ROAD SURFACE EVALUATION APPARATUS

A road surface evaluation apparatus includes a microprocessor and a memory connected to the microprocessor. The memory stores map information including roughness information indicating a roughness of a surface of a road, and the microprocessor is configured to perform: acquiring as driving information of a plurality of vehicles driving on the road, position information of the plurality of vehicles, acceleration information indicating accelerations of the plurality of vehicles, driving image information including a captured image of the surface of the road, and driving sound information indicating driving sound of the plurality of vehicles; evaluating the roughness of the surface of the road based on the driving information of the plurality of vehicles; and updating the roughness information corresponding to the road stored in the memory based on an evaluation result in the evaluating.

CROSS-REFERENCE TO RELATED APPLICATION

This application is based upon and claims the benefit of priority from Japanese Patent Application No. 2023-041765 filed on Mar. 16, 2023, the content of which is incorporated herein by reference.

BACKGROUND OF THE INVENTION

Field of the Invention

The present invention relates to a road surface evaluation apparatus that evaluates a road surface profile representing unevenness of a road surface.

Description of the Related Art

As an apparatus of this type, there has been conventionally known an apparatus configured to display a roughness index of a road surface calculated based on driving acceleration acquired from driving vehicles (see, for example, JP 2019-196680 A). The apparatus described in JP 2019-196680 displays the crack rate of a road surface detected based on the image captured by an in-vehicle camera on a map together with the roughness index of the road surface so as to allow efficient grasp of the condition of the road surface.

However, the condition of the road surface detected based on the captured image may vary depending on the environment outside the vehicle such as weather. Therefore, if the captured image is used as in the apparatus described in JP 2019-196680 A, the road surface condition may not be accurately evaluated.

SUMMARY OF THE INVENTION

An aspect of the present invention is a road surface evaluation apparatus including a microprocessor and a memory connected to the microprocessor. The memory stores map information including roughness information indicating a roughness of a surface of a road. The microprocessor is configured to perform: acquiring as driving information of a plurality of vehicles driving on the road, position information of the plurality of vehicles, acceleration information indicating accelerations of the plurality of vehicles, driving image information including a captured image of the surface of the road, and driving sound information indicating driving sound of the plurality of vehicles; evaluating the roughness of the surface of the road based on the driving information of the plurality of vehicles; and updating the roughness information corresponding to the road stored in the memory based on an evaluation result in the evaluating.

DETAILED DESCRIPTION OF THE INVENTION

An embodiment of the present invention will be described below with reference toFIGS.1to11. The road surface evaluation apparatus according to the present embodiment is an apparatus for evaluating the road surface profile of a road on which a vehicle is driving.FIG.1illustrates an example of the configuration of a road surface evaluation system including a road surface evaluation apparatus according to the present embodiment. As illustrated inFIG.1, a road surface evaluation system1includes a road surface evaluation apparatus10and in-vehicle terminals30. The road surface evaluation apparatus10includes, for example, a server device. The in-vehicle terminals30are configured to communicate with the road surface evaluation apparatus10via a communication network2.

The communication network2includes not only public wireless communication networks represented by Internet networks and cell phone networks, but also closed communication networks established for each predetermined administrative region, such as wireless LAN, Wi-Fi (registered trademark), and Bluetooth (registered trademark).

The in-vehicle terminals30are installed in vehicles20. The vehicles20include a plurality of vehicles20-1,20-2, . . . , and20-n. Note that the vehicles20may be manually operated vehicles or automated vehicles. The vehicles20may include vehicles of different models and grades.

FIG.2is a block diagram illustrating the key components of the in-vehicle terminal30according to the present embodiment. The in-vehicle terminal30includes an electronic control unit (ECU)31, a position measurement sensor32, an acceleration sensor33, a steering angle sensor34, a vehicle speed sensor35, an illuminance sensor36, a camera37, a microphone38, and a telematic control unit (TCU)39.

The position measurement sensor32is, for example, a GPS sensor, which receives positioning signals transmitted from GPS satellites and detects the absolute position (e.g., latitude and longitude) of the vehicle20. The position measurement sensor32includes not only GPS sensors but also sensors that use radio waves transmitted from satellites in various countries, known as GNSS satellites, including quasi-zenith orbit satellites. Hereinafter, information indicating the position of the vehicle detected by the position measurement sensor32is referred to as position information. The position information includes information indicating the time when the vehicle20has driven the position (hereinafter referred to as driving time information).

The acceleration sensor33detects the acceleration of the vehicle20in the left-right directions, that is, lateral acceleration. Note that the acceleration sensor33may be configured to detect acceleration in the front-back direction and vertical direction as well as lateral acceleration of the vehicle20. Hereinafter, the information of the acceleration detected by the acceleration sensor33is referred to as acceleration information or vehicle vibration data. The steering angle sensor34detects the steering angle of the steering wheel (not shown) of the vehicle20. The vehicle speed sensor35detects the vehicle speed of the vehicle20.

The illuminance sensor36includes a light receiving element and detects brightness (illuminance) of light incident on the light receiving element. The illuminance sensor36is installed outside the vehicle20(for example, on the roof) or inside the vehicle20(on the dashboard) so that it can detect the illuminance around the vehicle20. Since light passing through the windshield is somewhat attenuated by the glass, in a case where the illuminance sensor36is installed inside the vehicle20, the sensor value may be corrected in consideration of the attenuation amount.

The camera37includes an imaging device (image sensor) such as a CCD or a CMOS. The camera37continuously images a space in front of the vehicle20to acquire image data. The camera37is mounted at a predetermined position (front portion) of the vehicle20such that the road surface on which the vehicle20is driving is included in the imaging range. Hereinafter, the captured image data acquired by the camera37is referred to as road image data or simply a road image. The camera37may be a monocular camera or a stereo camera.

The microphone38converts the input sound into an electrical signal and outputs it. The microphone38is mounted at a predetermined position on the vehicle20so as to be capable of collecting driving sound of the vehicle20. Hereinafter, the data of the driving sound output from the microphone38is referred to as driving sound data. Note that the microphone38may be mounted inside or outside the vehicle. Furthermore, the microphone38may include a single microphone or may include a microphone array including a plurality of microphones.

As illustrated inFIG.2, the ECU31includes a computer including a processing unit310such as a CPU (processor), a memory unit320such as ROM and RAM, and other peripheral circuits such as I/O interfaces not illustrated. The processing unit310functions as a sensor value acquisition unit311and a communication control unit312by executing programs stored in the memory unit320in advance.

The sensor value acquisition unit311acquires the detection value of the position measurement sensor32(position information) and the detection value of the acceleration sensor33(vehicle vibration data) at a predetermined sampling cycle. The communication control unit312transmits the information acquired by the sensor value acquisition unit311(hereinafter referred to as driving information) to the road surface evaluation apparatus10at a predetermined cycle via the TCU36, together with the vehicle ID that allows the vehicle20to be identified.

The driving information acquired by the sensor value acquisition unit311further includes the detection value of the vehicle speed sensor35, that is, the measured driving speed of the vehicle20(hereinafter referred to as driving speed information), the detection value of the illuminance sensor36(hereinafter referred to as illuminance information), the detection value of the camera37(road image data), and the detection value of the microphone38(driving sound data). The driving information may include the detection value of the steering angle sensor34, that is, the measured steering angle of the steering wheel of the vehicle20(hereinafter referred to as steering angle information). The steering angle information may be configured to use information acquired by a yaw rate sensor (not shown) installed in the vehicle20(hereinafter referred to as yaw rate sensor information).

The road surface evaluation apparatus10detects the unevenness of the road surface, that is, the road surface roughness (hereinafter also referred to as a road surface profile), based on the values detected by the acceleration sensor33(vehicle vibration data) of the vehicle20(in-vehicle terminal30). The detected road surface profile information is output to, for example, a terminal owned by a road management company or the like, and is used as reference data by the road management company when considering whether or not repairs are necessary. Specifically, the detected values of the acceleration sensor are used to evaluate the road surface profile.

FIG.3is a block diagram illustrating the key components of the road surface evaluation apparatus10according to the present embodiment. The road surface evaluation apparatus10is configured to include a computer including a processing unit110, such as a CPU, a memory unit120such as ROM and RAM, and other peripheral circuits such as I/O interfaces not illustrated. The memory unit120stores map information including road maps, and various kinds of information processed by the processing unit110.

The processing unit110executes the programs stored in the memory unit120, thereby functioning as an information acquisition unit111, an evaluation unit112, an output unit113, and a communication control unit114.

The information acquisition unit111receives driving information from the in-vehicle terminals30of the vehicles20driving on the road via the communication control unit114. Note that the information acquisition unit111can identify the vehicle20that is the transmission source of the driving information by the vehicle ID associated with the driving information.

The information acquisition unit111stores driving information received from the plurality of vehicles20(in-vehicle terminals30) in the memory unit120in time series. Hereafter, the driving information stored in time series in the memory unit120is referred to as time-series driving information. The information acquisition unit111also acquires map information from the memory unit120, including information on the road on which the vehicles20are driving. Furthermore, the information acquisition unit111acquires weather information corresponding to the driving position and driving time of the vehicle20indicated by the driving information of the vehicle20from an external information distribution server (not illustrated) that distributes the weather information. The information acquisition unit111stores weather information in the memory unit120in association with driving information of the vehicle20.

The evaluation unit112evaluates the amount of unevenness (depth or height) of the road surface, or road surface roughness, based on the driving information of the plurality of vehicles20acquired by the information acquisition unit111within a predetermined period. More specifically, the evaluation unit112calculates the road surface roughness value indicating the degree of road surface roughness based on the lateral accelerations of the plurality of vehicles20acquired by the information acquisition unit111within a predetermined period. The road surface roughness values are, for example, values expressed in terms of the International Roughness Index (IRI), which is an international index. Hereinafter, the road surface roughness values may be simply referred to as roughness values.

FIG.4Ais a diagram illustrating an example of a map of the road on which the vehicles20are driving.FIG.4Aillustrates the predetermined road to be evaluated for the road surface roughness (latitude Y to Z on National Route X). InFIG.4A, the upper direction corresponds to the north direction, and the right direction corresponds to the east direction. The predetermined road to be evaluated for road surface roughness (hereinafter referred to as the road to be evaluated) can be designated by a user (for example, a road management company). More specifically, information that can identify the road to be evaluated, such as the name and position (latitude and longitude) of the road in the section to be evaluated, is transmitted from a terminal of a road management company or the like to the road surface evaluation apparatus10, whereby the road to be evaluated is designated. In a case where the road to be evaluated has a plurality of lanes on each side, the lane to be evaluated for road surface roughness may be designated by the user. In addition, the road to be evaluated may be designated by the distance from the start point coordinates.

The driving information acquired at a predetermined sampling cycle by the in-vehicle terminal30is transmitted to the road surface evaluation apparatus10via the communication control unit312.FIG.4Billustrates an example of time-series driving information obtained by the road surface evaluation apparatus10from the in-vehicle terminal30of the vehicle20-1driving on the road to be evaluated (latitude Y to Z on National Route X) inFIG.4A. The horizontal axis in the figure is the position (latitude) of the vehicles20-1in the driving direction along the traveling lane, and the vertical axis is the lateral acceleration of the vehicles20-1. The characteristics D1, D2, . . . , and Dn represent time-series driving information acquired when the vehicle20-1drives on the road to be evaluated for the first, second, . . . , and nth time, respectively.

In general, the greater the amount of unevenness of the road surface, the greater the lateral acceleration of the vehicles20, and the road surface roughness values and lateral acceleration have a certain correlation. The evaluation unit112uses this correlation information (hereafter referred to as a road surface condition inference model or simply an inference model) to calculate the road surface roughness value corresponding to the vehicle position on the road from the lateral acceleration.

First, the evaluation unit112performs machine learning (machine learning LN inFIG.7to be described later) using the pre-measured road surface roughness value and the lateral acceleration as training data to generate a road surface condition inference model.FIGS.5A and5Billustrate the training data for road surface roughness values and lateral acceleration, respectively. A vehicle VI illustrated inFIG.5Ais a dedicated vehicle including a measuring instrument MA that measures road surface roughness. The measuring instrument MA measures the road surface roughness values of the road RD when the vehicle VI is driving on a predetermined road (such as a course for measurement) RD. A characteristic P1inFIG.5Arepresents the road surface roughness value measured at this time, that is, the road surface roughness value used as the training data.

FIG.5Billustrates the vehicles20inFIG.1driving on the same road RD as that inFIG.5A. A characteristic P2inFIG.5Brepresents the lateral acceleration detected by the acceleration sensor33provided in the vehicle20while the vehicle20is driving on a predetermined road RD at a predetermined driving speed (hereinafter referred to as a reference driving speed) Vref, that is, the lateral acceleration used as training data.

The training data for road surface roughness values and lateral acceleration may be stored in the memory unit120of the road surface evaluation apparatus10or in an external memory device. The evaluation unit112executes machine learning using the training data for the road surface roughness value and the lateral acceleration read from the memory unit120or an external memory device to generate a road surface condition inference model. The longitudinal acceleration, front/rear acceleration, and steering angle may be added as training data for machine learning.

The evaluation unit112calculates the road surface roughness value of the road to be evaluated based on the driving information acquired while the vehicle20is driving on the road to be evaluated using the generated road surface condition inference model.

The output unit113executes processing of storing the road surface roughness information evaluated by the evaluation unit112, that is, the road surface roughness value calculated by the evaluation unit112, in association with the road information included in the map information of the memory unit120(hereinafter referred to as map information update processing). When the road surface roughness value corresponding to the road to be evaluated is already stored, the output unit113updates the road surface roughness value with the road surface roughness value calculated by the evaluation unit112.

The output unit113also outputs the road surface roughness information evaluated by the evaluation unit112in association with the road information acquired by the information acquisition unit111. The information output at this time is referred to as road surface profile information. When the output unit113receives an instruction to output the road surface profile from a terminal of a road management company or the like via the communication network2, it outputs the road surface profile information to the terminal from which the output instruction was transmitted or to a predetermined output destination terminal. The road surface profile information is information that can be displayed on a display device such as a display, and users can check road surface profiles by displaying the road surface profile information on a display included in the user's terminal.FIG.6illustrates an example of the road surface profile information. A characteristic P11inFIG.6represents the road surface roughness value calculated based on the time-series driving information D1inFIG.4B. A characteristic P12inFIG.6represents the road surface roughness value calculated based on the time-series driving information D2inFIG.4B. A characteristic P13inFIG.6represents the road surface roughness value calculated based on the time-series driving information Dn inFIG.4B.

By the way, the detection value of the acceleration sensor33included in the driving information of the vehicle20may include noise and the like caused by the external environment of the vehicle20, such as weather. Therefore, even when the vehicles20drive on the same road, as illustrated inFIG.6, different road surface roughness values are calculated for different external environments during driving. In this case, the road surface condition may not be accurately evaluated. Therefore, in order to address such a problem, the evaluation unit112evaluates the road surface roughness as follows.

FIG.7is a diagram for illustrating updating of the road surface condition inference model. The model RM inFIG.7is a road surface condition inference model generated by the machine learning LN using the training data inFIGS.5A and5B. As illustrated inFIG.7, the evaluation unit112inputs the vehicle vibration data acquired by the information acquisition unit111to the machine learning LN to update the inference model RM. At this time, the evaluation unit112inputs the road image data and the driving sound data detected at the same timing as that of the vehicle vibration data to the machine learning LN together with the vehicle vibration data. In this manner, the inference accuracy of the inference model RM can be improved by inputting the road image data and the driving sound data together with the vehicle vibration data to the machine learning LN.

On the other hand, the vehicle vibration data, the road image data, and the driving sound data change depending on the driving speed of the vehicle20even if the vehicle20drives on the same road in the same environment. Therefore, if the vehicle vibration data, the road image data, and the driving sound data are directly input to the machine learning LN, a desired road surface condition inference model RM may not be acquired. In consideration of this point, as illustrated inFIG.7, the evaluation unit112make corrections CR1, CR2, and CR3based on the driving speed of the vehicle20on the road image data, the vehicle vibration data, and the driving sound data.

FIG.8Ais a diagram for illustrating correction CR2on the vehicle vibration data.FIG.8Aillustrates how the vehicle vibration data before correction, that is, the vehicle vibration data included in the driving information acquired by the information acquisition unit111(left figure), is multiplied by the correction factor on the time axis to obtain the corrected vehicle vibration data (right figure). The correction factor is a value obtained by dividing the reference driving speed Vref by the driving speed indicated by the driving speed information acquired by the information acquisition unit111, that is, the driving speed V of the vehicle20at the time when the vehicle vibration data was detected.FIG.8Bis a diagram for illustrating correction CR3on the driving sound data. The correction of the driving sound data is performed in the same manner as the correction of the vehicle vibration data. Specifically, the driving sound data before correction, that is, the driving sound data included in the driving information acquired by the information acquisition unit111(left figure), is multiplied by the correction factor (Vref/V) on the time axis to obtain the corrected driving sound data (right figure).

FIG.9Ais a diagram for illustrating correction CRI on the road image data. As illustrated inFIG.9A, road image data SD is a moving image data including a plurality of frames. In the figure, numbers in parentheses represent frame numbers. The frame N is the current frame, and the frames N-1, . . . , and N-5are past frames consecutive to the frame N.FIG.9Bis a diagram for illustrating the position of the road surface imaged by the camera37of the in-vehicle terminal30. As illustrated inFIG.9B, position P1of the road surface imaged by the camera37is located a distance L ahead of the vehicle20. The distance L is calculated based on the position and orientation of the camera37with respect to the road surface. The road surface (more specifically, the road surface immediately under the tires of the vehicle20) at the driving position of the vehicle20at the time when the current frame N was acquired by the camera37is included in the frame (the frame N-3in the example inFIG.9A) acquired before the current frame N by a predetermined time Δt(=L/V). Therefore, the evaluation unit112acquires road image data with the time axis shifted past by Δt as corrected road image data.

In addition, as illustrated inFIG.7, the evaluation unit112attaches weights W1, W2, W3to the road image data, the vehicle vibration data, and the driving sound data, which have been corrected based on the driving speed of the vehicle20, respectively. The calculation of the weights W1, W2, and W3will be described.

The evaluation unit112calculates a weight W1to be given to the road image data based on the illuminance information included in the driving information acquired by the information acquisition unit111. Specifically, the evaluation unit112determines the brightness (illuminance) around the vehicle20based on the illuminance information, and makes the weight W1smaller when the illuminance is equal to or less than a predetermined value than when the illuminance is greater than the predetermined value. The evaluation unit112may decrease the weight W1as the illuminance decreases. In addition, the image recognition accuracy (recognition accuracy of the road surface condition based on the camera image) is lower when the driving time period is in bad weather than when in good weather. Therefore, the evaluation unit112may recognize the weather at the driving position during the driving time period of the vehicle20based on the weather information acquired by the information acquisition unit111, and may decrease the weight W1when the weather is bad than when the weather is good. The bad weather is weather that lowers the image recognition accuracy, and is, for example, rainfall, snowfall, snow cover, or fog.

In addition, the evaluation unit112calculates the weight W2to be attached to the vehicle vibration data based on the driving speed of the vehicle20. The vibration of the vehicle20caused by the unevenness of the road surface changes depending on the driving speed of the vehicle20. Specifically, the higher the driving speed, the greater the vibration of the vehicle caused by the unevenness of the road surface. In consideration of this point, the evaluation unit112decreases the weight W2as the driving speed of the vehicle20increases.

The evaluation unit112also calculates the weight W3of the driving sound data based on the weather during the driving time period of the vehicle20. For example, when it is raining during the driving time period, noise due to rain sound or splashing water may be included in the driving sound data. In consideration of this point, the evaluation unit112determines whether or not noise caused by the weather is included in the driving sound data based on the weather during the driving time period of the vehicle20. Then, the evaluation unit112makes the weight W3smaller when noise is included than when noise is not included.

The evaluation unit112periodically or intermittently repeats the machine learning as illustrated inFIG.7to grow the road surface condition inference model RM. For example, the evaluation unit112executes machine learning as illustrated inFIG.7every predetermined period or every time a predetermined amount of new driving information corresponding to the road to be evaluated is accumulated in the memory unit120. Alternatively, the processing is executed based on the user instruction input via the communication control unit114.

FIG.10is a diagram for illustrating the calculation of the road surface roughness value using the road surface condition inference model RM inFIG.7. The evaluation unit112inputs vehicle vibration data, road image data, and driving sound data acquired while the vehicle20is driving on the road to be evaluated to the road surface condition inference model RM, and calculates the road surface roughness value of the road to be evaluated. At this time, as illustrated inFIG.10, the evaluation unit112applies corrections CR1, CR2, and CR3and attach weights W1, W2, and W3to the vehicle vibration data, the road image data, and the driving sound data, and then inputs these data to the road surface condition inference model RM. As a result, the road surface roughness value can be accurately calculated without being affected by the driving speed of the vehicle20or the external environment.

FIG.11is a flowchart illustrating an example of processing executed by the processing unit110(CPU) of the road surface evaluation apparatus10(map information update processing) according to a predetermined program. The processing illustrated in this flowchart is repeated at a predetermined cycle while the road surface evaluation apparatus10is running. First, in step S11, it is determined whether driving information has been received from any of the in-vehicle terminals30of the vehicles20. If NO in step S11, the processing ends. If YES in step S11, in step S12, the vehicle information received in step S11is stored in the memory unit120. In step S13, it is determined whether the road to be evaluated is designated by the user.

If NO in step S13, the processing ends. If YES in step S13, it is determined in step S14, whether or not to update the road surface roughness value included in the map information in the memory unit120. More specifically, it is determined whether or not a predetermined period has elapsed from the time of the previous update (the time of the previous execution of step S18). Note that it may be determined whether or not a predetermined amount of new driving information corresponding to the road to be evaluated has been accumulated in the memory unit120. It may also be determined whether or not an update instruction has been received from a terminal of a road management company or the like via the communication network2.

If NO in step S14, the processing ends. If YES in step S14, driving information of the vehicle20corresponding to the road to be evaluated is acquired from the memory unit120in step S15. In step S16, corrections CR1, CR2, and CR3are applied and weights W1, W2, and W3are attached to the vehicle vibration data, the road image data, and the driving sound data included in the driving information acquired in step S15. In step S17, the road surface roughness is evaluated. Specifically, the vehicle vibration data, the road image data, and the driving sound data corrected and weighted in step S16are input to the road surface condition inference model RM to calculate the road surface roughness value. In step S18, the road surface roughness value calculated in step S17is stored in association with the road information included in the map information in the memory unit120. As a result, the road surface roughness value corresponding to the road to be evaluated included in the map information is updated.

According to the embodiment of the present invention, the following effects can be achieved.

(1) The road surface evaluation apparatus10includes: an information acquisition unit111configured to acquire position information of a plurality of vehicles20driving on a road, acceleration information indicating accelerations of the plurality of vehicles20, driving image information including captured images of the road surface, and driving sound information indicating driving sound of the plurality of vehicles20as driving information of the plurality of vehicles20; a memory unit120configured to store map information including roughness information indicating the road surface roughness; an evaluation unit112configured to evaluate the road surface roughness based on the driving information of the plurality of vehicles20acquired by the information acquisition unit111; and an output unit113configured to update the roughness information corresponding to the road stored in the memory unit120based on the evaluation result of the evaluation unit112. The information acquisition unit111acquires external environment information indicating the external environment of the plurality of vehicles20driving on a road. The evaluation unit112attaches weights to the acceleration information, the driving image information, and the driving sound information used to evaluate the road surface roughness. The evaluation unit112changes the weights attached to the acceleration information, the driving image information, and the driving sound information based on the external environment information acquired by the information acquisition unit111. This allows accurate evaluation of the condition of the road surface regardless of the external environment.

(2) The external environment information includes illuminance information indicating illuminance outside the plurality of vehicles20driving on the road. When the illuminance indicated by the illuminance information indicates a predetermined value or less, the evaluation unit112attaches a smaller weight to the driving image information than when the illuminance is larger than the predetermined value. This allows accurate evaluation of the condition of the road surface regardless of the brightness outside the vehicle20.

(3) The external environment information includes weather information indicating weather when the plurality of vehicles20are driving on the road. When the weather indicated by the weather information is bad, the evaluation unit112attaches a smaller weight to the driving image information than when the weather is not bad. Bad weather includes rainfall, snowfall, snow cover, or fog. This allows accurate evaluation of the condition of the road surface regardless of the weather when the vehicle20is driving.

(4) The information acquisition unit111further acquires, as driving information, driving speed information indicating the driving speeds of the plurality of vehicles calculated from the temporal transitions of the position information of the plurality of vehicles20, or measured driving speeds of the plurality of vehicles transmitted from the plurality of vehicles20. The evaluation unit112removes noise included in the driving sound information according to the speed band by spectrum analysis based on the driving speed information or the measured driving speed. More specifically, the evaluation unit112classifies the driving sound information of the plurality of vehicles20into speed bands based on the driving speed information or the measured driving speed corresponding to each vehicle, and performs noise removal using spectrum analysis on each of the driving sound information corresponding to each speed band. Then, the evaluation unit112uses the driving sound information from which the noise has been removed to evaluate the road surface roughness. As a result, noise included in the driving sound used to evaluate the road surface roughness can be appropriately removed according to the driving speed of the vehicle20. As a result, the road surface condition can be accurately evaluated.

The above embodiment can be modified into various forms. Modifications are described below.

In the above embodiment, the evaluation unit112inputs the vehicle vibration data acquired by the information acquisition unit111to the machine learning LN to update the inference model RM. Normally, even when a plurality of vehicles20drive on the same road, the road surface roughness values calculated by the evaluation unit112may differ when the models or grades of the vehicles20are different. The reason for this is that the suspension, tires, and other components installed in each vehicle20that affect the vehicle's motion are different for each model and grade. In consideration of this point, the evaluation unit may correct the vehicle vibration data included in the driving information of each vehicle20according to the model or grade of each vehicle20and then input the corrected data to the machine learning LN. In general, the lower the shock-absorbing performance (vertical shock absorption performance) of the suspension and tires, the more easily shocks and vibrations caused by uneven road surfaces are transmitted to the vehicle, and the greater the lateral acceleration detected by the acceleration sensors33in the vehicles20. Usually, the shock-absorbing performance of suspension and tires increases with the grade between the same models, and with the ride comfort between different models. This causes variation in the lateral acceleration detected in the vehicles20, even when the vehicles20drive on the same road. Therefore, the information acquisition unit111may identify the model and grade of the vehicle20based on the vehicle ID (for example, the chassis number) of the vehicle20associated with the driving information, recognize the shock-absorbing performance of the suspension and tires based on the identified vehicle model and grade, and attach more weights based on the corresponding shock-absorbing performance to the vehicle vibration data the higher the shock-absorbing performance. Specifically, it may decrease the weight attached to the vehicle vibration data the lower the shock-absorbing performance.

The above embodiment describes an example in which the evaluation unit112evaluates the road surface roughness. However, the evaluation unit may evaluate the condition of the road surface other than the road surface roughness. That is, data representing other condition of the road surface may be input to the road surface condition inference model RM. Other conditions of the road surface are, for example, cracking ratio of the road surface, rutting, flatness, potholes, step, dry road surface, wet road surface, snowy road surface, and frozen road surface. The road surface evaluation apparatus10may use a maintenance control index (MCI) instead of the IRI as an index indicating the road surface roughness.

In the above embodiment, the information acquisition unit111as the driving information acquisition unit acquires the detection value of the vehicle speed sensor35as the driving speed information. However, the driving information acquisition unit may calculate the driving speed of the vehicle20based on the temporal transition of the position information of the vehicle20and acquire the calculation result as the driving speed information.

In the above embodiment, as illustrated inFIG.7, the evaluation unit112performs the correction CR2and the correction CR3based on the driving speed on the vehicle vibration data and the driving sound data. However, the evaluation unit may perform machine learning on the vehicle vibration data and the driving sound data for each speed band instead of performing correction based on the driving speed. Specifically, the vehicle vibration data and the driving sound data may be classified by speed band based on the driving speed of the vehicle20at the time when the vehicle vibration data and the driving sound data are detected, and the vehicle vibration data and the driving sound data corresponding to each speed band may be input to the machine learning LN prepared for each speed band.

In the above embodiment, the information acquisition unit111as an environment information acquisition unit acquires weather information indicating the weather when the vehicle20is driving on a road, and the evaluation unit112recognizes the weather during the driving time period of the vehicle20based on the weather information, and attaches a weight to the driving sound data using the weight W3calculated based on the weather. However, instead of attaching a weight to the driving sound data, the evaluation unit may remove noise included in the driving sound information by spectrum analysis based on the weather information. More specifically, the evaluation unit may use spectrum analysis to detect noise (for example, sound of splashing water) corresponding to the weather during the driving time period included in the driving sound data and remove the detected noise from the driving sound data. Furthermore, in a case where the machine learning is performed for each speed band as described above, the evaluation unit may remove noise included in the driving sound data according to the speed band corresponding to the driving speed of the vehicle20by spectrum analysis based on the driving speed information.

In the above embodiment, the evaluation unit112as a weighting unit calculates the weight W1to be attached to the road image data based on the detection value of the illuminance sensor36. However, the weighting unit may calculate the weight W1to be attached to the road image data based on the driving time information included in the driving information acquired by the information acquisition unit111. More specifically, since the image recognition accuracy is lower at night (specifically, sunset to sunrise hours) than during the daytime, the evaluation unit112may attach a smaller weight W1when the driving time period of the vehicle20includes nighttime than when the driving time includes daytime. Note that the larger the percentage of nighttime hours included in the driving time period of the vehicle20, the smaller the weight W1may be. In the above embodiment, the illuminance sensor36detects the illuminance around the vehicle20, but the illuminance around the vehicle20may be detected based on the captured image of the camera37.

Furthermore, in the above embodiment, the output unit113stores the road surface roughness value calculated by the evaluation unit112in association with the road information included in the map information in the memory unit120. However, the output unit may store the road surface roughness value in association with the information of the road such that information indicating the road surface roughness value (text information or color information) is superimposed on the position of the road to be evaluated. In the above embodiment, the output unit113as an update unit updates the roughness value corresponding to the road stored in the memory unit120based on the evaluation result of the evaluation unit112, but the update unit may store information indicating that the roughness value has been updated in the memory unit120in association with the roughness value when the roughness value is updated.

The present invention allows accurate evaluation of the condition of the road surface.