Evaluation device, evaluation method, and evaluation program

Abstract

To provide an evaluation apparatus capable of objectively evaluating a driving mode of a driver to be evaluated.SOLUTION: The evaluation apparatus includes: a processing section 2 for acquiring mode information showing a driving mode of a driver; a violated action detection section 2E for determining whether or not the driving mode shown by the acquired mode information is a violated action which may cause an accident; and a scoring section 2F for evaluating the driving mode of the driver and creating and outputting evaluation information, based on an evaluation result by the violated action detection section 2E.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present application relates to the technical fields of an evaluation device, an evaluation method, and an evaluation program. More specifically, the present application relates to the technical fields of an evaluation device and an evaluation method, and a program for the evaluation device, which evaluate a driving behavior of a driver or the like and output evaluation information indicating the result of the evaluation. [Background technology]

[0002] In general, in order to improve the safety of driving a vehicle, it is necessary to objectively grasp and evaluate whether the driver's usual driving behavior is safe or dangerous. An example of a conventional technique for evaluating such driving behavior is the technique described in the following Patent Document 1. The technique described in Patent Document 1 is configured to evaluate the driving behavior using, for example, the frequency of sudden braking and sudden steering. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2016-81087 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, the technology described in Patent Document 1 above has a problem in that, for example, simply detecting sudden braking or steering is not enough to objectively evaluate the driving style of a driver, since the driving style can be influenced by external disturbances due to differences in the characteristics of the roads that the driver usually travels on, or external factors unrelated to the driver.

[0005] Therefore, the present application has been made in consideration of the above-mentioned problems, and one example of the object of the application is to provide an evaluation device and evaluation method, as well as a program for the evaluation device, that are capable of objectively evaluating the driving style of a driver to be evaluated. [Means for solving the problem]

[0006] In order to solve the above problem, the invention described in claim 1 provides an evaluation device that evaluates a driving behavior of a subject and outputs evaluation information indicating a result of the evaluation, comprising: an acquisition means for acquiring behavior information indicating the driving behavior of the subject; a determination means for determining whether the driving behavior indicated by the acquired behavior information is an accident-causing driving behavior that may cause an accident; and an evaluation means for evaluating the driving behavior of the subject based on a determination result by the determination means and a weighting associated with travel route characteristic information indicating characteristics of a travel route traveled by the subject, and generating and outputting the evaluation information. and a step, and the characteristics of the travel path are configured to be any one of (i) the travel path being a curve, (ii) the travel path being an intersection without traffic lights, (iii) the travel path being a railroad crossing, (iv) the travel path being near a traffic light, (v) the travel path being an intersection with a wide road, (vi) the travel path being a bend, (vii) the travel path being the top of an uphill slope, (viii) the travel path being a steep downhill slope, (ix) the travel path being a one-way road, or (x) the travel path being a no right or left turn point.

[0007] In order to solve the above problem, the invention described in claim 9 is an evaluation device that evaluates a driving behavior of a subject and outputs evaluation information indicating a result of the evaluation, the evaluation method being executed by the evaluation device including an acquisition means, a determination means, and an evaluation means, the evaluation method including: an acquisition step of acquiring behavior information indicating the driving behavior of the subject by the acquisition means; a determination step of determining by the determination means whether the driving behavior indicated by the acquired behavior information is an accident-causing driving behavior that may cause an accident; and an evaluation step of determining whether the driving behavior of the subject is an accident-causing driving behavior that may cause an accident based on a determination result in the determination step and a weighting associated with travel route characteristic information indicating characteristics of a travel route traveled by the subject. and an evaluation process of evaluating the driving behavior by the evaluation means and generating and outputting the evaluation information, wherein the characteristics of the travel path are configured to be any one of (i) the travel path being a curve, (ii) the travel path being an intersection without traffic lights, (iii) the travel path being a railroad crossing, (iv) the travel path being near a traffic light, (v) the travel path being an intersection with a wide road, (vi) the travel path being a bend, (vii) the travel path being the top of an uphill slope, (viii) the travel path being a steep downhill slope, (ix) the travel path being a one-way road, or (x) the travel path being a point where right or left turns are prohibited.

[0008] In order to solve the above problem, the invention described in claim 10 causes a computer to function as the evaluation device described in any one of claims 1 to 8. [Brief description of the drawings]

[0009] [Figure 1] 1 is a block diagram showing a schematic configuration of an evaluation device according to an embodiment. [Diagram 2] 1 is a block diagram showing a schematic configuration of an evaluation system according to a first embodiment. [Diagram 3] 1 is a block diagram showing a schematic configuration of a processing server included in the evaluation system according to a first embodiment. FIG. [Figure 4]1A and 1B are diagrams illustrating examples of the contents of data stored in a processing server in the first embodiment, where (a) is a diagram illustrating an example of the contents of history data, and (b) is a diagram illustrating an example of the contents of data for behavior evaluation. [Diagram 5] 1 is a block diagram showing a schematic configuration of a terminal device included in the evaluation system according to the first embodiment. [Figure 6] 4 is a flowchart showing an evaluation process according to the first embodiment. [Figure 7] 1A and 1B are diagrams illustrating examples of the contents of data stored in a processing server relating to a modified example of the evaluation process of the first embodiment, where (a) is a diagram illustrating the contents of history data, (b) is a diagram illustrating the contents of data for behavioral evaluation, and (c) is a diagram illustrating the contents of data for time period evaluation. [Figure 8] FIG. 11 is a diagram illustrating an example of the contents of road characteristic evaluation data according to the second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Next, an embodiment of the present invention will be described with reference to Fig. 1. Fig. 1 is a block diagram showing a schematic configuration of an evaluation device according to the embodiment.

[0011] As shown in FIG. 1, the evaluation device S according to the embodiment is an evaluation device S that evaluates the driving behavior of a subject and outputs evaluation information indicating the result of the evaluation, and is configured to include an acquisition means 2, a determination means 2E, and an evaluation means 2F.

[0012] In this configuration, the acquisition means 2 acquires behavior information indicating the driving behavior of the subject.

[0013] The determination means 2E then determines whether or not the driving behavior indicated by the behavior information acquired by the acquisition means 2 is an accident-causing driving behavior that may be the cause of an accident.

[0014] As a result, the evaluation means 2F evaluates the driving behavior of the subject based on the judgment result by the judgment means 2E and the weighting associated with the travel route characteristic information indicating the characteristics of the travel route traveled by the subject, and generates and outputs the evaluation information. At this time, the characteristics of the travel route are any one of (i) the travel route being a curve, (ii) the travel route being an intersection without a traffic light, (iii) the travel route being a railroad crossing, (iv) the travel route being near a traffic light, (v) the travel route being an intersection with a wide road, (vi) the travel route being a bend, (vii) the travel route being the top of an uphill slope, (viii) the travel route being a steep downhill slope, (ix) the travel route being a one-way road, or (x) the travel route being a point where right and left turns are prohibited.

[0015] As described above, according to the operation of the evaluation device S according to the embodiment, it is determined whether the driving behavior of the subject is an accident-causing driving behavior, and the driving behavior of the subject is evaluated based on the determination result and the weighting associated with the travel path characteristic information indicating the characteristics of the travel path on which the subject traveled, and the evaluation information is generated. At this time, the characteristics of the travel path are either (i) the travel path is a curve, (ii) the travel path is an intersection without a traffic light, (iii) the travel path is a railroad crossing, (iv) the travel path is near a traffic light, (v) the travel path is an intersection with a wide road, (vi) the travel path is a bend, (vii) the travel path is the top of an uphill slope, (viii) the travel path is a steep downhill slope, (ix) the travel path is a one-way road, or (x) the travel path is a point where right and left turns are prohibited, so that the driving behavior of the subject can be objectively evaluated and output. EXAMPLES

[0016] Next, specific examples corresponding to the above-mentioned embodiment will be described with reference to the drawings. Each of the examples described below is an example in which the present application is applied to an evaluation system that provides evaluation information to a driver who drives a vehicle as a result of evaluating the driving manner (or driving state) of the driver.

[0017] (1) First Example First, a first example corresponding to the embodiment will be described with reference to Figs. 2 to 6. Fig. 2 is a block diagram showing the schematic configuration of an evaluation system according to the first example, Fig. 3 is a block diagram showing the schematic configuration of a processing server included in the evaluation system, and Fig. 4 is a diagram illustrating the contents of data stored in the processing server. Furthermore, Fig. 5 is a block diagram showing the schematic configuration of a terminal device included in the evaluation system, and Fig. 6 is a flowchart showing the evaluation process according to the first example. In Figs. 2 to 5, the same component numbers as those of the evaluation device S are used for the components of the first example corresponding to the components of the evaluation device S according to the embodiment shown in Fig. 1.

[0018] (I) Overall configuration and operation of the evaluation system 2, the evaluation system SS according to the first embodiment is configured by connecting terminal devices T1, T2, ..., Tn (n is a natural number) used by drivers who drive vehicles, and a processing server SV so that data can be exchanged via the network NW. In the following explanation of each embodiment, when common matters regarding the terminal devices T1, T2, ..., Tn are explained, they will be collectively referred to as "terminal device T."

[0019] In this configuration, each terminal device T is carried by the driver who uses it, or is mounted on the vehicle driven by the driver, and moves with each driver. At this time, each terminal device T is equipped with a sensor unit that detects the current position of the terminal device T, and current position data indicating the current position detected by the sensor unit is transmitted to the processing server SV via the network NW together with time data indicating the time when the current position was detected and identification data identifying the terminal device T that is the transmission source. At this time, if the terminal device T is equipped with a camera that can capture, for example, the inside of the vehicle or the driver that moves with it, each terminal device T may be configured to transmit image data obtained by capturing an image with the camera to the processing server SV together with the current position data and the identification data.

[0020] Meanwhile, the processing server SV, which receives the current location data, time data, and identification data (as well as image data if the terminal device T is equipped with a camera) from each terminal device T, stores and accumulates history data indicating the movement mode of each terminal device T. In this case, the movement mode refers to the state of movement of the terminal device T, in other words, the driving mode of the driver moving together with the corresponding terminal device T, or the movement mode of the vehicle related to the driver's driving. Specifically, the movement mode includes the movement time, position, movement speed, movement direction, etc. Then, the processing server SV classifies the movement mode data indicating the movement mode into "violation behavior" and "road characteristics", and accumulates each as history data described later.

[0021] Here, the "violation behavior" refers to a movement pattern that violates the Road Traffic Act (Act No. 105 of 1960), such as "failure to stop temporarily," "speeding," "obstruction of priority traffic," or "violation of traffic division." In addition, the violation behavior according to the embodiment also includes a movement pattern that is generally recognized as having the possibility of leading to an accident, such as a movement pattern that violates the so-called safe driving obligation, such as "carelessness in one's movements," "distracted driving," or "failure to confirm safety." Among the violation behaviors, there are violation behaviors that can be identified for each terminal device T using only the current location data, and violation behaviors that can be identified for each terminal device T using the image data in addition to the current location data. This violation behavior corresponds to an example of the "accident-causing driving behavior" according to the present application.

[0022] Meanwhile, the "road characteristics" are characteristics of the road on which the terminal device T has traveled, such as "in an intersection with traffic lights," "general single road," or "in or near an intersection." The processing server SV accumulates the travel behavior data, which is the number of times that the travel behavior of each terminal device T falls into any of the above categories during one trip from the departure point to the destination of a vehicle traveling with the terminal device T, as the history data for each terminal device T. This history data will be described in detail later.

[0023] In addition, the processing server SV stores behavior evaluation data that associates the violation behavior with the road characteristics of the road that is considered to easily lead to an accident if the violation behavior is performed on that road. In this behavior evaluation data, for example, "not stopping temporarily" as a violation behavior is associated with "intersection without traffic lights" as a road characteristic, and data indicating weighting used for scoring (scoring) for generating the evaluation information is further associated with each association. In the evaluation mode in which the higher the score, the lower the driving evaluation, the data indicating the weighting is set so that the more likely the situation is to lead to an accident, the greater the weight. This evaluation data will also be described in detail later.

[0024] Then, the processing server SV uses the history data and the behavior evaluation data to generate evaluation information including score information indicating a score according to the scoring for each terminal device T, and transmits the generated evaluation information to the corresponding terminal device T via the network NW. As a result, the terminal device T that has received the evaluation information presents the score indicated by the score information included in the received evaluation information to the driver who moves with the terminal device T, for example, by displaying the score on a display of the terminal device T, which will be described later. Note that the evaluation information according to the embodiment may be provided to a third party other than the driver, such as an automobile insurance company with which the driver who uses the terminal device T has a contract, in addition to being transmitted to the terminal device T.

[0025] (II) Configuration and operation of the processing server Next, the configuration and operation of the processing server SV included in the evaluation system SS according to the first embodiment will be described with reference to FIGS.

[0026] 3, the processing server SV according to the first embodiment includes an interface 1, a processing unit 2 including a CPU, a RAM (Random Access Memory), a ROM (Read Only Memory), etc., a display 3 including a liquid crystal display, etc., an operation unit 4 including a mouse, a keyboard, etc., and a storage unit 5 including a HDD (Hard Disc Drive) or an SSD (Solid State Drive), etc. The processing unit 2 includes a map matching processing unit 2A, a traffic information adding unit 2B, a user information adding unit 2C, a road characteristic determining unit 2D, a violation behavior detecting unit 2E, and a scoring unit 2F. In this case, the map matching processing unit 2A, the traffic information adding unit 2B, the user information adding unit 2C, the road characteristic determining unit 2D, the violation behavior detecting unit 2E, and the scoring unit 2F may be realized by a hardware logic circuit including the CPU constituting the processing unit 2, or may be realized in a software manner by the CPU reading and executing a program corresponding to the evaluation processing according to the first embodiment described later from the storage unit 5 that non-volatilely stores the program. Moreover, the interface 1 corresponds to an example of the "current location information acquiring means", an example of the "behavior information acquiring means", and an example of the "output means" according to the present application, and the processing unit 2 corresponds to an example of the acquiring means 2 according to the embodiment. Furthermore, the traffic information adding unit 2B corresponds to an example of the "characteristic information acquiring means" according to the present application, the violation behavior detecting unit 2E corresponds to an example of the determining means 2E according to the embodiment, an example of the "movement behavior identifying means" and an example of the "driving behavior identifying means" according to the present application, and the scoring unit 2F corresponds to an example of the evaluating means 2F according to the embodiment and an example of the "evaluating means" according to the present application, respectively. Furthermore, the processing unit 2, the violating behavior detection unit 2E, and the scoring unit 2F constitute an example of an evaluation device S according to an embodiment.

[0027] In the above configuration, the storage unit 5 stores the history data HD and the behavior evaluation data WD in a non-volatile manner. The storage unit 5 outputs the history data HD and the like to the processing unit 2 in response to a request from the processing unit 2. On the other hand, the interface 1 controls data exchange with each of the terminal devices T via the network NW under the control of the processing unit 2. On the other hand, the operation unit 4 outputs an operation signal corresponding to an operation performed by a user of the processing server SV on the operation unit 4 to the processing unit 2. The processing unit 2 executes an evaluation process as the processing server SV according to the first embodiment, using the history data HD and the like stored in the storage unit 5, while exchanging necessary data with each of the terminal devices T via the interface 1 based on the operation signal. At this time, in addition to the history data HD and the behavior evaluation data WD, for example, map data used in the map matching process in the map matching processing unit 2A, a later-described calculation formula for the scoring by the scoring unit 2F, or the above program for executing the evaluation process as the processing server SV according to the first embodiment is stored in the storage unit 5. Information required for the processor 2 to execute the evaluation process according to the first embodiment is presented to the user via the display 3.

[0028] Next, the map matching processing unit 2A of the processing unit 2 matches the current position of the terminal device T with the positions of roads and the like included in the map data based on the current position data transmitted from any one of the terminal devices T and the map data stored in the storage unit 5, and specifies the road on which the terminal device T is located. Next, the traffic information adding unit 2B acquires traffic information on the road matched by the map matching processing unit 2A from, for example, a traffic information server (not shown) connected to the network NW via the interface 1, and adds this to the current position data. At this time, the traffic information includes, for example, information indicating the width and direction of the road on which the terminal device T is located, the number of lanes, and the traffic direction (whether it is one-way or not), as well as information indicating the state of congestion and information including whether or not a temporary road closure has occurred.

[0029] Next, the user information adding unit 2C acquires information about the driver who is the owner of the terminal device T traveling on the road matched by the map matching processing unit 2A from the terminal device T via the interface 1 based on, for example, the identification data, and adds this information to the current position data. At this time, the user information includes, for example, information indicating the driver's age, sex, driving history, etc.

[0030] Next, the road characteristic determination unit 2D determines the road characteristics of the road on which the vehicle together with the terminal device T is traveling, particularly based on the temporal change in the current position data stored as the history data HD and the traffic information added by the traffic information addition unit 2B. Furthermore, the violation behavior detection unit 2E detects whether the movement manner of the vehicle traveling together with the terminal device T (in other words, the driving manner of the driver driving the vehicle) corresponds to the violation behavior, particularly based on the temporal change in the current position data and the traffic information added by the traffic information addition unit 2B.

[0031] Finally, the scoring unit 2F performs scoring for the evaluation information that evaluates the movement mode of the vehicle traveling together with the terminal device T, based on the road characteristics determined by the road characteristics determination unit 2D, the violation behavior determined by the violation behavior detection unit 2E, and the behavior evaluation data WD. Then, the processing unit 2 generates the evaluation information including score information indicating the score by the scoring unit 2F for each terminal device T, and transmits the generated evaluation information to the corresponding terminal device T via the network NW. After that, the terminal device T that has received the evaluation information presents the score indicated by the score information included in the received evaluation information to the driver traveling together with the terminal device T.

[0032] Next, the history data HD etc. stored in the storage unit 5 of the processing server SV will be described in more detail with reference to FIG.

[0033] First, as illustrated in Fig. 4(a), the history data HD includes the number of times that the movement mode of each terminal device T falls under any of the items in each of the above categories during, for example, one trip from the corresponding starting point to the destination, for each of the above violation actions and road characteristics, and is accumulated for each terminal device T. At this time, violation actions such as "failure to stop temporarily", "speeding", "obstruction of priority traffic", and "violation of traffic division" illustrated in Fig. 4(a) are violation actions that can be determined for each terminal device T using only the current position data. On the other hand, violation actions such as "looking aside", "not paying attention to movements", "distracted driving", and "not confirming safety" are violation actions that can be determined for each terminal device T using, for example, the image data showing the driver's face in addition to the current position data.

[0034] Next, as illustrated in FIG. 4(b), the behavior evaluation data WD is associated with each of the violations and accident-related road characteristics such as "intersections without traffic lights", "curves" or "intersections without traffic lights" that are road characteristics that are likely to lead to accidents (i.e., have a high probability of leading to an accident) when the violation is performed, and is further associated with data indicating weighting used in the scoring. The data indicating the weighting indicates that the greater the value, the higher the risk of an accident (in other words, the higher the probability of an accident occurring). The weighting value may be a constant value or may be changed depending on the driver's travel conditions, such as the time of day and weather. More specifically, for example, if the weather is "rainy" and the time is "evening", and it is known that the risk of an accident at a curve increases, the weighting of the accident-related road characteristic "curve" is increased more than usual. In addition, as a basis for the weighting, for example, in the case of a violation of not stopping temporarily, a more generalized weighting is set in advance by considering the following cases targeting multiple people including the driver. - Residential areas have more fatal and injury accidents (twice as many as main roads). Accidents caused by failing to stop are mainly head-on collisions, and account for 40% of accidents on residential roads. In residential areas, you can relax, thinking, "I'm almost home." -The roads are narrow and there are many places where there are no guardrails to separate pedestrians from vehicles.

[0035] As a method of setting weights other than the method of setting weights exemplified in Fig. 4(b), for example, weights may be set using statistical information on traffic accidents that have occurred in the past. With the weights described above, even for the same violation, the impact on the scoring differs depending on the road characteristics on which the violation occurred, and therefore the likelihood of an accident being caused can be reflected in the scoring.

[0036] (III) Configuration and operation of terminal device Next, the configuration of each terminal device T included in the evaluation system SS according to the first embodiment will be described with reference to FIG.

[0037] As shown in FIG. 5, each terminal device T according to the first embodiment is composed of an interface 10, a processing unit 11 consisting of a CPU, RAM, ROM, etc., a display 12 consisting of an LCD display or the like, an operation unit 13 consisting of operation buttons and a touch panel, etc., a memory unit 14 consisting of an SSD or the like, and the above-mentioned sensor unit 15 consisting of, for example, a GPS (Global Positioning System) sensor and an independent sensor or an imaging camera, etc.

[0038] In this configuration, the interface 10 controls data transmission and reception with the processing server SV via the network NW under the control of the processing unit 11. On the other hand, the operation unit 13 outputs an operation signal corresponding to an operation performed by a vehicle occupant including a driver of the vehicle moving together with the terminal device T on the operation unit 13 to the processing unit 11. The sensor unit 15 detects the current position of the terminal device T using the GPS sensor or the independent sensor, generates the current position data and the time data as the detection results, and outputs them to the processing unit 11. If the sensor unit 15 is equipped with a camera, the sensor unit 15 generates the image data in addition to the current position data and the time data, and outputs them to the processing unit 11. The processing unit 11 then executes the evaluation process according to the embodiment as the terminal device T, while transmitting and receiving necessary data with the processing server SV via the interface 10 based on the operation signal and the current position data output from the sensor unit 15, using various data stored in the storage unit 14. At this time, the various data stored in the storage unit 14 include, for example, the identification data for identifying the terminal device T from other terminal devices T, as well as a program for executing evaluation processing as the terminal device T according to the embodiment. The evaluation processing as the terminal device T according to the embodiment includes, for example, periodic transmission of the current position data, the time data, and the identification data (as well as the image data in the case where the sensor unit 15 includes a camera) to the processing server SV, and presentation of the evaluation information transmitted from the processing server SV to the driver, etc., via display on the display 12.

[0039] (IV) Regarding the evaluation process according to the first embodiment Next, the evaluation process according to the first embodiment, which is executed mainly by the processing server SV included in the evaluation system SS having the above-mentioned configuration, will be specifically described with reference to FIG.

[0040] First, as an evaluation process according to the first embodiment, each terminal device T, for example, after its power switch is turned on, repeatedly transmits the current location data and the time data to the processing server SV at preset time intervals (for example, one second) via the network NW together with the identification data for identifying the terminal device T. At this time, if the terminal device T is equipped with the camera, the image data is transmitted together with the identification data in addition to the current location data and the time data.

[0041] On the other hand, as shown in the corresponding flowchart in FIG. 6, the evaluation process according to the first embodiment of the processing server SV is started, for example, when a preset processing start command is issued to the processing server SV. After the evaluation process is started, the processing unit 2 of the processing server SV first receives (acquires) the current location data and the like transmitted from any of the terminal devices T (step S1), and further rearranges them in chronological order (i.e., sorts them on the time axis) (step S2). Then, the processing unit 2 extracts data of a preset section to be the subject of the evaluation process as target data from the current location data and the like rearranged in step S2 (step S3). Next, the processing unit 2 checks the accuracy of the target data extracted in step S3 (particularly, the accuracy of the current location data included therein) and judges whether or not the accuracy is equal to or greater than a preset threshold accuracy (step S4). If the accuracy of the target data is less than the threshold accuracy in the judgment of step S4 (step S4: accuracy NG), the processing unit 2 excludes the target data from the evaluation process according to the embodiment (step S5), and then returns to the above step S1 to repeat the series of processes described above. On the other hand, if the accuracy of the target data is equal to or greater than the threshold accuracy in the judgment of step S4 (step S4: accuracy OK), then the processing unit 2 judges whether the number of current location data, etc. included in the target data is equal to or greater than the threshold data number preset as the number of target data in the preset section (step S6). If the number of current location data, etc. included in the target data is less than the threshold data number in the judgment of step S6 (step S6: insufficient), the processing unit 2 excludes the section corresponding to the target data from the evaluation processing according to the embodiment (step S7), and then returns to step S1 to repeat the series of processing described above. On the other hand, if the number of current location data, etc. included in the target data is equal to or greater than the threshold data number in the judgment of step S6 (step S6: sufficient), then the processing unit 2 temporarily stores the target data in the storage unit 5 (step S8). The target data stored in step S8 includes the time data and the identification data associated with the current location data (and the image data if image data is transmitted).

[0042] Next, the map matching processing unit 2A of the processing unit 2 matches the current position of the terminal device T indicated by each current position data included in the target data accumulated in step S8 with the position of the road on the map data stored in the storage unit 5, and specifies the road on which the terminal device T is located (step S9). In this step S9, the traffic information is added to the target data by the traffic information adding unit 2B of the processing unit 2, and the user information is added to the target data by the user information adding unit 2C of the processing unit 2. Using the result of step S9, the road characteristic determining unit 2D of the processing unit 2 determines the road characteristics of the road on which the vehicle is traveling together with the terminal device T, and reflects the result in the history data HD.

[0043] Next, the violation behavior detection unit 2E of the processing unit 2 compares the direction of the time change of each current position data included in the target data with respect to the road and the direction of the road and its travel direction indicated by the traffic information (step S10), and judges whether the state in which each current position data moves in the wrong-way direction on the road continues for a preset time (step S11). If the wrong-way driving state is not continuous in the judgment of step S11 (step S11: NO), the violation behavior detection unit 2E proceeds to step S19 described later. On the other hand, if the wrong-way driving state is continuous in the judgment of step S11 (step S11: YES), the violation behavior detection unit 2E detects a wrong-way driving violation as a violation behavior (step S12), reflects the detection result in the number of times in the history data HD, adds the detection result to the target data, and proceeds to step S19 described later.

[0044] On the other hand, in parallel with the above steps S10 to S12, the violation behavior detection unit 2E judges whether or not there is a position or a crossing where the vehicle should stop at the current position indicated by the current position data included in the target data based on the above traffic information added to the target data (step S13). Next, the violation behavior detection unit 2E judges whether or not the vehicle moving together with the terminal device T has stopped at the position or the crossing where the vehicle should stop by using a preset stop judgment method (step S14). As the stop judgment method, a conventionally used judgment method can be used. Next, if the vehicle does not stop (i.e., does not stop temporarily) in step S14, the violation behavior detection unit 2E detects the violation behavior as not stopping temporarily (step S15), reflects the detection result in the number of times in the history data HD, adds the detection result to the target data, and proceeds to step S19 described later.

[0045] On the other hand, in parallel with the above steps S10 to S15, the violation behavior detection unit 2E determines the movement mode (specifically, the movement direction and the movement speed, etc.) of the vehicle moving together with the terminal device T based on the traffic information added to the target data and the change in the current position indicated by each of the current position data (step S16), and further performs parallel detection of an entry direction violation at the intersection as a violation behavior (step S17) and detection of a speed violation as a violation behavior (step S18). At this time, the violation behavior detection unit 2E can detect a right / left turn violation and a no entry violation as a violation behavior using the detection result of the above step S17. Furthermore, the violation behavior detection unit 2E can detect a slow-down violation as a violation behavior using the detection result of the above step S18. Thereafter, the violation behavior detection unit 2E reflects the detection results of the above steps S17 and S18 in the number of times in the history data HD, adds the detection results to the target data, and proceeds to step S19 described later. In addition, when the image data is transmitted together with the current location data, etc., the violation behavior detection unit 2E uses the image data to detect the violation behavior according to the embodiment and reflects the number of occurrences of the violation behavior in the history data HD.

[0046] Based on the results of steps S10 to S18, the scoring unit 2F of the processing unit 2 performs scoring for the evaluation information to evaluate the movement mode of the vehicle moving with the terminal device T based on the road characteristics determined by the road characteristics determination unit 2D, each of the violation actions determined by the violation action detection unit 2E, and the behavior evaluation data WD (step S19). More specifically, the scoring unit 2F performs scoring for each accident-related road characteristic associated with each violation action in the behavior evaluation data WD illustrated in FIG. 4(b) for the target data of one journey from the departure point to the destination. That is, for example, for the violation action "not stopping temporarily", the scoring unit 2F calculates a corresponding score for each accident-related road characteristic based on the content of the violation action "not stopping temporarily" in the behavior evaluation data WD by scoring using the following calculation formulas (1) to (3), respectively (see the violation action "not stopping temporarily" in FIG. 4(b)). Accident-related road characteristics: "intersections without traffic lights excluding community roads" Score = (number of times not stopping at a stop sign ÷ number of times encountering intersections without traffic lights, excluding residential roads) × 30 … (1) Accident-related road characteristics: "residential roads and intersections without traffic lights" Score = (number of times not stopping at a stop sign ÷ number of times encountering intersections on residential roads without traffic lights) × 50 … (2) Accident-related road characteristics: "Stop positions other than 1 and 2" Score = (number of times not stopping / number of times stopping at a stop other than 1 or 2) × 20 … (3) Thereafter, the scoring unit 2F averages the scores calculated for each accident-related road characteristic, and sets the averaged score as the score for the violation (in the above case, "failure to stop temporarily").

[0047] The above calculation of the score by the scoring unit 2F is performed for each violation as exemplified in FIG. 4(b). As described above, by calculating a score for each violation and for each accident-related road characteristic corresponding thereto, it is possible to use the score to evaluate the driving behavior of the driver, to assist the driving, or to apply insurance to the driver as an insured person, as described later. At this time, the scoring unit 2F can also be configured to further average the scores for each violation and calculate a comprehensive score for all violations of the driver. By calculating such a comprehensive score, the driver can easily recognize an objective evaluation of his / her driving behavior.

[0048] According to the scoring by the scoring unit 2F described above, the higher the calculated score, the more likely the driver is to get into an accident (violating driving), but scoring may also be performed by deducting points from the base score. In this case, for example, it is preferable to calculate the score for failure to stop temporarily as a violation using the following calculation formula (4). According to the following calculation formula, the score for failure to stop temporarily is, for example, 50 points. Points for not stopping at a stop sign = Base points above (e.g. 100 points) - {(Number of times not stopping at a stop sign as a violation (e.g. 10 times)) ÷ (Number of times when a driver encountered a place where he / she should have stopped at a stop sign (e.g. 20 times)) × Base points above (e.g. 100 points)} … (4)

[0049] Thereafter, the processing unit 2 generates the evaluation information including score information indicating the score as a result of the scoring by the scoring unit 2F for each terminal device T, and transmits the generated evaluation information to the corresponding terminal device T via the network NW (step S20). Thereafter, the terminal device T that has received the evaluation information presents the score indicated by the score information included in the received evaluation information to the driver traveling with the terminal device T. In addition to this, the processing unit 2 provides the evaluation information according to the embodiment including the score information to a third party other than the driver as necessary.

[0050] Then, the processing unit 2 judges whether or not to end the evaluation process according to the embodiment as the processing server SV, for example, because a command to end the process has been issued to the processing server SV (step S21). If the evaluation process is to be continued in the judgment of step S21 (step S21: NO), the processing unit 2 returns to the above step S1 and repeats the above-mentioned process. On the other hand, if the evaluation process is to be ended in the judgment of step S21 (step S21: YES), the processing unit 2 ends the evaluation process as it is.

[0051] (V) Regarding a modified example of the evaluation process according to the first embodiment Next, a modified example of the evaluation process according to the first embodiment described above (hereinafter, the modified example of the evaluation process according to the first embodiment will be simply referred to as the "modified example of the first embodiment") will be described with reference to Fig. 7. Fig. 7 is a diagram illustrating an example of the contents of data stored in a processing server according to the modified example of the first embodiment. In the following description of the modified example of the first embodiment, the same components and processes as those of the evaluation system SS according to the first embodiment will be denoted by the same component numbers and step numbers, and detailed description will be omitted.

[0052] In the evaluation process according to the first embodiment described above, the driver's driving behavior was evaluated using behavior evaluation data WD (see FIG. 4(b)) that associates violation behavior with accident-related road characteristics. In contrast, in a modified example of the first embodiment described below, the driving behavior is evaluated by further associating it with time periods such as "early morning," "morning (rush hour)," "mid-morning," "afternoon," or "late night" in addition to the above.

[0053] That is, the processing server according to the modified example of the first embodiment classifies the movement pattern data showing the movement pattern of each terminal device T into "time period" in addition to "violation behavior" and "road characteristics" as exemplified in Fig. 7(a), and accumulates the data as history data HDD for each terminal device T. In this case, the history data HDD accumulates data for, for example, one journey from the departure point to the destination of a vehicle traveling with the terminal device T. In addition, the processing server according to the modified example of the first embodiment stores time period evaluation data TD that associates the time period with data showing a weighting used for scoring for generating the evaluation information for each time period as exemplified in Fig. 7(c).

[0054] The processing server relating to the modified example of the first embodiment uses the above-mentioned history data HDD, the behavioral evaluation data WD similar to that of the first embodiment, and the above-mentioned time period evaluation data TD to generate evaluation information including score information indicating the score based on the above-mentioned scoring for each terminal device T, and provides the generated evaluation information to the corresponding terminal device T or a third party other than the driver via the network NW.

[0055] On the other hand, the processing unit of the processing server according to the modified example of the first embodiment is configured to include a weather information adding unit (not shown) in addition to a map matching processing unit 2A, a traffic information adding unit 2B, a user information adding unit 2C, a road characteristics determining unit 2D, a violation behavior detecting unit 2E and a scoring unit 2F similar to the processing unit 2 of the processing server SV according to the first embodiment. In the following description, the processing unit of the processing server according to the modified example of the first embodiment will be simply referred to as the "processing unit of the modified example of the first embodiment."

[0056] In the above configuration, the storage unit of the processing server according to the modified example of the first embodiment (hereinafter, the storage unit of the processing server according to the modified example of the first embodiment will be simply referred to as the "storage unit of the modified example of the first embodiment") stores the history data HDD, the behavior evaluation data WD, and the time period evaluation data TD in a non-volatile manner. Then, in response to a request from the processing unit of the modified example of the first embodiment, the storage unit outputs the history data HDD, etc. to the processing unit. As a result, the processing unit of the modified example of the first embodiment executes the modified example of the evaluation process according to the first embodiment using the history data HDD, etc. stored in the storage unit of the modified example of the first embodiment.

[0057] Next, the map matching processing unit 2A, the traffic information adding unit 2B, the user information adding unit 2C, the road characteristic determining unit 2D, and the violation behavior detecting unit 2F of the processing unit of the modified example of the first embodiment execute the same functions as the map matching processing unit 2A of the processing unit 2 according to the first embodiment. In addition, the weather information adding unit acquires weather information indicating the weather when the terminal device T is moving on the road matched by the map matching processing unit 2A based on the corresponding time data from a weather information server (not shown) connected to the network NW via the interface 1, for example, and adds this to the current position data. At this time, the weather information includes, for example, information indicating the temperature, weather, and visibility (visibility) at the time when the terminal device T was moving.

[0058] Then, the scoring section of the processing section according to the modified example of the first embodiment (hereinafter, the scoring section of the processing section according to the modified example of the first embodiment will be simply referred to as the "scoring section of the modified example of the first embodiment") performs scoring for the evaluation information evaluating the movement mode of the vehicle moving together with the terminal device T, based on the road characteristics determined by the road characteristics determination section 2D, the violation behavior determined by the violation behavior detection section 2E, the behavior evaluation data WD, and the time period evaluation data TD. After that, the processing section according to the modified example of the first embodiment generates, for each terminal device T, the evaluation information including score information indicating the score obtained by the scoring section of the modified example of the first embodiment, and transmits the generated evaluation information to the corresponding terminal device T via the network NW.

[0059] Next, the history data HDD and the like stored in the storage unit of the modified example of the first embodiment will be described in more detail with reference to FIG.

[0060] First, as shown in Fig. 7(a), the history data HDD stores the number of times that the movement mode of each terminal device T corresponds to any of the items in each of the above categories for each of the above violation actions, road characteristics, and time periods during, for example, one journey from a corresponding starting point to a destination, for each terminal device T. At this time, the violation action such as "failure to stop at a temporary stop" shown in Fig. 7(a) is the same violation action as the violation action in the first embodiment.

[0061] Next, as shown in FIG. 7(b), the behavior evaluation data WD is the same as the behavior evaluation data WD according to the first embodiment.

[0062] Finally, as shown in FIG. 7(c), the time period evaluation data TD is associated with data indicating weighting for scoring each time period for each of the time periods of "early morning", "morning (rush hour)", "mid-morning", "afternoon", "evening (rush hour)", "night" and "late night". Note that FIG. 7(c) also shows a specific example of the time period such as "early morning". The weighting as the time period evaluation data TD is also set in advance as a more generalized weighting by taking into account the accident occurrence rate for each time period for multiple persons including the driver.

[0063] As a method for setting weights other than the method for setting weights illustrated in FIG. 7(b) or FIG. 7(c), for example, weights may be set by using statistical information on traffic accidents similar to that in the first embodiment.

[0064] Next, a specific description will be given of the modified example of the first embodiment that is executed mainly by the processing server according to the modified example of the first embodiment included in the evaluation system according to the modified example of the first embodiment having the above-mentioned configuration.

[0065] In the modified example of the first embodiment, first, steps S1 to S18 in the evaluation process according to the first embodiment are executed (see FIG. 6). Then, based on the results of steps S10 to S18, the scoring unit of the modified example of the first embodiment performs scoring for the evaluation information that evaluates the movement mode of the vehicle moving together with the terminal device T based on the road characteristics determined by the road characteristics determination unit 2D, the violation behaviors determined by the violation behavior detection unit 2E, the behavior evaluation data WD, and the time period evaluation data TD (step S19). More specifically, the scoring unit of the modified example of the first embodiment performs the scoring for the target data of one journey from the departure point to the destination, for example, using the following calculation formula (5).

[0066] Score = (number of violations / number of encounters with road characteristics that make violations more likely to lead to accidents) × weighting of road characteristics where violations occurred × weighting of time period … (5) At this time, the "number of violations" and "number of times of encountering road characteristics where violations are likely to lead to accidents" in the calculation formula (5) are referenced with reference to the history data HDD (see FIG. 7(a)), the "weighting of road characteristics where violations occurred" is referenced with reference to the behavior evaluation data WD, and the "weighting of time period" is referenced with reference to the time period evaluation data TD. More specifically, for example, if the violation of "failure to stop at a temporary stop" shown in FIG. 7 occurs in the evening, the scoring is performed using the following calculation formula (5).

[0067] Score = (number of times not stopping at a stop sign / number of times encountering intersections without traffic lights on residential roads) x 50 (see Figure 7(b)) x 30 (see Figure 7(c)) ... (5) When scoring using the above formula (5), weighting for each weather condition indicated by the weather information associated with the target data may be further added. In this case, the scoring unit of the modified example of the first embodiment preferably increases the weighting value when, for example, visibility is poor or there is rainfall, since these conditions are likely to lead to accidents.

[0068] Thereafter, the processing unit of the modified first embodiment generates the above-mentioned evaluation information including score information indicating the score by the scoring unit of the modified first embodiment for each terminal device T, and transmits the generated evaluation information to the corresponding terminal device T via the network NW (step S20). Thereafter, the processing unit of the modified first embodiment performs the process of step S21 similar to that of the processing unit 2 according to the first embodiment. In the case of the modified first embodiment, the score is calculated by multiplying the number of road characteristics, the number of time zone divisions, and the number of weather categories, and these are averaged to calculate the score for one violation. At this time, the more items there are as a violation, the smaller the value after averaging, so the scoring may be configured to be performed using a statistical method other than the averaging.

[0069] As described above, the evaluation system SS of the first embodiment (including the modified example of the first embodiment) judges whether the driving behavior of the driver to be evaluated is a violation or not, and generates evaluation information based on the judgment result, so that the driving behavior of the driver can be objectively evaluated and output, which can be useful for evaluating the driving behavior of the driver and / or providing driving assistance required in response thereto, and for applying insurance with the driver as the insured person.

[0070] Furthermore, since a determination as to whether or not a violation has occurred is made based on the history data HD (or history data HDD) and road characteristics, it is possible to more accurately determine, from among the violations, those violations that may be a cause of an accident.

[0071] Furthermore, by referring to the behavior evaluation data WD, it is determined whether the driver's driving behavior is an illegal behavior, so that it is possible to more objectively determine whether the driving behavior is an illegal behavior that may be a cause of an accident.

[0072] Furthermore, when generating evaluation information based on the time period evaluation data TD according to the modified example of the first embodiment, it is possible to perform an evaluation that is more accurate and in line with reality.

[0073] In addition, since the evaluation information is generated based on the number of times the road has been traveled, which is included in the history data HD (or history data HDD), no unfairness arises between drivers who have traveled on accident-related roads many times and drivers who have traveled on such roads less often, and the driving style of each driver can be evaluated fairly.

[0074] Furthermore, since the evaluation information is generated based on the road characteristics indicated by the characteristic information, it is possible to perform a more accurate evaluation in accordance with actual travel.

[0075] Furthermore, since evaluation information is generated and output by scoring, the more violations that may cause an accident and the more likely the driving style is to lead to an accident, the greater the impact on the score, so that evaluation information that is easier to recognize can be generated and output.

[0076] In the above-described first embodiment and the modified example of the first embodiment, when the illegal behavior is determined to be a driving manner that violates the Road Traffic Act, evaluation information that contributes to greater safety can be generated and output.

[0077] Furthermore, if the evaluation information is generated based on the weather indicated by the weather information, a more accurate and realistic evaluation can be performed.

[0078] Furthermore, the system may be configured to determine whether or not a violation is accident-related for each country or region that the road is in. In this case, safe driving support can be provided appropriately for each country or region through which the vehicle travels.

[0079] (2) Second Example Next, a second example, which is another example corresponding to the embodiment, will be described with reference to Fig. 8. Fig. 8 is a diagram illustrating the contents of road characteristic evaluation data according to the second example. In the following description, the same component parts as those in the evaluation system SS according to the first example described above will be denoted by the same component numbers and detailed descriptions will be omitted.

[0080] In the evaluation system SS according to the first embodiment described above, for example, a violation is first detected using the current position data of the terminal device T, and the driving manner of the driver is scored based on the detection result (see steps S9 to S19 in FIG. 6). In contrast, in the second embodiment described below, the road characteristics corresponding to the current position data are first detected based on the current position data by referring to map data, etc. Then, a violation that may be a cause of an accident is identified based on the detected road characteristics, and evaluation information is generated.

[0081] That is, in the storage unit of the processing server according to the second embodiment, road characteristic evaluation data DW according to the second embodiment, as illustrated in FIG. 8, is stored in place of the behavior evaluation data WD according to the first embodiment. As illustrated in FIG. 8, the road characteristic evaluation data DW according to the second embodiment is associated with the road characteristics and accident-related violation behaviors such as "failure to stop temporarily", "obstruction of priority traffic" or "violation of slow speed" as violation behaviors that are likely to lead to accidents (i.e., are highly likely to lead to accidents) when traveling on a road with the road characteristics, and data indicating weighting used in the scoring of the second embodiment is further associated with the road characteristics. The data indicating the weighting is similar to the behavior evaluation data WD according to the first embodiment in the sense that the larger the value, the higher the risk of an accident (in other words, the higher the possibility of an accident occurring), but the value is different from that of the behavior evaluation data WD. The weighting value may be a fixed value or may be changed depending on the driver's traveling conditions, such as the time of day and weather.

[0082] As the evaluation process according to the second embodiment, first, steps S1 to S9 in the evaluation process according to the first embodiment (see FIG. 6) are executed to identify the road on which the terminal device T is located and determine the road characteristics. Then, based on the determined road characteristics, the road characteristics evaluation data DW exemplified in FIG. 8 is referenced to determine the violation behavior that is likely to lead to an accident when moving on the road with the determined road characteristics. As a result, by determining the violation behavior that is likely to lead to an accident first, it is possible to determine in advance which of the processes of steps S10 to S12, steps S13 to S15, or steps S16 to S18 in the evaluation process according to the first embodiment is to be performed (or which of the processes is to be performed with priority), and as a result, it becomes unnecessary to determine the violation behavior (i.e., the violation behavior that is not likely to lead to an accident on the road with the determined road characteristics). As a result, the evaluation process according to the second embodiment can be simplified as a whole and its speed can be improved.

[0083] In addition, the evaluation result by the evaluation process according to the second embodiment using the road characteristic evaluation data DW can be used to assist the driver moving with the corresponding terminal device T. More specifically, in this case, for example, the road characteristic of the road on which the vehicle is moving together with the terminal device T is specified, and the road characteristic evaluation data DW is referenced to specify the accident-related violation behavior associated with the road characteristic. This is suitable for use in monitoring whether or not the driver will commit an accident-related violation behavior on the road on which the driver is currently moving or on a road to which the driver is scheduled to move in the future. When a violation behavior occurs, not only is evaluation information generated after the fact (for example, after the end of the one trip), but the evaluation information is presented on the terminal device T together with warning information immediately after the violation behavior occurs, so that the driver's driving assistance can be performed more effectively.

[0084] Also, for example, by presenting on the terminal device T accident-related violation behaviors corresponding to the road characteristics of the road on which the vehicle is traveling or is scheduled to travel, the terminal device T can be configured to issue a warning about the accident-related violation behavior before the vehicle travels on the road. In this case, for example, the system may be configured to determine whether or not to issue the warning or a corresponding warning based on the weighting data exemplified in Fig. 8. Furthermore, the system may be configured to refer to evaluation information (score information) corresponding to the driver's driving up to that point in time, and to present a warning about accident-related violation behaviors with low weighting (i.e. minor accident-related violation behaviors) to a driver with a low (bad) evaluation.

[0085] As described above, according to the evaluation system of the second embodiment, the driving manner of the vehicle that may cause an accident is identified based on the road characteristics of the road on which the vehicle is traveling together with the terminal device T, so that safe driving assistance can be provided accurately using a minimum amount of information.

[0086] In addition, since accident-related violations are identified from road characteristics while referring to the road characteristic evaluation data DW, accident-related violations can be identified more objectively.

[0087] Furthermore, since information for calling the driver's attention is presented to the driver based on road characteristics, safe driving support can be provided more accurately.

[0088] Furthermore, when the driver's driving behavior is evaluated based on accident-related violation behavior identified using the road characteristic evaluation data DW, the driving behavior can be objectively evaluated to provide the driver with safe driving support.

[0089] In the second embodiment described above, the road characteristic evaluation data DW shown in Fig. 8 can be set for each country or region where the road is located. In this case, accident-related violation behaviors can be identified based on the road characteristics for each country or region, so that safe driving support can be provided appropriately for each country, etc. where the vehicle travels.

[0090] [Variations] Next, a modification of the above-described embodiment will be described.

[0091] (1) First Modification First, as a first modified example, a so-called smartphone, a wearable device, an in-vehicle navigation device, a drive recorder, or the like may function as the terminal device T according to each embodiment, either alone or in combination. In this case, for example, when the wearable device or the like is used as the terminal device T and so-called biometric information is obtained via the wearable device or the like, the biometric information may be included in the information transmitted to the processing server SV, and the accident-related violation behavior may be determined using the biometric information in addition to the current position data, etc. and the image data.

[0092] (2) Second Variation Next, as a second variant, instead of or together with the above-mentioned identification data, driver identification data for identifying the driver using the terminal device T may be added to the current location data in the terminal device T and then transmitted to the processing server SV.

[0093] (3) Third Modification Next, as a third modified example, in each of the above-mentioned embodiments, the target data of one journey from the departure point to the destination is the target of scoring (evaluation), but in addition to this, for example, scoring may be performed for all accumulated journeys based on the history data HD according to the first embodiment. Alternatively, based on the date information given to the history data HD, scoring may be performed for target data divided into a predetermined period, such as a period divided by insurance renewal period or a month.

[0094] (4) Fourth Variation Next, as a fourth modified example, a part or all of the processes expressed as the functions of the processing server SV according to each of the above-mentioned embodiments may be executed by the terminal device T. More specifically, for example, the terminal device T may be configured to execute the processes from step S9 to step S19 in Fig. 6 and transmit the results to the processing server SV.

[0095] (5) Fifth Variation Next, as a fifth variant, when the accident-related violation behavior itself or its relationship with road characteristics differs depending on the country or region, national or regional borders may be detected based on current location data, etc., and the behavior evaluation data WD or road characteristic evaluation data DW corresponding to the country or region may be modified and used.

[0096] (6) Other variations Finally, as another variant, in each of the above-described embodiments, evaluation information was generated using scoring by the scoring unit 2F, but in addition to this, it is also possible to use the relationship between the scoring result and a threshold value indicating a predetermined level of safety to generate evaluation information evaluated using a binary value of safe / unsafe rather than a score, and to transmit this to the corresponding terminal device T.

[0097] In addition, a program corresponding to the flowchart shown in FIG. 6 can be recorded on a recording medium such as an optical disk or a hard disk, or obtained via a network such as the Internet, and then read out and executed by a general-purpose microcomputer or the like, thereby causing the microcomputer or the like to function as the processing unit 2 of the embodiment. [Explanation of symbols]

[0098] 2 Acquisition means (processing unit) 2E Judgment means (violation behavior detection unit) 2F Evaluation method (scoring section) 2A Map matching processing section 2B Traffic information addition section 2D road characteristic determination section S Evaluation Equipment T, T1, T2, Tn terminal equipment SV Processing Server SS Rating System HD, HDD history data WD Behavioral Assessment Data DW Road characteristic evaluation data TD Time Zone Evaluation Data

Claims

1. An evaluation device that evaluates a driving behavior of a subject and outputs evaluation information indicating a result of the evaluation, An acquisition means for acquiring behavior information indicating a driving behavior of the subject; A determination means for determining whether the driving behavior indicated by the acquired behavior information is an accident-causing driving behavior that may cause an accident; An evaluation means for evaluating the driving behavior of the subject based on a determination result by the determination means and a weighting associated with travel path characteristic information indicating characteristics of the travel path traveled by the subject, and generating and outputting the evaluation information; Equipped with The characteristics of the travel path are (i) the path of travel is curved; (ii) The route of travel is an intersection without a traffic light; (iii) The path of travel is a railroad crossing; (iv) The route of travel is near a traffic light; (v) The route of travel is at an intersection with a wide road; (vi) The path of travel is tortuous; (vii) The path of travel is uphill; (viii) The route of travel is a steep downward slope; (ix) The route of travel is a one-way road; or (x) The route of travel is a no-right or no-left turn point; The evaluation device according to any one of the above.

2. 2. The evaluation device according to claim 1, The evaluation device is characterized in that the evaluation means evaluates the driving behavior indicated by the acquired behavior information using a scoring method based on a weighting associated with the characteristics of the travel route and used for scoring the evaluation information, the weighting being set from cases targeting the driving behaviors of a plurality of people.

3. 3. The evaluation device according to claim 1, The acquisition means further acquires weather information indicating the weather when the subject drove in the driving style, The evaluation device, wherein the evaluation means generates the evaluation information further based on the weather indicated by the acquired weather information.

4. The evaluation device according to any one of claims 1 to 3, The acquisition means further acquires time period information indicating a time period during which the subject drove in the driving manner, The evaluation device, wherein the evaluation means generates the evaluation information further based on the time period indicated by the acquired time period information.

5. The evaluation device according to any one of claims 1 to 4, The acquisition means further acquires frequency information indicating a number of times the subject has moved along a moving path by driving the subject, The evaluation device, wherein the evaluation means generates the evaluation information further based on the number of times indicated by the acquired number of times information.

6. The evaluation device according to any one of claims 1 to 5, The evaluation device is characterized in that the evaluation means generates and outputs the evaluation information in which the driving manner of the subject is scored based on the judgment result by the judgment means.

7. The evaluation device according to any one of claims 1 to 6, The evaluation device is characterized in that the accident-causing driving behavior is a driving behavior that violates a road traffic law.

8. The evaluation device according to any one of claims 1 to 7, The evaluation device is characterized in that the determination means determines whether the driving behavior indicated by the acquired behavior information is an accident-causing driving behavior for each country in which the travel route is located.

9. An evaluation method executed by an evaluation device that evaluates a driving behavior of a subject and outputs evaluation information indicating a result of the evaluation, the evaluation device including an acquisition means, a determination means, and an evaluation means, An acquisition step of acquiring behavior information indicating a driving behavior of the subject by the acquisition means; a determination step of determining by the determination means whether or not the driving behavior indicated by the acquired behavior information is an accident-causing driving behavior that may cause an accident; an evaluation step of evaluating the driving behavior of the subject by the evaluation means based on a judgment result in the judgment step and a weighting associated with travel path characteristic information indicating characteristics of the travel path traveled by the subject, and generating and outputting the evaluation information; Including, The characteristics of the travel path are (i) the path of travel is curved; (ii) The route of travel is an intersection without a traffic light; (iii) The path of travel is a railroad crossing; (iv) The route of travel is near a traffic light; (v) The route of travel is at an intersection with a wide road; (vi) The path of travel is tortuous; (vii) The path of travel is uphill; (viii) The route of travel is a steep downward slope; (ix) The route of travel is a one-way road; or (x) The route of travel is a no-right or no-left turn point; The evaluation method according to any one of the above.

10. An evaluation program causing a computer to function as the evaluation device according to any one of claims 1 to 8.

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