WO2020204100A1

WO2020204100A1 - 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: WO2020204100A1
Application number: PCT/JP2020/015091
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: 2020-04-01
Publication date: 2020-10-08
Also published as: TW202037514A; TWI742596B; JP7280944B2; WO2020202451A1; JPWO2020204100A1

Abstract

Provided is a leaning vehicle traveling data analysis method capable of outputting analysis data which is unique to a leaning vehicle and which is based on traveling data of the leaning vehicle while increasing a degree of freedom in designing hardware resources. This leaning vehicle traveling data analysis method comprises: a leaning vehicle traveling reference data acquisition step for acquiring leaning vehicle traveling reference data; an analysis-use leaning vehicle traveling data acquisition step for acquiring analysis-use leaning vehicle traveling data; and an analysis data acquisition step for acquiring analysis data. The leaning vehicle traveling reference data is generated on the basis of reference-generation-use leaning vehicle traveling data which includes traveling data of leaning vehicles traveling on a public road in a middle density range more than travel data of leaning vehicles traveling on the public road in low and high density ranges. The analysis-use leaning vehicle traveling data includes analysis-use traveling-density-related data which relates to the density of a leaning vehicle to be analyzed in the case where a subject person to be analyzed drives the leaning vehicle to be analyzed and travels on the public road.

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 travel data analysis method for analyzing lean vehicle travel data of a lean vehicle, a lean vehicle travel data analyzer, an information processing method using the analysis data, and an information processing apparatus using the analysis data.

A device for determining the driving skill of a rider is known. As a device for determining a rider'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. Therefore, an analysis peculiar to a lean vehicle is required in consideration of various usage scenarios.

When trying to obtain data on various situations such as the driving environment in order to analyze various usage scenes of a lean vehicle, the amount of data processed by the information processing device becomes enormous, and the hardware of the device The load becomes high. 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.

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. Further, since the amount of steering rotation of the lean vehicle is smaller than 360 degrees, the amount of steering rotation of the lean vehicle is smaller than that of the non-lean vehicle. Further, a lean vehicle is a rider-active vehicle that can be actively operated by the driver, unlike a non-lean vehicle. Therefore, driving a lean vehicle is different from driving a non-lean vehicle. The driving data of a lean vehicle whose driving is different from that of a non-lean vehicle is significantly different from the driving data of a non-lean vehicle, for example, a four-wheeled 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.

For this reason, when the driver is driving a lean vehicle, the number of judgments and judgment options of the driver tend to be larger than when the driver is driving a non-lean vehicle.

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

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 lean vehicles in detail, the present inventors have an inverse relationship between the degree of freedom of driving by the driver of the lean vehicle and the density of the lean vehicle traveling on a public road. I noticed that I have. That is, when the density of lean vehicles traveling on public roads is low, the degree of freedom of driving by the driver of the lean vehicles is high, and when the density of lean vehicles traveling on public roads is high (for example, in Indonesia). Refer to the driving situation of the lean vehicle, etc., https: //www.youtube.com/watch? V = 0A1jYWojQXk, https: //www.youtube.com/watch?v=NqgDE-XqDVc) The degree of freedom of driving is low.

Since the four-wheeled vehicle is larger than the lean vehicle, the degree of freedom of driving by the driver of the four-wheeled vehicle is limited. Therefore, even if the density of the four-wheeled vehicle traveling on the public road changes, the degree of freedom of driving by the driver of the four-wheeled vehicle does not change or changes only slightly. That is, a four-wheeled vehicle traveling on a public road has a degree of freedom in the front-rear direction within the same lane, but the width of the vehicle relative to the width of the lane is larger than that of a lean vehicle, so that the vehicle travels in the left-right direction. The degree of freedom is low. Therefore, even if the density of the four-wheeled vehicle changes, the degree of freedom of traveling does not change so much. Therefore, in the case of a four-wheeled vehicle, the correlation between the degree of freedom of driving by the driver of the four-wheeled vehicle and the density of the four-wheeled vehicle traveling on a public road is extremely weak. The density of four-wheeled vehicles means the number of four-wheeled vehicles located within a predetermined length of a lane on a public road.

As described above, in the case of a lean vehicle, the present inventors have an inverse correlation between the degree of freedom of driving by the driver of the lean vehicle and the density of the lean vehicle traveling on a public road. When considering the degree of freedom of driving by the driver of a lean vehicle, I noticed that the density of lean vehicles traveling on public roads should be considered.

Furthermore, the present inventors analyzed the driving skill of the driver by using the driving data of the lean vehicle traveling on the public road in consideration of the degree of freedom of driving by the driver's intention, and made the following points. noticed.

By using lean vehicle driving data that considers the degree of freedom of driving by the driver's will, it is peculiar to lean vehicles that it was difficult to output until now, such as the skill to drive a lean vehicle considering various usage scenes. It turned out that the analysis data of can be output. For example, in the analysis using the lean vehicle driving data in a state where the degree of freedom is high, the skill of the driver to drive the lean vehicle can be analyzed more accurately and in more detail. Further, for example, in an analysis using lean vehicle driving data in a state where the degree of freedom is limited to some extent, the driver can predict the driving environment such as the movement of surrounding vehicles more accurately and in more detail. Can be analyzed.

Moreover, since the lean vehicle driving data that considers the degree of freedom of driving by the driver's intention is analyzed, the data to be processed is limited as compared with the case of analyzing all the driving data without considering the state. can do. It was found that this reduces the load on the hardware resources of the system and increases 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 are preferable to a driver who drives a lean vehicle for business use in analyzing driving data of a lean vehicle traveling on a public road in consideration of the degree of freedom of driving by the driver's intention. We found that we could provide analytical data. Until now, it has been difficult to output this, such as the skill of driving a lean vehicle in consideration of various usage scenarios, by using lean vehicle driving data that considers the degree of freedom of driving by the driver's intention. This is due to the ability to output analysis data specific to lean vehicles.

For example, a system for determining vehicle insurance premiums for lean vehicles can be considered. This system includes a mobile terminal equipped with a sensor that automatically collects driving data of a lean vehicle, a server that receives lean vehicle driving data collected by the mobile terminal, a database that stores the collected lean vehicle driving data, and a database. , A remote processing computer equipped with an evaluation engine that determines the insurance premium of the lean vehicle based on the collected lean vehicle running operation data can be considered.

The evaluation engine can determine insurance risk and insurance premium based on the driver's driving score obtained from the collected lean vehicle driving data.

In this way, the present inventors can simplify the analysis of the driver's risk by using the driver's driving skill in the risk evaluation of the driver who drives the lean vehicle, so that the system by data processing can be used. We have found that the load on hardware resources can be reduced and the degree of freedom in designing hardware resources can be increased.

In particular, from the viewpoint of risk evaluation, the present inventors analyze the driving skill of the driver in consideration of the degree of freedom of driving by the driver's intention, and to evaluate the risk in business using a lean vehicle. We found that it is possible to output data with high applicability. Furthermore, the present inventors can further simplify the data to be analyzed by considering the degree of freedom of driving by the driver of the lean vehicle, so that the load on the hardware resources of the system due to the data processing is increased. We have found that it can be further reduced and the degree of freedom in designing hardware resources can be increased.

Note that the analysis of driving skill includes not only the skill of driving a lean vehicle but also the skill related to prediction when driving a lean vehicle (predicted skill). This driving skill is analytical data obtained by analyzing lean vehicle driving data of a lean vehicle obtained when the analysis target person drives a lean vehicle on a public road as a driver based on lean vehicle driving reference data described later. include.

The lean vehicle running data analysis method according to an embodiment of the present invention acquires lean vehicle running reference data which is running reference data of a lean vehicle that leans to the right when turning right and leans to the left when turning left. Lean vehicle driving standard data acquisition process, analysis lean vehicle driving data acquisition process that acquires analysis lean vehicle driving data that is analysis target lean vehicle that is the analysis target lean vehicle, and the acquired lean vehicle By analyzing the acquired lean vehicle driving data for analysis based on the driving reference data, the analysis data acquisition to acquire the analysis data of at least one of the analysis target person who is the driver to be analyzed and the analysis target lean vehicle. It has a step, an output data generation step of generating output data for output using the analysis data, and an output step of outputting the output data. The lean vehicle driving reference data is the lowest density among the four density ranges when the density range between the minimum density and the maximum density of the lean vehicle traveling on a public road is divided into four density ranges. Based on the running data of lean vehicles traveling on public roads in the low density range and the highest density range, the running of lean vehicles traveling on public roads in the medium density range, which is the remaining two density ranges out of the above four density ranges. It is generated based on the lean vehicle running data for reference generation that contains a lot of data. The analysis lean vehicle travel data includes analysis travel density-related data related to the density of the lean vehicle when the analysis target person drives and travels on a public road with the analysis target lean vehicle. The lean vehicle running data analysis method analyzes the lean vehicle running data of the lean vehicle to be analyzed.

Lean vehicles have a higher degree of freedom in driving at the will of the driver than vehicles that do not lean. Therefore, the driver makes many decisions from many options when driving a lean vehicle. In addition, the driver of a lean vehicle is vulnerable to external stress. Further, the running of a lean vehicle is more influenced by the driving of the driver than the running of a non-lean vehicle.

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

As mentioned above, since the usage scenes in which the lean vehicle is used are various, the driving data of the lean vehicle differs depending on the degree of freedom of driving by the driver's intention.

Also, in the case of lean vehicles, the degree of freedom of driving by the driver's will differs depending on the density of lean vehicles traveling on public roads. Specifically, the degree of freedom of driving by the driver of the lean vehicle has an inverse correlation with the density of the lean vehicle traveling on the public road. For example, when the density of lean vehicles traveling on public roads is low, the degree of freedom of driving by the driver of the lean vehicle is high, and when the density of lean vehicles traveling on public roads is high, lean vehicles are lean. The degree of freedom of driving by the intention of the driver of the vehicle is low. Therefore, when considering the degree of freedom of driving by the driver of the lean vehicle, the density of the lean vehicle traveling on the public road may be considered.

Under the above-mentioned situation, as in the above configuration, by using the lean vehicle driving data considering the degree of freedom of driving by the driver's intention, a lean vehicle considering various usage scenes can be obtained. It is possible to output analysis data peculiar to lean vehicles, such as driving skill, which was difficult to output until now. For example, in the analysis using the lean vehicle driving data in a state where the degree of freedom is high, the skill of the driver to drive the lean vehicle can be analyzed more accurately and in more detail. Further, for example, in an analysis using lean vehicle driving data in a state where the degree of freedom is limited to some extent, the driver can predict the driving environment such as the movement of surrounding vehicles more accurately and in more detail. Can be analyzed.

Moreover, since the lean vehicle driving data that considers the degree of freedom of driving by the driver's intention is analyzed, the data to be processed is limited as compared with the case of analyzing all the driving data without considering the state. can do. As a result, the load on the hardware resources of the system can be reduced, and the degree of freedom in designing the hardware resources can be increased.

As a result, the types of data processed by the device that analyzes the lean vehicle running data 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 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 driving data includes classification-related data for classifying at least one of the driver and the lean vehicle. The analysis lean vehicle traveling data includes analysis classification-related data for classifying at least one of the analysis target person and the analysis target lean vehicle. In the analysis data acquisition step, the analysis classification-related data is analyzed by analyzing the analysis lean vehicle travel data based on the lean vehicle travel reference data generated based on the reference generation lean vehicle travel data. The analysis data of at least one of the analysis target person and the analysis target lean vehicle classified by using the above is acquired.

This makes it possible to process at least one of the driver and the lean vehicle in the device that analyzes the lean vehicle driving data. Therefore, the load on the hardware 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.

From another point of view, the lean vehicle driving data analysis method of the present invention preferably includes the following configurations. The analysis data is an analysis result of synchronization of the lean vehicle driving data for analysis with respect to the lean vehicle driving reference data including data having a density similar to that of the analysis driving density-related data among the lean vehicle driving reference data. Obtained using.

As a result, for example, when the driver who is the analysis target is driving the analysis target lean vehicle in a state of being densely packed with the other lean vehicle, the traveling data of the analysis target lean vehicle and the traveling of the other lean vehicle By evaluating the synchronization with the data, it is possible to obtain the analysis data peculiar to the lean vehicle in at least one of the analysis target person and the analysis target lean vehicle.

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. A simple lean vehicle driving data analysis method can be realized.

From another point of view, the lean vehicle driving data analysis method of the present invention preferably includes the following configurations. The analysis data includes data related to the evaluation result of the driving prediction skill when the analysis target person travels on a public road with the analysis target lean vehicle.

As a result, analysis data including data related to the evaluation result of the driving prediction skill of the analysis target person can be obtained. When driving a lean vehicle, driving prediction skill is more important than when driving a non-lean vehicle. By including the data related to the evaluation result of the driving prediction skill in the analysis data, the analysis data peculiar to the lean vehicle can be obtained.

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 the reference generation lean vehicle driving input data related to the driving input to the lean vehicle by the driver, and the reference generation lean vehicle related to the traveling position of the lean vehicle traveling on a public road. It includes at least one of the position data and the reference generation lean vehicle behavior data related to the behavior of the lean vehicle. The analysis lean vehicle driving data includes analysis lean vehicle driving input data related to driving input to the analysis target lean vehicle by the analysis target person, and analysis related to the traveling position of the analysis target lean vehicle traveling on a public road. It includes at least one of the lean vehicle position data for analysis and the lean vehicle behavior data for analysis related to the behavior of the lean vehicle to be analyzed.

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, the analysis lean vehicle position data regarding the traveling position of the analysis target lean vehicle is, for example, the positional relationship with other lean vehicles when the analysis target lean vehicle driven by the analysis target person is traveling at a predetermined density. It is used to identify. Further, the analysis lean vehicle behavior data related to the behavior of the analysis target lean vehicle is, for example, an analysis driven by the analysis target person when the analysis target lean vehicle driven by the analysis target person is traveling at a predetermined density. For analysis of the target lean vehicle It is used to detect the driving skill of the analysis target person from the behavior of the lean vehicle.

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-generating lean vehicle traveling data further includes reference-generating lean vehicle traveling environment data related to the traveling environment in which the lean vehicle travels. The analysis lean vehicle traveling data further includes analysis lean vehicle traveling environment data related to the traveling environment in which the analysis target lean vehicle travels.

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 traveling environment data can be used for analysis of lean vehicle traveling 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 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.

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 traveling 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. 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 in a state where the driver's judgment options are limited but multiple are left is peculiar to a lean vehicle as compared with lean vehicle driving data in a state where the driver's judgment options are not left. Contains the data of. Therefore, it is possible to obtain analysis data peculiar to the lean vehicle by using the lean vehicle driving data in a state where the driver's judgment options are limited but a plurality of them are left. 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.

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 data in a state where at least one of a passenger and an object is mounted. The analysis lean vehicle traveling data includes data in a state where at least one of a passenger and an object is mounted.

A lean vehicle equipped with at least one of a passenger and an object exhibits behavior peculiar to a lean vehicle compared to a state in which neither a passenger nor an object is mounted. Therefore, it is possible to more accurately analyze the lean vehicle driving data of the analysis target person who is the driver by using the lean vehicle driving data including the data in the state where at least one of the passenger and the object is mounted. 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.

From another point of view, the lean vehicle driving data analysis method of the present invention preferably includes the following configurations. The lean vehicle traveling data analysis method stores the acquired analysis data. The output data is generated using the plurality of 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.

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 increasing the degree of design freedom of 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 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 acquires lean vehicle travel reference data, which is travel reference data of a lean vehicle that tilts to the right when turning right and tilts to the left when turning left. Lean vehicle driving standard data acquisition unit, analysis lean vehicle driving data acquisition unit that acquires analysis lean vehicle driving data that is analysis target lean vehicle that is the analysis target lean vehicle, and the acquired lean vehicle By analyzing the acquired lean vehicle driving data for analysis based on the driving reference data, the analysis data acquisition to acquire the analysis data of at least one of the analysis target person who is the driver to be analyzed and the analysis target lean vehicle. A unit, an output data generation unit that generates output data for output using the analysis data, and a data output unit that outputs the output data are provided. The lean vehicle driving reference data is the lowest density among the four density ranges when the density range between the minimum density and the maximum density of the lean vehicle traveling on a public road is divided into four density ranges. Based on the running data of lean vehicles traveling on public roads in the low density range and the highest density range, the running of lean vehicles traveling on public roads in the medium density range, which is the remaining two density ranges out of the above four density ranges. It is generated based on the lean vehicle running data for reference generation that contains a lot of data. The analysis lean vehicle travel data includes analysis travel density-related data related to the density of the lean vehicle when the analysis target person drives and travels on a public road with the analysis target lean vehicle. The lean vehicle travel data analyzer analyzes the lean vehicle travel data of the lean vehicle to be analyzed.

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 classification-related data for classifying at least one of the driver and the lean vehicle. The analysis lean vehicle traveling data includes analysis classification-related data for classifying at least one of the analysis target person and the analysis target lean vehicle. The analysis data acquisition unit analyzes the analysis lean vehicle travel data based on the lean vehicle travel reference data generated based on the reference generation lean vehicle travel data, thereby performing the analysis classification-related data. The analysis data of at least one of the analysis target person and the analysis target lean vehicle classified by using the above is acquired.

From another point of view, the lean vehicle driving data analyzer of the present invention preferably includes the following configurations. The analysis data is an analysis result of synchronization of the lean vehicle driving data for analysis with respect to the lean vehicle driving reference data including data having a density similar to that of the analysis driving density-related data among the lean vehicle driving reference data. Obtained using.

From another point of view, the lean vehicle driving data analyzer of the present invention preferably includes the following configurations. The analysis data includes data related to the evaluation result of the driving prediction skill when the analysis target person travels on a public road with 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 driving data includes the reference generation lean vehicle driving input data related to the driving input to the lean vehicle by the driver, and the reference generation lean vehicle related to the traveling position of the lean vehicle traveling on a public road. It includes at least one of the position data and the reference generation lean vehicle behavior data related to the behavior of the lean vehicle. The analysis lean vehicle driving data includes analysis lean vehicle driving input data related to driving input to the analysis target lean vehicle by the analysis target person, and analysis related to the traveling position of the analysis target lean vehicle traveling on a public road. It includes at least one of the lean vehicle position data for analysis and the lean vehicle behavior data for analysis related to the behavior of the lean vehicle to be analyzed.

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

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.

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 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 techniques, 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 tilts 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. Moreover, the lean vehicle is a vehicle narrower than half the width of the four-wheeled vehicle, or a vehicle narrower than half the width of the lane in which the four-wheeled vehicle travels. 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.

[Density of lean vehicles]
In the present specification, the density of lean vehicles means, for example, the number of lean vehicles traveling within the width of the road on which one four-wheeled vehicle travels. The density of the lean vehicle means, for example, that the two four-wheeled vehicles travel within the front-rear length from the front end to the rear end of the two four-wheeled vehicles when traveling while maintaining a predetermined inter-vehicle distance. It means the number of lean vehicles. The density of lean vehicles is, for example, the width of the road on which one four-wheeled vehicle travels and the front-end to rear of the two four-wheeled vehicles when the two four-wheeled vehicles travel while maintaining a predetermined inter-vehicle distance. It means the number of lean vehicles running in the area having the front-rear length to the end.

The low density of lean vehicles means that the density range between the minimum density and the maximum density of lean vehicles traveling on public roads is divided into four density ranges, which is the highest of the four density ranges. Means a small density range. The density of lean vehicles is the highest among the four density ranges when the density range between the minimum density and the maximum density of lean vehicles traveling on public roads is divided into four density ranges. Means a high density range. The medium density of lean vehicles means that when the density range between the minimum density and the maximum density of lean vehicles traveling on public roads is divided into four density ranges, the rest of the four density ranges remains. Means two density ranges of.

The minimum density of lean vehicles means the state where the density of lean vehicles is the lowest. The minimum density is, for example, a state in which there are no other lean vehicles other than the own lean vehicle within a predetermined region when considering the density of the lean vehicle.

The highest density of lean vehicles means the state where the density of lean vehicles is the highest. The maximum density is, for example, a state in which the maximum number of lean vehicles are traveling within a predetermined region when considering the density of lean vehicles within a distance that can be traveled by each other.

[Freedom of driving]
In the present specification, the degree of freedom of driving means the degree of freedom of the driver to choose driving when the driver is driving a lean vehicle. The selection of the driving judgment includes, for example, the selection of the traveling route of the lean vehicle, the selection of acceleration / deceleration, the selection of the operation of the equipment in the lean vehicle, and the like.

The degree of freedom of driving by the driver's intention in a lean vehicle has an inverse correlation with the density of the lean vehicle traveling on a public road. That is, when the density of the lean vehicle traveling on the public road is low, the degree of freedom of the driver's intention to drive the lean vehicle is high. When the density of lean vehicles traveling on public roads is medium density, the degree of freedom of driving by the driver's intention in lean vehicles is medium. When the density of lean vehicles traveling on public roads is high, the degree of freedom of driving by the driver's intention in lean vehicles is low.

The low degree of freedom of driving by the driver's intention means that when the range between the case where the degree of freedom of driving is the lowest and the case where the degree of freedom of driving is the highest is divided into four ranges, the driving is performed out of the four ranges. Means the range with the lowest degree of freedom. The high degree of freedom of driving by the driver's intention means that when the range between the case where the degree of freedom of driving is the lowest and the case where the degree of freedom of driving is the highest is divided into four ranges, the driving is performed out of the four ranges. Means the range with the highest degree of freedom. The high degree of freedom of driving by the driver's intention means that the range between the case where the degree of freedom of driving is the lowest and the case where the degree of freedom of driving is the highest is divided into four ranges, and the rest of the four ranges remains. Means two ranges of.

In the case of a four-wheeled vehicle, if the density of the four-wheeled vehicle traveling on a public road is low, the degree of freedom of driving by the driver of the four-wheeled vehicle is medium. When the density of the four-wheeled vehicle traveling on a public road is medium density and high density, the degree of freedom of driving by the driver's intention in the four-wheeled vehicle is low.

[Degree of freedom related data]
In the present specification, the degree of freedom-related data is data related to the degree of freedom of driving, which means the degree of freedom for the driver to select driving when the driver is driving a lean vehicle. The degree-of-freedom-related data includes data related to the degree of freedom of selection in the traveling route of the lean vehicle, data related to the degree of freedom of selection in acceleration / deceleration, data related to the degree of freedom of selection of equipment operation in the lean vehicle, and the like. Including.

[Category-related data]
In the present specification, the classification-related data is data for classifying at least one of a driver and a lean vehicle. 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.).

[Public road]
In the present specification, the public road is not a simulation and circuit track, but a public road through which general vehicles can pass. The public roads also include private roads that general vehicles can pass through.

[Driving skill]
In the present specification, the driving skill means the 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 a predictive skill related to prediction when driving a lean vehicle.

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

[Lean vehicle driving input data]
In the present specification, the lean vehicle driving input data is data related to the driver's operation input performed when the driver drives the lean vehicle. Specifically, the lean vehicle driving input data may include data related to accelerator operation, braking operation, steering, or change in the position of the center of gravity due to a change in the posture of the driver. Further, specifically, the lean vehicle driving input data may include data related to the operation of various switches such as a horn switch, a blinker switch, and a lighting switch. Since the lean vehicle driving input data is data related to the driving input by the driver, it more reflects the driving skill of the driver and the like. In lean vehicles, there are many types of operations by the driver, and the degree of freedom of the driver's choice during driving is high, so the driving skill of the driver tends to be strongly reflected. Further, the lean vehicle driving input data may include processing data obtained by processing data acquired from a sensor or the like. The lean vehicle driving input data may include processing data processed using data acquired from a sensor or the like and 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 lean vehicle is driven by the driver. Specifically, the lean vehicle behavior data includes, for example, the acceleration, speed, and angle of the lean vehicle that changes when the driver who is the analysis target drives the vehicle. That is, when the driver who is the analysis target operates the accelerator or the brake to accelerate or decelerate the lean vehicle, the lean vehicle behavior data changes the posture including steering of the lean vehicle and changing the position of the center of gravity. It is data showing the behavior of a lean vehicle that occurs in such a case.

Further, the lean vehicle behavior data is generated in the lean vehicle not only by data on the acceleration, speed, and angle of the lean vehicle as described above, but also by a switch operation or the like performed on the lean vehicle by the driver who is the analysis target. The operation may be included. 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. The lean vehicle behavior data strongly reflects the result of the driver's driving input. Therefore, the lean vehicle behavior data also tends to strongly reflect the driver's lean vehicle driving skill. 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.

[Lean vehicle position data]
In the present specification, the lean vehicle position data is data related to the position of the lean vehicle. For example, the lean vehicle position data can be detected based on GPS and communication base station information of a communication mobile terminal. The lean vehicle position data can be calculated by various positioning techniques, SLAM, and the like. The lean vehicle position data strongly reflects the result of the driver's driving input. Therefore, the lean vehicle position data also includes data peculiar to the lean vehicle. 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.

[Lean vehicle driving environment data]
In the present specification, the lean vehicle driving environment data includes, for example, map data. The 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 traveling environment data can be used for analysis of lean vehicle traveling data together with 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 factors affecting the running of the lean vehicle. The lean vehicle driving environment data influences the driver's judgment, driving, and running of the lean vehicle. Therefore, by using the lean vehicle traveling environment data, the data obtained by analyzing the traveling data of the lean vehicle is more likely to include the data peculiar to the lean vehicle. Further, since the purpose and frequency of use of the lean vehicle are affected by using the lean vehicle driving environment data, the data obtained by analyzing the driving data of the lean vehicle includes more data peculiar to the lean vehicle. Easy to get.

The lean vehicle driving environment data can be obtained from various means. The means for acquiring the lean vehicle driving environment data is not limited to a certain means. For example, the means for acquiring the lean vehicle traveling environment data is an external environment recognition device mounted on the lean vehicle. More specifically, the means for acquiring the lean vehicle driving environment data includes a camera, a radar, and the like. Further, for example, the means for acquiring the lean vehicle traveling environment data is a communication device. More specifically, the means for acquiring the lean vehicle traveling environment data is a vehicle-to-vehicle communication device and a road-to-vehicle communication device. The lean vehicle driving environment data can also be obtained, for example, via the Internet.

[Synchronization of lean vehicle driving data]
In the present specification, the synchronism of the lean vehicle driving data means the lean vehicle driven by the analysis target person with respect to the group behavior including the lean vehicle driving data in a plurality of lean vehicles including the lean vehicle driven by the analysis target person. It means the degree of deviation of the lean vehicle driving data. The lower the degree of this divergence, the higher the synchronization of the analysis subjects. The group behavior may include, for example, data of an average value or behavior frequency obtained from lean vehicle running data in the plurality of lean vehicles. That is, the degree of deviation is the degree of deviation of the behavior frequency obtained from the lean vehicle running data of the lean vehicle driven by the analysis target person with respect to the group behavior frequency obtained from the lean vehicle running data of the plurality of lean vehicles. You may.

[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, when the density range between the minimum density and the maximum density of a lean vehicle traveling on a public road is divided into four density ranges, the lowest density range and the highest density of the four density ranges are obtained. It is said that more lean vehicles travel on public roads in the medium density range, which is the remaining two density ranges out of the above four density ranges, than the travel data of lean vehicles traveling on public roads in the high density range. It does not have to contain any travel data of lean vehicles traveling on public roads in low and high density ranges. For example, when the density range between the minimum density and the maximum density of a lean vehicle traveling on a public road is divided into four density ranges, the lowest density range and the highest density of the four density ranges are obtained. It is said that more lean vehicles travel on public roads in the medium density range, which is the remaining two density ranges out of the above four density ranges, than the travel data of lean vehicles traveling on public roads in the high density range. , The running data of the lean vehicle traveling on the public road in the low density range and the high density range may be partially included.

According to one embodiment of the present invention, lean vehicle driving capable of outputting analysis data peculiar to a lean vehicle based on driving data of a lean vehicle while reducing the load on the hardware resources and increasing the degree of freedom in designing the hardware resources. A data analysis method can be provided.

FIG. 1 is a diagram showing a schematic configuration of a lean vehicle traveling data analyzer according to an embodiment of the present invention. FIG. 2 is a flowchart showing an example of the operation of the lean vehicle traveling data analyzer. FIG. 3 is a diagram showing a schematic configuration of a lean vehicle traveling data analyzer according to the second embodiment. FIG. 4 is a diagram showing a schematic configuration of a lean vehicle traveling data analysis system according to the third embodiment. FIG. 5 is a flowchart showing an example of the operation of the information processing device. FIG. 6 is a top view of a lean vehicle to explain the density of the lean vehicle traveling on a public road. FIG. 7 is a diagram showing the relationship between the density of lean vehicles traveling on public roads and the degree of freedom of driving by the driver's will. FIG. 8 is a diagram showing the relationship between the density of four-wheeled vehicles traveling on public roads and the degree of freedom of driving by the driver's will. FIG. 9 is a diagram showing a schematic configuration of a lean vehicle driving data analyzer and a relationship between the density of lean vehicles traveling on a public road and the degree of freedom of driving by the driver's will.

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.

<Embodiment 1>
(Lean vehicle driving data analyzer)
1 and 9 show a schematic configuration of a lean vehicle traveling data analyzer 1 according to an embodiment of the present invention. The lean vehicle driving data analysis device 1 is a device that analyzes lean vehicle driving data when an analysis target person drives a lean vehicle X on a public road. The lean vehicle travel data analyzer 1 of the present embodiment obtains travel data (analysis target lean vehicle travel data) of the lean vehicle X (analysis target lean vehicle) obtained when the analysis target person drives the lean vehicle X on a public road. Analyze and output the analysis result.

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 drives the lean vehicle. Means the data to be.

Specifically, the lean vehicle driving data is related to the lean vehicle driving input data related to the driving input to the lean vehicle by the driver, the lean vehicle behavior data related to the behavior of the lean vehicle, and the running position of the lean vehicle. It includes lean vehicle position data, lean vehicle driving environment data 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 input data, the lean vehicle behavior data, the lean vehicle position data, and the lean vehicle traveling environment data. Further, the lean vehicle traveling data may include only one or a plurality of data among the lean vehicle driving input data, the lean vehicle behavior data, the lean vehicle position data, and the lean vehicle traveling environment data.

For example, in the case of a lean vehicle X in which the lean vehicle is a lean vehicle to be analyzed, the lean vehicle running data is the lean vehicle running data for analysis, and the lean vehicle driving input data is the lean vehicle driving input data for analysis. 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 traveling environment data is lean vehicle traveling environment data for analysis.

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

The lean vehicle driving input data is data related to the driver's operation input performed when the driver drives the lean vehicle. Specifically, the lean vehicle driving input data may include data related to accelerator operation, braking 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 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 input data is data related to the driving input by the driver, it more reflects the driving skill of the driver and the like. In lean vehicles, there are many types of operations by the driver, and the degree of freedom of the driver's choice during driving is high, so the driving skill of the driver tends to be strongly reflected. Further, the lean vehicle driving input data may include processing data obtained by processing data acquired from a sensor or the like. The lean vehicle driving 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 driving input of the driver when the lean vehicle is driven by the driver. Specifically, the lean vehicle behavior data includes, for example, the acceleration, speed, and angle of the lean vehicle that changes when the driver drives. 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. The lean vehicle behavior data strongly reflects the driving skill of the driver. Therefore, the driver's driving skill and the like tend to be strongly reflected in the lean vehicle behavior data. 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, information on a communication base station of a communication mobile terminal, or 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. Therefore, the driver's driving skill and the like tend to be strongly reflected in the lean vehicle position data. 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.

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 input data, the lean vehicle behavior data, and the lean vehicle position data.

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 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. In the present embodiment, the lean vehicle traveling 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 analysis lean vehicle driving data acquisition unit 20 acquires the analysis lean vehicle driving data including the driving data when the driver who is the analysis target drives the lean vehicle X on a public road.

Specifically, when the analysis target person drives the lean vehicle X, the analysis lean vehicle travel data acquisition unit 20 includes data included in the lean vehicle travel data of the lean vehicle X, that is, the analysis target lean vehicle operation. Acquires input data, lean vehicle behavior data for analysis, lean vehicle position data for analysis, lean vehicle driving environment data for analysis, and the like.

The analysis lean vehicle driving data acquisition unit 20 may acquire the analysis lean vehicle driving input data by, for example, acquiring the driving 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 that changes when the driver who is the analysis target drives the lean vehicle X, for example, the analysis lean vehicle behavior. It may be acquired as 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.

Further, even if the analysis lean vehicle driving data acquisition unit 20 acquires the operation generated in the lean vehicle X by the switch operation or the like performed on the lean vehicle X by the driver who is the analysis target, as the lean vehicle behavior data. Good. That is, the analysis lean vehicle travel data acquisition unit 20 acquires data related to the operation generated in the lean vehicle X by operating various switches such as the horn switch, the blinker switch, and the lighting switch as the analysis lean vehicle behavior data. You may. These data are transmitted from the lean vehicle X to the lean vehicle travel data analyzer 1.

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, or 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 travel data acquisition unit 20 may also acquire 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 lean vehicle running data includes analysis running density related data related to the density of the lean vehicle X traveling on a public road. The analysis lean vehicle traveling data may include analysis classification-related data for classifying the analysis target person.

The density of lean vehicles means, for example, the number of lean vehicles traveling within the width of the road on which one four-wheeled vehicle travels. The density of the lean vehicle means, for example, that the two four-wheeled vehicles travel within the front-rear length from the front end to the rear end of the two four-wheeled vehicles when traveling while maintaining a predetermined inter-vehicle distance. It means the number of lean vehicles. The density of lean vehicles is, for example, the width of the road on which one four-wheeled vehicle travels and the front-end to rear of the two four-wheeled vehicles when the two four-wheeled vehicles travel while maintaining a predetermined inter-vehicle distance. It means the number of lean vehicles running in the area having the front-rear length to the end.

FIG. 6 is a top view of the lean vehicle X in order to explain the density of the lean vehicle X traveling on a public road. 6 (A) shows the case where the density of the lean vehicle X is low, FIG. 6 (B) shows the case where the density of the lean vehicle X is high, and FIG. 6 (C) shows the case where the density of the four-wheeled vehicle P is high. Each is shown schematically. In FIG. 6, the thick solid line is the lane boundary line L of the public road. The road width means the distance between a pair of lane boundary lines L.

In FIG. 6, the number of lean vehicles X located between a pair of alternate long and short dash lines is the density of lean vehicles X traveling on a public road. As shown in FIG. 6, the four-wheeled vehicle P traveling on a public road is located between a pair of alternate long and short dash lines because it is necessary to secure a distance that can maintain an appropriate inter-vehicle distance as the front-rear distance. The number of four-wheeled vehicles P does not change so much. On the other hand, the lean vehicle X traveling on a public road has a smaller width than the four-wheeled vehicle, so that the density of the lean vehicle X is likely to change significantly.

FIG. 7 is a diagram showing the relationship between the density of the lean vehicle X traveling on a public road and the degree of freedom of driving by the driver's intention. As described above, the degree of freedom of driving of the lean vehicle X by the driver's intention has an inverse correlation with the density of the lean vehicle X traveling on the public road.

That is, when the density of the lean vehicle X traveling on the public road is low, the degree of freedom of the driver's intention to drive in the lean vehicle X is high. When the density of the lean vehicle X traveling on the public road is medium density, the degree of freedom of driving by the driver's intention in the lean vehicle X is medium. When the density of the lean vehicle X traveling on the public road is high, the degree of freedom of the driver's intention to drive in the lean vehicle X is low.

FIG. 8 is a diagram showing the relationship between the density (number of vehicles) of the four-wheeled vehicle P and the degree of freedom of driving according to the driver's intention. As described above, there is no strong inverse correlation between the density of the four-wheeled vehicle P traveling on the public road and the degree of freedom of driving by the driver's intention as in the case of the lean vehicle.

The driving density-related data is data related to the density including other lean vehicles traveling on the public road when the driver is driving the lean vehicle on the public road.

The degree of freedom of driving means the degree of freedom of the analysis target person to select a judgment when the analysis target person is driving a lean vehicle. The selection of the driving judgment includes, for example, the selection of the traveling route of the lean vehicle X, the selection of acceleration / deceleration, the selection of the operation of the equipment in the lean vehicle, and the like.

The degree of freedom-related data is data related to the degree of freedom of driving, which means the degree of freedom for the driver to select driving when the driver is driving a lean vehicle. Therefore, the degree-of-freedom-related data includes data related to the degree of freedom of selection in the traveling route of the lean vehicle, data related to the degree of freedom of selection in acceleration / deceleration, and data related to the degree of freedom of selection of device operation in the lean vehicle. And so on.

The degree-of-freedom-related data is generated using, for example, lean vehicle position data, lean vehicle driving environment data, and the like. The degree-of-freedom-related data may be generated using at least one of the lean vehicle driving input data and the lean vehicle behavior data.

Classification-related data is data for classifying at least one of a driver and a lean vehicle. 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, in the case of the lean vehicle X, which is the lean vehicle to be analyzed, the running density-related data is the running density-related data for analysis, the freedom-related data is the freedom-related data for analysis, and the classification-related data. The data is classification-related data for analysis.

The lean vehicle travel data analyzer 1 considers various usage scenarios by considering the analysis travel density-related data when analyzing the analysis lean vehicle travel data and generating the analysis data. It is possible to output analysis data peculiar to lean vehicles, such as the skill of driving a vehicle, which was difficult to output until now. In the lean vehicle driving data analyzer 1, for example, in an analysis using lean vehicle driving data in a state where the density of the lean vehicle is low, that is, a state where the driver has a high degree of freedom in driving, the analysis target examines the lean vehicle X. It is possible to analyze the driving skill more accurately and in more detail. Further, the lean vehicle driving data analyzer 1 analyzes, for example, in an analysis using lean vehicle driving data in a state where the density of the lean vehicle is medium, that is, in a state where the driver's degree of freedom of driving is limited to some extent. It is possible to analyze the prediction skill of the subject for predicting the driving environment such as the movement of surrounding vehicles with more accuracy and in more detail.

The analysis travel density-related data is limited to data having similar lean vehicle densities from among the lean vehicle travel reference data described later when analyzing the analysis lean vehicle travel data and generating the analysis data. It may be used when doing so. By using the data related to the driving density for analysis in this way, it is possible to limit the data to be processed when analyzing the lean vehicle driving data for analysis and generating the analysis data, and it is possible to reduce the load on the hardware resources. it can.

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 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.

The reference generation lean vehicle running data is the highest density among the four density ranges when the density range between the minimum density and the maximum density of the lean vehicle traveling on a public road is divided into four density ranges. Lean vehicles travel on public roads in the medium density range, which is the remaining two density ranges out of the above four density ranges, based on the driving data of lean vehicles traveling on public roads in the low density range and the highest density range. Contains a lot of driving data. Further, the reference generation lean vehicle driving data may include classification-related data for classifying at least one of the driver and the lean vehicle. Further, the reference generation lean vehicle travel data may include lean vehicle travel data of a plurality of lean vehicles having different categories.

The reference generation lean vehicle driving data includes reference generation lean vehicle driving input data related to driving inputs to the lean vehicle by different drivers, and driving positions of lean vehicles driven by different drivers and traveling on a plurality of public roads. Related to reference-generating lean vehicle position data, reference-generating lean vehicle behavior data related to the behavior of lean vehicles driven by different drivers and traveling on multiple public roads, and related to the driving environment in which lean vehicles drive. Includes lean vehicle driving environment data for reference generation.

The reference generation lean vehicle driving data is other than the reference generation lean vehicle driving 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. It may contain data. Further, the reference generation lean vehicle driving data includes the reference generation lean vehicle driving 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. , May contain only one or more data.

In the case of a lean vehicle in which a lean vehicle is driven by a different driver and travels on a plurality of public roads, the above-mentioned lean vehicle driving data is the reference generation lean vehicle driving data, and the above-mentioned lean vehicle driving input data is the reference generation. Lean vehicle driving input data, the above-mentioned lean vehicle behavior data is the reference generation lean vehicle behavior data, and the above-mentioned lean vehicle position data is the reference generation lean vehicle position data, and the above-mentioned lean vehicle. The driving environment data is lean vehicle driving environment data for reference generation.

The reference generation lean vehicle running data includes running density related data. The reference generation lean vehicle travel data may include classification-related data.

The travel density-related data is taken into consideration when analyzing the analysis lean vehicle travel data and generating the analysis data. As a result, 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 the lean vehicle X in consideration of various usage scenarios. For example, in an analysis using lean vehicle driving data in a state where the density of lean vehicles is low, that is, a state where the driver has a high degree of freedom in driving, the analysis target person can more accurately and accurately determine the skill of driving the lean vehicle X. It can be analyzed in more detail. Further, for example, in an analysis using lean vehicle driving data in a state where the density of lean vehicles is medium, that is, a state in which the degree of freedom of driving of the driver is limited to some extent, the analysis target person is the movement of surrounding vehicles. It is possible to analyze the prediction skill for predicting the driving environment of the vehicle more accurately and in more detail.

When analyzing the lean vehicle running data for analysis and generating the analysis data, the running density-related data is limited to data having similar lean vehicle densities from the lean vehicle running reference data described later. It may be used for. By using the travel density-related data in this way, it is possible to limit the data to be processed when analyzing the analysis lean vehicle travel data and generating the analysis data, and it is possible to reduce the load on the hardware resources.

The classification-related data may be 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. Good. 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 used when analyzing the analysis lean vehicle travel data. The lean vehicle driving reference data is used, for example, as a reference for classifying the lean vehicle driving skill of the driver who is the analysis target. The lean vehicle travel reference data is generated based on, for example, the reference generation lean vehicle travel data, and is stored in the data storage unit 60.

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 travel reference data obtained by the lean vehicle travel standard data acquisition unit 10. Acquire the analysis data obtained by this. This analysis data may be 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 lean vehicle of the analysis target person. 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 a predictive skill related to prediction when driving a lean vehicle. In addition, the analysis data includes, for example, data related to the traveling of the lean vehicle X. This data is, for example, data related to the 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.

With the above configuration, the lean vehicle travel data analyzer 1 analyzes the lean vehicle travel data of the lean vehicle X driven by the analysis target person based on the lean vehicle travel reference data, and outputs the analysis data as output data. can do.

(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 is the highest density among the four density ranges when the density range between the minimum density and the maximum density of the lean vehicle traveling on a public road is divided into four density ranges. Lean vehicles travel on public roads in the medium density range, which is the remaining two density ranges out of the above four density ranges, based on the driving data of lean vehicles traveling on public roads in the low density range and the highest density range. Contains a lot of driving data. The reference generation lean vehicle driving data may include classification-related data for classifying at least one of the driver and the lean vehicle. Further, the reference generation lean vehicle travel data may include 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 on the public road by the driving of the analysis target person (step SA2).

The analysis lean vehicle running data includes data for analysis running freedom related to the degree of freedom of running by the analysis target person's intention when traveling on a public road with the lean vehicle X. The analysis lean vehicle traveling data may include analysis classification-related data for classifying at least one of the analysis target person and the lean vehicle X.

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

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 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 an analysis target person traveling on a public road. The analysis data may be at least one of the classified analysis target person and the lean vehicle X.

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, the analysis data can be obtained by analyzing the lean vehicle running data of the lean vehicle X traveling on the public road by the driver who is the analysis target.

Therefore, as in the above configuration, by using the lean vehicle driving data considering the density of the lean vehicle traveling on the public road, the skill of driving the lean vehicle considering various usage scenes can be output up to now. It is possible to output analysis data peculiar to lean vehicles, which was difficult to do. For example, in an analysis using lean vehicle driving data when the density of lean vehicles is low, that is, when the driver has a high degree of freedom in driving, the driver's skill in driving a lean vehicle is more accurately and more detailed. Can be analyzed. Further, for example, in an analysis using lean vehicle driving data when the density of lean vehicles is medium, that is, when the degree of freedom of driving of the driver is limited to some extent, the driver may move the surrounding vehicles. It is possible to analyze the prediction skill for predicting the driving environment more accurately and in more detail.

Moreover, in order to analyze the lean vehicle driving data in consideration of the density of the lean vehicle traveling on the public road, 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. 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 is the highest density among the four density ranges when the density range between the minimum density and the maximum density of the lean vehicle traveling on a public road is divided into four density ranges. Lean vehicles travel on public roads in the medium density range, which is the remaining two density ranges out of the above four density ranges, based on the driving data of lean vehicles traveling on public roads in the low density range and the highest density range. Contains a lot of driving data.

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

For example, the reference generation lean vehicle travel data may be acquired by various sensors provided in the lean vehicle. Further, the reference generation lean vehicle travel data may be acquired by various sensors provided on the lean vehicle so as to be easily detachable. The reference generation lean vehicle travel data 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 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 by the analysis target person. The analysis target lean vehicle means a lean vehicle X driven 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 travel 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 acquires analysis data by analyzing the acquired lean vehicle travel data for analysis based on the acquired lean vehicle travel reference data.

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.

From another point of view, the lean vehicle driving data analysis method preferably includes the following configurations. The reference generation lean vehicle driving data includes lean vehicle driving input data related to driving input to the lean vehicle by the driver, reference generation lean vehicle position data related to the traveling position of the lean vehicle traveling on a public road, and the like. In addition, at least one of the reference generation lean vehicle behavior data related to the behavior of the lean vehicle is included. The analysis lean vehicle driving data includes analysis lean vehicle driving input data related to driving input to the analysis target lean vehicle by the analysis target person, and analysis related to the traveling position of the analysis target lean vehicle traveling on a public road. It includes at least one of the lean vehicle position data for analysis and the lean vehicle behavior data for analysis related to the behavior of the lean vehicle to be analyzed.

Lean vehicle driving input data is data related to driving input by the driver. In a lean vehicle, there are many types of operations by the driver, and the degree of freedom of the driver's selection during driving is high. Therefore, the driver's driving skill and the like tend to be strongly reflected in the lean vehicle driving input data.

The lean vehicle behavior data strongly reflects the result of the driver's driving input, which strongly reflects the driving skill of the driver. Therefore, the driver's driving skill and the like tend to be strongly reflected in the lean vehicle behavior data.

The lean vehicle position data strongly reflects the result of the driver's driving input, which strongly reflects the driving skill of the driver. Therefore, the driver's driving skill and the like tend to be strongly reflected in the lean vehicle position data.

As a result, the lean vehicle driving data used when generating the analysis data includes data that more reflects the driving skill of the analysis target person who is the driver.

From another point of view, the lean vehicle driving data analysis method preferably includes the following configurations. The reference-generating lean vehicle traveling data further includes reference-generating lean vehicle traveling environment data related to the traveling environment in which the lean vehicle travels. The analysis lean vehicle traveling data further includes analysis lean vehicle traveling environment data related to the traveling environment in which the analysis target lean vehicle travels.

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.

From another point of view, the lean vehicle driving data analysis method preferably includes the following configurations. The reference generation lean vehicle traveling 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. 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.

For example, the driver's judgment options are limited by the vehicles around the lean vehicle, but a plurality of remaining states may be determined from the lean vehicle position data and the lean vehicle driving environment data. More specifically, the state may be estimated based on the date, time, and place where the lean vehicle is traveling. Lean vehicle driving data when traveling in an urban area includes data in a state where a plurality of driver's judgment options are restricted by vehicles around the lean vehicle, but a plurality of them are left. In addition, data on the actual surrounding conditions of the lean vehicle may be acquired to estimate the state. A combination of methods for estimating a plurality of states may be used.

The driver's judgment options are limited by the vehicles around the lean vehicle, but a plurality of remaining options are defined as the driver of the lean vehicle driving in a group of a plurality of vehicles including the lean vehicle. It means the running state of the lean vehicle when a plurality of options are left although the options are limited when making the determination.

From another point of view, the lean vehicle driving data analysis method preferably includes the following configurations. The reference generation lean vehicle running data includes data in a state where at least one of a passenger and an object is mounted. The analysis lean vehicle traveling data includes data in a state where at least one of a passenger and an object is mounted.

For example, it may be determined from various sensors whether or not at least one of a passenger and an object is mounted. Further, the determination may be made based on the declaration by the driver.

From another point of view, the lean vehicle driving data analysis method preferably includes the following configurations. In the lean vehicle traveling data analysis method, the acquired analysis data is stored. In the lean vehicle traveling data analysis method, the output data is generated by using the plurality of stored analysis data.

Note that the memory includes not only the memory for storage but also the temporary memory of the result. For example, the analysis data stored in the storage and the analysis data stored in the temporary memory may be used for storage. These may be used to update the analysis data stored in the storage. These may be used to generate new analytical data. Statistical processing may be performed using these. These may be used to update the analysis data stored in the storage.

By using a plurality of analysis data as described above, for example, statistical processing can be performed, and lean vehicle driving data for analysis can be analyzed more accurately. More specifically, the old analysis data and the new analysis data can be used to more accurately analyze the lean vehicle running data of the lean vehicle X driven by the analysis target person.

This embodiment is an example of a lean vehicle driving data analyzer that analyzes lean vehicle driving data. The lean vehicle driving data analyzer of the present embodiment includes the following configurations.

The lean vehicle travel data analyzer of the present embodiment is a lean vehicle travel data analyzer that 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.

The lean vehicle travel data analyzer 1 of the present embodiment acquires lean vehicle travel reference data, which is travel reference data of a lean vehicle that tilts to the right when turning right and tilts to the left when turning left. The travel reference data acquisition unit 10, the analysis lean vehicle travel data acquisition unit 20 that acquires the analysis lean vehicle travel data that is the travel data of the analysis target lean vehicle that is the analysis target lean vehicle, and the acquired lean vehicle travel. Analysis data acquisition unit that acquires analysis data of at least one of the analysis target person who is the driver of the analysis target and the analysis target lean vehicle by analyzing the acquired lean vehicle driving data for analysis based on the reference data. 30 includes an output data generation unit 40 that generates output data for output using the analysis data, and a data output unit 50 that outputs the output data.

The lean vehicle driving reference data is the lowest density among the four density ranges when the density range between the minimum density and the maximum density of the lean vehicle traveling on a public road is divided into four density ranges. Based on the running data of lean vehicles traveling on public roads in the low density range and the highest density range, the running of lean vehicles traveling on public roads in the medium density range, which is the remaining two density ranges out of the above four density ranges. It is generated based on the lean vehicle running data for reference generation that contains a lot of data.

The analysis lean vehicle running data includes analysis running density related data related to the density of the lean vehicle when the analysis target person drives and travels on a public road with the analysis target lean vehicle.

The lean vehicle travel data analyzer 1 analyzes the lean vehicle travel data of the lean vehicle to be analyzed.

From another point of view, the lean vehicle driving data analyzer 1 preferably includes the following configurations. The reference generation lean vehicle driving data includes classification-related data for classifying at least one of the driver and the lean vehicle. The analysis lean vehicle traveling data includes analysis classification-related data for classifying at least one of the analysis target person and the analysis target lean vehicle. The analysis data acquisition unit analyzes the analysis lean vehicle travel data based on the lean vehicle travel reference data generated based on the reference generation lean vehicle travel data, thereby obtaining the analysis classification-related data. The analysis data of at least one of the analysis target person and the analysis target lean vehicle classified by using is acquired.

From another point of view, the lean vehicle driving data analyzer 1 preferably includes the following configurations. The analysis data is an analysis result of synchronization of the lean vehicle driving data for analysis with respect to the lean vehicle driving reference data including data having a density similar to that of the analysis driving density-related data among the lean vehicle driving reference data. Obtained using.

From another point of view, the lean vehicle driving data analyzer 1 preferably includes the following configurations. The analysis data includes data related to the evaluation result of the driving prediction skill when the analysis target person travels on a public road with the lean vehicle X.

From another point of view, the lean vehicle driving data analyzer 1 preferably includes the following configurations. The reference generation lean vehicle driving data includes the reference generation lean vehicle driving input data related to the driving input to the lean vehicle by the driver, and the reference generation lean vehicle related to the traveling position of the lean vehicle traveling on a public road. It includes at least one of the position data and the reference generation lean vehicle behavior data related to the behavior of the lean vehicle. The analysis lean vehicle driving data includes the analysis lean vehicle driving input data related to the driving input to the lean vehicle X by the analysis target person, and the analysis lean vehicle position related to the traveling position of the lean vehicle X traveling on a public road. It includes at least one of the data and the lean vehicle behavior data for analysis related to the behavior of the lean vehicle X.

From another point of view, the lean vehicle driving data analyzer 1 preferably includes the following configurations. The reference-generating lean vehicle traveling data further includes reference-generating lean vehicle traveling environment data related to the traveling environment in which the lean vehicle travels. The analytical lean vehicle traveling data further includes analytical lean vehicle traveling environment data related to the traveling environment in which the lean vehicle travels.

From another point of view, the lean vehicle driving data analyzer 1 preferably includes the following configurations. The reference generation lean vehicle traveling 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. 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.

From another point of view, the lean vehicle driving data analyzer 1 preferably includes the following configurations. The reference generation lean vehicle running data includes data in a state where at least one of a passenger and an object is mounted. The analysis lean vehicle traveling data includes data in a state where at least one of a passenger and an object is mounted.

From another point of view, the lean vehicle driving data analyzer 1 preferably includes the following configurations. The lean vehicle traveling data analysis device 1 has a data storage unit 60 that stores the acquired analysis data. The output data generation unit 40 generates the output data by using a plurality of analysis data stored in the data storage unit 60.

From another point of view, the lean vehicle driving data analyzer 1 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 traveling data analyzer 100. The lean vehicle driving data analyzer 100 analyzes the synchronization between the lean vehicle X driven by the analysis target person and the other lean vehicle Y traveling around the analysis target person, thereby predicting the driving skill of the analysis target person. Is evaluated, and the evaluation result is output as analysis data.

Specifically, the lean vehicle travel data analyzer 100 includes a group behavior calculation unit 110, a lean vehicle travel data acquisition unit 120 for analysis, a synchronization analysis unit 130, a prediction skill evaluation unit 140, and an evaluation output unit 150. And.

The analysis lean vehicle driving data acquisition unit 120 acquires the analysis lean vehicle driving data which is the driving data of the lean vehicle X driven by the analysis target person. The analysis lean vehicle travel data includes the analysis lean vehicle position data and the analysis lean vehicle behavior data of the lean vehicle X.

The group behavior calculation unit 110 acquires lean vehicle data for reference generation in a group (hereinafter referred to as a group) including the lean vehicle X and another lean vehicle Y. The reference generation lean vehicle data includes lean vehicle position data and lean vehicle behavior data of a plurality of lean vehicles. Whether or not the vehicle belongs to the group is determined by acquiring the lean vehicle position data of the lean vehicle X and the other lean vehicle Y, and the other lean vehicle Y is located within a predetermined range from the lean vehicle X. It is judged by whether or not it is. Lean vehicles belonging to the same group are traveling on public roads with similar densities, that is, similar degrees of freedom of travel.

The group behavior calculation unit 110 uses the acquired reference generation lean vehicle travel data to obtain travel data related to the group behavior. In the present embodiment, the travel data related to this group behavior is the average value of the lean vehicle travel data of a plurality of lean vehicles constituting the group. The travel data related to the group behavior corresponds to the lean vehicle travel reference data.

The synchronization analysis unit 130 uses the analysis lean vehicle travel data acquired by the analysis lean vehicle travel data acquisition unit 120 and the travel data related to the group behavior obtained by the group behavior calculation unit 110 to achieve synchronization. Perform an analysis.

The synchronism is the analysis lean vehicle running data of the lean vehicle X driven by the analysis target with respect to the group behavior including the lean vehicle running data in a plurality of lean vehicles including the lean vehicle X driven by the analysis target. It means the degree of divergence. The lower the degree of this divergence, the higher the synchronization of the analysis subjects. The group behavior may include, for example, data on the average value or behavior frequency of the behavior of the plurality of lean vehicles obtained from the lean vehicle traveling data in the plurality of lean vehicles. That is, the degree of deviation is obtained from the analysis lean vehicle running data of the lean vehicle X driven by the analysis target person with respect to the group behavior frequency obtained by using the average value of the lean vehicle running data in the plurality of lean vehicles. It may be the degree of deviation of the behavioral frequencies to be obtained.

In the present embodiment, the result of the synchronization analysis output from the synchronization analysis unit 130 corresponds to the analysis data in the first embodiment.

The prediction skill evaluation unit 140 evaluates the driving prediction skill related to the prediction when the analysis target person drives the lean vehicle X on a public road by using the result of the synchronization analysis of the synchronization analysis unit 130. That is, the prediction skill evaluation unit 140 classifies the driving prediction skill of the analysis target person into levels based on the result of the synchronization analysis. The evaluation result of the driving prediction skill obtained by the prediction skill evaluation unit 140 may be, for example, the result of leveling the analysis result of the synchronization according to the threshold value, or the numerical value obtained from the analysis result of the synchronization. Alternatively, it may be an evaluation value corresponding to it.

The evaluation output unit 150 outputs the evaluation result of the driving skill prediction obtained by the prediction skill evaluation unit 140 as output data. The output data may be output as it is from the lean vehicle traveling data analyzer 100. Further, the output data may be stored in a storage unit (not shown) of the lean vehicle traveling data analyzer 100 and then used when the evaluation output unit 150 performs arithmetic processing on the output data.

Note that the analysis data may be obtained from sources other than the above-mentioned synchronism analysis results. In addition, the analysis data may include an evaluation result of predictive skill obtained from other than the analysis result of synchronization described above.

In the lean vehicle travel data analysis device 100 of the present embodiment, the group behavior calculation unit 110 corresponds to the lean vehicle travel reference data acquisition unit 10 of the lean vehicle travel data analyzer 1 of the embodiment, and is a lean vehicle travel data acquisition unit for analysis. 120 corresponds to the lean vehicle travel data acquisition unit 20 for analysis of the lean vehicle travel data analyzer 1 of the first embodiment, and the synchronization analysis unit 130 corresponds to the analysis data acquisition unit 30 of the lean vehicle travel data analyzer 1 of the first embodiment. The predictive skill evaluation unit 140 corresponds to the output data generation unit 40 of the lean vehicle travel data analyzer 1 of the first embodiment, and the evaluation output unit 150 corresponds to the data output of the lean vehicle travel data analyzer 1 of the first embodiment. Corresponds to part 50.

In the present embodiment, the analysis data is an analysis of the synchronization of the lean vehicle driving data for analysis with respect to the lean vehicle driving reference data including the data having a degree of freedom similar to the data related to the degree of freedom for analysis among the lean vehicle driving reference data. Obtained using the results.

In the lean vehicle running data analysis method of the present embodiment, the analysis data is the same as the lean vehicle running reference data including the data having a degree of freedom similar to the analysis freedom-related data among the lean vehicle running reference data. It is obtained by using the analysis result of the synchronism of the lean vehicle driving data for analysis.

Similar driving degrees of freedom means not only when the driving degrees of freedom are exactly the same, but also when the analysis data obtained by analyzing the lean vehicle driving data is within a predetermined range. Is also included.

From another point of view, the lean vehicle driving data analysis method preferably includes the following configurations. The analysis data includes data related to the evaluation result of the driving prediction skill when the analysis target person travels on a public road with the analysis target lean vehicle.

When driving a lean vehicle, the driver's driving prediction skill is more important than when driving a non-lean vehicle. By including the data related to the evaluation result of the driving prediction skill in the analysis data, the analysis data peculiar to the lean vehicle can be obtained.

<Embodiment 3>
FIG. 4 shows an example of the lean vehicle driving data analysis system 200 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 travel data analysis system 200 includes a lean vehicle travel data analysis device 1 and a lean vehicle travel reference data generation device 201 that generates lean vehicle travel reference data.

The lean vehicle travel reference data generation device 201 is, for example, an information processing arithmetic unit capable of communicating with the lean vehicle travel data analyzer 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 201 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 201 acquires the lean vehicle running data and the classification-related data, and generates the 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 201 has a data storage unit 211 and a lean vehicle travel reference data generation unit 212. Although not particularly shown, the lean vehicle travel reference data generation device 201 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 201 has an output unit that outputs the generated lean vehicle travel reference data.

The data storage unit 211 stores lean vehicle running data for reference generation and lean vehicle running reference data. Specifically, the data storage unit 211 stores lean vehicle travel data for reference generation, including lean vehicle travel data and classification-related data obtained when a plurality of drivers drive the lean vehicle Y, respectively. Further, the data storage unit 211 stores the lean vehicle travel reference data generated by the lean vehicle travel reference data generation unit 212, which will be described later.

The lean vehicle running data includes, for example, lean vehicle driving 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 212 generates lean vehicle travel reference data based on the reference generation lean vehicle travel data stored in the data storage unit 211. The lean vehicle travel reference data generated by the lean vehicle travel reference data generation unit 212 is stored in the data storage unit 211.

The lean vehicle travel reference data stored in the data storage unit 211 is analyzed by the lean vehicle travel data analyzer 1 for lean vehicle travel data (lean vehicle travel data for analysis) of the lean vehicle X (lean vehicle for analysis). 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 lean vehicle travel data may be analyzed as in the lean vehicle travel data analyzer 100 of the second embodiment.

The output data output from the lean vehicle traveling data analyzer 1 may be input to, for example, the information processing device 202. In this case, the output data is generated in the lean vehicle traveling data analyzer 1 as information processing data used for information processing in the information processing device 202.

The information processing apparatus 202 provides 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, 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 202 may be the same device as the lean vehicle travel data analysis device 1. The information processing device 202 may be the same information processing calculation device as the lean vehicle travel reference data generation device 201.

The information processing device 202 has, for example, an output data acquisition unit 221, a first data acquisition unit 222, a second data generation unit 223, a second data output unit 224, and a data storage unit 225.

The output data acquisition unit 221 acquires the output data output from the lean vehicle travel data analyzer 1.

The first data acquisition unit 222 acquires the first data different from the output data. This first data is data to be processed by the information processing apparatus 202. 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 225.

The second data generation unit 223 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 224 outputs the second data generated by the second data generation unit 223.

(Information processing method using analytical data)
Next, an information processing method for performing information processing using output data by the information processing device 202 having the above configuration will be described with reference to the flowchart shown in FIG. FIG. 5 is a flowchart showing the operation of information processing by the information processing device 202.

As shown in FIG. 5, first, the output data acquisition unit 221 of the information processing device 202 acquires the output data output from the lean vehicle travel data analysis device 1 (step SB1).

Next, the first data acquisition unit 222 of the information processing device 202 acquires the first data stored in the data storage unit 225 (step SB2). This first data is different from the output data.

After that, the second data generation unit 223 of the information processing apparatus 202 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 202 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 driving data analysis method in each of the above-described embodiments is an example of the lean vehicle driving data analysis method for analyzing the lean vehicle driving data of the analysis target person.

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 202 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.

In each of the above embodiments, the lean vehicle travel reference data is generated using the lean vehicle travel data, but the lean vehicle travel is performed using not only the lean vehicle travel data but also data other than the lean vehicle travel data. Reference data may be generated.

Further, in each of the above-described embodiments, the lean vehicle travel data is acquired as the lean vehicle travel data for analysis, and the analysis data is acquired by analyzing the lean vehicle travel data for analysis based on the lean vehicle travel reference data. ing. However, the analysis data may be acquired by acquiring data other than the lean vehicle travel data for analysis and analyzing the data and the lean vehicle travel data.

Further, the output data may be used in combination with data other than the lean vehicle driving data.

As described above, each data described in each of the above embodiments may be combined with data other than the lean vehicle traveling data.

The present invention can be used for a lean vehicle driving data analysis method and a lean vehicle driving data analyzer for analyzing lean vehicle driving data of an analysis target person, and an information processing method using the analysis data obtained by these methods and the device. It can also be used for information processing devices.

1,100 Lean vehicle driving data analyzer 10 Lean vehicle driving reference

data acquisition unit

20, 120 Lean vehicle driving data acquisition unit 30 for analysis Analysis data acquisition unit 40 Output data generation unit 50

Data output unit

60, 211 Data storage unit 110 group Behavior calculation unit 130 Synchronous analysis unit 140 Prediction skill evaluation unit 150 Evaluation output unit 200 Lean vehicle driving data analysis system 201 Lean vehicle driving standard data generation device 202 Information processing device 212 Lean vehicle driving standard data generation unit 221 Output data acquisition unit 222 1st data acquisition unit 223 2nd data generation unit 224 2nd data output unit X lean vehicle (lean vehicle to be analyzed)
Y lean vehicle P four-wheeled vehicle L lane boundary line

Claims

A lean vehicle driving standard data acquisition process for acquiring lean vehicle driving standard data, which is driving standard data for a lean vehicle that tilts to the right when turning right and tilts to the left when turning left.
The analysis lean vehicle driving data acquisition process for acquiring the analysis lean vehicle driving data, which is the driving data of the analysis target lean vehicle, which is the lean vehicle to be analyzed, and the analysis lean vehicle driving data acquisition process.
By analyzing the acquired lean vehicle driving data for analysis based on the acquired lean vehicle driving reference data, the analysis data of at least one of the analysis target person who is the driver to be analyzed and the analysis target lean vehicle can be obtained. Analysis data acquisition process to be acquired and
An output data generation process that generates output data for output using the analysis data,
The output process for outputting the output data and
Have,
The lean vehicle driving reference data is the lowest density among the four density ranges when the density range between the minimum density and the maximum density of the lean vehicle traveling on a public road is divided into four density ranges. Based on the running data of lean vehicles traveling on public roads in the low density range and the highest density range, the running of lean vehicles traveling on public roads in the medium density range, which is the remaining two density ranges out of the above four density ranges. Generated based on lean vehicle driving data for reference generation that contains a lot of data
The analysis lean vehicle travel data includes analysis travel density-related data related to the density of the lean vehicle when the analysis target person drives and travels on a public road with the analysis target lean vehicle.
Analyzing the lean vehicle running data of the lean vehicle to be analyzed,
Lean vehicle driving data analysis method.
In the lean vehicle driving data analysis method according to claim 1,
The reference generation lean vehicle driving data includes classification-related data for classifying at least one of the driver and the lean vehicle.
The analysis lean vehicle traveling data includes analysis classification-related data for classifying at least one of the analysis target person and the analysis target lean vehicle.
In the analysis data acquisition step, the analysis classification-related data is analyzed by analyzing the analysis lean vehicle travel data based on the lean vehicle travel reference data generated based on the reference generation lean vehicle travel data. Acquire the analysis data of at least one of the analysis target person and the analysis target lean vehicle classified by using.
Lean vehicle driving data analysis method.
In the lean vehicle driving data analysis method according to claim 1 or 2.
The analysis data is the synchronization of the analysis lean vehicle travel data with respect to the reference generation lean vehicle travel reference data including the data having a density similar to that of the analysis travel density-related data among the lean vehicle travel reference data. Obtained using the analysis results,
Lean vehicle driving data analysis method.
In the lean vehicle driving data analysis method according to any one of claims 1 to 3.
The analysis data includes data related to the evaluation result of the driving prediction skill when the analysis target person travels on a public road with 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 4.
The reference generation lean vehicle driving data includes the reference generation lean vehicle driving input data related to the driving input to the lean vehicle by the driver, and the reference generation lean vehicle related to the traveling position of the lean vehicle traveling on a public road. Includes at least one of the position data and the reference generation lean vehicle behavior data related to the lean vehicle behavior.
The analysis lean vehicle driving data includes analysis lean vehicle driving input data related to driving input to the analysis target lean vehicle by the analysis target person, and analysis related to the traveling position of the analysis target lean vehicle traveling on a public road. Includes at least one of lean vehicle position data for analysis and lean vehicle behavior data for analysis related to the behavior of the lean vehicle to be analyzed.
Lean vehicle driving data analysis method.
In the lean vehicle driving data analysis method according to any one of claims 1 to 5.
The reference generation lean vehicle traveling data further includes the reference generating lean vehicle traveling environment data related to the traveling environment in which the lean vehicle travels.
The analysis lean vehicle traveling data further 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 6.
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 7.
The reference generation lean vehicle driving data includes data in a state where at least one of a passenger and an object is mounted.
The analysis lean vehicle driving data includes data in a state where at least one of a passenger and an object is mounted.
Lean vehicle driving data analysis method.
In the lean vehicle driving data analysis method according to any one of claims 1 to 8.
The acquired analysis data is stored and
The output data is generated using the plurality of stored analysis data.
Lean vehicle driving data analysis method.
In the lean vehicle driving data analysis method according to any one of claims 1 to 9.
The output data is generated as information processing analysis data used for further information processing.
Lean vehicle driving data analysis method.
A lean vehicle driving standard data acquisition unit that acquires lean vehicle driving standard data, which is driving standard data of a lean vehicle that tilts to the right when turning right and tilts to the left when turning left.
A lean vehicle driving data acquisition unit for analysis that acquires lean vehicle driving data for analysis, which is driving data of the lean vehicle to be analyzed, which is a lean vehicle to be analyzed.
By analyzing the acquired lean vehicle driving data for analysis based on the acquired lean vehicle driving reference data, the analysis data of at least one of the analysis target person who is the driver to be analyzed and the analysis target lean vehicle can be obtained. Analysis data acquisition department to acquire and
An output data generator that generates output data for output using the analysis data,
A data output unit that outputs the output data and
With
The lean vehicle driving reference data is the lowest density among the four density ranges when the density range between the minimum density and the maximum density of the lean vehicle traveling on a public road is divided into four density ranges. Based on the running data of lean vehicles traveling on public roads in the low density range and the highest density range, the running of lean vehicles traveling on public roads in the medium density range, which is the remaining two density ranges out of the above four density ranges. Generated based on lean vehicle driving data for reference generation that contains a lot of data
The analysis lean vehicle travel data includes analysis travel density-related data related to the density of the lean vehicle when the analysis target person drives and travels on a public road with the analysis target lean vehicle.
Analyzing the lean vehicle running data of the lean vehicle to be analyzed,
Lean vehicle driving data analyzer.
In the lean vehicle driving data analyzer according to claim 11.
The reference generation lean vehicle driving data includes classification-related data for classifying at least one of the driver and the lean vehicle.
The analysis lean vehicle traveling data includes analysis classification-related data for classifying at least one of the analysis target person and the analysis target lean vehicle.
The analysis data acquisition unit analyzes the analysis lean vehicle travel data based on the lean vehicle travel reference data generated based on the reference generation lean vehicle travel data, thereby performing the analysis classification-related data. Acquire the analysis data of at least one of the analysis target person and the analysis target lean vehicle classified by using.
Lean vehicle driving data analyzer.
In the lean vehicle driving data analyzer according to claim 11 or 12.
The analysis data is an analysis result of synchronization of the lean vehicle driving data for analysis with respect to the lean vehicle driving reference data including data having a density similar to that of the analysis driving density-related data among the lean vehicle driving reference data. Obtained by using
Lean vehicle driving data analyzer.
In the lean vehicle traveling data analyzer according to any one of claims 11 to 13.
The analysis data includes data related to the evaluation result of the driving prediction skill when the analysis target person travels on a public road with the analysis target lean vehicle.
Lean vehicle driving data analyzer.
In the lean vehicle traveling data analyzer according to any one of claims 11 to 14.
The reference generation lean vehicle driving data includes the reference generation lean vehicle driving input data related to the driving input to the lean vehicle by the driver, and the reference generation lean vehicle related to the traveling position of the lean vehicle traveling on a public road. Includes at least one of the position data and the reference generation lean vehicle behavior data related to the lean vehicle behavior.
The analysis lean vehicle driving data includes analysis lean vehicle driving input data related to driving input to the analysis target lean vehicle by the analysis target person, and analysis related to the traveling position of the analysis target lean vehicle traveling on a public road. The lean vehicle position data for analysis and at least one of the lean vehicle behavior data for analysis related to the behavior of the lean vehicle to be analyzed are included.
Lean vehicle driving data analyzer.
In the lean vehicle traveling data analyzer according to any one of claims 11 to 15.
The reference generation lean vehicle traveling data further includes the reference generating lean vehicle traveling environment data related to the traveling environment in which the lean vehicle travels.
The analysis lean vehicle traveling data further 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 11 to 16.
The output data is generated as information processing analysis data used for further information processing.
Lean vehicle driving data analyzer.
An information processing method using the output data generated as the information processing analysis data by the lean vehicle traveling data analysis method according to claim 10.
Acquire the output data and
Acquire the first data different from the output data,
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 apparatus that uses the output data generated as the information processing analysis data by the lean vehicle traveling data analyzer according to claim 17.
An output data acquisition unit that acquires the output data,
A first data acquisition unit that acquires first data different from 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.
A second data output unit that outputs the second data, and
To prepare
Information processing device that uses analytical data.