Road surface evaluation device

Abstract

To accurately monitor a road surface condition.SOLUTION: A road surface evaluation device 10 comprises: an information acquisition unit 111 that acquires positional information indicating a travel position of each of a plurality of vehicles traveling on a road, vehicle speed information indicating a travel speed corresponding to the travel position, wheel speed information indicating a rotation speed of a wheel and steering angle information indicating a steering angle value of a steering; a calculation unit 112 that calculates a slip ratio of a wheel at a travel position based on the vehicle speed information and the wheel speed information; a determination unit 113 that determines whether the road is a dry road or a non-dry road based on the slip ratio; and an output unit 114 that outputs road surface information including a determination result of the determination unit 113. The calculation unit 112 further calculates a complexity of a steering operation based on the steering angle information. When the determination unit 113 determines that the road is the non-dry road, the determination unit further determines whether the road surface is covered with slush based on the complexity of the steering operation.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] The present invention relates to a road surface evaluation device for evaluating the condition of a road surface. [Background technology]

[0002] A known example of this type of device is one that monitors road surface conditions in a target area based on information indicating whether or not drive wheel slip has occurred, obtained from multiple vehicles traveling on roads in the target area (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 4737853 Summary of the Invention [Problem to be solved by the invention]

[0004] However, there are various road surface conditions that can cause slippage, such as a wet road surface, a frozen road surface, etc. Therefore, it is difficult to accurately monitor the road surface conditions by only considering the presence or absence of slippage. [Means for solving the problem]

[0005] A road surface evaluation device according to one aspect of the present invention includes a travel information acquisition unit that acquires position information indicating the travel position of each of a plurality of vehicles traveling on a road, vehicle speed information indicating the travel speed, wheel speed information indicating the rotation speed of the wheels, and steering angle information indicating the steering angle value, which correspond to the travel position, a calculation unit that calculates a slip ratio of the wheels at the travel position based on the vehicle speed information and the wheel speed information acquired by the travel information acquisition unit, a determination unit that determines whether the road is a dry road surface or a non-dry road surface based on the slip ratio calculated by the calculation unit, and an output unit that outputs road surface information including the determination result of the determination unit. The calculation unit further calculates the degree of complexity of steering operation based on the steering angle information acquired by the travel information acquisition unit. When the determination unit determines that the road is a non-dry road surface, it further determines whether the road surface is sherbet-like based on the degree of complexity of steering operation calculated by the calculation unit. Effect of the Invention

[0006] According to the present invention, road surface conditions can be monitored with high accuracy. [Brief description of the drawings]

[0007] [Figure 1] 1 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. [Diagram 2] FIG. 2 is a block diagram showing a configuration of a main part of the in-vehicle device. [Diagram 3] FIG. 2 is a block diagram showing the configuration of a main part of a driving information storage device. [Figure 4] 1 is a block diagram showing a configuration of a main part of a road surface evaluation device according to an embodiment of the present invention. [Diagram 5] FIG. 2 is a diagram showing an example of a map of roads on which a vehicle travels. [Figure 6] FIG. 4 is a diagram showing an example of travel information. [Figure 7] FIG. 4 is a diagram showing an example of combined travel information. [Figure 8A] FIG. 4 is a diagram for explaining a method of deriving the correlation between a road surface roughness value and a lateral acceleration. [Figure 8B]FIG. 4 is a diagram for explaining a method of deriving the correlation between a road surface roughness value and a lateral acceleration. [Figure 9] FIG. 13 is a diagram showing an example of a histogram illustrating the distribution of a slip ratio. [Figure 10A] 5 is a diagram for explaining a prediction error of a steering angle value; [Figure 10B] FIG. 4 is a diagram showing an example of a histogram illustrating the distribution of the magnitude of prediction errors of steering angle values. [Figure 11] 5 is a flowchart showing an example of processing executed by a calculation unit in FIG. 4. [Figure 12] 12 is a flowchart showing details of the process in step S15 of FIG. 11. [Figure 13] FIG. 13 is a diagram showing another example of a histogram showing the distribution of a slip ratio. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] Hereinafter, an embodiment of the present invention will be described with reference to Figs. 1 to 13. 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 travels. Fig. 1 is a diagram showing an example of the configuration of a road surface evaluation system including a road surface evaluation device according to this embodiment. As shown in Fig. 1, the road surface evaluation system 1 includes a road surface evaluation device 10, an in-vehicle terminal 30, and a travel information storage device (hereinafter simply referred to as a storage device) 40. The road surface evaluation device 10 and the storage device 40 are configured, for example, by a server device. The in-vehicle terminal 30 is configured to be able to communicate with the road surface evaluation device 10 and the storage device 40 via a communication network 2.

[0009] The communication network 2 includes not only public wireless communication networks such as the Internet and mobile phone networks, but also closed communication networks established for each designated management area, such as wireless LAN, Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0010] The in-vehicle terminal 30 is mounted on a vehicle 20. The vehicles 20 include a plurality of vehicles 20-1, 20-2, . . . , 20-n. The vehicles 20 may be manually driven vehicles or automatically driven vehicles. The vehicles 20 may include vehicles of different models and grades.

[0011] 2 is a block diagram showing the main configuration of the in-vehicle terminal 30 according to this embodiment. The in-vehicle terminal 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, a wheel speed sensor 36, and a TCU (Telematic Control Unit) 37.

[0012] The positioning sensor 32 is, for example, a GPS sensor that receives a positioning signal transmitted from a GPS satellite and detects the absolute position (latitude, longitude, etc.) of the vehicle 20. The positioning sensor 32 includes not only a GPS sensor but also a sensor that performs positioning using radio waves transmitted from satellites of various countries called GNSS satellites, including quasi-zenith orbit satellites.

[0013] The acceleration sensor 33 detects the acceleration in the left-right direction of the vehicle 20, i.e., the lateral acceleration. The acceleration sensor 33 may be configured to detect the acceleration in the front-rear direction and the acceleration in the up-down direction as well as the lateral acceleration of the vehicle 20. The steering angle sensor 34 detects the steering angle of a steering wheel (not shown) of the vehicle 20. The vehicle speed sensor 35 detects the vehicle speed of the vehicle 20. The wheel speed sensor 36 detects the rotation speed of the wheels of the vehicle 20, i.e., the wheel speed.

[0014] 2, the ECU 31 includes a computer having a calculation unit 310 such as a CPU, a storage unit 320 such as a ROM, a RAM, and other peripheral circuits (not shown) such as an I / O interface. The calculation unit 310 functions as a sensor value acquisition unit 311 and a communication control unit 312 by executing a program stored in advance in the storage unit 320.

[0015] The sensor value acquisition unit 311 acquires the detection values ​​of the sensors 33-36 and the absolute position of the vehicle 20 detected by the positioning sensor 32 at a predetermined period. The communication control unit 312 transmits the information acquired by the sensor value acquisition unit 311 (hereinafter referred to as traveling information) together with a vehicle ID capable of identifying the vehicle 20 to the communication network 2 via the TCU 37 at a predetermined period.

[0016] 3 is a block diagram showing the main configuration of the storage device 40 according to this embodiment. The storage device 40 includes a computer having a calculation unit 41 such as a CPU, a storage unit 42 such as a ROM and a RAM, a communication unit 43, and other peripheral circuits (not shown) such as an I / O interface. The calculation unit 41 functions as an information receiving unit 411 by executing a program stored in the storage unit 42. The communication unit 43 is a communication interface that connects the storage device 40 to the communication network 2.

[0017] The information receiving unit 411 receives driving information transmitted from the in-vehicle terminal 30 of each vehicle 20 traveling on a road. The driving information includes position information indicating the position of the vehicle 20, driving time information indicating the time when the vehicle 20 traveled through that position, and acceleration information indicating the acceleration of the vehicle 20. The acceleration information includes information on the lateral acceleration of the vehicle 20 detected by the acceleration sensor 33. The driving information also includes vehicle speed information indicating the driving speed of the vehicle 20. The vehicle speed information indicates the sensor value of the vehicle speed sensor 35, i.e., the measured value of the driving speed of the vehicle 20. Furthermore, the driving information includes steering angle information indicating the steering angle of the steering wheel of the vehicle 20. The steering angle information indicates the sensor value of the steering angle sensor 34, i.e., the measured value of the steering angle of the vehicle 20. Note that the vehicle 20 that is the source of the driving information can be identified by the vehicle ID associated with the driving information.

[0018] The information receiving unit 411 stores the travel information received from the multiple vehicles 20 (vehicle-mounted terminals 30) in chronological order in the storage unit 120. Hereinafter, the travel information stored in chronological order in the storage unit 120 will be referred to as time-series travel information.

[0019] 4 is a block diagram showing the main configuration of the road surface evaluation device 10 according to this embodiment. The road surface evaluation device 10 detects the uneven shape of the road surface, i.e., the roughness of the road surface (hereinafter also referred to as the road surface profile), based on the travel information of a plurality of vehicles 20 stored in a storage device 40, specifically, the acceleration information. This detected road surface profile information is output to a terminal owned by a road management company or the like, and is used as reference data when the road management company or the like considers the necessity of repairs, etc. In other words, the acceleration information is used to evaluate the road surface profile.

[0020] The road surface evaluation device 10 includes a computer having a calculation unit 110 such as a CPU, a storage unit 120 such as a ROM, a RAM, a communication unit 13, and other peripheral circuits (not shown) such as an I / O interface. The storage unit 120 stores map information including a road map and various information processed by the calculation unit 110. The communication unit 13 is a communication interface that connects the road surface evaluation device 10 to a communication network 2.

[0021] The calculation unit 110 executes the programs stored in the storage unit 120 to function as an information acquisition unit 111 , a calculation unit 112 , a determination unit 113 , and an output unit 114 .

[0022] The information acquisition unit 111 acquires from the storage device 40 via the communication unit 13 the travel information of the vehicles 20 corresponding to the period (hereinafter referred to as the evaluation period) and road (hereinafter referred to as the evaluation road) to be evaluated for road surface roughness. The evaluation period and the evaluation road are specified by an output instruction for a road surface profile, which will be described later. FIG. 5 is a diagram showing an example of a map of the road on which the vehicle 20 travels. In FIG. 5, the section (latitude Y to Z of National Route X) designated as the evaluation road is shaded with diagonal lines. In FIG. 5, the upward direction corresponds to the north direction, and the rightward direction corresponds to the east direction. When the evaluation road has multiple lanes on one side, the lane to be evaluated for road surface roughness may be specified by the user. In addition, the information acquisition unit 111 acquires map information including information on the evaluation road from the storage unit 120.

[0023] The calculation unit 112 calculates a road surface roughness value indicating the amount of unevenness (depth or height) of the road surface of the evaluation target road, that is, the road surface roughness, based on the travel information of the multiple vehicles 20 corresponding to the evaluation target road and the evaluation target period acquired by the information acquisition unit 111. More specifically, the calculation unit 112 calculates the road surface roughness value based on the evaluation target period and the lateral acceleration of the multiple vehicles 20 corresponding to the evaluation target road. The road surface roughness value may be expressed, for example, by IRI (International Roughness Index), which is an international index, or may be expressed by a value correlated with IRI. Hereinafter, the road surface roughness value may be simply expressed as a roughness value.

[0024] Fig. 6 is a diagram showing an example of time-series travel information acquired by the road surface evaluation device 10 from the in-vehicle terminal 30 of the vehicle 20 traveling on the evaluation target road (latitude Y to Z of national highway X) in Fig. 5. The horizontal axis in the diagram represents the position (latitude) of the vehicle 20 in the traveling direction along the travel lane, and the vertical axis represents the lateral acceleration of the vehicle 20. Characteristics D1, D2, ..., Dn represent the time-series travel information of the vehicles 20-1, 20-2, ..., 20-n, respectively.

[0025] Fig. 7 is a diagram showing an example of composite travel information generated based on travel information acquired from the in-vehicle terminals 30 of multiple vehicles 20 traveling on the road in Fig. 5. The composite travel information is information obtained by combining acceleration information of each vehicle 20 based on position information of each vehicle 20. The composite travel information shown in Fig. 7 is obtained by overlapping the vertical axis (lateral acceleration) values ​​of each vehicle 20 shown in Fig. 6 based on the horizontal axis (latitude). The calculation unit 112 calculates the road surface roughness value of the road on which each vehicle 20 is traveling based on the composite travel information obtained in this manner.

[0026] In general, the greater the amount of unevenness on the road surface, the greater the lateral acceleration of the vehicle 20, and there is a predetermined correlation between the road surface roughness value and the lateral acceleration. The calculation unit 112 uses information indicating this correlation (hereinafter referred to as correlation data) to calculate the road surface roughness value corresponding to the vehicle position on the road from the lateral acceleration.

[0027] First, the calculation unit 112 performs machine learning using previously measured road surface roughness values ​​and lateral acceleration as teacher data, and calculates the correlation between the road surface roughness value and the lateral acceleration. FIG. 8A and FIG. 8B are diagrams for explaining teacher data of road surface roughness values ​​and lateral acceleration, respectively. The vehicle V1 shown in FIG. 8A is a dedicated vehicle equipped with a measuring device MA for measuring road surface roughness. The measuring device MA measures the road surface roughness value of the road RD when the vehicle V1 is traveling on a predetermined road (measurement course, etc.) RD. The characteristic P1 in FIG. 8A indicates the road surface roughness value measured at this time, that is, the road surface roughness value used as teacher data.

[0028] Fig. 8B shows a state in which vehicle 20 in Fig. 1 travels on the same road RD as in Fig. 8A. A characteristic P2 in Fig. 8B shows the lateral acceleration detected by acceleration sensor 33 provided on vehicle 20 while vehicle 20 is traveling on a predetermined road RD, that is, the lateral acceleration used as teacher data.

[0029] The teacher data of the road surface roughness value and the lateral acceleration may be stored in the memory unit 120 of the road surface evaluation device 10, or may be stored in an external storage device. The calculation unit 112 performs machine learning using the teacher data of the road surface roughness value and the lateral acceleration read from the memory unit 120 or the external storage device, and calculates the correlation between the road surface roughness value and the lateral acceleration. Note that the machine learning may be performed by adding the traveling speed, the longitudinal acceleration, and the steering angle as the teacher data.

[0030] The calculation unit 112 calculates the road surface roughness value of the road to be evaluated based on the correlation between the calculated road surface roughness value and the lateral acceleration, and on the combined travel information corresponding to the road to be evaluated.

[0031] The calculation unit 112 also calculates the slip ratio of the wheels of the vehicle 20 on the road to be evaluated based on the travel information acquired by the information acquisition unit 111. Specifically, the calculation unit 112 calculates the slip ratio at the travel position indicated by the position information included in the travel information by the following formulas (i) and (ii) based on the vehicle speed information and wheel speed information included in the travel information. When the travel speed of the vehicle 20 indicated by the vehicle speed information is higher than the wheel speed of the vehicle 20 indicated by the wheel speed information, the calculation unit 112 calculates the slip ratio by using formula (i). On the other hand, when the travel speed of the vehicle 20 indicated by the vehicle speed information is lower than the wheel speed of the vehicle 20 indicated by the wheel speed information, the calculation unit 112 calculates the slip ratio by using formula (ii).

[0032] Slip ratio = (vehicle speed - wheel speed) / vehicle speed ... (i) Slip ratio = (vehicle speed - wheel speed) / wheel speed ... (ii)

[0033] The calculation unit 112 further calculates the degree of complexity of the steering operation of the vehicle 20 based on the steering angle information included in the traveling information acquired by the information acquisition unit 111.

[0034] The determining unit 113 determines the road surface condition of the road to be evaluated, specifically, whether the road surface is dry or non-dry, based on the slip ratio calculated by the calculating unit 112. More specifically, the determining unit 113 determines whether the road surface is dry or non-dry for each predetermined section along the traveling direction of the road to be evaluated. When the determination result indicates that the road to be evaluated has a non-dry road surface, the determining unit 113 further determines whether the non-dry road surface is sherbet-like, based on the complexity of the steering operation calculated by the calculating unit 112.

[0035] The output unit 114 generates information (hereinafter referred to as road surface profile information or road surface information) in which the road surface roughness value of the road to be evaluated calculated by the calculation unit 112 is associated with the road information acquired by the information acquisition unit 111, and outputs the generated road surface profile information. When the output unit 114 receives an output instruction of the road surface profile from a terminal of a road management company or the like via the communication network 2, the output unit 114 outputs the road surface profile information to the terminal that sent the output instruction or a predetermined output destination terminal. More specifically, when the output unit 114 receives the output instruction, the information acquisition unit 111 acquires the travel information of the plurality of vehicles 20 corresponding to the evaluation target period and the evaluation target road designated in the output instruction from the storage device 40 via the communication unit 13. Then, the calculation unit 112 calculates the road surface roughness value of the road to be evaluated, and the slip ratio and the complexity of the steering operation of the vehicle 20 on the evaluation target road based on the travel information acquired by the information acquisition unit 111. The determining unit 113 determines the road surface condition of the road to be evaluated for each predetermined section based on the slip ratio and the complexity of the steering operation calculated by the calculating unit 112. The output unit 114 outputs road surface profile information including the road surface roughness value calculated by the calculating unit 112 to the destination terminal. At this time, the output unit 114 also includes the determination result by the determining unit 113 in the road surface profile information.

[0036] The road surface profile output instruction includes target road information that can identify the road to be evaluated. The target road information includes information indicating the name of the road to be evaluated and the range of the section, such as "Road: National Highway No. X, Section: Latitude Y to Z". If the road has multiple lanes on one side, such as two lanes on one side, the target road information may include information on the lanes to be evaluated, such as "Road: National Highway No. X, Lane: Right End, Section: Latitude Y to Z". Information other than latitude may be used to specify the road to be evaluated. For example, longitude may be used instead of latitude, or longitude may be used in addition to latitude. Also, the distance from the start point of the section may be used. The road surface profile output instruction further includes target period information that specifies the evaluation target period. The target period information includes information indicating the evaluation target period, such as "one month from X month Y day". The road surface profile output instruction may include information specifying whether or not to output the determination result of the road surface condition, or information instructing the output of only the determination result of the road surface condition.

[0037] An instruction to output the road surface profile may be input to the road surface evaluation device 10 via an operation unit (not shown) that the road surface evaluation device 10 has. The road surface profile information is information that can be displayed on a display device such as a display, and a user (e.g., a road management company) can check the road surface profile and road surface conditions by displaying the road surface profile information on a display of the user's terminal. The output unit 114 may output the road surface profile information to the storage unit 120. For example, the road surface profile information may be output so as to be mapped to map information stored in the storage unit 120.

[0038] Here, the determination of the road surface condition by the determination unit 113 will be described in detail. FIG. 9 is a diagram showing an example of a histogram showing the distribution of the slip ratio in a predetermined section of the road to be evaluated (hereinafter referred to as the evaluation target section). The slip ratio is calculated by the calculation unit 112 for each point that the vehicle 20 traveling in the evaluation target section passes at a predetermined time interval or a predetermined distance interval. FIG. 9 shows the distribution of the slip ratio calculated based on the driving information acquired from the vehicle 20 traveling in the evaluation target section. The characteristic P11 shows the distribution of the slip ratio calculated based on the driving information acquired when the road surface of the evaluation target section is in a dry state. The characteristic P12 shows the distribution of the slip ratio calculated based on the driving information acquired when the road surface of the evaluation target section is in a non-dry state. As shown in FIG. 9, the peak value r12 of the histogram of the characteristic P12 (dry road surface) is larger than the peak value r11 of the histogram of the characteristic P11 (non-dry road surface). Considering this, the determining unit 113 determines that the evaluation target section has a dry road surface when the peak value of the slip ratio histogram of the evaluation target section is less than a predetermined value, and determines that the evaluation target section has a non-dry road surface when the peak value is equal to or greater than the predetermined value. The predetermined value is set to a value greater than the peak value r11 and less than the peak value r12. As shown in FIG. 9, the histogram of the characteristic P12 has a wider base to the right of the peak value compared to the histogram of the characteristic P11. Therefore, the determining unit 113 may include in the above-mentioned determination conditions whether or not the maximum value of the slip ratio on the evaluation target road is equal to or greater than a predetermined threshold value. More specifically, when the peak value of the slip ratio on the evaluation target road is equal to or greater than a predetermined value and the maximum value is equal to or greater than a predetermined threshold value, the determining unit 113 may determine that the evaluation target section has a non-dry road surface.

[0039] Next, when the determination unit 113 determines that the evaluation target section is a non-dry road surface, it determines the type of the non-dry road surface. First, the determination unit 113 determines whether the non-dry road surface is a slush-like road surface (hereinafter referred to as a slush road surface). Specifically, the determination unit 113 predicts a steering angle value at a predetermined point based on steering angle information corresponding to a plurality of points (hereinafter referred to as reference points) located before a predetermined point in the evaluation target section in the traveling direction, which are acquired by the information acquisition unit 111. The predetermined point is a point that the vehicle 20 traveling in the evaluation target section passes at a predetermined time interval or a predetermined distance interval. The determination unit 113 calculates the complexity of the steering operation based on the predicted steering angle value at the predetermined point. More specifically, the determination unit 113 calculates the difference (hereinafter referred to as a prediction error) between the predicted steering angle value at the predetermined point and the steering angle value indicated by the steering angle information corresponding to the predetermined point acquired by the information acquisition unit 111 as the complexity of the steering operation of the vehicle 20.

[0040] FIG. 10A is a diagram for explaining the prediction error of the steering angle value. n ) is the time at a given point (time t n θp(t n ) is a reference point (time t n -1,t n -2,t n The steering angle value at a given point is predicted based on the steering angle information corresponding to the driving position of the vehicle (t n ) is the prediction error at a given point and represents the complexity of the steering operation.

[0041] FIG. 10B is a diagram showing an example of a histogram showing the distribution of the magnitude of the prediction error of the steering angle value in the evaluation target section. FIG. 10B shows the distribution of the magnitude of the prediction error of the steering angle value calculated based on the driving information (steering angle information) acquired from the vehicle 20 traveling in the evaluation target section in a non-dry state. The characteristic P21 shows the distribution of the magnitude of the prediction error of the steering angle value calculated based on the steering angle information acquired when the road surface of the evaluation target section is in a wet state (wet state) due to rain or the like. The characteristic P22 shows the distribution of the magnitude of the prediction error of the steering angle value calculated based on the steering angle information acquired when the road surface of the evaluation target section is in a snowy state. The characteristic P23 shows the distribution of the magnitude of the prediction error of the steering angle value calculated based on the steering angle information acquired when the snow on the road surface of the evaluation target section has melted and the road surface is in a slush state. As shown in FIG. 10B, the height of the peak (frequency d23 at the peak value) of the histogram of the characteristic P23 (slush road surface) is lower than the heights d21 and d22 of the peaks of the histogram of the characteristic P21 (wet road surface) and the characteristic P22 (snow-covered road surface). Taking this into consideration, when the frequency of the peak value of the histogram of the magnitude of the prediction error of the steering angle value in the evaluation target section is less than a predetermined value, the determination unit 113 determines that the prediction error of the steering angle value is equal to or greater than a predetermined level, and determines that the evaluation target section is a slush road surface. On the other hand, when the frequency of the peak value is equal to or greater than a predetermined value, the determination unit 113 determines that the prediction error of the steering angle value is less than a predetermined level, and determines that the evaluation target section is not a slush road surface. Note that this predetermined value is set to a value smaller than d23 and larger than d21 and d22.

[0042] When the determination unit 113 determines that the section to be evaluated is not a slush road surface, it determines whether the section to be evaluated is an icy and snowy road surface (snow-covered road surface or frozen road surface) or a wet road surface based on the statistical value of the road surface roughness value of the section to be evaluated calculated by the calculation unit 112. The statistical value is the average value of the road surface roughness value in the section to be evaluated. The statistical value may be a value other than the average value as long as it represents the degree of road surface roughness of the entire section to be evaluated. When the roughness of the road surface is equal to or greater than a predetermined level, it is estimated that the unevenness of the road surface is caused by ice and snow. Therefore, when the statistical value of the road surface roughness value is equal to or greater than a predetermined threshold value, the determination unit 113 determines that the roughness of the road surface is equal to or greater than a predetermined level, and determines that the section to be evaluated is an icy and snowy road surface. On the other hand, when the statistical value of the road surface roughness value is less than the predetermined value, i.e., when the flatness of the road surface is maintained, the determination unit 113 determines that the ice and snow on the road surface have completely melted or that the road surface is wet due to rain or the like, and determines that the section to be evaluated is a wet road surface.

[0043] 11 is a flowchart showing an example of processing executed in the calculation unit 110 (CPU) of the road surface evaluation device 10 according to a predetermined program. The processing shown in this flowchart is repeated at a predetermined cycle while the road surface evaluation device 10 is running. First, in step S11, it is determined whether or not an instruction to output a road surface profile has been input (received).

[0044] If the result in step S11 is negative, the process is terminated. If the result in step S11 is positive, in step S12, road information included in the map information stored in the storage unit 120 is acquired. In step S13, travel information of the vehicle 20 is acquired from the storage unit 40 via the communication unit 13. More specifically, based on the target road information and target period information included in the output instruction of the road surface profile and the road information acquired in step S12, travel information corresponding to the evaluation target road specified by the target road information and corresponding to the evaluation target period specified by the target period information is read from the storage unit 120. At this time, if there are multiple vehicles 20 that have traveled on the evaluation target road, travel information corresponding to each of the multiple vehicles 20 is acquired. In addition, when the target road information included in the output instruction of the road surface profile includes information on the lane to be evaluated, travel information including position information corresponding to the lane is read from the storage unit 120.

[0045] In step S14, synthetic travel information is generated based on the travel information read from the storage unit 120 in step S13, and road surface roughness is estimated based on the synthetic travel information. More specifically, a road surface roughness value is calculated based on the synthetic travel information. In step S15, the road surface condition of the road to be evaluated is estimated based on the synthetic travel information. Here, the process of step S15 will be described in detail with reference to FIG. 12.

[0046] FIG. 12 is a flowchart showing the details of the process of step S15 in FIG. 11. The processes of steps S21 to S30 are executed for each predetermined section along the traveling direction of the road to be evaluated. In step S21, a slip ratio is calculated for each point that the vehicle 20 traveling in the predetermined section passes at a predetermined time interval or a predetermined distance interval, based on the combined travel information. In step S22, it is determined whether or not the peak value of a histogram showing the distribution of the slip ratio calculated in step S21 is equal to or greater than a predetermined value. If the result in step S22 is negative, in step S23, it is determined that the predetermined section is a dry road surface, and the process proceeds to step S30. On the other hand, if the result in step S22 is positive, it is determined that the predetermined section is a non-dry road surface, and the process proceeds to a process of determining the type of non-dry road surface (steps S24 to S28).

[0047] In step S24, the degree of complexity of the steering operation of the vehicle 20, that is, the prediction error of the steering angle value, is calculated based on the combined travel information, more specifically, the steering angle information of the vehicle 20 included in the combined travel information. In step S25, it is determined whether the prediction error of the steering angle value calculated in step S24 is equal to or greater than a predetermined level. If the result in step S25 is positive, in step S26, it is determined that the predetermined section is a slush road surface, and the process proceeds to step S30. If the result in step S25 is negative, in step S27, it is determined whether the roughness of the road surface of the predetermined section is equal to or greater than a predetermined level. If the result in step S27 is positive, in step S28, it is determined that the predetermined section is an icy and snowy road surface, and the process proceeds to step S30. If the result in step S27 is negative, in step S29, it is determined that the predetermined section is a wet road surface, and the process proceeds to step S30. In step S30, it is determined whether there is a section on the road to be evaluated where the road surface condition has not been determined, that is, whether the determination of the road surface condition has been completed for the entire road to be evaluated. If the result in step S30 is NO, the process returns to step S21, whereas if the result in step S30 is YES, the process ends.

[0048] Returning to the description of FIG. 11, after step S15, in step S16, information is generated in which the road surface roughness value calculated in step S14 is associated with the road information acquired in step S12, i.e., road surface profile information. At this time, the road surface condition judgment result in step S15 (hereinafter referred to as road surface condition information) is also stored in the road surface profile information in association with the road information acquired in step S12. In step S17, the road surface profile information generated in step S16 is output. This allows the road surface profile information to be displayed on a display device such as a display, and the user can check not only the unevenness of the road surface but also the road surface condition (dry road surface, icy and snowy road surface, slush road surface, etc.). Note that the road surface condition information may be mapped and output on map information including the road to be evaluated as shown in FIG. 5 so that the user can visually recognize the road surface condition. More specifically, the road surface profile information may be mapped onto the map information and output so that the road to be evaluated is displayed for each specified section along the direction of travel in a color corresponding to the determined road surface condition (e.g., dry road surface: white, snow-covered road surface: red, wet road surface: light blue, slush road surface: yellow).

[0049] According to the embodiment of the present invention, the following advantageous effects can be obtained. (1) The road surface evaluation device 10 includes an information acquisition unit 111 that acquires position information indicating the traveling position of each of a plurality of vehicles 20 traveling on a road, vehicle speed information indicating the traveling speed, wheel speed information indicating the rotation speed of the wheels, and steering angle information indicating the steering angle value, which correspond to the traveling position, of each of the vehicles 20 traveling on the road, a calculation unit 112 that calculates a slip ratio of the wheels at the traveling position based on the vehicle speed information and the wheel speed information acquired by the information acquisition unit 111, a determination unit 113 that determines whether the road is a dry road surface or a non-dry road surface based on the slip ratio calculated by the calculation unit 112, and an output unit 114 that outputs road surface information including the determination result of the determination unit 113. The calculation unit 112 further calculates the complexity of the steering operation based on the steering angle information acquired by the information acquisition unit 111. When the determination unit 113 determines that the road is a non-dry road surface, it further determines whether the road surface is sherbet-like based on the complexity of the steering operation calculated by the calculation unit 112. The calculation unit 112 predicts a steering angle value at a predetermined point based on steering angle information corresponding to each of a plurality of reference points (second driving positions) located before the predetermined point (first driving position) in the traveling direction, which is acquired by the information acquisition unit 111, and calculates a prediction error, which is a difference between the predicted steering angle value at the predetermined point and the steering angle value corresponding to the predetermined point indicated by the steering angle information acquired by the information acquisition unit 111, as a degree of complexity of steering operation at the predetermined point. This makes it possible to monitor the road surface condition with high accuracy. In addition, the user can be made to appropriately recognize the road surface condition. As a result, it becomes possible to promptly take measures such as snow removal. In addition, the user can instruct a response such as the dispatch of a snowplow to an appropriate road by the above configuration, thereby suppressing the unnecessary dispatch of a snowplow and improving the fuel efficiency of the vehicle. As a result, carbon dioxide emission is reduced, and climate change can be mitigated.

[0050] (2) The determining unit 113 determines that the road is a non-dry road surface when the peak value of the histogram showing the distribution of the slip ratio on the road calculated by the calculating unit 112 is equal to or greater than a predetermined value (first predetermined value). This makes it possible to more accurately determine whether the road is a non-dry road surface.

[0051] (3) The determination unit 113 determines that the road surface is slush-like when the frequency of peak values ​​of a histogram showing the distribution of the magnitude (absolute value) of the prediction error on the road calculated by the calculation unit 112 is equal to or less than a predetermined value (second predetermined value). This makes it possible to accurately determine whether the road surface is slush-like. As a result, it becomes possible to take measures such as removing snow before the road surface freezes, and the occurrence of slipping on the road surface can be suppressed.

[0052] (4) The information acquisition unit 111 further acquires acceleration information indicating the acceleration of each of the multiple vehicles 20 corresponding to the traveling position. The calculation unit 112 further calculates a roughness value indicating the roughness of the road surface based on the acceleration information. When the determination unit 113 determines that the road surface is a non-dry road surface and not slushy, and the statistical value of the roughness values ​​calculated by the calculation unit 112 is equal to or greater than a predetermined threshold value, the determination unit 113 determines that the road surface is in a snowy or icy state. On the other hand, when the determination unit 113 determines that the road surface is a non-dry road surface and not slushy, and the statistical value of the roughness values ​​calculated by the calculation unit 112 is less than the predetermined threshold value, the determination unit 113 determines that the road surface is in a wet state. This allows the type of non-dry road surface to be determined in more detail.

[0053] The above embodiment can be modified in various ways. Modifications will be described below. In the above embodiment, when the evaluation target section is a non-dry road surface, the determination unit 113 determines whether the non-dry road surface is a slush road surface based on the degree of complexity of the steering operation calculated by the calculation unit 112. However, the determination unit may determine whether the non-dry road surface is a slush road surface based on the distribution of the slip ratio. FIG. 13 is a diagram showing another example of a histogram showing the distribution of the slip ratio in the evaluation target section. FIG. 13 shows the distribution of the slip ratio calculated based on the driving information acquired from the vehicle 20 traveling in the evaluation target section in a non-dry state. The characteristic P31 shows the distribution of the slip ratio calculated based on the driving information acquired when the road surface of the evaluation target section is in a wet state due to rain or the like. The characteristic P32 shows the distribution of the slip ratio calculated based on the driving information acquired when the road surface of the evaluation target section is in a snow-covered state. The characteristic P33 shows the distribution of slip ratios calculated based on the travel information acquired when the snow on the road surface of the evaluation target section melts and the road surface becomes slushy. As shown in FIG. 13, the height of the peak (frequency at the peak value) of the histogram of the characteristic P33 (slush road surface) is lower than the height of the peak of the histogram of the characteristic P31 (wet road surface) and the characteristic P32 (snow-covered road surface). Furthermore, the spread of the base of the histogram of the characteristic P33 (difference between the minimum and maximum values ​​of the slip ratio) is larger than the histograms of the characteristic P31 and the characteristic P32. Therefore, the determination unit 113 may determine that the road to be evaluated is a slush road surface when the peak value of the histogram of the slip ratio of the evaluation target section is equal to or greater than a predetermined value (third predetermined value) and the spread of the base of the histogram is equal to or greater than a predetermined value (fourth predetermined value). 13, the histogram of the characteristic P33 has a distribution entirely on the right side compared to the histograms of the characteristic P31 and the characteristic P32. Therefore, the determining unit 113 may include in the conditions for the above determination whether or not the maximum value of the slip ratio on the road to be evaluated is equal to or greater than a predetermined value (a fifth predetermined value).

[0054] Also, in the above embodiment, the storage device 40 stores the travel information of the multiple vehicles 20, and the information acquisition unit 111 as a travel information acquisition unit acquires the travel information of the multiple vehicles 20 corresponding to the evaluation target period and the evaluation target road from the storage device 40 via the communication unit 13. However, the travel information of the multiple vehicles 20 may be stored in the storage unit 120 of the road surface evaluation device 10. In that case, the information acquisition unit reads out and acquires the travel information of the multiple vehicles 20 corresponding to the evaluation target period and the evaluation target road from the storage unit 120.

[0055] Furthermore, in the above embodiment, the information acquisition unit 111 acquires the map information including the information of the roads on which the vehicle 20 travels from the storage unit 120. However, the information acquisition unit may acquire the map information including the information of the roads on which the vehicle 20 travels from an external server device or the like.

[0056] The above description is merely an example, and the present invention is not limited to the above-mentioned embodiment and modifications, as long as the features of the present invention are not impaired. One or more of the above-mentioned embodiment and modifications can be arbitrarily combined, and modifications can also be combined with each other. [Explanation of symbols]

[0057] 10 Road surface evaluation device, 20, 20-1 to 20-n Vehicle, 30 Vehicle-mounted terminal, 40 Travel information storage device, 110 Calculation unit, 111 Information acquisition unit, 112 Calculation unit, 113 Determination unit, 114 Output unit, 120 Storage unit

Claims

1. a travel information acquisition unit that acquires position information indicating a travel position of each of a plurality of vehicles traveling on a road, vehicle speed information indicating a travel speed, wheel speed information indicating a rotation speed of the wheels, and steering angle information indicating a steering angle value, each of which corresponds to the travel position; a calculation unit that calculates a slip ratio of the wheels at the traveling position based on the vehicle speed information and the wheel speed information acquired by the traveling information acquisition unit; a determination unit that determines whether the road surface is a dry road surface or a non-dry road surface based on the slip ratio calculated by the calculation unit; an output unit that outputs road surface information including a determination result of the determination unit; The calculation unit further calculates a degree of complexity of a steering operation based on the steering angle information acquired by the traveling information acquisition unit, A road surface evaluation device characterized in that when the judgment unit determines that the road is a non-dry road surface, it further judges whether the road surface is slush-like or not based on the complexity of the steering operation calculated by the calculation unit.

2. The road surface evaluation device according to claim 1, The road surface evaluation device is characterized in that the judgment unit judges that the road is a non-dry road surface when a peak value of a histogram showing the distribution of the slip ratio on the road calculated by the calculation unit is equal to or greater than a predetermined value.

3. The road surface evaluation device according to claim 1, The calculation unit predicts the steering angle value of the first driving position based on the steering angle information corresponding to each of a plurality of second driving positions that are located earlier in the direction of travel than the first driving position and that are acquired by the driving information acquisition unit, and calculates a prediction error, which is the difference between the predicted steering angle value of the first driving position and the steering angle value corresponding to the first driving position indicated by the steering angle information acquired by the driving information acquisition unit, as the complexity of the steering operation at the first driving position.

4. 4. The road surface evaluation device according to claim 3, the predetermined value is a first predetermined value, A road surface evaluation device characterized in that the judgment unit judges that the road surface is sorbet-like when the frequency of peak values ​​of a histogram showing the distribution of the absolute values ​​of the prediction errors calculated by the calculation unit on the road is less than a second predetermined value.

5. The road surface evaluation device according to any one of claims 1 to 4, The travel information acquisition unit further acquires acceleration information indicating the acceleration of each of the plurality of vehicles corresponding to the travel position, The calculation unit further calculates a roughness value indicating roughness of the road surface of the road based on the acceleration information, A road surface evaluation device characterized in that when the judgment unit determines that the road surface is a non-dry road surface and not slush-like, and the statistical value of the roughness value calculated by the calculation unit is equal to or greater than a predetermined threshold value, the judgment unit further judges that the road surface is in a snow-covered or icy state.

6. The road surface evaluation device according to claim 5, A road surface evaluation device characterized in that when the judgment unit determines that the road is a non-dry road surface and not slush-like, and the statistical value of the roughness value calculated by the calculation unit is less than the specified threshold, the judgment unit further judges that the road surface is in a wet state.

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