Information processing method, information processing device, and program

Abstract

To provide a driving evaluation method that can maintain motivation for safe driving. [Solution] The method includes acquiring driving information of a moving object in a time series, calculating a driving score for each of the multiple periods based on the driving information for each of the multiple periods, determining whether the driving score for the first period satisfies the convergence condition based on the driving scores for periods prior to the first period included in the multiple periods, and in the determination, if it is determined that the convergence condition is met, increasing the past convergence score calculated for the period immediately preceding the first period by a first predetermined amount to obtain the convergence score, and if it is determined that the convergence has not occurred, decreasing the past convergence score by a second predetermined amount to obtain the convergence score, and performing a driving evaluation for the first period based on the driving score for the first period and the convergence score for the first period.

Description

Technical Field

[0001] The present invention relates to information processing technology for providing a driving evaluation method capable of maintaining the motivation for safe driving.

Background Art

[0002] For business companies that develop businesses using vehicles, administrators who manage vehicles, and operators (drivers) who operate vehicles, it is important to manage and operate vehicles so as to improve driving quality and avoid events such as traffic accidents. As one of the indicators, a driving score that scores driving quality is calculated, and the calculated driving score is presented to the administrator and the operator, so that they can recognize the driving quality of the operator at that time. If a high driving score is calculated, the driving quality is maintained. Conversely, if a low driving score is calculated, measures such as making the operator aware of improving the driving quality or receiving appropriate education are taken.

[0003] For example, regarding the calculation of the driving score, there is a conventional technique as described in Patent Document 1.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] Herein lies the problem that driving scores converge, for example, when an operator regularly operates a mobile vehicle in a similar area. Convergence refers to a state where, even if the operator makes every effort possible under the given environment, it becomes difficult to further improve the driving score. For example, this could occur when the driving score approaches perfect, or even when the driving score is not near perfect, but the operating environment, such as the nature of the area being operated in (not limited to specific areas, but including, for example, steep slopes, sharp curves, roads that are easy to speed on but are narrow), makes it difficult to achieve a score above a certain level. In such cases, operators may find that their driving scores do not improve no matter how hard they try, which can lead to a decrease in their motivation for safe driving. Furthermore, managers face the problem of lacking effective means to prevent operators' motivation for safe driving from declining.

[0006] The objective of this invention is to provide a driving evaluation method that can maintain motivation for safe driving. [Means for solving the problem]

[0007] According to one aspect of the present invention, a method for evaluating operation in an information processing system, comprising: acquiring driving information of a moving object in a time series; calculating an operation score for each of a plurality of periods based on the driving information for each of the plurality of periods; determining whether the operation score for the first period satisfies convergence conditions based on the operation scores for periods prior to the first period included in the plurality of periods; if it is determined that the convergence conditions are met, the convergence score is obtained by increasing the past convergence score calculated for the period immediately preceding the first period by a first predetermined amount; if it is determined that the convergence has not occurred, the convergence score is obtained by decreasing the past convergence score by a second predetermined amount, or resetting the convergence score and calculating the convergence score for the first period; and performing an operation evaluation for the first period based on the operation score for the first period and the convergence score for the first period. [Effects of the Invention]

[0008] The system and other features according to the present invention provide a driving evaluation method that can maintain motivation for safe driving. [Brief explanation of the drawing]

[0009] [Figure 1] This is a system configuration diagram of an operation evaluation system according to one embodiment. [Figure 2] This is a block diagram showing the functional configuration of a driving information acquisition device 10 according to one embodiment. [Figure 3] This is a flowchart of the driving information transmission process according to one embodiment of the model. [Figure 4] This is a block diagram showing the functional configuration of server 30 according to one embodiment. [Figure 5] This is a block diagram showing the functional configuration of an administrator terminal 40 according to one embodiment of the model. [Figure 6] This is a flowchart of the operation evaluation process according to one embodiment of the model. [Figure 7] This figure illustrates an example of determining whether convergence is occurring based on the driving score distribution, according to one embodiment of the system. [Figure 8] This figure illustrates an example of a case in which an operational evaluation is performed based on an operational score and a convergence score, according to one embodiment of the model. [Figure 9] This figure illustrates an example of determining whether convergence has occurred based on the driving score distribution, according to a modified embodiment. [Figure 10] This figure illustrates an example of how location information is used as a criterion in calculating the convergence score, according to a modified embodiment. [Modes for carrying out the invention]

[0010] Examples of embodiments for carrying out the present invention will be described below with reference to the drawings. In addition, in the descriptions of the drawings, the same reference numeral is used for identical elements, and redundant explanations may be omitted. Furthermore, the components described in these embodiments are merely illustrative and are not intended to limit the scope of the present invention to them.

[0011] <First Embodiment> The following describes a first embodiment, which is an example of realizing the information processing technology of the present invention. Furthermore, the contents described in this embodiment are applicable to any of the other embodiments, examples, or modifications.

[0012] <Functional Configuration> Figure 1 is a system configuration diagram of a driving evaluation system according to one embodiment of the first model. In this system, there is a mobile unit 1 operated by an operator who is the subject of the driving evaluation, a driving information acquisition device 10 mounted on the mobile unit 1 that acquires and transmits driving information of the mobile unit 1, a server 30 that receives and processes the driving information, and an administrator terminal 40 for managing the mobile unit 1 are connected via a network NW.

[0013] The moving body 1 is, for example, a passenger car or a truck vehicle, but is not limited thereto, and may be any moving body as long as it can be operated (driven) by an operator. Also, it may be a gasoline vehicle, an electric vehicle, or a hybrid vehicle.

[0014] FIG. 2 is a block diagram showing the functional configuration of the traveling information acquisition device 10. The traveling information acquisition device 10 in the present embodiment includes, for example, a configuration for collecting traveling information according to the ETC 2.0 method and transmitting it to the server 30. As another example, it is a device that can be inserted into a socket of the moving body 1 (for example, a cigarette socket, an electric supply socket, or a connection socket) and fixed inside the moving body 1 (referred to as a "cigarette socket device" in the present application). The electric supply socket or the connection socket is, for example, a socket that supports USB (Universal Serial Bus). The traveling information acquisition device 10 is not limited to these, and may be in any form or device, such as a device provided in the moving body 1 such as a drive recorder, a car navigation device, a digital tachograph, or even integrated with a portable terminal such as a smartphone held by the driver of the moving body 1, as long as it can at least collect the traveling information of the moving body 1 and transmit it to the server 30.

[0015] The traveling information acquisition device 10 is configured to include, for example, a processing unit 110, a storage unit 120, a communication unit 130, a traveling information acquisition unit 170, and a clock unit 180.

[0016] The traveling information acquisition device 10 collects traveling information in a time series via the traveling information acquisition unit 170, and stores it in the storage unit 120 in association with the time information acquired by the clock unit 180. Then, the stored acquired traveling information is transmitted to the server 30 connected to the network NW through the communication unit 130 at a predetermined timing.

[0017] The processing unit 110 is composed of a processing arithmetic unit including, for example, a CPU (Central Processing Unit) and an MPU (Micro-Processing Unit). The processing unit 110 performs various processing on each data and controls various functional units such as the communication unit 130, the driving information acquisition unit 170, and the clock unit 180 by reading and executing programs stored in the storage unit 120.

[0018] The storage unit 120 includes, for example, an HDD (Hard Disk Drive), SSD (Solid State Drive), EEPROM (Electrically Erasable Programmable Read-Only Memory), ROM (Read-Only Memory), RAM (Random Access Memory), etc., and stores control programs processed by the processing unit 110, various data, such as information acquired by each functional unit. Note that the storage unit 120 is not limited to one built into the driving information acquisition device 10, but may also be an external storage device connected via a digital input / output port such as USB (Universal Serial Bus).

[0019] In this embodiment, the storage unit 120 stores, for example, a driving information transmission processing program 121 and a driving information database 122.

[0020] The driving information transmission processing program 121 is a program that implements driving information transmission processing to send driving information acquired via the driving information acquisition unit 170 to the server 30 at an appropriate timing.

[0021] The driving information database 122 is a database in which driving information acquired for, for example, the mobile body 1 is stored. Driving information is stored in the driving information database 122 in association with time information issued by the clock unit 180, which will be described later. In other words, the driving information is stored in the driving information database 122 in a manner that allows it to be determined when the driving information was acquired. For example, when the driving information acquisition device 10 acquires location information as driving information, the coordinate information as acquired location information and the time of acquisition are stored in association with each other.

[0022] Here, "driving information" refers to information relating to the movement of the mobile body 1, and is not limited to these. Examples include position information, speed information, and acceleration information, which are related to the movement of the mobile body 1, as well as information from each sensor, actuator, etc. of the mobile body 1 (for example, whether an abnormality has occurred, how much wear and tear there is, etc.), or information relating to the conditions in which the mobile body 1 is placed, for example, temperature information, inclination angle information relating to the inclination angle of the road it is traveling on, and is not limited to these.

[0023] Examples of driving information include, but are not limited to, any information that is directly or indirectly related to the driving evaluation of mobile unit 1. • Vehicle behavior and driving operation related information Speed ​​(instantaneous speed, average speed, maximum speed, etc.) Acceleration (frequency of sudden acceleration and deceleration, degree of acceleration and deceleration, etc.) Steering wheel operation (steering angle, steering speed, etc.) Brake operation (brake pedal pressure, number of sudden braking incidents, number of ABS activations, etc.) Accelerator opening Shift operation (including gear change timing in manual transmission vehicles) Timing and frequency of using turn signals and changing lanes • Vehicle internal sensor information Engine speed (RPM) and fuel consumption (fuel efficiency) Battery level and power consumption (for electric vehicles) Status of various warning lights • Driving event information Number of sudden braking, sudden steering, and sudden acceleration Presence and frequency of collisions / contacts (accidents or minor contacts) Number of times the lane departure warning system activated Frequency of activation of safety devices (such as ESC) • Information related to vehicle location and route selection GPS location information Mileage (how the same distance is covered, and under what driving conditions) Compliance with speed limits on the route (frequency and amount of speeding). Behavior that demonstrates adherence to traffic rules, such as stopping at stop signs or traffic lights (the extent to which the driver's intentions can control the vehicle). • Information related to the in-vehicle environment and driver operation status Seatbelt usage status (whether the driver is wearing a seatbelt, and whether they have instructed passengers to wear one) Driver's gaze and facial expression Smartphone and mobile device operation status Air conditioning and audio controls • Driving time • Date and time • Break time Start time and end time of operation Continuous operating time and timing / frequency of breaks • Status of operations related to mobile vehicles Timing of alcohol checks Timing of periodic inspections or pre-boarding inspections of mobile vehicles

[0024] Furthermore, the driving information is not limited to being composed of only one type of information, but may include multiple types of information. For example, the driving information may include position information and acceleration information. Also, certain driving information may be collected by the driving information acquisition device 10 at predetermined timings (for example, in chronological order), and similarly, the driving information stored in the driving information acquisition device 10 may be transmitted to the server 30 at predetermined timings. For example, in one driving information acquisition device 10, the information may be collected every second and transmitted to the server 30 every minute. As another example, in another driving information acquisition device 10, the driving information may be collected every 10 seconds and transmitted via the roadside unit when the mobile body 1 passes near the roadside unit connected to the server 30 (for example, the ETC2.0 method).

[0025] The communication unit 130 is a module that connects to a public network such as the Internet using, for example, mobile communication such as LTE (Long Term Evolution), 3G, 4G, or 5G, or narrowband communication such as DSRC (Dedicated Short Range Communication), and is capable of data communication with various devices such as the server 30 connected to the same network. Alternatively, information may be exchanged using ETC2.0-compatible communication, which performs bidirectional communication using DSRC. The driving information stored in the driving information database 122 is transmitted to the external server 30 via the communication unit 130.

[0026] The driving information acquisition unit 170, for example, if the driving information includes location information, acquires location information (e.g., latitude and longitude information) of the driving information acquisition device 10 at predetermined intervals based on radio waves arriving from GNSS satellites (e.g., GPS satellites). In other words, it can acquire location information of the mobile body 1 equipped with the driving information acquisition device 10. By extension, by using the mobile body 1 equipped with the driving information acquisition device 10, it is possible to substantially acquire location information of the mobile body 1. Also, for example, if the driving information includes speed information, it acquires vehicle speed pulse information acquired by a vehicle speed pulse acquisition unit (not shown) mounted on the mobile body 1, and acquires the speed information of the mobile body 1 at predetermined intervals based on that vehicle speed pulse information. Alternatively, speed information may be calculated based on separately acquired location information. Also, for example, if the driving information includes acceleration information, acceleration is acquired by a piezoelectric acceleration sensor. Alternatively, the acceleration of the vehicle may be calculated based on separately acquired location information or speed information.

[0027] Furthermore, the driving information acquisition unit 170 may include a temperature information acquisition unit (not shown). That is, it may measure temperature over time and acquire it as driving information. Similarly, the driving information acquisition unit 170 may include any of the following: a pressure information acquisition unit, an altitude information acquisition unit, a humidity information acquisition unit, etc. That is, it may acquire information about some environment inside or outside the mobile body 1 as driving information.

[0028] Furthermore, the driving information acquisition unit 170 may also include an image information acquisition unit (not shown), which may appropriately acquire images inside and outside the mobile body 1 and acquire such image information as driving information. For example, road surface image information and weather image information may be acquired as driving information outside the mobile body 1, or driver image information may be acquired as driving information inside the mobile body 1, and the content is not particularly limited.

[0029] The acquired driving information is associated with information regarding the time (current time) at which the driving information was acquired by the clock unit 180 (described later), and is stored in the driving information database 122 of the storage unit 120.

[0030] If location information is included in the driving information, a precision value (e.g., DOP value) indicating the accuracy of the location information may be obtained when acquiring the location information. In this case, the acquired location information and precision value may be associated with the current time and stored in the storage unit 120.

[0031] The method for acquiring driving information is not limited to those described above, and any method for acquiring driving information may be applied. For example, the driving information may be acquired by the driving information acquisition unit 170 when a mobile body 1 equipped with the driving information acquisition device 10 approaches a roadside unit that emits radio waves containing driving information from the roadside unit installed on the side of the road.

[0032] The clock unit 180 is the built-in clock of the driving information acquisition device 10 and outputs time information (timing information). The clock unit 180 is configured to include, for example, a clock using a crystal oscillator. The clock unit 180 may be configured with a clock that conforms to the NITZ (Network Identity and Time Zone) standard or the like.

[0033] Figure 3 is a flowchart of the driving information transmission process in this embodiment, in which the driving information acquisition device 10 acquires driving information, transmits the acquired driving information to the server 30, and the server 30 appropriately records the acquired driving information.

[0034] First, in order to accumulate driving information for the mobile body 1, the driving information acquisition device 10 acquires driving information (S1001) and transmits the acquired driving information to the server 30 (S1002). The driving information acquisition device 10 repeatedly performs these steps to acquire driving information in chronological order and transmit it to the server 30. Server 30 receives driving information from the driving information acquisition device 10 (S3002) and stores the received driving information in the database 322 of the storage unit 320 (S3003).

[0035] Next, it is determined whether the amount of driving information stored in database 322 exceeds a predetermined amount (S3004). This determination is made to appropriately delete outdated driving information as time passes and to continue processing based on new driving information. If the predetermined amount is exceeded (S3004; Y), the oldest driving information, for example, the driving information with the oldest stored date is deleted as predetermined driving information (S3005). Then, the process returns to step S3002 and the same process is repeated thereafter. If the predetermined amount has not been exceeded (S3004;N), the driving information will not be deleted, and the process will return to step S3002, and the same process will be repeated thereafter.

[0036] In this way, the driving information related to the mobile body 1 can be stored in the memory unit 320 while being updated as needed so that the content is replaced with new information. Furthermore, it is not always necessary to delete such driving information, and steps S3004 and S3005 may be omitted.

[0037] Figure 4 is a block diagram showing the functional configuration of the server 30 in Figure 1. In this embodiment, the server 30 is configured to include a processing unit 310, a storage unit 320, and a communication unit 330. Server 30 is connected to the driving information acquisition device 10, administrator terminal 40, etc., via a network such as the Internet, and transmits and receives information to each other, and processes the received information as appropriate.

[0038] The processing unit 310 is composed of a processing arithmetic unit including, for example, a CPU (Central Processing Unit) and an MPU (Micro-Processing Unit). The processing unit 310 performs various processing on each data, and also reads and executes programs stored in the storage unit 320. Furthermore,

[0039] The storage unit 320 includes, for example, an HDD (Hard Disk Drive), SSD (Solid State Drive), EEPROM (Electrically Erasable Programmable Read-Only Memory), ROM (Read-Only Memory), RAM (Random Access Memory), etc., and stores control programs processed by the processing unit 310, various data, such as tables containing information about each mobile unit 1. Note that the storage unit 320 is not limited to one built into the server 30, but may also be an external storage device connected via a digital input / output port such as USB (Universal Serial Bus).

[0040] In this embodiment, the storage unit 320 stores, for example, a driving score calculation processing program 321, a driving score convergence degree calculation processing program 322, and a driving information database 323.

[0041] The driving score calculation processing program 321 is a program that is read by the processing unit 310 and executed as the driving score calculation process. Details of the driving score calculation process will be described later.

[0042] The driving score convergence calculation processing program 322 is read by the processing unit 310 and executed as the driving score convergence calculation process. Details of the driving score convergence calculation process will be described later.

[0043] The driving information database 323 is a database for storing driving information of the mobile body 1 transmitted from the driving information acquisition device 10, and information generated by processing the driving information as appropriate.

[0044] The communication unit 330 is a module that connects to a network such as the Internet using a wired or wireless communication interface, for example, using mobile communication such as LTE (Long Term Evolution), 3G, 4G, or 5G, or narrowband communication such as DSRC (Dedicated Short Range Communication), and is capable of data communication with various devices such as the driving information acquisition device 10 and the administrator terminal 40 that are connected to the same network.

[0045] The administrator terminal 40 is a device operated by the administrator of multiple mobile devices, including mobile device 1 (for example, an employee of the first business operator), and manages information about the multiple mobile devices under its management.

[0046] Figure 5 is a block diagram showing the functional configuration of the administrator terminal 40 in Figure 1. The administrator terminal 40 in this embodiment is not limited to but may be any electronic device such as a tablet or laptop PC, and may be configured to include, for example, a processing unit 410, a storage unit 420, a communication unit 430, a display unit 440, an operation unit 450, and an audio output unit 460. The configurations of these functional units, the processing unit 410, the storage unit 420, and the communication unit 430, can be substantially the same as those of the processing unit 310, storage unit 320, and communication unit 330 of the server 30, so a detailed explanation of these will be omitted.

[0047] The display unit 440 is a display device configured, for example, with an LCD, and performs various displays based on display signals output from the processing unit 410. The display unit 440 may be integrated with a touch panel to form an operation unit 450 that functions as a touchscreen.

[0048] The operation unit 450 is configured to have input devices such as operation buttons and operation switches for the administrator to input various operations to the administrator terminal 40. The operation unit 450 may also have a touch panel integrated with the display unit 440, and this touch panel may function as an input interface between the administrator and the administrator terminal 40. Operation signals corresponding to user operations by the administrator or other user are output from the operation unit 450 to the processing unit 410.

[0049] Furthermore, the operation unit 550 may be configured as an integral part of an image acquisition unit (not shown). For example, image information captured by the image acquisition unit may be acquired as input information.

[0050] The sound output unit 460 is a sound output device that includes a speaker and other components, and performs various sound outputs based on the sound output signal output from the processing unit 410.

[0051] In this embodiment, the storage unit 420 stores, for example, a mobile device management processing program 421 and a database 422.

[0052] The mobile object management processing program 421 is read by the processing unit 410 and is used to perform mobile object management processing. Mobile object management processing is performed using the output from the driving score calculation processing and the driving score convergence degree calculation processing, which will be described later. For example, it generates and outputs ranking information that summarizes the driving score and convergence score of each operator, as shown in Figure 8.

[0053] Database 422 is a database for managing data used in processing carried out based on the mobile object management processing program 421, etc.

[0054] <Driving score calculation process> As described above, the driving score calculation process scores and outputs the quality of the operator's operation of the mobile vehicle. The driving score calculation in this invention scores the driving quality over a predetermined period (for example, one month, one week, one day, etc.), and can be any driving information, regardless of its content, as long as the same driving information results in the same driving score. For example, the following methods can be used to calculate driving scores. 1. Output a score corresponding to the value obtained by integrating the scalar values ​​of acceleration. In this case, values ​​below a predetermined scalar value may not be accumulated, and only the scalar values ​​for accelerations above the predetermined scalar value may be accumulated, and the score corresponding to that accumulated value may be output. 2. Output a score corresponding to the number of times the acquired scalar values ​​of acceleration and / or velocity exceeded a predetermined threshold. 3.1 and 2 combination

[0055] In this way, the driving score is calculated and output. However, considering that there may be periods or days when the vehicle is not driven, or that the number of days included may differ when calculating the driving score on a monthly basis, it is preferable to adjust the score appropriately so that there are no differences between predetermined periods (for example, in method 1, the score is calculated based on the value obtained by dividing the cumulative value by the driving time).

[0056] <Calculation process for the degree of convergence of driving scores> Figure 6 is a flowchart of the operation evaluation process in this embodiment. The operation evaluation process in this invention is a process that determines whether the operation score calculated by the operation score calculation process has converged, and if it has converged, calculates the degree of convergence and outputs it together with the operation score. As shown in Figure 6, first, the driving score is calculated using the aforementioned driving score calculation process (S3011). This driving score is calculated for each predetermined period, for example, every month, according to the driving information for that month, and these are stored in the storage unit 320. Figure 7 shows the calculation status of the driving score from the most recent to some time in the past. The driving score convergence calculation process will now be explained with reference to Figure 7.

[0057] Next, the most recently calculated operating score is compared with a predetermined number of operating scores going back from the most recent month to determine whether the convergence condition is met (S3012). The most recently calculated operating score is, for example, the operating score for the previous month, and the predetermined number of operating scores going back from the most recent month are the operating scores from two months prior, three months prior, and four months prior. Of course, the predetermined number can be any number. Here, the convergence condition is a condition used to determine whether the driving score is converging, and it can be any condition, but for example, the following conditions can be given. In other words, the convergence condition is determined to be met if either (or both) of the following conditions are met. [Example Condition 1] The difference between the maximum and minimum values ​​of the driving scores for a first predetermined number of periods (e.g., 3) going back from the most recent period is within the first predetermined range (e.g., 4 points or less). [Example Condition 2] The most recently calculated driving score is within a second predetermined range (for example, within 3 points) of the intermediate value of the driving scores for a second predetermined number of years preceding the most recent score (for example, median, mean (including concepts such as arithmetic mean, weighted mean (for example, weighting closer to the most recent score), geometric mean, harmonic mean, trimmed mean, etc.), quantile, mode, midrange, interval median, midpoint, etc.).

[0058] In the situation shown in Figure 7, the most recent driving score for September is 88. When combined with the three driving scores corresponding to the first predetermined number of minutes prior—86 for July and 87 for August—the maximum value in the set is 88 for September, the minimum value is 86 for July, and the difference is 2 points. Since this falls within the first predetermined range (4 points or less), the condition example 1 is satisfied. Furthermore, the driving score of 88 calculated most recently for September is within 3 points of 86.67, which is the midpoint (in this case, the arithmetic mean) of the three driving scores corresponding to the period two predetermined times prior to the most recent: 87 for June, 86 for July, and 87 for August. Therefore, it satisfies example condition 2. Therefore, under any given example, the operating score for September is judged to satisfy the convergence condition in the situation shown in Figure 7.

[0059] If it is determined that the convergence conditions are not met (S3012; N), the convergence score is reset (S3013). Here, the convergence score refers to a score indicating the degree to which the operation score has converged, and resetting means setting the score to a value that indicates a state of non-convergence, such as setting it to 0. In this embodiment, resetting means setting the convergence score to 0, and it is explained that the convergence score increases as the convergence state continues. Here, instead of resetting the convergence score, we may decrease the convergence score by a predetermined amount (second predetermined amount). Decreasing by a predetermined amount may be done by subtracting a predetermined value, or by multiplying it by a predetermined value that is between 0 and 1 (inclusive).

[0060] On the other hand, if it is determined that the convergence conditions are met (S3012; Y), the convergence score is increased by 1 (first predetermined amount) (S3014). Here, the convergence score is a score corresponding to how long the operating score has been converged, and the larger the score, the longer the operating score has been converged. Furthermore, the score to be increased is not limited to 1, but can be any number, and if the convergence has continued for a long time, the amount to be increased may be larger than in the normal case. Alternatively, instead of adding predetermined points, the convergence score may be increased by a predetermined percentage (for example, 100%) (in this case, when the convergence score is reset, it must be a number greater than 0, such as 1). In other words, by performing any operation such as addition, multiplication, or a combination thereof, to increase the convergence score by a predetermined amount, it is sufficient that the score becomes better the longer the period of convergence. In the example shown in Figure 7, convergence is judged for June, July, and August, so the convergence score as of September is 4.

[0061] The server 30 then outputs a pair of the calculated driving score and convergence score (S3015). For example, if the calculated driving score is 88 and the convergence score is 4, it outputs (88,4).

[0062] Here, for example, if the calculated driving score gradually decreases while staying within the convergence range, the intermediate value itself will also decrease. If this continues, the level of the second predetermined range used to determine whether convergence is occurring will decrease. Therefore, if the intermediate value of a newly calculated second predetermined number of driving scores, calculated by going back from the most recent, does not exceed the intermediate value of a second predetermined number of driving scores calculated in the past, it is preferable to continue using the intermediate value of that previously calculated second predetermined number of driving scores as the reference value and determine whether the score is within the predetermined range from that value. In this way, if the driving score gradually decreases while remaining within a predetermined range, it will eventually be judged as not converging, while if the driving score gradually increases, it will continue to be judged as converging.

[0063] Furthermore, if the system is judged to have converged even if low operating scores persist, as long as the convergence conditions are met, there is a concern that the motivation for operators to strive to obtain high operating scores will decrease. Therefore, it is desirable to add the requirement of achieving a certain score or higher to the convergence conditions. In the example shown in Figure 7, for example, the convergence condition could be set as an operating score of 80 points or higher.

[0064] <Mobile object management processing> Mobile object management processing involves obtaining travel information from mobile objects such as Mobile Object 1, which are the objects to be managed, and generating and outputting information necessary for the management of the mobile objects based on the acquired travel information. Mobile object management processing is performed by a mobile object management processing program 321 stored in the storage unit 320 of the server 30 and a mobile object management processing program 421 stored in the storage unit 420 of the administrator terminal 40.

[0065] Figure 8 shows one example of ranking information generated by the mobile device management process, based on the driving score and convergence score of each operator, and displayed, for example, on the display unit 440 of the administrator terminal 40. As shown in Figure 8, the ranking information shows the operator name, operation score, and convergence score in descending order of evaluation.

[0066] In the example shown in Figure 8, operator AA, with a driving score of 92, is ranked higher than operator BB, who has a higher driving score of 94. This is because the convergence score is also taken into account in addition to the driving score. For example, this ranking is the result of comparing an overall evaluation that takes into account both the driving score and the convergence score. As an overall evaluation, the value of the overall evaluation can be calculated using any operation, such as simply adding the driving score and the convergence score, adding them with a weight, or multiplying them (it is preferable to add a predetermined value, such as +1 for the convergence score, before multiplying). At first glance, this might seem like an unfair evaluation for operator BB, but operator AA has a convergence score of 12, having achieved an operating score of around 92 for at least 12 consecutive times, while operator BB has a convergence score of 2, having achieved a score significantly lower than 94 three times prior. This suggests that there is still variability in the stability of their operation. In light of this situation, for example, operator AA has been given a higher evaluation.

[0067] In other words, the main feature of this invention is that, instead of using a high driving score as the sole evaluation criterion, the degree of convergence of the driving score is also taken into consideration, so that the ability to maintain a stable and consistent (preferably high) driving score can also be used as an evaluation criterion. Furthermore, with this configuration, the above-mentioned problems can be solved, namely, when an operator operates a mobile vehicle in the same area on a regular basis, even if the operator makes every effort possible, the driving score will not increase any further (for example, when the driving score is close to perfect, or even if the driving score is not close to perfect, when it is difficult to achieve a score above a certain level due to the nature of the area of ​​operation (not limited to, for example, when there are steep slopes, sharp curves, roads that are easy to speed on but are narrow, etc.)), which can solve the problem for operators that their driving score does not increase no matter how hard they try, and their motivation for safe driving decreases, and for managers that there is no useful means to prevent the operator's motivation for safe driving from decreasing.

[0068] <Example 1> In the above-described embodiment, when determining whether convergence has occurred under convergence conditions such as example condition 2, if the target operating score falls below the second predetermined range, it is determined that convergence has not occurred (the convergence state has been released). However, due to various factors such as the operator's poor health or differences in the operational area, even if the operator approaches the operation of the mobile vehicle with the same level of attention as usual, the operation score may drop once it has occurred, meaning it is no longer in a convergence state, and in order to receive a high evaluation, the convergence score must be accumulated again.

[0069] In light of such cases, for example, if the driving score drops only for a short period and then quickly returns to the same level as the previous convergence period, it may be considered that the convergence state was not broken (the convergence state continued). Specifically, if the following conditions (referred to as "conditions for returning to a converged state") are met, it is determined that the converged state has not been released. [Example Condition 3] When the number of times the driving score falls below the second predetermined range is within the third predetermined number (for example, 2 times), and the driving score immediately after that returns to the second predetermined range.

[0070] Furthermore, the conditions for returning to a converged state may include any of the following additional conditions. [Example of additional condition 1] The driving score when the second predetermined range is outside the predetermined range is less than a predetermined number of points (for example, 3 points) from the lower limit of the second predetermined range. [Additional Condition Example 2] The driving score returned to the second predetermined range within a fourth predetermined number of times (e.g., 3 times) after falling outside the second predetermined range. [Additional Condition Example 3] After returning to the second predetermined range, the driving score remained within the second predetermined range for a fifth predetermined number of times (for example, 3 times). Furthermore, during the period from when the operating score falls outside the second predetermined range until the conditions for returning to a converged state are met, it is not determined that the converged state was still in effect, and therefore the converged score is temporarily reset. Also, when the conditions for returning to a converged state are met, the converged score may or may not include the portion that fell outside the second predetermined range.

[0071] For example, consider the case where the driving score is calculated as shown in Figure 9. As of the fourth week of September, although the system was in a convergence state up to the third week of September, the calculated driving score of 81 falls outside the second predetermined range (here, the second predetermined range is defined as driving scores from 83.67 to 89.67), thus ending the convergence state. However, since it is less than 3 points below the lower limit of the second predetermined range, it satisfies additional condition example 1. Then, in the next week of October (week 1), the driving score was 83, which again fell outside the second specified range for the second consecutive week. However, from the second to fourth weeks of October, the driving scores returned to within the second specified range for three consecutive weeks, at 86, 87, and 85 respectively. In other words, additional condition examples 2 and 3 were met as of the fourth week of October.

[0072] Therefore, in the example shown in Figure 9, it is determined that the data returned to a converged state as of the fourth week of October. In this case, the convergence score may be increased by considering that the weeks of September (fourth week) and October (first week), which fell outside the second predetermined range, were also converged, and the convergence score may be set to 8 as of the fourth week of October (assuming that +1 is added when it is determined that the data is converged), or the convergence score may not be increased for those weeks, and the convergence score as of the fourth week of October may be set to 6.

[0073] Similarly, adjustments may be made in light of changes in the operational area. For example, if the driving information includes the location information of the moving object, and there is a significant difference in the operating area when comparing the location information of the operating object in the converged state with the location information of the operating object when the operation that caused the converged state to break, and the driving score returns to the same level as when the converged state was in place within a certain period of time, it may be determined that the converged state was maintained.

[0074] Specifically, the adjustments will be made under the following conditions: [Example Condition 4] The percentage of the movement range defined by the location information included in the travel information going back a predetermined number of times (e.g., 5) in the past, and the movement range defined by the location information included in the target travel information, that does not overlap, is greater than or equal to a predetermined percentage (e.g., 50% or more). Here, the method for defining the range of movement based on location information may be arbitrary, but for example, if a predetermined relationship is established between the location information included in the travel information going back a predetermined number of times (6th predetermined number) and the location information included in the target travel information, it may be determined that there is an overlap. More specifically, if a travel path (change in location information) as shown in Figure 10(1) is obtained from the travel information going back a predetermined number of times (6th predetermined number), the range of movement may be defined as a predetermined distance range from the location information (for example, a distance range of 10 meters), and the area surrounding the travel path as shown in Figure 10(2) may be defined as the range of movement. Alternatively, for example, if the location information included in the target travel information is within a predetermined distance from at least a predetermined number (for example, 3) of the location information included in the travel information going back a predetermined number of times (6th predetermined number), it may be determined that there is an overlap.

[0075] <Variation 2 (including evaluation of high-difficulty sections)> In the above embodiment, an example was described in which all driving routes were treated equally and driving evaluations were performed regardless of the nature of the driving route. However, in the following modified example, an example is described in which the region is divided based on the nature of the driving route and driving evaluations are performed for each region.

[0076] In the driving evaluation process related to this modified version, if the driving information includes location information, the location information is compared with data related to the properties of the road, such as road data (data related to gradient, curvature, width, etc.), weather data, etc., to divide the driving route into sections. For example, if any of the following conditions are met for a certain distance or a certain period of time for each section, that section is identified as a "high difficulty section". • Sections where the gradient exceeds a specified value. • Sections with a series of sharp curves where the curvature is below a predetermined value. • Narrow sections with an effective width less than or equal to a predetermined value • Sections where weather conditions such as snow cover and freezing persist. For example, sections where there is a continuous uphill slope of 100 meters or more, or a series of sharp curves, are considered environments that require drivers to maintain a high level of concentration and are designated as high-difficulty sections.

[0077] For each section (or for a region that combines multiple such sections into one as a high-difficulty section), the driving score and convergence score are calculated in the same manner as in the embodiment described above. However, in high-difficulty sections, the absolute value of the driving score tends not to increase easily due to environmental factors, so the convergence score may be given more relative weight in the evaluation. On the other hand, in normal sections, the driving score is used as the primary evaluation method, as in the conventional method, and the convergence score may or may not be used as a supplementary factor.

[0078] Furthermore, in high-difficulty sections, it is assumed that the score when convergence is determined will be lower compared to normal sections. Therefore, it is preferable to set the convergence score according to the level of the score when convergence is determined (for example, if the score when convergence is determined is low, the convergence score should be lower even if convergence has occurred. The reverse may also be applied). Also, the evaluation of how low the score when convergence is determined is possible using any method, such as an absolute evaluation (for example, by the numerical value of the score), or the difference from the convergence score in the normal section.

[0079] The final driving evaluation may be calculated by integrating the evaluations for each section, or scores may be calculated separately for normal sections and high-difficulty sections. When integrating the data, a weighting system can be applied based on the proportion of high-difficulty sections and normal sections (by distance or time), and the overall score can be calculated by taking a weighted average of both. When calculating separately, scores may be calculated for regular sections while also calculating scores for each section's characteristics (such as gradient, curvature, width, and weather), or scores may be calculated for high-difficulty sections as a single unit, or scores may be calculated for geographically unified sections (for example, a wide section in a mountainous area with multiple elements that make it difficult in terms of gradient, curvature, and weather).

[0080] Alternatively, only the driving score may be calculated for normal sections, and both the driving score and the convergence score may be calculated only for high-difficulty sections. In other words, the evaluation results may be calculated and output based on how stably the vehicle can be driven in difficult sections.

[0081] Furthermore, the target section designated as a section for evaluating driving quality different from that of normal sections is not limited to high-difficulty sections determined by the nature of the travel route, but any section or area may be designated as a target section. For example, an area where dangerous driving is frequently observed in the group of managed vehicles may be designated as a target section, or an arbitrary area (for example, an area including routes frequently traveled by the group of managed vehicles, or an area arbitrarily designated as a "safe driving area" according to some intention) may be designated as a target section by a user such as an administrator via the administrator terminal 40.

[0082] Furthermore, if the scores are calculated separately, each of these scores may be displayed on a map in a manner that shows what kind of driving evaluation it represents for the target section. Of course, scores may also be displayed for normal sections. By doing so, it becomes easy to understand the quality of operation of the mobile object of interest in each section and area.

[0083] This allows drivers who continuously travel through areas including highly challenging sections to have their stability properly evaluated and maintain their motivation for safe driving.

[0084] Furthermore, in the above embodiments, various programs and data related to various processes are stored in the storage unit, and the processing unit reads and executes these programs to realize the processes in each of the above embodiments. In this case, the storage unit of each device may have recording media (recording media, external storage devices, storage media) such as memory cards (SD cards), CompactFlash® cards, Memory Sticks, USB memory, CD-RWs (optical discs), and MOs (magneto-optical discs), in addition to internal storage devices such as ROM, EEPROM, flash memory, hard disks, and RAM, and the above programs and data may be stored on these recording media.

[0085] Although embodiments and modifications of the present invention have been described in detail above, the scope of the present invention is not limited to the embodiments and modifications described above. Furthermore, the embodiments and modifications described above can be improved or modified in various ways without departing from the spirit of the present invention. In addition, the embodiments and modifications described above can be combined in any way. [Explanation of Symbols]

[0086] 1 Mobile Unit 10. Driving Information Acquisition Device 30 servers 40 Administrator terminals

Claims

1. A method for evaluating the operation of an information processing system, To acquire time-series information on the movement of a moving object, Calculating a driving score for each of the multiple periods based on the driving information for each of the multiple periods, Based on the operating scores in the periods prior to the first period included in the aforementioned multiple periods, it is determined whether the operating score in the first period satisfies the convergence condition, In the aforementioned judgment, If it is determined that the convergence conditions are met, the past convergence score calculated for the period immediately preceding the first period is increased by a predetermined amount to obtain the convergence score. If it is determined that convergence has not occurred, the convergence score is obtained by reducing the past convergence score by a second predetermined amount, or the convergence score is reset and the convergence score for the first period is calculated. Based on the operating score during the first period and the convergence score during the first period, an operational evaluation is performed during the first period. A driving evaluation method, including the following.

2. A method for evaluating operation according to claim 1, The acquisition of the aforementioned moving object's travel information in a time series is performed for each region defined based on the characteristics of the road it travels on. The calculation of the aforementioned driving score is performed for each of the aforementioned regions. Calculating the aforementioned convergence score is performed for areas within the aforementioned region that satisfy predetermined conditions, or areas specified by the user. Driving evaluation method.

3. A method for evaluating operation according to claim 1 or 2, The aforementioned convergence conditions are: The difference between the maximum and minimum values ​​of the driving score in the first period and in each of the second plurality of periods preceding the first period by a predetermined number of minutes is within a predetermined range. A driving evaluation method, including the following.

4. A method for evaluating operation according to claim 1 or 2, The aforementioned convergence conditions are: The driving score during the first period is within a second predetermined range from the midpoint of the driving scores in each of the third plurality of periods that precede the first period by a second predetermined number of minutes. A driving evaluation method, including the following.

5. An information processing system, An acquisition unit that acquires time-series information on the movement of a moving object, A driving score calculation unit that calculates a driving score for each of the multiple periods based on the driving information for each of the multiple periods, A determination unit that determines whether the operating score in the first period satisfies the convergence condition based on the operating score in the period prior to the first period included in the plurality of periods, In the determination made by the aforementioned determination unit, If it is determined that the convergence conditions are met, the past convergence score calculated for the period immediately preceding the first period is increased by a predetermined amount to obtain the convergence score. If it is determined that convergence has not occurred, the convergence score calculation unit calculates the convergence score for the first period by reducing the past convergence score by a second predetermined amount, or by resetting the convergence score. An operation evaluation unit that performs an operation evaluation during the first period based on the operation score during the first period and the convergence score during the first period, A driving evaluation system equipped with the following features.

6. On the computer, To acquire time-series information on the movement of a moving object, Calculating a driving score for each of the multiple periods based on the driving information for each of the multiple periods, Based on the operating scores in the periods prior to the first period included in the aforementioned multiple periods, it is determined whether the operating score in the first period satisfies the convergence condition, In the aforementioned judgment, If it is determined that the convergence conditions are met, the past convergence score calculated for the period immediately preceding the first period is increased by a predetermined amount to obtain the convergence score. If it is determined that convergence has not occurred, the convergence score is obtained by reducing the past convergence score by a second predetermined amount, or the convergence score is reset and the convergence score for the first period is calculated. Based on the operating score during the first period and the convergence score during the first period, an operational evaluation is performed during the first period. A program that executes something.

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