JP7280944B2 - Lean vehicle travel data analysis method, lean vehicle travel data analysis device, information processing method using analysis data, and information processing device using analysis data - Google Patents

Description

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 analysis device, an information processing method using analysis data, and an information processing device using analysis data.

Devices for determining the driving skill of a rider are known. As a device for determining the driving skill of a rider, for example, the configuration disclosed in Patent Document 1 is known.

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

International publication WO2015/050038

Lean vehicles are used in various situations due to their high mobility and convenience. Therefore, there is a demand for lean vehicle-specific analysis that considers various usage scenarios.

When trying to obtain data on various situations such as the driving environment in order to analyze various lean vehicle usage scenes, the amount of data to be processed by the information processing device becomes enormous, and the hardware of the device becomes unusable. Higher load. As a result, the number of hardware resources required by the information processing apparatus increases, which imposes restrictions on the design of the hardware resources. Therefore, the degree of freedom in designing the hardware resources of the information processing apparatus is reduced.

SUMMARY OF THE INVENTION It is an object of the present invention to provide a lean vehicle travel data analysis method capable of outputting analysis data specific to a lean vehicle based on lean vehicle travel data while increasing the degree of freedom in designing hardware resources.

In analyzing the travel data of the lean vehicle, the inventors have noticed that the travel data of the lean vehicle and the travel data of the non-lean vehicle are significantly different. A lean vehicle is a vehicle that leans to the right when turning right and leans to the left when turning left.

A lean vehicle has a smaller body size than a non-lean vehicle. That is, the lean vehicle is smaller in size in the longitudinal direction and/or the lateral direction of the vehicle body than the non-lean vehicle. In addition, since the amount of rotational operation of the steering wheel of a lean vehicle is less than 360 degrees, the amount of rotational operation of the steering wheel of a lean vehicle is smaller than that of a vehicle that does not lean. Furthermore, a lean vehicle is a rider active vehicle that can be actively steered by a driver, unlike a vehicle that does not lean. Thus, driving a lean vehicle is different from driving a non-lean vehicle. The travel data of a lean vehicle that operates differently from a vehicle that does not lean in this way is significantly different from the travel data of a vehicle that does not lean, ie, a four-wheeled vehicle, for example.

The inventors of the present invention have studied the driving conditions of lean vehicles in more detail, and have found that lean vehicles have a much higher degree of freedom in driving according to the driver's will than vehicles that do not lean.

Therefore, when the driver is driving the lean vehicle, the driver tends to make more decisions and have more choices than when the driver is driving the non-lean vehicle.

Also, the driver is more likely to be exposed to external stresses when driving a lean vehicle than when driving a non-lean vehicle. Furthermore, the external stresses on a driver operating a lean vehicle are very diverse.

Also, a lean vehicle is lighter than a non-lean vehicle. As such, lean vehicles are more maneuverable and convenient than non-lean vehicles. Lean vehicles are used for various purposes and tend to be used more frequently. Therefore, the lean vehicle is used in various scenes.

The inventors of the present invention have studied various usage scenes of lean vehicles in detail, and found that the degree of freedom of lean vehicle driving based on the intention of the driver is inversely correlated with the density of lean vehicles traveling on public roads. I noticed that I have a In other words, when the density of lean vehicles running on public roads is low, the degree of freedom of lean vehicle driving is high, and when the density of lean vehicles running on public roads is high (for example, in Indonesia) For lean vehicle driving conditions, etc., https://www.youtube.com/watch?v=0A1jYWojQXk, https://www.youtube.com/watch?v=NqgDE-XqDVc), lean vehicle driving The degree of freedom of running according to the intention of the person is low.

Since a four-wheeled vehicle is larger than a lean vehicle, the driver of the four-wheeled vehicle has limited freedom of movement. Therefore, even if the density of four-wheeled vehicles running on public roads changes, the degree of freedom of driving of the four-wheeled vehicle does not change or changes only slightly. In other words, a four-wheeled vehicle running on a public road has a degree of freedom in the longitudinal direction in the same lane, but compared to a lean vehicle, the width of the vehicle relative to the width of the lane is large, so it is difficult to travel in the lateral direction. less freedom. Therefore, even if the density of the four-wheeled vehicle changes, the degree of freedom of travel does not change much. Therefore, in the case of a four-wheeled vehicle, the correlation between the degree of freedom of driving by the intention of the driver of the four-wheeled vehicle and the density of four-wheeled vehicles running on public roads 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.

In this way, the inventors of the present invention have found that, in the case of a lean vehicle, the degree of freedom of travel by the driver's intention of the lean vehicle has an inverse relationship with the density of lean vehicles traveling on public roads. It has been found that when considering the degree of freedom of the lean vehicle's driver's intentional travel, the density of lean vehicles traveling on public roads should be taken into consideration.

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

By using lean vehicle driving data that considers the degree of freedom of driving according to the intention of the driver, it is possible to obtain information unique to lean vehicles that has been difficult to output, such as the skill of driving a lean vehicle that takes into account various usage scenarios. analysis data can be output. For example, in an analysis using lean vehicle travel data with a high degree of freedom, the driver's ability to drive a lean vehicle can be analyzed more accurately and in more detail. In addition, for example, in an analysis using lean vehicle driving data in a state where the degree of freedom is limited to some extent, the prediction skill of the driver to predict the driving environment such as the movement of the surrounding vehicle can be more accurately and in detail. can be analyzed.

Moreover, since the lean vehicle driving data is analyzed with consideration given to the degree of freedom of driving according to the driver's intention, the data to be processed is limited compared to the case where all driving data is analyzed 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.

As described above, the present inventors created a lean vehicle travel data analysis method capable of outputting analysis data specific to a lean vehicle based on lean vehicle travel data while increasing the degree of freedom in designing hardware resources.

Furthermore, the present inventors analyzed the driving data of a lean vehicle traveling on a public road in consideration of the degree of freedom of driving according to the driver's intention, and found that it is more preferable for a driver who drives a lean vehicle for business use. It was found that analytical data can be provided. By using lean vehicle driving data that considers the degree of freedom of driving according to the driver's intention, it has been difficult to output information such as the skill of driving a lean vehicle that takes into account various usage scenarios. This is due to the ability to output analysis data unique to lean vehicles.

For example, consider a system that determines vehicle insurance premiums for lean vehicles. This system includes a mobile terminal equipped with a sensor that automatically collects lean vehicle travel data, a server that receives the lean vehicle travel data collected by the mobile terminal, a database that accumulates the collected lean vehicle travel data, and , a remote processing computer with a rating engine that determines a lean vehicle premium based on the collected lean vehicle travel data .

The rating engine can determine insurance risk and premium based on a driver's driving score determined from the collected lean vehicle travel data.

In this way, the inventors of the present invention can simplify the analysis of the risk of the driver by using the driving skill of the driver to evaluate the risk of the driver who drives the lean vehicle. 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 assessment, the present inventors analyzed the driving skill of the driver in consideration of the degree of freedom of driving according to the intention of the driver, thereby contributing to risk assessment in business using lean vehicles. We found that it is possible to output data with high applicability for Furthermore, the inventors of the present invention can simplify the data to be analyzed by considering the degree of freedom of driving according to the intention of the driver of the lean vehicle, so that the load on the hardware resources of the system due to data processing can be reduced. It was found that the design freedom of the hardware resources can be further improved by further reducing the load.

The analysis of driving skill includes not only the skill of driving a lean vehicle but also the skill (predictive skill) related to prediction when driving a lean vehicle. This driving skill is analytical data obtained by analyzing the lean vehicle driving data obtained when the person to be analyzed drives a lean vehicle on a public road based on the lean vehicle driving standard data described later. include.

A lean vehicle travel data analysis method according to an embodiment of the present invention acquires lean vehicle travel reference data, which is the travel reference data of a lean vehicle that leans to the right when turning to the right and leans to the left when turning to the left. a step of acquiring lean vehicle travel reference data for analysis; a step of acquiring lean vehicle travel data for analysis, which is travel data of the lean vehicle to be analyzed, which is the lean vehicle to be analyzed; Analysis data acquisition for acquiring analysis data of at least one of an analysis target person who is an analysis target driver and the analysis target lean vehicle by analyzing the acquired analysis lean vehicle travel data based on the driving reference data. an output data generation step of generating output data for output using the analysis data; and an output step of outputting the output data. When the density range between the lowest density and the highest density of a lean vehicle traveling on a public road is divided into four density ranges, the lean vehicle driving reference data is the lowest density among the four density ranges. From the driving data of a lean vehicle running on public roads in the low density range and the highest density range, the lean vehicle runs on public roads in the remaining two medium density ranges of the four density ranges. It is generated based on data-rich lean vehicle travel data for reference generation. The lean vehicle travel data for analysis includes travel density-related data for analysis relating to the density of lean vehicles when the person to be analyzed drives the lean vehicle to be analyzed and travels on public roads. The lean vehicle travel data analysis method analyzes the lean vehicle travel data of the lean vehicle to be analyzed.

A lean vehicle has a higher degree of freedom in driving according to the driver's intention than a non-lean vehicle. Therefore, the driver makes many decisions among many options when driving a lean vehicle. Also, the driver of a lean vehicle is susceptible to external stress. Furthermore, the running of a lean vehicle is more influenced by the driver's driving than the running of a non-lean vehicle.

Also, a lean vehicle is lighter than a non-lean vehicle. As such, lean vehicles are more maneuverable 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 described above, lean vehicles are used in a variety of situations, and lean vehicles have different travel data depending on the degree of freedom of travel according to the intention of the driver.

In addition, in the case of lean vehicles, the degree of freedom of travel according to the intention of the driver differs according to the density of lean vehicles traveling on public roads. Specifically, the degree of freedom of driving a lean vehicle according to the intention of the driver has an inverse correlation with the density of lean vehicles traveling on public roads. For example, when the density of lean vehicles traveling on public roads is low, the degree of freedom of lean vehicle driving is high, and when the density of lean vehicles traveling on public roads is high, lean The degree of freedom of travel according to the intention of the driver of the vehicle is low. Therefore, the density of lean vehicles traveling on public roads should be taken into consideration when considering the degree of freedom of driving of lean vehicles based on the intention of the driver.

Under the circumstances described above, by using lean vehicle travel data that considers the degree of freedom of travel according to the driver's intention, as in the configuration described above, a lean vehicle that takes into consideration various usage scenes can be developed. It is possible to output analysis data unique to lean vehicles, such as driving skills, which has been difficult to output until now. For example, in an analysis using lean vehicle travel data with a high degree of freedom, the driver's ability to drive a lean vehicle can be analyzed more accurately and in more detail. In addition, for example, in an analysis using lean vehicle driving data in a state where the degree of freedom is limited to some extent, the prediction skill of the driver to predict the driving environment such as the movement of the surrounding vehicle can be more accurately and in detail. can be analyzed.

Moreover, since the lean vehicle driving data is analyzed with consideration given to the degree of freedom of driving according to the driver's intention, the data to be processed is limited compared to the case where all driving data is analyzed without considering the state. can do. This reduces the load on the hardware resources of the system and increases the degree of freedom in designing the hardware resources.

As a result, the types of data to be processed by the device for analyzing lean vehicle travel data can be reduced, and the hardware load of the device can be reduced. Moreover, 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 reduce the load on the hardware resources of a device that analyzes lean vehicle travel data and increase the degree of freedom in designing the hardware resources of the device, while outputting analytical data specific to lean vehicles based on lean vehicle travel data. It is possible to provide a lean vehicle running data analysis method.

According to another aspect, the lean vehicle travel data analysis method of the present invention preferably includes the following configuration. The reference-generating lean vehicle running data includes segment-related data for segmenting at least one of a driver and a lean vehicle. The analysis lean vehicle travel 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 obtaining step, the classification-related data for analysis is obtained by analyzing the lean vehicle traveling data for analysis based on the lean vehicle traveling reference data generated based on the lean vehicle traveling data for reference generation. to acquire analysis data of at least one of the analysis target person and the analysis target lean vehicle classified using .

This enables the device for analyzing the lean vehicle travel data to process for each segment of at least one of the driver and the lean vehicle. Therefore, the hardware load of the device can be reduced. Moreover, 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.

According to another aspect, the lean vehicle travel data analysis method of the present invention preferably includes the following configuration. The analysis data is an analysis result of the synchrony of the lean vehicle travel data for analysis with respect to the lean vehicle travel reference data including data similar in density to the analysis travel density related data among the lean vehicle travel 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 crowded with other lean vehicles, the travel data of the analysis target lean vehicle and the travel data of the other lean vehicle By evaluating the synchronism with the data, it is possible to obtain analysis data unique to the lean vehicle for at least one of the analysis target person and the analysis target lean vehicle.

Therefore, it is possible to reduce the load on the hardware resources of a device that analyzes lean vehicle travel data and increase the degree of freedom in designing the hardware resources of the device, while outputting analytical data specific to lean vehicles based on lean vehicle travel data. A lean vehicle running data analysis method can be realized.

According to another aspect, the lean vehicle travel data analysis method of the present invention preferably includes the following configuration. The analysis data includes data related to evaluation results of predictive driving skills when the person to be analyzed travels on a public road in the lean vehicle to be analyzed.

As a result, analysis data including data relating to the evaluation result of the predicted driving skill of the person to be analyzed is obtained. Driving anticipation skills are more important when driving a lean vehicle than when driving a non-lean vehicle. By including the data related to the evaluation result of the predictive driving skill in the analysis data, the analysis data unique to the lean vehicle can be obtained.

Therefore, it is possible to reduce the load on the hardware resources of a device that analyzes lean vehicle travel data and increase the degree of freedom in designing the hardware resources of the device, while outputting analytical data specific to lean vehicles based on lean vehicle travel data. A lean vehicle running data analysis method can be realized.

According to another aspect, the lean vehicle travel data analysis method of the present invention preferably includes the following configuration. The lean vehicle driving data for reference generation includes: lean vehicle driving input data for reference generation related to driving input to the lean vehicle by the driver; lean vehicle driving input data for reference generation related to a running position of the lean vehicle traveling on a public road and at least one of position data and lean vehicle behavior data for generating a reference relating to behavior of the lean vehicle. The lean vehicle driving data for analysis includes: lean vehicle driving input data for analysis related to the driving input to the lean vehicle to be analyzed by the person to be analyzed, and analysis related to the running position of the lean vehicle to be analyzed traveling on public roads. lean vehicle position data for analysis; and lean vehicle behavior data for analysis related to behavior of the lean vehicle to be analyzed.

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

That is, the analysis lean vehicle position data relating to the travel position of the analysis target lean vehicle is, for example, when the analysis target lean vehicle driven by the analysis target travels at a predetermined density, the positional relationship with other lean vehicles. used to identify Further, the analysis lean vehicle behavior data related to the behavior of the analysis target lean vehicle is, for example, when the analysis target lean vehicle driven by the analysis target travels at a predetermined density, the analysis target lean vehicle is driven by the analysis target. It is used to detect the driving skill of the person to be analyzed from the lean vehicle behavior for analysis of the target lean vehicle.

With this configuration, it is possible to more accurately analyze the lean vehicle travel data for analysis based on the lean vehicle travel reference data. In addition, by using the lean vehicle travel data for analysis in which the type of data is specified, the types of data to be processed by the device for analyzing the lean vehicle travel data can be reduced, and the hardware load of the device can be further reduced. Moreover, 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 realize a lean vehicle travel data analysis method capable of outputting lean vehicle specific analysis data based on the lean vehicle travel data while increasing the degree of freedom in designing the hardware resources of the device that analyzes the lean vehicle travel data.

According to another aspect, the lean vehicle travel data analysis method of the present invention preferably includes the following configuration. The reference-generating lean vehicle running data further includes reference-generating lean vehicle running environment data relating to a running environment in which the lean vehicle travels. The lean vehicle travel data for analysis further includes lean vehicle travel environment data for analysis related to the travel environment in which the lean vehicle to be analyzed travels.

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 signals and facilities, regulation information on road travel, and the like. The lean vehicle driving environment data, together with the lean vehicle behavior data and the lean vehicle position data, can be used to analyze lean vehicle driving data.

With this configuration, the lean vehicle travel data for analysis obtained when the lean vehicle driven by the person to be analyzed travels on a public road can be analyzed more accurately using the lean vehicle travel reference data. Further, by using the lean vehicle travel data for which the type of data is specified, it is possible to reduce the types of data to be processed by the device for analyzing the lean vehicle travel data, thereby further reducing the load on the hardware of the device. Moreover, 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 realize a lean vehicle travel data analysis method capable of outputting analysis data specific to a lean vehicle based on lean vehicle travel data while increasing the degree of freedom in designing hardware resources.

According to another aspect, the lean vehicle travel data analysis method of the present invention preferably includes the following configuration. The reference-generating lean vehicle running data includes data in a state in which a plurality of options for a driver's judgment are limited by vehicles around the lean vehicle, but a plurality of options remain. The lean vehicle travel data for analysis includes data in a state in which a plurality of options for judgment of the person to be analyzed is limited by vehicles surrounding the lean vehicle to be analyzed, but a plurality of options remain.

Lean vehicle travel data with limited but multiple driver decision options are more lean vehicle specific than lean vehicle travel data with no driver decision options left. contains data for Therefore, it is possible to obtain analytical data specific to a lean vehicle by using the lean vehicle travel data in which the driver's decision options are limited but left in a plurality of options. In addition, by using the lean vehicle travel data for which the type of data is specified, it is possible to reduce the types of data to be processed by the device for analyzing the lean vehicle travel data, thereby further reducing the load on the hardware of the device. Moreover, 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 realize a lean vehicle travel data analysis method capable of outputting lean vehicle specific analysis data based on the lean vehicle travel data while increasing the degree of freedom in designing the hardware resources of the device that analyzes the lean vehicle travel data.

According to another aspect, the lean vehicle travel data analysis method of the present invention preferably includes the following configuration. The lean vehicle travel data for reference generation includes data of a state in which at least one of a fellow passenger and an object is mounted. The lean vehicle travel data for analysis includes data of a state in which at least one of a passenger and an object is loaded.

A lean vehicle loaded with at least one of a passenger and an object exhibits behavior specific to a lean vehicle compared to a state where neither a passenger nor an object is loaded. Therefore, it is possible to more accurately analyze the lean vehicle travel data of the person to be analyzed, who is the driver, by using the lean vehicle travel data including the data of the state in which at least one of the passenger and the object is mounted. In addition, by using the lean vehicle travel data for which the type of data is specified, it is possible to reduce the types of data to be processed by the device for analyzing the lean vehicle travel data, thereby further reducing the load on the hardware of the device. Moreover, 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 realize a lean vehicle travel data analysis method capable of outputting lean vehicle specific analysis data based on the lean vehicle travel data while increasing the degree of freedom in designing the hardware resources of the device that analyzes the lean vehicle travel data.

According to another aspect, the lean vehicle travel data analysis method of the present invention preferably includes the following configuration. The lean vehicle travel data analysis method stores the acquired analysis data. The output data is generated using the stored plurality of analytical data.

By using a plurality of pieces of analysis data, the lean vehicle travel data for analysis of the person to be analyzed can be analyzed with higher accuracy.

Therefore, it is possible to realize a lean vehicle travel data analysis method capable of outputting analysis data unique to a lean vehicle based on the lean vehicle travel data while increasing the degree of freedom in designing hardware resources of an apparatus for analyzing lean vehicle travel data.

According to another aspect, the lean vehicle travel data analysis method of the present invention preferably includes the following configuration. The output data is generated as analysis data for information processing used for further information processing.

As a result, the analysis data obtained by the lean vehicle travel data analysis method using the lean vehicle travel data for analysis of the lean vehicle driven by the analysis subject 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 the lean vehicle travel data.

A lean vehicle travel data analyzer according to an embodiment of the present invention acquires lean vehicle travel reference data, which is the travel reference data of a lean vehicle that leans to the right when turning to the right and leans to the left when turning to the left. a lean vehicle travel reference data acquisition unit that acquires lean vehicle travel data for analysis that is travel data of the lean vehicle to be analyzed that is the lean vehicle to be analyzed; Analysis data acquisition for acquiring analysis data of at least one of an analysis target person who is an analysis target driver and the analysis target lean vehicle by analyzing the acquired analysis lean vehicle travel data based on the driving reference data. 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. When the density range between the lowest density and the highest density of a lean vehicle traveling on a public road is divided into four density ranges, the lean vehicle driving reference data is the lowest density among the four density ranges. From the driving data of a lean vehicle running on public roads in the low density range and the highest density range, the lean vehicle runs on public roads in the remaining two medium density ranges of the four density ranges. It is generated based on data-rich lean vehicle travel data for reference generation. The lean vehicle travel data for analysis includes travel density-related data for analysis relating to the density of lean vehicles when the person to be analyzed drives the lean vehicle to be analyzed and travels on public roads. The lean vehicle travel data analysis device analyzes the lean vehicle travel data of the lean vehicle to be analyzed.

According to another aspect, the lean vehicle travel data analysis device of the present invention preferably includes the following configuration. The reference-generating lean vehicle running data includes segment-related data for segmenting at least one of a driver and a lean vehicle. The analysis lean vehicle travel 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 lean vehicle travel data for analysis based on the lean vehicle travel reference data generated based on the lean vehicle travel data for reference generation, thereby obtaining the classification related data for analysis. to acquire analysis data of at least one of the analysis target person and the analysis target lean vehicle classified using .

According to another aspect, the lean vehicle travel data analysis device of the present invention preferably includes the following configuration. The analysis data is an analysis result of the synchrony of the lean vehicle travel data for analysis with respect to the lean vehicle travel reference data including data similar in density to the analysis travel density related data among the lean vehicle travel reference data. obtained using

According to another aspect, the lean vehicle travel data analysis device of the present invention preferably includes the following configuration. The analysis data includes data related to evaluation results of predictive driving skills when the person to be analyzed travels on a public road in the lean vehicle to be analyzed.

According to another aspect, the lean vehicle travel data analysis device of the present invention preferably includes the following configuration. The lean vehicle driving data for reference generation includes: lean vehicle driving input data for reference generation related to driving input to the lean vehicle by the driver; lean vehicle driving input data for reference generation related to a running position of the lean vehicle traveling on a public road and at least one of position data and lean vehicle behavior data for generating a reference relating to behavior of the lean vehicle. The lean vehicle driving data for analysis includes: lean vehicle driving input data for analysis related to the driving input to the lean vehicle to be analyzed by the person to be analyzed, and analysis related to the running position of the lean vehicle to be analyzed traveling on public roads. lean vehicle position data for analysis; and lean vehicle behavior data for analysis related to behavior of the lean vehicle to be analyzed.

According to another aspect, the lean vehicle travel data analysis device of the present invention preferably includes the following configuration. The reference-generating lean vehicle running data further includes reference-generating lean vehicle running environment data relating to a running environment in which the lean vehicle travels. The lean vehicle travel data for analysis further includes lean vehicle travel environment data for analysis related to the travel environment in which the lean vehicle to be analyzed travels.

According to another aspect, the lean vehicle travel data analysis device of the present invention preferably includes the following configuration. The output data is generated as analysis data for information processing used for further information processing.

An information processing method using analysis data according to an embodiment of the present invention is an information processing method using the output data generated as the information processing analysis data by the lean vehicle traveling data analysis method described above. This information processing method acquires the output data, acquires first data different from the output data, and uses the output data and the first data to obtain a second data different from the output data and the first data. Generate data and output the second data.

The information processing method using analysis data may be any information processing method as long as it is an information processing method using analysis data obtained by analyzing lean vehicle travel data. For example, the first data and the second data relate to markets, products, services, environments or customers used in businesses such as lean vehicle sharing, lean vehicle rental, lean vehicle leasing, lean vehicle 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 traveling data, and the output data Using the first data different from the above, the obtained output data and the second data different from the first data are generated and output. Therefore, second data with higher accuracy can be generated and output.

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

An information processing apparatus using analysis data according to an embodiment of the present invention is an information processing apparatus using the output data generated as the analysis data for information processing by the lean vehicle travel data analysis apparatus described above. This information processing apparatus includes an output data acquisition unit that acquires the output data, a first data acquisition unit that acquires first data different from the output data, and the output data and the first data, the 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 terminology used herein is for the purpose of defining particular embodiments only and is not intended to limit the invention by the terminology.

As used herein, "and/or" includes all combinations of one or more of the associated listed constructs.

As used herein, the use of "including," "comprising," or "having," and variations thereof, refers to the features, steps, elements, components, and/or , may include one or more of the steps, acts, elements, components and/or groups thereof, although specifying the presence of equivalents thereof.

As used herein, "attached," "connected," "coupled," and/or equivalents thereof are used broadly and include "direct and indirect" attachment, It includes both connection and coupling. Furthermore, "connected" and "coupled" are not limited to physical or mechanical connections or couplings, but can include direct or indirect connections or couplings.

Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

Terms defined in commonly used dictionaries are to be construed to have a meaning consistent with their meaning in the context of the relevant art and this disclosure, unless explicitly defined herein. , is not to be interpreted in an idealized or overly formal sense.

In describing the present invention, it is understood that a number of techniques and processes are disclosed. Each of these has individual benefits and can also be used in conjunction with one or more, or possibly all, of the other disclosed techniques.

Therefore, for the sake of clarity, the description of the invention refrains from unnecessarily repeating all possible combinations of the 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.

In this specification, embodiments of a lean vehicle travel data analysis method, a lean vehicle travel data analysis apparatus, an information processing method using analysis data, and an information processing apparatus using analysis data according to the present invention will be described.

In the following description, numerous specific examples are set forth to provide a thorough understanding of the invention. However, it will be obvious to one skilled in the art that the invention may be practiced without these specific examples.

Accordingly, the following disclosure should be considered illustrative of the invention and is not intended to limit the invention to the specific embodiments illustrated by the following drawings or description.

[Lean vehicle]
In this specification, a lean vehicle is a vehicle that turns in an inclined posture. Specifically, a lean vehicle is a vehicle that leans to the left when turning to the left and to the right when turning to the right in the lateral direction of the vehicle. Moreover, a lean vehicle is a vehicle whose width is narrower than half the width of a four-wheeled vehicle, or a vehicle whose width is narrower than half the width of the lane in which the four-wheeled vehicle travels. A lean vehicle may be a single-seat vehicle or a vehicle that can accommodate multiple people. Note that lean vehicles include not only two-wheeled vehicles but also all vehicles that turn in an inclined posture, such as three-wheeled vehicles and four-wheeled vehicles.

[Lean Vehicle Density]
In this specification, the density of lean vehicles means, for example, the number of lean vehicles traveling within the width of a road on which one four-wheeled vehicle travels. The density of a lean vehicle means, for example, that two four-wheeled vehicles are traveling within the front-to-rear length from the front end to the rear end of the two four-wheeled vehicles 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 a road on which one four-wheeled vehicle travels, and the width 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 an area having a longitudinal length to the end.

When the density range between the lowest density and the highest density of a lean vehicle traveling on a public road is divided into four density ranges, the density of the lean vehicle is the lowest among the four density ranges. implying a small density range. The high density of a lean vehicle means that when the density range between the lowest density and the highest density of a lean vehicle traveling on a public road is divided into four density ranges, the density is the highest among the four density ranges. means high density range. When the density range of a lean vehicle traveling on a public road is divided into four density ranges between the lowest density and the highest density, the remaining density of the four density ranges means two density ranges of

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

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

[Driving freedom]
As used herein, driving freedom refers to the degree of freedom the driver has to choose to drive when the driver is driving a lean vehicle. The selection of driving decisions includes, for example, selection of a lean vehicle traveling route, selection of acceleration/deceleration, selection of operation of equipment in a lean vehicle, and the like.

The driver's intentional driving freedom in a lean vehicle has an inverse correlation with the density of lean vehicles traveling on public roads. In other words, when the density of lean vehicles traveling on public roads is low, the degree of freedom of driving in lean vehicles according to the driver's will is high. When the density of a lean vehicle traveling on a public road is medium density, the degree of freedom of driving in the lean vehicle according to the intention of the driver is medium. When the density of lean vehicles traveling on public roads is high, the degree of freedom of driving in lean vehicles is low.

When the range between the lowest and highest degrees of freedom of travel is divided into four ranges, the degree of freedom of travel according to the driver's will is low, and the degree of freedom of travel in the four ranges is means the range with the lowest degree of freedom. A high degree of freedom of driving according to the driver's intention means that when the range between the lowest and highest degrees of freedom of driving is divided into four ranges, means the range with the highest degree of freedom. When the range between the lowest degree of freedom and the highest degree of freedom of travel is divided into four ranges, the remaining four ranges means two ranges of

In the case of a four-wheeled vehicle, when the density of four-wheeled vehicles running on public roads is low, the four-wheeled vehicle has a medium degree of freedom of travel according to the intention of the driver. When the densities of four-wheeled vehicles running on public roads are medium density and high density, the four-wheeled vehicle has a low degree of freedom in driving according to the intention of the driver.

[Degree-of-freedom related data]
In this specification, the degree-of-freedom-related data is data related to the degree of freedom of travel, 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 travel 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 in operating equipment in the lean vehicle, and the like. include.

[Category-related data]
In this specification, 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. (For example, the type and output of the drive source, the performance of the suspension, etc.).

[Public road]
In this specification, a public road is a road for public use that general vehicles can pass, not a road for simulations and circuits. The public road includes a private road through which general vehicles can pass.

[Driving skill]
In this specification, 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 the predictive skill of predicting when driving a lean vehicle.

[Lean vehicle driving data]
In this specification, lean vehicle travel data is data related to travel of a lean vehicle. Specifically, the lean vehicle driving data includes lean vehicle driving input data related to the driver's driving input to the lean vehicle, lean vehicle behavior data related to the behavior of the lean vehicle, and lean vehicle driving position related to the lean vehicle travel position. 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 is driven. Further, the lean vehicle running data may include processed data obtained by processing lean vehicle behavior data, lean vehicle position data, lean vehicle running environment data, and the like. The lean vehicle travel data may include processed data processed using lean vehicle behavior data, lean vehicle position data, lean vehicle travel environment data, and other data.

[Lean vehicle operation input data]
In this specification, the lean vehicle driving input data is data related to the driver's operation input when the driver drives the lean vehicle. Specifically, the lean vehicle driving input data may include data related to accelerator operation, brake operation, steering, or change in the position of the center of gravity due to a change in driver's posture. Further, specifically, the lean vehicle driving input data may include data related to operation of various switches such as a horn switch, a winker 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. In a lean vehicle, there are many kinds of operations by the driver, and the driver has a high degree of freedom of selection during driving, so the driver's driving skill tends to be strongly reflected. Further, the lean vehicle driving input data may include processed data obtained by processing data obtained from a sensor or the like. The lean vehicle driving input data may include processed data processed using data acquired from a sensor or the like and other data.

[Lean vehicle behavior data]
In this specification, the lean vehicle behavior data is data related to the lean vehicle behavior caused by the driver's operation input 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 change when the driver who is the subject of analysis drives. That is, the lean vehicle behavior data is such that when the driver who is the person to be analyzed accelerates or decelerates the lean vehicle by operating the accelerator or the brake, the steering of the lean vehicle and the change in the position of the center of gravity are performed. This data expresses the behavior of a lean vehicle that occurs when

As described above, the lean vehicle behavior data includes not only data related to the acceleration, speed, and angle of the lean vehicle, but also data generated in the lean vehicle by switch operations performed by the driver, who is the subject of analysis, on the lean vehicle. May include actions. In other words, the lean vehicle behavior data includes data related to operations of the lean vehicle caused by operation of various switches such as a horn switch, a winker 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 lean vehicle driving skill of the driver. Further, the lean vehicle behavior data may include processed data obtained by processing data acquired from a sensor or the like. The lean vehicle behavior data may include processed data processed using data acquired from a sensor or the like and other data.

[Lean vehicle position data]
As used herein, lean vehicle position data is data relating to the position of a lean vehicle. For example, the lean vehicle position data can be detected based on GPS, communication base station information of the 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 lean vehicle specific 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 processed data processed using data acquired from a sensor or the like and other data.

[Lean Vehicle Driving Environment Data]
In this specification, 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 signals and facilities, regulation information on driving on roads, and the like. Map data may also be associated with environmental data such as weather, temperature, or humidity. The lean vehicle driving environment data, together with the lean vehicle behavior data and the lean vehicle position data, can be used to analyze lean vehicle driving data.

The information on road conditions includes information on roads (areas) in a congested environment, such as frequent traffic jams and many parked vehicles. By combining this information with the time period, the accuracy of the information is further improved. Further, the information on road conditions includes information on roads that are likely to be flooded when there is a squall.

The lean vehicle driving environment data is considered to be an example of a factor affecting lean vehicle driving. The lean vehicle driving environment data influences driver judgment, driving and lean vehicle driving. Therefore, by using the lean vehicle driving environment data, the data obtained by analyzing the lean vehicle driving data is more likely to include data specific to the lean vehicle. In addition, since the use of the lean vehicle driving environment data affects the purpose and frequency of use of the lean vehicle, the data obtained by analyzing the lean vehicle driving data includes more data specific to the lean vehicle. easy to fall off.

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 running environment data is an external environment recognition device mounted on the lean vehicle. More specifically, means for acquiring the lean vehicle driving environment data include cameras, radars, and the like. Further, for example, the means for acquiring the lean vehicle running environment data is a communication device. More specifically, the means for acquiring the lean vehicle driving 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 via the Internet, for example.

[Synchronization of lean vehicle driving data]
In this specification, the synchrony of the lean vehicle travel data means that the group behavior including the lean vehicle travel data of a plurality of lean vehicles including the lean vehicle driven by the person to be analyzed is compared with the lean vehicle driven by the person to be analyzed. means the degree of divergence of lean vehicle travel data. The lower the degree of divergence, the higher the synchronicity of the person to be analyzed. The group behavior may include, for example, an average value or behavior frequency data obtained from lean vehicle running data of the plurality of lean vehicles. That is, the degree of deviation is the degree of deviation between the group behavior frequency obtained from the lean vehicle traveling data of the plurality of lean vehicles and the behavior frequency obtained from the lean vehicle traveling data of the lean vehicle driven by the person to be analyzed. may

[Contains more B than A]
In the present specification, "containing more B than A" may not contain A at all. The phrase "containing more B than A" may include A in part.

For example, when the density range between the lowest density and the highest density of a lean vehicle traveling on a public road is divided into four density ranges, the lowest density range and the highest density among the four density ranges. It means that more data of lean vehicles traveling on public roads in the medium density ranges, which are the remaining two density ranges of the four density ranges, is included than the traveling data of lean vehicles traveling on public roads in the high density range of . , the low density range and the high density range may not contain any driving data for lean vehicles traveling on public roads. For example, when the density range between the lowest density and the highest density of a lean vehicle traveling on a public road is divided into four density ranges, the lowest density range and the highest density among the four density ranges. It means that more data of lean vehicles traveling on public roads in the medium density ranges, which are the remaining two density ranges of the four density ranges, is included than the traveling data of lean vehicles traveling on public roads in the high density range of . , a portion of driving data of a lean vehicle traveling on a public road in a low density range and a high density range.

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

FIG. 1 is a diagram showing a schematic configuration of a lean vehicle travel data analysis device according to an embodiment of the present invention. FIG. 2 is a flow chart showing an example of the operation of the lean vehicle travel data analysis device. FIG. 3 is a diagram showing a schematic configuration of a lean vehicle travel data analysis device according to the second embodiment. FIG. 4 is a diagram showing a schematic configuration of a lean vehicle travel data analysis system according to the third embodiment. FIG. 5 is a flow chart showing an example of the operation of the information processing device. FIG. 6 is a top view of a lean vehicle for explaining the density of lean vehicles traveling on public roads. FIG. 7 is a diagram showing the relationship between the density of lean vehicles traveling on public roads and the degree of freedom of travel determined by the intention of the driver. 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 travel determined by the intention of the driver. FIG. 9 is a diagram showing the schematic configuration of the lean vehicle travel data analysis device and the relationship between the density of lean vehicles traveling on public roads and the degree of freedom of travel determined by the intention of the driver.

Each embodiment will be described below with reference to the drawings. Note that the dimensions of the constituent members in each drawing do not faithfully represent the actual dimensions of the constituent members, the dimensional ratios of the respective constituent members, and the like.

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

The lean vehicle running data in the present embodiment is data related to lean vehicle running. The lean vehicle travel data is used to obtain analysis data including data relating to the driving skill of the driver, among the data relating to the travel of the lean vehicle obtained when the driver drives the lean vehicle. means the data that is

Specifically, the lean vehicle driving data includes lean vehicle driving input data related to a driver's driving input to the lean vehicle, lean vehicle behavior data related to behavior of the lean vehicle, and lean vehicle running position. It includes lean vehicle position data, lean vehicle driving environment data related to the driving environment in which the lean vehicle drives, and the like. The lean vehicle running 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 running environment data. The lean vehicle running data may include only one or more of the lean vehicle driving input data, the lean vehicle behavior data, the lean vehicle position data, and the lean vehicle running environment data.

For example, when the lean vehicle is a lean vehicle X which is a lean vehicle to be analyzed, the lean vehicle travel data is lean vehicle travel data for analysis, the lean vehicle driving input data is 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 driving environment data is lean vehicle driving environment data for analysis.

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

The lean vehicle driving input data is data related to operation input by the driver when the driver drives the lean vehicle. Specifically, the lean vehicle driving input data may include data related to accelerator operation, brake operation, steering, or change in the position of the center of gravity due to a change in driver's posture. Further, specifically, the lean vehicle driving input data may include operation of various switches such as a horn switch, a winker 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. In a lean vehicle, there are many kinds of operations by the driver, and the driver has a high degree of freedom of selection during driving, so the driver's driving skill tends to be strongly reflected. Further, the lean vehicle driving input data may include processed data obtained by processing data obtained from a sensor or the like. The lean vehicle driving input data may include processed data processed using data acquired from a sensor or the like and other data.

The lean vehicle behavior data is data relating to the behavior of the lean vehicle caused 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, acceleration, velocity, and angle of the lean vehicle that change 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 when the attitude of the lean vehicle including steering or changing the position of the center of gravity of the lean vehicle is changed. This data represents the behavior of a lean vehicle.

As described above, the lean vehicle behavior data may include not only data related to the acceleration, speed, and angle of the lean vehicle, but also operations that occur in the lean vehicle due to switch operations performed by the driver on the lean vehicle. In other words, the lean vehicle behavior data includes data related to operations of the lean vehicle caused by operation of various switches such as a horn switch, a winker switch, and a lighting switch. The lean vehicle behavior data strongly reflects the driving skill of the driver. Therefore, the lean vehicle behavior data also tends to strongly reflect the driving skill of the driver. Further, the lean vehicle behavior data may include processed data obtained by processing data acquired from a sensor or the like. The lean vehicle behavior data may include processed data processed using data acquired from a sensor or the like and other data.

The lean vehicle position data is data relating to the running position of the lean vehicle. For example, the lean vehicle position data can be detected based on GPS, communication base station information of the communication portable 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 lean vehicle position data also tends to strongly reflect the driving skill of the driver. Further, the lean vehicle position data may include processed data obtained by processing data acquired from a sensor or the like. The lean vehicle position data may include processed 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 signals and facilities, regulation information on road travel, and the like. Further, the map data may be associated with environmental data such as weather, temperature or humidity. The lean vehicle driving environment data, together with the lean vehicle driving input data, the lean vehicle behavior data and the lean vehicle position data, can be used to analyze lean vehicle driving data.

The information on road conditions includes information on roads (areas) in a congested environment, such as frequent traffic jams and many parked vehicles. By combining this information with the time period, the accuracy of the information is further improved. Further, the information on road conditions includes information on roads that are likely to be flooded when there is a squall.

The lean vehicle driving environment data is considered to be an example of external stress that the driver receives. The lean vehicle driving environment data affects the driving of the driver. Therefore, by using the lean vehicle running environment data, the driving skill of the driver tends to appear more strongly in the lean vehicle running data. In addition, the use of the lean vehicle driving environment data affects the purpose and frequency of use of the lean vehicle, so the lean vehicle driving data is more likely to include data specific to the lean vehicle.

The lean vehicle travel data analysis device 1 includes a lean vehicle travel reference data acquisition unit 10, an analytical lean vehicle travel data acquisition unit 20, an analysis data acquisition unit 30, an output data generation unit 40, a data output unit 50, and a data storage unit 60 . In this embodiment, the lean vehicle travel data analysis device 1 is, for example, a portable terminal owned by the person to be analyzed. Note that the lean vehicle travel data analysis device 1 may be an arithmetic processing device that acquires data through communication and performs arithmetic processing.

The lean vehicle travel data acquisition unit 20 for analysis acquires lean vehicle travel data for analysis including travel data obtained when the driver, who is the person to be analyzed, drives the lean vehicle X on a public road.

Specifically, when the person to be analyzed drives the lean vehicle X, the analysis lean vehicle travel data acquisition unit 20 acquires data included in the lean vehicle travel data of the lean vehicle X, that is, the lean vehicle driving data to be analyzed. Input data, lean vehicle behavior data for analysis, lean vehicle position data for analysis, lean vehicle driving environment data for analysis, and the like are acquired.

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

The lean vehicle travel data acquisition unit 20 for analysis acquires data including, for example, the acceleration, speed, and angle of the lean vehicle X that change when the driver who is the person to be analyzed drives the lean vehicle X, as the lean vehicle behavior for analysis. You may acquire it as data. The lean vehicle travel data acquisition unit 20 for analysis acquires the lean vehicle behavior data for analysis using, for example, a gyro sensor. The lean vehicle behavior data for analysis is such that when the driver who is the person to be analyzed accelerates or decelerates the lean vehicle X by operating the accelerator or the brake, the attitude change including the change in the steering or the position of the center of gravity of the lean vehicle X is performed. This is data representing the behavior of the lean vehicle X that occurs when, for example,

Further, the lean vehicle travel data acquisition unit 20 for analysis acquires, as the lean vehicle behavior data, an operation that occurs in the lean vehicle X due to a switch operation or the like performed on the lean vehicle X by the driver who is the person to be analyzed. good. That is, the lean vehicle traveling data acquisition unit 20 for analysis acquires, as the lean vehicle behavior data for analysis, data related to the operation of the lean vehicle X due to the operation of various switches such as a horn switch, a blinker switch, and a lighting switch. may These data are transmitted from the lean vehicle X to the lean vehicle travel data analysis device 1 .

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

The lean vehicle travel data acquisition unit 20 for analysis may acquire the lean vehicle travel environment data for analysis 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 signals and facilities, or regulation information on road travel. Further, 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 relating to the road traffic environment (accompanying information for the road such as traffic lights) and rule information relating to driving on the road are associated with each other.

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

In the present embodiment, the lean vehicle travel data acquisition unit 20 for analysis may also acquire information (for example, classification-related data) regarding the person to be analyzed 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 analysis device 1. You may The analytical lean vehicle travel data acquisition unit 20 may acquire information from the lean vehicle X. FIG.

Note that the analytical lean vehicle traveling data acquisition unit 20 may receive and acquire detection signals from a gyro sensor provided in the lean vehicle X, a GPS, a detection unit that detects operation signals of various switches, and the like.

The lean vehicle travel data for analysis includes travel density-related data for analysis relating to the density of lean vehicles X traveling on public roads. The lean vehicle travel data for analysis may include classification-related data for analysis for classifying the person to be analyzed.

The density of lean vehicles means, for example, the number of lean vehicles traveling within the width of a road on which one four-wheeled vehicle travels. The density of a lean vehicle means, for example, that two four-wheeled vehicles are traveling within the front-to-rear length from the front end to the rear end of the two four-wheeled vehicles 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 a road on which one four-wheeled vehicle travels, and the width 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 an area having a longitudinal length to the end.

FIG. 6 is a top view of the lean vehicle X for explaining the density of the lean vehicle X traveling on the public road. 6A shows a case where the density of lean vehicles X is low, FIG. 6B shows a case where the density of lean vehicles X is high, and FIG. 6C shows a case where the density of four-wheeled vehicles P is high. Each is shown schematically. In addition, 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. As shown in FIG.

In FIG. 6, the number of lean vehicles X positioned between a pair of dashed lines is the density of lean vehicles X traveling on public roads. As shown in FIG. 6, a four-wheeled vehicle P traveling on a public road needs to secure a distance in front and behind that can maintain an appropriate inter-vehicle distance. The number of four-wheeled vehicles P that do not change much. On the other hand, since the lean vehicle X traveling on the public road has a smaller width than the four-wheeled vehicle, the density of the lean vehicle X tends to change greatly.

FIG. 7 is a diagram showing the relationship between the density of lean vehicles X traveling on public roads and the degree of freedom of travel determined by the intention of the driver. As described above, the degree of freedom of driving the lean vehicle X according to the intention of the driver 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 driver's freedom of driving 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 the lean vehicle X to travel according to the intention of the driver is medium. When the density of the lean vehicles X traveling on the public road is high, the degree of freedom of the lean vehicle X to drive according to the driver's intention is low.

FIG. 8 is a diagram showing the relationship between the density (the number of vehicles) of four-wheeled vehicles P and the degree of freedom of travel according to the intention of the driver. In this way, there is no strong negative correlation between the density of four-wheeled vehicles P traveling on public roads and the degree of freedom of travel determined by the driver's intention, as in the case of lean vehicles.

When the density range between the lowest density and the highest density of a lean vehicle traveling on a public road is divided into four density ranges, the density of the lean vehicle is the lowest among the four density ranges. implying a small density range. The high density of a lean vehicle means that when the density range between the lowest density and the highest density of a lean vehicle traveling on a public road is divided into four density ranges, the density is the highest among the four density ranges. means high density range. When the density range of a lean vehicle traveling on a public road is divided into four density ranges between the lowest density and the highest density, the remaining density of the four density ranges means two density ranges of

The driving density related data is data related to the density of other lean vehicles traveling on public roads when the driver is driving the lean vehicle on public roads.

The degree of freedom of driving means the degree of freedom of the person to be analyzed to make a decision when the person to be analyzed is driving a lean vehicle. The selection of driving judgment includes, for example, selection of the travel route of the lean vehicle X, selection of acceleration/deceleration, selection of operation of equipment in the lean vehicle, and the like.

The degree-of-freedom-related data is data related to the degree of freedom of travel, which means the degree of freedom for the driver to select driving when the driver is driving the lean vehicle. Therefore, the degree-of-freedom-related data are 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 in operating equipment in the lean vehicle. and so on.

The degree-of-freedom related data is generated using, for example, lean vehicle position data and lean vehicle running environment data. The degree of freedom related data may also be generated using at least one of lean vehicle driving input data and 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. (For example, the type and output of the drive source, the performance of the suspension, etc.).

For example, when the lean vehicle is a lean vehicle X which is a lean vehicle to be analyzed, the travel density related data is travel density related data for analysis, the degree of freedom related data is degree of freedom related data for analysis, and the classification related The data is segment-related data for analysis.

The lean vehicle travel data analysis device 1 analyzes the lean vehicle travel data for analysis and generates analysis data by considering the travel density related data for analysis, thereby obtaining a lean vehicle X in which various usage scenes are considered. It is possible to output analysis data unique to lean vehicles, such as driving skills, which was difficult to output until now. For example, the lean vehicle travel data analysis device 1 performs analysis using lean vehicle travel data in a state in which the density of lean vehicles is low, that is, in a state in which the degree of freedom of driving by the driver is high. Driving skill can be analyzed more accurately and in more detail. In addition, the lean vehicle travel data analysis device 1 performs analysis using lean vehicle travel data in a state where the density of lean vehicles is medium, that is, in a state where the driver's degree of freedom in driving is restricted to some extent, for example. The subject's ability to predict the driving environment, such as the movement of surrounding vehicles, can be analyzed more accurately and in more detail.

When the lean vehicle travel data for analysis is analyzed to generate the analysis data, the travel density related data for analysis is limited to data having similar lean vehicle densities among the lean vehicle travel standard data described later. It may be used when By using the analysis-use travel density-related data in this way, it is possible to limit the data to be processed when analyzing the analysis-use lean vehicle travel data to generate analysis data, thereby reducing the load on hardware resources. can.

When generating analysis data by analyzing the lean vehicle travel data for analysis, the classification-related data for analysis is selected from the lean vehicle travel standard data described later, the attributes (gender, age, etc.) of the person to be analyzed, the manufacturer Also, it is used when limiting to data corresponding to categories such as car models. By using this classification-related data for analysis, it is possible to limit the data to be processed when analyzing the lean vehicle travel data for analysis to generate the analysis data, thereby reducing the load on hardware resources.

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

When the density range between the lowest density and the highest density of a lean vehicle traveling on a public road is divided into four density ranges, the lean vehicle travel data for reference generation has the highest density among the four density ranges. From the driving data of a lean vehicle running on public roads in the low density range and the highest density range, the lean vehicle runs on public roads in the medium density range, which is the remaining two density ranges of the four density ranges. It contains a lot of running data. Further, the lean vehicle running data for reference generation may include classification related data for classifying at least one of the driver and the lean vehicle. Furthermore, the lean vehicle travel data for reference generation may include lean vehicle travel data of a plurality of lean vehicles in different classifications.

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

Note that the reference-generating lean vehicle traveling data includes data other than the reference-generating lean vehicle driving input data, the reference-generating lean vehicle behavior data, the reference-generating lean vehicle position data, and the reference-generating lean vehicle traveling environment data. may contain data. The reference-generating lean vehicle traveling data is one of the reference-generating lean vehicle driving input data, the reference-generating lean vehicle behavior data, the reference-generating lean vehicle position data, and the reference-generating lean vehicle traveling environment data. , may contain only one or more data.

In the case of a lean vehicle that is driven by different drivers and travels on a plurality of public roads, the lean vehicle driving data described above is lean vehicle driving data for reference generation, and the lean vehicle driving input data described above is reference generation. is lean vehicle operation input data for reference generation, the aforementioned lean vehicle behavior data is reference generation lean vehicle behavior data, the aforementioned lean vehicle position data is reference generation lean vehicle position data, and the aforementioned lean vehicle position data is reference generation lean vehicle position data. The driving environment data is lean vehicle driving environment data for reference generation.

The lean vehicle travel data for reference generation includes travel density related data. The reference-generating lean vehicle travel data may include segment-related data.

The travel density related data is taken into account when analyzing the analytical lean vehicle travel data to generate analytical data. As a result, it is possible to output analysis data unique to lean vehicles, which has been difficult to output until now, such as the skill of driving the lean vehicle X in consideration of various usage scenes. For example, in an analysis using lean vehicle travel data in a state in which the density of lean vehicles is low, that is, in a state in which the driver has a high degree of freedom in driving, the skill of the person to be analyzed to drive the lean vehicle X can be obtained more accurately and It can be analyzed in more detail. In addition, for example, in an analysis using lean vehicle travel data in a state where the lean vehicle density is medium, that is, in a state where the driver's degree of freedom in driving is limited to some extent, the subject of analysis is the movement of surrounding vehicles. It is possible to analyze the prediction skill of predicting the driving environment of the vehicle more accurately and in more detail.

When the lean vehicle travel data for analysis is analyzed to generate analysis data, the travel density related data is limited to data with similar lean vehicle densities from lean vehicle travel standard data described later. 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 lean vehicle travel data for analysis to generate the analysis data, thereby reducing the load on 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 travel data for analysis. good. By using this segment-related data, data to be processed when generating analysis data can be limited, and the load on hardware resources can be reduced.

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

The analysis data acquisition unit 30 analyzes the lean vehicle travel data for analysis obtained by the lean vehicle travel data acquisition unit 20 for analysis based on the lean vehicle travel reference data obtained by the lean vehicle travel reference data acquisition unit 10. Acquire analytical data obtained by This analysis data may be analysis data of at least one of the person to be analyzed and the lean vehicle X classified using the classification-related data for analysis.

The analysis data includes, for example, data related to the lean vehicle driving skill of the person to be analyzed. The driving skill means the driving skill of the driver who drives the lean vehicle. The driving skill includes not only the skill of driving a lean vehicle but also the predictive skill of predicting when driving a lean vehicle. Further, the analysis data includes, for example, data related to running of the lean vehicle X. FIG. This data is, for example, data related to the driving skill of the person to be analyzed who is the driver.

The output data generator 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. FIG. Thereby, accurate output data can be generated. Note that the output data generation unit 40 may generate the analysis data as it is as the output data.

The data output unit 50 outputs the output data generated by the output data generation unit 40 from the lean vehicle travel data analysis device 1 .

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

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

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

When the density range between the lowest density and the highest density of a lean vehicle traveling on a public road is divided into four density ranges, the lean vehicle travel data for reference generation has the highest density among the four density ranges. From the driving data of a lean vehicle running on public roads in the low density range and the highest density range, the lean vehicle runs on public roads in the medium density range, which is the remaining two density ranges of the four density ranges. It contains a lot of running data. The reference-generating lean vehicle travel data may include classification-related data for classifying at least one of the driver and the lean vehicle. Furthermore, the lean vehicle travel data for reference generation may include lean vehicle travel data of a plurality of lean vehicles in different classifications.

Next, the lean vehicle travel data acquisition unit for analysis 20 acquires lean vehicle travel data for analysis, which is the travel data of the lean vehicle X traveling on the public road driven by the person to be analyzed (step SA2).

The lean vehicle travel data for analysis includes travel degree-of-freedom-for-analysis related data relating to the degree of freedom of travel according to the will of the person to be analyzed when the lean vehicle X travels on a public road. The lean vehicle travel data for analysis may include classification-related data for analysis for classifying at least one of the person to be analyzed and the lean vehicle X.

In addition, the lean vehicle driving data for analysis includes lean vehicle driving input data for analysis related to the driving input to the lean vehicle by the person to be analyzed, and lean vehicle driving input data for analysis related to the running position of the lean vehicle X traveling on the public road. position data; analytical lean vehicle behavior data relating to the behavior of the lean vehicle X traveling on public roads; and analytical lean vehicle driving environment data relating to the driving environment of the lean vehicle X traveling on public roads.

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

After that, the analysis data acquisition unit 30 acquires analysis data by analyzing the lean vehicle travel data for analysis based on the lean vehicle travel reference data (step SA3).

The analysis data includes, for example, data relating to the lean vehicle driving skill of the person to be analyzed traveling on public roads. The analysis data may be analysis data of at least one of the classified analysis target person and the lean vehicle X. FIG.

The output data generator 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). Terminate this flow (end).

With the above configuration, analysis data can be obtained by analyzing the lean vehicle travel data of the lean vehicle X traveling on a public road driven by the driver who is the subject of analysis.

Therefore, as in the above configuration, by using lean vehicle driving data that takes into account the density of lean vehicles traveling on public roads, the ability to drive a lean vehicle that takes into account various usage scenes, etc., can be output up to now. It is possible to output analysis data unique to lean vehicles, which has been difficult to do. For example, when the lean vehicle density is low, i.e., when the driver has a high degree of freedom in driving, an analysis using lean vehicle driving data can provide more accurate and detailed information about the driver's ability to drive a lean vehicle. can be analyzed to In addition, for example, when the density of lean vehicles is medium, that is, when the driver's freedom of driving is restricted to some extent, an analysis using lean vehicle travel data reveals that the driver may not be able to detect the movements of surrounding vehicles. Prediction skills for predicting the driving environment can be analyzed more accurately and in more detail.

In addition, since lean vehicle driving data is analyzed considering the density of lean vehicles traveling on public roads, the data to be processed is limited compared to the case of analyzing all driving data without considering the state. be able to. As a result, the load on the hardware resources of the lean vehicle travel data analysis device 1 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 lean vehicle travel data analysis device 1 can be reduced, and the hardware load of the device can be reduced. Moreover, 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 obtain analytical data unique to lean vehicles based on lean vehicle travel data while increasing the degree of freedom in designing hardware resources.

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

In the lean vehicle travel data analysis method of the present embodiment, lean vehicle travel reference data generated based on lean vehicle travel data for reference generation is acquired. When the density range between the lowest density and the highest density of a lean vehicle traveling on a public road is divided into four density ranges, this lean vehicle travel data for reference generation is the highest density among the four density ranges. From the driving data of a lean vehicle running on public roads in the low density range and the highest density range, the lean vehicle runs on public roads in the medium density range, which is the remaining two density ranges of the four density ranges. It contains a lot of running data.

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

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

In the lean vehicle travel data analysis method, the lean vehicle travel data for analysis related to the travel data of the lean vehicle X obtained when the person to be analyzed drives the lean vehicle X which is the lean vehicle to be analyzed is acquired.

The lean vehicle travel data for analysis means the lean vehicle travel data of the lean vehicle X driven by the person to be analyzed. The lean vehicle to be analyzed means the lean vehicle X driven by the person to be analyzed, which is a target for acquiring lean vehicle traveling data for analysis.

The person to be analyzed 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 lean vehicles for which the lean vehicle travel data for reference generation is acquired. The lean vehicle to be analyzed may not be included in the lean vehicles for which the lean vehicle travel data for reference generation is to be acquired. The lean vehicle data to be analyzed may be included in the lean vehicle travel data for reference generation. The lean vehicle travel data for analysis may not be included in the lean vehicle travel data for reference generation.

For example, the lean vehicle travel data for analysis may be acquired by various sensors provided on the lean vehicle to be analyzed. Further, the lean vehicle travel data for analysis may be acquired by various sensors that are easily detachable from the lean vehicle to be analyzed. The lean vehicle travel data for analysis may be acquired by various sensors temporarily provided in the lean vehicle to be analyzed for data collection.

The various sensors for collecting the lean vehicle travel data for analysis may have lower detection accuracy than the various sensors for collecting the lean vehicle travel data for reference generation.

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

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

The lean vehicle travel data analysis device 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 travel data analysis device 1 uses the analysis data to generate output data for output.

The lean vehicle travel data analysis device 1 outputs the output data.

According to another aspect, the lean vehicle travel data analysis method preferably includes the following configuration. The reference-generating lean vehicle travel data includes: lean-vehicle driving input data relating to driving input to the lean vehicle by the driver; reference-generating lean vehicle position data relating to the travel position of the lean vehicle traveling on a public road; and at least one of reference-generating lean vehicle behavior data relating to the behavior of the lean vehicle. The lean vehicle driving data for analysis includes: lean vehicle driving input data for analysis related to the driving input to the lean vehicle to be analyzed by the person to be analyzed, and analysis related to the running position of the lean vehicle to be analyzed traveling on public roads. lean vehicle position data for analysis; and lean vehicle behavior data for analysis related to behavior of the lean vehicle to be analyzed.

Lean vehicle driving input data is data associated with driving inputs by the driver. In a lean vehicle, there are many types of operations by the driver, and the driver has a high degree of freedom of selection during driving. Therefore, the lean vehicle driving input data tends to strongly reflect the driver's driving skill and the like.

The lean vehicle behavior data strongly reflects the result of the driver's driving input, which strongly reflects the driver's driving skill. Therefore, the lean vehicle behavior data also tends to strongly reflect the driving skill of the driver.

The lean vehicle position data strongly reflects the result of the driver's driving input, which strongly reflects the driver's driving skill. Therefore, the lean vehicle position data also tends to strongly reflect the driving skill of the driver.

As a result, the lean vehicle travel data used when generating the analysis data includes data that better reflects the driving skill of the person to be analyzed who is the driver.

According to another aspect, the lean vehicle travel data analysis method preferably includes the following configuration. The reference-generating lean vehicle running data further includes reference-generating lean vehicle running environment data relating to a running environment in which the lean vehicle travels. The lean vehicle travel data for analysis further includes lean vehicle travel environment data for analysis related to the travel environment in which the lean vehicle to be analyzed travels.

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 signals and facilities, regulation information on road travel, and the like. The lean vehicle driving environment data can be used in conjunction with the lean vehicle behavior data and the lean vehicle position data to analyze lean vehicle driving data.

With this configuration, the lean vehicle travel data for analysis obtained when the lean vehicle driven by the person to be analyzed travels on a public road can be analyzed more accurately using the lean vehicle travel reference data. In addition, by using the lean vehicle travel data for which the type of data is specified, it is possible to reduce the types of data to be processed by the device for analyzing the lean vehicle travel data, thereby further reducing the load on the hardware of the device. Moreover, 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 realize a lean vehicle travel data analysis method capable of outputting analysis data specific to a lean vehicle based on lean vehicle travel data while increasing the degree of freedom in designing hardware resources.

According to another aspect, the lean vehicle travel data analysis method preferably includes the following configuration. The reference-generating lean vehicle running data includes data in a state in which a plurality of options for a driver's judgment are limited by vehicles around the lean vehicle, but a plurality of options remain. The lean vehicle travel data for analysis includes data in a state in which a plurality of options for judgment of the person to be analyzed is limited by vehicles surrounding the lean vehicle to be analyzed, but a plurality of options remain.

For example, a state in which the driver's decision options are limited by the vehicles surrounding the lean vehicle, but a plurality of options remain, may be determined from the lean vehicle position data and the lean vehicle running environment data. More specifically, the state may be estimated by the date, time, and location where the lean vehicle is traveling. Lean vehicle travel data when the vehicle is traveling in an urban area includes data in a state in which the driver's decision options are limited by vehicles around the lean vehicle, but a plurality of options remain. Also, the state may be estimated by obtaining data on the actual surroundings of the lean vehicle. A method of estimating a plurality of states may be combined.

The state in which the driver's decision options are limited by the vehicles surrounding the lean vehicle, but a plurality of choices remain, means that the driver of the lean vehicle is driving in a group of a plurality of vehicles including a lean vehicle. This means the running state of the lean vehicle when the options are limited but a plurality of options are left when making the determination.

According to another aspect, the lean vehicle travel data analysis method preferably includes the following configuration. The lean vehicle travel data for reference generation includes data of a state in which at least one of a fellow passenger and an object is mounted. The lean vehicle travel data for analysis includes data of a state in which at least one of a passenger and an object is loaded.

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

According to another aspect, the lean vehicle travel data analysis method preferably includes the following configuration. In the lean vehicle travel data analysis method, the acquired analysis data is stored. In the lean vehicle travel data analysis method, the output data is generated using the plurality of stored analysis data.

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

By using a plurality of pieces of analysis data as described above, for example, statistical processing can be performed, and the lean vehicle travel data for analysis can be analyzed with higher accuracy. More specifically, using the old analysis data and the new analysis data, it is possible to more accurately analyze the lean vehicle travel data of the lean vehicle X driven by the person to be analyzed.

This embodiment is an example of a lean vehicle travel data analysis device that analyzes lean vehicle travel data. The lean vehicle travel data analysis device of this embodiment includes the following configuration.

The lean vehicle travel data analysis device of the present embodiment is a lean vehicle travel data analysis device that analyzes the lean vehicle travel data of a lean vehicle that travels leaning to the right when turning right and leaning to the left when turning left.

A lean vehicle travel data analysis apparatus 1 of the present embodiment acquires lean vehicle travel reference data, which is the travel reference data of a lean vehicle that leans to the right when turning to the right and leans to the left when turning to the left. a running reference data acquisition unit 10; an analysis lean vehicle running data acquisition unit 20 that acquires analysis lean vehicle running data that is running data of an analysis target lean vehicle that is an analysis target lean vehicle; and the acquired lean vehicle running data. an analysis data acquisition unit that acquires analysis data of at least one of the analysis target person who is the analysis target driver and the analysis target lean vehicle by analyzing the acquired analysis lean vehicle traveling data based on the reference data; 30, 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.

When the density range between the lowest density and the highest density of a lean vehicle traveling on a public road is divided into four density ranges, the lean vehicle driving reference data is the lowest density among the four density ranges. From the driving data of a lean vehicle running on public roads in the low density range and the highest density range, the lean vehicle runs on public roads in the remaining two medium density ranges of the four density ranges. It is generated based on data-rich lean vehicle travel data for reference generation.

The lean vehicle travel data for analysis includes travel density-related data for analysis relating to the density of lean vehicles when the person to be analyzed drives the lean vehicle to be analyzed and travels on public roads.

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

From another point of view, the lean vehicle travel data analysis device 1 preferably includes the following configuration. The reference-generating lean vehicle running data includes segment-related data for segmenting at least one of a driver and a lean vehicle. The analysis-use lean vehicle traveling data includes analysis-use classification-related data for classifying at least one of the analysis target person and the analysis target lean vehicle. The analysis data acquisition unit obtains the classification-related data for analysis by analyzing the lean vehicle travel data for analysis based on the lean vehicle travel reference data generated based on the lean vehicle travel data for reference generation. Analysis data of at least one of the person to be analyzed and the lean vehicle to be analyzed classified using the analysis data is obtained.

From another point of view, the lean vehicle travel data analysis device 1 preferably includes the following configuration. The analysis data is an analysis result of the synchrony of the lean vehicle travel data for analysis with respect to the lean vehicle travel reference data including data similar in density to the analysis travel density related data among the lean vehicle travel reference data. obtained using

From another point of view, the lean vehicle travel data analysis device 1 preferably includes the following configuration. The analysis data includes data related to the evaluation result of the driving prediction skill when the person to be analyzed travels the lean vehicle X on the public road.

From another point of view, the lean vehicle travel data analysis device 1 preferably includes the following configuration. The lean vehicle driving data for reference generation includes: lean vehicle driving input data for reference generation related to driving input to the lean vehicle by the driver; lean vehicle driving input data for reference generation related to a running position of the lean vehicle traveling on a public road and at least one of position data and lean vehicle behavior data for generating a reference relating to behavior of the lean vehicle. The lean vehicle travel data for analysis includes lean vehicle driving input data for analysis related to the driving input to the lean vehicle X by the person to be analyzed, and lean vehicle position for analysis related to the travel position of the lean vehicle X traveling on a public road. and analytical lean vehicle behavior data related to lean vehicle X behavior.

From another point of view, the lean vehicle travel data analysis device 1 preferably includes the following configuration. The reference-generating lean vehicle running data further includes reference-generating lean vehicle running environment data relating to a running environment in which the lean vehicle travels. The lean vehicle driving data for analysis further includes lean vehicle driving environment data for analysis related to the driving environment in which the lean vehicle travels.

From another point of view, the lean vehicle travel data analysis device 1 preferably includes the following configuration. The reference-generating lean vehicle running data includes data in a state in which a plurality of options for a driver's judgment are limited by vehicles around the lean vehicle, but a plurality of options remain. The lean vehicle travel data for analysis includes data in a state in which a plurality of options for judgment of the person to be analyzed is limited by vehicles surrounding the lean vehicle to be analyzed, but a plurality of options remain.

From another point of view, the lean vehicle travel data analysis device 1 preferably includes the following configuration. The lean vehicle travel data for reference generation includes data of a state in which at least one of a fellow passenger and an object is mounted. The lean vehicle travel data for analysis includes data of a state in which at least one of a passenger and an object is loaded.

From another point of view, the lean vehicle travel data analysis device 1 preferably includes the following configuration. The lean vehicle travel 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 using a plurality of analysis data stored in the data storage unit 60 .

From another point of view, the lean vehicle travel data analysis device 1 preferably includes the following configuration. The output data is generated as analysis data for information processing used for further information processing.

<Embodiment 2>
FIG. 3 shows an example of the lean vehicle travel data analysis device 100. As shown in FIG. This lean vehicle travel data analysis device 100 analyzes the synchrony between a lean vehicle X driven by a person to be analyzed and another lean vehicle Y traveling around it, thereby predicting the driving skill of the person to be analyzed. are evaluated, and the evaluation results are output as analysis data.

Specifically, the lean vehicle travel data analysis device 100 includes a group behavior calculation unit 110, an analysis lean vehicle travel data acquisition unit 120, a synchrony analysis unit 130, a predictive skill evaluation unit 140, and an evaluation output unit 150. and

The lean vehicle travel data acquisition unit 120 for analysis acquires lean vehicle travel data for analysis, which is the travel data of the lean vehicle X driven by the person to be analyzed. The analytical lean vehicle travel data includes analytical lean vehicle position data and analytical lean vehicle behavior data of lean vehicle X. FIG.

The group behavior calculator 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. FIG. The reference-generating lean vehicle data includes lean vehicle position data and lean vehicle behavior data for a plurality of lean vehicles. Whether or not the vehicle belongs to the lean vehicle group is determined by obtaining the lean vehicle position data of the lean vehicle X and the other lean vehicle Y, and determining whether the other lean vehicle Y is located within a predetermined range from the lean vehicle X. It is determined by whether or not Lean vehicles belonging to the same group travel on public roads with similar densities, ie similar degrees of freedom of travel.

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

The synchronism analysis unit 130 uses the lean vehicle travel data for analysis acquired by the lean vehicle travel data acquisition unit for analysis 120 and the travel data relating to group behavior obtained by the group behavior calculation unit 110 to determine the synchrony. do the analysis.

The synchronism is determined by comparing the lean vehicle travel data for analysis of the lean vehicle X driven by the person to be analyzed with respect to the group behavior including the lean vehicle travel data of a plurality of lean vehicles including the lean vehicle X driven by the person to be analyzed. It means the degree of divergence. The lower the degree of divergence, the higher the synchronicity of the person to be analyzed. The group behavior may include, for example, an average value or behavior frequency data of behaviors of the plurality of lean vehicles obtained from lean vehicle travel data of the plurality of lean vehicles. That is, the degree of deviation is obtained from the lean vehicle travel data for analysis of the lean vehicle X driven by the person to be analyzed with respect to the group behavior frequency obtained using the average value of the lean vehicle travel data of the plurality of lean vehicles. It may be the degree of divergence of the behavioral frequency that is obtained.

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

The predictive skill evaluation unit 140 uses the result of the synchronicity analysis of the synchronicity analysis unit 130 to evaluate the driving predictive skill related to prediction when the person to be analyzed drives the lean vehicle X on a public road. That is, the predictive skill evaluation unit 140 classifies the predictive driving skill of the person to be analyzed into levels based on the result of the synchrony analysis. The predicted driving skill evaluation result obtained by the predicted driving skill evaluation unit 140 may be, for example, a result obtained by classifying the synchronism analysis result into levels according to a threshold value, or a numerical value obtained from the synchronism analysis result. Alternatively, it may be an evaluation value corresponding thereto.

The evaluation output unit 150 outputs the driving skill prediction evaluation result obtained by the predicted skill evaluation unit 140 as output data. Note that this output data may be output from the lean vehicle travel data analysis device 100 as it is. Further, the output data may be stored in a storage unit (not shown) of the lean vehicle travel data analysis device 100 and then used when the output data is arithmetically processed by the evaluation output unit 150 .

The analysis data may be obtained from other than the synchrony analysis results described above. Further, the analysis data may include evaluation results of predictive skill obtained from other than the synchrony analysis results 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 analysis device 1 of the embodiment, and the lean vehicle travel data acquisition unit for analysis 120 corresponds to the analytical lean vehicle travel data acquisition unit 20 of the lean vehicle travel data analysis device 1 of the first embodiment, and the synchronism analysis unit 130 corresponds to the analysis data acquisition unit 30 of the lean vehicle travel data analysis device 1 of the first embodiment. , the prediction skill evaluation unit 140 corresponds to the output data generation unit 40 of the lean vehicle travel data analysis device 1 of the first embodiment, and the evaluation output unit 150 corresponds to the data output of the lean vehicle travel data analysis device 1 of the first embodiment corresponds to the part 50;

In the present embodiment, the analysis data is an analysis of the synchronism of the lean vehicle travel data for analysis with respect to the lean vehicle travel reference data including data similar in degree of freedom to the analysis degree-of-freedom related data among the lean vehicle travel reference data. obtained using the results.

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 crowded with other lean vehicles, the travel data of the analysis target lean vehicle and the travel data of the other lean vehicle By evaluating the synchronism with the data, it is possible to obtain analysis data unique to the lean vehicle for at least one of the analysis target person and the analysis target lean vehicle.

Therefore, it is possible to reduce the load on the hardware resources of a device that analyzes lean vehicle travel data and increase the degree of freedom in designing the hardware resources of the device, while outputting analytical data specific to lean vehicles based on lean vehicle travel data. A lean vehicle running data analysis method can be realized.

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

In the lean vehicle traveling data analysis method of the present embodiment, the analysis data is the lean vehicle traveling reference data including data similar in degree of freedom to the analysis degree-of-freedom related data among the lean vehicle traveling reference data. Analytical lean vehicle travel data is obtained using synchrony analysis results.

The degree of freedom of driving is similar not only when the degrees of freedom of driving are completely the same, but also when the analysis data obtained by analyzing the lean vehicle driving data is within a predetermined range. is also included.

According to another aspect, the lean vehicle travel data analysis method preferably includes the following configuration. The analysis data includes data related to evaluation results of predictive driving skills when the person to be analyzed travels on a public road in the lean vehicle to be analyzed.

When driving a lean vehicle, the driver's predictive driving skills are more important than when driving a non-lean vehicle. By including data associated with the evaluation results of predictive driving skill in the analytical data, analytical data specific to lean vehicles is obtained.

<Embodiment 3>
FIG. 4 shows an example of a lean vehicle travel data analysis system 200 including the lean vehicle travel data analysis device 1 of the first embodiment. In the following, components similar to those of the first embodiment are assigned the same reference numerals, descriptions thereof are omitted, and only configurations different from the first embodiment are described.

A 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 device capable of communicating with the lean vehicle travel data analysis device 1 and having a processor. If the lean vehicle travel data analysis device 1 is an information processing device having a processor, the lean vehicle travel reference data generation device 201 may be the same information processing device as the lean vehicle travel data analysis device 1 .

The lean vehicle driving reference data generation device 201 acquires lean vehicle driving data and classification-related data, and generates lean vehicle driving reference data based on lean vehicle driving data for reference generation including these data.

Specifically, the lean vehicle running reference data generation device 201 has a data storage unit 211 and a lean vehicle running reference data generation unit 212 . Although not shown, the lean vehicle running reference data generation device 201 has an acquisition unit that acquires the lean vehicle running data and the classification related data. Although not shown, the lean vehicle running reference data generation device 201 has an output unit that outputs the generated lean vehicle running 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 running data for reference generation including lean vehicle running data and classification related data obtained when a plurality of drivers drive the lean vehicle Y respectively. The data storage unit 211 also stores lean vehicle running reference data generated by a lean vehicle running reference data generation unit 212, which will be described later.

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

The lean vehicle running reference data generation unit 212 generates lean vehicle running reference data based on the lean vehicle running data for reference generation stored in the data storage unit 211 . The lean vehicle running standard data generated by the lean vehicle running standard data generating unit 212 is stored in the data storage unit 211 .

The lean vehicle travel reference data stored in the data storage unit 211 is used by the lean vehicle travel data analysis device 1 to analyze the lean vehicle travel data (lean vehicle travel data for analysis) of the lean vehicle X (lean vehicle for analysis). used in some cases. Since the method of analyzing the lean vehicle travel data in the lean vehicle travel data analysis device 1 is the same as that of the first embodiment, detailed description thereof will be omitted.

That is, the lean vehicle travel data analysis device 1 analyzes the lean vehicle travel data of the lean vehicle X based on the lean vehicle travel reference data, and divides the analyzed data of at least one of the person to be analyzed and the lean vehicle X. and outputs output data generated from the analysis data. Since the configuration of the lean vehicle travel data analysis device 1 is the same as that of the first embodiment, detailed description of the lean vehicle travel data analysis device 1 is omitted. The lean vehicle travel data may be analyzed as in the lean vehicle travel data analysis device 100 of the second embodiment.

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

The information processing device 202 stores data related to insurance, markets, products, services, environments, or customers used in businesses such as lean vehicle sharing, lean vehicle rental, lean vehicle leasing, and lean vehicle insurance. It may be a device that performs the processing of If the lean vehicle travel data analysis device 1 is an information processing 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 arithmetic device as the lean vehicle running reference data generation device 201 .

The information processing device 202 has 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, for example.

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

The first data acquisition unit 222 acquires first data different from the output data. This first data is data to be processed by the information processing device 202 . The first data is, for example, data related to insurance, markets, products, services, environments, or customers used in businesses such as lean vehicle sharing, lean vehicle rental, lean vehicle leasing, and lean vehicle vehicle insurance. is. The first data is stored in the data storage section 225 .

The second data generator 223 uses the output data and the first data to generate second data different from the output data and the first data. This second data, like the first data, is used in businesses such as lean vehicle sharing, lean vehicle rental, lean vehicle leasing, and lean vehicle vehicle insurance, markets, products, services, It is environmental or customer related data.

The second data output section 224 outputs the second data generated by the second data generation section 223 .

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

After that, the second data generation unit 223 of the information processing device 202 generates second data using the acquired output data and the acquired first data (step SB3). The second data is data 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 traveling data analysis device 1 in this way can be used by an information processing device in fields such as lean vehicle sharing, lean vehicle rental, lean vehicle leasing, and lean vehicle insurance. It can be used in computing credit risk or credit score. In other words, the analytical data obtained by analyzing lean vehicle driving data is used for arithmetic processing of information processing equipment 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 insurance, an information processing device acquires output data that has been output, and outputs the acquired output data. The data can be used to computationally output a credit risk or credit score.

In the fields of lean vehicle sharing, lean vehicle rental, lean vehicle leasing, lean vehicle insurance, etc., the information processing method includes a step of acquiring output data output from the lean vehicle traveling data analysis device 1; and using the obtained output data to output credit risk data relating to credit risk or credit score data relating to credit scores.

In the fields of lean vehicle sharing, lean vehicle rental, lean vehicle leasing, lean vehicle vehicle insurance, etc., the information processing device acquires output data output from the lean vehicle traveling data analysis device 1. 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 using the acquired output data.

In the above-described information processing method and information processing apparatus, when the output credit risk is low or the credit score is high, for example, it is possible to make it easier for the person to be analyzed to rent a lean vehicle, or to make it easier for the person to be analyzed to rent a lean vehicle. In some cases, the fee may be given preferential treatment, or the person to be analyzed may receive preferential insurance premium treatment.

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

In addition, the lean vehicle travel data analysis method of the present invention preferably includes the following configuration. The output data is generated as information processing data used for further information processing.

For example, the further information processing may be related to insurance, markets, products, services, environments or customers used in businesses such as lean vehicle sharing, lean vehicle rental, lean vehicle leasing, lean vehicle vehicle insurance. It may be the processing of data to be processed.

According to another aspect, it is preferable to use the output data output by the lean vehicle travel data analysis method of the present invention for the following information processing method using analysis data. In this information processing method, the outputted output data is obtained. In the information processing method, first data different from the output data is obtained. 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 using analysis data obtained by analyzing lean vehicle travel data. For example, the first data and the second data may be insurance, markets, products, services, environments or It may be customer related data.

With the configuration of this embodiment, analysis data that can be used by the information processing device 202 can be acquired by the lean vehicle travel data analysis device 1 and the lean vehicle travel data analysis method. Further, as described in the first embodiment, by analyzing the lean vehicle travel data to obtain the analysis data, the types of data processed by the system can be reduced, and the hardware load of the lean vehicle travel data analysis device 1 can be reduced. can be reduced.

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

In each of the above-described embodiments, the lean vehicle running data is used to generate the lean vehicle running reference data. Reference data may be generated.

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

Also, the output data may be used in combination with data other than the lean vehicle travel data.

In this manner, each data described in each of the above embodiments may be combined with data other than the lean vehicle travel data.

INDUSTRIAL APPLICABILITY The present invention is applicable to a lean vehicle travel data analysis method and a lean vehicle travel data analysis device for analyzing lean vehicle travel data of an analysis subject, and an information processing method using analysis data obtained by these methods and devices. and an information processing device.

1, 100 lean vehicle travel data analyzer 10 lean vehicle travel reference data acquisition units 20, 120 lean vehicle travel data acquisition unit for analysis 30 analysis data acquisition unit 40 output data generation unit 50 data output units 60, 211 data storage unit 110 group Behavior calculation unit 130 Synchronization analysis unit 140 Predictive skill evaluation unit 150 Evaluation output unit 200 Lean vehicle travel data analysis system 201 Lean vehicle travel reference data generation device 202 Information processing device 212 Lean vehicle travel reference data generation unit 221 Output data acquisition unit 222 First data acquisition unit 223 Second data generation unit 224 Second data output unit X Lean vehicle (analyzed lean vehicle)
Y Lean vehicle P Four-wheeled vehicle L Lane boundary line

Claims (19)

respectively performed by a lean vehicle travel data analyzer;
a lean vehicle driving reference data acquisition step for obtaining lean vehicle driving reference data, which is the driving reference data of a lean vehicle that leans to the right when turning to the right and tilts to the left when turning to the left;
a lean vehicle travel data acquisition step for acquiring lean vehicle travel data for analysis, which is travel data of the lean vehicle to be analyzed, which is the lean vehicle to be analyzed;
By analyzing the acquired lean vehicle travel data for analysis based on the acquired lean vehicle travel reference data, analysis data of at least one of the analysis target person who is the analysis target driver and the analysis target lean vehicle is obtained. an analysis data acquisition step to be acquired;
an output data generation step of generating output data for output using the analysis data;
an output step of outputting the output data;
has
When the density range between the lowest density and the highest density of a lean vehicle traveling on a public road is divided into four density ranges, the lean vehicle driving reference data is the lowest density among the four density ranges. From the driving data of a lean vehicle running on public roads in the low density range and the highest density range, the lean vehicle runs on public roads in the remaining two medium density ranges of the four density ranges. Generated based on data-rich lean vehicle driving data for reference generation,
The lean vehicle travel data for analysis includes travel density-related data for analysis relating to the density of lean vehicles when the person to be analyzed drives the lean vehicle to be analyzed and travels on public roads,
analyzing lean vehicle travel data of the lean vehicle to be analyzed;
Lean vehicle driving data analysis method. In the lean vehicle travel data analysis method according to claim 1,
the reference-generating lean vehicle travel data includes segment-related data for segmenting at least one of a driver and a lean vehicle;
The analysis lean vehicle travel 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 lean vehicle travel data analysis device analyzes the lean vehicle travel data for analysis based on the lean vehicle travel reference data generated based on the lean vehicle travel data for reference generation. obtains analysis data of at least one of the person to be analyzed and the lean vehicle to be analyzed classified using the classification-related data for analysis;
Lean vehicle driving data analysis method. In the lean vehicle travel data analysis method according to claim 1 or 2,
The analysis data is an analysis result of the synchrony of the lean vehicle travel data for analysis with respect to the lean vehicle travel reference data including data similar in density to the analysis travel density related data among the lean vehicle travel reference data. obtained by using
Lean vehicle driving data analysis method. In the lean vehicle travel data analysis method according to any one of claims 1 to 3,
The analysis data includes data related to evaluation results of predictive driving skills when the person to be analyzed travels on a public road in the lean vehicle to be analyzed,
Lean vehicle driving data analysis method. In the lean vehicle travel data analysis method according to any one of claims 1 to 4,
The lean vehicle driving data for reference generation includes: lean vehicle driving input data for reference generation related to driving input to the lean vehicle by the driver; lean vehicle driving input data for reference generation related to a running position of the lean vehicle traveling on a public road at least one of position data and reference-generating lean vehicle behavior data relating to behavior of the lean vehicle;
The lean vehicle driving data for analysis includes: lean vehicle driving input data for analysis related to the driving input to the lean vehicle to be analyzed by the person to be analyzed, and analysis related to the running position of the lean vehicle to be analyzed traveling on public roads. lean vehicle position data for analysis; and lean vehicle behavior data for analysis related to behavior of the lean vehicle to be analyzed.
Lean vehicle driving data analysis method. In the lean vehicle travel data analysis method according to any one of claims 1 to 5,
The reference-generating lean vehicle driving data further includes reference-generating lean vehicle driving environment data related to a driving environment in which the lean vehicle travels,
The lean vehicle travel data for analysis further includes lean vehicle travel environment data for analysis related to a travel environment in which the lean vehicle to be analyzed travels.
Lean vehicle driving data analysis method. In the lean vehicle travel data analysis method according to any one of claims 1 to 6,
The reference-generating lean vehicle running data includes data in a state in which a plurality of options for a driver's judgment are limited by vehicles surrounding the lean vehicle, but a plurality of options remain,
The lean vehicle travel data for analysis includes data in a state in which a plurality of options for judgment of the person to be analyzed is limited by vehicles surrounding the lean vehicle to be analyzed, but a plurality of choices remain.
Lean vehicle driving data analysis method. In the lean vehicle travel data analysis method according to any one of claims 1 to 7,
The lean vehicle travel data for reference generation includes data of a state in which at least one of a fellow passenger and an object is mounted,
The lean vehicle travel data for analysis includes data of a state in which at least one of a passenger and an object is loaded,
Lean vehicle driving data analysis method. In the lean vehicle travel data analysis method according to any one of claims 1 to 8,
The lean vehicle travel data analysis device,
storing the acquired analytical data;
using the stored plurality of analytical data to generate the output data;
Lean vehicle driving data analysis method. In the lean vehicle travel data analysis method according to any one of claims 1 to 9,
The output data is generated as information processing data used for further information processing.
Lean vehicle driving data analysis method. a lean vehicle driving reference data acquisition unit that acquires lean vehicle driving reference data, which is the driving reference data of a lean vehicle that leans to the right when turning right and leans to the left when turning left;
a lean vehicle travel data acquisition unit for analysis that acquires lean vehicle travel data for analysis that is travel data of a lean vehicle to be analyzed that is a lean vehicle to be analyzed;
By analyzing the acquired lean vehicle travel data for analysis based on the acquired lean vehicle travel reference data, analysis data of at least one of the analysis target person who is the analysis target driver and the analysis target lean vehicle is obtained. an analysis data acquisition unit to acquire;
an output data generation unit that generates output data for output using the analysis data;
a data output unit that outputs the output data;
with
When the density range between the lowest density and the highest density of a lean vehicle traveling on a public road is divided into four density ranges, the lean vehicle driving reference data is the lowest density among the four density ranges. From the driving data of a lean vehicle running on public roads in the low density range and the highest density range, the lean vehicle runs on public roads in the remaining two medium density ranges, which are the remaining two density ranges of the four density ranges. Generated based on data-rich lean vehicle driving data for reference generation,
The lean vehicle travel data for analysis includes travel density-related data for analysis relating to the density of lean vehicles when the person to be analyzed drives the lean vehicle to be analyzed and travels on public roads,
analyzing lean vehicle travel data of the lean vehicle to be analyzed;
Lean vehicle travel data analyzer. The lean vehicle travel data analysis device according to claim 11,
the reference-generating lean vehicle travel data includes segment-related data for segmenting at least one of a driver and a lean vehicle;
The analysis lean vehicle travel 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 lean vehicle travel data for analysis based on the lean vehicle travel reference data generated based on the lean vehicle travel data for reference generation, thereby obtaining the classification related data for analysis. obtaining analysis data of at least one of the analysis target person and the analysis target lean vehicle classified using
Lean vehicle travel data analyzer. In the lean vehicle travel data analysis device according to claim 11 or 12,
The analysis data is an analysis result of the synchrony of the lean vehicle travel data for analysis with respect to the lean vehicle travel reference data including data similar in density to the analysis travel density related data among the lean vehicle travel reference data. obtained by using
Lean vehicle travel data analyzer. In the lean vehicle travel data analysis device according to any one of claims 11 to 13,
The analysis data includes data related to evaluation results of predictive driving skills when the person to be analyzed travels on a public road in the lean vehicle to be analyzed,
Lean vehicle travel data analyzer. In the lean vehicle travel data analysis device according to any one of claims 11 to 14,
The lean vehicle driving data for reference generation includes: lean vehicle driving input data for reference generation related to driving input to the lean vehicle by the driver; lean vehicle driving input data for reference generation related to a running position of the lean vehicle traveling on a public road at least one of position data and reference-generating lean vehicle behavior data relating to behavior of the lean vehicle;
The lean vehicle driving data for analysis includes: lean vehicle driving input data for analysis related to the driving input to the lean vehicle to be analyzed by the person to be analyzed, and analysis related to the running position of the lean vehicle to be analyzed traveling on public roads. lean vehicle position data for analysis; and lean vehicle behavior data for analysis related to behavior of the lean vehicle to be analyzed.
Lean vehicle travel data analyzer. In the lean vehicle travel data analysis device according to any one of claims 11 to 15,
The reference-generating lean vehicle driving data further includes reference-generating lean vehicle driving environment data related to a driving environment in which the lean vehicle travels,
The lean vehicle travel data for analysis further includes lean vehicle travel environment data for analysis related to a travel environment in which the lean vehicle to be analyzed travels.
Lean vehicle travel data analyzer. In the lean vehicle travel data analysis device according to any one of claims 11 to 16,
The output data is generated as information processing data used for further information processing.
Lean vehicle travel data analyzer. An information processing method using the output data generated as the information processing data by the lean vehicle travel data analysis method according to claim 10,
The information processing device
obtaining the output data;
obtaining first data different from the output data;
using the output data and the first data to generate second data different from the output data and the first data;
outputting the second data;
Information processing method using analytical data. An information processing device using the output data generated as the information processing data by the lean vehicle traveling data analysis device 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 generator 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;
comprising
Information processing equipment using analytical data.

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