JP6210654B2 - Driving characteristic evaluation method, program, and driving characteristic evaluation apparatus - Google Patents

Description

The present invention relates to a driving characteristic evaluation method, a program, and a driving characteristic evaluation apparatus.
This application claims priority on May 20, 2014 based on Japanese Patent Application No. 2014-104639 for which it applied to Japan, and uses the content for it here.

  2. Description of the Related Art Conventionally, a traffic jam prediction method is known in which a single regression line is calculated by performing a single regression analysis on the power spectrum of vehicle acceleration, and a traffic jam prediction is performed based on the maximum value of the slope of the single regression line (for example, Patent Document 1).

International Publication No. 2012/081209

  By the way, according to the traffic jam prediction method according to the above-described prior art, the driving characteristics of the vehicle are evaluated in the process of traffic jam prediction, and the suppression of the traffic jam generation is supported using the evaluation results of the driving characteristics that cause the traffic jam. Is desired.

  Aspects of the present invention have been made in view of the above circumstances, and a driving characteristic evaluation method, a program, and a driving characteristic capable of supporting the suppression of the occurrence of traffic congestion using the evaluation result of the driving characteristic that causes the occurrence of the traffic jam An evaluation device is provided.

In order to solve the above problems and achieve the object, the present invention employs the following aspects.
(1) The driving characteristic evaluation method according to the first aspect of the present invention includes acceleration information acquisition means for acquiring information on accelerations in the directions of the first to third axes forming an orthogonal coordinate system in a three-dimensional space. A driving characteristic evaluation method executed by an electronic device, wherein the electronic device calculates an acceleration vector in the three-dimensional space using the acceleration information acquired by the acceleration information acquisition means, and has two different timings. An input data calculation step for calculating a norm of the vector difference as input data, and a frequency at which the electronic device calculates a power spectrum by calculating an autocorrelation of the input data and performing a Fourier transform on the autocorrelation An analysis step, an angle information acquisition step in which the electronic device converts the power spectrum into angle information, and the electronic device, Fluctuation width information acquisition step for acquiring fluctuation width information in time-series fluctuation of the angle information, and evaluation for evaluating the driving characteristics of the moving body in which the electronic device moves together with the electronic device according to the fluctuation width information Steps.

  (2) In the driving characteristic evaluation method according to the above (1), the electronic device may be configured such that, in the evaluation step, the goodness of the driving characteristic changes in an increasing tendency as the fluctuation range decreases. Evaluation may be performed such that the goodness of the driving characteristics changes in a decreasing tendency as the fluctuation range increases.

  (3) In the driving characteristic evaluation method according to (1) or (2), the electronic device acquires the angle information and the speed of the electronic device in time series in the fluctuation range information acquisition step, The fluctuation range information may be acquired from the frequency distribution of the combination of the angle information and the speed of the electronic device.

  (4) In the driving characteristic evaluation method described in (3) above, the electronic device is at least one of the position of the electronic device, the movement environment of the electronic device, and date / time information in the fluctuation range information acquisition step. The angle information and the speed of the electronic device may be acquired in association with one.

  (5) In the driving characteristic evaluation method according to the above (4), the electronic device is at least one of the position of the electronic device, the movement environment of the electronic device, and date / time information in the fluctuation range information acquisition step. The frequency information of the combination of the angle information and the speed of the electronic device may be generated by normalizing the angle information and the speed of the electronic device based on one.

  (6) In the driving characteristic evaluation method described in (4) above, the electronic device is at least one of the position of the electronic device, the environment of movement of the electronic device, and date / time information in the fluctuation information acquisition step. The frequency information of the combination of the angle information and the speed of the electronic device may be generated for each group by grouping the angle information and the speed of the electronic device based on one.

  (7) In the driving characteristic evaluation method according to any one of (1) to (6), in the evaluation step, the electronic device includes the speed of the electronic device, the angle information, and the angle information. A state that does not contribute to the evaluation of the driving characteristics is detected based on at least one of the change information according to time, and the state that does not contribute to the evaluation of the driving characteristics from the information of the fluctuation range The driving characteristic of the moving body may be evaluated by excluding the acquired information on the fluctuation range.

  (8) The driving characteristic evaluation method according to the second aspect of the present invention includes acceleration information for acquiring acceleration information in the axial directions of the first to third axes forming the orthogonal coordinate system of the server device and the three-dimensional space. And a driving characteristic evaluation method executed by a driving characteristic evaluation system including an acquisition device and a current position information acquisition unit that acquires current position information, wherein the electronic device is acquired by the acceleration information acquisition unit. An input data calculation step of calculating an acceleration vector in the three-dimensional space using the acceleration information and calculating a norm of a difference between the vectors at two different timings as input data; and A frequency analysis step of calculating an autocorrelation of input data and calculating a power spectrum by performing a Fourier transform on the autocorrelation; The electronic device converts the power spectrum into angle information, the angle information acquisition step, the electronic device acquires the fluctuation range information in the time series fluctuation of the angle information, the electronic device The electronic device transmission step of transmitting the information on the fluctuation range and the information on the current position acquired by the current position information acquisition means to the server device, and the server device includes at least one of the Using the information on the current position received from the electronic device and the information on the fluctuation range, the information on the fluctuation range in an appropriate position range is within the position range according to the number and ratio of the electronic devices that are the predetermined conditions. A position range driving characteristic detecting step for detecting a driving characteristic of a moving body that moves together with the electronic device, and the server device is within the position range. Including a server transmission step of transmitting the information of the operating characteristics to the electronic device in the position range, the.

  (9) A program according to a third aspect of the present invention is an electronic device including an acceleration information acquisition unit that acquires information on acceleration in each of the first to third axes forming an orthogonal coordinate system in a three-dimensional space. The computer which comprises comprises calculating the acceleration vector in the said three-dimensional space using the said acceleration information acquired by the said acceleration information acquisition means, and calculates the norm of the difference of the said vector of two different timings as input data. By the input data calculation means, the frequency analysis means for calculating the autocorrelation of the input data calculated by the input data calculation means, and calculating the power spectrum by performing Fourier transform on the autocorrelation, and the frequency analysis means Angle information acquisition means for converting the calculated power spectrum into angle information; A variation width information obtaining means for obtaining information of the variation width in the column varies, and evaluation means for evaluating the operating characteristics of a moving body moving together with the electronic apparatus in accordance with the information of the fluctuation range, to function as a.

  (10) In the program according to (9), the evaluation unit evaluates so that the goodness of the driving characteristics changes in an increasing tendency as the fluctuation range decreases, and the fluctuation range increases. Accordingly, evaluation may be made so that the goodness of the driving characteristics changes in a decreasing tendency.

  (11) In the program according to (9) or (10), the fluctuation range information acquisition unit acquires the angle information and the speed of the electronic device in time series, and the angle information and the speed of the electronic device. The fluctuation range information may be acquired from the frequency distribution of the combinations.

  (12) In the program according to (11), the fluctuation range information acquisition unit associates the angle with at least one of the position of the electronic device, the movement environment of the electronic device, and date and time information. Information and speed of the electronic device may be acquired.

  (13) In the program according to (12), the fluctuation range information acquisition unit may calculate the angle based on at least one of the position of the electronic device, the movement environment of the electronic device, and date / time information. The frequency distribution of the combination of the angle information and the speed of the electronic device may be generated by normalizing the information and the speed of the electronic device.

  (14) In the program according to (12), the fluctuation range information acquisition unit may calculate the angle based on at least one of the position of the electronic device, the movement environment of the electronic device, and date / time information. A frequency distribution of the combination of the angle information and the speed of the electronic device may be generated for each group by grouping the information and the speed of the electronic device.

  (15) In the program according to any one of (9) to (14), the evaluation unit includes information on a speed of the electronic device, the angle information, and change information according to time of the angle information. Based on at least one of them, the state that does not contribute to the evaluation of the driving characteristics is detected, and the information on the fluctuation range acquired in the state that does not contribute to the evaluation of the driving characteristics from the information on the fluctuation range May be excluded, and the driving characteristics of the moving body may be evaluated.

  (16) The driving characteristic evaluation apparatus according to the fourth aspect of the present invention acquires acceleration information for acquiring acceleration information of an electronic device in each of the first to third axes forming an orthogonal coordinate system in a three-dimensional space. Input data for calculating an acceleration vector in the three-dimensional space using the acceleration information acquired by the means and the acceleration information acquisition means, and calculating a norm of a difference between the vectors at two different timings as input data A calculation means, a frequency analysis means for calculating an autocorrelation of the input data calculated by the input data calculation means, and calculating a power spectrum by performing a Fourier transform on the autocorrelation, and a frequency analysis means. Angle information acquisition means for converting the power spectrum into angle information; and time series fluctuation of the angle information. It comprises a fluctuation width information obtaining means for obtaining information of fluctuation range, and a evaluation means for evaluating the operating characteristics of a mobile that moves together with the electronic device in accordance with the information of the fluctuation range.

  (17) In the driving characteristic evaluation apparatus according to (16), the evaluation unit evaluates so that the goodness of the driving characteristic changes in an increasing tendency as the fluctuation range decreases, and the fluctuation You may evaluate so that the favorable degree of the said driving | running characteristic may change to a downward tendency with a width | variety increasing.

  (18) In the driving characteristic evaluation apparatus according to (16) or (17), the fluctuation range information acquisition unit acquires the angle information and the speed of the electronic device in time series, and the angle information and the electronic The fluctuation range information may be acquired from a frequency distribution of combinations of device speeds.

  (19) In the driving characteristic evaluation apparatus according to (18), the fluctuation range information acquisition unit is associated with at least one of the position of the electronic device, the movement environment of the electronic device, and date and time information. The angle information and the speed of the electronic device may be acquired.

  (20) In the driving characteristic evaluation apparatus according to (19), the fluctuation range information acquisition unit is based on at least one of the position of the electronic device, the movement environment of the electronic device, and date / time information. The frequency distribution of the combination of the angle information and the speed of the electronic device may be generated by normalizing the angle information and the speed of the electronic device.

  (21) In the driving characteristic evaluation apparatus according to (19), the fluctuation range information acquisition unit is based on at least one of the position of the electronic device, the movement environment of the electronic device, and date / time information. The frequency distribution of the combination of the angle information and the speed of the electronic device may be generated for each group by grouping the angle information and the speed of the electronic device.

  (22) In the driving characteristic evaluation apparatus according to any one of (16) to (21), the evaluation unit changes the speed of the electronic device, the angle information, and the time of the angle information. Based on at least one of the information, the state that does not contribute to the evaluation of the driving characteristic is detected, and the fluctuation obtained in the state that does not contribute to the evaluation of the driving characteristic from the information of the fluctuation range The driving characteristics of the moving body may be evaluated by excluding width information.

  According to the driving characteristic evaluation method according to the aspect described in (1) above, it is possible to evaluate the degree of driving consistency using information on the fluctuation range in the time-series fluctuation of the angle information, and cause the occurrence of traffic congestion. It is possible to properly evaluate the driving characteristics.

  Furthermore, in the case of (2) above, it is possible to appropriately evaluate the degree of good driving characteristics, that is, the degree that traffic congestion is unlikely to occur, according to the fluctuation range in the time-series fluctuation of the angle information.

  Furthermore, in the case of the above (3), it is possible to appropriately evaluate the driving characteristics that cause the occurrence of traffic jam from the frequency distribution of the combination of the angle information and the speed of the electronic device.

  Furthermore, in the case of the above (4), the angle information and the speed of the electronic device are determined based on the position of the electronic device and the moving environment (for example, the type of road on which a moving body such as a vehicle moving together with the electronic device travels) In addition, by associating with at least one of the date and time information, a combination of angle information and speed of the electronic device under different conditions can be acquired.

  Further, in the case of (5) above, the frequency distribution of the combination of the angle information and the speed of the electronic device under different conditions is obtained by normalization based on at least one of the position and moving environment of the electronic device and the date and time information. Can be put together properly.

  Further, in the case of (6) above, the frequency distribution of the combination of the angle information and the speed of the electronic device under different conditions is obtained by grouping based on at least one of the position and moving environment of the electronic device and the date and time information. It can be used for each group.

  Furthermore, in the case of (7) above, information obtained in a state that does not contribute to the evaluation of the driving characteristics is excluded from the information on the fluctuation range, and the driving characteristics of the mobile body are evaluated. Can do.

  According to the driving characteristic evaluation method according to the aspect described in (8) above, a plurality of moving bodies that move with the electronic device in addition to the fluctuation range in the time-series fluctuation of the angle information of each electronic device within an appropriate position range The driving characteristics can be comprehensively evaluated while taking the above conditions into consideration. Furthermore, by providing the information on the driving characteristics within this position range to each electronic device, it is possible to efficiently improve the driving characteristics in conjunction with a plurality of moving bodies that move together with the electronic device.

  According to the program according to the aspect described in (9) above, it is possible to evaluate the degree of driving consistency using information on the fluctuation range in the time series fluctuation of the angle information, and driving that causes traffic congestion Properties can be properly evaluated.

  Furthermore, in the case of the above (10), it is possible to appropriately evaluate the degree of good driving characteristics, that is, the degree of hardly causing traffic jams, according to the fluctuation range in the time series fluctuation of the angle information.

  Furthermore, in the case of the above (11), it is possible to appropriately evaluate the driving characteristics that cause the occurrence of traffic congestion from the frequency distribution of the combination of the angle information and the speed of the electronic device.

  Further, in the case of (12) above, the angle information and the speed of the electronic device are determined based on the position of the electronic device and the moving environment (for example, the type of road on which a moving body such as a vehicle moving with the electronic device travels and the congestion state), In addition, by associating with at least one of the date and time information, a combination of angle information and speed of the electronic device under different conditions can be acquired.

  Further, in the case of (13) above, the frequency distribution of the combination of the angle information and the speed of the electronic device under different conditions is obtained by normalization based on at least one of the position and moving environment of the electronic device and the date and time information. Can be put together properly.

  Furthermore, in the case of (14) above, the frequency distribution of the combination of the angle information and the speed of the electronic device under different conditions is obtained by grouping based on at least one of the position and moving environment of the electronic device and the date and time information. It can be used for each group.

  Furthermore, in the case of (15) above, since the information acquired in a state that does not contribute to the evaluation of the driving characteristics is excluded from the information on the fluctuation range, the driving characteristics of the moving body are evaluated, so that appropriate evaluation is performed. Can do.

  According to the driving characteristic evaluation apparatus according to the aspect described in (16) above, it is possible to evaluate the degree of driving consistency using information on the fluctuation range in the time-series fluctuation of the angle information, and cause the occurrence of traffic congestion. It is possible to properly evaluate the driving characteristics.

  Furthermore, in the case of the above (17), it is possible to appropriately evaluate the degree of good driving characteristics, that is, the degree that traffic congestion is unlikely to occur, according to the fluctuation range in the time-series fluctuation of the angle information.

  Furthermore, in the case of the above (18), it is possible to appropriately evaluate the driving characteristics that cause the occurrence of the traffic jam from the frequency distribution of the combination of the angle information and the speed of the electronic device.

  Furthermore, in the case of (19) above, the angle information and the speed of the electronic device are determined based on the position of the electronic device and the moving environment (for example, the type of road on which a moving body such as a vehicle moving with the electronic device travels and the congestion state). In addition, by associating with at least one of the date and time information, a combination of angle information and speed of the electronic device under different conditions can be acquired.

  Further, in the case of the above (20), the frequency distribution of the combination of the angle information and the speed of the electronic device under different conditions is obtained by normalization based on at least one of the position and moving environment of the electronic device and the date and time information. Can be put together properly.

  Further, in the case of the above (21), the frequency distribution of the combination of the angle information and the speed of the electronic device under different conditions is obtained by grouping based on at least one of the position and moving environment of the electronic device and the date and time information. It can be used for each group.

  Furthermore, in the case of the above (22), the information obtained in a state that does not contribute to the evaluation of the driving characteristics is excluded from the information on the fluctuation range, and the driving characteristics of the mobile body are evaluated. Can do.

It is a block diagram of the driving characteristic evaluation apparatus which implement | achieves the driving characteristic evaluation method which concerns on embodiment of this invention. It is a figure which shows an example of the difference of the vector of the acceleration which concerns on embodiment of this invention. It is a figure which shows an example of the acceleration spectrum which concerns on embodiment of this invention. It is a figure which shows the example according to the embodiment of this invention of the fluctuation | variation according to the time of the acceleration and spectrum angle, and an average behavior. It is a figure which shows the example of the frequency of the combination of the spectrum angle average and speed average according to the driving | running characteristic which concerns on embodiment of this invention. (A) is a figure of the state where the favorable degree of driving characteristics is high, (B) is a figure of the state where the favorable degree of driving characteristics is low. It is a flowchart which shows the driving | operation characteristic evaluation method which concerns on embodiment of this invention. It is a block diagram of the traffic congestion sign detection system which implement | achieves the driving characteristic evaluation method which concerns on the modification of embodiment of this invention. It is a flowchart which shows the driving | running characteristic evaluation method which concerns on the modification of embodiment of this invention. It is a flowchart which shows the network operation | movement shown in FIG. It is a figure which shows the example of the display screen of the driving characteristic evaluation apparatus which concerns on embodiment and modification of this invention, (A) is a screen of the stop state or constant-speed driving | running state of a vehicle, (B) is a gentle vehicle (C) is a screen when the vehicle is suddenly started, suddenly braked, accelerated and decelerated frequently, and (D) is a screen when it can be properly connected to the server device. It is. It is a figure which shows the example of the display screen of the driving characteristic evaluation apparatus which concerns on embodiment and modification of this invention, (A) is a screen of the stop state or constant-speed driving | running state of a vehicle, (B) is a gentle vehicle (C) is a screen when the vehicle is suddenly started, suddenly braked, accelerated and decelerated frequently, and (D) is a screen when it can be properly connected to the server device. It is.

Hereinafter, an embodiment of an operation characteristic evaluation method, a program, and an operation characteristic evaluation apparatus according to the present invention will be described with reference to the accompanying drawings.
The driving characteristic evaluation apparatus 10 according to the present embodiment is, for example, a mobile terminal carried by a passenger of a moving body such as a vehicle, an information device detachably mounted on a moving body such as a vehicle, or a mobile body such as a vehicle in advance. Electronic devices such as on-board navigation devices.
The driving characteristic evaluation device 10 is capable of bidirectional communication with an external device, for example, by wireless communication via a wireless communication network system including a base station.

Note that the wireless communication network system includes, for example, a base station for wireless communication and a public communication network such as the Internet that connects the base station and an external device in a wired manner. In this wireless communication network system, information transmitted from an external device by wired communication is received by the base station, and transferred from the base station to the driving characteristic evaluation device 10 by wireless communication.
Further, information transmitted from the driving characteristic evaluation device 10 by wireless communication is received by the base station, and transferred from the base station to an external device by wired communication.

  As shown in FIG. 1, the driving characteristic evaluation device 10 includes a device communication device 11, a positioning signal receiver 12, a current position acquisition unit 13, a three-dimensional acceleration sensor 14, an input device 15, and a display device 16. The device control unit 17 and the map data storage unit 18 are provided.

  The device communication apparatus 11 can communicate with an external apparatus via various wireless communication network systems such as a client server type, and transmits and receives various signals. Note that the communication between the driving characteristic evaluation device 10 and the external device is not limited to the communication mode described above, and other communication such as communication via a communication satellite may be employed.

The positioning signal receiver 12 is used in, for example, a positioning system (for example, Global Positioning System: GPS or Global Navigation Satellite System: GNSS) for measuring the position of the driving characteristic evaluation apparatus 10 using an artificial satellite. Receive positioning signals.
The current position acquisition unit 13 detects the current position of the driving characteristic evaluation device 10 using the positioning signal received by the positioning signal receiver 12.

  The three-dimensional acceleration sensor 14 is a three-axis acceleration sensor having a so-called three-axis number of detection axes, and the acceleration generated in the driving characteristic evaluation apparatus 10 in a predetermined sampling cycle is formed in an orthogonal coordinate system in a three-dimensional space. Detected as acceleration in the axial direction of the axis, the Y axis, and the Z axis.

The input device 15 includes, for example, a switch, a touch panel, a keyboard, a voice input device, and the like, and outputs signals according to various input operations by the operator.
The display device 16 is, for example, various displays such as a liquid crystal display device, and displays various information output from the device control unit 17.

The device control unit 17 controls various operations of the driving characteristic evaluation device 10.
The device control unit 17 includes an input data calculation unit 21 (input data calculation unit), a frequency analysis unit 22 (frequency analysis unit), a single regression line calculation unit 23 (angle information acquisition unit), a determination data calculation unit 24, The driving characteristic determination unit 25 (variation range information acquisition unit, evaluation unit) and the traffic jam prediction unit 26 are provided.

The input data calculation unit 21 calculates an acceleration vector (acceleration vector) A in a three-dimensional space using the accelerations in the X-axis, Y-axis, and Z-axis directions detected by the three-dimensional acceleration sensor 14. Then, the norm u of the difference (acceleration vector difference) ΔA between two different timing vectors with a time interval such as the sampling period ΔT is calculated as input data to be input to the frequency analysis unit 22.
As shown in FIG. 2, the input data calculation unit 21, for example, has an acceleration vector A (t) = (ax _t , ay _t , az _t ) at an appropriate time t and a sampling period ΔT before this time t. Acceleration vector A (t−ΔT) = (ax _t−ΔT , ay _t−ΔT , az _t−ΔT ) at time t−ΔT at the time t−ΔT, the acceleration vector difference ΔA = A (t) −A (t−ΔT) Is calculated. Then, as shown in the following formula (1), a norm u _t of the acceleration vector difference ΔA is calculated.
Note that the buffer size of a buffer (not shown) that can store acceleration information in the X-axis, Y-axis, and Z-axis directions detected by the three-dimensional acceleration sensor 14, that is, the number of samples of acceleration information is For example, an appropriate setting screen displayed on the display device 16 can be appropriately set by the operator.

The frequency analysis unit 22 performs frequency analysis on the input data calculated by the input data calculation unit 21 and calculates a power spectrum (acceleration spectrum) corresponding to the frequency.
For example, the frequency analysis unit 22 calculates the autocorrelation of the input data by using the number of input / output points of the input data with respect to the frequency analysis and the autocorrelation delay number. Then, an acceleration spectrum is calculated by performing a fast Fourier transform on the autocorrelation. The number of input / output points of input data for frequency analysis, the number of autocorrelation delays, and the selection of whether or not to subtract the average value from the autocorrelation input value are, for example, an appropriate setting screen displayed on the display device 16, etc. It can be set by the operator.
For example, the frequency analysis unit 22 calculates the acceleration spectrum for a predetermined period by performing autocorrelation calculation and fast Fourier transform on the input / output points of the input data calculated by the input data calculation unit 21 at the sampling period ΔT. .

The single regression line calculation unit 23 calculates a single regression line in a predetermined frequency range of the acceleration spectrum calculated by the frequency analysis unit 22, and converts the inclination of the single regression line into information on an angle (spectrum angle).
For example, in chaos theory, the influence of a power spectrum at a low frequency is larger than a high frequency on the prediction of a traffic jam. For this reason, as shown in FIG. 3, the single regression line calculation unit 23 performs the minimum two for the acceleration spectrum in the low frequency region (for example, the frequency region above the lower limit frequency fa and below the predetermined frequency fb) of the predetermined frequency fb or less. A single regression line L is calculated by multiplication or the like. Then, the calculated inclination of the single regression line L (that is, the inclination with respect to the axial direction assuming that the axial direction of the frequency is zero) is converted into information of angle (spectral angle) θ.

For example, as the spectral angle θ increases in the minus direction (decrease direction of the acceleration spectrum) (that is, as the absolute value increases with a minus sign), the delay in the dynamic time response of acceleration and deceleration increases. However, the speed variation increases. As a result, it becomes difficult to limit the driving range in which the vehicle's energy efficiency (such as fuel efficiency or power consumption) is prioritized, and traffic congestion is likely to occur and the energy efficiency is lowered.
For example, when the absolute value of the spectral angle θ is small, this corresponds to a case where a shock wave (vibration, fluctuation) received by the vehicle moving with the driving characteristic evaluation apparatus 10 from the preceding vehicle is small, and the reaction delay with respect to the preceding vehicle is small. This is equivalent to a case where the vehicle group is difficult to be formed due to a long period of time, that is, there is little possibility of traffic congestion.
Conversely, when the absolute value of the spectral angle θ is large, this corresponds to the case where the vehicle moving with the driving characteristic evaluation apparatus 10 receives a large shock wave (vibration, fluctuation) from the preceding vehicle, and the reaction delay with respect to the preceding vehicle is large. This corresponds to a case where the group tends to become dense, that is, there is a high possibility of congestion. The shock wave (vibration, fluctuation) referred to here means that this operation (back and forth movement) is propagated to the rear vehicle as a kind of vibration by repeating the acceleration and deceleration operations.

The determination data calculation unit 24 uses the angle information calculated by the single regression line calculation unit 23 to indicate information indicating a change in angle according to time (for example, information on a duration in which an angle value is maintained, an angle Information on the convergence time required for the absolute value of the value to converge to zero is calculated as determination data to be input to the traffic jam prediction unit 26.
For example, as shown in Equation (2) below, the determination data calculation unit 24 determines the determination interval N (N is a natural number) and the angle threshold θ _T, and the angle θ _j calculated by the single regression line calculation unit 23 in the determination interval N. The determination data S _N is calculated based on (j is a natural number equal to or less than N). Note that the determination section N and the angle threshold θ _T can be set by the operator on an appropriate setting screen displayed on the display device 16, for example. The determination section N is, for example, a score of angle information corresponding to a period that can be appropriately set by the operator, that is, a score of angle information calculated by the single regression line calculation unit 23 during this period.
For example, the determination data calculation unit 24 determines the determination data S _N of the determination section N corresponding to a predetermined period based on the angle θ _j (1 ≦ j ≦ N) calculated by the single regression line calculation unit 23 in the sampling period ΔT. Is calculated. The angle threshold θ _T can be set to an arbitrary value by the operator. For example, other than “−45 degrees” or “−45 degrees”, which is generally known as (1 / f) fluctuation characteristics, Other values.

Determination data S _N of the equation (2) shows the total power of the acceleration and deceleration of a predetermined time period corresponding to the determined interval N, the comparison with a predetermined threshold corresponding to a predetermined angle threshold theta _T. For example, when this total power exceeds a predetermined threshold, traffic congestion is likely to occur, and the vehicle energy efficiency (fuel consumption, electricity consumption, etc.) decreases.
For example, in the case of shifting from a stationary state of the vehicle to constant speed running with appropriate acceleration, such as a change in acceleration and spectral angle and an average behavior during the period from time ta to time tb shown in FIG. Is small. Even if the absolute value of the spectrum angle temporarily increases, it immediately converges toward zero, so the total power for acceleration and deceleration becomes a small value.
Further, for example, in the case where the vehicle is decelerated moderately by driving at a constant speed or by engine braking or the like, such as the change in acceleration and spectrum angle and the average behavior during the period from time ta to time tb shown in FIG. Small fluctuation. And since the absolute value of a spectrum angle maintains a small value, the total power of acceleration and deceleration becomes a small value. In this case, even if the absolute value of the spectrum angle temporarily increases due to vibration or the like, it immediately converges toward zero, so that the total power of acceleration and deceleration becomes a small value. Further, even if the absolute value of the spectrum angle temporarily increases due to, for example, the detection error of the three-dimensional acceleration sensor 14, it immediately converges toward zero, so the total power of acceleration and deceleration becomes a small value.
On the other hand, for example, when the vehicle suddenly decelerates or decelerates immediately after acceleration, as in the case of acceleration and spectral angle variation and average behavior during the period from time tb to time tc shown in FIG. . The absolute value of the spectrum angle becomes a large value, and the time required for convergence toward zero becomes long. Therefore, the total power for acceleration and deceleration becomes a large value.

The driving characteristic determination unit 25 acquires and accumulates information on time-series fluctuations of an average value of spectrum angles (spectrum angle average) and an average value of speed (speed average) every predetermined time, and FIG. ) To generate frequency distribution information of a combination of spectral angle average and velocity average. The driving characteristic determination unit 25 is, for example, a signal output from a speed sensor or the like provided in a moving body such as a vehicle that moves with the driving characteristic evaluation device 10 or a time-series change in the current position detected by the current position acquisition unit 13. Based on the above, the speed of the driving characteristic evaluation device 10 is acquired. The driving characteristic determination unit 25 generates, for example, three-dimensional histogram data by sequentially associating the spectrum angle average, the speed average, and the frequency with the X axis, the Y axis, and the Z axis.
The driving characteristic determination unit 25 stores the information of the spectrum angle average and the speed average in association with other information (for example, at least one of the current position, the road type and congestion status of the traveling road, and the date / time information). . The driving characteristic determination unit 25 performs a process such as normalization or grouping using other information associated with the spectral angle average and speed average information, thereby performing spectral angle averaging and other information reflected. Generate information on frequency distribution of velocity average. For example, in the normalization process, the driving characteristic determination unit 25 responds to a plurality of pieces of information acquired for each type of other information (for example, the current position, the road type and congestion status of the traveling road, and date / time information). Then, each of the spectral angle average and the velocity average is normalized within a numerical range previously divided into a plurality of stages, and information on the frequency distribution of the spectral angle average and the velocity average is generated. For example, in the grouping process, the driving characteristic determination unit 25 has substantially the same information acquired for each type of other information (for example, the current position, the road type and congestion status of the traveling road, and date / time information). Information on frequency distribution of spectral angle average and velocity average is generated for each group.

The driving characteristic determination unit 25 excludes information on the spectral angle and speed acquired in a state where it is determined not to contribute to the evaluation of the driving characteristic when generating the frequency distribution information of the average spectral angle and the average speed. May be. The driving characteristic determination unit 25 is a state that does not contribute to the evaluation of the driving characteristics, for example, a state in which a difference in driving behavior among a plurality of drivers is recognized as being small, such as a traffic jam state, a slowing state, and a stopped state of the vehicle. And The driving characteristic determination unit 25 uses, for example, at least one of speed information, spectrum angle information, determination data S _N , and information on a traffic jam predictor calculated by the traffic jam prediction unit 26 described later, Detects conditions that do not contribute to the evaluation of driving characteristics.

The driving characteristic determination unit 25 obtains the fluctuation range of the spectral angle average from the information of the frequency distribution of the spectral angle average and the speed average, and drives the vehicle moving together with the driving characteristic evaluation device 10 according to the fluctuation range of the spectral angle average. Evaluate characteristics. For example, in the frequency distribution of the spectrum angle average and the speed average, the driving characteristic determination unit 25 extracts the fluctuation range of the spectrum angle average in a region where each of the speed average and the frequency satisfies a predetermined condition.
The driving characteristic determination unit 25 determines that the consistency of driving increases as the fluctuation range of the spectrum angle average decreases, and evaluates so that the goodness of driving characteristics changes in an increasing tendency.
The driving characteristic determination unit 25 determines that the consistency of driving becomes smaller as the fluctuation range of the spectrum angle average increases, and evaluates so that the goodness of the driving characteristic changes to a decreasing tendency. For example, when the fluctuation range of the spectrum angle average is large, the driving characteristic determination unit 25 is in a situation where the vehicle cannot travel at an optimal speed according to the flow of the traveling path, and acceleration / deceleration increases. From the viewpoint of safe driving and fuel consumption, it is determined that the degree of good driving characteristics is lower than that of a driver with a small fluctuation range of the average spectral angle. For example, when the fluctuation range of the spectrum angle average is less than a predetermined fluctuation threshold, the driving characteristic determination unit 25 sets “1” for the flag value of the driving characteristic good flag indicating that the driving characteristic is good, When the fluctuation range of the spectrum angle average is equal to or larger than a predetermined fluctuation threshold, “0” is set to the flag value of the driving characteristic good flag. The driving characteristic determination unit 25 may display the driving characteristic evaluation result on the display device 16 according to the flag value of the driving characteristic good flag.

The driving characteristic determination unit 25 does not evaluate the driving characteristic when the number of accumulated spectral angle average and speed average information is less than a predetermined number (for example, the accumulated number over several days to several weeks). The driving characteristics may be evaluated when the number of accumulated average and speed average information exceeds a predetermined number.
In addition, the driving characteristic determination unit 25 accumulates the evaluation result of the driving characteristic in association with the position information and the time information, and thereby, for example, information such as in what time zone and driving place the driving characteristic changes. You may be comprised so that it can show.

The traffic jam prediction unit 26 determines whether the traffic jam (traffic traffic jam) in the future in accordance with at least one of the spectral angle θ calculated by the single regression line calculation unit 23 and the determination data _SN calculated by the determination data calculation unit 24. ) Or a traffic jam sign indicating the possibility of traffic jam already occurring. The degree of traffic jam sign indicating the magnitude of the traffic jam sign increases when there is a high possibility of a traffic jam ahead in the traveling direction of the vehicle moving together with the driving characteristic evaluation device 10, and decreases when the possibility is low.
For example, the traffic jam prediction unit 26 determines whether or not the spectral angle θ exceeds a predetermined angle threshold θ _T and the determination data S _N exceeds a predetermined determination threshold (that is, a threshold of acceleration change intensity). It is determined whether or not. When the spectral angle θ exceeds the angle threshold θ _T and the determination data S _N exceeds the determination threshold, the vehicle energy efficiency (fuel consumption, electricity consumption, etc.) tends to decrease and traffic congestion is likely to occur. judge. Note that the predetermined determination threshold for the determination data _SN can be set by the operator on an appropriate setting screen displayed on the display device 16, for example.

For example, the traffic jam prediction unit 26 obtains in advance a function (for example, y = αx + β, etc.) indicating the relationship between the magnitude (x) where the judgment data _SN exceeds the judgment threshold and the traffic jam sign degree (y), It is possible to calculate the traffic jam sign degree (y) for the combination of the determination data _SN calculated by the determination data calculation unit 24 and the determination threshold.
In addition, the traffic jam prediction unit 26 creates a correspondence relationship between the determination data _SN and the determination threshold value and the corresponding traffic jam sign degree value in advance and stores it as a table, and the traffic jam sign for the determination data _SN and the determination threshold value. The degree can also be obtained by referring to the table.

The map data storage unit 18 stores map data.
The map data includes, for example, road coordinate data indicating position coordinates on the road required for map matching processing based on information on the current position of the driving characteristic evaluation device 10, and a road map required for calculating the guidance route. Data. The road map data includes, for example, nodes, links, link costs, road shapes, pavement presence / absence, road surface unevenness, road conditions such as vehicle running conditions, road types, and the like. The node is a coordinate point composed of the latitude and longitude of a predetermined point on the road such as an intersection and a branch point. A link is a line that connects nodes, and is a road section that connects points. The link cost is information indicating the distance of the road section corresponding to the link or the time required for the movement of the road section.

  The driving characteristic evaluation apparatus 10 that realizes the driving characteristic evaluation method according to the present embodiment has the above-described configuration. Next, the operation of the driving characteristic evaluation apparatus 10, that is, the driving characteristic evaluation method will be described.

First, in step S01 shown in FIG. 6, the device control unit 17 determines whether or not the three-dimensional acceleration sensor 14 has detected accelerations in the X-axis, Y-axis, and Z-axis directions.
When the determination result is “NO”, the device control unit 17 repeatedly executes the determination process of step S01.
On the other hand, if the determination result is “YES”, the device control unit 17 advances the process to step S02.
Next, in step S02, the input data calculation unit 21 calculates the acceleration vector A in the three-dimensional space using the accelerations in the X-axis, Y-axis, and Z-axis directions detected by the three-dimensional acceleration sensor 14. To do. Then, the norm u of the difference (acceleration vector difference) ΔA between the acceleration vectors A at two different timings with a time interval of the sampling period ΔT is calculated as input data.

Next, in step S03, the frequency analysis unit 22 calculates the autocorrelation of the input data by using the number of delays that can be appropriately set by the operator at the number of input / output points that can be appropriately set by the operator. Then, a power spectrum (acceleration spectrum) is calculated by performing a fast Fourier transform on the autocorrelation.
Next, in step S04, the single regression line calculation unit 23 calculates a single regression line in a predetermined frequency range of the acceleration spectrum, and converts the inclination of the single regression line into information of an angle (spectrum angle) θ.

Next, in step S05, the driving characteristic determination unit 25 acquires time-series fluctuation information of the average value of spectrum angles (spectrum angle average) and the average value of speed (speed average). Get information on frequency distribution of combinations. The driving characteristic determination unit 25 acquires the fluctuation range of the spectrum angle average from the information of the frequency distribution of the spectrum angle average and the speed average, and determines whether or not the fluctuation range of the spectrum angle average is less than a predetermined fluctuation threshold.
If the determination result is “NO”, the driving characteristic determination unit 25 advances the process to step S08.
On the other hand, when the determination result is “YES”, the driving characteristic determination unit 25 advances the process to step S06.
Next, in step S06, the driving characteristic determination unit 25 sets “1” to the flag value of the driving characteristic good flag indicating that the driving characteristic is good.
Next, in step S07, the driving characteristic determination unit 25 displays a screen indicating that the driving characteristic is good on the display device 16.
In step S08, the driving characteristic determination unit 25 sets “0” to the flag value of the driving characteristic good flag.
Next, in step S09, the driving characteristic determination unit 25 displays a screen indicating that the driving characteristic is not good on the display device 16.

Next, in step S10, the traffic jam prediction unit 26 determines whether the spectrum angle theta is greater than the angle threshold theta _T.
If the determination result is “YES”, the traffic jam prediction unit 26 advances the process to step S11.
On the other hand, when the determination result is “NO”, the traffic jam prediction unit 26 advances the process to step S12.
In step S <b> 11, the traffic jam prediction unit 26 sets “1” indicating permission to execute the alarm to the flag value of the alarm flag.
In step S12, the traffic jam prediction unit 26 sets “0” indicating that the warning is not permitted to the flag value of the warning flag.

In step S13, the traffic jam prediction unit 26 uses the information on the spectrum angle θ to calculate the determination data _SN shown in the above equation (2) as information indicating the change in the spectrum angle θ according to time.
Next, in step S14, the traffic jam prediction unit 26 determines whether or not the determination data S _N has exceeded a determination threshold value (that is, a threshold value of acceleration change intensity). Determine whether convergence is slow.
If the determination result is “NO”, the traffic jam prediction unit 26 proceeds with the process to step S17.
On the other hand, when the determination result is “YES”, the traffic jam prediction unit 26 advances the process to step S15.
In step S15, the traffic jam prediction unit 26 determines whether or not “1” indicating permission to execute the alarm is set in the flag value of the alarm flag.
If the determination result is “NO”, the traffic jam prediction unit 26 proceeds with the process to step S17.
On the other hand, if the determination result is “YES”, the traffic jam prediction unit 26 advances the process to step S16.

In step S16, the traffic jam prediction unit 26 displays a predetermined warning screen (for example, a first interface screen P shown in FIG. 10C and a second interface screen Q shown in FIG. 11C) described later on the display device 16. And a predetermined alarm sound is output from a speaker (not shown). Then, the traffic jam prediction unit 26 ends a series of processes.
In step S17, the traffic jam prediction unit 26 displays a predetermined normal screen (for example, a first interface screen P shown in FIG. 10A described later and a second interface screen Q shown in FIG. 11A) on the display device 16. Etc.). Then, the traffic jam prediction unit 26 ends a series of processes.

  In addition, when displaying a predetermined alarm screen on the display device 16 and when outputting a predetermined alarm sound from a speaker (not shown), the alarm screen is displayed at a very short time interval such as ms units. In order to prevent the alarm sound from being output, an appropriate suppression time may be provided. Note that the appropriate suppression time can be appropriately set by the operator on, for example, an appropriate setting screen displayed on the display device 16. Depending on the suppression time, display of a continuous alarm screen and output of an alarm sound can be prohibited.

As described above, according to the driving characteristic evaluation apparatus 10 and the driving characteristic evaluation method of the present embodiment, the degree of consistency of vehicle driving is evaluated using information on the fluctuation range in the time-series fluctuation of the spectral angle θ. Therefore, it is possible to appropriately evaluate the driving characteristics that cause traffic congestion.
Furthermore, the driving characteristic determination unit 25 can appropriately evaluate the degree of good driving characteristics, that is, the degree that traffic congestion is unlikely to occur, according to the fluctuation range of the average spectral angle.
Furthermore, the driving characteristic determination unit 25 can appropriately evaluate the driving characteristic causing the occurrence of traffic jam from the frequency distribution of the combination of the spectrum angle average and the speed average.

Further, the driving characteristic determination unit 25 uses the information of the spectrum angle average and the speed average as the position of the driving characteristic evaluation device 10 and the moving environment (for example, the current position, the road type of the traveling road, the congestion situation, etc.) and the date and time information. By associating with at least one of them, it is possible to acquire information on the average spectral angle and the average velocity under different conditions.
Further, the driving characteristic determination unit 25 performs normalization based on at least one of the position and the moving environment of the driving characteristic evaluation device 10 and the date and time information, and the frequency distribution of the combination of the spectrum angle average and the speed average under different conditions. Can be properly combined into one data.
Further, the driving characteristic determination unit 25 performs a frequency distribution of combinations of spectral angle averages and speed averages under different conditions by grouping based on at least one of the position and moving environment of the driving characteristic evaluation device 10 and date and time information. Can be used for each group.
Furthermore, since the driving characteristic determination unit 25 evaluates the driving characteristic by excluding information on the spectrum angle average and the speed average acquired in a state that does not contribute to the evaluation of the driving characteristic, an appropriate evaluation can be performed.

In the above-described embodiment, for example, as in the modification shown in FIG. 7, the driving characteristic evaluation is performed by at least one driving characteristic evaluation apparatus 10 and the server apparatus 31 that can communicate with the driving characteristic evaluation apparatus 10. System 30 may be configured.
The server device 31 of this modification includes a server communication device 32, a server control unit 33, a map data storage unit 34, and a range traffic jam prediction unit 35.

  The server communication device 32 can bidirectionally communicate with the device communication device 11 of the driving characteristic evaluation device 10 by, for example, wireless communication via a wireless communication network system or road-to-vehicle communication via a roadside communication device. Send and receive various information.

The server control unit 33 outputs various types of information received from the driving characteristic evaluation device 10 by the server communication device 32 to the range traffic jam prediction unit 35.
In this modification, the driving characteristic evaluation apparatus 10 uses, for example, a single regression line calculation unit as information based on the accelerations in the X-axis, Y-axis, and Z-axis directions detected by the three-dimensional acceleration sensor 14. 23, the spectrum angle θ calculated by the control unit 23, the determination data _SN calculated by the determination data calculation unit 24, the information on the traffic jam predictor calculated by the traffic jam prediction unit 26, and the current position acquired by the current position acquisition unit 13. The position information can be transmitted to the server device 31.

The map data storage unit 34 stores map data.
The map data includes, for example, road coordinate data indicating position coordinates on the road required for map matching processing based on information on the current position of the driving characteristic evaluation device 10, and a road map required for calculating the guidance route. Data. The road map data includes, for example, nodes, links, link costs, road shapes, pavement presence / absence, road surface unevenness, road conditions such as vehicle running conditions, road types, and the like. The node is a coordinate point composed of the latitude and longitude of a predetermined point on the road such as an intersection and a branch point. A link is a line that connects nodes, and is a road section that connects points. The link cost is information indicating the distance of the road section corresponding to the link or the time required for the movement of the road section.

The range congestion prediction unit 35 determines, for example, the spectrum angle θ received from the driving characteristic evaluation device 10 with respect to an appropriate position range based on the current position information received from at least one driving characteristic evaluation device 10. The traffic sign in this position range is detected based on the number and the ratio of the driving characteristic evaluation devices 10 in which the data S _N or the traffic congestion sign degree is equal to or greater than a predetermined threshold. Then, information on a traffic jam sign in the position range is transmitted to the driving characteristic evaluation apparatus 10 in the position range via the server communication device 32.

  The range traffic jam prediction unit 35 may, for example, change the average spectral angle received from the driving characteristic evaluation device 10 within an appropriate position range based on the current position information received from at least one driving characteristic evaluation device 10. The driving characteristics within this position range are evaluated based on the number and ratio of the driving characteristics evaluation devices 10 whose width is less than a predetermined fluctuation threshold. Then, information on the driving characteristics within this position range is transmitted to the driving characteristics evaluation apparatus 10 within this position range via the server communication device 32.

  The driving characteristic evaluation system 30 that realizes the driving characteristic evaluation method according to this modified example has the above-described configuration. Next, the operation of the driving characteristic evaluation system 30, particularly the operation of the driving characteristic evaluation apparatus 10, will be described.

First, in step S21 shown in FIG. 8, the server control unit 33 connects the operating characteristic evaluation apparatus 10 to a communication network such as a wireless communication network system, and the server apparatus without any communication failure through the communication network. It is determined whether or not it can be properly connected to 31.
If the determination result is “NO”, the server control unit 33 repeatedly executes the process of step S21.
On the other hand, if the determination result is “YES”, the server control unit 33 advances the process to step S22.

In step S <b> 22, the server control unit 33 determines whether or not a stand-alone operation execution instruction independent of an external device such as the server device 31 is generated by an instruction from the operator.
If the determination result is “YES”, that is, if there is no instruction to execute the stand-alone operation, the server control unit 33 advances the process to step S23. In step S23, the server control unit 33 executes a network operation described later and ends the process.
On the other hand, if the determination result is “NO”, the server control unit 33 advances the process to step S24. In step S24, the server control unit 33 performs the processing from step S01 to step S17 in the above-described embodiment as a stand-alone operation.

Hereinafter, the network operation in step S23 described above will be described.
First, in step S31 shown in FIG. 9, the device control unit 17 displays a predetermined communication indicator display on the display device 16. The device control unit 17 properly connects the display of the communication indicator to the server device 31 with the driving characteristic evaluation device 10 connected to a communication network such as a wireless communication network system and without any communication failure. The display indicates that it is possible.

Next, in step S <b> 32, the device control unit 17 detects the acceleration in the X-axis, Y-axis, and Z-axis directions by the three-dimensional acceleration sensor 14, and the current position acquisition unit 13 obtains the current position information. It is determined whether or not it has been acquired.
When the determination result is “NO”, the device control unit 17 repeatedly executes the determination process of step S32.
On the other hand, if the determination result is “YES”, the device control unit 17 advances the process to step S33.
Next, in step S33, the input data calculation unit 21 calculates the acceleration vector A in the three-dimensional space using the accelerations in the X-axis, Y-axis, and Z-axis directions detected by the three-dimensional acceleration sensor 14. To do. Then, the norm u of the difference (acceleration vector difference) ΔA between the acceleration vectors A at two different timings with a time interval of the sampling period ΔT is calculated as input data.

Next, in step S34, the frequency analysis unit 22 calculates the autocorrelation of the input data using the number of delays that can be appropriately set by the operator at the number of input / output points that can be appropriately set by the operator. Then, a power spectrum (acceleration spectrum) is calculated by performing a fast Fourier transform on the autocorrelation.
Next, in step S35, the single regression line calculation unit 23 calculates a single regression line in a predetermined frequency range of the acceleration spectrum, and converts the inclination of the single regression line into information of an angle (spectrum angle) θ.
Next, in step S <b> 36, the driving characteristic determination unit 25 acquires information on the fluctuation range of the spectrum angle average from information on the frequency distribution of the spectrum angle average and the speed average.
Next, in step S37, the traffic jam prediction unit 26 uses the information on the spectrum angle θ to calculate the determination data S _N shown in the above equation (2) as information indicating the change in the spectrum angle θ according to time. .

Next, in step S <b> 38, the device control unit 17 determines the spectrum angle average fluctuation range information, the spectrum angle θ, the determination data S _N , the traffic congestion predictor information calculated by the traffic congestion prediction unit 26, and the current position information. Is transmitted to the server device 31 via the device communication device 11.
Next, in step S <b> 39, the device control unit 17 determines whether or not the information on the driving characteristics and the traffic jam sign information in the appropriate position range detected by the server device 31 has been received from the server device 31.
When the determination result is “NO”, the device control unit 17 ends the series of processes.
On the other hand, if the determination result is “YES”, the device control unit 17 advances the process to step S40. In step S <b> 40, the device control unit 17 displays on the display device 16 a display screen corresponding to the driving characteristic information or the traffic jam sign information within the appropriate position range received from the server device 31, and proceeds to return.

  According to the driving characteristic evaluation system 30 and the driving characteristic evaluation method according to this modification, in addition to the fluctuation range of the average spectral angle of each driving characteristic evaluation apparatus 10, the driving characteristic evaluation apparatus 10 moves within an appropriate position range. The driving characteristics can be comprehensively evaluated in consideration of the states of a plurality of moving bodies such as vehicles. Furthermore, by providing information on driving characteristics within this position range to each driving characteristic evaluation device 10, the driving characteristics are efficiently improved in conjunction with a plurality of moving bodies such as vehicles moving with the driving characteristic evaluation device 10. Can be made.

  In the embodiment and the modification described above, the driving characteristic determination unit 25 uses information on time-series fluctuations of the average value of spectrum angles (spectrum angle average) and the average value of speeds (speed average) every predetermined time. Although the driving characteristics are evaluated, the present invention is not limited to this. For example, the driving characteristic determination unit 25 may evaluate the driving characteristic using a spectral angle and a speed at a predetermined time instead of the spectral angle average and the speed average.

  In the embodiment and the modification described above, the driving characteristic determination unit 25 acquires the fluctuation range of the spectrum angle average from the information of the frequency distribution of the spectrum angle average and the speed average. However, the present invention is not limited to this. For example, the driving characteristic determination unit 25 may acquire the fluctuation range from the information of the frequency distribution of only the spectral angle average without using the speed average. In addition, for example, the driving characteristic determination unit 25 may acquire the fluctuation range of the spectrum angle average from the frequency distribution information using other parameters in addition to or instead of the speed average.

  In the above-described embodiment and modification, the driving characteristic evaluation device 10 includes a plurality of interface screens (for example, the first interface screen P and the first interface screen P shown in FIGS. 10A to 10D) that can be appropriately selected by the operator. In the second interface screen Q shown in FIGS. 11A to 11D, the traffic jam sign detected by the traffic jam prediction unit 26 or the calculated traffic jam sign degree is displayed in a plurality of stages (for example, three stages, etc.). It can be displayed on the device 16.

  For example, the first interface screen P shown in FIG. 10 (A) and the second interface screen Q shown in FIG. 11 (A) have no or a predetermined change in acceleration, such as when the vehicle is stopped or at a constant speed. Is displayed when the absolute value of the spectrum angle is zero or less than a predetermined value near zero. The first interface screen P shown in FIG. 10A includes, for example, a figure Pa1 such as an ellipsoid of a predetermined color (for example, bright green) indicating stability and a wave figure Pb1 having a small wave height. The second interface screen Q shown in FIG. 11A includes, for example, a graphic Qa1 such as a clover of a predetermined color (for example, bright green) indicating stability and a wave graphic Qb1 having a small wave height.

  For example, the first interface screen P shown in FIG. 10 (B) and the second interface screen Q shown in FIG. 11 (B) are normal, such as when the vehicle is slowly accelerating or decelerating, or when the engine brake is operating. Even if the absolute value of the spectrum angle temporarily increases due to vibration during operation, it is displayed when it immediately converges toward zero. The first interface screen P shown in FIG. 10B includes, for example, a figure Pa2 such as an ellipsoid of a predetermined color (for example, dark green) indicating a normal state, and a wave figure Pb2 having a slightly large wave height. Further, the second interface screen Q shown in FIG. 11B includes, for example, a graphic Qa2 such as a clover of a predetermined color (for example, dark green) indicating a normal state and a wave graphic Qb2 having a slightly large wave height. .

  For example, the first interface screen P shown in FIG. 10 (C) and the second interface screen Q shown in FIG. 11 (C) are used when the vehicle starts suddenly, suddenly brakes, frequently repeats acceleration and deceleration, etc. In addition, when the acceleration change is larger than the predetermined change, the absolute value of the spectrum angle becomes a large value, which is displayed when the time required to converge toward zero becomes long. The first interface screen P shown in FIG. 10C includes, for example, a figure Pa3 such as an ellipsoid of a predetermined color (eg, blue) indicating instability and a wave figure Pb3 having a large wave height. The second interface screen Q shown in FIG. 11C includes, for example, a graphic Qa3 such as a clover of a predetermined color (for example, blue) indicating instability and a wave graphic Qb3 having a large wave height.

  In addition, when the driving characteristic evaluation device 10 is connected to a communication network such as a wireless communication network system and can be properly connected to the server device 31 through this communication network without communication failure, for example, As shown in the first interface screen P shown in FIG. 10D and the second interface screen Q shown in FIG. 11D, predetermined communication indicator displays Pc and Qc are displayed.

  Note that the driving characteristic evaluation device 10 according to the above-described embodiment and the modification and the server device 31 of the driving characteristic evaluation system 30 may be realized by dedicated hardware, or the driving characteristic. A program for realizing the functions of the evaluation device 10 and the server device 31 is recorded on a computer-readable recording medium, and the program recorded on the recording medium is read into a computer system and executed to thereby execute an operation characteristic evaluation device. 10 and server device 31 may be operated. The computer system referred to here includes an OS and hardware such as peripheral devices. The computer system also includes a WWW system provided with a homepage providing environment (or display environment).

  The computer-readable recording medium refers to a storage device such as a portable medium such as a flexible disk, a magneto-optical disk, a ROM, and a CD-ROM, and a hard disk built in the computer system. Further, the computer-readable recording medium is a volatile memory (RAM) inside a computer system that becomes a server or a client when a program is transmitted via a network such as the Internet or a communication line such as a telephone line. In addition, those that hold a program for a certain period of time are also included.

The program may be transmitted from a computer system storing the program in a storage device or the like to another computer system via a transmission medium or by a transmission wave in the transmission medium. Here, the transmission medium for transmitting the program refers to a medium having a function of transmitting information, such as a network (communication network) such as the Internet or a communication line (communication line) such as a telephone line.
The program may be for realizing a part of the functions described above.
Furthermore, what can implement | achieve the function mentioned above in combination with the program already recorded on the computer system, what is called a difference file (difference program) may be sufficient.

  The above-described embodiments are presented as examples, and are not intended to limit the scope of the invention. The above-described novel embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the spirit of the invention. The above-described embodiments and modifications thereof are included in the scope and gist of the invention, and are included in the invention described in the claims and the equivalents thereof. For example, in the above embodiment, the server device 31 is configured as one device. However, a plurality of devices may be connected by a communication line or the like.

10 Driving characteristic evaluation device (electronic equipment)
12 positioning signal receiver 13 current position acquisition unit (current position information acquisition means)
14 Three-dimensional acceleration sensor (acceleration information acquisition means)
DESCRIPTION OF SYMBOLS 15 Input device 16 Display apparatus 17 Apparatus control part 21 Input data calculation part (input data calculation means)
22 Frequency analysis unit (frequency analysis means)
23 Single regression line calculation unit (angle information acquisition means)
24 determination data calculation unit 25 driving characteristic determination unit (variation width information acquisition means, evaluation means)
26 Congestion Prediction Unit 30 Driving Characteristic Evaluation System (Congestion Prediction Detection System)
31 Server device 35 Range traffic jam prediction unit

Claims (22)

A driving characteristic evaluation method executed by an electronic device including acceleration information acquisition means for acquiring information on acceleration in the direction of each axis of the first to third axes forming an orthogonal coordinate system in a three-dimensional space,
The electronic device calculates an acceleration vector in the three-dimensional space using the acceleration information acquired by the acceleration information acquisition means, and calculates a norm of a difference between the vectors at two different timings as input data. An input data calculation step;
The electronic device calculates an autocorrelation of the input data, and a frequency analysis step of calculating a power spectrum by performing a Fourier transform on the autocorrelation;
An angle information acquisition step in which the electronic device converts the power spectrum into angle information;
A fluctuation range information acquisition step in which the electronic device acquires information on a fluctuation range in the time-series fluctuation of the angle information;
An evaluation step in which the electronic device evaluates driving characteristics of a moving body that moves with the electronic device according to the information on the fluctuation range;
including,
A method for evaluating driving characteristics. In the evaluation step, the electronic device evaluates so that the goodness of the driving characteristics changes in an increasing tendency as the fluctuation range decreases, and the driving characteristics increase as the fluctuation range increases. Evaluate so that the goodness of changes to a downward trend,
The operating characteristic evaluation method according to claim 1. In the fluctuation range information acquisition step, the electronic device acquires the angle information and the speed of the electronic device in time series, and obtains information on the fluctuation range from a frequency distribution of a combination of the angle information and the speed of the electronic device. get,
The driving characteristic evaluation method according to claim 1, wherein the driving characteristic is evaluated. In the fluctuation range information acquisition step, the electronic device determines the angle information and the speed of the electronic device in association with at least one of the position of the electronic device, the movement environment of the electronic device, and date and time information. get,
The operating characteristic evaluation method according to claim 3. The electronic device determines the angle information and the speed of the electronic device based on at least one of the position of the electronic device, the movement environment of the electronic device, and date / time information in the fluctuation range information acquisition step. Generating a frequency distribution of the combination of the angle information and the speed of the electronic device by normalizing;
The operating characteristic evaluation method according to claim 4. The electronic device determines the angle information and the speed of the electronic device based on at least one of the position of the electronic device, the movement environment of the electronic device, and date / time information in the fluctuation range information acquisition step. By generating a group, a frequency distribution of a combination of the angle information and the speed of the electronic device is generated for each group.
The operating characteristic evaluation method according to claim 4. In the evaluation step, the electronic device evaluates the driving characteristics based on at least one of the speed of the electronic device, the angle information, and information on change of the angle information according to time. Detect non-contributing conditions,
Excluding the fluctuation range information acquired in a state that does not contribute to the evaluation of the driving characteristics from the fluctuation range information, to evaluate the driving characteristics of the mobile body,
The driving characteristic evaluation method according to any one of claims 1 to 6, wherein: An electronic apparatus comprising a server device, acceleration information acquisition means for acquiring acceleration information in the directions of the first to third axes forming an orthogonal coordinate system in a three-dimensional space, and current position information acquisition means for acquiring current position information An operation characteristic evaluation method executed by an operation characteristic evaluation system comprising:
The electronic device calculates an acceleration vector in the three-dimensional space using the acceleration information acquired by the acceleration information acquisition means, and calculates a norm of a difference between the vectors at two different timings as input data. An input data calculation step;
The electronic device calculates an autocorrelation of the input data, and a frequency analysis step of calculating a power spectrum by performing a Fourier transform on the autocorrelation;
An angle information acquisition step in which the electronic device converts the power spectrum into angle information;
A fluctuation range information acquisition step in which the electronic device acquires information on a fluctuation range in the time-series fluctuation of the angle information;
An electronic device transmitting step in which the electronic device transmits the information on the fluctuation range and the information on the current position acquired by the current position information acquiring unit to the server device;
The server device uses the information on the current position received from at least one or more electronic devices and the information on the fluctuation range, and the information on the fluctuation range within an appropriate position range is a predetermined condition. A position range driving characteristic detection step for detecting a driving characteristic of a moving body that moves together with the electronic device within the position range according to the number and ratio of the devices;
A server transmission step in which the server device transmits information on driving characteristics within the position range to the electronic device within the position range; and
including,
Driving characteristic evaluation method. A computer constituting an electronic device comprising acceleration information acquisition means for acquiring acceleration information in the directions of the first to third axes forming a Cartesian coordinate system in a three-dimensional space;
Input data calculation means for calculating an acceleration vector in the three-dimensional space using the acceleration information acquired by the acceleration information acquisition means, and calculating a norm of a difference between the vectors at two different timings as input data; ,
Frequency analysis means for calculating autocorrelation of the input data calculated by the input data calculation means, and calculating a power spectrum by performing Fourier transform on the autocorrelation;
Angle information acquisition means for converting the power spectrum calculated by the frequency analysis means into angle information;
Fluctuation width information acquisition means for acquiring fluctuation width information in the time-series fluctuation of the angle information;
Evaluation means for evaluating the operating characteristics of a moving body that moves with the electronic device according to the information of the fluctuation range;
Function as
A program characterized by that. The evaluation means evaluates so that the goodness of the driving characteristics changes in an increasing trend as the fluctuation range decreases, and the goodness of the driving characteristics decreases as the fluctuation range increases. Evaluate as the trend changes,
The program according to claim 9. The fluctuation range information acquisition means acquires the angle information and the speed of the electronic device in time series, and acquires the information of the fluctuation range from a frequency distribution of a combination of the angle information and the speed of the electronic device.
The program according to claim 9 or 10, characterized by the above. The fluctuation range information acquisition means acquires the angle information and the speed of the electronic device in association with at least one of the position of the electronic device, the movement environment of the electronic device, and date and time information.
The program according to claim 11. The fluctuation range information acquisition unit normalizes the angle information and the speed of the electronic device based on at least one of the position of the electronic device, the movement environment of the electronic device, and date and time information. Generating a frequency distribution of a combination of the angle information and the speed of the electronic device,
The program according to claim 12, wherein: The fluctuation range information acquisition unit groups the angle information and the speed of the electronic device based on at least one of the position of the electronic device, the movement environment of the electronic device, and the date and time information. A frequency distribution of a combination of the angle information and the speed of the electronic device is generated for each group.
The program according to claim 12, wherein: The evaluation means detects a state that does not contribute to the evaluation of the driving characteristics based on at least one of the speed of the electronic device, the angle information, and information on change of the angle information according to time. And
Excluding the fluctuation range information acquired in a state that does not contribute to the evaluation of the driving characteristics from the fluctuation range information, to evaluate the driving characteristics of the mobile body,
The program according to any one of claims 9 to 14, characterized in that: Acceleration information acquisition means for acquiring information on the acceleration of the electronic device in each of the first to third axis directions forming an orthogonal coordinate system in a three-dimensional space;
Input data calculation means for calculating an acceleration vector in the three-dimensional space using the acceleration information acquired by the acceleration information acquisition means, and calculating a norm of a difference between the vectors at two different timings as input data; ,
Frequency analysis means for calculating autocorrelation of the input data calculated by the input data calculation means, and calculating a power spectrum by performing Fourier transform on the autocorrelation;
Angle information acquisition means for converting the power spectrum calculated by the frequency analysis means into angle information;
Fluctuation width information acquisition means for acquiring fluctuation width information in the time-series fluctuation of the angle information;
Evaluation means for evaluating the operating characteristics of a moving body that moves with the electronic device according to the information of the fluctuation range;
Comprising
A driving characteristic evaluation apparatus characterized by that. The evaluation means evaluates so that the goodness of the driving characteristics changes in an increasing trend as the fluctuation range decreases, and the goodness of the driving characteristics decreases as the fluctuation range increases. Evaluate as the trend changes,
The driving characteristic evaluation apparatus according to claim 16. The fluctuation range information acquisition means acquires the angle information and the speed of the electronic device in time series, and acquires the information of the fluctuation range from a frequency distribution of a combination of the angle information and the speed of the electronic device.
The driving characteristic evaluation apparatus according to claim 16 or claim 17, characterized in that: The fluctuation range information acquisition means acquires the angle information and the speed of the electronic device in association with at least one of the position of the electronic device, the movement environment of the electronic device, and date and time information.
The driving characteristic evaluation apparatus according to claim 18. The fluctuation range information acquisition unit normalizes the angle information and the speed of the electronic device based on at least one of the position of the electronic device, the movement environment of the electronic device, and date and time information. Generating a frequency distribution of a combination of the angle information and the speed of the electronic device,
The driving characteristic evaluation apparatus according to claim 19. The fluctuation range information acquisition unit groups the angle information and the speed of the electronic device based on at least one of the position of the electronic device, the movement environment of the electronic device, and the date and time information. A frequency distribution of a combination of the angle information and the speed of the electronic device is generated for each group.
The driving characteristic evaluation apparatus according to claim 19. The evaluation means detects a state that does not contribute to the evaluation of the driving characteristics based on at least one of the speed of the electronic device, the angle information, and information on change of the angle information according to time. And
Excluding the fluctuation range information acquired in a state that does not contribute to the evaluation of the driving characteristics from the fluctuation range information, to evaluate the driving characteristics of the mobile body,
The driving characteristic evaluation apparatus according to any one of claims 16 to 21, wherein