JP7626461B2 - Information processing device, information processing system, information processing method, and program - Google Patents

Description

The present disclosure relates to an information processing device, an information processing system, an information processing method, and a program.

For transportation companies, the social and economic losses that result from accidents are enormous, so managing the driving behavior of drivers is important. For example, transportation companies that own five or more commercial vehicles are required to appoint a safety driving manager. The safety driving manager assesses the driving skills of drivers and provides safe driving guidance.

For example, Patent Document 1 discloses a method for generating instructional information based on detection data from an in-vehicle device and measuring the viewing time of the instructional information by the driver. The detection data includes the vehicle's position, time, speed, etc.

JP 2020-102040 A

However, the method described in the above-mentioned Patent Document 1 does not take into account the characteristics of the driver when changing pedal positions in driving behavior.

In view of the above-mentioned problems, the objective of the present disclosure is to provide an information processing device, an information processing system, an information processing method, and a program that can appropriately analyze driving behavior based on measurement data during pedal changeover.

An information processing device according to one aspect of the present disclosure includes an acquisition unit that acquires time-series measurement data related to the movement state of the soles of the driver's feet, a section identification unit that identifies, from the measurement data, a section having a high-frequency component whose frequency is equal to or greater than a predetermined value as a step-change section, an analysis unit that analyzes the driving behavior of the driver based at least on the characteristics of the measurement data in the step-change section, and an output unit that outputs the results of the analysis.

An information processing system according to one aspect of the present disclosure includes an inertial sensor that measures the movement state of the soles of the feet of a driver, and an information processing device. The information processing device includes an acquisition unit that acquires time-series measurement data measured by the inertial sensor, a section identification unit that identifies a section having a high-frequency component equal to or higher than a predetermined frequency from the measurement data as a step-change section, an analysis unit that analyzes the driving behavior of the driver based at least on the characteristics of the measurement data in the step-change section, and an output unit that outputs the results of the analysis.

An information processing method according to one aspect of the present disclosure acquires time-series measurement data relating to the movement state of the soles of the driver's feet, identifies from the measurement data a section having high-frequency components equal to or greater than a predetermined frequency as a step-change section, analyzes the driving behavior of the driver based at least on the characteristics of the measurement data in the step-change section, and outputs the results of the analysis.

The program according to one aspect of the present disclosure causes a computer to realize an acquisition function for acquiring time-series measurement data regarding the movement state of the soles of the driver's feet, an identification function for identifying, from the measurement data, a section having high-frequency components equal to or higher than a predetermined frequency as a step-change section, an analysis function for analyzing the driving behavior of the driver based at least on the characteristics of the measurement data in the step-change section, and an output function for outputting the results of the analysis.

This disclosure provides an information processing device, information processing system, information processing method, and program that can optimally analyze driving behavior based on measurement data when changing pedal positions.

1 is a block diagram showing a configuration of an information processing device according to a first embodiment. FIG. 11 is a block diagram showing a schematic configuration of an information processing system according to a second embodiment. FIG. 11 is a diagram showing a usage state of a sensor according to a second embodiment. FIG. 11 is a diagram showing an example of measurement data according to the second embodiment. 11A and 11B are diagrams for explaining the behavior of the sole of the foot when changing pedals. FIG. 11 is a block diagram showing a configuration of a server according to a second embodiment. 13 is a diagram for explaining an example of section identification and analysis by a server according to the second embodiment. FIG. 10 is a flowchart showing a flow of an information processing method by a server according to a second embodiment. FIG. 11 is a block diagram showing a configuration of an administrator terminal according to the second embodiment. FIG. 11 is a diagram illustrating an example of an image displayed on an administrator terminal according to the second embodiment. FIG. 11 is a block diagram showing a configuration of an information processing system according to a third embodiment.

Embodiments of the present disclosure will now be described in detail with reference to the drawings. In each drawing, identical or corresponding elements are denoted by the same reference numerals, and duplicated descriptions will be omitted as necessary for clarity of explanation.

<Embodiment 1>
First, a first embodiment of the present disclosure will be described. Fig. 1 is a block diagram showing a configuration of an information processing device 10 according to the first embodiment. The information processing device 10 is a computer that analyzes the driving behavior of a driver. The information processing device 10 includes an acquisition unit 11, a section identification unit 13, an analysis unit 14, and an output unit 16.

The measurement data used as the basis for analysis in this embodiment 1 is time-series data measuring physical quantities that represent the movement state of the sole of the driver's foot. The physical quantity that represents the movement state of the sole of the foot is at least one of the displacement, speed, and acceleration of the sole of the foot. The measurement data represents not only the behavior of the sole of the foot when the driver places the sole of the foot on the pedal and presses it down, but also the behavior of the sole of the foot during the step-down section. The step-down section is the section from when the sole of the foot leaves the pedal after pressing it down, after the sole of the foot leaves the pedal, and until the sole of the foot next presses down on the pedal. The step-down section is divided into a section where the driver switches from the accelerator pedal to the brake pedal, and a section where the driver switches from the brake pedal to the accelerator pedal.

By analyzing the behavior of the soles of the feet in the changeover section, it is possible to obtain driving behavior in the changeover section that cannot be obtained from vehicle information such as mileage and speed recorded by digital tachographs, etc.

First, the acquisition unit 11 acquires measurement data.

Next, the section identification unit 13 identifies a step-changing section from the measurement data acquired by the acquisition unit 11. Specifically, the section identification unit 13 identifies a section having high-frequency components as a step-changing section. A high-frequency component refers to data among the measurement data whose waveform frequency is equal to or greater than a predetermined value.

Next, the analysis unit 14 analyzes the driving behavior of the driver based at least on the characteristics of the measurement data of the step-change section. For example, the analysis unit 14 analyzes the number of occurrences of the step-change section, the time interval between step-change sections, or the amplitude or frequency of the step-change section as the characteristics of the measurement data of the step-change section, and analyzes the driving behavior of the driver based on at least one of these. The driving behavior is, for example, sudden acceleration, sudden braking, unstable driving, roughness, etc.

The output unit 16 then outputs the analysis results.

According to the first embodiment, the information processing device 10 can appropriately analyze the driving behavior of the driver based on the behavior of the sole of the foot in the pedal change section. This allows the safe driving manager to accurately grasp the driving operation of the driver and provide appropriate driving guidance.

<Embodiment 2>
Next, a description will be given of a second embodiment of the present disclosure. Fig. 2 is a block diagram showing a schematic configuration of an information processing system 1 according to the second embodiment. The information processing system 1 is a system including one or more computers for supporting driving instruction by a safety driving manager.

The information processing system 1 includes a sensor 20, a communication control device 30, a server 40, and an administrator terminal 50. The communication control device 30, the server 40, and the administrator terminal 50 are connected to a network N.

Network N is a communications network such as the Internet.

The sensor 20 is an inertial sensor that measures the displacement of the soles of the driver's feet. As an example, the sensor 20 is an acceleration sensor, particularly a three-axis acceleration sensor. The sensor 20 may include a gyro sensor in addition to the acceleration sensor.
The sensor 20 transmits measurement data to the communication control device 30. The communication between the sensor 20 and the communication control device 30 may be wired communication or wireless communication. For example, the sensor 20 supplies the measurement data to the communication control device 30 using short-range wireless communication such as Bluetooth (registered trademark).

The communication control device 30 is an in-vehicle communication device. For example, the communication control device 30 is an in-vehicle navigation device, a dedicated communication device, or a smartphone or tablet terminal used by the driver. The communication control device 30 relays data transmission from the sensor 20 to the server 40. Specifically, the communication control device 30 receives measurement data from the sensor 20 and transmits the measurement data to the server 40 via the network N.

The server 40 is an example of the information processing device 10 of embodiment 1. The server 40 receives measurement data of the sensor 20 from the communication control device 30 via the network N. The server 40 then analyzes the driving behavior of the driver based on the measurement data. The server 40 then transmits the analysis results to the administrator terminal 50.

The manager terminal 50 is an information terminal used by the safety driving manager. The manager terminal 50 receives the analysis results of the driver's driving behavior from the server 40 via the network N and outputs the analysis results. As an example of output, the manager terminal 50 displays the analysis results on a display unit (not shown). By viewing the output analysis results, the safety driving manager can understand the driver's driving operation and use them for driving guidance.

Figure 3 is a diagram showing the usage status of the sensor 20 according to the second embodiment. The sensor 20 is mounted on a part that is linked to the movement of the sole SL of the driver's foot. For example, the sensor 20 is mounted on the insole 310 of the driver's shoe 300. As an example, the sensor 20 is mounted on a part of the insole 310 that comes into contact with the heel of the driver. The sensor 20 may be mounted on the midsole or outsole of the shoe 300, or may be attached to the driver's socks or the sole SL itself.

The X-axis direction of the sensor 20 is the width direction of the sole SL. The Y-axis direction of the sensor 20 is the length direction of the sole SL. The Z-axis direction of the sensor 20 is perpendicular to the X-axis and Y-axis, that is, the normal direction of the surface of the sole SL. The sensor 20 detects acceleration in each axial direction and measures displacement in each axial direction based on the acceleration. Of the measurement data, displacement in the X-axis direction is referred to as X-axis data, displacement in the Y-axis direction is referred to as Y-axis data, and displacement in the Z-axis direction is referred to as Z-axis data.

Figure 4 is a diagram showing an example of measurement data according to the second embodiment. The vertical axis of Figure 4 is the sensor output, and the horizontal axis is time. The solid line in Figure 4 is the X-axis data, the diagonal line is the Y-axis data, and the dashed line is the Z-axis data.

Here, from the value of the X-axis data, it can be ascertained whether the sole of the driver's foot SL is located on the brake pedal in the positive direction of the X-axis, or on the accelerator pedal in the negative direction of the X-axis. For example, in section 1, the X-axis data indicates a positive value, so the driver is depressing the brake pedal. Also, in section 3, the X-axis data indicates a negative value, so the driver is depressing the accelerator pedal. Therefore, section 2 between sections 1 and 3 is a change-over section where the driver changes his/her foot from the brake pedal to the accelerator pedal. Similarly, section 4 is a change-over section where the driver changes his/her foot from the accelerator pedal to the brake pedal.

Figure 5 is a diagram for explaining the behavior of the sole of the foot SL when changing pedals. As shown in Figure 5(a), the driver releases the sole of the foot SL from the pedal before changing pedals. At this time, the sole of the foot SL moves in the negative direction of the Y axis and then in the positive direction of the Z axis. As shown in Figure 5(b), the driver places the sole of the foot SL on the pedal while moving the sole of the foot SL in the X axis direction or after moving it. At this time, the sole of the foot SL moves in the positive direction of the Y axis and then in the negative direction of the Z axis.

As shown in Figure 4, in the step-change section, the values of the Y-axis data and Z-axis data fluctuate significantly between positive and negative. If the driver's step-change is rough, the values of the Y-axis data and Z-axis data will be larger and will change in a shorter period of time. By utilizing this phenomenon, it is possible to detect step-change sections from the measurement data, and ultimately to analyze the driver's driving behavior.

FIG. 6 is a block diagram showing the configuration of the server 40 according to the second embodiment. The server 40 includes a communication unit 41, a control unit 42, an output unit 46, and a storage unit 47.

The communication unit 41 is a communication interface for connecting to the network N, and is an example of the acquisition unit 11 of embodiment 1. The communication unit 41 receives measurement data of the sensor 20 from the communication control device 30 via the network N. The measurement data includes X-axis data, Y-axis data, and Z-axis data. The communication unit 41 supplies the acquired measurement data to the section identification unit 43 of the control unit 42.

The communication unit 41 is also an example of the output unit 16 of the first embodiment. The communication unit 41 receives the analysis results from the output control unit 45 and transmits them to the administrator terminal 50 via the network N.

The control unit 42 controls the hardware of the server 40. The control unit 42 includes a section determination unit 43, an analysis unit 44, and an output control unit 45.

The section identification unit 43 is an example of the section identification unit 13 of the first embodiment. The section identification unit 43 identifies the step-change section from at least one of the Y-axis data and the Z-axis data among the measurement data acquired by the communication unit 41.

The analysis unit 44 is an example of the analysis unit 14 in embodiment 1. The analysis unit 44 analyzes the driving behavior of the driver based on at least one of the characteristics of the Y-axis data and the Z-axis data of the pedal change section.

The output control unit 45 converts the analysis results into a predetermined output format and outputs them via the communication unit 41 or the output unit 46.

The output unit 46 is an example of the output unit 16 of embodiment 1. The output unit 46 receives the analysis results from the output control unit 45 and outputs them to the outside.

The memory unit 47 is a storage device that stores information necessary for processing by the control unit 42.

Figure 7 is a diagram for explaining an example of section identification and analysis by the server 40 according to the second embodiment. Figure 7 shows an example of Y-axis data for the period to be analyzed, from time T0 to time T1.

In FIG. 7, the section identification unit 43 identifies a section having high-frequency components from the Y-axis data acquired by the communication unit 41, and sets this as a step-change section.

For example, the section identification unit 43 performs frequency analysis on the waveform of the Y-axis data using a Fourier transform or the like. The section identification unit 43 then identifies a section having a high-frequency component whose amplitude is equal to or greater than a predetermined value and whose frequency is equal to or greater than a predetermined value f _0. In order to remove noise from the Y-axis data, the section identification unit 43 may first remove noise with a frequency of f _n (≫f ₀ ) or greater using a low-pass filter or the like, and then identify a section having a frequency of f ₀ or greater from the remaining waveform.

Alternatively, for example, the section identification unit 43 detects peaks whose maximum values are equal to or greater than a predetermined value from the waveform of the Y-axis data, and identifies, among the detected peaks, a section in which a peak width or half-width is equal to or less than a predetermined width exists as a section having high-frequency components. Here, too, the section identification unit 43 may perform noise removal on the waveform of the Y-axis data before performing peak detection.

In FIG. 7, the section identification unit 43 detects sections 2, 4, and 6 as pedal change sections.

The analysis unit 44 then analyzes the driver's driving behavior based at least on the characteristics of the Y-axis data of the pedal change section.

For example, the analysis unit 44 calculates the number of pedal change sections that occur during the analysis period. In FIG. 7, there are three pedal change sections during the period from time T0 to T1. If the number of occurrences is equal to or greater than a predetermined number, the analysis unit 44 determines that the driving behavior is abnormal. Note that the analysis unit 44 may determine that the more occurrences there are, the rougher the driver's driving operation is.

For example, the analysis unit 44 also calculates the time intervals between adjacent pedal change sections included in the analysis period. In FIG. 7, the analysis unit 44 calculates the period between section 2 and section 4, i.e., the duration of section 3, and the period between section 4 and section 6, i.e., the duration of section 5.

Then, the analysis unit 44 determines whether the driving behavior is abnormal based on the average of the calculated time intervals or the variance of the time intervals. Specifically, the analysis unit 44 may determine that the driver's driving operation is inappropriate if the average of the calculated time intervals is equal to or less than a predetermined threshold. Furthermore, the analysis unit 44 may determine that the driver's driving operation tends to be rough, that the driver is engaging in abnormal driving such as tailgating, or that the driver is engaging in unstable driving due to drunk driving or drowsy driving, etc., if the variance of the calculated time intervals is equal to or greater than a predetermined threshold.

For example, the analysis unit 44 calculates the amplitude in each step-change section of the Y-axis data. The amplitude calculated here may be the maximum value minus the minimum value of the amplitude of the waveform included in one step-change section. If frequency analysis has been performed, it may be the maximum value or average value of the amplitude of the high-frequency component extracted by frequency analysis for one step-change section. If peaks have been detected, it may be the maximum value or average value of the peak height detected for one step-change section.

The analysis unit 44 then determines whether the driving behavior is abnormal based on the amplitude calculated for each step-change section. Specifically, the analysis unit 44 may determine that the driver's driving operation is rough if the average of the amplitudes calculated for multiple step-change sections is equal to or greater than a predetermined threshold. Furthermore, the analysis unit 44 may determine that the driver is driving unstably if the variance in the amplitudes calculated for multiple step-change sections is equal to or greater than a predetermined threshold.

For example, the analysis unit 44 also calculates the frequency of the Y-axis data included in the measurement data in each step-change section. If frequency analysis is performed, the frequency calculated here may be the maximum or average frequency of the high-frequency components extracted by frequency analysis for one step-change section. If peak detection is performed, the frequency calculated here may be the reciprocal of the maximum or average of the peak width or half-width detected for one step-change section.

Then, the analysis unit 44 determines whether the driving behavior is abnormal based on the frequency. Specifically, the analysis unit 44 may determine that the driver's driving operation is rough if the average of the frequencies calculated in multiple pedal change sections is equal to or greater than a predetermined threshold. Furthermore, the analysis unit 44 may determine that the driver is driving unstably if the variance of the frequencies calculated in multiple pedal change sections is equal to or greater than a predetermined threshold.

In this way, various analyses of the step-change sections can be performed to analyze driving behavior in detail. The analysis unit 44 may also perform a comprehensive analysis of driving behavior by combining analysis items such as the number of occurrences of the step-change sections, time intervals, and amplitudes described above.

Note that although an example of analysis using Y-axis data has been described in FIG. 7, analysis may also be performed using Z-axis data. The analysis method using Z-axis data is similar to the analysis method using Y-axis data, and therefore a description thereof will be omitted. Furthermore, the analysis unit 44 may perform a comprehensive analysis by combining the analysis using Y-axis data and the analysis using Z-axis data.

The analysis may also use the type of step-change indicating whether the step-change is from the accelerator pedal to the brake pedal or from the brake pedal to the accelerator pedal. In this case, the section identification unit 43 first identifies the type of each step-change section, whether it is a step-change from the accelerator pedal to the brake pedal or from the brake pedal to the accelerator pedal, based on the value of the X-axis data before and after each step-change section. The analysis unit 44 then analyzes the driver's driving behavior based on the type of step-change corresponding to each step-change section. This allows the driver's driving habits to be understood in more detail.

The analysis may also use vehicle information about the vehicle driven by the driver. The vehicle information is data that can be obtained from a digital tachograph or various sensing data that can be collected via a CAN (Controller Area Network), and includes at least one of the following: mileage, driving speed, driving time, information about sudden acceleration and deceleration, information about door opening and closing, and idling information. In this case, the communication control device 30 may obtain vehicle information from the digital tachograph or other on-board sensors and transmit it to the server 40.

The analysis unit 44 of the server 40 may analyze the driving behavior of the driver based on various characteristics of the pedal change section in the measurement data and vehicle information of the vehicle driven by the driver. By comparing the characteristics obtained from the vehicle information with the characteristics of the pedal change section in the measurement data, the driving behavior can be analyzed from multiple perspectives. Such a multifaceted analysis can also be applied to analyzing the situation at the time of a traffic accident.

Figure 8 is a flowchart showing the flow of an information processing method by the server 40 according to the second embodiment. First, the communication unit 41 of the server 40 acquires measurement data from the sensor 20 (S20). Next, the section identification unit 43 of the control unit 42 detects high-frequency components by performing frequency analysis and peak analysis (S21). Next, the section identification unit 43 identifies the section having the detected high-frequency components as a step-change section (S22).

Next, the analysis unit 44 analyzes the characteristics of the pedal change section (e.g., the number of occurrences of the pedal change section, the time interval, the amplitude, or the frequency) (S23). Then, the analysis unit 44 determines whether the driving behavior is abnormal based on the characteristics of the pedal change section (S24).

Finally, the output control unit 45 transmits the analysis results, including the judgment results, to the administrator terminal 50 via the communication unit 41 (S25).

FIG. 9 is a block diagram showing the configuration of an administrator terminal 50 according to the second embodiment. The administrator terminal 50 includes a communication unit 51, a control unit 52, a display unit 56, and a storage unit 57.

The communication unit 51 is a communication interface for connecting to the network N. The control unit 52 controls the hardware of the administrator terminal 50. The display unit 56 is a display device such as a liquid crystal display or an organic EL display. The display unit 56 displays the analysis results that the communication unit 51 receives from the server 40 via the network N. The memory unit 57 is a storage device that stores information necessary for processing by the control unit 52.

Fig. 10 is a diagram showing an example of an image 500 displayed on the administrator terminal 50 according to the second embodiment. Image 500 shows the driving behavior status of each driver as an analysis result. For example, image 500 shows that driver A suddenly accelerated three times in one hour, and drove abnormally. On the other hand, image 500 shows that drivers B and C did not show any abnormal driving behavior in one hour, and drove safely.

The safe driving manager can understand the driving skills of each driver based on the image 500. This makes it easy to create individual driving instruction plans for each driver.

According to the second embodiment, the server 40 can thus analyze the stability of the sole of the foot movement during the pedal change section, thereby enabling a detailed analysis of the driving situation at that time and the driver's driving tendencies. This allows for more optimal support of driving instruction.

<Embodiment 3>
Next, a third embodiment of the present disclosure will be described. In the third embodiment, an administrator terminal instead of a server analyzes the driving behavior of a driver. Fig. 11 is a block diagram showing a configuration of an information processing system 1a according to the third embodiment. The information processing system 1a includes an external storage device 25 and an administrator terminal 50a.

The external storage device 25 is a storage device that stores the measurement data of the sensor 20. The external storage device 25 is, for example, a flash memory such as an SD card or a USB memory.

The administrator terminal 50a is an example of the information processing device 10 of embodiment 1. The administrator terminal 50a is similar to the administrator terminal 50 in that it includes a communication unit 51, a display unit 56, and a memory unit 57. However, the administrator terminal 50a differs from the administrator terminal 50 in that it includes an input unit 58 and a control unit 52a instead of the control unit 52. Note that the communication unit 51 is not essential for the administrator terminal 50a.

The input unit 58 reads the measurement data from the external storage device 25 and supplies the measurement data to the section determination unit 53 of the control unit 52a.

The control unit 52a controls the hardware of the administrator terminal 50a. The control unit 52a includes a section identification unit 53, an analysis unit 54, and an output control unit 55.

The section identification unit 53, the analysis unit 54, and the output control unit 55 have the same functions as the section identification unit 43, the analysis unit 44, and the output control unit 45 of the server 40. The output control unit 55 displays the analysis results by the analysis unit 54 on the display unit 56.

Even if the analysis is performed offline in this manner, the same effect as in embodiment 2 can be achieved.

In the above embodiment, a hardware configuration has been described, but the present disclosure is not limited to this. Any processing of the present disclosure can also be realized by causing a processor to execute a computer program.

In the above examples, the program includes instructions (or software code) that, when loaded into a computer, cause the computer to perform one or more functions described in the embodiments. The program may be stored on a non-transitory computer-readable medium or a tangible storage medium. By way of example and not limitation, computer-readable media or tangible storage media include random-access memory (RAM), read-only memory (ROM), flash memory, solid-state drive (SSD) or other memory technology, CD-ROM, digital versatile disc (DVD), Blu-ray® disk or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage device. The program may be transmitted on a transitory computer-readable medium or communication medium. By way of example and not limitation, the transitory computer-readable medium or communication medium includes electrical, optical, acoustic, or other forms of propagated signals.

Note that this disclosure is not limited to the above embodiment, and can be modified as appropriate without departing from the spirit of the present disclosure. For example, although the sensor 20 measures the displacement of the sole of the foot, the measurement target may be speed or acceleration.

A part or all of the above-described embodiments can be described as, but is not limited to, the following supplementary notes.
(Appendix 1)
An acquisition unit that acquires time-series measurement data relating to the movement state of the soles of the driver;
a section identification unit that identifies a section having a high-frequency component having a frequency equal to or higher than a predetermined value as a step-changing section from the measurement data;
an analysis unit that analyzes a driving behavior of the driver based at least on features of the measurement data in the pedal change section;
and an output unit that outputs a result of the analysis.
(Appendix 2)
The analysis unit includes:
Calculating the number of occurrences of the step-change section included in a predetermined period of time;
The information processing device according to claim 1, further comprising: determining that the driving behavior is abnormal when the number of occurrences is equal to or greater than a predetermined number of times.
(Appendix 3)
The analysis unit includes:
Calculating time intervals between adjacent change sections included in a predetermined period,
The information processing device according to claim 1 or 2, further comprising: determining whether or not the driving behavior is abnormal based on an average of the time intervals or a variance in the time intervals.
(Appendix 4)
The measurement data includes X-axis data relating to a movement state of the sole in a width direction, Y-axis data relating to a movement state of the sole in a length direction, and Z-axis data relating to a state in a normal direction of the sole,
The information processing device according to any one of claims 1 to 3, wherein the section identification unit identifies the step-changing section from at least one of the Y-axis data and the Z-axis data.
(Appendix 5)
The analysis unit includes:
Calculating the amplitude of at least one of the Y-axis data and the Z-axis data included in the measurement data in each changeover section;
The information processing device according to claim 4, further comprising: determining whether or not a driving behavior is abnormal based on the amplitude in each pedal change section.
(Appendix 6)
The analysis unit includes:
Calculating a frequency of at least one of Y-axis data and Z-axis data included in the measurement data in the step-change section;
The information processing device according to claim 4 or 5, further comprising: determining whether or not a driving behavior is abnormal based on the frequency.
(Appendix 7)
the section identification unit identifies a type of each step-change section, whether it is a step-change from the accelerator pedal to the brake pedal or a step-change from the brake pedal to the accelerator pedal, based on values of the X-axis data before and after each step-change section;
The information processing device according to any one of claims 4 to 6, wherein the analysis unit analyzes a driving behavior of the driver further based on a type of pedal change corresponding to each pedal change section.
(Appendix 8)
The information processing device according to any one of claims 1 to 7, wherein the analysis unit analyzes a driving behavior of the driver based on a feature of the measurement data in the pedal change section and vehicle information of a vehicle driven by the driver.
(Appendix 9)
an inertial sensor that measures the movement state of the soles of the driver's feet;
An information processing device;
The information processing device includes:
an acquisition unit that acquires time-series measurement data measured by the inertial sensor;
a section identification unit that identifies a section having a high-frequency component equal to or higher than a predetermined frequency as a step-changing section from the measurement data;
an analysis unit that analyzes a driving behavior of the driver based at least on features of the measurement data in the pedal change section;
and an output unit that outputs a result of the analysis.
(Appendix 10)
The information processing system according to claim 9, wherein the inertial sensor is mounted in an insole of a shoe of the driver.
(Appendix 11)
The information processing system further includes an administrator terminal,
The information processing system according to claim 9 or 10, wherein the output unit outputs a result of the analysis to a display unit of the administrator terminal.
(Appendix 12)
Acquire time-series measurement data regarding the movement state of the soles of the driver's feet;
Identifying a section having a high-frequency component equal to or higher than a predetermined frequency as a step-change section from the measurement data;
Analyzing a driving behavior of the driver based at least on characteristics of the measurement data in the step-change section;
and outputting a result of said analysis.
(Appendix 13)
An acquisition function for acquiring time-series measurement data regarding the movement state of the soles of the driver's feet;
A function of identifying a section having a high-frequency component equal to or higher than a predetermined frequency as a step-change section from the measurement data;
An analysis function that analyzes a driving behavior of the driver based at least on characteristics of the measurement data in the pedal change section;
and an output function for outputting the results of the analysis.

REFERENCE SIGNS LIST 1, 1a Information processing system 10 Information processing device 11 Acquisition unit 13 Section identification unit 14 Analysis unit 16 Output unit 20 Sensor 25 External storage device 30 Communication control device 40 Server 41 Communication unit 42 Control unit 43 Section identification unit 44 Analysis unit 45 Output control unit 46 Output unit 47 Memory unit 50 Administrator terminal 51 Communication unit 52, 52a Control unit 53 Section identification unit 54 Analysis unit 55 Output control unit 56 Display unit 57 Memory unit 58 Input unit 120 Pedal 140 Mat 300 Shoes 310 Insole 500 Image N Network SL Sole of foot

Claims (10)

An acquisition unit that acquires time-series measurement data relating to the movement state of the soles of the driver;
a section identification unit that identifies a section having a high-frequency component having a frequency equal to or higher than a predetermined value as a step-changing section from the measurement data;
an analysis unit that analyzes a driving behavior of the driver based at least on features of the measurement data in the pedal change section;
An output unit that outputs a result of the analysis,
The analysis unit includes:
Calculating the number of occurrences of the step-change section included in a predetermined period of time;
When the number of occurrences is equal to or greater than a predetermined number, the information processing device determines that the driving behavior is abnormal. An acquisition unit that acquires time-series measurement data relating to the movement state of the soles of the driver;
a section identification unit that identifies a section having a high-frequency component having a frequency equal to or higher than a predetermined value as a step-changing section from the measurement data;
an analysis unit that analyzes a driving behavior of the driver based at least on features of the measurement data in the pedal change section;
An output unit that outputs a result of the analysis,
The analysis unit includes:
Calculating time intervals between adjacent change sections included in a predetermined period,
and determining whether or not the driving behavior is abnormal based on an average of the time intervals or a variation in the time intervals. The measurement data is
X-axis data relating to the movement state in the width direction of the sole;
Y-axis data relating to the movement state in the length direction of the sole of the foot;
Z-axis data relating to a state in a normal direction of the sole of the foot;
The information processing device according to claim 1 , wherein the section identification unit is configured to identify the step-changing section from at least one of the Y-axis data and the Z-axis data. The analysis unit includes:
Calculating an amplitude of at least one of the Y-axis data and the Z-axis data included in the measurement data in each changeover section;
The information processing device according to claim 3 , further comprising: a stepping-down control unit configured to determine whether or not a driving behavior is abnormal based on the amplitude in each pedal-changing section. The information processing device according to claim 1 , wherein the analysis unit analyzes a driving behavior of the driver based on a feature of the measurement data in the pedal change section and vehicle information of a vehicle driven by the driver. an inertial sensor that measures the movement state of the soles of the driver's feet;
An information processing system comprising: the information processing device according to claim 1 . The information processing system according to claim 6 , wherein the inertial sensor is mounted in an insole of a shoe of the driver. An information processing device,
A step of acquiring time-series measurement data regarding a movement state of the soles of the driver's feet;
identifying a section having a high-frequency component equal to or higher than a predetermined frequency from the measurement data as a step-changing section;
Analyzing a driving behavior of the driver based at least on characteristics of the measurement data in the change section;
and outputting a result of the analysis,
The information processing device,
In the analyzing step,
Calculating the number of occurrences of the step-change section included in a predetermined period of time;
When the number of occurrences is equal to or greater than a predetermined number, the driving behavior is determined to be abnormal. An information processing device,
A step of acquiring time-series measurement data regarding a movement state of the soles of the driver's feet;
identifying a section having a high-frequency component equal to or higher than a predetermined frequency from the measurement data as a step-changing section;
Analyzing a driving behavior of the driver based at least on characteristics of the measurement data in the change section;
and outputting a result of the analysis,
The information processing device,
In the analyzing step,
Calculating time intervals between adjacent change sections included in a predetermined period,
Determining whether the driving behavior is abnormal based on the average of the time intervals or the variance of the time intervals
Information processing methods. A program causing the information processing device to execute the information processing method according to claim 8 or 9.

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