WO2020202452A1

WO2020202452A1 - Leaning vehicle traveling data analysis method, leaning vehicle traveling data analysis device, information processing method using analysis data, and information processing device using analysis data

Info

Publication number: WO2020202452A1
Application number: PCT/JP2019/014559
Authority: WO
Inventors: 圭祐森島; 謙作磯部; 中尾　浩; 佑輔梅澤; 裕章木邨
Original assignee: ヤマハ発動機株式会社
Priority date: 2019-04-01
Filing date: 2019-04-01
Publication date: 2020-10-08
Also published as: JP7210704B2; WO2020204111A1; JPWO2020204111A1

Abstract

Provided is a leaning vehicle traveling data analysis method for outputting analysis data of a leaning vehicle while increasing a designing freedom for hardware resources. This leaning vehicle traveling data analysis method involves: acquiring leaning vehicle traveling reference data generated on the basis of reference-generation-use leaning vehicle traveling data which includes leaning vehicle traveling data of a leaning vehicle in a state where a fellow passenger and/or baggage is on board, includes class-associated data for classifying a driver and/or a vehicle, and includes leaning vehicle traveling data of a plurality of leaning vehicles of different classes; acquiring analysis-use leaning vehicle traveling data which includes the traveling data of a leaning vehicle in the state where the fellow passenger and/or the baggage is on board and includes analysis-use class-associated data for classifying a driver to be analyzed and/or a leaning vehicle to be analyzed; and acquiring analysis data on the driver to be analyzed and/or on the leaning vehicle to be analyzed by analyzing the acquired analysis-use leaning vehicle traveling data on the basis of the acquired leaning vehicle traveling reference data.

Description

Lean vehicle driving data analysis method, lean vehicle driving data analyzer, information processing method using analysis data and information processing device using analysis data

The present invention relates to a lean vehicle driving data analysis method, a lean vehicle driving data analyzer, an information processing method using the analysis data, and an information processing device using the analysis data.

A device for determining the driving skill of a driver is known. As a device for determining a driver's driving skill, for example, a configuration disclosed in Patent Document 1 is known.

Patent Document 1 discloses an evaluation device capable of evaluating the driving skill of a vehicle. This Patent Document 1 analyzes a rider's riding skill using the running data of a lean vehicle.

International Publication WO2015 / 050038

Lean vehicles are used in various scenes because of their high mobility and convenience. There is a need for a lean vehicle-specific analysis that takes into account various usage scenarios.

When trying to obtain data on various situations such as the driving environment in order to analyze various usage scenarios of lean vehicles, the amount of data processed by the information processing device becomes enormous, and the hardware load of the device becomes large. Will be higher. Therefore, the hardware resources required by the information processing apparatus increase, which imposes restrictions on the design of the hardware resources. Therefore, the degree of freedom in designing the hardware resources of the information processing device is reduced.

An object of the present invention is to provide a lean vehicle driving data analysis method capable of outputting analysis data peculiar to a lean vehicle based on the driving data of a lean vehicle while increasing the degree of freedom in designing hardware resources.

The lean vehicle travel data analysis method according to an embodiment of the present invention analyzes lean vehicle travel data of a lean vehicle that tilts to the right when turning right and tilts to the left when turning left. This is a data analysis method. This lean vehicle running data analysis method includes more lean vehicle running data of a lean vehicle with at least one of a passenger and an object than the lean vehicle running data of a lean vehicle without a passenger and an object. Lean vehicle generated based on the reference generation lean vehicle driving data including the classification related data for classifying at least one of the driver and the vehicle, and including the lean vehicle driving data of a plurality of lean vehicles having different classifications. Analysis classification for acquiring driving reference data, including lean vehicle driving data with at least one of passengers and objects mounted, and classifying at least one of the driver who is the analysis target and the lean vehicle to be analyzed. Based on the process of acquiring the lean vehicle driving data for analysis including the related data and the acquired lean vehicle driving reference data, the acquired lean vehicle driving data for analysis is analyzed, and the classification-related data is used. The analysis data of at least one of the driver and the vehicle classified by the above is acquired, the output data for output is generated using the analysis data, and the output data is output.

The lean vehicle driving data includes classification-related data for classifying at least one of the driver and the vehicle, and the analysis data analyzes the lean vehicle driving data for analysis based on the lean vehicle driving reference data, and the classification-related data is described. It is the analysis data of at least one of the driver and the vehicle classified by using the data. In this way, by classifying at least one of the driver and the vehicle by the classification-related data, it is possible to process the data corresponding to at least one of the driver and the vehicle when acquiring the analysis data. .. This makes it possible to limit the amount of data processed by the device that analyzes the lean vehicle travel data. Further, by classifying at least one of the driver and the vehicle by the classification-related data, analysis data corresponding to the classification can be obtained. For example, analysis data corresponding to a vehicle type, driver's gender, and analysis data corresponding to an age group can be easily obtained.

Therefore, it is possible to output analysis data peculiar to a lean vehicle, which has been difficult to output until now, such as the skill of driving and operating a lean vehicle in consideration of various usage scenes.

Moreover, at least one of the passenger and the object is mounted by using the lean vehicle driving standard generated based on the lean vehicle driving data including a large amount of the lean vehicle driving data in the state where at least one of the passenger and the object is mounted. By analyzing the lean vehicle driving data including a large amount of lean vehicle driving data in the state of being in the state, the number of data to be processed is limited as compared with the case of analyzing all the driving data without considering the state. be able to. As a result, the load on the hardware resource of the device that analyzes the lean vehicle driving data can be reduced, and the degree of freedom in designing the hardware resource can be increased.

Therefore, it is possible to output analysis data peculiar to the lean vehicle based on the running data of the lean vehicle while reducing the load on the hardware resource of the device for analyzing the running data of the lean vehicle and increasing the degree of freedom in designing the hardware resource of the device. Can provide a lean vehicle driving data analysis method.

From another point of view, the lean vehicle driving data analysis method of the present invention preferably includes the following configurations. The reference generation lean vehicle traveling data includes reference generation lean vehicle driving operation input data related to operation input to the lean vehicle, reference generation lean vehicle behavior data related to the behavior of the lean vehicle, and the lean vehicle. The analysis lean vehicle driving data includes at least one of the reference generation lean vehicle position data related to the position, and the analysis lean vehicle driving data is the analysis lean vehicle driving operation input data related to the operation input to the analysis target lean vehicle. It includes at least one of the analysis lean vehicle behavior data related to the behavior of the analysis target lean vehicle and the analysis lean vehicle position data related to the position of the analysis target lean vehicle.

As a result, the lean vehicle driving data used when analyzing the lean vehicle driving data for analysis includes data that more reflects the driving skill of the driver's lean vehicle.

That is, since the lean vehicle operation input data is data related to the operation input by the driver, it more reflects the result of the driver's judgment. In lean vehicles, there are many types of driver's operations and they are complicatedly related, so there are many variations in operations. The result of the operation input is strongly reflected in the lean vehicle behavior data and the lean vehicle position data. With this configuration, the lean vehicle driving data more strongly reflects the change in the operation of the lean vehicle after the driver makes a judgment. The analysis lean vehicle position data regarding the traveling position of the analysis target lean vehicle is reflected in the operation input of the analysis target person, for example, and is based on the analysis target lean vehicle behavior of the analysis target lean vehicle driven by the analysis target person. It is used to detect driving skills.

With this configuration, it is possible to analyze the lean vehicle driving data for analysis with higher accuracy based on the lean vehicle driving standard data. Further, by using the analysis lean vehicle traveling data in which the data type is specified, the type of data processed by the apparatus for analyzing the lean vehicle traveling data can be reduced, and the hardware load of the apparatus can be further reduced. Further, since the hardware resources required by the device can be reduced, the degree of freedom in designing the hardware resources of the device can be further increased.

Therefore, it is possible to realize a lean vehicle driving data analysis method capable of outputting analysis data peculiar to a lean vehicle based on the driving data of the lean vehicle while further increasing the degree of freedom in designing the hardware resource of the device for analyzing the driving data of the lean vehicle.

From another point of view, the lean vehicle driving data analysis method of the present invention preferably includes the following configurations. The reference generation lean vehicle traveling data includes reference generation lean vehicle traveling environment data related to the traveling environment in which the lean vehicle travels, and the analysis lean vehicle traveling data includes traveling in which the analysis target lean vehicle travels. Includes lean vehicle driving environment data for analysis related to the environment.

Lean vehicle driving environment data includes, for example, map data. This map data may be associated with, for example, information on road conditions, information on road traffic environments such as traffic lights and equipment, and regulatory information on road travel. The lean vehicle driving environment data can be used for analyzing the lean vehicle driving data together with the lean vehicle driving operation input data, the lean vehicle behavior data, and the lean vehicle position data.

With this configuration, it is possible to more accurately analyze the analysis lean vehicle driving data obtained when the analysis target lean vehicle driven and operated by the analysis target person is driven by using the lean vehicle driving reference data. Further, by using the lean vehicle traveling data in which the data type is specified, the type of data processed by the apparatus for analyzing the lean vehicle traveling data can be reduced, and the hardware load of the apparatus can be further reduced. Further, since the hardware resources required by the device can be reduced, the degree of freedom in designing the hardware resources of the device can be further increased.

Therefore, it is possible to realize a lean vehicle driving data analysis method capable of outputting analysis data peculiar to a lean vehicle based on the driving data of a lean vehicle while further increasing the degree of freedom in designing hardware resources.

From another point of view, the lean vehicle driving data analysis method of the present invention preferably includes the following configurations. The reference generation lean vehicle travel data further includes more travel data when the lean vehicle travels on a public road than data when the lean vehicle travels on a road other than a public road, and the analysis lean vehicle travel data includes the analysis lean vehicle travel data. It includes more travel data when the lean vehicle to be analyzed travels on a public road than travel data when the lean vehicle to be analyzed travels on a road other than a public road.

When a driver traveling on a public road is operating a lean vehicle, the driver has more judgments, has more choices of judgment, and is easily exposed to external stress. The data contains more variations. Therefore, by using lean vehicle driving data including a large amount of data when driving on a public road, it becomes easier to analyze the driving skill and / or driving skill of the driver from the driving data of the lean vehicle. By using such lean vehicle driving data, it is possible to acquire more accurate analysis data while ensuring the degree of freedom in designing the hardware resources of the information processing device.

From another point of view, the lean vehicle driving data analysis method of the present invention preferably includes the following configurations. The reference generation lean vehicle driving data includes data in a state where a plurality of judgment options of the driver are limited by vehicles around the lean vehicle, but a plurality of them are left, and the analysis lean vehicle driving data includes. It includes data in a state where a plurality of judgment options of the analysis target person are limited by vehicles around the analysis target lean vehicle, but a plurality of them are left.

By using lean vehicle driving data in a state where the driver's judgment options are limited but multiple are left, it becomes easier to analyze the driver's driving skill from the driving data of the lean vehicle. By using such lean vehicle driving data, it is possible to acquire more accurate analysis data while ensuring the degree of freedom in designing the hardware resources of the information processing device.

From another point of view, the lean vehicle driving data analysis method of the present invention preferably includes the following configurations. The lean vehicle running reference data includes evaluation data related to riding by a passenger of the lean vehicle for reference generation or evaluation data related to transportation by a requester of transportation of goods, and lean vehicle running data for reference generation. Generated based on.

With this configuration, the lean vehicle driving reference data is generated by adding evaluation data related to boarding by a passenger or evaluation data related to evaluation related to transportation by a requester of transportation of goods. From this, the lean vehicle traveling standard data can reflect the evaluation related to the riding by the passenger or the evaluation related to the transportation by the requester of the transportation of the object.

As a result, it is possible to output analysis data peculiar to the lean vehicle such as a tendency of preferable driving behavior to the lean vehicle in which at least one of a passenger and an object is mounted.

From another point of view, the lean vehicle driving data analysis method of the present invention preferably includes the following configurations. Lean vehicle driving data of a lean vehicle with at least one of a passenger and an object is included more than lean vehicle traveling data of a lean vehicle with no passenger and an object, and at least one of a driver and a vehicle is classified. Acquires the non-mounted state lean vehicle running standard data generated based on the lean vehicle running data for generating the non-mounted state standard including the lean vehicle running data of a plurality of lean vehicles having different classifications. Lean vehicle for non-mounted state analysis, which includes lean vehicle driving data related to passengers and non-loaded conditions, and includes classification-related data for classifying at least one of the analyzed person and the lean vehicle to be analyzed. By acquiring the driving data and analyzing the acquired lean vehicle driving data for non-mounted state analysis based on the acquired non-mounted state lean vehicle driving reference data, the analysis classified using the classification related data is performed. The non-mounted state analysis data of at least one of the target person and the analysis target vehicle is acquired, the output data for output is generated using the acquired non-mounted state analysis data, and the generated output data is output.

With this configuration, it is possible to further acquire non-mounted state analysis data related to a lean vehicle in a state where passengers and objects are not mounted. That is, when the driver drives the lean vehicle X, there are driving with at least one of the passenger and the object mounted and driving without the passenger and the object. Therefore, the lean vehicle driving data can obtain the lean vehicle driving data based on the driving with at least one of the passenger and the object and the lean vehicle traveling state related to the lean vehicle without the passenger and the object. it can.

Therefore, according to the above configuration, in addition to the analysis based on the lean vehicle driving data based on the driving with at least one of the passenger and the object, the analysis based on the lean vehicle without the passenger and the object is performed. be able to.

As a result, the degree of change can be seen by comparing the non-mounted state analysis data with the analysis data obtained by analyzing the lean vehicle running data in the state where at least one of the passenger and the object is mounted. Further, more accurate analysis data can be obtained by using the analysis data in the state where at least one of the passenger and the object is mounted and the analysis data in the non-mounted state in which the passenger and the object are not mounted.

From another point of view, the lean vehicle driving data analysis method of the present invention preferably includes the following configurations. The acquired analysis data is stored, and the output data of at least one of the analysis target person and the analysis target vehicle is generated by using the stored analysis data.

By using a plurality of analysis data, it is possible to analyze the analysis target lean vehicle driving data with higher accuracy.

According to another viewpoint, the output data is generated as information processing analysis data used for further information processing.

Thereby, the analysis data obtained by the lean vehicle driving data analysis method using the analysis lean vehicle traveling data of the analysis target lean vehicle driven by the analysis target person can be used in a further information processing device.

Therefore, it is possible to acquire analysis data that can be used for further information processing while increasing the degree of freedom in designing the hardware resources of the device that analyzes lean vehicle driving data.

The lean vehicle travel data analyzer according to an embodiment of the present invention analyzes lean vehicle travel data of a lean vehicle that tilts to the right when turning right and tilts to the left when turning left. It is a data analyzer. This lean vehicle driving data analyzer includes more lean vehicle driving data of a lean vehicle with at least one of a passenger and an object than the lean vehicle traveling data of a lean vehicle without a passenger and an object. Lean vehicle generated based on the reference generation lean vehicle driving data including the classification related data for classifying at least one of the driver and the vehicle, and including the lean vehicle driving data of a plurality of lean vehicles having different classifications. A lean vehicle driving standard acquisition unit that acquires driving standard data, and data related to the loading state of at least one of the passenger and the object related to the state in which at least one of the passenger and the object is mounted, and the analysis target person and the analysis target The analysis lean vehicle data acquisition unit that acquires the analysis lean vehicle driving data including the classification-related data for classifying at least one of the lean vehicles, and the acquired analysis lean vehicle based on the acquired lean vehicle driving reference data. An analysis data acquisition unit that analyzes vehicle running data and acquires analysis data of at least one of an analysis target person and a vehicle classified using the classification-related data, and output data for output using the analysis data. Includes an output data generation unit that generates the output data and a data output unit that outputs the output data.

From another point of view, the lean vehicle driving data analyzer of the present invention preferably includes the following configurations. The reference generation lean vehicle traveling data includes reference generation lean vehicle driving operation input data related to operation input to the lean vehicle, reference generation lean vehicle behavior data related to the behavior of the lean vehicle, and the lean vehicle. The analysis lean vehicle driving data includes at least one of the reference generation lean vehicle position data related to the position, and the analysis lean vehicle driving data is the analysis lean vehicle driving operation input data related to the operation input to the analysis target lean vehicle. It includes at least one of the analysis target lean vehicle behavior data related to the behavior of the analysis target lean vehicle and the analysis target lean vehicle position data related to the position of the analysis target lean vehicle.

From another point of view, the lean vehicle driving data analyzer of the present invention preferably includes the following configurations. The reference generation lean vehicle running data includes reference generation lean vehicle running environment data related to the running environment in which the lean vehicle runs, and the analysis lean vehicle running data further runs the analysis target lean vehicle. Includes analytical lean vehicle driving environment data related to the driving environment.

From another point of view, the lean vehicle driving data analyzer of the present invention preferably includes the following configurations. The reference generation lean vehicle traveling data includes more data when the lean vehicle travels on a public road than data when the lean vehicle travels on a road other than a public road, and the analysis lean vehicle traveling data is the analysis target. It includes more data when the lean vehicle to be analyzed travels on the public road than the data when the lean vehicle travels on a road other than the public road.

From another point of view, the lean vehicle driving data analyzer of the present invention preferably includes the following configurations. The reference generation lean vehicle driving data includes data in a state where a plurality of judgment options of the driver are limited by vehicles around the lean vehicle, but a plurality of them are left, and the analysis lean vehicle driving data includes. It includes data in a state where a plurality of judgment options of the analysis target person are limited by vehicles around the analysis target lean vehicle, but a plurality of them are left.

From another point of view, the lean vehicle driving data analyzer of the present invention preferably includes the following configurations. The analysis data corresponding to at least one category of the analysis target person and the vehicle is generated as information processing analysis data used for further information processing.

The information processing method using the analysis data according to the embodiment of the present invention is an information processing method using the output data generated as the information processing analysis data by the above-mentioned lean vehicle traveling data analysis method. In this information processing method, the output data is acquired, the first data different from the output data is acquired, and the output data and the first data are used to obtain the output data and the second data different from the first data. Data is generated and the second data is output.

The information processing method using the analysis data may be any information processing method as long as it is an information processing method using the analysis data obtained by analyzing the lean vehicle driving data. For example, the first and second data relate to markets, goods, services, environments or customers used in business such as lean vehicle sharing, lean vehicle rental, lean vehicle leasing, lean vehicle insurance, etc. It may be data.

As a result, the output data output using the analysis data of at least one of the analysis target person and the analysis target lean vehicle obtained and classified by analyzing the analysis lean vehicle running data, and the output output data described above. Using the first data different from the above, the acquired output data and the second data different from the first data are generated and output. Therefore, it is possible to generate and output the second data with higher accuracy.

Therefore, it is possible to generate and output more accurate second data using the analysis data while increasing the degree of freedom in designing the hardware resources of the device that executes the information processing method using the analysis data.

The information processing device using the analysis data according to the embodiment of the present invention is the information processing device using the output data generated as the information processing analysis data by the lean vehicle traveling data analysis device described above. This information processing apparatus uses the output data acquisition unit for acquiring the output data, the first data acquisition unit for acquiring the first data different from the output data, the output data, and the first data. It includes a second data generation unit that generates output data and second data different from the first data, and a second data output unit that outputs the second data.

The technical terms used in the present specification are used for the purpose of defining only specific examples, and there is no intention of limiting the invention by the technical terms.

As used herein, "and / or" includes all combinations of one or more relatedly listed components.

As used herein, the use of "including, including," "comprising," or "having" and variations thereof are described as features, processes, elements, components, and / or. , Identifying the existence of their equivalents, but may include one or more of steps, actions, elements, components, and / or groups thereof.

In the present specification, "attached", "connected", "combined", and / or their equivalents are used in a broad sense and are "direct and indirect" attachments. Includes both connection and connection. Further, "connected" and "connected" are not limited to physical or mechanical connections or connections, but can include direct or indirect connections or connections.

Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

Terms defined in commonly used dictionaries should be construed to have meanings consistent with their meaning in the context of the relevant technology and disclosure, unless expressly defined herein. , Is not interpreted in an ideal or overly formal sense.

It is understood that a number of techniques and processes are disclosed in the description of the present invention. Each of these has its own interests and can be used with one or more of the other disclosed technologies, or in some cases all.

Therefore, for the sake of clarity, the description of the present invention refrains from unnecessarily repeating all possible combinations of individual steps. However, the specification and claims should be read with the understanding that all such combinations are within the scope of the present invention.

This specification describes an embodiment of a lean vehicle traveling data analysis method, a lean vehicle traveling data analyzer, an information processing method using analysis data, and an information processing apparatus using analysis data according to the present invention.

In the following description, a number of specific examples will be given to provide a complete understanding of the present invention. However, it will be apparent to those skilled in the art that the present invention can be practiced without these specific examples.

Therefore, the following disclosure should be considered as an example of the invention and is not intended to limit the invention to the particular embodiments set forth in the drawings or description below.

[Lean vehicle]
In the present specification, the lean vehicle is a vehicle that turns in an inclined posture. Specifically, a lean vehicle is a vehicle that inclines to the left when turning to the left and to the right when turning to the right in the left-right direction of the vehicle. The lean vehicle may be a single-seater vehicle or a vehicle that can accommodate a plurality of people. The lean vehicle includes not only a two-wheeled vehicle but also all vehicles that turn in an inclined posture, such as a three-wheeled vehicle or a four-wheeled vehicle.

[Analytical data]
In the present specification, the analysis data is data obtained by analyzing the driving operation of the driver. For example, it is data obtained by analyzing a driver's driving skill and / or driving skill according to a lean vehicle driving standard.

[Evaluation data]
In the present specification, the evaluation data is evaluation data related to the evaluation regarding the boarding by the passenger or the evaluation regarding the transportation by the requester of the transportation of the goods. The evaluation data is, for example, an evaluation of the driver and / or the vehicle when the customer gets on the vehicle, and the comprehensive evaluation felt by the customer is given, for example, on a five-point scale. The customer's evaluation of boarding is a subjective and qualitative evaluation of the customer when the customer boarded. Further, the evaluation data is, for example, an evaluation for the requested transportation by a customer who requested the transportation of goods, and a comprehensive evaluation felt by the customer is given, for example, on a five-point scale. The highest evaluation is 5, and the smaller the number, the lower the evaluation. Further, the evaluation data may include the degree of reliability, comfort, economic efficiency including quickness (short time to reach the destination), and the like. The evaluation axis in the evaluation data is not limited to one. The evaluation axis in the evaluation data may have a plurality of items.

[Vehicle driving data]
In the present specification, the vehicle traveling data is data related to the traveling of a lean vehicle. Specifically, the vehicle driving data includes lean vehicle driving operation input data related to driving operation input to a lean vehicle by a driver, lean vehicle behavior data related to lean vehicle behavior, and lean vehicle traveling. It includes lean vehicle position data regarding the position, lean vehicle driving environment data related to the driving environment in which the lean vehicle travels, and the like. Further, the vehicle traveling data may include processed data obtained by processing lean vehicle driving operation input data, lean vehicle behavior data, lean vehicle position data, lean vehicle traveling environment data, and the like. The vehicle traveling data may include processing data processed using other data such as lean vehicle driving operation input data, lean vehicle behavior data, lean vehicle position data, and lean vehicle traveling environment data.

[Category-related data]
In the present invention, the classification-related data includes data for classifying the individual driver, data for classifying the gender of the driver, data for classifying the age group of the driver, data for classifying the manufacturer of the vehicle, and classifying the vehicle type. Includes data, data that classifies vehicle performance (eg, drive source output, drive source type, suspension, etc.). This classification-related data is used to limit the range of use of lean vehicle driving standard data and analytical lean vehicle driving data in response to classifications such as driver attributes, manufacturers, and vehicle types when generating analysis data. Used.

[Lean vehicle driving operation input data]
In the present specification, the lean vehicle driving operation input data is data related to the driver's operation input performed when the driver drives and operates the lean vehicle. Specifically, it may include data related to accelerator operation, brake operation, steering, or change in the position of the center of gravity due to a change in the driver's posture. Specifically, the lean vehicle driving operation input data may include operations of various switches such as a horn switch, a blinker switch, and a lighting switch. Since the lean vehicle driving operation input data is data related to the driving operation input by the driver, the result of the driver's judgment is more reflected. In lean vehicles, there are many types of driver's driving operations and they are complicatedly related, so there is a tendency for many variations. Further, the lean vehicle driving operation input data may include processing data obtained by processing data acquired from a sensor or the like. The lean vehicle driving operation input data may include data acquired from a sensor or the like and processing data processed using other data.

[Lean vehicle behavior data]
In the present specification, the lean vehicle behavior data is data related to the behavior of the lean vehicle generated by the operation input of the driver when the driver operates the lean vehicle. Specifically, the data related to the behavior of the lean vehicle includes, for example, the acceleration, speed, and angle of the lean vehicle that change when the driver who is the analysis target operates the driving operation. That is, the data on the behavior of the lean vehicle shows the posture change including the steering of the lean vehicle and the change of the position of the center of gravity when the driver who is the analysis target operates the accelerator or the brake to accelerate or decelerate the lean vehicle X. It is the data showing the behavior of the lean vehicle that occurs when the above is performed.

Further, as described above, the lean vehicle behavior data relating to the behavior of the lean vehicle includes not only the data relating to the acceleration, speed, and angle of the lean vehicle, but also the switch operation performed on the lean vehicle by the driver who is the analysis target. May include movements that occur in lean vehicles. That is, the data relating to the behavior of the lean vehicle includes data related to the operation generated in the lean vehicle by operating various switches such as a horn switch, a blinker switch, and a lighting switch. The lean vehicle behavior data strongly reflects the result of the input of the driver's driving operation. Further, the lean vehicle behavior data may include processing data obtained by processing data acquired from a sensor or the like. The lean vehicle behavior data may include data acquired from a sensor or the like and processing data processed using other data.

[Lean vehicle running position data]
In the present specification, the traveling position data of the lean vehicle is the data related to the position of the lean vehicle. For example, it can be detected based on GPS and communication base station information of a communication mobile terminal. The traveling position data of the lean vehicle can be calculated by various positioning techniques, SLAM, and the like. The lean vehicle position data affects the driving operation of the driver and is reflected in the behavior of the vehicle. Therefore, the lean vehicle position data also tends to strongly reflect the driving skill and / or driving skill of the driver. Further, the lean vehicle position data may include processing data obtained by processing data acquired from a sensor or the like. The lean vehicle position data may include data acquired from a sensor or the like and processing data processed using other data.

[Lean vehicle driving environment data]
In the present specification, the lean vehicle driving environment data includes, for example, map data. For example, map data may be associated with information on road conditions, information on road traffic environments such as traffic lights and equipment, and regulatory information on road travel. It may also be associated with environmental data such as weather, temperature or humidity.

The driving environment in which a lean vehicle travels is considered to be an example of external stress that the driver receives. Lean vehicle driving environment data influences the driver's judgment and influences the driver's driving operation. Therefore, the driver's driving skill and / or driving skill is likely to appear in the lean vehicle driving environment data.

Lean vehicle driving environment data can be obtained from various means. It is not limited to a certain means. For example, it is an external environment recognition device mounted on a lean vehicle. More specifically, there are cameras, drivers, radars, and the like. Also, for example, a communication device. More specifically, it is an inter-vehicle communication device and a road-to-vehicle communication device. For example, it can be obtained via the Internet.

[Contains more B than A]
In the present specification, "containing more B than A" does not have to contain A at all. "Contains more B than A" may include a part of A. For example, reference generation lean vehicle running data that includes more lean vehicle running data of a lean vehicle with at least one of a passenger and an object than lean vehicle running data of a lean vehicle without a passenger and an object. Does not have to include any lean vehicle travel data of a lean vehicle with no passengers or objects on it. For example, reference generation lean vehicle running data that includes more lean vehicle running data of a lean vehicle with at least one of a passenger and an object than lean vehicle running data of a lean vehicle without a passenger and an object. May include a part of lean vehicle travel data of a lean vehicle in a state where passengers and objects are not mounted.

For example, the fact that the lean vehicle includes more travel data when the lean vehicle travels on a public road than the travel data when the lean vehicle travels on a non-public road includes all the travel data when the lean vehicle travels on a non-public road. It does not have to be. For example, the fact that the lean vehicle includes more travel data when the lean vehicle travels on a public road than the travel data when the lean vehicle travels on a non-public road is a part of the travel data when the lean vehicle travels on a non-public road. It may be included.

For example, when the analysis target lean vehicle travels on a non-public road, the analysis target lean vehicle includes more travel data when the analysis target lean vehicle travels on a public road than the travel data when the analysis target lean vehicle travels on a non-public road. It is not necessary to include the driving data of. The fact that the analysis target lean vehicle includes more travel data when the analysis target lean vehicle travels on a public road than the travel data when the analysis target lean vehicle travels on a non-public road means that the analysis target lean vehicle travels on a non-public road. May be partially included.

For example, it means that at least one of the passengers and the object is included in the lean vehicle traveling data of the lean vehicle in the state of not carrying the passenger and the object more than the lean vehicle traveling data of the lean vehicle in the state of carrying at least one of the passenger and the object. It is not necessary to include the lean vehicle running data of the lean vehicle in which one is mounted. To include more lean vehicle driving data of a lean vehicle without passengers and objects than lean vehicle driving data of a lean vehicle with at least one of passengers and objects included means that at least one of passengers and objects is included. It may include a part of the lean vehicle running data of the lean vehicle in the mounted state.

According to the lean vehicle running data analysis method according to the embodiment of the present invention, lean vehicle running data capable of outputting analysis data peculiar to the lean vehicle based on the running data of the lean vehicle while increasing the degree of freedom in designing hardware resources. An analysis method can be provided.

FIG. 1 is a diagram showing a schematic configuration of a lean vehicle traveling data analyzer according to the first embodiment of the present invention. FIG. 2 is a flowchart showing a lean vehicle traveling data analysis method according to the first embodiment of the present invention. FIG. 3 is a diagram showing a schematic configuration of a lean vehicle traveling data analysis processing system according to a second embodiment of the present invention. FIG. 4 is a flowchart showing an information processing method using the analysis data.

Hereinafter, each embodiment will be described with reference to the drawings. The dimensions of the constituent members in each drawing do not faithfully represent the actual dimensions of the constituent members and the dimensional ratio of each constituent member.

While analyzing the running data of the lean vehicle, the present inventors noticed that the running data of the lean vehicle and the running data of the non-lean vehicle are significantly different. A lean vehicle is a vehicle that tilts to the right when turning right and tilts to the left when turning left.

Lean vehicles are smaller in size than non-lean vehicles. That is, the lean vehicle is smaller in the front-rear direction and / or the left-right direction of the vehicle body than the non-lean vehicle. In addition, the lean vehicle has a smaller amount of steering rotation operation than the non-lean vehicle. The amount of rotational operation of the steering of a lean vehicle is less than 360 degrees. Furthermore, the operation of a lean vehicle is a rider-active vehicle that the driver can actively operate, unlike a non-lean vehicle. Therefore, the operation of a lean vehicle is different from the operation of a non-lean vehicle. The running data of a lean vehicle whose operation is different from that of a non-lean vehicle is significantly different from the running data of a non-lean vehicle.

When the present inventors examined the driving situation of the lean vehicle in more detail, they noticed that the lean vehicle had a much higher degree of freedom of driving by the driver's intention than the non-lean vehicle.

Therefore, when the driver is operating the lean vehicle, the number of judgments and the judgment options of the driver tend to be larger than when the driver is operating the non-lean vehicle.

In addition, the driver is more likely to be exposed to external stress when operating a lean vehicle than when operating a non-lean vehicle. Moreover, the external stress exerted on the driver operating the lean vehicle is very diverse.

Therefore, it was found that the driving data of the lean vehicle has more variations than the driving data of the non-lean vehicle due to the difference in the driver who operates the lean vehicle, the difference in the lean vehicle, the difference in the driving environment, and the like.

Also, lean vehicles are lighter than non-lean vehicles. For this reason, lean vehicles are more manoeuvrable and convenient than non-lean vehicles. Lean vehicles are used for a variety of purposes and tend to be used more frequently. Therefore, the lean vehicle is used in various scenes.

In examining various usage scenarios of the lean vehicle in detail, the present inventors consider that the weight of the lean vehicle is higher when the lean vehicle is equipped with at least one of a person and an object and when the lean vehicle is not equipped with the person and the object. It is very different, and the behavior of lean vehicles is also different. That is, a lean vehicle has a higher ratio of the weight of a person or object to the weight of the vehicle than a non-lean vehicle. Therefore, I noticed that the running data of the lean vehicle is different between the state in which at least one of the person and the object is mounted and the state in which the person and the object are not mounted.

Furthermore, the present inventors noticed the following points while analyzing the running data of a lean vehicle equipped with at least one of a person and an object.

By using the driving data of a lean vehicle carrying at least one of a passenger and an object, not only the skill of driving the lean vehicle but also, for example, at least one of the passenger and the object is loaded. Analytical data peculiar to lean vehicles, such as the skill (ability) of the driver who operates the lean vehicle in the state and the tendency of the behavior of the lean vehicle with at least one of the passenger and the object, which has been difficult to output until now. It turned out that it can be output.

Moreover, in order to analyze the driving data of the lean vehicle with at least one of the passenger and the object mounted, the data to be processed is limited as compared with the case of analyzing all the driving data without considering the state. be able to. It was found that this can reduce the load on the hardware resources of the system and increase the degree of freedom in designing the hardware resources.

From the above, the present inventors have created a lean vehicle driving data analysis method capable of outputting analysis data peculiar to a lean vehicle based on the driving data of a lean vehicle while increasing the degree of freedom in designing hardware resources.

Furthermore, the present inventors pay attention to the state in which at least one of the passenger and the object is mounted, and while analyzing the driving data of the lean vehicle in that state, the driver who operates the lean vehicle for business use. It was found that more favorable analytical data can be provided.

During the examination, the driver of the lean vehicle and the passenger have different feelings about the input received from the lean vehicle. Therefore, it has been found that when a lean vehicle is used as a business vehicle for carrying a customer, the driver who operates the lean vehicle is required to have a different skill than when riding the lean vehicle alone. I have noticed that it is possible to give analytical data such as preferable driving skills to a driver who operates a lean vehicle for such business use.

In addition, when a lean vehicle is used as a business vehicle for carrying a customer, evaluation data for the customer's boarding can be collected at that time.

The present inventors examined in detail the relationship between the driving data of the lean vehicle and the evaluation data of the customer, and found that the evaluation data of the customer is related to the comfort felt by the customer with respect to the driving data of the lean vehicle. Do you get it. From this, it is considered that if the data on the driver's skill and skill necessary for driving that the customer feels comfortable can be output, the driver can perform the driving operation desired by the customer with reference to the data.

Therefore, the present inventors use lean vehicle driving data in a state where at least one of the passenger and an object is mounted in order to obtain a driving operation of the driver that the passenger feels comfortable with. I came up with a method to analyze. By using the driving data of a lean vehicle carrying at least one of a passenger and an object, the types of data processed by the system can be reduced, and the hardware load of the system that analyzes the lean driving data can be reduced. .. In addition, since the hardware resources required by the system can be reduced, the degree of freedom in designing the hardware resources of the system that analyzes the lean driving data of the lean vehicle can be increased.

From the above, the present inventors have created a lean vehicle driving data analysis method capable of acquiring analysis data peculiar to a lean vehicle based on the driving data of a lean vehicle while increasing the degree of freedom in designing hardware resources.

<Embodiment 1>
(Lean vehicle driving data analyzer 1)
FIG. 1 shows a schematic configuration of a lean vehicle traveling data analyzer 1 according to an embodiment of the present invention. The lean vehicle driving data analyzer 1 analyzes lean vehicle driving data when the analysis target drives a lean vehicle X that tilts to the right when turning right and tilts to the left when turning left. It is a device.

The lean vehicle running data in this embodiment is data related to the running of the lean vehicle. The lean vehicle driving data is used when obtaining analysis data including data related to the driving skill of the driver among the data related to the driving of the lean vehicle obtained when the driver operates the lean vehicle. Means the data used.

Specifically, the lean vehicle driving data includes lean vehicle driving operation input data related to driving operation input to the lean vehicle by the driver, _Hlk4750909 lean vehicle behavior data related to the behavior of the lean vehicle, and driving of the lean vehicle. Includes lean vehicle position data related to position, _Hlk4750921 lean vehicle driving environment data _Hlk4750921 related to the driving environment in which the lean vehicle travels, and the like. The lean vehicle traveling data may include data other than the lean vehicle driving operation input data, the lean vehicle behavior data, the lean vehicle position data, and the lean vehicle traveling environment data. Further, the lean vehicle driving data may include only one or a plurality of data among the lean vehicle driving operation input data, the lean vehicle behavior data, the lean vehicle position data, and the lean vehicle driving environment data. ..

For example, in the case of a lean vehicle X in which the lean vehicle is a lean vehicle for analysis, the lean vehicle driving data is the lean vehicle driving data for analysis, and the lean vehicle driving operation input data is the lean vehicle driving operation input data for analysis. Yes, the lean vehicle behavior data is lean vehicle behavior data for analysis, the lean vehicle position data is lean vehicle position data for analysis, and the lean vehicle running environment data is lean vehicle running environment data for analysis.

The lean vehicle driving data may include processed data obtained by processing lean vehicle driving operation input data, lean vehicle behavior data, lean vehicle position data, lean vehicle driving environment data, and the like. Further, the vehicle traveling data may include processing data processed by using lean vehicle driving operation input data, lean vehicle behavior data, lean vehicle position data, lean vehicle traveling environment data, and other data and other data. ..

The lean vehicle driving operation input data is data related to the driver's operation input performed when the driver drives and operates the lean vehicle. Specifically, the lean vehicle driving operation input data may include data related to accelerator operation, braking operation, steering, or change of the center of gravity position due to a change in the driver's posture. Specifically, the lean vehicle driving operation input data may include operations of various switches such as a horn switch, a blinker switch, and a lighting switch. Since the lean vehicle driving operation input data is data related to the driving operation input by the driver, it more reflects the driving skill of the driver and the like. In lean vehicles, there are many types of driving operations by the driver and they are complicatedly related, so that the driving skill of the driver tends to be strongly reflected. Further, the lean vehicle driving operation input data may include processing data obtained by processing data acquired from a sensor or the like. The lean vehicle driving operation input data may include processing data processed using data acquired from a sensor or the like and other data.

The lean vehicle behavior data is data related to the behavior of the lean vehicle generated by the driver's operation input when the lean vehicle is driven and operated by the driver. Specifically, the lean vehicle behavior data includes, for example, the acceleration, speed, and angle of the lean vehicle that change when the driver operates the vehicle. That is, the lean vehicle behavior data is generated when the driver accelerates or decelerates the lean vehicle by operating the accelerator or the brake, or changes the posture including steering of the lean vehicle or changing the position of the center of gravity. This is data showing the behavior of a lean vehicle.

As described above, the lean vehicle behavior data may include not only data on the acceleration, speed, and angle of the lean vehicle, but also movements that occur in the lean vehicle due to a switch operation or the like performed by the driver on the lean vehicle. That is, the lean vehicle behavior data includes data related to the operation generated in the lean vehicle by operating various switches such as a horn switch, a blinker switch, and a lighting switch. _Hlk4751612 The lean vehicle behavior data strongly reflects the driving skill of the driver. Further, the lean vehicle behavior data may include processing data obtained by processing data acquired from a sensor or the like. The lean vehicle behavior data may include processing data processed using data acquired from a sensor or the like and other data.

The lean vehicle position data is data related to the running position of the lean vehicle. For example, the lean vehicle position data can be detected based on GPS_Hlk4751473, information of a communication base station of a communication mobile terminal, and the like. The lean vehicle position data can be calculated by various positioning techniques, SLAM, and the like. The lean vehicle position data strongly reflects the driving skill of the driver. Further, the lean vehicle position data may include processed data obtained by processing data acquired from a sensor or the like. The lean vehicle position data may include processing data processed using data acquired from a sensor or the like and other data. _Hlk4751473

The lean vehicle driving environment data includes, for example, map data. This map data may be associated with, for example, information on road conditions, information on road traffic environments such as traffic lights and equipment, and regulatory information on road travel. In addition, the map data may be associated with environmental data such as weather, temperature or humidity. The lean vehicle driving environment data can be used for analysis of lean vehicle driving data together with the lean vehicle driving operation input data, the lean vehicle behavior data, and the lean vehicle position data.

The information on the road conditions includes information on roads (regions) in a congested environment such as frequent traffic jams and many vehicles parked on the street. By combining this information with the time zone, the accuracy of the information is further improved. In addition, the information on the road condition includes information on a road that is easily flooded when there is a squall.

The lean vehicle driving environment data is considered to be an example of external stress received by the driver. The lean vehicle driving environment data affects the driving operation of the driver. Therefore, by using the lean vehicle driving environment data, the driving skill of the driver and the like are more likely to appear in the driving data of the lean vehicle. Further, since the purpose and frequency of use of the lean vehicle are affected by using the lean vehicle running environment data, the running data of the lean vehicle is more likely to include data peculiar to the lean vehicle.

The lean vehicle driving data analysis device 1 includes a lean vehicle driving reference data acquisition unit 10, a lean vehicle driving data acquisition unit 20 for analysis, an analysis data acquisition unit 30, an output data generation unit 40, and a data output unit 50. A data storage unit 60 is provided. _Hlk4511320 In the present embodiment, the lean vehicle travel data analyzer 1 is, for example, a mobile terminal owned by the person to be analyzed. The lean vehicle travel data analysis device 1 may be an arithmetic processing unit that acquires data via communication and performs arithmetic processing.

The lean vehicle driving standard data acquisition unit 10 acquires the lean vehicle driving standard data used when analyzing the lean vehicle driving data for analysis. This lean vehicle travel reference data is generated based on the reference generation lean vehicle travel data.

In the present embodiment, the reference generation lean vehicle running data is based on the lean vehicle running data of the lean vehicle in which the passenger and the object are not mounted, and the lean vehicle in which at least one of the passenger and the object is mounted. Contains a lot of vehicle driving data. In addition, the reference generation lean vehicle driving data includes classification-related data for classifying at least one of the driver and the lean vehicle. Further, the reference generation lean vehicle travel data includes lean vehicle travel data of a plurality of lean vehicles having different categories.

The reference generation lean vehicle driving data is related to the reference lean vehicle driving operation input data related to the driving operation input to the lean vehicle by different drivers, and the traveling position of the lean vehicle driven and traveled by different drivers. Reference generation lean vehicle position data, reference generation lean vehicle behavior data related to the behavior of lean vehicles driven and driven by different drivers, and reference lean vehicle driving environment related to the driving environment in which the lean vehicle travels. Includes data etc. The reference generation lean vehicle driving data is other than the reference generation lean vehicle driving operation input data, the reference generation lean vehicle behavior data, the reference generation lean vehicle position data, and the reference generation lean vehicle driving environment data. Data may be included. Further, the reference generation lean vehicle driving data includes the reference generation lean vehicle driving operation input data, the reference generation lean vehicle behavior data, the reference generation lean vehicle position data, and the reference generation lean vehicle driving environment data. Of these, only one or more data may be included.

When the lean vehicle is a lean vehicle driven by a different driver, the above-mentioned lean vehicle driving data is the reference generation lean vehicle driving data, and the above-mentioned lean vehicle driving operation input data is the reference generation lean vehicle. It is driving operation input data, the above-mentioned lean vehicle behavior data is lean vehicle behavior data for reference generation, and the above-mentioned lean vehicle position data is reference generation lean vehicle position data, and the above-mentioned lean vehicle running. The environmental data is lean vehicle driving environment data for reference generation.

The reference generation lean vehicle running data includes reference generation classification related data.

The reference classification-related data is used to generate analysis data corresponding to classifications such as driver attributes (gender, age, etc.), manufacturer, and vehicle type when analyzing lean vehicle driving data for analysis. .. By using this division-related data, it is possible to limit the data to be processed when generating the analysis data, and it is possible to reduce the load on the hardware resources.

The lean vehicle travel reference data is generated based on the reference generation lean vehicle travel data including the lean vehicle travel data of a plurality of lean vehicles having different categories. The lean vehicle travel reference data is used when analyzing the analysis lean vehicle travel data.

The lean vehicle driving standard data is used, for example, as a standard for classifying the lean vehicle driving skill of the driver who is the analysis target. The lean vehicle reference travel data is generated based on, for example, the reference generation lean vehicle travel data, and is stored in the data storage unit 60.

The lean vehicle running reference data may be data generated in advance, or may be data generated by the lean vehicle running reference data acquisition unit 10.

The analysis lean vehicle driving data acquisition unit 20 of the present embodiment acquires the analysis lean vehicle driving data including the driving data when the driver who is the analysis target drives the lean vehicle X.

Specifically, the analysis lean vehicle driving data acquisition unit 20 uses the lean vehicle driving data of the lean vehicle X when the analysis target person drives the lean vehicle X with at least one of a passenger and an object mounted on the vehicle. The included data, that is, the lean vehicle driving operation input data to be analyzed, the lean vehicle behavior data for analysis, the lean vehicle position data for analysis, the lean vehicle driving environment data for analysis, and the like are acquired.

When the person to be analyzed drives the lean vehicle X, there is also driving in a state where no passengers or objects are loaded. Lean vehicle running data in a state where the passenger and an object are not mounted is also acquired, but in the present embodiment, lean vehicle running including a large amount of lean vehicle running data in a state where at least one of the passenger and the object is mounted. The data is acquired as lean vehicle driving data for analysis.

The analysis lean vehicle driving data acquisition unit 20 may acquire the analysis lean vehicle driving operation input data by, for example, acquiring the driving operation of the analysis target person with respect to the lean vehicle X as an operation signal. Specifically, the analysis lean vehicle driving data acquisition unit 20 changes the position of the center of gravity due to data related to the driver's operation input in the lean vehicle X, that is, accelerator operation, brake operation, steering, or change in the driver's posture. Data related to the above, data related to the operation of various switches such as a horn switch, a blinker switch, and a lighting switch may be acquired. These data are transmitted from the lean vehicle X.

The analysis lean vehicle driving data acquisition unit 20 obtains data including the acceleration, speed, and angle of the lean vehicle X, which changes when the driver who is the analysis target drives and operates the lean vehicle X, for example. It may be acquired as behavior data. The analysis lean vehicle travel data acquisition unit 20 acquires the analysis lean vehicle behavior data by, for example, a gyro sensor. The lean vehicle behavior data for analysis is a posture change including steering of the lean vehicle X or a change in the position of the center of gravity when the driver who is the analysis target operates the accelerator or the brake to accelerate or decelerate the lean vehicle X. This is data showing the behavior of the lean vehicle X that occurs when the above is performed.

The analysis lean vehicle travel data acquisition unit 20 may acquire the analysis lean vehicle position data related to the travel position of the lean vehicle X, for example, based on the information of GPS and the communication base station of the communication mobile terminal. The lean vehicle position data for analysis can be calculated by various positioning techniques, SLAM, and the like.

The analysis lean vehicle traveling data acquisition unit 20 may acquire the analysis lean vehicle traveling environment data from, for example, map data. This map data may be associated with, for example, information on road conditions, information on road traffic environments such as traffic lights and equipment, and regulatory information on road travel. In addition, the map data may be associated with environmental data such as weather, temperature or humidity. The map data may include information in which road information and information on the road traffic environment (information incidental to the road such as a signal) are associated with rule information related to road travel.

The analysis lean vehicle driving data acquisition unit 20 may acquire the analysis lean vehicle driving environment data by, for example, an external environment recognition device mounted on the lean vehicle X. More specifically, the analysis lean vehicle traveling data acquisition unit 20 may acquire the analysis lean vehicle traveling environment data from a camera, radar, or the like. Further, the analysis lean vehicle traveling data acquisition unit 20 may acquire the analysis lean vehicle traveling environment data by, for example, a communication device. More specifically, the analysis lean vehicle traveling data acquisition unit 20 may acquire the analysis lean vehicle traveling environment data by the vehicle-to-vehicle communication device and the road-to-vehicle communication device. The analysis lean vehicle traveling data acquisition unit 20 may acquire the analysis lean vehicle traveling environment data via the Internet, for example. As described above, the lean vehicle traveling environment data for analysis can be obtained from various means. The means for acquiring the analysis lean vehicle driving environment data is not limited to a certain means.

In the present embodiment, the analysis lean vehicle driving data acquisition unit 20 also acquires information (for example, classification-related data) related to the analysis target person and the lean vehicle X, for example. The lean vehicle travel data acquisition unit 20 for analysis may acquire the data from the data storage unit 60 in which the input data is stored, or acquire the data directly input to the lean vehicle travel data analyzer 1. You may. The analysis lean vehicle travel data acquisition unit 20 may acquire information from the lean vehicle X.

The analysis lean vehicle travel data acquisition unit 20 may receive and acquire detection signals from a gyro sensor, GPS, a detection unit that detects operation signals of various switches, etc. provided in the lean vehicle X.

The analysis classification-related data is data for classifying at least one of the driver and the lean vehicle, which are the analysis target persons. The classification-related data includes data for classifying the individual driver, data for classifying the gender of the driver, data for classifying the age group of the driver, data for classifying the manufacturer of the vehicle, data for classifying the vehicle type, and vehicle performance. Includes data that classifies (for example, drive source type and output, suspension performance, etc.).

For example, when the lean vehicle is a lean vehicle X to be analyzed, the reference generation lean vehicle driving operation input data related to the operation input to the lean vehicle, the lean vehicle behavior data related to the behavior of the lean vehicle, and the lean vehicle behavior data The lean vehicle position data related to the position of the lean vehicle is the lean vehicle traveling data for analysis, and the classification-related data is the classification-related data for analysis.

When analyzing the analysis lean vehicle driving data and generating the analysis data, the analysis classification-related data includes the attributes (gender, age, etc.) of the analysis target person and the manufacturer from the lean vehicle driving reference data described later. And when limiting to the data corresponding to the classification such as vehicle type. By using this analysis classification-related data, it is possible to limit the data to be processed when analyzing the analysis lean vehicle driving data and generating the analysis data, and it is possible to reduce the load on the hardware resources.

The analysis data acquisition unit 30 analyzes the analysis lean vehicle travel data obtained by the analysis lean vehicle travel data acquisition unit 20 based on the lean vehicle reference travel data obtained by the lean vehicle travel reference data acquisition unit 10. Acquire the analysis data obtained by this. This analysis data is analysis data of at least one of the analysis target person and the lean vehicle X classified using the analysis classification-related data.

The analysis data includes, for example, data related to the driving skill of the classified lean vehicle to be analyzed. The driving skill means a driving skill of a driver who drives a lean vehicle. The driving skill includes not only the skill of driving a lean vehicle but also the driving skill of driving a lean vehicle. In addition, the analysis data includes, for example, data related to the traveling of the classified lean vehicle X. This data is, for example, data related to the driving skill and / or driving skill of the analysis target person who is the driver.

The output data generation unit 40 generates output data for output from the analysis data. For example, the output data generation unit 40 generates output data using a plurality of analysis data stored in the data storage unit 60. As a result, it is possible to generate highly accurate output data. The output data generation unit 40 may generate the analysis data as it is as output data.

The data output unit 50 outputs the output data generated by the output data generation unit 40 from the lean vehicle traveling data analyzer 1.

As described above, the weight of the lean vehicle differs greatly between the case where at least one of the person and the object is mounted and the case where the person and the object are not mounted, and the behavior of the lean vehicle also differs. Then, by using the driving data of the lean vehicle in which at least one of the passenger and the object is mounted, not only the driving skill (skill) for driving and operating the lean vehicle but also, for example, at least one of the passenger and the object is used. It is possible to output analysis data peculiar to a lean vehicle, such as the driving skill (ability) of a driver who operates a lean vehicle equipped with the vehicle and the tendency of the behavior of the lean vehicle equipped with at least one of a passenger and an object.

Further, the analysis data is analysis data of at least one of the driver and the vehicle classified using the classification-related data. In this way, by classifying at least one of the driver and the vehicle according to the classification-related data, analysis data corresponding to the classification can be obtained. For example, analysis data corresponding to a vehicle type, driver's gender, and analysis data corresponding to an age group can be easily obtained.

(Lean vehicle driving data analysis method)
Next, a lean vehicle running data analysis method performed by the lean vehicle running data analysis device 1 having the above-described configuration will be described with reference to FIG. FIG. 2 is a flow showing a lean vehicle driving data analysis method.

First, the lean vehicle driving standard data acquisition unit 10 acquires the lean vehicle driving standard data generated based on the standard generation lean vehicle driving data (step SA1). The lean vehicle travel reference data is generated based on the reference generation lean vehicle travel data and is stored in advance in the data storage unit 60.

The reference generation lean vehicle running data includes more lean vehicle running data of a lean vehicle with at least one of a passenger and an object than the lean vehicle running data of a lean vehicle without a passenger and an object. Including. In addition, the reference generation lean vehicle driving data includes classification-related data for classifying at least one of the driver and the lean vehicle. Further, the reference generation lean vehicle travel data includes lean vehicle travel data of a plurality of lean vehicles having different categories.

Next, the analysis lean vehicle driving data acquisition unit 20 acquires the analysis lean vehicle driving data which is the driving data of the lean vehicle X traveling by the driver who is the analysis target (step SA2).

The analysis lean vehicle running data classifies at least one of the analysis target person and the lean vehicle X from the analysis lean vehicle running data in a state where at least one of a passenger and an object is mounted when traveling on the lean vehicle X. Includes analysis category-related data for

Further, the analysis lean vehicle driving data includes the analysis lean vehicle driving operation input data related to the driving operation input to the lean vehicle by the analysis target person and the analysis lean vehicle related to the traveling position of the traveling lean vehicle X. It includes position data, analytical lean vehicle behavior data related to the behavior of the traveling lean vehicle X, and analytical lean vehicle traveling environment data related to the traveling environment of the traveling lean vehicle X.

The analysis lean vehicle travel data acquisition unit 20 includes, for example, an information acquisition unit that acquires information about the analysis target person and the lean vehicle X, and a detection sensor including a gyro sensor, GPS, and the like. The analysis lean vehicle travel data acquisition unit 20 acquires, for example, the analysis lean vehicle position data and the analysis lean vehicle behavior data from the output of the detection sensor. The analysis lean vehicle travel data acquisition unit 20 acquires, for example, analysis classification-related data from the data acquired by the information acquisition unit. The analytical travel degree-of-freedom-related data is acquired using, for example, the analytical lean vehicle position data obtained from the output of the detection sensor.

After that, the analysis data acquisition unit 30 acquires the analysis data of at least one of the classified analysis target person and the lean vehicle X by analyzing the analysis lean vehicle travel data based on the lean vehicle travel reference data. (Step SA3).

The analysis data includes, for example, data related to the driving skill of a lean vehicle of a driver who is an analysis target traveling on a public road.

The output data generation unit 40 generates output data for output from the analysis data (step SA4). After that, the data output unit 50 outputs the output data (step SA5). End this flow (end).

With the above configuration, by analyzing the lean vehicle running data of the lean vehicle X traveling by the driver who is the analysis target person, the analysis data of at least one of the classified analysis target person and the lean vehicle X is acquired. be able to.

Therefore, as in the above configuration, the lean vehicle running data of the lean vehicle with at least one of the passenger and the object is included more than the lean vehicle running data of the lean vehicle without the passenger and the object. By using the lean vehicle driving data, it is possible to output analysis data peculiar to the lean vehicle, which has been difficult to output until now, such as the skill of driving and operating the lean vehicle in consideration of various usage scenes. For example, by analyzing the driving data of a lean vehicle carrying at least one of a passenger and an object, the driving skill with at least one of the passenger and the object can be more accurately and more accurately obtained. Can be analyzed in detail.

Moreover, in order to analyze the lean vehicle running data of the lean vehicle with at least one of the passenger and the object mounted, the data to be processed is compared with the case of analyzing all the running data without considering the state. Can be limited. As a result, the load on the hardware resource of the lean vehicle traveling data analyzer 1 can be reduced, and the degree of freedom in designing the hardware resource can be increased.

As a result, the types of data processed by the lean vehicle traveling data analyzer 1 can be reduced, and the hardware load of the device can be reduced. Further, since the hardware resources required by the device can be reduced, the degree of freedom in designing the hardware resources of the device can be increased.

Therefore, it is possible to acquire analysis data peculiar to a lean vehicle based on the driving data of the lean vehicle while increasing the degree of freedom in designing hardware resources.

This embodiment is an example of a lean vehicle driving data analysis method for analyzing lean vehicle driving data. The lean vehicle driving data analysis method of the present embodiment includes the following steps.

In the lean vehicle running data analysis method of the present embodiment, the lean vehicle running reference data generated based on the lean vehicle running data for reference generation is acquired. This reference generation lean vehicle driving data includes more lean vehicle driving data of a lean vehicle with at least one of a passenger and an object than lean vehicle driving data of a lean vehicle without a passenger and an object. Includes and includes classification-related data for classifying at least one of the driver and the lean vehicle, and includes lean vehicle travel data of a plurality of lean vehicles having different classifications.

The reference generation lean vehicle driving data means lean vehicle driving data by a plurality of drivers.

For example, the reference generation lean vehicle running data may be acquired by various sensors provided in the lean vehicle. Further, the lean vehicle travel data for generating the reference may be acquired by various sensors provided so as to be easily detachable from the lean vehicle. The lean vehicle travel data for reference generation may be acquired by various sensors temporarily provided in the lean vehicle for data collection.

In the lean vehicle driving data analysis method, the lean vehicle driving data for analysis related to the driving data of the lean vehicle X obtained when the analysis target person drives and operates the lean vehicle X which is the analysis target lean vehicle is acquired.

The analysis lean vehicle running data means the lean vehicle running data of the lean vehicle X driven and operated by the analysis target person. The analysis target lean vehicle means a lean vehicle X driven and operated by the analysis target person, which is a target for acquiring analysis lean vehicle travel data.

The analysis target person may be included in the plurality of drivers. The person to be analyzed may not be included in the plurality of drivers. The lean vehicle to be analyzed may be included in the lean vehicle that acquires the reference generation lean vehicle travel data. The lean vehicle to be analyzed may not be included in the lean vehicle that acquires the reference generation lean vehicle travel data. The analysis target lean vehicle data may be included in the reference generation lean vehicle travel data. The lean vehicle travel data for analysis may not be included in the lean vehicle travel data for reference generation.

For example, the analysis lean vehicle running data may be acquired by various sensors provided in the analysis target lean vehicle. Further, the analysis lean vehicle travel data may be acquired by various sensors provided so as to be easily detachable from the analysis target lean vehicle. The analysis lean vehicle traveling data may be acquired by various sensors temporarily provided in the analysis target lean vehicle for data collection.

It should be noted that the various sensors for collecting the lean vehicle running data for analysis may have lower detection accuracy than the various sensors for collecting the lean vehicle running data for reference generation.

The various sensors for collecting the lean vehicle running data for analysis may be the same as the various sensors for collecting the lean vehicle running data for reference generation.

The type of data included in the analysis lean vehicle travel data may be less than the type of data included in the reference generation lean vehicle travel data. The type of data included in the analysis lean vehicle travel data may be the same as the type of data included in the reference generation lean vehicle travel data.

The lean vehicle travel data analyzer 1 analyzes the acquired lean vehicle travel data for analysis based on the acquired lean vehicle travel reference data, and thereby classifies the analysis target using the analysis classification-related data. The analysis data of at least one of the person and the lean vehicle to be analyzed is acquired.

The lean vehicle driving data analyzer 1 uses the analysis data to generate output data for output.

The lean vehicle driving data analyzer 1 outputs the output data.

In the lean vehicle travel data analysis method of the present embodiment, the reference generation lean vehicle travel data is related to the reference generation lean vehicle driving operation input data related to the operation input to the lean vehicle and the behavior of the lean vehicle. The lean vehicle behavior data for reference generation and the lean vehicle position data for reference generation related to the position of the lean vehicle are included, and the lean vehicle travel data for analysis is an operation input to the lean vehicle to be analyzed. At least one of the analysis lean vehicle driving operation input data related to, the analysis target lean vehicle behavior data related to the analysis target lean vehicle behavior, and the analysis target lean vehicle position data related to the analysis target lean vehicle position. Including one.

The reference generation lean vehicle driving data means lean vehicle driving data by a plurality of drivers. Further, the lean vehicle is a vehicle that tilts to the right when turning right and tilts to the left when turning left.

That is, the analysis lean vehicle position data regarding the traveling position of the analysis target lean vehicle is, for example, the position with another lean vehicle when the analysis target lean vehicle driven by the analysis target person is traveling with a predetermined degree of freedom. Used to identify relationships. Further, the analysis lean vehicle behavior data related to the behavior of the analysis target lean vehicle is, for example, driven by the analysis target person when the analysis target lean vehicle driven by the analysis target person is traveling with a predetermined degree of freedom. It is used to detect the driving skill of the analysis target person from the analysis lean vehicle behavior of the analysis target lean vehicle.

From another point of view, the lean vehicle driving data analysis method of the present invention preferably includes the following configurations. The reference generation lean vehicle running data includes reference generation lean vehicle running environment data related to the running environment in which the lean vehicle runs, and the analysis lean vehicle running data further runs the analysis target lean vehicle. Includes analytical lean vehicle driving environment data related to the driving environment.

Lean vehicle driving environment data includes, for example, map data. This map data may be associated with, for example, information on road conditions, information on road traffic environments such as traffic lights and equipment, and regulatory information on road travel. The lean vehicle driving environment data can be used for analyzing the lean vehicle driving data together with the lean vehicle behavior data and the lean vehicle position data.

With this configuration, it is possible to more accurately analyze the analysis lean vehicle driving data obtained when the analysis target lean vehicle driven and operated by the analysis target person travels on a public road by using the lean vehicle driving reference data. Further, by using the lean vehicle traveling data in which the data type is specified, the type of data processed by the apparatus for analyzing the lean vehicle traveling data can be reduced, and the hardware load of the apparatus can be further reduced. Further, since the hardware resources required by the device can be reduced, the degree of freedom in designing the hardware resources of the device can be further increased.

When a driver traveling on a public road is operating a lean vehicle, the driver has more judgments, has more choices of judgment, and is easily exposed to external stress. The data contains more variations. Therefore, by using the lean vehicle driving data including a large amount of data when traveling on a public road, it becomes easier to analyze the driving skill of the driver from the driving data of the lean vehicle. By using such lean vehicle driving data, it is possible to acquire more accurate analysis data while ensuring the degree of freedom in designing the hardware resources of the information processing device.

By using lean vehicle driving data in a state where the driver's judgment options are limited but multiple are left, it becomes easier to analyze the driver's driving skill and / or driving skill from the driving data of the lean vehicle. .. By using such lean vehicle driving data, it is possible to acquire more accurate analysis data while ensuring the degree of freedom in designing the hardware resources of the information processing device.

From another point of view, the lean vehicle driving data analysis method of the present invention preferably includes the following configurations. The lean vehicle running reference data includes evaluation data related to evaluation related to boarding by a passenger of the lean vehicle for reference generation or evaluation related to transportation by a requester of transportation of goods, and lean vehicle running data for reference generation. Is generated based on.

With this configuration, the lean vehicle driving reference data is generated by adding the evaluation data related to the evaluation related to boarding by the passenger or the evaluation related to the transportation by the requester of the transportation of the object. From this, the lean vehicle traveling standard data can reflect the evaluation related to the riding by the passenger or the evaluation related to the transportation by the requester of the transportation of the object.

As a result, it is possible to output analysis data peculiar to the lean vehicle such as a tendency of preferable driving behavior to the lean vehicle with at least one of the passenger and the object mounted.

When the driver drives the lean vehicle X, there are driving with at least one of the passenger and the object mounted and driving without the passenger and the object. Therefore, the lean vehicle driving data can obtain the lean vehicle driving data based on the driving with at least one of the passenger and the object and the lean vehicle traveling state related to the lean vehicle without the passenger and the object. it can.

That is, the non-mounted state is based on the data including more lean vehicle running data of the lean vehicle in the state where the passenger and the object are not mounted than the lean vehicle running data of the lean vehicle in which at least one of the passenger and the object is mounted. Acquired as lean vehicle driving standard data. Then, the lean vehicle running data of the lean vehicle in the state where the passenger and the object are not mounted is acquired as the lean vehicle running data in the non-mounted state.

With this configuration, it is possible to acquire non-mounted state analysis data related to a lean vehicle in a state where passengers and objects are not mounted. It is possible to see the degree of change by comparing the non-mounted state data with the analysis data obtained by analyzing the lean vehicle running data including a large amount of lean vehicle running data in the state where a passenger or an object is mounted. Further, more detailed analysis data can be obtained by using the analysis data in the state where the passenger and the object are mounted and the non-mounted state data in the state where the passenger and the object are not mounted.

From another point of view, the lean vehicle driving data analysis method of the present invention preferably includes the following configurations. The output data is generated as information processing analysis data used for further information processing.

From another point of view, the lean vehicle driving data analyzer of the present invention preferably includes the following configurations. The reference generation lean vehicle traveling data includes reference generation lean vehicle traveling environment data related to the traveling environment in which the lean vehicle travels, and the analysis lean vehicle traveling data includes traveling in which the analysis target lean vehicle travels. Includes lean vehicle driving environment data for analysis related to the environment.

From another point of view, the lean vehicle driving data analyzer of the present invention preferably includes the following configurations. The reference generation lean vehicle travel data includes data in a state where a plurality of driver's judgment options are limited by vehicles around the lean vehicle, but a plurality of data are left, and the analysis lean vehicle travel data includes the data. Includes data in which the analysis target's judgment options are limited by the vehicles around the analysis target lean vehicle, but a plurality of data are left.

From another point of view, the lean vehicle driving data analyzer of the present invention preferably includes the following configurations. The lean vehicle running reference data includes evaluation data related to evaluation related to boarding by a passenger of the lean vehicle for reference generation or evaluation related to transportation by a requester of transportation of goods, and lean vehicle running data for reference generation. Is generated based on.

From another point of view, the lean vehicle travel data analyzer of the present invention preferably includes the following configurations. It contains more lean vehicle driving data of a lean vehicle without passengers and objects than lean vehicle driving data of a lean vehicle with at least one of passengers and objects, and classifies at least one of the driver and the vehicle. Acquires the non-mounted state lean vehicle driving standard data generated based on the lean vehicle driving data for generating the non-mounted state standard including the lean vehicle driving data of a plurality of lean vehicles having different classifications. And
Lean vehicle driving data for non-mounted condition analysis, including lean vehicle driving data related to passengers and non-loaded conditions, and classification-related data for classifying at least one of the analyzed person and the lean vehicle to be analyzed. By analyzing the acquired lean vehicle running data for non-mounted state analysis based on the acquired lean vehicle running standard data in the non-mounted state, the analysis target person classified using the classification-related data. And at least one of the non-mounted state analysis data of the vehicle to be analyzed is acquired, output data for output is generated using the acquired non-mounted state analysis data, and the generated output data is output.

From another point of view, the lean vehicle driving data analyzer of the present invention preferably includes the following configurations. At least one of the acquired analysis data and the non-mounted state analysis data is stored, and at least one of the stored plurality of analysis data and the non-mounted state analysis data is used to at least one of the analysis target person and the analysis target vehicle. Generate one output data.

From another point of view, the lean vehicle driving data analyzer of the present invention preferably includes the following configurations. The output data is generated as information processing analysis data used for further information processing.

<Embodiment 2>
FIG. 3 shows an example of the lean vehicle driving data analysis system 100 including the lean vehicle traveling data analysis device 1 of the first embodiment. Hereinafter, the same components as those of the first embodiment are designated by the same reference numerals and the description thereof will be omitted, and only the configurations different from the first embodiment will be described.

The lean vehicle driving data analysis system 100 includes a lean vehicle driving data analysis device 1 and a lean vehicle driving standard data generation device 101 that generates lean vehicle driving reference data.

The lean vehicle travel reference data generation device 101 is, for example, an information processing arithmetic unit capable of communicating with the lean vehicle travel data analysis device 1 and having a processor. When the lean vehicle travel data analysis device 1 is an information processing arithmetic unit having a processor, the lean vehicle travel reference data generation device 101 may be the same information processing arithmetic unit as the lean vehicle travel data analysis device 1.

The lean vehicle running standard data generation device 101 acquires lean vehicle running data and classification-related data, and generates lean vehicle running reference data based on the reference generation lean vehicle running data including these data.

Specifically, the lean vehicle travel reference data generation device 101 has a data storage unit 111 and a lean vehicle travel reference data generation unit 112. Although not particularly shown, the lean vehicle travel reference data generation device 101 has an acquisition unit for acquiring lean vehicle travel data and classification-related data. Further, although not particularly shown, the lean vehicle travel reference data generation device 101 has an output unit that outputs the generated lean vehicle travel reference data.

The data storage unit 111 stores lean vehicle running data for reference generation, lean vehicle running reference data, evaluation data, and analysis data. Specifically, the data storage unit 111 stores lean vehicle travel data for reference generation, including lean vehicle travel data and classification-related data obtained when a plurality of drivers drive and operate the lean vehicle Y, respectively. .. Further, the data storage unit 111 stores the lean vehicle travel reference data generated by the lean vehicle travel reference data generation unit 112, which will be described later. The data storage unit 111 stores evaluation data of a plurality of customers for the boarding at that time. Further, the data storage unit 111 stores the analysis data analyzed by the lean vehicle traveling data analyzer 1 described later.

The lean vehicle running data includes, for example, lean vehicle driving operation input data of lean vehicle Y, lean vehicle behavior data of lean vehicle Y, lean vehicle position data of lean vehicle Y, lean vehicle running environment data of lean vehicle Y, and the like. ..

The lean vehicle travel reference data generation unit 112 generates lean vehicle travel reference data based on the reference generation lean vehicle travel data stored in the data storage unit 111. The lean vehicle travel reference data generated by the lean vehicle travel reference data generation unit 112 is stored in the data storage unit 111.

The lean vehicle travel reference data stored in the data storage unit 111 analyzes the lean vehicle travel data (lean vehicle travel data for analysis) of the lean vehicle X (lean vehicle for analysis) by the lean vehicle travel data analyzer 1. Used when. Since the method of analyzing the lean vehicle running data in the lean vehicle running data analyzer 1 is the same as that of the first embodiment, detailed description thereof will be omitted.

That is, the lean vehicle travel data analyzer 1 analyzes the lean vehicle travel data of the lean vehicle X based on the lean vehicle travel reference data, and thereby analyzes at least one of the classified analysis target person and the lean vehicle X. Is acquired, and the output data generated from the analysis data is output. Since the configuration of the lean vehicle travel data analyzer 1 is the same as that of the first embodiment, detailed description of the lean vehicle travel data analyzer 1 will be omitted.

The output data output from the lean vehicle traveling data analyzer 1 may be input to, for example, the information processing device 102. In this case, the output data is generated in the lean vehicle traveling data analysis device 1 as information processing data used for information processing in the information processing device 102.

The information processing device 102 provides data related to insurance, markets, goods, services, environment or customers used in business such as sharing of lean vehicles, rental of lean vehicles, leasing of lean vehicles, and vehicle insurance of lean vehicles. It may be an apparatus that performs the processing of. When the lean vehicle travel data analysis device 1 is an information processing calculation device, the information processing device 102 may be the same device as the lean vehicle travel data analysis device 1. The information processing device 102 may be the same information processing calculation device as the lean vehicle travel reference data generation device 101.

The information processing device 102 includes, for example, an output data acquisition unit 121, a first data acquisition unit 122, a second data generation unit 123, a second data output unit 124, and a data storage unit 125.

The output data acquisition unit 121 acquires the output data output from the lean vehicle travel data analyzer 1.

The first data acquisition unit 122 acquires the first data different from the output data. This first data is data to be processed by the information processing apparatus 102. The second data is data related to insurance, markets, products, services, environment or customers used in business such as sharing of lean vehicles, rental of lean vehicles, leasing of lean vehicles, vehicle insurance of lean vehicles, etc. Is. The first data is stored in the data storage unit 125.

The second data generation unit 123 uses the output data and the first data to generate second data different from the output data and the first data. Similar to the first data, this second data also includes insurance, markets, products, services, etc. used in business such as sharing of lean vehicles, rental of lean vehicles, leasing of lean vehicles, and vehicle insurance of lean vehicles. Data related to the environment or customers.

The second data output unit 124 outputs the second data generated by the second data generation unit 123.

(Information processing method using analytical data)
Next, an information processing method for performing information processing using output data by the information processing apparatus 102 having the above configuration will be described with reference to the flowchart shown in FIG. FIG. 4 is a flowchart showing the operation of information processing by the information processing device 102.

As shown in FIG. 4, first, the output data acquisition unit 121 of the information processing device 102 acquires the output data output from the lean vehicle travel data analysis device 1 (step SB1).

Next, the first data acquisition unit 122 of the information processing device 102 acquires the first data stored in the data storage unit 125 (step SB2). This first data is different from the output data.

After that, the second data generation unit 123 of the information processing apparatus 102 generates the second data by using the acquired output data and the acquired first data (step SB3). This second data is different from the output data and the first data.

Subsequently, the second data output unit 224 of the information processing device 102 outputs the generated second data (step SB4).

The output data output from the lean vehicle driving data analyzer 1 in this way can be used as an information processing device in fields such as lean vehicle sharing, lean vehicle rental, lean vehicle leasing, and lean vehicle vehicle insurance. It can be used when processing credit risk or credit score. That is, the analysis data obtained by analyzing the lean vehicle driving data is used for arithmetic processing of the information processing device in fields such as lean vehicle sharing, lean vehicle rental, lean vehicle leasing, and lean vehicle insurance. can do.

Specifically, in fields such as lean vehicle sharing, lean vehicle rental, lean vehicle leasing, and lean vehicle vehicle insurance, the information processing device acquires the output output data and the acquired output. Using the data, credit risk or credit score can be output by arithmetic processing.

In fields such as lean vehicle sharing, lean vehicle rental, lean vehicle leasing, and lean vehicle vehicle insurance, information processing methods include a process of acquiring output data output from the lean vehicle driving data analyzer 1 and a process of acquiring output data. It may include a step of outputting credit risk data related to credit risk or credit score data related to credit score using the acquired output data.

In fields such as lean vehicle sharing, lean vehicle rental, lean vehicle leasing, and lean vehicle vehicle insurance, the information processing device acquires output data output from the lean vehicle driving data analyzer 1. A unit and a credit risk output unit that outputs credit risk data related to credit risk or a credit score output unit that outputs credit score data related to credit score may be included by using the acquired output data.

In the above-mentioned information processing method and information processing device, when the output credit risk is low or the credit score is high, for example, the analysis target person can easily rent a lean vehicle, or the analysis target person rents a lean vehicle. In some cases, the fee may be given preferential treatment, or the person to be analyzed may receive preferential treatment of insurance premiums.

The lean vehicle running data analysis method in each of the above-described embodiments is an example of the lean vehicle running data analysis method for analyzing the lean vehicle running data to be analyzed.

The lean vehicle driving data analysis method of the present invention preferably includes the following configurations. The output data is generated as information processing data used for further information processing.

For example, the further information processing is related to business insurance, markets, goods, services, environment or customers such as lean vehicle sharing, lean vehicle rental, lean vehicle leasing, lean vehicle vehicle insurance, etc. It may be the processing of the data to be processed.

From another viewpoint, it is preferable that the output data output by the lean vehicle driving data analysis method of the present invention is used in the information processing method using the following analysis data. In this information processing method, the output data is acquired. In the information processing method, first data different from the output data is acquired. In the information processing method, the output data and the acquired first data are used to generate second data different from the output data and the acquired first data. In the information processing method, the generated second data is output.

The information processing method may be any information processing method as long as it uses the analysis data obtained by analyzing the lean vehicle traveling data. For example, the first data and the second data are insurance, markets, goods, services, environments or environments used in business such as lean vehicle sharing, lean vehicle rental, lean vehicle leasing, lean vehicle vehicle insurance, etc. It may be data related to the customer.

According to the configuration of the present embodiment, the analysis data available in the information processing device 102 can be acquired by the lean vehicle driving data analysis device 1 and the lean vehicle driving data analysis method. Further, as described in the first embodiment, by analyzing the lean vehicle travel data and obtaining the analysis data, the types of data processed by the system can be reduced, and the load on the hardware of the lean vehicle travel data analyzer 1 can be reduced. Can be reduced.

Therefore, it is possible to acquire analysis data that can be used in the information processing device while increasing the degree of freedom in designing hardware resources.

(Other embodiments)
Although the embodiments of the present invention have been described above, the above-described embodiments are merely examples for carrying out the present invention. Therefore, the embodiment is not limited to the above-described embodiment, and the above-described embodiment can be appropriately modified and implemented within a range that does not deviate from the gist thereof.

The present invention can be used for a lean vehicle driving data analysis method, a lean vehicle driving data analysis processing device, an information processing method using analysis data, and an information processing device using analysis data.

1: Lean vehicle driving data analyzer 10: Lean vehicle driving reference data acquisition unit 20: Lean vehicle driving data acquisition unit 30 for analysis: Analysis data acquisition unit 40: Output data generation unit 50: Data output unit 60, 111: Data storage Part 100: Lean vehicle driving data analysis system 101: Lean vehicle driving reference data generation device 102: Information processing device

Claims

It is a lean vehicle running data analysis method that analyzes lean vehicle running data of a lean vehicle that leans to the right when turning right and leans to the left when turning left.
It contains more lean vehicle driving data of a lean vehicle with at least one of the passenger and the object than the lean vehicle traveling data of the lean vehicle without at least one of the passenger and the object, and also includes the driver and the vehicle. Acquires lean vehicle driving reference data generated based on reference-generating lean vehicle driving data including classification-related data for classifying at least one of the above and including lean vehicle driving data of a plurality of lean vehicles having different classifications. ,
Lean vehicle driving data for analysis, including lean vehicle driving data with at least one of a passenger and an object, and analysis classification-related data for classifying at least one of the analysis target person and the analysis target lean vehicle. To get
By analyzing the acquired lean vehicle driving data for analysis based on the acquired lean vehicle driving reference data, at least one of the analysis target person and the analysis target vehicle classified using the analysis classification-related data. Get the analysis data,
Output data for output is generated using the acquired analysis data.
Output the generated output data,
Lean vehicle driving data analysis method.
In the lean vehicle analysis method according to claim 1,
The reference generation lean vehicle running data includes reference generation lean vehicle driving operation input data related to operation input to the lean vehicle, reference generation lean vehicle behavior data related to the behavior of the lean vehicle, and the lean vehicle. Contains at least one of the lean vehicle position data for reference generation related to the position,
The analysis lean vehicle traveling data includes analysis lean vehicle driving operation input data related to operation input to the analysis target lean vehicle, analysis lean vehicle behavior data related to the behavior of the analysis target lean vehicle, and the analysis target. Includes at least one of the analytical lean vehicle position data related to the lean vehicle position,
Lean vehicle driving data analysis method.
In the lean vehicle driving data analysis method according to claim 1 or 2.
The reference generation lean vehicle running data includes reference generation lean vehicle running environment data related to the running environment in which the lean vehicle runs.
The analysis lean vehicle traveling data includes analysis lean vehicle traveling environment data related to the traveling environment in which the analysis target lean vehicle travels.
Lean vehicle driving data analysis method.
In the lean vehicle driving data analysis method according to any one of claims 1 to 3.
The reference generation lean vehicle traveling data includes more traveling data when the lean vehicle travels on a public road than data when the lean vehicle travels on a road other than a public road.
The analysis lean vehicle traveling data includes more traveling data when the analysis target lean vehicle travels on a public road than travel data when the analysis target lean vehicle travels on a road other than a public road.
Lean vehicle driving data analysis method.
In the lean vehicle driving data analysis method according to any one of claims 1 to 4.
The reference generation lean vehicle driving data includes data in a state where a plurality of judgment options of the driver are limited by vehicles around the lean vehicle, but a plurality thereof are left.
The analysis lean vehicle traveling data includes data in a state where a plurality of analysis target lean vehicle travel data are left, although the analysis target person's judgment options are limited by the vehicles around the analysis target lean vehicle.
Lean vehicle driving data analysis method.
In the lean vehicle driving data analysis method according to any one of claims 1 to 5.
The lean vehicle running reference data includes evaluation data related to riding by a passenger of the lean vehicle for reference generation or evaluation data related to transportation by a requester of transportation of goods, and lean vehicle running data for reference generation. Generated based on
Lean vehicle driving data analysis method.
In the lean vehicle driving data analysis method according to any one of claims 1 to 6.
It contains more lean vehicle driving data of a lean vehicle without passengers and objects than lean vehicle driving data of a lean vehicle with at least one of passengers and objects, and classifies at least one of the driver and the vehicle. Acquires the non-mounted state lean vehicle driving standard data generated based on the lean vehicle driving data for generating the non-mounted state standard including the lean vehicle driving data of a plurality of lean vehicles having different classifications. And
Lean vehicle driving data for non-mounted condition analysis, including lean vehicle driving data related to passengers and non-mounted conditions, and classification-related data for classifying at least one of the analysis target person and the analysis target lean vehicle. To get
By analyzing the acquired lean vehicle running data for non-mounted state analysis based on the acquired lean vehicle running standard data, the analysis target person and the analysis target vehicle classified using the classification-related data. Obtain at least one of the non-installed state analysis data,
Output data for output is generated using the acquired non-mounted state analysis data.
Output the generated output data,
Lean vehicle driving data analysis method.
In the lean vehicle driving data analysis method according to any one of claims 1 to 6.
The acquired analysis data is stored and
Using the plurality of stored analysis data, output data of at least one of the analysis target person and the analysis target vehicle is generated.
Lean vehicle driving data analysis method.
In the lean vehicle driving data analysis method according to any one of claims 1 to 8.
The output data is a lean vehicle traveling data analysis method generated as information processing analysis data used for further information processing.
It is a lean vehicle driving data analyzer that analyzes lean vehicle driving data of a lean vehicle that tilts to the right when turning right and tilts to the left when turning left.
It contains more lean vehicle driving data of a lean vehicle with at least one of a passenger and an object than lean vehicle driving data of a lean vehicle without a passenger and an object, and at least one of a driver and a vehicle. Lean vehicle driving to acquire lean vehicle driving reference data generated based on the reference generation lean vehicle driving data including the classification related data for classifying and including the lean vehicle driving data of a plurality of lean vehicles having different classifications. Standard acquisition department and
Classification-related data that includes at least one of the passengers and objects related to the state in which at least one of the passengers and objects is mounted, and for classifying at least one of the analysis target person and the analysis target lean vehicle. Lean vehicle data acquisition unit for analysis that acquires lean vehicle driving data for analysis, including
Based on the acquired lean vehicle driving reference data, the acquired lean vehicle driving data for analysis is analyzed, and at least one of the analysis target person and the vehicle classified using the classification-related data is acquired. Analysis data acquisition department and
An output data generator that generates output data for output using the analysis data,
A lean vehicle driving data analysis method device including a data output unit that outputs the output data.
In the lean vehicle driving data analyzer according to claim 10.
The reference generation lean vehicle running data includes reference generation lean vehicle driving operation input data related to operation input to the lean vehicle, reference generation lean vehicle behavior data related to the behavior of the lean vehicle, and the lean vehicle. Contains at least one of the lean vehicle position data for reference generation related to the position,
The analysis lean vehicle traveling data includes analysis lean vehicle driving operation input data related to operation input to the analysis target lean vehicle, analysis target lean vehicle behavior data related to the analysis target lean vehicle behavior, and the analysis target. Includes at least one of the analytical lean vehicle position data related to the lean vehicle position,
Lean vehicle driving data analyzer.
In the lean vehicle driving data analyzer according to claim 10 or 11.
The reference generation lean vehicle running data includes reference generation lean vehicle running environment data related to the running environment in which the lean vehicle runs.
The analysis lean vehicle traveling data includes analysis lean vehicle traveling environment data related to the traveling environment in which the analysis target lean vehicle travels.
Lean vehicle driving data analyzer.
In the lean vehicle traveling data analyzer according to any one of claims 10 to 12.
The reference generation lean vehicle traveling data further includes more data when the lean vehicle travels on a public road than data when the lean vehicle travels on a road other than a public road.
The analysis lean vehicle traveling data further includes more data when the analysis target lean vehicle travels on a public road than data when the analysis target lean vehicle travels on a road other than a public road.
Lean vehicle driving data analyzer.
In the lean vehicle traveling data analyzer according to any one of claims 10 to 13.
The reference generation lean vehicle driving data includes data in a state where a plurality of driver's judgment options are limited by vehicles around the lean vehicle, but a plurality of data are left.
The analysis lean vehicle traveling data includes data in a state where a plurality of analysis target lean vehicle travel data are left, although the analysis target person's judgment options are limited by the vehicles around the analysis target lean vehicle.
Lean vehicle driving data analyzer.
In the lean vehicle traveling data analyzer according to any one of claims 10 to 14.
The analysis data corresponding to at least one category of the analysis target person and the vehicle is generated as information processing analysis data used for further information processing.
Lean vehicle driving data analyzer.
In the lean vehicle traveling data analyzer according to any one of claims 10 to 15.
The lean vehicle travel reference data includes evaluation data related to the evaluation related to riding by a passenger of the lean vehicle travel data for reference generation or evaluation related to transportation by a requester of transportation of goods, and the reference generation lean vehicle. Generated based on driving data,
Lean vehicle driving data analyzer.
An information processing method using the analysis data generated as the information processing analysis data in the lean vehicle traveling data analysis method of claim 9.
Acquire the output data and
Using the output data, first data different from the analysis data is acquired, and
Using the output data and the first data, a second data different from the output data and the first data is generated.
Output the second data,
Information processing method using analytical data.
An information processing device that uses the analysis data generated as the information processing analysis data by the lean vehicle traveling data analyzer according to claim 15.
With the analysis data acquisition unit that acquires the output data,
A first data acquisition unit that acquires first data different from the data using the output data,
A second data generation unit that uses the output data and the first data to generate second data different from the output data and the first data.
It has an output unit that outputs the second data.
Information processing device that uses analytical data.