Road surface state estimation device, road surface state estimation method, and road surface state estimation program

Abstract

To provide a technology that accurately estimates a road surface state.SOLUTION: A road surface state estimation device comprises acquisition means 23a, measurement means 23c, and estimation means 23d. The acquisition means 23a acquires travel information including acceleration of a moving body. The measurement means 23c measures information about the vibration of the moving body based on a distribution of acceleration in multiple axes of the moving body, based on the travel information acquired by the acquisition means 23a. The estimation means 23d estimates the road surface state based on the information about the vibration of the moving body measured by the measurement means 23c. The measurement means 23c measures vibration position deviation of the moving body as the information about the vibration of the moving body. The estimation means 23d estimates the road surface state based on the vibration position deviation of the moving body measured by the measurement means 23c.SELECTED DRAWING: Figure 3

Description

Technical Field

[0001] The present invention relates to a road surface condition estimation device, a road surface condition estimation method, and a road surface condition estimation program.

Background Art

[0002] Conventionally, there has been known a technique for determining whether or not there are irregularities on a road surface without using a road surface property measurement vehicle equipped with a laser scanning device or a camera by acquiring the vertical acceleration of a vehicle (see, for example, Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, in the above prior art, since only the vertical acceleration of the vehicle is acquired, there is still room for further improvement in detecting the detailed state of the irregularities existing on the road surface.

[0005] The present invention has been made in view of the above, and an object thereof is to provide, for example, a road surface condition estimation device, a road surface condition estimation method, and a road surface condition estimation program capable of accurately estimating a road surface condition.

Means for Solving the Problems

[0006] The road surface condition estimation device according to claim 1 includes an acquisition means, a measurement means, and an estimation means. The acquisition means acquires running information including the acceleration of the moving body. The measurement means measures information regarding the vibration of the moving body based on the distribution of the acceleration of the moving body in a plurality of axes of the running information acquired by the acquisition means. The estimation means estimates the road surface condition based on the information regarding the vibration of the moving body measured by the measurement means. The measurement means measures the deviation of the vibration position of the moving body as information regarding the vibration of the moving body. The estimation means estimates the road surface condition based on the deviation of the vibration position of the moving body measured by the measurement means.

[0007] Further, the road surface condition estimation method according to claim 9 is a road surface condition estimation method implemented by a road surface condition estimation device, including an acquisition step of acquiring running information including the acceleration of the moving body, a measurement step of measuring information regarding the vibration of the moving body based on the distribution of the acceleration of the moving body in a plurality of axes of the acquired running information, and an estimation step of estimating the road surface condition based on the information regarding the vibration of the moving body measured in the measurement step. The measurement step measures the deviation of the vibration position of the moving body as information regarding the vibration of the moving body, and the estimation step estimates the road surface condition based on the deviation of the vibration position of the moving body measured in the measurement step.

[0008] Further, the road surface condition estimation program according to claim 10 is a road surface condition estimation program for causing a computer to execute a process of including an acquisition means for acquiring running information including the acceleration of the moving body, a measurement means for measuring information regarding the vibration of the moving body based on the distribution of the acceleration of the moving body in a plurality of axes of the acquired running information, and an estimation means for estimating the road surface condition based on the information regarding the vibration of the moving body measured by the measurement means. The measurement means measures the deviation of the vibration position of the moving body as information regarding the vibration of the moving body, and the estimation means estimates the road surface condition based on the deviation of the vibration position of the moving body measured by the measurement means.

Brief Description of the Drawings

[0009]

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Embodiments for Carrying Out the Invention

[0010] Hereinafter, embodiments for carrying out the present invention (hereinafter referred to as embodiments) will be described with reference to the drawings. Note that the present invention is not limited by the embodiments described below. Further, in the description of the drawings, the same parts are denoted by the same reference numerals.

[0011] <Configuration of the Control System> First, the configuration of the control system 1 according to Embodiment 1 will be described with reference to FIG. 1. FIG. 1 is an explanatory diagram showing an example of the configuration of the control system 1 according to Embodiment 1. As shown in FIG. 1, the control system 1 according to Embodiment 1 includes a server 2 and a plurality of vehicles 3. The vehicle 3 is an example of a moving body.

[0012] The server 2 manages the plurality of vehicles 3 as one control network. The plurality of vehicles 3 each have a control device 4.

[0013] The server 2 and the plurality of vehicles 3 are connected via a network (e.g., the Internet) N by, for example, wireless LAN (Local Area Network) communication, WAN (Wide Area Network) communication, mobile phone communication, etc., and various types of information can be communicated between the two.

[0014] <Configuration of the server> Next, the configuration of the server 2 according to Embodiment 1 will be described with reference to FIG. 2. FIG. 2 is an explanatory diagram showing an example of the configuration of the server 2 according to Embodiment 1. As shown in FIG. 2, the server 2 includes a communication unit 11, a storage unit 12, and a control unit 13.

[0015] Note that the server 2 may have an input unit (e.g., a keyboard, a mouse, etc.) for receiving various operations from an administrator or the like who uses such a server 2, and a display unit (e.g., a liquid crystal display, etc.) for displaying various types of information.

[0016] The communication unit 11 is realized by, for example, a NIC (Network Interface Card) or the like. The communication unit 11 is connected to the network N by wire or wirelessly, and transmits and receives information to and from the plurality of vehicles 3 via the network N.

[0017] The storage unit 12 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk. As shown in FIG. 2, the storage unit 12 includes a road information storage unit 12a, a road surface information storage unit 12b, and a cargo shift information storage unit 12c.

[0018] The road information storage unit 12a stores road position information indicating the position of a road, such as map information. The road surface information storage unit 12b stores information regarding the road surface condition of a road (hereinafter also referred to as "road surface information"). In such a road surface information storage unit 12b, for example, information regarding the position of a road surface with unevenness and information regarding details (such as shape) of such unevenness are stored in association with each other.

[0019] The cargo shift information storage unit 12c stores information regarding cargo shift in the vehicle 3 (hereinafter also referred to as "cargo shift information"). In such a cargo shift information storage unit 12c, for example, information regarding a position where the degree of influence on cargo shift is large and information regarding the degree of influence on cargo shift are stored in association with each other.

[0020] The control unit 13 is a controller and is realized, for example, by various programs stored in a storage device inside the server 2 being executed with the RAM as a work area by a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like. Further, the control unit 13 is, for example, a controller and is realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0021] As shown in FIG. 2, the control unit 13 includes an acquisition means 13a and a transmission means 13b, and realizes or executes the functions and operations of various processes described below. Note that the internal configuration of the control unit 13 is not limited to the configuration shown in FIG. 2, and any other configuration may be used as long as it can perform the various processes described later.

[0022] The acquisition means 13a acquires the road surface information estimated by the control device 4 of the vehicle 3 and transmitted from this control device 4, and stores it in the road surface information storage unit 12b. Further, the acquisition means 13a acquires the load collapse information specified by the control device 4 of the vehicle 3 and transmitted from this control device 4, and stores it in the load collapse information storage unit 12c.

[0023] The transmission means 13b transmits the road surface information stored in the road surface information storage unit 12b and the load collapse information stored in the load collapse information storage unit 12c to the control device 4 based on a command from the control device 4 of the vehicle 3.

[0024] <Road surface state estimation process> Next, the configuration of the vehicle 3 according to the first embodiment and the details of the road surface state estimation process performed by this vehicle 3 will be described with reference to FIGS. 3 to 6. FIG. 3 is an explanatory diagram showing an example of the configuration of the vehicle 3 according to the first embodiment.

[0025] As shown in FIG. 3, the vehicle 3 includes a control device 4, a three-axis acceleration sensor 5, a GPS (Global Positioning System) sensor 6, a speed sensor 7, and a display unit 8. The control device 4 is an example of a road surface state estimation device and also an example of a driving support device.

[0026] The three-axis acceleration sensor 5 is a sensor that detects accelerations in the X-axis (for example, the longitudinal direction of the vehicle 3), Y-axis (for example, the lateral direction of the vehicle 3), and Z-axis (for example, the vertical direction of the vehicle 3), and supplies its detection signal to the control device 4. Note that the three-axis acceleration sensor 5 may further detect the angular velocity and angular acceleration of the operation of the vehicle 3.

[0027] The GPS sensor 6 receives radio waves carrying downlink data including positioning data from a plurality of GPS satellites, and supplies such positioning data to the control device 4. The control device 4 can detect the absolute position of the vehicle 3 from the position information (for example, latitude and longitude) included in such positioning data.

[0028] The speed sensor 7 is, for example, a sensor that detects the speed of the vehicle 3, and supplies its detection signal to the control device 4. The display unit 8 is provided, for example, on the instrument panel of the vehicle 3, and is composed of a liquid crystal display, an organic EL (Electro Luminescence) element, etc.

[0029] The control device 4 includes a communication unit 21, a storage unit 22, and a control unit 23. The communication unit 21 is realized by, for example, a NIC or the like. The communication unit 21 is connected to the network N by wire or wirelessly, and transmits and receives information to and from the server 2 via the network N.

[0030] The storage unit 22 is realized by, for example, a semiconductor memory element such as a RAM or a flash memory, or a storage device such as a hard disk or an optical disk.

[0031] The control unit 23 is a controller, and is realized, for example, by various programs stored in the storage device inside the control device 4 being executed with the RAM as a working area by a CPU, an MPU, or the like. Also, the control unit 23 is, for example, a controller, and is realized by an integrated circuit such as an ASIC or an FPGA.

[0032] As shown in FIG. 3, the control unit 23 includes an acquisition means 23a, a generation means 23b, a measurement means 23c, an estimation means 23d, a storage means 23e, an extraction means 23f, a specification means 23g, and a presentation means 23h, and realizes or executes the functions and actions of various processes described below. Note that the internal configuration of the control unit 23 is not limited to the configuration shown in FIG. 3, and any other configuration may be used as long as it can perform the various processes described below.

[0033] The acquisition means 23a acquires information regarding the running of the vehicle 3 (hereinafter also referred to as running information). As such running information, for example, the acquisition means 23a acquires information regarding the acceleration in the longitudinal direction, lateral direction, and vertical direction of the vehicle 3 from the three-axis acceleration sensor 5.

[0034] Also, as running information, the acquisition means 23a acquires the position information of the vehicle 3 from the GPS sensor 6 and the speed information of the vehicle 3 from the speed sensor 7, for example.

[0035] The generation means 23b generates an acceleration value distribution diagram which is a diagram plotting the distribution of the three axes of acceleration in a predetermined unit time in a three-dimensional coordinate system using the information regarding the acceleration in the longitudinal direction, lateral direction, and vertical direction of the vehicle 3 acquired by the acquisition means 23a. The details of such an acceleration value distribution diagram will be described below.

[0036] FIG. 4 is a diagram showing an example of the acceleration value distribution diagram according to the first embodiment. For example, when the vehicle 3 is stopped with the engine turned off, the plotting range of the acceleration value distribution diagram is the plotting range D1 near the origin of the three-dimensional coordinate system.

[0037] Also, when the vehicle 3 is stopped while idling the engine, the plotting range of the acceleration value distribution diagram is a spherical plotting range D2 centered on the origin of the three-dimensional coordinate system and wider than the above-mentioned plotting range D1.

[0038] Furthermore, when the vehicle 3 is running on a flat road surface, the plotting range of the acceleration value distribution diagram is a spherical plotting range D3 centered on the origin of the three-dimensional coordinate system and wider than the above-mentioned plotting range D2. In FIG. 4 or the text, a point taking into account the gravitational acceleration at the origin (with 1g as the initial value in the vertical direction) is used.

[0039] Next, the road surface state estimation process using this acceleration value distribution diagram will be described. FIG. 5 is a diagram for explaining an example of the road surface state estimation process according to the first embodiment. As shown in FIG. 5, it is assumed that the vehicle 3 passes through a road surface deterioration section X with asphalt cracks or the like.

[0040] In this case, while the left front wheel FL and the left rear wheel RL of the vehicle 3 pass through the road surface deterioration section X in order, the right front wheel FR and the right rear wheel RR of the vehicle 3 do not pass through the road surface deterioration section X. Therefore, the vehicle 3 is shaken in the front-rear direction and shaken more greatly in the left direction than in the right direction.

[0041] Then, in the unit time when passing through such a road surface deterioration section X, the generation means 23b generates an acceleration value distribution diagram as shown in FIG. 6. FIG. 6 is a diagram showing an example of the acceleration value distribution diagram according to the first embodiment.

[0042] In the unit time when passing through the road surface deterioration section X, the generation means 23b generates an acceleration value distribution diagram with a plot range D4 that spreads in the front-rear direction and the up-down direction and spreads more greatly in the left direction than in the right direction, as shown in FIG. 6.

[0043] Returning to the description of FIG. 3, the measurement means 23c of the control unit 23 measures information regarding the vibration of the vehicle 3 (hereinafter also referred to as vibration information) based on the distribution of a plurality of axes of the acceleration of the vehicle 3 (for example, the acceleration value distribution diagram generated by the generation means 23b).

[0044] Examples of the vibration information measured by the measurement means 23c include, for example, the absolute value |A| of the magnitude in the front-rear direction in the acceleration value distribution diagram of the plot range D4 shown in FIG. 6. Also, examples of the vibration information measured by the measurement means 23c include, for example, the absolute value |B| of the magnitude in the left-right direction in the acceleration value distribution diagram of the plot range D4.

[0045] In addition, examples of the vibration information measured by the measuring means 23c include the absolute value |B / C| of the ratio between the magnitude B in the left-right direction in the acceleration value distribution diagram of the plot range D4 and the magnitude C in the horizontal direction (the left direction in the figure) with a large spread with respect to the origin.

[0046] In addition, examples of the vibration information measured by the measuring means 23c include the absolute value |D| of the magnitude in the vertical direction in the acceleration value distribution diagram of the plot range D4.

[0047] Returning to the description of FIG. 3, the estimation means 23d of the control unit 23 estimates the road surface condition of the road surface on which the vehicle 3 has traveled based on the vibration information measured by the measuring means 23c.

[0048] For example, the estimation means 23d estimates that the degree of deterioration (for example, unevenness, etc.) of the road surface deterioration section X is greater as the value of the absolute value |A| measured by the measuring means 23c is greater.

[0049] In addition, the estimation means 23d estimates that the degree of deterioration (for example, unevenness, etc.) of the road surface deterioration section X is greater as the value of the absolute value |B| measured by the measuring means 23c is greater.

[0050] In addition, the estimation means 23d estimates that the partial deterioration of the road surface deterioration section X is progressing more as the value of the absolute value |B / C| measured by the measuring means 23c is greater.

[0051] In addition, the estimation means 23d can estimate the horizontal position of the road surface deterioration section X (the relative position in the left-right direction of the road surface deterioration section X with respect to the vehicle 3) based on the value of the absolute value |B / C| measured by the measuring means 23c.

[0052] In addition, when the fluctuation value of the absolute value |D| measured by the measuring means 23c is large, the estimation means 23d can estimate that the contribution of the degree of deterioration of the road surface deterioration section X to the value of the absolute value |B / C| is greater than the influence of the partial deterioration than the horizontal position of the road surface deterioration section X.

[0053] As described so far, in the first embodiment, vibration information of the vehicle 3 is measured based on the distribution of a plurality of axes of the acceleration of the vehicle 3 (for example, an acceleration value distribution diagram), and the road surface condition is estimated based on such vibration information of the vehicle 3. Thereby, the road surface condition can be accurately estimated.

[0054] Further, in the first embodiment, the estimation means 23d may estimate the road surface condition based on the magnitude of the vibration of the vehicle 3 (for example, the absolute value |A|, the absolute value |B|, the absolute value |D|) and the deviation of the vibration position of the vehicle 3 (for example, the absolute value |B / C|), and the temporal length of the vibration.

[0055] In this way, by estimating the degree of deterioration of the road surface deterioration section X with various parameters, the road surface condition can be estimated more accurately.

[0056] Further, in the first embodiment, the measurement means 23c may measure the magnitude of the vibration of the vehicle 3 and the deviation of the vibration position of the vehicle 3 based on the shape of the acceleration value distribution diagram generated by the generation means 23b.

[0057] Thereby, the magnitude of the vibration of the vehicle 3 and the deviation of the vibration position of the vehicle 3 can be accurately measured. Therefore, according to the first embodiment, the road surface condition can be estimated more accurately.

[0058] Further, in the first embodiment, the estimation means 23d may estimate the undulation of the road surface based on the magnitude of the vibration of the vehicle 3, and estimate the lateral position of the point where the road surface condition is damaged in the driving lane based on the deviation of the vibration position of the vehicle 3.

[0059] Thereby, the degree of deterioration of the road surface deterioration section X can be estimated with various parameters, and the lateral position of the road surface deterioration section X can also be estimated. Therefore, according to the first embodiment, the road surface condition can be estimated more accurately.

[0060] Further, in Embodiment 1, the estimation means 23d may directly estimate the road surface condition based on the shape of the acceleration value distribution diagram generated by the generation means 23b. For example, the estimation means 23d can estimate whether there are steps on the road surface or whether there are cracks on the road surface based on the shape of the acceleration value distribution diagram. Therefore, according to Embodiment 1, the road surface condition can be estimated with higher accuracy.

[0061] Further, in Embodiment 1, the acquisition means 23a may acquire driving information from one three-axis acceleration sensor 5 located inside the vehicle 3. As a result, since the acceleration value distribution diagram of the vehicle 3 can be easily generated, the road surface condition can be estimated at low cost.

[0062] Note that the three-axis acceleration sensor 5 is not limited to being mounted on the vehicle 3. For example, it may be a three-axis acceleration sensor mounted on an information terminal such as a smartphone and placed inside the vehicle 3.

[0063] Also, in the present disclosure, the number of three-axis acceleration sensors 5 for measuring the acceleration of the vehicle 3 is not limited to one, and may be plural. As a result, since the acceleration of the vehicle 3 can be measured accurately, the road surface condition can be estimated with higher accuracy.

[0064] Returning to the description of FIG. 3. The storage means 23e of the control unit 23 associates the position information of the vehicle 3 acquired by the acquisition means 23a with the road surface condition estimated by the estimation means 23d and stores it in the road surface information storage unit 12b (see FIG. 2) of the server 2 (see FIG. 2).

[0065] As a result, information regarding a location with a poor road surface condition is stored in the road surface information storage unit 12b of the server 2, so that the information regarding such a location with a poor road surface condition can be utilized by a plurality of vehicles 3 connected to the network N. Examples of utilization of such road surface conditions will be described later.

[0066] In addition, in Embodiment 1, the shape and size of the generated acceleration value distribution diagram may differ depending on the speed of the vehicle 3, the vehicle type of the vehicle 3, the type of tires mounted on the vehicle 3, and the like.

[0067] Therefore, in Embodiment 1, in addition to the various vibration information described above, the road surface condition may be estimated based on information such as the speed of the vehicle 3 when passing through the road surface deterioration section X, the vehicle type of the vehicle 3, and the type of tires mounted on the vehicle 3.

[0068] For example, calibration processing may be performed on each vehicle 3 by causing the vehicle 3 to travel on a road surface whose uneven shape is known in advance.

[0069] Further, the server 2 or the control device 4 generates a learning model in which the acceleration value distribution diagram is input information and the degree of deterioration of the road surface is output information based on a set of acceleration value distribution diagrams of one vehicle 3. Then, each time the acceleration value distribution diagram is generated, the estimation means 23d may estimate the degree of deterioration of the road surface using this learning model.

[0070] Further, the server 2 generates a learning model in which the acceleration value distribution diagram is input information and the degree of deterioration of the road surface is output information based on a set of acceleration value distribution diagrams of a plurality of vehicles 3 connected to the network N. Then, each time the acceleration value distribution diagram is generated, the estimation means 23d may estimate the degree of deterioration of the road surface using this learning model.

[0071] In addition, in Embodiment 1, the generation means 23b may generate an acceleration value distribution diagram for each unit time at all driving times during driving, or may generate an acceleration value distribution diagram from the time when the triaxial acceleration sensor 5 detects a specific acceleration.

[0072] <Driving support process> Next, the details of the driving support process according to Embodiment 1 will be described with reference to FIGS. 3 and 6 to 9.

[0073] The acquisition means 23a of the control unit 23 shown in FIG. 3 acquires the driving information of the vehicle 3. As such driving information, for example, the acquisition means 23a acquires information regarding the acceleration in the longitudinal direction, lateral direction, and vertical direction of the vehicle 3 from the three-axis acceleration sensor 5.

[0074] Further, as driving information, the acquisition means 23a acquires the position information of the vehicle 3 from the GPS sensor 6, for example, and acquires the speed information of the vehicle 3 from the speed sensor 7.

[0075] The generation means 23b generates an acceleration value distribution diagram in which the three-axis distribution of the acceleration in a predetermined unit time is plotted in a three-dimensional coordinate system by using the information regarding the acceleration in the longitudinal direction, lateral direction, and vertical direction of the vehicle 3 acquired by the acquisition means 23a.

[0076] The measurement means 23c measures the vibration information of the vehicle 3 based on the distribution of a plurality of axes of the acceleration of the vehicle 3 (for example, the acceleration value distribution diagram generated by the generation means 23b).

[0077] The measurement means 23c measures, for example, the magnitude of the vibration of the vehicle 3 (for example, the absolute values |A|, |B|, |D|) and the deviation of the vibration position of the vehicle 3 (for example, the absolute value |B / C|) in the same manner as the above-described road surface state estimation process.

[0078] The specifying means 23g specifies information regarding load collapse in the vehicle 3 (that is, load collapse information) based on various types of information. Such load collapse information includes, for example, the probability of load collapse occurring and the degree of the load collapse that has occurred.

[0079] In the following description, the probability of load collapse occurring and the degree of the load collapse that has occurred, which are included in the load collapse information, are collectively referred to as the "degree of influence on load collapse".

[0080] The specifying means 23g specifies the load collapse information of the vehicle 3 by using, for example, the vibration information of the vehicle 3 measured by the measurement means 23c.

[0081] For example, the specifying means 23g specifies that the greater the value of the absolute value |A| (see FIG. 6) of the magnitude in the longitudinal direction in the acceleration value distribution diagram, the greater the degree of influence on the load collapse. In this case, the specifying means 23g may specify the degree of influence on the load collapse in consideration of the duration of the vibration in the longitudinal direction in the vehicle 3 as well.

[0082] Note that the specifying means 23g may provide a dead zone for the vibration in the longitudinal direction in the vehicle 3. Thereby, the degree of influence on the load collapse can be specified accurately.

[0083] Further, for example, the specifying means 23g specifies that the greater the value of the absolute value |B| (see FIG. 6) of the magnitude in the lateral direction in the acceleration value distribution diagram, the greater the degree of influence on the load collapse. In this case, the specifying means 23g may specify the degree of influence on the load collapse in consideration of the duration of the vibration in the lateral direction in the vehicle 3 as well.

[0084] Further, for example, the specifying means 23g specifies that the greater the value of the above-described absolute value |B / C| (see FIG. 6) indicating the deviation of the vibration position of the vehicle 3, the greater the degree of influence on the load collapse. In this case, the specifying means 23g may specify the degree of influence on the load collapse in consideration of the duration of the deviation of the vibration position of the vehicle 3 as well.

[0085] Further, the specifying means 23g may specify the load collapse information of the vehicle 3 using information different from the vibration information measured by the measuring means 23c. FIG. 7 is a diagram for explaining an example of the driving support process according to the first embodiment, and is a plot of the transition of each acceleration at each time in a three-dimensional coordinate system in which the distributions of the three axes of acceleration are plotted.

[0086] Specifically, in FIG. 7, first, the distributions of the three axes of acceleration are plotted at the position of the plot P1, and then the distributions of the three axes of acceleration are plotted in the order of the plot P2, the plot P3, the plot P4, the plot P5, and the plot P6.

[0087] In this case, the extraction means 23f of the control unit 23 extracts information regarding the lateral acceleration component of the acceleration of the vehicle 3 from, for example, the transition of the three-axis acceleration in the vehicle 3. For example, the extraction means 23f extracts the value of the absolute value |E| of the magnitude of the lateral component in each plot (for example, plot P5) shown in FIG. 7.

[0088] Then, the specifying means 23g specifies that the greater the value of the absolute value |E| of the magnitude of such a lateral component, the greater the degree of influence on the cargo collapse.

[0089] Further, the extraction means 23f extracts, for example, a value obtained by taking into account the distance between such a plot P5 and the origin in the absolute value |E| of the magnitude of the lateral component in each plot (for example, plot P5) shown in FIG. 7.

[0090] Then, the specifying means 23g specifies that the greater the value of the absolute value |E| of the magnitude of the lateral component taking into account the distance from the origin, the greater the degree of influence on the cargo collapse.

[0091] Further, the extraction means 23f extracts, for example, the value of the absolute value |F| of the deviation (the difference from the immediately preceding plot, for example, the difference between plot P6 and the immediately preceding plot P5) in each plot shown in FIG. 7.

[0092] Then, the specifying means 23g specifies that the greater the value of the absolute value |F| of such a deviation, the greater the degree of influence on the cargo collapse.

[0093] Further, in the first embodiment, an acceleration value distribution diagram for one operation of the vehicle 3 may be generated, and the degree of influence on the cargo collapse may be specified based on such an acceleration value distribution diagram for one operation of the vehicle 3. FIG. 8 is a diagram for explaining an example of the driving support process according to the first embodiment, and is a diagram showing an example of an acceleration value distribution diagram for one operation of the vehicle 3.

[0094] The acceleration value distribution diagram (plot range D5) for one run of the vehicle 3 shown in FIG. 8 is generated by the generation means 23b of the control unit 23. Then, the extraction means 23f extracts the degree of irregularity (for example, the deviation from the origin) of such a plot range D5.

[0095] And the specifying means 23g specifies that the greater the degree of irregularity of the acceleration value distribution diagram for such one run, the greater the degree of influence on the load collapse.

[0096] Also, in the first embodiment, the moment applied to the load loaded on the vehicle 3 may be extracted from the running information of the vehicle 3 acquired by the acquisition means 23a, and the degree of influence on the load collapse may be specified based on the moment applied to such a load. FIG. 9 is a diagram for explaining an example of the driving support process according to the first embodiment and is a diagram for explaining the moment applied to the load.

[0097] In the example of FIG. 9, the position of the center of gravity G of the load in the initial stage coincides with the origin (for example, the center of gravity position of the vehicle 3). Next, consider the case where the vehicle 3 turns to the right and the center of gravity G of the load moves from the origin to the left rear.

[0098] In this case, the extraction means 23f can extract the moment M applied to the load based on the following formula (1). M∝F C ×L ···(1) F C : Centrifugal force L: Distance between the origin and the center of gravity G

[0099] And the specifying means 23g specifies that the greater the value of the moment M applied to such a load (the greater L is, that is, the greater the difference in the initial value of the center of gravity, and / or the greater the centrifugal force F of the moment M applied to such a load, that is, the greater the speed and the sharper the curve), the greater the degree of influence on the load collapse. Note that the position of the center of gravity G of the load can be obtained from the acceleration, angular velocity, angular acceleration, etc. detected by the three-axis acceleration sensor 5.

[0100] Return to the description of FIG. 3. The presentation means 23h of the control unit 23 presents the load collapse information of the vehicle 3 specified by the specifying means 23g to the driver of the vehicle 3. For example, the presentation means 23h presents the load collapse information of the vehicle 3 to the driver by displaying the load collapse information of the vehicle 3 on the display unit 8.

[0101] For example, when the degree of influence on the load collapse is greater than a given threshold, the presentation means 23h presents to the driver that there is a high possibility of load collapse. Thereby, the control device 4 can suppress the occurrence of driving in which load collapse is likely to occur.

[0102] Further, for example, when the degree of influence on the load collapse is greater than a given threshold, the presentation means 23h may present the degree of the occurred load collapse to the driver. Also by this, the control device 4 can suppress the occurrence of driving in which the degree of load collapse is likely to become even larger.

[0103] Therefore, according to the first embodiment, the load collapse in the vehicle 3 can be suppressed.

[0104] Further, in the first embodiment, among the various factors described above (for example, the absolute value |A|, the absolute value |B|, the absolute value |B / C|, the absolute value |E|, the absolute value |F|, the moment M), the factors having a large degree of influence on the load collapse may be individually presented to the driver.

[0105] Further, in the first embodiment, the above-described various factors (for example, the absolute value |A|, the absolute value |B|, the absolute value |B / C|, the absolute value |E|, the absolute value |F|, the moment M) may be comprehensively considered, and the magnitude of the comprehensively considered degree of influence may be presented to the driver.

[0106] Further, in the first embodiment, information on the location where the degree of influence on the load collapse is large may be stored in the server 2. For example, the storage means 23e associates the position information of the vehicle 3 acquired by the acquisition means 23a with the load collapse information specified by the specifying means 23g, and stores it in the load collapse information storage unit 12c of the server 2.

[0107] As a result, information regarding locations where cargo collapse is likely to occur is stored in the cargo collapse information storage unit 12c of the server 2, so that such information regarding locations where cargo collapse is likely to occur can be utilized by a plurality of vehicles 3 connected to the network N.

[0108] For example, the control device 4 may present route guidance for the route on which the vehicle 3 is scheduled to travel to the driver based on the information stored in the road surface information storage unit 12b and the cargo collapse information storage unit 12c of the server 2.

[0109] Specifically, the control device 4 may present to the driver route guidance that avoids, for example, locations with poor road surface conditions stored in the road surface information storage unit 12b and locations where cargo collapse is likely to occur (for example, locations where the degree of influence on cargo collapse is greater than a given threshold) stored in the cargo collapse information storage unit 12c.

[0110] This can prevent the vehicle 3 from entering locations with poor road surface conditions (for example, locations with large bumps and depressions on the road surface) or locations where cargo collapse is likely to occur (for example, curves with a small curvature). Therefore, according to the first embodiment, cargo collapse in the vehicle 3 can be effectively suppressed.

[0111] In addition, when the control device 4 refers to the road surface information storage unit 12b and during travel on a road surface that is known in advance to have good road surface conditions, if the degree of influence on cargo collapse becomes greater than a given threshold, the control device 4 estimates that the increase in the degree of this influence is due to the driver's driving manner rather than the road surface conditions.

[0112] And in this case, the presentation means 23h may present (advise) the driver of a driving method for reducing the degree of influence on cargo collapse. For example, when the degree of skewness of the acceleration value distribution diagram in one operation shown in FIG. 8 is large, the presentation means 23h may present to the driver a driving method for reducing the degree of skewness of the acceleration value distribution diagram during the break after one operation.

[0113] As a result, the control device 4 can suppress the occurrence of operations in which cargo collapse is likely to occur. Therefore, according to the first embodiment, cargo collapse in the vehicle 3 can be suppressed.

[0114] Also, in the first embodiment, when the position of the center of gravity G of the cargo is gradually moving, guidance may be presented to the driver. Further, when it is assumed that the balance of the load is significantly disrupted, the presenting means 23h may present to the driver to temporarily stop the vehicle 3 and check the load.

[0115] <Second Embodiment> Next, the configuration of the vehicle 3A according to the second embodiment will be described with reference to FIG. 10. FIG. 10 is an explanatory diagram showing an example of the configuration of the vehicle 3A according to the second embodiment. The vehicle 3A of this second embodiment is a stand-alone type vehicle not connected to the network N (see FIG. 1).

[0116] As shown in FIG. 10, the vehicle 3A includes a control device 4A, a three-axis acceleration sensor 5, a GPS sensor 6, a speed sensor 7, and a display unit 8. The control device 4A is another example of a road surface state estimation device and also another example of a driving support device.

[0117] The three-axis acceleration sensor 5 is, for example, a sensor that detects accelerations in the respective directions of the X-axis, Y-axis, and Z-axis, and supplies its detection signal to the control device 4A. Note that the three-axis acceleration sensor 5 may further detect the angular velocity and angular acceleration of the operation of the vehicle 3A.

[0118] The GPS sensor 6 receives a radio wave that conveys downlink data including positioning data from a plurality of GPS satellites, and supplies such positioning data to the control device 4A. The control device 4A can detect the absolute position of the vehicle 3A from the position information (for example, latitude and longitude) included in such positioning data.

[0119] The speed sensor 7 is, for example, a sensor that detects the speed of the vehicle 3A and supplies its detection signal to the control device 4A. The display unit 8 is provided, for example, on the instrument panel of the vehicle 3A and is composed of a liquid crystal display, an organic EL element, and the like.

[0120] As shown in FIG. 10, the control device 4A includes a storage unit 31 and a control unit 32. The storage unit 31 is realized by, for example, a semiconductor memory element such as a RAM or a flash memory, or a storage device such as a hard disk or an optical disk. As shown in FIG. 10, the storage unit 31 has a road information storage unit 31a, a road surface information storage unit 31b, and a load collapse information storage unit 31c.

[0121] The road information storage unit 31a stores road position information indicating the position of the road, for example, map information. The road surface information storage unit 31b stores road surface information of the road. The load collapse information storage unit 31c stores load collapse information.

[0122] Note that since the road information storage unit 31a, the road surface information storage unit 31b, and the load collapse information storage unit 31c have the same configurations as the road information storage unit 12a, the road surface information storage unit 12b, and the load collapse information storage unit 12c of the first embodiment shown in FIG. 2, respectively, detailed descriptions thereof are omitted.

[0123] The control unit 32 is a controller and is realized, for example, by various programs stored in the storage device inside the control device 4A being executed with the RAM as a work area by a CPU, an MPU, or the like. Also, the control unit 32 is, for example, a controller and is realized by an integrated circuit such as an ASIC or an FPGA.

[0124] As shown in FIG. 10, the control unit 32 includes an acquisition unit 32a, a generation unit 32b, a measurement unit 32c, an estimation unit 32d, a storage unit 32e, an extraction unit 32f, a specification unit 32g, and a presentation unit 32h, and realizes or executes the functions and operations of various processes described below. Note that the internal configuration of the control unit 32 is not limited to the configuration shown in FIG. 10, and other configurations may be used as long as they can perform the various processes described below.

[0125] The acquisition unit 32a acquires the driving information of the vehicle 3A. As such driving information, for example, the acquisition unit 32a acquires information regarding the acceleration in the front-rear direction, left-right direction, and up-down direction of the vehicle 3A from the three-axis acceleration sensor 5.

[0126] Also, as driving information, the acquisition unit 32a acquires the position information of the vehicle 3A from the GPS sensor 6, and acquires the speed information of the vehicle 3A from the speed sensor 7.

[0127] The generation unit 32b generates an acceleration value distribution diagram in which the distribution of the three axes of acceleration in a predetermined unit time is plotted in a three-dimensional coordinate system by using the information regarding the acceleration in the front-rear direction, left-right direction, and up-down direction of the vehicle 3A acquired by the acquisition unit 32a.

[0128] The measurement unit 32c measures the vibration information of the vehicle 3A based on the distribution of a plurality of axes of the acceleration of the vehicle 3A (for example, the acceleration value distribution diagram generated by the generation unit 32b). The estimation unit 32d estimates the road surface condition of the road surface on which the vehicle 3A has traveled based on the vibration information measured by the measurement unit 32c.

[0129] The storage unit 32e associates the position information of the vehicle 3A acquired by the acquisition unit 32a with the road surface condition estimated by the estimation unit 32d, and stores the associated information in the road surface information storage unit 31b of the storage unit 31.

[0130] Note that since the acquisition means 32a, generation means 32b, measurement means 32c, estimation means 32d, and storage means 32e have the same configurations as the acquisition means 23a, generation means 23b, measurement means 23c, estimation means 23d, and storage means 23e of Embodiment 1 shown in FIG. 3, respectively, detailed descriptions thereof are omitted.

[0131] Thus, in Embodiment 2, similarly to Embodiment 1 described above, vibration information of the vehicle 3A is measured based on the distribution of a plurality of axes of the acceleration of the vehicle 3A (for example, an acceleration value distribution diagram), and the road surface condition is estimated based on such vibration information of the vehicle 3A. Thereby, the road surface condition can be estimated with high accuracy.

[0132] Further, in Embodiment 2, the estimation means 32d may estimate the road surface condition based on the magnitude of the vibration of the vehicle 3A (for example, absolute values |A|, |B|, |D|) and the deviation of the vibration position of the vehicle 3A (for example, absolute value |B / C|).

[0133] Thus, by estimating the degree of deterioration of the road surface deterioration section X with various parameters, the road surface condition can be estimated with even higher accuracy.

[0134] Further, in Embodiment 2, the measurement means 32c may measure the magnitude of the vibration of the vehicle 3A and the deviation of the vibration position of the vehicle 3A based on the shape of the acceleration value distribution diagram generated by the generation means 32b.

[0135] Thereby, the magnitude of the vibration of the vehicle 3A and the deviation of the vibration position of the vehicle 3A can be measured with high accuracy. Therefore, according to Embodiment 2, the road surface condition can be estimated with even higher accuracy.

[0136] Further, in Embodiment 2, the estimation means 32d may estimate the undulation of the road surface based on the magnitude of the vibration of the vehicle 3A, and may estimate the lateral position of the point where the road surface condition is damaged in the traveling lane based on the deviation of the vibration position of the vehicle 3A.

[0137] Accordingly, the degree of deterioration of the road surface deterioration section X can be estimated by various parameters, and the lateral position of the road surface deterioration section X can also be estimated. Therefore, according to the second embodiment, the road surface condition can be estimated with higher accuracy.

[0138] In addition, in the second embodiment, the estimation means 32d may directly estimate the road surface condition based on the shape of the acceleration value distribution diagram generated by the generation means 32b. For example, the estimation means 32d can estimate whether there is a step on the road surface or whether there is a crack on the road surface based on the shape of the acceleration value distribution diagram. Therefore, according to the second embodiment, the road surface condition can be estimated with higher accuracy.

[0139] In addition, in the second embodiment, the acquisition means 32a may acquire the travel information from one three-axis acceleration sensor 5 located inside the vehicle 3A. Thereby, since the acceleration value distribution diagram of the vehicle 3A can be easily generated, the road surface condition can be estimated at low cost.

[0140] Note that the three-axis acceleration sensor 5 is not limited to being mounted on the vehicle 3A, and may be, for example, a three-axis acceleration sensor mounted on an information terminal such as a smartphone and placed inside the vehicle 3A.

[0141] In addition, in the present disclosure, the number of three-axis acceleration sensors 5 for measuring the acceleration of the vehicle 3A is not limited to one, and may be plural. Thereby, since the acceleration of the vehicle 3A can be measured accurately, the road surface condition can be estimated with higher accuracy.

[0142] In addition, in the second embodiment, the storage means 32e may associate the position information of the vehicle 3A acquired by the acquisition means 32a with the road surface condition estimated by the estimation means 32d and store it in the road surface information storage section 31b of the storage section 31.

[0143] As a result, since the locations with poor road surface conditions are stored in the road surface information storage unit 31b, the information on the road surface conditions estimated by the vehicle 3A can be utilized when the vehicle 3A travels after the next time.

[0144] Also, in the second embodiment, similar to the first embodiment described above, depending on the speed of the vehicle 3A, the vehicle type of the vehicle 3A, and the type of tires mounted on the vehicle 3A, etc., the shape and size of the generated acceleration value distribution diagram may be different.

[0145] Therefore, in the second embodiment, in addition to the various vibration information described above, the road surface conditions may be estimated based on information such as the speed of the vehicle 3A when passing through the road surface deterioration section X, the vehicle type of the vehicle 3A, and the type of tires mounted on the vehicle 3A.

[0146] For example, the vehicle 3A may be made to travel on a road surface whose uneven shape is known in advance, and the vehicle 3A may be subjected to calibration processing.

[0147] Further, the control device 4A generates a learning model in which the acceleration value distribution diagram is input information and the degree of road surface deterioration is output information based on the set of acceleration value distribution diagrams of the vehicle 3A. Then, each time the acceleration value distribution diagram is generated, the estimation means 32d may estimate the degree of road surface deterioration using this learning model.

[0148] Also, in the second embodiment, the generation means 32b may generate an acceleration value distribution diagram for each unit time at all travel times during travel, or may generate an acceleration value distribution diagram from the time when the triaxial acceleration sensor 5 detects a specific acceleration.

[0149] The extraction means 32f extracts, for example, information regarding the lateral direction component of the acceleration of the vehicle 3A from the transition of the triaxial acceleration of the vehicle 3A. The specifying means 32g specifies the load collapse information of the vehicle 3A based on various information.

[0150] The notification means 32h notifies the driver of the vehicle 3A of the cargo collapse information identified by the identification means 32g. For example, the notification means 32h notifies the driver of the vehicle 3A of the cargo collapse information by displaying the cargo collapse information of the vehicle 3A on the display unit 8.

[0151] Note that since the extraction means 32f, the identification means 32g, and the notification means 32h have the same configurations as the extraction means 23f, the identification means 23g, and the notification means 23h in the first embodiment shown in FIG. 3, detailed descriptions thereof are omitted.

[0152] As described above, in the second embodiment, similar to the first embodiment, the cargo collapse information of the vehicle 3A identified by the identification means 32g is notified to the driver of the vehicle 3A.

[0153] For example, when the degree of influence on the cargo collapse is greater than a given threshold, the notification means 32h notifies the driver that there is a high possibility of a cargo collapse occurring. Thereby, the control device 4A can suppress the occurrence of driving in which a cargo collapse is likely to occur.

[0154] Further, for example, when the degree of influence on the cargo collapse is greater than a given threshold, the notification means 32h may notify the driver of the degree of the occurred cargo collapse. Also by this, the control device 4A can suppress the occurrence of driving in which the degree of the cargo collapse is likely to become even greater.

[0155] Therefore, according to the second embodiment, the cargo collapse in the vehicle 3A can be suppressed.

[0156] Also, in the second embodiment, among the various factors (for example, the absolute value |A|, the absolute value |B|, the absolute value |B / C|, the absolute value |E|, the absolute value |F|, the moment M) shown in the first embodiment, a factor having a large degree of influence on the cargo collapse may be individually notified to the driver.

[0157] Further, in Embodiment 2, various factors described above (for example, absolute value |A|, absolute value |B|, absolute value |B / C|, absolute value |E|, absolute value |F|, moment M) may be comprehensively considered, and the degree of the comprehensively considered influence may be presented to the driver.

[0158] Further, in Embodiment 2, information on points where the degree of influence on load collapse is large may be stored in the storage unit 31. For example, the storage means 32e associates the position information of the vehicle 3A acquired by the acquisition means 32a with the load collapse information specified by the specifying means 32g, and stores it in the load collapse information storage unit 31c of the storage unit 31.

[0159] As a result, since information on locations where load collapse is likely to occur is stored in the road surface information storage unit 31b, the information on locations where such load collapse is likely to occur can be utilized when the vehicle 3A travels after the next time.

[0160] For example, the control device 4A may present route guidance for the vehicle 3A's planned travel to the driver based on the information stored in the road surface information storage unit 31b and the load collapse information storage unit 31c of the storage unit 31.

[0161] Specifically, the control device 4A may present, for example, route guidance that avoids locations with poor road surface conditions stored in the road surface information storage unit 31b and locations where load collapse is likely to occur (for example, locations where the degree of influence on load collapse is greater than a given threshold) stored in the load collapse information storage unit 31c to the driver.

[0162] As a result, it is possible to prevent the vehicle 3A from entering locations with poor road surface conditions (for example, locations where large unevenness occurs on the road surface) or locations where load collapse is likely to occur (for example, curves with large curvatures). Therefore, according to Embodiment 2, load collapse in the vehicle 3A can be effectively suppressed.

[0163] Further, while the vehicle 3A is traveling on a road surface that is known in advance to have good road surface conditions, when the degree of influence on load shift becomes greater than a given threshold value, the control device 4A refers to the road surface information storage unit 31b and estimates that the increase in the degree of such influence is due to the driving manner of the driver rather than the road surface conditions.

[0164] And in this case, the presentation means 32h may present (advise) the driver of a driving method for reducing the degree of influence on load shift. For example, when the degree of distortion of the acceleration value distribution diagram in one operation is large, the presentation means 32h may present to the driver a driving method for reducing the degree of distortion of the acceleration value distribution diagram during the rest period after one operation is completed.

[0165] Thereby, the control device 4A can suppress the occurrence of driving that is likely to cause load shift. Therefore, according to the second embodiment, load shift in the vehicle 3A can be suppressed.

[0166] Also, in the second embodiment, when the position of the center of gravity G of the load gradually moves, guidance may be presented to the driver. Further, when it is assumed that the balance of the load is significantly disrupted, the presentation means 32h may present to the driver to once stop the vehicle 3A and check the load.

[0167] <Processing Procedure> Subsequently, the procedures of various processes according to the first embodiment will be described with reference to FIGS. 11 and 12. FIG. 11 is a flowchart showing the procedure of the road surface state estimation process according to the first embodiment.

[0168] First, the acquisition means 23a acquires the driving information of the vehicle 3 (step S101). As such driving information, for example, the acquisition means 23a acquires information regarding the acceleration of the vehicle 3 in the front-rear direction, left-right direction, and up-down direction from the triaxial acceleration sensor 5.

[0169] Next, the generation means 23b generates an acceleration value distribution diagram in which the distribution of the three axes of acceleration in a predetermined unit time is plotted in a three-dimensional coordinate system by using the information on the acceleration in the longitudinal direction, lateral direction, and vertical direction of the vehicle 3 acquired by the acquisition means 23a (step S102).

[0170] Next, the measurement means 23c measures vibration information of the vehicle 3 based on the distribution of a plurality of axes of acceleration of the vehicle 3 (for example, the acceleration value distribution diagram generated by the generation means 23b) (step S103).

[0171] Next, the estimation means 23d estimates the road surface condition of the road surface on which the vehicle 3 has traveled based on the vibration information measured by the measurement means 23c (step S104).

[0172] Finally, the storage means 23e associates the position information of the vehicle 3 acquired by the acquisition means 23a with the road surface condition estimated by the estimation means 23d, stores it in the road surface information storage unit 12b of the server 2 (step S105), and ends a series of road surface condition estimation processes.

[0173] FIG. 12 is a flowchart showing the procedure of the driving support process according to the first embodiment. First, the acquisition means 23a acquires the driving information of the vehicle 3 (step S201). The acquisition means 23a acquires information on the acceleration in the longitudinal direction, lateral direction, and vertical direction of the vehicle 3 from, for example, the three-axis acceleration sensor 5 as such driving information.

[0174] Next, the specifying means 23g specifies the load collapse information of the vehicle 3 based on the distribution of a plurality of axes of acceleration of the vehicle 3 based on the driving information acquired by the acquisition means 23a (step S202).

[0175] The specifying means 23g specifies the load collapse information of the vehicle 3 based on, for example, the vibration information of the vehicle 3 measured by the measurement means 23c and various types of information (such as the absolute value |E| and the absolute value |F| described above) extracted by the extraction means 23f.

[0176] Finally, the presentation means 23h presents the load collapse information of the vehicle 3 identified by the identification means 23g to the driver of the vehicle 3 (step S203), and ends the series of driving support processes.

[0177] As described above, the embodiments of the present invention have been described. However, the present invention is not limited to the above embodiments, and various modifications are possible without departing from the gist thereof. For example, in the above embodiments, various processes performed by the vehicle 3 have been shown. However, the subject to which the present disclosure is applied is not limited to vehicles, and can also be applied to various moving bodies (for example, motorcycles, trains, etc.).

[0178] Also, in the above embodiment, an example in which the presentation means 23h presents the load collapse information to the driver of the vehicle 3 has been shown. However, the target to which the presentation means 23h presents the load collapse information is not limited to the driver of the vehicle 3, and may be the driver of another vehicle 3, the administrator of the server 2, or the like.

[0179] Also, in the above embodiment, an example has been shown in which an acceleration value distribution diagram, which is a diagram plotting the distribution of the three axes of acceleration in a predetermined unit time in a three-dimensional coordinate system by the three-axis acceleration sensor 5, is generated, and a road surface state estimation process and a driving support process are performed based on such an acceleration value distribution diagram.

[0180] However, the above embodiment is not limited to such an example. For example, an acceleration value distribution diagram, which is a diagram plotting the distribution of the two axes of acceleration in a predetermined unit time in a two-dimensional coordinate system using the two-axis acceleration in the X-axis direction and the Y-axis direction measured by the acceleration sensor, may be generated, and a road surface state estimation process and a driving support process may be performed based on such an acceleration value distribution diagram.

[0181] Further effects and modifications can be easily derived by those skilled in the art. Therefore, the broader aspects of the present invention are not limited to the specific details and representative embodiments represented and described as above. Accordingly, various changes are possible without departing from the spirit or scope of the general inventive concept defined by the appended claims and their equivalents.

Description of Symbols

[0182] 1 Control system 2 Server 3, 3A Vehicle (an example of a moving body) 4, 4A Control device (an example of a road surface condition estimation device and a driving support device) 5 Triaxial acceleration sensor 6 GPS sensor 7 Speed sensor 8 Display unit 12, 22, 31 Storage unit 13, 23, 32 Control unit 23a, 32a Acquisition means 23b, 32b Generation means 23c, 32c Measurement means 23d, 32d Estimation means 23e, 32e Storage means 23f, 32f Extraction means 23g, 32g Identification means 23h, 32h Presentation means

Claims

1. An acquisition means for acquiring travel information including an acceleration of a moving object; a measuring means for measuring information regarding vibration of the moving body based on a multi-axial distribution of acceleration of the moving body based on the traveling information acquired by the acquiring means; an estimation means for estimating a road surface condition based on information regarding the vibration of the moving body measured by the measurement means, the measuring means measures a deviation of a vibration position of the moving body as information related to the vibration of the moving body; The estimation means estimates the road surface condition based on a deviation of the vibration position of the moving body measured by the measurement means. A road surface condition estimating device comprising:

2. A generating means for generating an acceleration value distribution diagram in which a three-axis distribution of acceleration in a predetermined unit time is plotted on a three-dimensional coordinate system using the traveling information acquired by the acquiring means, The measuring means measures a deviation in a vibration position of the moving body based on a shape of the acceleration value distribution map generated by the generating means.

2. The road surface condition estimating device according to claim 1 .

3. The estimation means estimates a lateral position of a point where the road surface condition is damaged in the travel lane based on a deviation in the vibration position of the moving body.

3. The road surface condition estimating device according to claim 1 or 2.

4. the measuring means measures a magnitude of the vibration of the moving body as information regarding the vibration of the moving body; The estimation means estimates the road surface condition based on the magnitude of vibration of the moving body measured by the measurement means.

4. The road surface condition estimating device according to claim 1,

5. A generating means for generating an acceleration value distribution diagram in which a three-axis distribution of acceleration in a predetermined unit time is plotted on a three-dimensional coordinate system using the traveling information acquired by the acquiring means, The measuring means measures the magnitude of vibration of the moving body based on the shape of the acceleration value distribution map generated by the generating means.

5. The road surface condition estimating device according to claim 4.

6. The estimation means estimates the undulations of a road surface based on the magnitude of vibration of the moving body.

6. The road surface condition estimating device according to claim 4 or 5.

7. The acquiring means acquires the traveling information from one three-axis acceleration sensor located inside the moving body.

7. The road surface condition estimating device according to claim 1,

8. The acquisition means further acquires position information of the moving object as the traveling information, a storage unit that stores the location information acquired by the acquisition unit and the road surface condition estimated by the estimation unit in a road surface information storage unit in association with each other.

8. The road surface condition estimating device according to claim 1,

9. A road surface condition estimation method implemented by a road surface condition estimation device, comprising: An acquisition step of acquiring travel information including an acceleration of a moving object; a measuring step of measuring information regarding vibration of the moving body based on a multi-axial distribution of acceleration of the moving body based on the acquired running information; an estimation step of estimating a road surface condition based on information regarding the measured vibration of the moving body; Including, the measuring step measures a deviation of a vibration position of the moving body as information related to the vibration of the moving body; The estimation step estimates the road surface condition based on a deviation of the vibration position of the moving body measured in the measurement step. A road surface condition estimating method comprising:

10. An acquisition means for acquiring travel information including an acceleration of a moving object; a measuring means for measuring information regarding vibration of the moving body based on a multi-axial distribution of acceleration of the moving body based on the acquired travel information; an estimation means for estimating a road surface condition based on information regarding the measured vibration of the moving body; Includes the measuring means measures a deviation of a vibration position of the moving body as information related to the vibration of the moving body; The estimation means estimates the road surface condition based on a deviation of the vibration position of the moving body measured by the measurement means. A road surface condition estimation program for causing a computer to execute the processing.

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