Road surface evaluation device and road surface evaluation method

Abstract

The present invention provides a road surface evaluation device (10), comprising: an information acquisition unit (111), which acquires vehicle information including information indicating the movement of a traveling vehicle and position information of the vehicle, and acquires map information including information about a road on which the vehicle is traveling; a road surface profile derivation unit (112), which derives flatness information indicating the flatness of a road surface and the reliability of the flatness information based on the vehicle information acquired by the information acquisition unit (111); and a road surface profile output unit (113), which establishes a correspondence between the flatness information and the reliability derived by the flatness information derivation unit (112) and the road information acquired by the map information acquisition unit (111), and outputs the result.

Description

Technical Field

[0001] The present invention provides a road surface evaluation device and a road surface evaluation method for evaluating a road surface profile representing the uneven shape of a road surface. Background Art

[0002] As such a device, there is conventionally known a device that detects a road surface profile representing the uneven shape of a road on which a vehicle has traveled based on lateral acceleration (lateral direction relative to the traveling direction) measured by an acceleration sensor installed in the vehicle (for example, see Patent Document 1).

[0003] Prior art literature

[0004] Patent Literature

[0005] Patent Document 1: Japanese Patent Application Publication No. 2002-12138. Summary of the invention

[0006] Problem that the invention aims to solve

[0007] However, the road surface profile detected based on the acceleration measured by the acceleration sensor may deviate due to various factors such as the position of the tire on the road surface being traveled, the weather, etc. Therefore, the road surface profile cannot be fully evaluated by detecting the road surface profile based only on the acceleration measured by the acceleration sensor as in the device described in the above-mentioned patent document 1.

[0008] Solutions for solving problems

[0009] A road surface evaluation device according to a technical solution of the present invention comprises: a vehicle information acquisition unit, which acquires vehicle information including information indicating the movement of a traveling vehicle and position information of the vehicle; a map information acquisition unit, which acquires map information including information of a road on which the vehicle is traveling; a flatness information derivation unit, which derives flatness information indicating the flatness of a road surface and reliability of the flatness information based on the vehicle information acquired by the vehicle information acquisition unit; and an output unit, which establishes a correspondence between the flatness information and reliability derived by the flatness information derivation unit and the road information acquired by the map information acquisition unit and outputs the result.

[0010] Another technical solution of the present invention is a road surface evaluation method, which includes the following steps executed by a computer: a step of obtaining vehicle information including information indicating the movement of a moving vehicle and position information of the vehicle; a step of obtaining map information including information of a road on which the vehicle is traveling; a step of deriving flatness information indicating the flatness of the road surface and the reliability of the flatness information based on the obtained vehicle information; and a step of establishing a correspondence between the derived flatness information and reliability and the obtained road information and outputting the correspondence.

[0011] Effects of the Invention

[0012] The present invention can fully evaluate the road surface profile. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a diagram showing an example of the configuration of a road surface evaluation system including a road surface evaluation device according to an embodiment of the present invention.

[0014] Figure 2 It is a block diagram showing the main part configuration of the vehicle-mounted device.

[0015] Figure 3 This is a block diagram showing a main part configuration of a road surface evaluation device according to an embodiment of the present invention.

[0016] Figure 4A This is a diagram for explaining teaching data of road surface roughness values.

[0017] Figure 4B This is a diagram for explaining teaching data of lateral acceleration.

[0018] Figure 5A The figure shows an example of a map of a road on which a vehicle travels.

[0019] Figure 5B The road surface evaluation device shows the Figure 5A FIG. 1 is a diagram showing an example of vehicle information obtained by an on-board device of a vehicle traveling on a road.

[0020] Figure 6 It is shown by Figure 3 A flowchart of an example of processing performed by a calculation unit.

[0021] Figure 7 It is shown Figure 3 A diagram showing an example of information output by a road surface profile output unit.

[0022] Figure 8 It is a diagram for explaining data processing in the road surface evaluation device according to the embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following reference Figures 1 to 8 An embodiment of the present invention will be described. A road surface evaluation device according to an embodiment of the present invention is a device for evaluating a road surface profile of a road on which a vehicle is traveling. Figure 1 FIG. 2 is a diagram showing an example of the configuration of a road surface evaluation system including the road surface evaluation device according to the present embodiment. Figure 1 As shown, the road surface evaluation system 1 includes a road surface evaluation device 10 and an onboard device 30 . The road surface evaluation device 10 is configured as a server device. The onboard device 30 is configured to be able to communicate with the road surface evaluation device 10 via a communication network 2 .

[0024] The communication network 2 includes not only public wireless communication networks represented by the Internet and mobile phone networks, but also closed communication networks set up for each specified management area, such as wireless LAN, Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] The vehicle-mounted device 30 is mounted on various vehicles 20. The vehicles 20 include various vehicles 20-1, 20-2, ..., 20-n having different vehicle widths. For example, the vehicle 20-1 is a sedan, the vehicle 20-2 is a single-cab vehicle having a wider vehicle width than the vehicle 20-1, and the vehicle 20-n is a van having a wider vehicle width than the vehicle 20-1 and a narrower vehicle width than the vehicle 20-2. The vehicle 20 may be a manually driven vehicle or an automatically driven vehicle.

[0026] Figure 2 The in-vehicle device 30 of the present embodiment is a block diagram showing a main part of the configuration. The in-vehicle device 30 includes an electronic control unit (ECU) 31 , a positioning sensor 32 , an acceleration sensor 33 , a steering angle sensor 34 , a vehicle speed sensor 35 , and a TCU (Telematic Control Unit) 36 .

[0027] The positioning sensor 32 is, for example, a GPS (Global Positioning System) sensor, which receives positioning signals sent from GPS satellites and detects the absolute position (latitude, longitude, etc.) of the vehicle 20. It should be noted that the positioning sensor 32 is not only a GPS sensor, but also includes a sensor that uses radio waves sent from satellites of various countries called GNSS (Global Navigation Satellite System) satellites represented by quasi-zenith satellites for positioning. In addition, a hybrid method with an inertial navigation method can also be used to obtain the vehicle position.

[0028] The acceleration sensor 33 detects the left-right acceleration of the vehicle 20, that is, the lateral acceleration. It should be noted that the acceleration sensor 33 can also be configured to detect the lateral acceleration of the vehicle 20, the acceleration in the front-rear direction, and the acceleration in the up-down direction. The steering angle sensor 34 detects the steering angle of the steering wheel (not shown) of the vehicle 20. The vehicle speed sensor 35 detects the vehicle speed of the vehicle 20.

[0029] like Figure 2 As shown, the ECU 31 includes a computer having a computing unit 310 such as a CPU (central processing unit), a storage unit 320 such as a ROM (read only memory) and a RAM (random access memory), and other peripheral circuits not shown such as an I / O interface. The computing unit 310 functions as a sensor value acquisition unit 311 and a communication control unit 312 by executing a program pre-stored in the storage unit 320.

[0030] The sensor value acquisition unit 311 acquires information (values) detected by each sensor 32 to 35, that is, vehicle information. The sensor value acquisition unit 311 acquires vehicle information including the acceleration of the vehicle 20 detected by the acceleration sensor 33 and the absolute position of the vehicle 20 detected by the positioning sensor 32 at a predetermined period, for example, in units of 10 ms. The vehicle information includes at least the lateral acceleration of the vehicle 20 detected by the acceleration sensor 33. The communication control unit 312 transmits the vehicle information acquired by the sensor value acquisition unit 311 to the road surface evaluation device 10 via the TCU 36 at a predetermined period. More specifically, in order not to increase the processing load and unnecessarily compress the frequency band of the communication network 2, the communication control unit 312 extracts the vehicle information acquired by the sensor value acquisition unit 311 at intervals, for example, in units of 1 s.

[0031] The road surface evaluation device 10 detects the uneven shape of the road surface, that is, the road surface profile, based on the detection value of the acceleration sensor 33 provided in the vehicle 20. The detected road surface profile is output to a terminal owned by, for example, a road management company, and is used by the road management company as reference data when examining whether repairs are necessary. That is, the detection value of the acceleration sensor is used to evaluate the road surface profile.

[0032] However, as described above, the road surface profile detected by the acceleration detected by the acceleration sensor of the vehicle may be biased. Therefore, it is difficult to fully evaluate the road surface profile detected by the acceleration sensor of the vehicle 20. Therefore, in this embodiment, in order to fully evaluate the road surface profile, the road surface evaluation device is constructed as follows.

[0033] Figure 3 1 is a block diagram showing the main part of the road surface evaluation device 10 of the present embodiment. The road surface evaluation device 10 is composed of a computer having a computing unit 110 such as a CPU, a storage unit 120 such as a ROM and a RAM, and other peripheral circuits (not shown) such as an I / O interface. The storage unit 120 stores map information including a map of roads and various information processed by the computing unit 110.

[0034] The calculation unit 110 executes a program stored in the storage unit 120 to function as an information acquisition unit 111 , a road surface profile derivation unit 112 , a road surface profile output unit 113 , and a communication control unit 114 .

[0035] The information acquisition unit 111 acquires information indicating the motion of the vehicle 20 including acceleration in each direction of the vehicle 20 and vehicle information including position information of the vehicle 20. The information acquisition unit 111 receives the vehicle information from the vehicle-mounted device 30 of the vehicle 20 traveling on the road through the communication control unit 114. The information acquisition unit 111 stores the acquired vehicle information in the storage unit 120 in a time series. The information indicating the motion of the vehicle 20 is a mixture of information indicating the rolling motion of the vehicle, information indicating the motion due to centrifugal force, and information indicating the motion due to the unevenness of the road surface.

[0036] The information acquisition unit 111 acquires map information including information on the road on which the vehicle 20 travels from the storage unit 120 .

[0037] The road surface profile deriving unit 112 derives the amount of unevenness (depth or height) of the road surface, that is, the flatness information indicating the flatness of the road surface, based on the vehicle information obtained by the information obtaining unit 111. More specifically, the road surface profile deriving unit 112 derives the flatness information of the road surface by machine learning based on the vehicle information obtained by the information obtaining unit 111. The flatness information is a road surface flatness value indicating the degree of flatness of the road surface, for example, a value expressed by IRI (International Roughness Index) which is an international index. In the following, the road surface flatness value may be expressed simply as the flatness value.

[0038] Generally, the greater the amount of unevenness of the road surface, the greater the lateral acceleration of the vehicle 20, and the road surface roughness value and the lateral acceleration have a predetermined correlation. The road surface profile deriving unit 112 uses the correlation to derive the road surface roughness value corresponding to the vehicle position on the road from the lateral acceleration. Specifically, the road surface profile deriving unit 112 first performs machine learning using the pre-measured road surface roughness value and the lateral acceleration as teaching data, and derives the correlation between the road surface roughness value and the lateral acceleration.

[0039] Figure 4A and Figure 4B They are diagrams for explaining teaching data of road surface roughness value and lateral acceleration, respectively. Figure 4A The vehicle V1 shown is a special vehicle equipped with a measuring device MA for measuring the road surface roughness. The measuring device MA measures the road surface roughness value of the road RD when the vehicle V1 travels on a predetermined road (such as a measurement route) RD. Figure 4A The characteristic P1 represents the road surface roughness value measured at this time.

[0040] Figure 4B Shown in Figure 1 Vehicle 20 in Figure 4A The state of traveling on the same road RD. Figure 4BThe characteristic P2 represents the lateral acceleration detected in units of 10 ms by the acceleration sensor 33 provided in the vehicle 20 while the vehicle 20 is traveling on the predetermined road RD. Figure 4A Characteristics P1 and Figure 4B The characteristics P2 shown are used as teaching data when the road surface profile deriving unit 112 derives the correlation between the road surface roughness value and the lateral acceleration.

[0041] The data of the characteristics P1 and P2, i.e., the teaching data of the road surface roughness value and the lateral acceleration, can also be stored in the storage unit 120 of the road surface evaluation device 10, or can also be stored in an external storage device. When deriving the correlation between the road surface roughness value and the lateral acceleration, the road surface profile derivation unit 112 reads out each teaching data from the storage unit 120 or the external storage device. It should be noted that the road surface profile derivation unit 112 can also derive the correlation between the road surface roughness value and the lateral acceleration from the teaching data of the road surface roughness value and the lateral acceleration in advance, and store the information of the derived correlation in the storage unit 120, etc. In addition, the road surface profile derivation unit 112 can also add the driving speed, the acceleration in the front and rear directions, and the steering angle (steering angle) as teaching data for machine learning.

[0042] The road surface profile deriving unit 112 uses the correlation between the road surface roughness value and the lateral acceleration to derive the road surface roughness value of the road traveled by the vehicle 20 based on the vehicle information of the vehicle 20 stored in the storage unit 120 in a time series. The vehicle information stored in the storage unit 120 in a time series is hereinafter referred to as time series vehicle information.

[0043] Figure 5A 1 is a diagram showing an example of a map of a road on which the vehicle 20 travels. Figure 5A The prescribed range (the interval between latitudes Y and Z on National Highway X) from which the road surface roughness value is derived is shown in FIG. Figure 5A In the figure, the upper direction corresponds to the north direction, and the right direction corresponds to the east direction. The range of the road surface roughness value to be derived can be specified by the user as described later. In the case where the road surface roughness value to be derived is a single-sided multi-lane road, the lane to be derived from the road surface roughness value is specified by the user. Figure 5B The road surface evaluation device 10 is shown from the Figure 5A The horizontal axis of the figure is the position (latitude) along the travel direction of the driving lane of the vehicle 20, and the vertical axis is the lateral acceleration of the vehicle 20.

[0044] The vehicle information is transmitted from the communication control unit 312 of the vehicle-mounted device 30 in units of a predetermined time (1 second in this embodiment). Figure 5B As shown in the figure, the time-series vehicle information of the vehicle 20 becomes discrete information, and there will be intervals with less information. For example, when the driving speed of the vehicle 20 becomes faster, as shown in the interval a in the figure, the distance from the location where the vehicle information was sent last time to the location where the vehicle information is sent next becomes longer, and the amount of information becomes less. In addition, in the case of a single-sided multi-lane road, there will be an interval (interval b in the figure) where the vehicle information cannot be sent even if a lane change is performed. Interval b is an interval where the vehicle 20 has traveled in a lane other than the lane designated as the object of the road surface roughness value.

[0045] The road surface profile deriving unit 112 estimates the road surface roughness value for a section where the amount of vehicle information is small or a section where vehicle information cannot be transmitted, using vehicle information obtained from the vehicle-mounted device 30 of the same vehicle 20 in the past in the section. In addition, the road surface profile deriving unit 112 estimates the road surface roughness value for the section using vehicle information obtained from the vehicle-mounted device 30 of another vehicle 20 in the section. In addition, the road surface profile deriving unit 112 estimates the road surface roughness value for the section based on vehicle information obtained before and after the section where vehicle information cannot be obtained from any vehicle 20.

[0046] However, when estimating the road surface roughness value using vehicle information obtained from the same vehicle in the past or from other vehicles, the vehicle information may be biased due to the position of the tires when the vehicle is traveling, weather, etc. Figure 1 Since the vehicle widths of the vehicles 20-1, 20-2, and 20-n are different, the positions of the tires used to detect the road surface roughness values ​​are different. Therefore, even when the vehicles 20-1, 20-2, and 20-n are traveling on the same road, the vehicle information obtained from the vehicle-mounted device 30 of each vehicle may deviate. When such a deviation occurs, the estimation accuracy of the road surface roughness value varies, and the greater the deviation, the lower the estimation accuracy.

[0047] In view of this, the road surface profile derivation unit 112 is configured to estimate the road surface roughness value by machine learning. More specifically, the road surface profile derivation unit 112 estimates the road surface roughness value using a complementary algorithm using a Gaussian process, which is a learning method of a Bayesian system. The Gaussian process is used because the estimated accuracy (reliability) of the value can be obtained together with the estimated value in the Gaussian process.

[0048] The road surface profile output unit 113 associates the road surface roughness value and its reliability derived by the road surface profile deriving unit 112 with the road information acquired by the information acquiring unit 111 and outputs the associated values.

[0049] The communication control unit 114 controls a communication unit (not shown) to transmit and receive data with an external device or the like. More specifically, the communication control unit 114 transmits and receives data with the vehicle-mounted device 30 of the vehicle 20 and a terminal of a road management company or the like through the communication network 2. In addition, the communication control unit 114 receives an output instruction of a road profile described later from a terminal of a road management company or the like through the communication network 2. Furthermore, the communication control unit 114 obtains map information or the like from various servers connected to the communication network 2 periodically or at any time. The communication control unit 114 stores the information obtained from various servers in the storage unit 120.

[0050] Figure 6 1 is a flowchart showing an example of processing executed by the computing unit 110 (CPU) of the road surface evaluation device 10 according to a predetermined program. The processing shown in the flowchart is repeatedly performed at a predetermined cycle while the road surface evaluation device 10 is activated. First, in step S11, it is determined whether vehicle information is received from the vehicle-mounted device 30 of the vehicle 20. When the step S11 is negative (S11: No), the process proceeds to step S13. When the step S11 is positive (S11: Yes), in step S12, the vehicle information received in step S11 is stored in the storage unit 120.

[0051] In step S13, it is determined whether an output instruction of the road surface profile is input (received). The output instruction of the road surface profile is sent to the road surface evaluation device 10 from, for example, a terminal of a user (a road management company, etc.) via the communication network 2. It should be noted that the output instruction of the road surface profile can also be input to the road surface evaluation device 10 via an operation unit (not shown) provided in the road surface evaluation device 10.

[0052] The output instruction of the road profile includes the section information that can identify the section of the road to be output. The section information is information indicating the name and section of the road to be output, such as "Road: National Highway Line X, Section: Latitude Y-Z". It should be noted that in the case of a single-sided multi-lane road such as a single-sided two-lane road, the section information may not include the information of the lane to be output, such as "Road: National Highway Line X, Lane: Right End, Section: Latitude Y-Z". In addition, information other than latitude can be used to specify the section to be output. For example, longitude can be used instead of latitude, or longitude can be used on the basis of latitude. In addition, the distance from the starting point coordinates can also be used.

[0053] When the step S13 is negative (S13: No), the processing is terminated. When the step S13 is positive (S13: Yes), in step S14, the map information is read from the storage unit 120, and the road information included in the map information is obtained. In step S15, the time-series vehicle information of the vehicle 20 is obtained from the storage unit 120. More specifically, based on the section information included in the output instruction of the road profile and the road information obtained in step S14, the time-series vehicle information of the section to be output stored in the storage unit 120 is obtained from the storage unit 120.

[0054] In step S16, the road surface roughness value of the output section is estimated using a complementary algorithm using a Gaussian process based on the previously derived correlation between the road surface roughness value and the lateral acceleration and the time series vehicle information of the vehicle 20 obtained in step S15. At this time, the reliability of the estimated road surface roughness value is obtained together with the road surface roughness value.

[0055] Finally, in step S17, the road surface roughness value and its reliability estimated (derived) in step S16 are associated with the road information obtained in step S14 and output. More specifically, based on the road information obtained in step S14, the road surface roughness value and its reliability estimated in step S16 are associated with each position of the output section and output. Hereinafter, the information output at this time is referred to as road surface profile information. The road surface profile information is transmitted to the user through the communication network 2 in a manner that can be transmitted to the user ... Figure 7 The method of displaying the information in the manner shown is to the terminal of the source of the output instruction of the road surface profile, or the terminal of the predetermined output destination. As a result, the road surface profile information can be displayed on a display device such as a display, and the road surface profile information can be confirmed and evaluated by the user. It should be noted that even if step S13 is negative (S13: No), if the time-series vehicle information that has not been output in the time-series vehicle information of the vehicle 20 stored in the storage unit 120 accumulates a prescribed amount, step S14 can be entered. In addition, even if step S13 is positive (S13: Yes), if the time-series vehicle information that has not been output in the time-series vehicle information of the vehicle 20 stored in the storage unit 120 is less than a prescribed amount, the processing can be terminated. At this time, information (text information, audio information, image information) for notifying that the time-series vehicle information that has not been output is less than a prescribed amount can also be output to the terminal of the source of the output instruction of the road surface profile.

[0056] Figure 7 FIG. 1 is a diagram showing an example of the road surface profile information output by the road surface profile output unit 113. Figure 7, the vertical axis is the road surface roughness value, and the horizontal axis is the position of the vehicle 20. The dotted line represents the road surface roughness value estimated by the road surface profile derivation unit 112 (referred to as the estimated roughness value). The solid line represents the road surface roughness value of the actual road (referred to as the actual roughness value). It should be noted that the actual roughness value is not included in the road surface profile information, but is shown in the figure for the purpose of explanation.

[0057] The area between the lines L1 and L2 represents the reliability of the estimated flatness value at each position. For example, the width CR in the longitudinal direction at point P represents the reliability of the estimated flatness value at point P. Figure 7 In the example shown, the width CR represents the confidence interval of 2σ when the estimated flatness value follows Gaussian distribution, and the estimated flatness value at point P falls within this range with a probability of about 95%. That is, the narrower the width CR is, the higher the accuracy of the estimated flatness value at that point is.

[0058] The data processing in the road surface evaluation device 10 of the present embodiment is summarized as follows. Figure 8 First, the correlation between the road surface roughness value and the lateral acceleration is derived in advance by machine learning based on the teaching data TD1 of the road surface roughness value and the teaching data TD2 of the lateral acceleration ((a), (b)).

[0059] When the output instruction of the road profile is input, the road roughness value and its reliability of the road traveled by each vehicle are estimated based on the correlation between the road roughness value and the lateral acceleration and the time series vehicle information VD1 to VDn of the vehicles 20-1 to 20-n ((c)). Finally, the estimated road roughness value and its reliability are associated with the information of the road included in the map information read from the storage unit 120 and output as information OD ((d)).

[0060] The following effects can be achieved by adopting the embodiments of the present invention.

[0061] (1) The road surface evaluation device 10 includes: an information acquisition unit 111 that acquires vehicle information including information indicating the movement of a traveling vehicle 20 and position information of the vehicle 20, and acquires map information including information about a road on which the vehicle 20 is traveling; a flatness road surface profile derivation unit 112 that derives, based on the vehicle information of the vehicle 20 acquired by the information acquisition unit 111, flatness information indicating the flatness of a road surface on which the vehicle 20 is traveling and reliability of the flatness information; and a road surface profile output unit 113 that associates the flatness information and reliability derived by the road surface profile derivation unit 112 with the road information acquired by the information acquisition unit 111 and outputs the result. Figure 3). With this configuration, the road surface profile of the road can be fully evaluated. In addition, the evaluation result can be presented to users of road management companies, so that users can estimate the roads that need to be repaired without going to the site, which can reduce the cost required for road management.

[0062] (2) The flatness information is a flatness value indicating the degree of flatness of the road surface, and the reliability of the flatness information (flatness value) is indicated by the range of the flatness value derived by the road surface profile deriving unit 112 based on the vehicle information acquired by the information acquiring unit 111. Thus, the accuracy of the flatness information (flatness value) derived by the road surface profile deriving unit 112 can be identified, and the road surface profile can be more fully evaluated.

[0063] (3) The road surface profile output unit 113 displays information in which the flatness information and reliability derived by the road surface profile deriving unit 112 are associated with the road information acquired by the information acquiring unit 111. This allows the user to visually recognize the road surface profile and more easily infer the road that needs repair.

[0064] (4) The road surface profile derivation unit 112 derives the flatness information and the reliability of the flatness information at each position of the road on which the vehicle has traveled by machine learning using a Gaussian process. Thus, even if the vehicle information obtained by the information acquisition unit 111 is discrete information, the flatness information and the reliability of the flatness information can be derived for each position of the road on which the vehicle 20 has traveled.

[0065] (5) When the information acquisition unit 111 fails to acquire vehicle information corresponding to a third location between the first location and the second location, the road surface profile deriving unit 112 interpolates and derives the flatness information of the third location and the reliability of the flatness information using the vehicle information corresponding to the first location and the vehicle information corresponding to the second location. Thus, even for a section of the road on which the vehicle has traveled and for which the information acquisition unit 111 fails to acquire vehicle information, the flatness information and the reliability of the flatness information can be derived.

[0066] (6) The road surface evaluation device 10 of this embodiment can also be used as a road surface evaluation method. In the road surface evaluation method, the following steps are executed by a computer: Figure 6): a step of obtaining vehicle information including information indicating the movement of the traveling vehicle 20 and position information of the vehicle 20 (step S15); a step of obtaining map information including information of the road on which the vehicle is traveling (step S14); a step of deriving flatness information indicating the flatness of the road surface and the reliability of the flatness information based on the obtained vehicle information (step S16); and a step of establishing a corresponding relationship between the derived flatness information and the reliability and the obtained road information and outputting them (step S17). With this configuration, the road surface profile of the road can be fully evaluated.

[0067] The above-mentioned embodiment can be modified into various forms. The following describes a modified example. In the above-mentioned modified example, the information acquisition unit 111, as a vehicle information acquisition unit, acquires the lateral acceleration of the vehicle 20 detected by the acceleration sensor 33 as information indicating the movement of the vehicle 20, but the information indicating the movement of the vehicle 20 is not limited to the lateral acceleration of the vehicle 20 detected by the acceleration sensor. That is, as long as the information indicating the movement of the vehicle 20 is acquired, the configuration of the information acquisition unit 111 can be any form such as detecting the front-rear acceleration of the vehicle 20 by the acceleration sensor.

[0068] In addition, in the above-mentioned embodiment, the information acquisition unit 111 as the map information acquisition unit acquires the map information including the information of the road on which the vehicle 20 travels from the storage unit 120, but the map information may be stored in an external server or an external storage device. That is, as long as the map information including the information of the road on which the vehicle 20 travels is acquired, the configuration of the information acquisition unit 111 may be in any form.

[0069] In addition, the road surface profile deriving unit 112 can also correct the derived road surface roughness value based on the vehicle speed detected by the vehicle speed sensor 35 and the steering angle detected by the steering angle sensor 34. When the vehicle 20 is traveling on a curved road, the acceleration sensor 33 detects not only the lateral acceleration caused by the unevenness of the road surface, but also the lateral acceleration caused by the centrifugal force generated in accordance with the traveling speed and steering angle of the vehicle 20. Therefore, in such a case, the road surface profile deriving unit 112 can also correct the road surface roughness value so that the component based on the lateral acceleration caused by the centrifugal force is excluded from the road surface roughness value derived from the lateral acceleration detected by the acceleration sensor 33. Therefore, the road surface of the road other than the straight road can also be fully evaluated. It should be noted that the road surface profile deriving unit 112 can also add the traveling speed, the front-rear acceleration and the steering angle (steering angle) as teaching data, and perform the above-mentioned machine learning to correct the road surface roughness value.

[0070] In addition, in the above-mentioned embodiment, the road surface profile derivation unit 112 as a flatness information derivation unit uses the pre-determined road surface flatness value and lateral acceleration as teaching data for machine learning, and derives the correlation between the road surface flatness value and the lateral acceleration. However, the flatness information derivation unit can also generate a formula or table representing the correlation between the flatness value and the lateral acceleration based on the pre-determined flatness value and lateral acceleration instead of using machine learning. Then, the flatness information derivation unit can also use the formula or table to derive the road surface flatness value and its reliability. That is, as long as the road surface flatness value and its reliability are derived, the structure of the flatness information derivation unit can be in any form.

[0071] In addition, in the above-mentioned embodiment, the road surface profile output unit 113 is used as an output unit to be able to use Figure 7 The road surface contour information is output in a manner of displaying the road surface contour information in the manner shown in the figure, but the road surface contour information can also be output in a manner that can be displayed in other manners, for example, in a manner of plotting the road surface roughness value and reliability on a map. More specifically, the road surface contour information can also be output in a manner of displaying the road surface on a map with a color and width corresponding to the roughness value and reliability. That is, as long as the road surface contour information is output in a manner that allows the user to recognize the road surface roughness value and reliability, the configuration of the output unit can be any manner.

[0072] In addition, in the above embodiment, an example of using IRI to represent the road surface roughness value is shown, but the road surface roughness value can also be represented by other indicators. For example, when the road surface roughness value obtained as teaching data is represented by an indicator other than IRI, the road surface profile derivation unit 112 can also derive the road surface roughness value represented by the indicator.

[0073] Furthermore, when there is a road (section) whose reliability estimated in step S13 is lower than a predetermined value, the road profile output unit 113 may transmit a travel request information requesting the vehicle 20 to travel on the road through the communication control unit 114. In this case, the road profile output unit 113 may transmit the travel request information to vehicles 20 within a predetermined distance (e.g., within 1 km) from the road whose reliability of the road surface flatness value is lower than a predetermined value, instead of to all vehicles 20. Furthermore, a reward may be given to the user of the vehicle 20 who travels on the road specified by the travel request information. In this way, the reliability of the flatness value derived by the road profile deriving unit 112 can be improved in the section where the reliability is low.

[0074] The above description is merely an example, and the above embodiment and modified examples do not limit the present invention unless the features of the present invention are impaired. One or more of the above embodiment and modified examples can be arbitrarily combined, and the modified examples can be combined with each other.

[0075] Description of Reference Numerals

[0076] 10: Road surface evaluation device;

[0077] 20, 20-1 to 20-n: vehicles;

[0078] 30: Vehicle-mounted device;

[0079] 110: Operation unit;

[0080] 111: Information acquisition department;

[0081] 112: road surface profile derivation unit (flatness information derivation unit);

[0082] 113: road surface profile output unit (output unit);

[0083] 120: Storage unit.

Claims

1. A road surface evaluation device, characterized in that: have: a vehicle information acquisition unit that acquires vehicle information including information indicating the movement of a traveling vehicle and position information of the vehicle; a map information acquisition unit that acquires map information including information on a road on which the vehicle travels; a flatness information deriving unit for deriving flatness information indicating the flatness of a road surface and the reliability of the flatness information based on the vehicle information acquired by the vehicle information acquiring unit; as well as an output unit that establishes a correspondence between the flatness information and the reliability derived by the flatness information deriving unit and the road information acquired by the map information acquiring unit and outputs the correspondence; The flatness information is a flatness value indicating the degree of flatness of the road surface. The reliability of the flatness value is represented by a range of the flatness value derived by the flatness information deriving unit based on the vehicle information acquired by the vehicle information acquiring unit.

2. The road surface evaluation device according to claim 1, characterized in that: The output unit outputs information in a displayable form in which the flatness information and reliability derived by the flatness information deriving unit and the road information acquired by the map information acquiring unit are associated with each other.

3. The road surface evaluation device according to claim 1 or 2, characterized in that: The roughness information deriving unit derives the roughness information at each position of the road on which the vehicle traveled and the reliability of the roughness information by machine learning using a Gaussian process.

4. The road surface evaluation device according to claim 1 or 2, characterized in that: The output unit further outputs, to the vehicle, travel request information requesting the vehicle to travel on a road whose reliability derived by the roughness information deriving unit is lower than a predetermined value.

5. The road surface evaluation device according to claim 4, characterized in that: The output unit outputs the travel request information to a vehicle that is within a predetermined distance from a road whose reliability derived by the roughness information deriving unit is lower than the predetermined value.

6. The road surface evaluation device according to claim 1, 2 or 5, characterized in that: When the vehicle information acquisition unit fails to acquire the vehicle information corresponding to a third location between the first location and the second location, the flatness information extraction unit uses the vehicle information corresponding to the first location and the vehicle information corresponding to the second location to interpolate and extract the flatness information of the third location and the reliability of the flatness information.

7. The road surface evaluation device according to claim 1, 2 or 5, characterized in that: The flatness information deriving unit corrects the derived flatness information so as to exclude a component of acceleration in a left-right direction of the vehicle due to centrifugal force, based on a traveling speed and a steering angle of the vehicle indicated by the vehicle information acquired by the vehicle information acquiring unit.

8. The road surface evaluation device according to claim 1, 2 or 5, characterized in that: The vehicle information acquired by the vehicle information acquisition unit includes, as information indicating the movement of the vehicle, acceleration in a left-right direction, a travel speed, acceleration in a front-rear direction, and a steering angle of the vehicle.

9. A road surface evaluation method, characterized in that: The process comprises the following steps executed by a computer: The step of acquiring vehicle information including information indicating the movement of a traveling vehicle and position information of the vehicle; A step of acquiring map information including information on a road on which the vehicle is traveling; A step of deriving, based on the acquired vehicle information, flatness information indicating the flatness of the road surface and the reliability of the flatness information; as well as The step of establishing a corresponding relationship between the derived flatness information and reliability and the acquired road information and outputting the corresponding relationship; The flatness information is a flatness value indicating the degree of flatness of the road surface. The reliability of the flatness value is represented by a range of the flatness value derived from the acquired vehicle information.

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