Information processor, information processing method and information processing program

Abstract

To provide an information processor capable of timely executing safe driving support using a minimum volume of information.SOLUTION: The information processor includes: an interface 1 for acquiring current position information showing a current position of a vehicle; a road information addition section 2B for acquiring characteristics information showing the characteristics of a road on which a vehicle travels on the basis of the acquired current position information; and a violated action detection section 2E for identifying an accident cause driving mode on the basis of the road characteristics shown by the acquired characteristics information.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present application relates to the technical fields of an information processing device, an information processing method, and an information processing program. More specifically, the present application relates to the technical fields of an information processing device, an information processing method, and a program for the information processing device that processes information about a route along which a moving body such as a vehicle moves. [Background technology]

[0002] Conventionally, in order to improve the safety of vehicle driving, there are known technologies such as a technology that uses so-called probe information to warn of accident-prone locations and a technology that detects sudden braking and issues a warning against sudden braking. An example of a prior art document that illustrates such a technology is the technology described in Patent Document 1 below. The technology described in Patent Document 1 is configured to evaluate driving behavior using, for example, the frequency of sudden braking and sudden steering. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2016-81087 Summary of the Invention [Problem to be solved by the invention]

[0004] However, in the prior art including the technology described in Patent Document 1, for example, warnings about locations where accidents are likely to occur require the accumulation of the above-mentioned probe information, and warnings about sudden braking can only be given after the fact, resulting in the problem that it is not possible to provide accurate support for safe driving using minimal information.

[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 information processing device, an information processing method, and a program for the information processing device that can accurately provide safe driving assistance using minimal information. [Means for solving the problem]

[0006] In order to solve the above problem, the invention described in claim 1 comprises a current location information acquisition means for acquiring current location information indicating the current location of a mobile body, a characteristic information acquisition means for acquiring characteristic information indicating the path characteristics of the path along which the mobile body is traveling based on the acquired current location information, and a determination means for determining a violating behavior, which is a movement pattern of a mobile body that violates the Road Traffic Act, by referring to road characteristic evaluation data in which accident-causing movement patterns, which are movement patterns of a mobile body that may cause an accident, are pre-associated with accident-causing movement patterns that are pre-assigned a weight corresponding to the likelihood of the accident occurring and the path characteristics indicated by the acquired characteristic information.

[0007] In order to solve the above problem, the invention described in claim 6 is an information processing method executed in an information processing device equipped with a current location information acquisition means, a characteristic information acquisition means, and a determination means, and includes a current location information acquisition step of acquiring current location information indicating the current location of a moving body by the current location information acquisition means, a characteristic information acquisition step of acquiring characteristic information indicating the path characteristics of the path along which the moving body is moving by the characteristic information acquisition means based on the acquired current location information, and a determination step of determining, by the determination means, a violating behavior, which is a movement pattern of a moving body that violates the Road Traffic Act, by referring to road characteristic evaluation data in which accident-causing movement patterns, which are movement patterns of a moving body that may cause an accident, are pre-associated with the path characteristics indicated by the acquired characteristic information.

[0008] In order to solve the above problem, the invention described in claim 7 causes a computer to function as the information processing device described in any one of claims 1 to 5. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a block diagram showing a schematic configuration of an information processing apparatus according to an embodiment; [Figure 2] 1 is a block diagram showing a schematic configuration of an evaluation system according to a first embodiment. [Figure 3] FIG. 2 is a block diagram showing a schematic configuration of a processing server included in the evaluation system according to the first embodiment. [Figure 4] 1A and 1B are diagrams illustrating examples of the contents of data stored in a processing server according to a first embodiment, where FIG. 1A is a diagram illustrating an example of the contents of history data, and FIG. 1B is a diagram illustrating an example of the contents of behavior evaluation data. [Figure 5] FIG. 2 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] 10A and 10B are diagrams illustrating 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 behavior evaluation, and (c) is a diagram illustrating the contents of data for time period evaluation. [Figure 8] FIG. 10 is a diagram illustrating an example of the contents of road characteristic evaluation data according to the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[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 information processing device according to the embodiment.

[0011] As shown in FIG. 1, an information processing device S according to the embodiment is configured to include a current location information acquisition means 1, a characteristics information acquisition means 2B, and a movement pattern identification means 2E.

[0012] In this configuration, the current location information acquisition means 1 acquires current location information indicating the current location of a mobile object.

[0013] On the other hand, the characteristics information acquisition means 2B acquires characteristics information indicating the characteristics of the path along which the moving object is moving, based on the current position information acquired by the current position information acquisition means 1.

[0014] The movement pattern identifying means 2E identifies an accident-causing movement pattern, which is a movement pattern of a moving object that may cause an accident, based on the movement path characteristics indicated by the characteristic information acquired by the characteristic information acquiring means 2B.

[0015] As described above, according to the operation of the information processing device S of the embodiment, the accident-causing movement pattern is identified based on the path characteristics of the path along which the moving body is traveling, so that safe driving assistance can be provided accurately using minimal information. [Example]

[0016] Next, specific examples corresponding to the above-described embodiments will be described with reference to the drawings. Note that each example described below is an example in which the present application is applied to an evaluation system that provides evaluation information to a driver of a vehicle as a result of evaluating the driver's driving style (or driving state).

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

[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 explanations of the embodiments, when common matters regarding terminal device 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 travels with the driver. At this time, each terminal device T is equipped with a sensor unit that detects the current location of the terminal device T, and current location data indicating the current location 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 location was detected and identification data that identifies the terminal device T as the sender. At this time, if the terminal device T is equipped with a camera that can capture images of the interior of the vehicle or the driver traveling with it, for example, each terminal device T may be configured to transmit image data obtained by the image capture by the camera to the processing server SV together with the current location data and the identification data.

[0020] Meanwhile, the processing server SV, which receives the current location data, time data, and identification data (and 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 behavior of each terminal device T. In this case, the movement behavior refers to the state of movement of the terminal device T, in other words, the driving behavior of the driver moving together with the corresponding terminal device T, or the movement behavior of the vehicle driven by the driver. The movement behavior specifically includes the movement time, position, movement speed, movement direction, etc. Then, the processing server SV classifies the movement behavior data indicating the movement behavior into "violation behavior" and "road characteristics," and accumulates them as history data, which will be 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," "speeding," "obstruction of right-of-way," or "violation of traffic division." In addition, the violation behaviors in the embodiments also include movement patterns that are generally recognized as having the potential to lead to accidents, such as "carelessness," "distracted driving," or "failure to check for safety," which are so-called violations of the safe driving obligation, even though there are no clear legal standards for them. 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. These violation behaviors correspond to examples of "accident-causing driving patterns" in the present application.

[0022] Meanwhile, the "road characteristics" refer to the 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 stores the travel behavior data, which is the number of times that the travel behavior of each terminal device T falls into any of the categories in a single journey 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 with road characteristics of the road where the violation is likely to lead to an accident if committed on that road. For example, the behavior evaluation data associates a violation such as "failure to stop at a stop sign" with a road characteristic such as "intersection without traffic lights." Each association is further associated with data indicating a weighting used for scoring (scoring) to generate the evaluation information. In the case of an evaluation mode in which a higher score indicates a lower driving evaluation, the weighting is set so that the more likely a situation is to lead to an accident, the greater the weighting. This evaluation data will be described in detail later.

[0024] Then, the processing server SV uses the history data and the behavior evaluation data to generate evaluation information for each terminal device T, including score information indicating a score according to the scoring, 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 a driver traveling with the terminal device T, for example, by displaying the score on a display (to be described later) of the terminal device T. Note that the evaluation information according to the embodiment may be transmitted to the terminal device T, or may be provided to a third party other than the driver, such as an automobile insurance company with which the driver using the terminal device T has a contract.

[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 or the like, 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 also includes 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. 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 each be realized by a hardware logic circuit including the CPU and the like constituting the processing unit 2, or may be realized in software by the CPU and the like reading and executing a program corresponding to the evaluation processing according to the first embodiment described later from a storage unit 5 that non-volatilely stores the program. The interface 1 corresponds to an example of current location information acquiring means 1 according to the embodiment, and an example of an "output means" and an example of a "behavior information acquiring means" according to the present application, respectively. The traffic information adding unit 2B corresponds to an example of a characteristic information acquiring means 2B according to the embodiment. The violation behavior detecting unit 2E corresponds to an example of a travel behavior identifying means 2E according to the embodiment, and an example of a "driving behavior identifying means" and an example of a "determination means" according to the present application, respectively. The processing unit 2 corresponds to an example of an "acquisition means" according to the present application, and the scoring unit 2F corresponds to an example of an "evaluation means" according to the present application. Furthermore, the interface 1, the traffic information adding unit 2B, and the violation behavior detecting unit 2E constitute an example of an information processing device S according to the embodiment.

[0027] In the above configuration, the storage unit 5 non-volatilely stores the history data HD and the behavior evaluation data WD. 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. Under the control of the processing unit 2, the interface 1 controls data exchange with each of the terminal devices T via the network NW. 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 then executes the 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 and exchanging necessary data with each of the terminal devices T via the interface 1 based on the operation signal. In this case, the storage unit 5 stores, in addition to the history data HD and the behavior evaluation data WD, map data used in the map matching process in the map matching processing unit 2A, a formula (described later) for the scoring by the scoring unit 2F, the program for executing the evaluation process as the processing server SV according to the first embodiment, and the like. Information required for the processing unit 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 any terminal device T with the positions of roads, etc. included in the map data based on the current position data transmitted from any terminal device T and the map data stored in the storage unit 5, thereby identifying the road on which the terminal device T is located. Next, the traffic information adding unit 2B obtains traffic information about 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 information 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 status of congestion and information indicating whether 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, for example, based on the identification data, and adds this information to the current location data. At this time, the user information includes, for example, information indicating the driver's age, gender, driving history, etc.

[0030] Next, the road characteristics determination unit 2D determines the road characteristics of the road on which the vehicle traveling 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 of 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. Thereafter, the processing unit 2 generates the evaluation information for each terminal device T, including score information indicating the score obtained by the scoring by the scoring unit 2F, and transmits the generated evaluation information to the corresponding terminal device T via the network NW. 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 together with the terminal device T.

[0032] Next, the history data HD and the like 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, for each of the violations and the road characteristics, the number of times that the movement mode of each terminal device T falls under any of the items in each of the categories during, for example, one journey from the corresponding starting point to the destination, accumulated for each terminal device T. In this case, violations such as "failure to stop temporarily," "speeding," "obstruction of right of way," and "violation of traffic division" illustrated in Fig. 4(a) are violations that can be identified for each terminal device T using only the current location data. On the other hand, violations such as "looking aside," "not paying attention to movements," "distracted driving," and "failure to confirm safety" are violations that can be identified for each terminal device T using, for example, the image data showing the driver's face in addition to the current location data.

[0034] Next, as shown in FIG. 4(b), the behavior evaluation data WD associates each of the violations with accident-related road characteristics, such as "intersections without traffic lights," "curves," or "intersections without traffic lights," which are road characteristics that are likely to lead to accidents if the violation is performed (i.e., the probability of an accident occurring is high). Data indicating weightings used in the scoring are also associated with each of the violations. The larger the weighting data, the higher the risk of an accident (i.e., the higher the probability of an accident occurring). The weighting values ​​may be constant 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 on a curve increases, the weighting of the accident-related road characteristic "curves" is increased compared to normal. Furthermore, as a basis for the weighting, for example, in the case of a violation of failing to stop temporarily, a more generalized weighting is preset by taking into account the following cases involving 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 that occur on residential roads. In residential areas, you can relax knowing that you'll be home soon. ·Since the road width is narrow, there are many places without guardrails to distinguish pedestrians from vehicles.

[0035] In addition, as another method for setting weights other than the method for setting weights illustrated in FIG. 4(b), for example, weights may be set using statistical information on traffic accidents that occurred in the past. With the weights as described above, even for the same violation behavior, the influence on scoring differs depending on the road characteristics where the violation occurred, and thus the likelihood of leading to an accident can be reflected in the scoring.

[0036] (III) Configuration and operation of terminal equipment 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. 5.

[0037] As shown in FIG. 5, each terminal device T according to the first embodiment includes an interface 10, a processing unit 11 composed of a CPU, RAM, ROM, etc., a display 12 composed of a liquid crystal display, etc., an operation unit 13 composed of operation buttons and a touch panel, etc., a storage unit 14 composed of an SSD, etc., and the sensor unit 15 composed of, for example, a GPS (Global Positioning System) sensor, an inertial sensor, or an imaging camera.

[0038] In this configuration, the interface 10 controls the exchange of data with the processing server SV via the network NW under the control of the processing unit 11. Meanwhile, the operation unit 13 outputs to the processing unit 11 an operation signal corresponding to an operation performed on the operation unit 13 by a vehicle occupant, including a driver of a vehicle traveling with the terminal device T. The sensor unit 15 detects the current location of the terminal device T using the GPS sensor or the autonomous sensor, generates the current location 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 location 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 of the terminal device T, using various data stored in the memory unit 14, while exchanging necessary data with the processing server SV via the interface 10 based on the operation signal and the current location data output from the sensor unit 15. At this time, the various data stored in the storage unit 14 include, for example, the identification data for distinguishing 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 according to the embodiment as the terminal device T includes, for example, periodically transmitting the current position data, the time data, and the identification data (and the image data in the case where the sensor unit 15 includes a camera) to the processing server SV, and presenting the evaluation information transmitted from the processing server SV to the driver or the like 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-described configuration, will be specifically described with reference to FIG.

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

[0041] 6, the evaluation process according to the first embodiment of the processing server SV is initiated, for example, when a preset processing start command is issued to the processing server SV. After the evaluation process is initiated, the processing unit 2 of the processing server SV first receives (acquires) the current location data and other data transmitted from any of the terminal devices T (step S1) and then sorts the data in chronological order (i.e., time axis) (step S2). The processing unit 2 then extracts data from the current location data and other data sorted in step S2 as target data for the evaluation process (step S3). The processing unit 2 then checks the accuracy of the target data extracted in step S3 (particularly the accuracy of the current location data included therein) and determines whether the accuracy is equal to or exceeds a preset threshold accuracy (step S4). If the accuracy of the target data is determined to be less than the threshold accuracy in 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 step S1 and repeats the above-described series of processes. On the other hand, if the accuracy of the target data is determined to be equal to or greater than the threshold accuracy in step S4 (step S4: accuracy OK), the processing unit 2 then determines whether the number of pieces of current location data, etc. included in the target data is equal to or greater than a threshold data number preset as the number of pieces of target data in the preset section (step S6). If the number of pieces of current location data, etc. included in the target data is less than the threshold data number in step S6 (step S6: insufficient), the processing unit 2 excludes the section corresponding to the target data from the evaluation process according to the embodiment (step S7), and then returns to step S1 to repeat the series of processes described above. On the other hand, if the number of pieces of current location data, etc. included in the target data is determined to be equal to or greater than the threshold data number in step S6 (step S6: sufficient), the processing unit 2 then 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 identifies the road on which the terminal device T is located (step S9). In this step S9, the traffic information adding unit 2B of the processing unit 2 adds the traffic information to the target data, and the user information adding unit 2C of the processing unit 2 adds the user information to the target data. Furthermore, by 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 together with the terminal device T is traveling, 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 change over time in each current position data included in the target data with the direction of the road and the travel direction thereof indicated by the traffic information (step S10), and determines whether the state in which each current position data moves in the wrong-way direction on the road has continued for a preset period of time (step S11). If the determination in step S11 shows that the wrong-way driving state is not continuous (step S11: NO), the violation behavior detection unit 2E proceeds to step S19, which will be described later. On the other hand, if the determination in step S11 shows that the wrong-way driving state is continuous (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, which will be described later.

[0044] Meanwhile, in parallel with steps S10 to S12, the violation behavior detection unit 2E determines, based on the traffic information added to the target data, whether or not the current position indicated by the current position data included in the target data has a position where a stop should be made or a railroad crossing where a stop should be made (step S13). Next, the violation behavior detection unit 2E determines, using a preset stop determination method, whether or not the vehicle traveling with the terminal device T has stopped at the position where a stop should be made or a railroad crossing where a stop should be made (step S14). This stop determination method may be a conventionally used determination method. Next, if the vehicle does not stop (i.e., does not stop temporarily) in step S14, the violation behavior detection unit 2E detects the failure to stop temporarily as a violation behavior (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, which will be described later.

[0045] On the other hand, in parallel with steps S10 to S15, the violation behavior detection unit 2E determines the movement mode (specifically, the movement direction and movement speed, etc.) of the vehicle traveling together with the terminal device T based on the traffic information added to the target data and changes in the current location indicated by each of the current location data (step S16), and further detects an entry direction violation at the intersection as a violation behavior (step S17) and a speeding 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 step S17. Furthermore, the violation behavior detection unit 2E can detect a slow-down violation as a violation behavior using the detection result of step S18. Thereafter, the violation behavior detection unit 2E reflects the detection results of 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, which will be 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 traveling with the terminal device T, based on the road characteristics determined by the road characteristics determination unit 2D, each of the violations determined by the violation behavior detection unit 2E, and the behavior evaluation data WD (step S19). More specifically, for the target data of one journey from the departure point to the destination, the scoring unit 2F performs scoring for each accident-related road characteristic associated with each violation behavior in the behavior evaluation data WD illustrated in FIG. 4(b). That is, for example, for the violation behavior "failure to stop at a stop sign," the scoring unit 2F calculates a corresponding score for each accident-related road characteristic based on the content of the violation behavior "failure to stop at a stop sign" in the behavior evaluation data WD, using the following calculation formulas (1) to (3), respectively (see the violation behavior "failure to stop at a stop sign" in FIG. 4(b)). Accident-related road characteristics: "intersections without traffic lights excluding residential roads" Score = (Number of times you didn't stop / Number of times you encountered an intersection without a traffic light, excluding residential roads) × 30 ... (1) Accident-related road characteristics: "residential roads and intersections without traffic lights" Score = (Number of times you didn't stop at a stop sign / Number of times you encountered intersections on residential roads without traffic lights) x 50 ... (2) Accident-related road characteristics: "Stop positions other than 1 and 2" Score = (number of times not stopping / number of times encountering a stopping point 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 scoring unit 2F calculates the score for each violation as illustrated in FIG. 4(b). As described above, by calculating a score for each violation and for each corresponding accident-related road characteristic, the score can be used to evaluate the driver's driving behavior, assist the driver, or apply insurance to the driver, as will be described later. In this case, the scoring unit 2F can also be configured to further average the scores for each violation to calculate an overall score for all of the driver's violations. By calculating such an overall score, the driver can easily recognize an objective evaluation of his or 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 (violative driving). However, scoring may also be performed by deducting points from the base score. In this case, for example, it is preferable to calculate the points for failing to stop temporarily as a violation using the following calculation formula (4). According to the calculation formula below, the points for failing to stop temporarily will be, for example, 50 points. Points for failing to stop = the base points (for example, 100 points) - {(number of times the driver failed to stop as a violation (for example, 10 times)) ÷ (number of times the driver encountered a place where he or she should have stopped (for example, 20 times)) × the base points (for example, 100 points)} ... (4)

[0049] Thereafter, the processing unit 2 generates the evaluation information for each terminal device T, including score information indicating the score as a result of the scoring by the scoring unit 2F, 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 also 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 determines whether or not to terminate the evaluation process according to the embodiment as the processing server SV, for example, because a command to terminate the process has been issued to the processing server SV (step S21). If the evaluation process is to be continued in the determination of step S21 (step S21: NO), the processing unit 2 returns to step S1 and repeats the above-described process. On the other hand, if the evaluation process is to be terminated in the determination of step S21 (step S21: YES), the processing unit 2 terminates the evaluation process as is. (V) Modification 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 using FIG. 7. FIG. 7 is a diagram illustrating 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 assigned the same component numbers and step numbers, and detailed description will be omitted.

[0051] 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 violations with accident-related road characteristics. In contrast, in a modified version 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."

[0052] That is, as shown in Fig. 7(a), the processing server according to the modified example of the first embodiment classifies the movement pattern data indicating the movement pattern of each terminal device T into "time period" in addition to "violation behavior" and "road characteristics," and stores the data as history data HDD for each terminal device T. In this case, the history data HDD stores, for example, one journey from the departure point to the destination of a vehicle traveling with the terminal device T. In addition, as shown in Fig. 7(c), 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 indicating a weighting used for scoring to generate the evaluation information for each time period.

[0053] The processing server according to the modified example of the first embodiment uses the history data HDD, the behavioral evaluation data WD similar to that of the first embodiment, and the time period evaluation data TD to generate evaluation information for each terminal device T, including score information indicating the score obtained by the scoring, and provides the generated evaluation information to the corresponding terminal device T or a third party other than the driver via the network NW.

[0054] 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 addition unit (not shown) in addition to a map matching processing unit 2A, a traffic information addition unit 2B, a user information addition unit 2C, a road characteristics determination unit 2D, a violation behavior detection unit 2E, and a scoring unit 2F, which are 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."

[0055] 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.

[0056] Next, the map matching processing unit 2A, traffic information adding unit 2B, user information adding unit 2C, road characteristics determining unit 2D, and violation behavior detecting unit 2F of the processing unit in the modified example of the first embodiment perform the same functions as the map matching processing unit 2A of the processing unit 2 in the first embodiment. In addition, the weather information adding unit acquires weather information indicating the weather when the terminal device T was traveling on the road matched by the map matching processing unit 2A, for example, from a weather information server (not shown) connected to the network NW via the interface 1 based on the corresponding time data, and adds this to the current location 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 traveling.

[0057] 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 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 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. Thereafter, 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 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.

[0058] 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.

[0059] First, as shown in Fig. 7(a), the history data HDD stores, for each of the categories of the violation, the road characteristics, and the time period, the number of times that the movement mode of each terminal device T corresponds to any of the items in each of the categories during, for example, one journey from the corresponding starting point to the destination, for each terminal device T. In this case, the violation such as "failure to stop at a temporary stop" shown in Fig. 7(a) is the same violation as the violation in the first embodiment.

[0060] 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.

[0061] Finally, as shown in Figure 7(c), the time period evaluation data TD associates the time periods, "early morning," "morning (rush hour)," "mid-morning," "afternoon," "evening (rush hour)," "night," and "late night," with data indicating weightings for scoring each time period. Figure 7(c) also shows specific examples of time periods such as "early morning." The weightings for the time period evaluation data TD are also pre-set as more generalized weightings, taking into account factors such as the accident occurrence rate for each time period for multiple people, including the driver.

[0062] 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 using statistical information on traffic accidents similar to that in the first embodiment.

[0063] Next, a specific description will be given of a modification of the first embodiment that is executed mainly by a processing server according to the modification of the first embodiment, which is included in the evaluation system according to the modification of the first embodiment having the above-described configuration.

[0064] In the modification 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 modification of the first embodiment performs scoring for the evaluation information that evaluates the movement mode of the vehicle traveling 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 modification 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):

[0065] Score = (number of violations / number of times of encountering road characteristics that make violations more likely to lead to accidents) × weighting of road characteristics where violations occurred × weighting of time period … (5) In this case, the "number of violations" and "number of times encountering road characteristics that make violations likely to lead to accidents" in 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 formula (5).

[0066] Score = (Number of times not stopping at a stop sign / Number of times encountering intersections without traffic lights on residential roads) × 50 (see Figure 7(b)) × 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 rain, since these conditions are likely to lead to accidents.

[0067] Thereafter, the processing unit of the modified first embodiment generates the evaluation information for each terminal device T, including score information indicating the score obtained by the scoring performed by the scoring unit of the modified first embodiment, 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 same process as the processing unit 2 of the first embodiment in step S21. In 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 that constitute a violation, the smaller the value after averaging, so scoring may be performed using a statistical method other than averaging.

[0068] As explained above, the evaluation system SS according to the first embodiment (including the modified example of the first embodiment) determines whether the driving behavior of the driver to be evaluated is a violation or not, and generates evaluation information based on the determination result, so that the driving behavior of the driver can be objectively evaluated and output, which can be used to evaluate the driving behavior of the driver and / or provide the driving assistance required in response thereto, and to apply insurance to the driver as the insured person.

[0069] 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, among the violations, violations that may be the cause of an accident.

[0070] Furthermore, by referring to the behavior evaluation data WD, it is determined whether the driver's driving behavior is a violation, so it is possible to more objectively determine whether the behavior is a violation that could be a cause of an accident.

[0071] Furthermore, when the evaluation information is generated 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.

[0072] Furthermore, since the evaluation information is generated based on the number of times the road has been passed, etc., included in the history data HD (or history data HDD), there is no unfairness between drivers who have driven on accident-related roads many times and drivers who have driven on accident-related roads less often, and the driving style of each driver can be evaluated fairly.

[0073] Furthermore, since the evaluation information is generated based on the road characteristics indicated by the characteristic information, it is possible to perform an evaluation more accurately in line with actual travel.

[0074] Furthermore, since evaluation information is generated and output by scoring, the more violations that could be the cause of an accident and the more likely the driving style is to lead to an accident, the more the score is affected, so evaluation information that is easier to recognize can be generated and output.

[0075] In the first embodiment and the modified example of the first embodiment described above, 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.

[0076] Furthermore, if the evaluation information is generated based on the weather indicated by the weather information, a more accurate and realistic evaluation can be made.

[0077] Furthermore, the system may be configured to determine whether or not an accident-related violation is occurring 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.

[0078] (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 will be designated by the same component numbers and detailed description thereof will be omitted.

[0079] In the evaluation system SS according to the first embodiment described above, for example, violations are first detected using the current location data of the terminal device T, and the driver's driving style is scored based on the detection results (see steps S9 to S19 in FIG. 6). In contrast, in the second embodiment described below, the road characteristics corresponding to the current location data are first detected by referring to map data or the like based on the current location data. Thereafter, violations that may be the cause of an accident are identified based on the detected road characteristics, and evaluation information is generated.

[0080] That is, the storage unit of the processing server according to the second embodiment stores road characteristic evaluation data DW according to the second embodiment, as shown in FIG. 8, instead of the behavior evaluation data WD according to the first embodiment. As shown in FIG. 8, the road characteristic evaluation data DW according to the second embodiment associates the road characteristics with accident-related violations such as "failure to stop," "obstruction of right of way," and "violation of slow speed" that are likely to lead to accidents (i.e., have a high probability of leading to an accident) when traveling on a road with the road characteristics, and further associates these with data indicating weightings used in the scoring of the second embodiment. This weighting data is similar to the behavior evaluation data WD according to the first embodiment in the sense that a larger value indicates a higher risk of an accident (in other words, a higher probability of an accident), but the weighting values ​​differ from those of the behavior evaluation data WD. The weighting values ​​may be constant or may be changed depending on the driver's travel conditions, such as the time of day and weather.

[0081] In the evaluation process according to the second embodiment, steps S1 to S9 in the evaluation process according to the first embodiment (see FIG. 6) are first executed to identify the road on which the terminal device T is located and determine its road characteristics. Then, based on the determined road characteristics, the road characteristics evaluation data DW illustrated in FIG. 8 is referenced to determine the violations likely to lead to accidents when traveling on a road with the determined road characteristics. By first determining the violations likely to lead to accidents, it is possible to determine in advance which of steps S10 to S12, S13 to S15, or S16 to S18 in the evaluation process according to the first embodiment should be performed (or which should be prioritized). This eliminates the need to determine unnecessary violations (i.e., violations that are not likely to lead to accidents on a road with the determined road characteristics). This simplifies the entire evaluation process according to the second embodiment and improves its speed.

[0082] Furthermore, the evaluation results of the evaluation process according to the second embodiment using the road characteristic evaluation data DW can be used to provide driving assistance to a driver traveling with the corresponding terminal device T. More specifically, in this case, for example, the road characteristics of the road on which the vehicle is traveling with the terminal device T are identified, and the road characteristic evaluation data DW is referenced to identify an accident-related violation associated with the road characteristics. This is suitable for use in monitoring whether the driver will engage in an accident-related violation on the road on which the driver is currently traveling or on a road to which the driver is scheduled to travel in the future. When a violation occurs, not only is evaluation information generated after the fact (for example, after the end of the one trip), but the evaluation information is also displayed on the terminal device T together with warning information immediately after the violation occurs, thereby enabling more effective driving assistance to the driver.

[0083] In addition, for example, by presenting on the terminal device T accident-related violations corresponding to the road characteristics of the road on which the driver is traveling or is scheduled to travel, the terminal device T can issue a warning about the accident-related violations before the driver travels on that road. In this case, for example, the system may be configured to determine whether 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, and to issue a warning about accident-related violations with a low weighting (i.e., minor accident-related violations) for a driver with a low evaluation (bad).

[0084] As described above, the evaluation system of the second embodiment identifies the driving behavior of the vehicle that may cause an accident based on the road characteristics of the road on which the vehicle is traveling together with the terminal device T, thereby enabling accurate safe driving assistance to be provided using minimal information.

[0085] Furthermore, 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.

[0086] Furthermore, since information for drawing attention to the driver is presented based on road characteristics, safe driving support can be provided more accurately.

[0087] 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 and the driver's safe driving assistance can be provided.

[0088] In the second embodiment described above, the road characteristics 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 violations 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.

[0089] [Variations] Next, a modification of the above-described embodiment will be described.

[0090] (1) First Modification First, as a first modification, a so-called smartphone, a wearable device, an in-vehicle navigation device, a drive recorder, or the like may function alone or in conjunction with a plurality of devices to function as the terminal device T according to each embodiment. 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 location data, etc. and the image data.

[0091] (2) Second Modification 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.

[0092] (3) Third Modification Next, as a third modification, in each of the above-described embodiments, the target data for one journey from the departure point to the destination was the target of scoring (evaluation), but in addition to this, scoring may be performed for all accumulated journeys, for example, based on the history data HD according to the first embodiment. Alternatively, scoring may be performed for target data divided into predetermined periods, such as periods divided by insurance renewal periods or by one month, based on the date information added to the history data HD.

[0093] (4) Fourth Modification Next, as a fourth modification, a part or all of the processes expressed as functions of the processing server SV according to each of the above-mentioned embodiments may be configured to be executed by the terminal device T. More specifically, for example, the terminal device T may be configured to perform the processes from step S9 to step S19 in Fig. 6 and transmit the results to the processing server SV.

[0094] (5) Fifth Modification Next, as a fifth variant, when the accident-related violation behavior itself and its relationship with road characteristics differ depending on the country or region, it is possible to detect national or regional borders based on current location data, etc., and modify and use the above behavior evaluation data WD or the above road characteristic evaluation data DW corresponding to the country or region.

[0095] (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.

[0096] In addition, a program corresponding to the flowchart shown in Figure 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]

[0097] 1 Current location information acquisition method (interface) 2B characteristic information acquisition means (traffic information addition unit) 2E Movement pattern identification means (violation behavior detection unit) 2A Map matching processing section 2D road characteristic determination section 2F Scoring Section S Information processing device 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

[Claim 1] a current location information acquiring means for acquiring current location information indicating the current location of a mobile object; a characteristic information acquisition means for acquiring characteristic information indicating a path characteristic of a path along which the moving object is moving, based on the acquired current position information; a movement pattern specifying means for specifying an accident-causing movement pattern, which is a movement pattern of a moving object that may cause an accident, based on the movement path characteristics indicated by the acquired characteristic information; An information processing device comprising:

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