, the icy and snowy period ΔTs is long, for example, about four to six months. Apart from the icy and snowy period ΔTs, the target period ΔT can also be set. For example, the remaining period of one year excluding the icy and snowy period ΔTs is a period without ice and snow and with a dry road surface, and this can be set as the target period ΔT. The target period ΔT in this case is referred to as the dry period ΔTd. The target period ΔT may be set in units of months, weeks, days, or hours.

[0017] The predetermined area AR1 is an area that includes points where the mutual snow and ice periods ΔTs and dry periods ΔTd are the same. Specifically, the predetermined area AR1 is an area that includes points where the mutual weather is the same, and for example, it is an area divided as an administrative district such as a municipality or a prefecture. The predetermined area AR1 may be wider or narrower than the administrative district. For example, when the evaluation target road RD1 extends from Town A adjacent to each other to Town B, the predetermined area AR1 may include two administrative districts (Town A and Town B).

[0018] The evaluation target road RD1 is a road with the same road name, such as Route 〇〇 of the national highway or Route 〇〇 of the prefectural road. The area including this evaluation target road RD1 may be used as the predetermined area AR1. It is also possible to include the entire section from the starting point to the ending point of the road with the same road name within the same predetermined area AR1. It is also possible to include a part of the road with the same road name in the predetermined area AR1. That is, the predetermined area AR1 may be set for each road unit.

[0019] The configurations of the in-vehicle device 30 of the target vehicle 20A in FIG. 1 and the in-vehicle devices 30 of other vehicles 20B and 20C are the same as each other. FIG. 3 is a block diagram showing the schematic configuration of the in-vehicle device 30. As shown in FIG. 3, the in-vehicle device 30 includes a positioning sensor 31, a sensor group 32, a communication unit 33, and a controller 35. The positioning sensor 31, the sensor group 32, and the communication unit 33 are each communicably connected to the controller 35.

[0020] The positioning sensor 31 receives a positioning signal transmitted from a positioning satellite. The positioning satellite is an artificial satellite such as a GPS satellite or a quasi-zenith satellite, and the current position (latitude, longitude, altitude) of the vehicle 20 can be detected using the positioning information from the positioning satellite received by the positioning sensor 31. Therefore, the positioning sensor 31 functions as a position detection unit that detects the position of the vehicle 20.

[0021] The sensor group 32 is a general term for a plurality of sensors that detect the driving state of the vehicle 20. The sensor group 32 includes a steering angle sensor 321 that detects the steering angle of the steering wheel. Although not shown, a vehicle speed sensor, an acceleration sensor, a wheel speed sensor, etc. are also included in the sensor group 32.

[0022] The controller 35 is an electronic control unit configured to include a computer having a CUP, a ROM, a RAM, and other peripheral circuits. The controller 35 reads signals from the positioning sensor 31 and the sensor group 32 at a predetermined period. The controller 35 associates time information with the position data of the vehicle 20 detected by the positioning sensor 31 to generate position information of the vehicle 20. The controller 35 associates time information with the steering angle data detected by the steering angle sensor 321 to generate steering angle information. The position information and the steering angle information can be stored in the storage unit of the controller 35.

[0023] The communication unit 33 communicates with the server device 10 via the network 200 according to a command from the controller 35. Thereby, the time-series position information and steering angle information of the vehicle 20 can be transmitted to the server device 10 with a vehicle ID attached. The communication unit 33 transmits driving information including the position information and steering angle information of the vehicle 20 at a predetermined period during the driving of the vehicle 20. The communication unit 33 may transmit driving information including the position information and steering angle information of the vehicle 20 at a predetermined timing according to a request from the server device 10.

[0024] FIG. 4 is a block diagram showing the configuration of the server device 10. As shown in FIG. 4, the server device 10 includes an ECU having an arithmetic unit 10A such as a CPU, a storage unit 10B such as a ROM and a RAM, and other peripheral circuits (not shown) such as an I / O interface, and a communication unit 15. The communication unit 15 communicates with the in-vehicle device 30 via the network 2. The in-vehicle devices 30 with which the communication unit 15 communicates include not only the in-vehicle devices 30 of the vehicles 20 (target vehicle 20A, other vehicles 20B, 20C) within the predetermined area AR1 but also the in-vehicle devices 30 of the vehicles 20 outside the predetermined area AR1. The communication unit 15 can also communicate with the server device of an insurance company that operates a vehicle insurance business via the network 2.

[0025] Travel information is transmitted to the server device 10 from the in-vehicle devices 30 of a plurality of vehicles 20 within the predetermined area AR1 and outside the predetermined area AR1 via the network 2. This travel information is stored in the storage unit 10B together with the vehicle ID. Further, the storage unit 10B stores road map information, user information, various programs executed by the arithmetic unit 10A, thresholds used in the arithmetic of the arithmetic unit 10A, and the like.

[0026] The road map information includes information on the evaluation target road RD1 (information such as the position, name, road width, and traffic volume of the road) and information on the predetermined area AR1 (FIG. 2) to which the evaluation target road RD1 belongs. The user information is information on the driver including the user ID associated with the vehicle ID. The user information includes the user's address, name, and contract information of the automobile insurance subscribed by the user.

[0027] Instead of storing all the above-described information in the storage unit 10B of the server device 10, some of the information may be stored in the storage unit of another device provided communicable with the server device 10. For example, at least one of the travel information and the road map information of the plurality of vehicles 20 may be stored in a device different from the server device 10.

[0028] The arithmetic unit 10A functions as an information acquisition unit 11, an error information generation unit 12, a driving evaluation unit 13, and an information output unit 14 by executing the program stored in the storage unit 10B. Hereinafter, for the sake of easy explanation, the configuration of the arithmetic unit 10A will be described on the premise of evaluating the driving skill of the target driver based on the running information obtained during the target period ΔT in FIG. 2.

[0029] The information acquisition unit 11 acquires predetermined running information necessary for calculating the driving skill of the target driver from among the running information of a plurality of vehicles 20 stored in the storage unit 10B. Specifically, the information acquisition unit 11 first acquires running information including the time-series position information and steering information of the target vehicle 20A. The acquired running information includes the running information at time ta in FIG. 2. Further, the information acquisition unit 11 acquires road map information, and based on the road map information and the position information of the target vehicle 20, identifies the road (evaluation target road RD1) on which the target vehicle 20 has traveled during the target period ΔT.

[0030] Then, the information acquisition unit 11 acquires the running information of other vehicles 20B and 20C that have traveled on the evaluation target road RD1 during the target period ΔT from among the running information of a plurality of vehicles 20 stored in the storage unit 10B. The target period ΔT is, for example, a dry period ΔTd or a snow and ice period ΔTs. That is, if time ta is the time when the road surface becomes a snow and ice road, the target period ΔT is the snow and ice period ΔTs. On the other hand, if time ta is the time when the road surface becomes a dry road, the target period ΔT is the dry period ΔTd.

[0031] The error information generation unit 12 generates steering error information on the evaluation target road RD1 based on the running information (steering information) of a plurality of vehicles 20 acquired by the information acquisition unit 11. The steering error information is information on the error between the measured value of the steering angle of the vehicle 20 and the predicted value of the steering angle of the vehicle 20 on the evaluation target road RD1. Such an error may be referred to as a prediction error of the steering angle.

[0032] FIG. 5 is a diagram for explaining the prediction error of the steering angle. In the figure, θ(n) is the steering angle (measured steering angle) included in the steering angle information acquired by the information acquisition unit 11 at a predetermined time interval, among which the steering angle information at time point n (for example, time point ta in FIG. 2). On the other hand, θp(n) is the steering angle (predicted steering angle) calculated based on the steering angle information at reference time points n-1, n-2, n-3 before time point n. The predicted steering angle θp(n) can be calculated, for example, by a second-order Taylor expansion centered at time point n-1 using the steering angles of the past three points. The difference between the measured steering angle θ(n) and the predicted steering angle θp(n) is the prediction error e(n). The prediction error e(n) is a physical quantity representing the complexity of the steering operation.

[0033] FIG. 6 is a diagram showing an example of a histogram indicating the distribution of the magnitudes of the prediction errors e(n) obtained from a plurality of vehicles 20 traveling on the same evaluation target road RD1 within the target period ΔT. In FIG. 6, the prediction error e(n) obtained from the steering angle information of a plurality of vehicles 20 during the dry period ΔTd and the prediction error e(n) obtained from the steering angle information of a plurality of vehicles 20 during the ice and snow period ΔTs are shown in different patterns.

[0034] The coefficient of friction of the road surface on an ice and snow road is smaller and the degree of unevenness of the road surface is larger than that on a dry road. Therefore, steering is likely to become unstable on an ice and snow road. Thus, if the prediction error corresponding to the peak frequency is defined as the mode, as shown in FIG. 6, the mode es of the prediction error during the ice and snow period ΔTs is larger than the mode ed of the prediction error during the dry period ΔTd. Also, if the value obtained by dividing the sum of the prediction error data by the number of data is defined as the average value, the average value of the prediction error during the ice and snow period ΔTs is larger than the average value of the prediction error during the dry period ΔTd. Furthermore, if the middle value when the entire data is arranged in order is defined as the median, the median of the prediction error during the ice and snow period ΔTs is larger than the median of the prediction error during the dry period ΔTd.

[0035] Such mode values es, ed, average values, and median values are representative values representing data of prediction errors e(n) obtained from driving information of a plurality of vehicles 20 (for example, a plurality of vehicles 20 excluding the target vehicle 20A) traveling on the same evaluation target road RD1 within the target period ΔT. Such representative values calculated by the error information generation unit 12 are referred to as reference error values α0. Hereinafter, as an example, the mode values es, ed are used as the reference error values αs0, αd0. The reference error value αs0 (mode value es) during the ice and snow period ΔTs is larger than the reference error value αd0 (mode value ed) during the dry period ΔTd.

[0036] Furthermore, the error information generation unit 12 calculates a target error value α1 representing the data of the prediction error e(n) based on a histogram (not shown) showing the distribution of the magnitudes of the prediction errors e(n) of the target vehicle 20A within the target period ΔT. The target error value α1 is calculated by the same method as the reference error value α0. Therefore, the mode value corresponding to the peak frequency in the histogram of the prediction error e(n) of the target vehicle 20A is used for the target error value α1.

[0037] The driving evaluation unit 13 calculates a prediction difference Δα (=α1 - α0), which is the difference between the reference error value α0 and the target error value α1 within the target period ΔT calculated by the error information generation unit 12, and evaluates the driving skill of the target driver based on the prediction difference Δα. More specifically, the driving evaluation unit 13 first sets a threshold value β used for evaluating the driving skill according to the target period ΔT. Specifically, when the target period ΔT is the ice and snow period ΔTs, the threshold value βs is set, and when the target period ΔT is the dry period ΔTd, the threshold value βd is set. As shown in FIG. 6, the histogram during the ice and snow period ΔTs has a larger dispersion (variation) than the histogram during the dry period ΔTd. Considering this point, the threshold value βs is set to a value larger than the threshold value βd.

[0038] Next, the driving evaluation unit 13 determines whether the prediction difference Δα is greater than the threshold value β using the threshold value β corresponding to the target period ΔT. When the prediction difference Δα is greater than the threshold value β, the driving evaluation unit 13 decreases the evaluation value of the driving skill of the target driver. For example, when the evaluation value of the driving skill is set in three levels of A, B, and C, when the prediction difference Δα is greater than the threshold value β, the evaluation value of the driving skill of the target driver is decreased by one level from A to B. When the prediction difference Δα is greater than a predetermined value or more than the threshold value β, the evaluation value of the driving skill may be decreased by two levels from A to C.

[0039] On the other hand, when the prediction difference Δα is less than or equal to the threshold value β, the driving evaluation unit 13 maintains the evaluation value of the driving skill of the target driver. When the target error value α1 is smaller than the reference error value α0 and the prediction difference Δα is a negative value, the driving evaluation unit 13 may increase the evaluation value of the driving skill of the target driver (for example, increase it by one or two levels).

[0040] The information output unit 14 outputs the evaluation value of the driving skill of the target driver obtained by the driving evaluation unit 13. The output evaluation value is transmitted to the server device of the insurance company that operates the vehicle insurance business via the communication unit 15. The insurance company determines the insurance premium of the target driver based on the transmitted evaluation value.

[0041] FIG. 7 is a flowchart showing an example of the processing executed by the arithmetic unit 10A of the server device 10 according to the program stored in the storage unit 10B in advance. The processing shown in this flowchart is executed every time the target period ΔT (the ice and snow period ΔTs or the dry period ΔTd) ends in order to evaluate the driving skill of the target driver in the most recent target period ΔT, for example.

[0042] First, in step S1, information stored in the storage unit 10B, that is, running information of a plurality of vehicles 20 associated with time information within the target period ΔT and road map information are acquired. Next, in step S2, based on the running information of the evaluation target road RD1 within the predetermined area AR1 among the acquired running information, steering error information about the target vehicle 20A and steering error information about the plurality of vehicles 20 are generated. The steering error information about the target vehicle 20A is information (information of the histogram of the prediction error) of the prediction error e(n) of the steering angle obtained from the steering angle information of the target vehicle 20A when the target vehicle 20A travels on the evaluation target road RD1 within the target period ΔT. The steering error information about the plurality of vehicles 20 is information (FIG. 6) of the prediction error e(n) of the steering angle obtained from the steering angle information of the plurality of vehicles 20 when a plurality of vehicles 20 (for example, other vehicles 20B, 20C) excluding the target vehicle 20A travel on the evaluation target road RD1 within the same target period ΔT.

[0043] Next, in step S3, based on the steering error information about the plurality of vehicles 20 generated in step S2, a reference error value α0, which is a representative value of the data of the prediction error e(n) of the plurality of vehicles 20, is calculated. For example, the mode values ed, es in FIG. 6 are calculated as the reference error values α0 (αd0, αs0). Next, in step S4, based on the steering error information about the target vehicle 20A generated in step S2, a target error value α1, which is a representative value of the data of the prediction error e(n) of the target vehicle 20A, is calculated. Next, in step S5, the reference error value α0 is subtracted from the target error value α1 to calculate a prediction difference Δα.

[0044] Next, in step S6, based on the time information included in the running information, it is determined whether the most recent target period ΔT is an ice and snow period ΔTs. If it is affirmed in step S6, the process proceeds to step S7, and it is determined whether the prediction difference Δα is greater than a threshold value βs corresponding to the ice and snow period ΔTs stored in advance. On the other hand, if the target period ΔT is a dry period ΔTd, it is negated in step S6. In this case, the process proceeds to step S8, and it is determined whether the prediction difference Δα is greater than a threshold value βd corresponding to the dry period ΔTd stored in advance.

[0045] If it is affirmed in step S7 or affirmed in step S8, proceed to step S9. In step S9, the evaluation value of the driving skill of the target driver is decreased. For example, the evaluation value is uniformly decreased by one level. Note that instead of uniformly decreasing the evaluation value by one level, it may be determined how much to decrease according to the difference between the prediction difference Δα and the threshold values βs, βd. If it is negated in step S7 or negated in step S8, step S9 is skipped and the process proceeds to step S10. In this case, the evaluation value of the driving skill of the target driver is maintained. Depending on the magnitude of the prediction difference Δα (for example, when the prediction difference Δα is a negative value), the evaluation value may be increased.

[0046] In step S10, the calculated evaluation value of the driving skill of the target driver is output to the outside. Specifically, the evaluation value is transmitted to the server device of the insurance company via the communication unit 15. The server device of the insurance company as the transmission destination can be specified based on the user information of the target driver stored in the storage unit 10B. The insurance company determines the insurance premium of the target driver using the transmitted evaluation value.

[0047] Summarizing the operation of the driving evaluation device according to this embodiment, it is as follows. When the target vehicle 20A travels on the evaluation target road RD1 during the dry period ΔTd, the driving skill of the target driver is evaluated based on the steering information included in the driving information of the plurality of vehicles 20 that have traveled on the evaluation target road RD1 within the dry period ΔTd. More specifically, based on the histogram (FIG. 6) corresponding to the dry period ΔTd, a reference error value αd0, which is a representative value of the prediction error e(n), is calculated (step S3). Further, based on the steering information of the target vehicle 20A, a target error value α1 is calculated (step S4).

[0048] In this case, as shown in FIG. 6, the reference error value αd0 (= ed) is small. However, since the target vehicle 20A is traveling on a dry road, the target error value α1 also tends to be small. At this time, as shown in Example 1 of FIG. 8, if the prediction difference Δα1 during the dry period ΔTd is equal to or less than the threshold value βd, the evaluation value is maintained (step S8 → step S10). On the other hand, as shown in Example 2 of FIG. 8, if the prediction difference Δα2 is greater than the threshold value βd, the evaluation value is decreased (step S8 → step S9 → step S10). For example, the evaluation value is decreased by one level. The greater the difference Δα10 between the prediction difference Δα2 and the threshold value βd, the more the evaluation value may be decreased significantly.

[0049] On the other hand, when the target vehicle 20A travels on the evaluation target road RD1 during the ice and snow period ΔTs, the driving skill of the target driver is evaluated based on the steering information included in the driving information of a plurality of vehicles 20 that have traveled on the evaluation target road RD1 within the ice and snow period ΔTs. More specifically, based on the histogram (FIG. 6) corresponding to the ice and snow period ΔTs, a reference error value αs0 is calculated, and based on the steering information of the target vehicle 20A, a target error value α1 is calculated (steps S3, S4).

[0050] In this case, since the target vehicle 20A is traveling on an ice and snow road, the target error value α1 tends to be large. However, as shown in FIG. 6, the reference error value αs0 (= es) is also large. Therefore, an increase in the prediction difference Δα is suppressed. At this time, as shown in Example 3 of FIG. 8, if the prediction difference Δα2 during the ice and snow period ΔTs is equal to or less than the threshold value βs, the evaluation value is maintained (step S7 → step S10). On the other hand, as shown in Example 4 of FIG. 8, if the prediction difference Δα4 is greater than the threshold value βs, the evaluation value is decreased (step S7 → step S9 → step S10). For example, the evaluation value is decreased by one level. The greater the difference Δα20 between the prediction difference Δα4 and the threshold value βs, the more the evaluation value may be decreased significantly.

[0051] In Example 3 of FIG. 8, for reference, the prediction difference Δα, which is the difference between the target error value α1 during the ice and snow period ΔTs and the reference error value αd0 during the dry period ΔTd, is shown by a dotted line. In this case, since the reference error value αd0 is small, the prediction difference Δα becomes larger than the threshold value βs. As a result, using the reference error value αd0 may reduce the evaluation value of the driver's driving skill. On the contrary, as in this embodiment, when using the reference error value αs0 during the ice and snow period ΔTs, the prediction difference Δα does not increase too much (Δα3), and the driving skill of the target driver can be appropriately evaluated.

[0052] Even if the reference error value αs0 during the ice and snow period ΔTs is used, since the variance of the histogram of the prediction error e(n) is large on the ice and snow road, the prediction difference Δα tends to be large. In this regard, in this embodiment, the threshold value βs used during the ice and snow period ΔTs is made larger than the threshold value βd used during the dry period ΔTd, so that it is possible to prevent the driving skill from being evaluated strictly. Note that when the difference between the prediction difference Δα (for example, Δα1 in FIG. 8) during the ice and snow period ΔTs and the prediction difference Δα (for example, Δα3 in FIG. 8) during the dry period ΔTd is small, a common threshold value β may be set for each period ΔTs, ΔTd.

[0053] According to this embodiment, the following effects can be achieved. (1) The driving evaluation device includes an information acquisition unit 11 that acquires driving information associated with time information within a predetermined target period ΔT, the driving information including position information of a plurality of vehicles 20 including the target vehicle 20A and steering information that is operation information of the steering wheel, and road map information; an error information generation unit 12 that generates steering error information, which is information on an error (prediction error e(n)) between an actual measurement value and a predicted value of steering in an evaluation target road RD1 specified by the road map information, based on the driving information of the plurality of vehicles 20 acquired by the information acquisition unit 11; and a driving evaluation unit 13 that evaluates the driving skill of the driver of the target vehicle 20A based on a difference (prediction difference Δα) between a reference error value α0 determined by the steering error information of the plurality of vehicles 20 for the evaluation target road RD1 within the target period ΔT generated by the error information generation unit 12 and a target error value α1 determined by the steering error information of the target vehicle 20A for the evaluation target road RD1 within the target period ΔT generated by the error information generation unit 12 (FIG. 4).

[0054] With this configuration, the driving skill of the target driver is evaluated based on the prediction difference Δα, which is the difference between the reference error value α0 obtained from the plurality of vehicles 20 and the target error value α1 obtained from the target vehicle 20A within the same target period ΔT. Therefore, it is possible to appropriately evaluate the driving skill even for a target driver who frequently drives on a road surface that has an adverse effect on driving operations. That is, during the ice and snow period ΔTs, steering is likely to become unstable, so the target error value α1 of the target driver is likely to increase. If the driving skill of the target driver is evaluated without considering this point, the driving skill of the target driver who drives during the ice and snow period ΔTs will be evaluated as low, which is harsh on the target driver. In this regard, in the present embodiment, when evaluating the driving skill using the steering information of the target driver during the ice and snow period ΔTs, since the evaluation is performed by comparing the steering information of a plurality of drivers during the ice and snow period ΔTs, it is possible to appropriately evaluate the driving skill.

[0055] (2) The road map information includes information on the road names assigned to each road. The road RD1 to be evaluated includes roads with the same road name. Although the road surface conditions often vary by road, the driving skills of the target driver can be appropriately evaluated by evaluating the driving skills of the target driver using the steering information for each road.

[0056] (3) The road RD1 to be evaluated includes roads within a predetermined administrative district. Although the road surface conditions may vary by administrative district, the driving skills of the target driver can be appropriately evaluated by evaluating the driving skills of the target driver using the steering information for each administrative district.

[0057] (4) The target period ΔT is the snow and ice period ΔTs during which snow and ice occur on the road surface. Thus, the driving skills of the target driver can be appropriately evaluated using the steering information of the target driver during the snow and ice period ΔTs.

[0058] (5) The driving evaluation device further includes an information output unit 14 that outputs information on the driving skills so that the information on the driving skills evaluated higher than 13 is used in the calculation of the target driver's vehicle insurance (Fig. 4). Thereby, an appropriate insurance premium can be calculated according to the driving skills of the target driver.

[0059] (6) The driving evaluation unit 13 sets threshold values βs and βd according to the road surface condition of the road RD1 to be evaluated during the target period ΔT, and determines the degree of the driving skills of the driver of the target vehicle 20A by comparing the predicted difference Δα between the reference error value α0 and the target error value α1 during the target period ΔT with the threshold values βs and βd (Fig. 7). In this way, the degree of the driving skills of the driver of the target vehicle can be appropriately determined using the predicted difference Δα between the reference error value α0 and the target error value α1 during the target period ΔT.

[0060] (7) When the target period ΔT is the dry period ΔTd, the driving evaluation unit 13 sets a threshold value βd. On the other hand, when the target period ΔT is the ice and snow period ΔTs in which the friction coefficient of the road surface becomes smaller or the degree of unevenness of the road surface becomes larger than that in the dry period ΔTd, a threshold value βs larger than the threshold value βd is set. Further, when the difference (prediction difference Δα) between the reference error value αd0 and the target error value α1 in the dry period ΔTd is larger than the threshold value βd, and when the difference (prediction difference Δα) between the reference error value αs0 and the target error value α1 in the ice and snow period ΔTs is larger than the threshold value βs, the driving skill level of the driver of the target vehicle 20A is lowered (Fig. 7). For example, when the driving skill is evaluated in three levels of A, B, and C, it is lowered from A to B or from A to C. In the ice and snow period ΔTs, since the variance of the prediction error e(n) of the steering angle is large, the prediction difference Δα in the ice and snow period ΔTs may be larger than the prediction difference Δα in the dry period ΔTd. In this regard, by using different threshold values β in the ice and snow period ΔTs and the dry period ΔTd, the driving skill level of the driver of the target vehicle can be appropriately determined.

[0061] (8) The above-described driving evaluation device can also be configured as a driving evaluation method. In this case, the driving evaluation method includes a step of acquiring travel information in which time information within a predetermined target period ΔT is associated with travel information including position information of a plurality of vehicles 20 including the target vehicle 20A and steering information which is operation information of the steering wheel (step S1), a step of generating steering error information which is information on an error (prediction error e(n)) between an actual measured value and a predicted value of steering on an evaluation target road RD1 specified by road map information based on the travel information of the plurality of vehicles 20 (step S2), and a step of evaluating the driving skill of the driver of the target vehicle 20A based on the difference (prediction difference Δα) between a reference error value α0 determined by the steering error information of the plurality of vehicles 20 targeted at the evaluation target road RD1 within the target period ΔT and a target error value α1 determined by the steering error information of the target vehicle 20A targeted at the evaluation target road RD1 within the target period ΔT (steps S3 to S9) (Fig. 7). Thereby, the driving skill of the target driver can be appropriately evaluated.

[0062] The above-described embodiment can be modified into various forms. Hereinafter, several modification examples will be described. In the above-described embodiment, an administrative district is used as the predetermined area AR1, and the driving skill of the target driver is evaluated using the driving information of the vehicle 20 traveling on the evaluation target road RD1 within the same administrative district. However, the predetermined area AR1 is not limited to an administrative district. For example, as shown in FIG. 9A, a predetermined map mesh obtained by dividing a road map may be used as the predetermined area AR2, and the driving skill of the target driver may be evaluated using the driving information of the vehicle 20 traveling on the evaluation target road RD1 within the predetermined map mesh.

[0063] In the above-described embodiment, the steering error information is generated using the driving information of other vehicles 20B and 20C traveling at the same point (the same evaluation target road RD1) as the target vehicle 20A. However, even if the evaluation target roads are different, as long as they are within the same predetermined area AR1, it is assumed that the road surface conditions are the same. Considering this point, the steering error information may be generated using the driving information of other vehicles different from those described above. For example, as shown in FIG. 9B, the steering error information may be generated using the driving information of the vehicle 20 traveling on the evaluation target road RD1 within the predetermined area AR1 and the vehicle 20 traveling on another evaluation target road RD2. That is, the other vehicles 20B and 20C in FIG. 1 are not limited to vehicles traveling on the same evaluation target road as the target vehicle 20A.

[0064] When including the vehicle 20 traveling on the evaluation target road RD2 different from the evaluation target road RD1 in the other vehicles in FIG. 1, it is preferable to include, in the other vehicles, the vehicle 20 traveling on the evaluation target road RD2 having a traffic volume level similar to that of the evaluation target road RD1. This is because if the traffic volume levels are similar, it is assumed that the road surface conditions are similar. Note that the traffic volume information is included in the road map information acquired by the information acquisition unit 11.

[0065] In the above embodiment, it was stated that the target vehicle 20A in FIG. 1 and the other vehicles 20B and 20C do not necessarily have the same vehicle type or class. However, if the vehicle types and classes are the same, the behaviors of the vehicles 20 when driving on roads with the same road surface conditions are likely to be equal to each other. Considering this point, other vehicles with the same vehicle type and class as the target vehicle 20A may be used as the other vehicles 20B and 20C in FIG. 1. The vehicle type and class can be specified by the vehicle ID acquired by the information acquisition unit 11. In the above embodiment, steering error information for a plurality of vehicles 20 was generated using the steering information of a plurality of vehicles 20 excluding the target vehicle 20A. However, steering information for a plurality of vehicles 20 may be generated using the steering information of a plurality of vehicles 20 including the target vehicle 20A.

[0066] In the above embodiment, the information on the driving skill output from the information output unit 14 was transmitted to the server of the insurance company. However, the information on the driving skill may be used not only for calculating insurance premiums but also for other purposes (for example, evaluating cognitive functions). Therefore, the information output unit may transmit the information on the driving skill to other devices.

[0067] In the above embodiment, the dry period ΔTd (the first predetermined period) and the ice and snow period ΔTs (the second predetermined period) were used as the predetermined period (target period ΔT) during which driving information for evaluating the driver's driving skill can be obtained. However, other periods may be used as the predetermined period. Therefore, roads other than ice and snow roads can be used as the evaluation target roads. In the above embodiment, the threshold value βd (the first threshold value) and the threshold value βs (the second threshold value) were set according to the target period ΔT. However, the same threshold value may be set. In the above embodiment, when the prediction difference Δ is greater than the threshold value β, the driving skill of the target driver is decreased from A (the first level) to B (the second level) or from A (the first level) to C (the second level). However, the degree of decrease is not limited to that described above.

[0068] In the above embodiment, the error information generation unit 12 generates information on the prediction error e(n), which is information on the error between the measured value and the predicted value of steering in the evaluation target road RD1 specified by the road map information, based on the driving information of a plurality of vehicles 20 acquired by the information acquisition unit 11. However, the generated steering error information is not limited to the information on the prediction error e(n). For example, information on the steering entropy value, which is a value obtained by quantifying the smoothness of steering, may be generated as the steering error information.

[0069] In the above embodiment, the server device 10 constitutes the driving evaluation device. However, the driving evaluation device can also be constituted by other means than the server device 10. The driving evaluation device may be constituted by a plurality of devices (for example, the server device 10 and the in-vehicle device 30).

[0070] The above description is merely an example, and the present invention is not limited to the above-described embodiments and modified examples as long as the features of the present invention are not impaired. It is also possible to arbitrarily combine one or more of the above embodiments and modified examples, and it is also possible to combine the modified examples with each other.

Description of Reference Numerals

[0071] 10 Server device, 11 Information acquisition unit, 12 Error information generation unit, 13 Driving evaluation unit, 14 Information output unit, 20 Vehicle, 20A Target vehicle, 20B, 20C Other vehicles, ΔT Target period, ΔTs Snow and ice period, ΔTd Dry period, RD1 Evaluation target road, α0 Reference error value, α1 Target error value, Δα Prediction difference, β Threshold value

Claims

1. Travel information associated with time information within a predetermined period, the travel information including position information of a plurality of vehicles including the vehicle to be evaluated and steering information which is operation information of a steering wheel, and road map information, and an information acquisition unit that acquires the same; An error information generation unit that generates steering error information which is information on an error between an actually measured value and a predicted value of steering on an evaluation target road specified by the road map information based on the travel information of the plurality of vehicles acquired by the information acquisition unit; A driving evaluation unit that evaluates the driving skill of a driver of the vehicle to be evaluated based on a difference between a reference error value determined by the steering error information of the plurality of vehicles targeted at the evaluation target road within the predetermined period generated by the error information generation unit and a target error value determined by the steering error information of the vehicle to be evaluated targeted at the evaluation target road within the predetermined period generated by the error information generation unit. A driving evaluation device characterized by comprising the same.

2. In the driving evaluation device according to Claim 1, the road map information includes information on road names assigned to each road, and the evaluation target road includes roads assigned the same road name. A driving evaluation device characterized by this.

3. In the driving evaluation device according to Claim 1, the evaluation target road includes roads within a predetermined map mesh or within a predetermined administrative area obtained by dividing a road map. A driving evaluation device characterized by this.

4. In the driving evaluation device according to Claim 1, the road map information includes information on traffic volume on the road, and the evaluation target road includes roads with the same level of traffic volume. A driving evaluation device characterized by this.

5. In the driving evaluation device according to any one of Claims 1 to 4, the predetermined period is a period during which ice and snow occur on the road surface. A driving evaluation device characterized by this.

6. In the driving evaluation device according to any one of claims 1 to 4, further comprising an information output unit that outputs the information on the driving skill so that the information on the driving skill evaluated by the driving evaluation unit is used for calculating the vehicle insurance of the driver. The driving evaluation device is characterized by this.

7. In the driving evaluation device according to any one of claims 1 to 4, the driving evaluation unit sets a threshold value according to the road surface condition of the evaluation target road in the predetermined period, and compares the difference between the reference error value and the target error value in the predetermined period with the threshold value, thereby determining the degree of the driving skill of the driver of the evaluation target vehicle. The driving evaluation device is characterized by this.

8. In the driving evaluation device according to claim 7, the driving evaluation unit when the predetermined period is the first predetermined period, sets a first threshold value, while when the predetermined period is a second predetermined period in which the coefficient of friction of the road surface becomes smaller or the degree of unevenness of the road surface becomes larger than the first predetermined period, sets a second threshold value larger than the first threshold value, when the difference between the reference error value and the target error value in the first predetermined period is larger than the first threshold value, and when the difference between the reference error value and the target error value in the second predetermined period is larger than the second threshold value, reducing the degree of the driving skill of the driver of the evaluation target vehicle from the first degree to the second degree. The driving evaluation device is characterized by this.

9. a step of acquiring driving information associated with time information within a predetermined period, the driving information including position information of a plurality of vehicles including the evaluation target vehicle and steering information that is operation information of a steering wheel, and road map information; a step of generating steering error information, which is information on an error between an actual measurement value and a predicted value of steering on an evaluation target road specified by the road map information, based on the driving information of the plurality of vehicles; A driving evaluation method, characterized by including having a computer execute a step of evaluating a driver's driving skill of the vehicle to be evaluated based on a difference between a reference error value determined by steering error information of the plurality of vehicles targeted at the road to be evaluated within the predetermined period and a target error value determined by steering error information of the vehicle to be evaluated targeted at the road to be evaluated within the predetermined period.

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