Abnormal determination method and abnormal determination device

Abstract

To provide an abnormality determination method and an abnormality determination device that enable detection of degradation in service level of a vehicle and a difference in service level between vehicles.SOLUTION: In an abnormality determination method and an abnormality determination device, characteristic values of a vehicle are calculated from data measured by a sensor mounted to the vehicle, in order to determine whether a service level of a first vehicle is maintained, on the basis of a first characteristic value calculated according to data of the first vehicle out of the characteristic values of the vehicle, and constraint conditions associated with the first vehicle, and then, output a determination result.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to an abnormality determination method and an abnormality determination device.

Background Art

[0002] A method has been proposed for analyzing the operating state of a sensor and detecting an abnormality in the sensor itself based on diagnostic information of the sensor self-diagnosed by a control device mounted on each vehicle (see Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, according to the technique described in Patent Document 1, an abnormality in the sensor itself is detected based on the self-diagnosis result of the sensor in each vehicle. Therefore, it is impossible to detect a decrease in the service level of the vehicle, such as abnormal noise due to loosening of bolts in the vehicle, or a decrease in straight-line running performance caused by deterioration or wear of bushes, tires, etc., which is different from a sensor abnormality. Furthermore, it is also impossible to detect a difference in the service level of each vehicle compared to other vehicles. Such problems are likely to occur in self-driving vehicles that provide unmanned mobility services.

[0005] The present invention has been made in view of the above problems, and an object thereof is to provide an abnormality determination method and an abnormality determination device capable of detecting a decrease in the service level of a vehicle and a difference in the service level between vehicles.

Means for Solving the Problems

[0006] In order to solve the above problems, an abnormality determination method and an abnormality determination apparatus according to an aspect of the present invention calculate vehicle characteristic values from data measured by sensors mounted on a vehicle, and among the vehicle characteristic values, based on a first characteristic value calculated based on data of a first vehicle and a constraint condition associated with the first vehicle, it is determined whether the service level of the first vehicle is maintained, and a determination result is output.

Effects of the Invention

[0007] According to the present invention, it is possible to detect a decrease in the service level of a vehicle and a difference in service level between vehicles compared to other vehicles.

Brief Description of the Drawings

[0008]

Figure 1

Figure 2

Figure 3A

Figure 3B

Modes for Carrying Out the Invention

[0009] Next, embodiments of the present invention will be described in detail with reference to the drawings. In the description, the same components are denoted by the same reference numerals and redundant descriptions are omitted.

[0010] FIG. 1 is a block diagram showing the configuration of the abnormality determination apparatus according to the present embodiment.

[0011] [Configuration of Abnormality Determination Apparatus] The abnormality determination device 200 is connected so as to be able to communicate with one or more vehicles 100 wirelessly or by wire, and determines whether there is a decrease in the service level (LOS) of the vehicle 100 or a difference in the service level compared to other vehicles. Note that the abnormality determination device 200 may be mounted on the vehicle 100 or may be installed outside the vehicle 100.

[0012] The vehicle 100 is, for example, an autonomous vehicle provided by an autonomous driving service provider (service provider). The vehicle 100 is not limited to an autonomous vehicle provided by a service provider, and may be a general autonomous vehicle capable of traveling without a driver. Examples of service providers include vehicle dispatching service providing companies such as DeNA (registered trademark) and UBER (registered trademark).

[0013] As shown in FIG. 1, the vehicle 100 includes an in-vehicle sensor 110. The in-vehicle sensor 110 acquires data of the vehicle 100. The in-vehicle sensor 110 is composed of, for example, an in-vehicle microphone, a speedometer, an accelerometer, a tire air pressure sensor, a raindrop sensor, an ammeter for measuring the current value of a brake actuator, a steering angle sensor for detecting the steering angle of a steering wheel, a vibration meter, etc., and as data of the vehicle 100, it acquires the sound pressure and frequency of sound in the vehicle interior, vehicle speed, acceleration, tire air pressure, the current value of the raindrop sensor, the current value of the brake actuator, the steering angle, the vibration amount, etc. The acquired data is transmitted to the abnormality determination device 200.

[0014] Note that the data of the vehicle 100 may include, in addition to the data acquired by the in-vehicle sensor 110, information on the driving route of the vehicle 100 and information on road surface data indicating the road surface condition on which the vehicle 100 travels.

[0015] As shown in FIG. 1, the abnormality determination device 200 includes an acquisition unit 210, a database 220, a control unit 230 (controller), and an output unit 240. The control unit 230 is connected so as to be able to communicate with the acquisition unit 210, the database 220, and the output unit 240.

[0016] The acquisition unit 210 receives the data transmitted from the in-vehicle sensor 110. The acquisition unit 210 sequentially receives the data and generates time-series data representing the time change of the data acquired from each in-vehicle sensor 110. Note that the acquisition unit 210 may receive data not only from the in-vehicle sensors 110 of a specific vehicle 100 but also from the in-vehicle sensors 110 of a plurality of vehicles 100.

[0017] When the abnormality determination device 200 is mounted on the vehicle 100, the acquisition unit 210 may be connected to the in-vehicle sensor 110 via an in-vehicle network such as CAN (Controller Area Network). When the abnormality determination device 200 is not mounted on the vehicle 100, the acquisition unit 210 may be connected to the in-vehicle sensor 110 via a wireless network such as 4G or LTE.

[0018] The database 220 stores the time-series data generated by the acquisition unit 210. The database 220 also stores various vehicle characteristic values calculated by the control unit 230 described later and reference values used for abnormality determination. In addition, the database 220 may store the time-series data, vehicle characteristic values, reference values, etc. in association with identification information for identifying the vehicle 100 and the driving conditions of the vehicle 100 (such as driving route, driving time zone, weather, loaded cargo weight, etc.).

[0019] The output unit 240 outputs the result of the processing in the control unit 230 to a management terminal (not shown) for managing the vehicle 100. Specifically, the output unit 240 outputs the result of the abnormality determination regarding the vehicle 100.

[0020] An administrator (such as a service provider) who manages the vehicle 100 grasps the state of the vehicle 100 by checking the result output from the output unit 240. For example, based on the state of the vehicle 100, the administrator changes the maintenance plan based on the deterioration of the service level of the vehicle 100 or performs emergency maintenance on the vehicle 100.

[0021] The control unit 230 (an example of a controller and a processing unit) is a general-purpose microcomputer including a CPU (Central Processing Unit), a memory, and an input / output unit. A computer program (abnormality determination program) for causing the control unit 230 to function as part of the abnormality determination device is installed in the control unit 230. By executing the computer program, the control unit 230 functions as a plurality of information processing circuits (231, 233, 235, 237).

[0022] Here, an example is shown in which a plurality of information processing circuits (231, 233, 235, 237) included in the control unit 230 are realized by software. However, it is also possible to prepare dedicated hardware for executing each of the information processes shown below to configure the information processing circuits (231, 233, 235, 237). Further, the plurality of information processing circuits (231, 233, 235, 237) may be configured by individual hardware.

[0023] The control unit 230 includes, as a plurality of information processing circuits (231, 233, 235, 237), a characteristic value calculation unit 231, a reference value determination unit 233, a deviation calculation unit 235, and an abnormality determination unit 237.

[0024] The characteristic value calculation unit 231 (characteristic value calculation means) calculates a vehicle characteristic value of the vehicle 100 based on the time-series data stored in the database 220. Here, the vehicle characteristic value is an index value obtained by statistically processing one or a plurality of time-series data, and is, for example, a moving average or a variance. In addition, the characteristic value calculation unit 231 may calculate a component amount of a specific frequency obtained by frequency-analyzing the time-series data as the vehicle characteristic value of the vehicle 100.

[0025] Further, the characteristic value calculation unit 231 may calculate the vehicle characteristic value using a neural network. Specifically, by performing machine learning using the time-series data as input data, the neural network is caused to acquire features that well represent the input data, and the output data of the neural network may be used as the vehicle characteristic value.

[0026] In addition, in order to enable comparison between vehicle characteristic values, the characteristic value calculation unit 231 may calculate vehicle characteristic values based on time-series data obtained under the same driving conditions. Specifically, the characteristic value calculation unit 231 may calculate vehicle characteristic values based on time-series data composed of data obtained by the in-vehicle sensor 110 when the vehicle 100 travels on a specific driving route.

[0027] Furthermore, the characteristic value calculation unit 231 may calculate vehicle characteristic values by comparing the current data of the vehicle 100 acquired by the in-vehicle sensor 110 with the initial data of the vehicle 100. Here, the initial data of the vehicle 100 means data measured under predetermined driving conditions immediately after the vehicle 100 is delivered.

[0028] The characteristic value calculation unit 231 may calculate both a current index value obtained by statistically processing time-series data composed of the current data of the vehicle 100 and an initial index value obtained by statistically processing time-series data composed of the initial data, and calculate the ratio of the current index value to the initial index value as the vehicle characteristic value.

[0029] In addition, the characteristic value calculation unit 231 is not limited to calculating only the vehicle characteristic values of one vehicle. The characteristic value calculation unit 231 may calculate the vehicle characteristic value calculated based on the data of the first vehicle as the first characteristic value, and calculate the vehicle characteristic value calculated based on the data of the second vehicle different from the first vehicle as the second characteristic value.

[0030] Furthermore, the characteristic value calculation unit 231 may calculate vehicle characteristic values for each vehicle for a plurality of vehicles of the same vehicle type, and calculate the average value of the obtained plurality of vehicle characteristic values as the average characteristic value representing the vehicle type.

[0031] The reference value determination unit 233 (reference value determination means) determines the reference value used for abnormality determination. Specifically, among the reference values used for abnormality determination stored in the database 220, the reference value associated with the vehicle 100 that is the target of abnormality determination is read out and transmitted to the deviation calculation unit 235 and the abnormality determination unit 237.

[0032] The reference value is composed of a first reference value T1 and a second reference value T2. The first reference value T1 is used to determine a mild abnormality of the vehicle 100, and the second reference value T2 is used to determine a severe abnormality of the vehicle 100. More specifically, the first reference value T1 is a value serving as a reference for determining a decrease in the service level of the vehicle 100, and the second reference value T2 is a value serving as a reference for determining whether emergency maintenance of the vehicle 100 is necessary.

[0033] Note that the first reference value T1 and the second reference value T2 may be appropriately set by an administrator who manages the vehicle 100 according to the vehicle type of the vehicle 100 and the service content provided by the vehicle 100, and stored in the database 220. Furthermore, the first reference value T1 may be the average characteristic value calculated by the characteristic value calculation unit 231.

[0034] The deviation calculation unit 235 (deviation calculation means) calculates the deviation of the vehicle characteristic value calculated by the characteristic value calculation unit 231 with the reference value set by the reference value determination unit 233 as the comparison target. Specifically, the deviation calculation unit 235 calculates the value obtained by subtracting the first reference value T1 from the vehicle characteristic value and the value obtained by subtracting the second reference value T2 from the vehicle characteristic value, respectively, to obtain the deviation of the vehicle characteristic value with respect to the reference value.

[0035] The abnormality determination unit 237 (abnormality determination means) determines whether the vehicle characteristic value of the vehicle 100 satisfies the constraint conditions associated with the vehicle 100 based on the deviation calculated by the deviation calculation unit 235. For example, the constraint conditions are defined by the vehicle characteristic value being less than or equal to the first reference value T1 and the vehicle characteristic value being less than or equal to the second reference value T2. In addition, the constraint conditions may be defined by the deviation calculated by the deviation calculation unit 235 being less than or equal to a predetermined threshold value.

[0036] In addition, in the present embodiment, a case where the constraint conditions associated with the vehicle 100 are defined by the first reference value T1 and the second reference value T2 is shown. However, there are various constraint conditions, and the present embodiment is not limited to the example.

[0037] An example of the process in the abnormality determination unit 237 will be described with reference to FIGS. 3A and 3B. FIG. 3A is a diagram showing an example of identifying a vehicle with a degraded service level. FIG. 3B is a diagram showing an example of identifying a vehicle that requires emergency maintenance. FIGS. 3A and 3B show the results of the characteristic value calculation unit 231 calculating the respective vehicle characteristic values of vehicle A, vehicle B, and vehicle C.

[0038] In FIG. 3A, although the vehicle characteristic values of vehicle A and vehicle C are below the first reference value T1, the vehicle characteristic value of vehicle B is above the first reference value T1. Therefore, the abnormality determination unit 237 determines that vehicle B does not satisfy the constraint conditions. In particular, the abnormality determination unit 237 determines that vehicle B is a vehicle with a degraded service level. On the other hand, the abnormality determination unit 237 determines that vehicle A and vehicle C are vehicles with a maintained service level.

[0039] In FIG. 3B, although the vehicle characteristic values of vehicle A and vehicle C are below the first reference value T1, the vehicle characteristic value of vehicle B is above the second reference value T2. Therefore, the abnormality determination unit 237 determines that vehicle B does not satisfy the constraint conditions. In particular, the abnormality determination unit 237 determines that vehicle B is a vehicle that requires emergency maintenance. On the other hand, the abnormality determination unit 237 determines that vehicle A and vehicle C are vehicles that do not require emergency maintenance.

[0040] To explain with a more specific example, for instance, a state where the service level of the vehicle 100 has deteriorated, such as abnormal noise due to loosening of bolts in the vehicle 100, or a decrease in straight-line running performance caused by deterioration or wear of bushes or tires, etc., can be detected as a case where the vehicle characteristic value of the vehicle 100 gradually increases and changes from a state below the first reference value T1 to a state above the first reference value T1.

[0041] When the vehicle characteristic value exceeds the first reference value T1, it may give a sense of discomfort to the passengers of the vehicle 100. Therefore, the administrator of the vehicle 100 needs to grasp the state where the vehicle characteristic value exceeds the first reference value T1. When the abnormality determination device 200 cannot be used, in order to grasp such a state, an operator has to get on the vehicle 100 and directly check the state of the vehicle 100 through the operator's vision and hearing.

[0042] However, in the present embodiment, since the abnormality determination unit 237 determines whether the vehicle characteristic value of the vehicle 100 satisfies the constraint condition characterized by the first reference value T1, the operator and the administrator do not need to directly check the state of the vehicle 100.

[0043] In addition, a state that requires emergency maintenance, such as a flat tire or damage to the windshield, can be detected as a case where the vehicle characteristic value of the vehicle 100 rapidly changes from a state below the second reference value T2 to a state above the second reference value T2.

[0044] When the vehicle characteristic value exceeds the second reference value T2, it may affect the passengers of the vehicle 100 and is a state where the mobility service provided by the vehicle 100 has to be interrupted. Therefore, for the administrator of the vehicle 100, it is necessary to grasp the state where the vehicle characteristic value exceeds the second reference value T2 at an early stage. When the abnormality determination device 200 cannot be used, in order to grasp such a state, an operator has to get on the vehicle 100 and directly check the state of the vehicle 100 through the operator's vision and hearing.

[0045] However, in the present embodiment, since the abnormality determination unit 237 determines whether the vehicle characteristic value of the vehicle 100 satisfies the constraint condition characterized by the second reference value T2, the operator and the administrator do not need to directly check the state of the vehicle 100.

[0046] [Processing Procedure for Abnormality Determination] Next, based on the flowchart of FIG. 2, an example of the processing procedure for abnormality determination according to this embodiment will be described. FIG. 2 is a flowchart showing the operation of the abnormality determination device according to this embodiment. The abnormality determination process shown in FIG. 2 starts when an instruction to start abnormality diagnosis is received from the service center or at a predetermined timing during the period when the vehicle 100 is running.

[0047] First, in step S101, the acquisition unit 210 acquires data related to the vehicle 100 from the in-vehicle sensor 110. Then, the database 220 stores the data related to the vehicle 100 acquired by the acquisition unit 210.

[0048] In step S103, the characteristic value calculation unit 231 calculates the vehicle characteristic value of the vehicle 100 based on the data related to the vehicle 100 stored in the database 220.

[0049] In step S105, the reference value determination unit 233 determines the reference value used for the abnormality determination performed by the abnormality determination unit 237.

[0050] In step S107, the deviation calculation unit 235 calculates the deviation of the vehicle characteristic value calculated by the characteristic value calculation unit 231.

[0051] In step S111, the abnormality determination unit 237 determines whether the vehicle characteristic value of the vehicle 100 satisfies the constraint conditions associated with the vehicle 100 based on the deviation calculated by the deviation calculation unit 235. For example, the abnormality determination unit 237 determines whether the deviation calculated by the deviation calculation unit 235 is greater than a predetermined threshold value.

[0052] If the deviation calculated by the deviation calculation unit 235 is greater than a predetermined threshold value (YES in step S111, does not satisfy the constraint conditions), the process proceeds to step S113. On the other hand, if the deviation calculated by the deviation calculation unit 235 is less than or equal to the predetermined threshold value (NO in step S111, satisfies the constraint conditions), the process proceeds to step S121, and the abnormality determination unit 237 determines that the service level (LOS) of the vehicle 100 is maintained.

[0053] In step S113, the abnormality determination unit 237 determines whether the vehicle characteristic value calculated by the characteristic value calculation unit 231 is equal to or less than the upper limit of the standard. For example, the upper limit of the standard is the second reference value T2 (a reference value for determining whether emergency maintenance is required).

[0054] When the vehicle characteristic value calculated by the characteristic value calculation unit 231 is equal to or less than the upper limit of the standard (YES in step S113), the process proceeds to step S123, and the abnormality determination unit 237 determines that the service level of the vehicle 100 has decreased. On the other hand, when the vehicle characteristic value calculated by the characteristic value calculation unit 231 is greater than the upper limit of the standard (NO in step S113), the process proceeds to step S123, and the abnormality determination unit 237 determines that emergency maintenance of the vehicle 100 is necessary.

[0055] After the processes of steps S121, S123, and S125 are completed, in step S127, the output unit 240 outputs the determination results in steps S121, S123, and S125.

[0056] Note that in the flowchart of FIG. 2, two-stage determinations are made in steps S111 and S113, and the description is given as obtaining three types of determination results: "the service level of the vehicle 100 is maintained", "the service level of the vehicle 100 has decreased", and "emergency maintenance of the vehicle 100 is necessary".

[0057] However, the present embodiment is not limited to this, and either determination in steps S111 and S113 may be omitted to perform a one-stage determination.

[0058] For example, the determination in step S111 may be omitted, and only the determination in step S113 may be performed to obtain two types of determination results, namely, "emergency maintenance of vehicle 100 is necessary" or "emergency maintenance of vehicle 100 is unnecessary". Further, the determination in step S113 may be omitted, and only the determination in step S111 may be performed to obtain two types of determination results, namely, "the service level of vehicle 100 is maintained" and "the service level of vehicle 100 is decreased".

[0059] [Effects of the Embodiment] As described in detail above, according to the abnormality determination method and the abnormality determination device according to the present embodiment, when determining an abnormality based on a vehicle characteristic value calculated from data measured by a sensor mounted on a vehicle, among the vehicle characteristic values, based on a first characteristic value calculated based on data of a first vehicle and a constraint condition associated with the first vehicle, it is determined whether the service level of the first vehicle is maintained, and the result of the determination is output.

[0060] Thereby, it is possible to detect a state in which the service level of the vehicle has decreased. In particular, even when there is no abnormality in the sensor itself, it is possible to detect a state in which the service level has decreased. Further, in order to determine whether or not a first characteristic value calculated from data measured by a sensor mounted on a vehicle satisfies a constraint condition, an operator or a manager can grasp a state in which the service level of the vehicle has decreased without directly checking the state of the vehicle. As a result, emergency maintenance of the vehicle can be performed.

[0061] Further, according to the abnormality determination method and the abnormality determination device according to the present embodiment, the determination may be made based on the deviation between the first characteristic value and the second characteristic value calculated based on the data of the second vehicle different from the first vehicle among the vehicle characteristic values. Thereby, when there are a plurality of vehicles, the difference in the service level between the first vehicle and the second vehicle can be detected. As a result, it is possible to detect a state in which the service level of a specific vehicle among the plurality of vehicles is lower than the service levels of other vehicles, and it is possible to change the maintenance plan of the vehicle based on the decrease in the service level.

[0062] Furthermore, according to the abnormality determination method and the abnormality determination device according to the present embodiment, when determining an abnormality based on the vehicle characteristic value calculated from the data measured by the sensor mounted on the vehicle, among the vehicle characteristic values, the first characteristic value calculated based on the data of the first vehicle and the second characteristic value calculated based on the data of the second vehicle different from the first vehicle among the vehicle characteristic values, based on the deviation between them, it may be determined whether the service level of the first vehicle is maintained, and the result of the determination may be output.

[0063] Thereby, it is possible to detect a state in which the service level of the vehicle has decreased. In particular, even when there is no abnormality in the sensor itself, it is possible to detect a state in which the service level has decreased. Also, it is possible to detect a state in which the service level of a specific vehicle among the plurality of vehicles is lower than the service levels of other vehicles. As a result, it is possible to change the maintenance plan of the vehicle based on the decrease in the service level or perform emergency maintenance on the vehicle.

[0064] Further, according to the abnormality determination method and the abnormality determination device according to the present embodiment, the vehicle characteristic value may be calculated by comparing the current data measured by the sensors mounted on the vehicle with the initial data of the vehicle. Thereby, it is possible to detect the aging change of the vehicle when the vehicle is continuously operated. Furthermore, it is possible to predict the timing of vehicle maintenance in the future from the degree of aging change of the vehicle.

[0065] Furthermore, according to the abnormality determination method and the abnormality determination device according to the present embodiment, the initial data may be data measured under predetermined driving conditions immediately after the vehicle is delivered. Thereby, it becomes possible to align the conditions at the time of acquiring the initial data serving as a reference for evaluating the vehicle characteristic value, and it becomes possible to make a comparison between the same vehicle models. Furthermore, the accuracy of determination using the vehicle characteristic value can be improved.

[0066] Each function shown in the above embodiment can be implemented by one or a plurality of processing circuits. The processing circuit includes a programmed processor, an electric circuit, etc., and further includes a device such as an application-specific integrated circuit (ASIC) and circuit components arranged to execute the described functions.

[0067] As described above, the content of the present invention has been described along with the embodiments. However, it is obvious to those skilled in the art that the present invention is not limited to these descriptions, and various modifications and improvements are possible. It should not be understood that the discussion and drawings forming part of this disclosure limit the present invention. Various alternative embodiments, examples, and operation techniques will become apparent to those skilled in the art from this disclosure.

[0068] Of course, the present invention includes various embodiments not described herein. Therefore, the technical scope of the present invention is defined only by the invention specific matters according to the reasonable claims based on the above description.

Explanation of Reference Numerals

[0069] 100 Vehicle 110 In-vehicle Sensor 200 Abnormality determination device 210 Acquisition unit 220 Database 230 Control unit 231 Characteristic value calculation unit 233 Reference value determination unit 235 Deviation calculation unit 237 Abnormality determination unit 240 Output unit

Claims

1. An abnormality determination device including a controller connected to an acquisition unit that acquires data measured by a sensor mounted on a vehicle, The controller: Obtaining a first reference value and a second reference value; Calculating a vehicle characteristic value from the data for each of the vehicles; When the vehicle characteristic value is equal to or less than the first reference value, it is determined that a service level of the vehicle is maintained; determining that maintenance of the vehicle is required when the vehicle characteristic value is not equal to or less than the second reference value; when the vehicle characteristic value is not equal to or less than the first reference value and is equal to or less than the second reference value, determining whether or not a service level of the vehicle is being maintained based on a constraint condition that is predetermined for each vehicle type and the vehicle characteristic value; Abnormality determination device.

2. The abnormality determination device according to claim 1 , wherein the vehicle is a vehicle managed by a servicer that provides a vehicle dispatch service.

3. 2 . The abnormality determination device according to claim 1 , wherein the controller calculates the vehicle characteristic value by statistically processing one or more pieces of time-series data obtained for a plurality of the vehicles of the same model.

4. 2. The abnormality determination device according to claim 1, wherein the first reference value is an average value of the vehicle characteristic values ​​obtained for a plurality of vehicles of the same model.

5. 2. The abnormality determination device according to claim 1, wherein the first reference value is determined in advance so that a state in which the vehicle characteristic value exceeds the first reference value includes a state in which an occupant of the vehicle feels uncomfortable.

6. The abnormality determination device according to any one of claims 1 to 5, wherein when the controller determines that the service level of the vehicle is not being maintained, the controller outputs information to a manager of the vehicle indicating that the service level of the vehicle is not being maintained.

7. 1. A method for determining an abnormality, comprising: controlling a controller connected to an acquisition unit that acquires data measured by a sensor mounted on a vehicle, the method comprising: The controller: Obtaining a first reference value and a second reference value; Calculating a vehicle characteristic value from the data for each of the vehicles; When the vehicle characteristic value is equal to or less than the first reference value, it is determined that a service level of the vehicle is maintained; determining that maintenance of the vehicle is required when the vehicle characteristic value is not equal to or less than the second reference value; when the vehicle characteristic value is not equal to or less than the first reference value and is equal to or less than the second reference value, determining whether or not a service level of the vehicle is being maintained based on a constraint condition that is predetermined for each vehicle type and the vehicle characteristic value; Abnormality determination method.

8. A vehicle dispatch service management method, comprising the steps of: suspending provision of service by the vehicle when it is determined by the abnormality determination method according to claim 7 that maintenance of the vehicle is required.

9. A vehicle dispatch service management method, which, when it is determined that the service level of the vehicle is not being maintained by the abnormality determination method described in claim 7, outputs information to a manager of the vehicle indicating that the service level of the vehicle is not being maintained.

Images