WO2023189972A1

WO2023189972A1 - Computation device, computation method, and program

Info

Publication number: WO2023189972A1
Application number: PCT/JP2023/011323
Authority: WO
Inventors: 陽支増渕; 伸一 ▲高▼松; 敦俊長谷部; 悠首藤
Original assignee: Ｋｙｂ株式会社
Priority date: 2022-03-31
Filing date: 2023-03-22
Publication date: 2023-10-05
Also published as: JP2023151410A

Abstract

The present invention minutely ascertains the road surface condition on a per-position basis. This computation device includes: a position information acquisition unit that acquires position information regarding a position sensor (10A) installed in a vehicle (10) traveling on a road, said position information having been detected by the position sensor (10A); a relationship information acquisition unit that acquires relationship information indicating the positional relationship between the position of the position sensor (10A) of the vehicle (10) and the position of a vehicle wheel (TR) of the vehicle (10); and a vehicle wheel position calculation unit that, on the basis of the position information regarding the position sensor (10A) and the relationship information, calculates position information regarding the vehicle wheel (TR).

Description

Arithmetic device, arithmetic method and program

The present invention relates to an arithmetic device, an arithmetic method, and a program.

For example, as shown in Patent Document 1, a technique is known that estimates the state of the road surface by detecting the acceleration of a vehicle running on a road and inputting the acceleration data to a learning model. As an index indicating the road surface condition, for example, as shown in Patent Document 2, IRI (International Roughness Index) may be used.

JP2020-86960A Japanese Patent Application Publication No. 2010-66040

When estimating road surface conditions, it is required to understand the road surface conditions at each location in more detail.

The present invention has been made in view of the above, and it is an object of the present invention to provide a calculation device, a calculation method, and a program that allow detailed understanding of road surface conditions at each location.

In order to solve the above-mentioned problems and achieve the objective, a computing device according to the present disclosure acquires position information of a position sensor detected by a position sensor mounted on a vehicle moving on a road. a relationship information acquisition unit that acquires relationship information indicating a positional relationship between the position of the position sensor in the vehicle and the position of a wheel in the vehicle, based on the position information of the position sensor and the relationship information. , a wheel position calculation unit that calculates position information of the wheel;
including.

In order to solve the above-mentioned problems and achieve the objective, a calculation method according to the present disclosure includes the steps of: acquiring position information of a vehicle that is detected by a position sensor mounted on a vehicle moving on a road; acquiring relationship information indicating a positional relationship between the position of the position sensor on the vehicle and the position of a wheel on the vehicle; and calculating position information of the wheel based on the position information and the relationship information. and, including.

In order to solve the above-mentioned problems and achieve the purpose, a program according to the present disclosure includes the steps of acquiring position information of a vehicle that is detected by a position sensor mounted on a vehicle moving on a road; acquiring relationship information indicating a positional relationship between the position of the position sensor and the position of a wheel on the vehicle; and calculating position information of the wheel based on the position information and the relationship information. , cause the computer to execute.

FIG. 1 is a schematic block diagram of a detection system according to this embodiment. FIG. 2 is a schematic diagram of the vehicle. FIG. 3 is a schematic block diagram of the arithmetic device. FIG. 4 is a schematic diagram showing an example of each position on the road. FIG. 5 is a schematic diagram for explaining the calculation of IRI. FIG. 6 is a flowchart illustrating a calculation flow of road surface conditions in non-overlapping sections. FIG. 7 is a schematic block diagram of the arithmetic device according to the second embodiment. FIG. 8 is a schematic diagram showing an example of the positional relationship between the position sensor and the wheels.

Below, preferred embodiments of the present invention will be described in detail based on the drawings. Note that the present invention is not limited to the embodiments described below.

(Detection system)
FIG. 1 is a schematic block diagram of a detection system according to this embodiment. As shown in FIG. 1, the detection system 1 according to the present embodiment includes a vehicle 10, a measurement data acquisition device 12, and a calculation device 14. The detection system 1 uses the calculation device 14 to calculate the road surface condition of the road based on the behavior information. In this embodiment, the road surface condition is an index indicating the degree of unevenness of the road surface. More specifically, in this embodiment, the road surface condition is an index based on IRI (International Roughness Index).

In the detection system 1, the vehicle 10 detects behavior information and position information while traveling on a road, and transmits the detected behavior information and position information to the measurement data acquisition device 12. Behavior information and position information will be described later. The measurement data acquisition device 12 is, for example, a device (computer) managed by a road management entity. The measurement data acquisition device 12 transmits the behavior information and position information transmitted from the vehicle 10 to the calculation device 14. In this way, the calculation device 14 acquires behavior information and position information via the measurement data acquisition device 12, but is not limited thereto. For example, the detection system 1 may not include the measurement data acquisition device 12, and the calculation device 14 may acquire behavior information and position information from the vehicle 10.

(vehicle)
FIG. 2 is a schematic diagram of the vehicle. As shown in FIG. 2, the vehicle 10 includes a position sensor 10A, a behavior sensor 10B, and a measuring device 10C. The position sensor 10A is a sensor that acquires its own position information. The position information of the position sensor 10A is information indicating the earth coordinates of the position sensor 10A. In this embodiment, the position information of the position sensor 10A detected by the position sensor 10A is treated as the position information (earth coordinates) of the vehicle 10. In this embodiment, the position sensor 10A is a module for GNSS (Global Navigation Satellite System). Note that the Z direction in FIG. 2 refers to the upper side in the vertical direction, and FIG. 2 can be said to be a schematic diagram when the vehicle 10 is viewed from above in the vertical direction.

The behavior sensor 10B is a sensor that detects behavior information indicating the behavior of the vehicle 10. The behavior information may be any information that indicates the behavior of the vehicle 10 while traveling on the road. In this embodiment, it is preferable that the behavior sensor 10B detects the acceleration of the vehicle 10 as behavior information. In this case, the behavior sensor 10B is an acceleration sensor that detects acceleration, more preferably an acceleration sensor that detects acceleration in three axes. Further, the behavior information detected by the behavior sensor 10B is not limited to acceleration, and includes, for example, acceleration, image data captured around the vehicle 10, speed of the vehicle 10, angular velocity of the vehicle 10, steering angle of the vehicle 10, It may be at least one of the amount of braking of the vehicle 10, the operation of the wiper of the vehicle 10, and the amount of operation of the suspension of the vehicle 10. Note that the image data around the vehicle 10 changes depending on the movement of the vehicle 10, and therefore can be said to be information indicating the behavior of the vehicle 10. The behavior sensor 10B that detects a captured image around the vehicle 10 is, for example, a camera, the behavior sensor 10B that detects the speed of the vehicle 10 is, for example, a speed sensor, and the behavior sensor 10B that detects the speed of the vehicle 10 is, for example, a three-axis sensor. The behavior sensor 10B, which is a gyro sensor and detects the steering angle of the vehicle 10, is, for example, a steering sensor, and the behavior sensor 10B, which detects the amount of braking of the vehicle 10, is, for example, a brake sensor, and detects the operation of the wiper of the vehicle 10. An example of the behavior sensor 10B is a wiper sensor, and an example of the behavior sensor 10B that detects the amount of operation of the suspension of the vehicle 10 is a suspension sensor.

In this embodiment, the vehicle 10 is equipped with a plurality of behavior sensors 10B. The respective behavior sensors 10B are mounted at different positions in the vehicle 10. In the example of FIG. 2, the behavior sensors 10B include a behavior sensor 10B1 provided on the Z direction side (upward side in the vertical direction) of the left front wheel TR1, and a behavior sensor 10B1 provided on the Z direction side of the right front wheel TR2. A behavior sensor 10B2 provided on the Z direction side of the wheel TR3 which is the left rear wheel, and a behavior sensor 10B4 provided on the Z direction side of the right rear wheel TR4. However, the position where the behavior sensor 10B is provided is arbitrary. Further, the number of behavior sensors 10B is not limited to four, and may be any number. Further, in the example of FIG. 2, the number of wheels TR is four, but the number may be arbitrary, for example, an arbitrary number of two or more. Furthermore, in the example of FIG. 2, the behavior sensors 10B1 to 10B4 detect the same type of behavior information (here, acceleration), but each behavior sensor 10B detects different types of behavior information. It's good. For example, a plurality of behavior sensors 10B (for example, a plurality of acceleration sensors) that detect the same type of behavior information and a behavior sensor 10B (for example, a speed sensor) that detects different behavior information may be provided.

The measuring device 10C is a device that controls the position sensor 10A and the behavior sensor 10B to detect the position information and behavior information of the vehicle 10, and records the detected position information and behavior information. That is, the measuring device 10C functions as a data logger that records position information and behavior information. The measuring device 10C can be said to be a computer, and includes a control section 10C1, a storage section 10C2, and a communication section 10C3. The control unit 10C1 is an arithmetic device, and includes, for example, an arithmetic circuit such as a CPU (Central Processing Unit). The storage unit 10C2 is a memory that stores various information such as calculation contents and programs of the control unit 10C1, position information and behavior information of the vehicle 10, and includes, for example, RAM (Random Access Memory) and ROM (Read Only Memory). The storage device includes at least one of a main storage device such as the above, and a nonvolatile storage device such as a flash memory or a hard disk drive (HDD). Note that the program for the control unit 10C1 stored in the storage unit 10C2 may be stored in a recording medium readable by the measuring device 10C. The communication unit 10C3 is a communication module that communicates with an external device, and is, for example, an antenna.

The control unit 10C1 reads the program stored in the storage unit 10C2 and executes control of the position sensor 10A and the behavior sensor 10B. While the vehicle 10 is traveling on the road, the control unit 10C1 causes the position sensor 10A to detect the position information of the vehicle 10 at predetermined time intervals, and causes the behavior sensor 10B to detect behavior information at predetermined time intervals. Obtain location information and behavior information. That is, the control unit 10C1 causes the position sensor 10A and the behavior sensor 10B to perform detection every time the vehicle 10 travels for a predetermined period of time. The predetermined time here is preferably a fixed time, such as one minute, but the predetermined time is not limited to a fixed time and may be any length. That is, the predetermined time may change each time.

The control unit 10C1 associates the acquired behavior information and position information and stores them in the storage unit 10C2. That is, behavior information and position information detected at the same timing are associated. The associated information is stored in the storage unit 10C2 at each detected timing. Note that although these pieces of associated information are detected at the same timing, they are not limited to strictly the same timing, and may be detected at different timings. In this case, for example, behavior information and position information for which the difference in detection timing is less than or equal to a predetermined value are treated as being detected at the same timing and are associated with each other. In addition, although the above description was made on the assumption that the sampling periods of all the sensors are the same, if the sampling periods of each sensor are different, adjustments are made as appropriate.

The control unit 10C1 transmits the associated behavior information and position information to the measurement data acquisition device 12 via the communication unit 10C3. The measurement data acquisition device 12 transmits the behavior information and position information received from the vehicle 10 to the calculation device 14. Note that if the measurement data acquisition device 12 is not provided, the control unit 10C1 may directly transmit the behavior information and position information to the arithmetic device 14.

(computing device)
FIG. 3 is a schematic block diagram of the arithmetic device. As shown in FIG. 3, the arithmetic device 14 is, for example, a computer, and includes a communication section 20, a storage section 22, and a control section 24. The communication unit 20 is a communication module that communicates with an external device, and is, for example, an antenna. The storage unit 22 is a memory that stores calculation contents and programs of the control unit 24, and includes at least one of a RAM, a main storage device such as a ROM, and a non-volatile storage device such as a flash memory or an HDD. Including one. Note that the program for the control unit 24 stored in the storage unit 22 may be stored in a recording medium that can be read by the arithmetic unit 14.

The control unit 24 is an arithmetic device, and includes, for example, an arithmetic circuit such as a CPU. The control unit 24 includes a road surface information acquisition unit 30, an IRI calculation unit 32, a road condition calculation unit 34, a position information acquisition unit 36, a behavior information acquisition unit 38, a learning unit 40, and a calculation unit 42. . The control unit 24 reads the program (software) from the storage unit 22 and executes it, thereby controlling the road surface information acquisition unit 30, the IRI calculation unit 32, the road surface condition calculation unit 34, the position information acquisition unit 36, and the behavior information acquisition unit 38. A learning section 40 and a calculation section 42 are implemented to execute their processing. Note that the control unit 24 may execute these processes using one CPU, or may include a plurality of CPUs and execute the processes using the plurality of CPUs. Further, at least a portion of the road surface information acquisition section 30, the IRI calculation section 32, the road surface condition calculation section 34, the position information acquisition section 36, the behavior information acquisition section 38, the learning section 40, and the calculation section 42 are realized by hardware. It's okay.

(Calculation of road surface conditions for teacher data)
In the present embodiment, the calculation device 14 uses behavior information of the vehicle 10 traveling on a road whose position (height) in the Z direction at each position on the road surface is known and the road surface condition of the road as training data. , Machine learning is performed on a learning model to determine the correspondence between behavior information and road surface conditions. Then, the arithmetic device 14 calculates the road surface condition of the road by inputting the behavior information of the vehicle 10 that has traveled on a road whose position in the Z direction and road surface condition are unknown to the machine-learned learning model. Here, it is conceivable to use IRI as the road surface condition used for the teacher data. However, IRI is calculated as an average value of road surface conditions over a unit distance of 20 m or more. In this way, since the unit distance in IRI is long, the sampling rate becomes long, and if IRI is used as teacher data, there is a risk that the accuracy of the learning model will decrease. In contrast, in the present embodiment, as will be described later, the road surface condition in the non-overlapping section S3 is calculated and used as the road surface condition for teacher data, thereby suppressing a decrease in the accuracy of the learning model.

Hereinafter, a method for calculating the road surface condition in the non-overlapping section S3 will be explained, and then a method for calculating the road surface condition on a road with an unknown road surface condition using a learning model will be explained.

(Road surface information acquisition department)
FIG. 4 is a schematic diagram showing an example of each position on the road. The road surface information acquisition unit 30 acquires road surface height information indicating the position (height) of the road surface in the Z direction. The road surface information acquisition unit 30 acquires road surface height information for each position on the road. It is preferable that the road surface information acquisition unit 30 acquires road surface height information for each position in the extending direction of the road, and also acquires road surface height information for each position in a direction intersecting the extending direction of the road. FIG. 4 shows each position P on the road R, and the road surface information acquisition unit 30 acquires the position of the road surface in the Z direction at each position P as road surface height information. In FIG. 4, as positions P, positions PA1 to PA8 are aligned in the extending direction of the road, positions PB1 to PB8 are aligned in the extending direction of the road, and positions PC1 to PC8 are aligned in the extending direction of the road. It is shown. Positions PA1 to PA8, positions PB1 to PB8, and positions PC1 to PC8 are lined up in a direction intersecting the extending direction of the road. Note that the distance between adjacent positions P, that is, the distance between the positions where road surface height information is acquired, may be any length, but may be shorter than 20 m, which is the unit distance of IRI, for example, 0.1 m. It may be.

The road surface information acquisition unit 30 may acquire road surface height information for each position P using any method. For example, the road surface height information may be set (measured) in advance, and the road surface information acquisition unit 30 acquires information on the position of the road surface measured in advance in the Z direction and information on the measurement position of the road surface height information (for example, (Earth coordinates of the measurement position) may be acquired as the road surface height information. Alternatively, for example, the road surface height information may be measured by a measurement vehicle using LIDAR (Light Detection and Ranging), and the road surface information acquisition unit 30 may obtain the measurement results.

(IRI calculation department)
FIG. 5 is a schematic diagram for explaining the calculation of IRI. The IRI calculation unit 32 calculates the IRI in the first section and the IRI in the second section on the road R based on the road surface height information for each position P.

The IRI calculation unit 32 calculates the IRI using a known method. That is, the IRI calculation unit 32 calculates the IRI by executing a simulation in which a one-wheeled vehicle model travels at 80 km/h in the section for which the IRI is to be calculated. Specifically, the IRI calculation unit 32 sets the position (height) in the Z direction of each position in the section for which IRI is to be calculated from the road surface height information for each position P. Then, the IRI calculation unit 32 executes an analysis in which a one-wheeled vehicle model travels at a speed of 80 km/h in a section with a set height, and calculates the unsprung Z-direction position of the wheel suspension for each unit time, The position on the spring in the Z direction is calculated. The IRI calculation unit 32 calculates the IRI in that section from the difference between the unsprung Z-direction position of the wheel suspension and the sprung Z-direction position. Specifically, the IRI calculation unit 32 calculates the IRI using the following equation (1).

In equation (1), L is the length (distance) of the section, v is the speed of the vehicle model, Z _S is the position on the spring in the Z direction, and Z _u is the Z It is the position of the direction. That is, the IRI calculation unit 32 sums up the difference between the Z-direction position on the spring and the Z-direction position on the unsprung part for each position in the section, and divides the total value by the distance of the section. Calculated as IRI.

In the present embodiment, the IRI calculation unit 32 calculates the IRI in the first section on the road R based on the road surface height information for each position P. In this case, as shown in FIG. 5, the IRI calculation unit 32 sets a starting point position (starting point) PS1a and an ending point position (goal point) PS1b on the road R. The IRI calculation unit 32 sets the starting point position PS1a to an arbitrary position (earth coordinates) on the road R, and sets a position separated by the first unit distance from the starting point position PS1a to the ending point position PS1b. The first unit distance is longer than the unit distance in IRI, in other words, it is 20 m or more. The IRI calculation unit 32 sets the section from the start point position PS1a set in this way to the end point position PS1b as the first section S1. Then, the IRI calculation unit 32 sets the position (height) in the Z direction at each position on the first section S1 based on the road surface height information for each position P, and uses the method described above to set the position (height) in the Z direction at each position on the first section S1. Calculate the IRI of S1. Note that the IRI calculation unit 32 sets the starting point position PS1a and the ending point position PS1b so that they overlap with any of the positions P indicated by the road surface height information, and determines the Z-direction position of each position P that overlaps with the first section S1. is the position in the Z direction at each position on the first section S1. However, the present invention is not limited thereto, and the IRI calculation unit 32 may set at least one of the starting point position PS1a and the ending point position PS1b so as to be shifted from the position P indicated by the road surface height information.

Furthermore, the IRI calculation unit 32 calculates the IRI in the second section S2 on the road R based on the road surface height information for each position P. The IRI calculation unit 32 sets the second section so that it overlaps with the first section S1 and is longer than the first section S1 by a predetermined distance W. The predetermined distance W here may be set to any length, but is preferably less than the unit distance (20 m) in IRI, and the distance between adjacent positions P (the position where road surface height information is acquired) is preferably less than the unit distance (20 m) in IRI. It is more preferable that the distance between the two is the same. In the present embodiment, the second section S2 is longer than the first section S1 (first unit distance) and less than twice as long as the first section S1 (first unit distance). Specifically, the IRI calculation unit 32 defines the section from the start point PA1a of the first section S1 to the end point PS2b, which is a predetermined distance W from the end point PS1b, passing through the end point PS1b of the first section S1, as the second section S2. Set. That is, the second section S2 overlaps with the first section S1 from the start point PA1a to the end point PS1b, and does not overlap with the first section S1 from the end point PS1b to the end point PS2b. Hereinafter, the section of the second section S2 that does not overlap with the first section S1 (the section from the end point PS1b to the end point PS2b) will be referred to as a non-overlapping section S3. The IRI calculation unit 32 sets the position (height) in the Z direction at each position on the second section S2 based on the road surface height information for each position P, and calculates the height of the second section S2 by the above method. Calculate IRI.

The IRI calculation unit 32 changes the positions of the first section S1 and the second section S2, and calculates the IRI in the first section S1 and the IRI in the second section S2 for each position of the first section S1 and the second section S2. Calculated as follows. For example, the first section S1 for which the IRI has been calculated is defined as the first section S1A, and the first section S1 and the second section S2 for which the IRI is calculated are defined as the first section S1B and the second section S2B. In this case, the IRI calculation unit 32 sets the starting point PS1a of the first section S1B from which the IRI is to be calculated at a different position from the starting point PS1a of the first section S1A. Then, the IRI calculation unit 32 sets the first section S1B by setting a position separated by the first unit distance from the starting point PS1a of the first section S1B as the end point PS1b of the first section S1B. The IRI calculation unit 32 calculates the IRI of this first section S1B using the same method as described above. In this case, it is preferable that the first sections S1 having different positions have the same length. In other words, the distance (first unit distance) from the starting point PS1a to the ending point PS1b of each first section S1 is as follows: Preferably they are the same.

Furthermore, the IRI calculation unit 32 sets the second section S2B so that it overlaps with the first section S1B and is longer than the first section S1B by a predetermined distance W. That is, the IRI calculation unit 32 converts the section from the starting point PS1a of the first section S1B, passing through the ending point PS1b of the first section S1B, to the ending point PS2b, which is a predetermined distance W away from the ending point PS1b, as the second section. Set as S2B. The IRI calculation unit 32 calculates the IRI of this second section S2B using the same method as described above. In this case, it is preferable that the second sections S2 having different positions have the same length. In other words, it is preferable that the distances from the starting point PS1a to the ending point PS2b of the respective second sections S2 are the same. . That is, it is preferable that the predetermined distance W (length of the non-overlapping section S3) of each second section S2 is the same.

The above-described calculation of IRI for each position in the first section S1 and the second section S2 will be explained using FIG. 4 as an example. For example, in FIG. 4, a first section S1A starting from position PA1 and ending at position PA4, and a second section S2A starting from position PA1, passing through position PA4, and ending at position PA5 are set. Suppose that In this case, the IRI calculation unit 32 calculates the IRI of the first section S1A from the position PA1 to the position PA4, and the IRI of the second section S2A from the position PA1 to the position PA5. In addition, the IRI calculation unit 32 further sets a first section S1B in which the starting point is at the position PA2 and an ending point in the position PA5, and a second section S2B in which the starting point is at the position PA2 and the ending point is at the position PA6. , the IRI of the first section S1B from position PA2 to position PA5 and the IRI of the second section S2B from position PA2 to position PA6 are calculated. In this way, the IRI calculation unit 32 makes the positions of the first section S1 and the second section S2 different by shifting the starting point position and the ending point position while keeping the lengths of the first section S1 and the second section S2 constant. In addition, the IRI of the first section S1 and the second section S2 at each position is calculated. In addition, in the above explanation, an example is given in which two sets of IRIs are calculated for the first section S1 and the second section S2 at different positions, but the number of sets is not limited to two, but may be any plurality of three or more. It's fine.

(Road surface condition calculation unit)
The road surface condition calculation unit 34 calculates the road surface condition in a non-overlapping section S3 that does not overlap with the first section S1 in the second section S2, based on the IRI of the first section S1 and the IRI of the second section. In this embodiment, the road surface condition calculation unit 34 calculates the road surface condition of the non-overlapping section S3 based on the difference between the IRI of the second section and the IRI of the first section S1. Furthermore, it is preferable that the road surface condition calculation unit 34 calculates the road surface condition IRI _S3 of the non-overlapping section S3 using the following equation (2).

IRI _S3 = (IRIS ₂ × L _S2 - IRI _S1 × L _S1 )/L _S2 (2)

Here, IRI _S1 is the IRI of the first section S1, L _S1 is the length of the first section, IRI _S2 is the IRI of the second section S2, and L _S2 is the IRI of the second section S1. It is the length.

In this way, in this embodiment, by taking the difference between the IRI of the second section and the IRI of the first section S1, it is possible to determine the non-overlapping between the end point PS1b of the first section S1 and the end point PS2b of the second section S2. The road surface condition of section S3 is calculated. The road surface condition of the non-overlapping section S3 can be said to be a value corresponding to the IRI at the length (predetermined distance W) of the non-overlapping section S3.

The road surface condition calculation unit 34 calculates the road surface condition of the non-overlapping section S3 for each of the first section S1 and the second section S2, which are located at different positions. That is, for each set of the first section S1 and the second section S2 having different positions, the position of the non-overlapping section S3 will also be different, so the road surface condition calculation unit 34 By executing calculations similar to those described above, the road surface condition for each non-overlapping section S3 at a different position is calculated. That is, in the example of FIG. 4, the road surface condition calculation unit 34 calculates the IRI from position PA4 to position PA4 based on the IRI of first section S1 from position PA1 to position PA4 and the IRI of second section S2 from position PA1 to position PA5. The road surface condition of the non-overlapping section S3 up to the position PA5 is calculated. Furthermore, the road surface condition calculation unit 34 calculates the non-standard area from the position PA5 to the position PA6 based on the IRI of the first section S1 from the position PA2 to the position PA5 and the IRI of the second section S2 from the position PA2 to the position PA6. The road surface condition of the overlapping section S3 is calculated. That is, it can be said that the road surface condition calculation unit 34 calculates the road surface condition for each position on the road R for each distance between adjacent positions P (every predetermined distance W).

The processing flow for calculating the road surface condition of the non-overlapping section described above will be explained. FIG. 6 is a flowchart illustrating a calculation flow of road surface conditions in non-overlapping sections. As shown in FIG. 6, the calculation device 14 uses the road surface information acquisition unit 30 to acquire road surface height information indicating the height of each road surface position of the road R (step S10), and the IRI calculation unit 32 acquires road surface height information indicating the height of each road surface position of the road R. The IRI of the first section and the IRI of the second section are calculated based on the IRI of the first section and the IRI of the second section (step S12). The road surface condition (IRI) of section S3 is calculated (step S14). After that, if road surface height information at other road surface positions remains (step S16; No), that is, the calculation process of the road surface condition of the non-overlapping section S3 using the road surface height information at all road surface positions is performed. If it has not been completed, shift the starting point position (step S18), return to step S12, calculate the IRI of the first section S1 and the second section S2 at a different position, and calculate the non-overlapping section S3 for each road surface position. Calculate the road surface condition. On the other hand, if there is no road surface height information remaining at other road surface positions (step S16; Yes), that is, the calculation process of the road surface condition of the non-overlapping section S3 using the road surface height information at all road surface positions is performed. When the process is finished, this process is finished. Note that in this flow, the process of calculating the IRI of the first section S1 and the second section S2 and the process of calculating the non-overlapping section S3 are repeated, but the process is not limited to repeating the processes in this order. For example, after calculating the IRI of the first section and the IRI of the second section at each position, the road surface condition of the non-overlapping section S3 at each position may be calculated.

As explained above, the road surface condition calculation unit 34 calculates the road surface condition of the non-overlapping section S3 whose length is the predetermined distance W for each position on the road R. Therefore, it is possible to calculate the road surface condition for each section which is shorter than the IRI, which has a unit distance of 20 m, so that the road surface condition for each position can be grasped in detail.

(Learning process of learning model)
Next, a process of learning a learning model using the road surface condition of the non-overlapping section S3 as teacher data will be described.

(Position information acquisition unit and behavior information acquisition unit)
In the case of learning the learning model, the behavior information and the position information of the vehicle 10 are acquired while the vehicle 10 is traveling on the road R on which the road surface condition of the non-overlapping section S3 has been calculated (the road R with a known height). Let it be detected. The position information acquisition unit 36 of the calculation device 14 acquires the position information of the vehicle 10 detected by the position sensor 10A while traveling on the road R. Further, the behavior information acquisition unit 38 of the arithmetic device 14 acquires behavior information of the vehicle 10 detected by the behavior sensor 10B while traveling on the road R.

(Study Department)
The learning unit 40 causes the learning model to perform machine learning using the behavior information of the vehicle 10 that has moved on the road R and the road surface condition in the non-overlapping section S3 as training data. Specifically, the learning unit 40 associates the behavior information of the vehicle 10 with the road surface condition in the non-overlapping section S3 based on the position information of the vehicle 10 that is associated with the behavior information of the vehicle 10 that has moved on the road R. . That is, for example, the learning unit 40 extracts a non-overlapping section S3 that is within a predetermined distance (preferably a position that overlaps with the position of the vehicle 10) from the position information of the vehicle 10. The learning unit 40 then associates the behavior information associated with the position information of the vehicle 10 with the road surface condition of the extracted non-overlapping section S3. The learning unit 40 sets, as teacher data, a data set in which the behavior information is an input value and the road surface condition of the non-overlapping section S3 associated with the behavior information is an output value, and the teacher data is used as a learning model. input. In this case, the learning unit 40 prepares a plurality of data sets consisting of the behavior information and the road surface condition of the non-overlapping section S3 for each behavior information of the vehicle 10, that is, for each position, and learns each of the plurality of data sets. Preferably input into the model. As a result, the learning model performs machine learning on the correspondence between behavior information and the road surface condition at the position where the behavior information is detected. It becomes a model (program) that can be calculated.

Note that the learning model is a learning model learned by deep learning, and is composed of a model (neural network configuration information) that defines a neural network that constitutes a classifier learned by deep learning, and variables. A learning model can determine the label of input data based on that data. In the example of this embodiment, the learning model is a CNN (Conventional Neural Network) model, but is not limited to the CNN model, and may be any type of learning model.

(Calculation of road surface condition of unknown road)
(calculation section)
The calculation unit 42 of the calculation device 14 uses the learned learning model to calculate the road surface condition of a road whose road surface condition and height are unknown. Specifically, the position information acquisition unit 36 and the behavior information acquisition unit 38 acquire position information and behavior information of the vehicle 10 detected by the vehicle 10 moving on a road with an unknown road surface condition. The calculation unit 42 inputs the acquired behavior information into the trained learning model. In the learning model, behavior information is input as input data and calculations are executed. As a result, the learning model outputs the road surface condition at the position where the behavior information was detected as output data. It can be said that the calculation unit 42 calculates the road surface condition output as output data as the road surface condition of the road. The calculation unit 42 inputs the behavior information for each position indicated by the position information of the vehicle 10 into the learning model, and calculates the road surface condition for each position of the road.

As explained above, in this embodiment, the road surface condition of the road is calculated using a learning model that uses the road surface condition of the non-overlapping section S3 as teacher data. According to this embodiment, by using the road surface condition of the non-overlapping section S3, which can be set for each shorter section, as training data, rather than the IRI whose unit distance is defined as 20 m, the road surface condition at each position can be grasped in detail. This allows the learning model to improve the accuracy of calculating road surface conditions.

However, the use of the road surface condition in the non-overlapping section S3 is not limited to teacher data for a learning model, and may be used for any purpose. For example, the calculation device 14 may output information on the calculated road surface condition of the non-overlapping section S3, may perform arbitrary processing based on the road surface condition of the non-overlapping section S3, or may output information on the calculated road surface condition of the non-overlapping section S3. The road surface condition at S3 may be transmitted to another device, or may be displayed on a display device of the arithmetic device 14 (not shown).

(effect)
As described above, the calculation device 14 according to the present embodiment uses the road surface information acquisition unit 30 that acquires road surface height information indicating the height position of the road surface of the road R in the vertical direction for each road surface position on the road R. , based on the road surface height information for each road surface position, the IRI in the first section S1 of the unit distance (first unit distance) on the road, and the IRI of the unit distance (first unit distance) that overlaps with the first section and ) and an IRI in a second section S2 whose length is less than twice the unit distance (first unit distance), and an IRI in the first section S1 and IRI in the second section S2. a road surface condition calculation unit 34 that calculates a road surface condition (IRI) in a non-overlapping section S3 that does not overlap with the first section S1 in the second section S2 based on the following. According to this embodiment, the road surface condition of the non-overlapping section S3, which is a predetermined distance W, is calculated from the IRI of the first section S1 and the IRI of the second section S2. Furthermore, since the second section S2 is longer than the first section S1, the non-overlapping section S3 can be made shorter than the first section S1, and since the second section S2 is less than twice the first section, the non-overlapping section S3 can be made shorter than the first section S1. It can be said that by making the road surface condition shorter than the first section S1, it is possible to grasp the road surface condition at each position in detail. For example, when the first section S1 (first unit distance) is 20 m, the second section S2 is longer than 20 m and less than 40 m, so the non-overlapping section S3 is longer than 0 m (for example, 0.1 m) and 20 m. (for example, 19 m, etc.), and the road surface condition at each position can be grasped in more detail than in the first section S1. Therefore, according to this embodiment, it is possible to grasp the road surface condition in detail for each position.

The IRI calculation section 32 calculates the IRI in the first section S1 and the IRI in the second section S2 for each position by differentiating the starting point positions of the first section S1 and the second section S2, and calculates the IRI in the first section S1 and the IRI in the second section S2 for each position. calculates the road surface condition (IRI) in the non-overlapping section S3 for each position of the non-overlapping section. According to this embodiment, since the road surface condition of the non-overlapping section S3 is calculated for each position of the road R, the road surface condition for each position can be grasped in detail.

The IRI calculation unit 32 makes the distance of the first section S1 the same for each position, and the distance of the second section S2 for each position the same. According to this embodiment, since the lengths of the first section S1 and the second section S2 are the same, the road surface condition for each position can be calculated with high accuracy.

The IRI calculation unit 32 calculates the IRI in the second section S2, with the section from the start point PS1a of the first section S1 passing through the end point PS1b of the first section S1 to the end point PS2b of the non-overlapping section S3 as the second section S2. do. According to this embodiment, by setting the second section S2 in this way, the road surface condition for each position can be calculated with high accuracy.

The arithmetic device 14 further includes a learning section 40. The learning unit 40 uses the behavior information indicating the behavior of the vehicle 10 that has traveled in the non-overlapping section S3 and the road surface condition in the non-overlapping section S3 as training data, and automatically creates a correspondence relationship between the behavior information and the road surface condition in the learning model. Let them learn. According to the present embodiment, by using the road surface state of the non-overlapping section S3 as the teacher data, it is possible to improve the calculation accuracy of the road surface state by the learning model.

(Second embodiment)
Next, a second embodiment will be described. In the first embodiment, the position information of the position sensor 10A detected by the position sensor 10A is treated as the position information of the vehicle 10. On the other hand, in the second embodiment, the position information of the wheel TR is calculated based on the position information of the position sensor 10A and the relationship information indicating the relative position between the position sensor 10A and the wheel TR, and The position information is treated as position information of the vehicle 10. In the second embodiment, descriptions of parts that have the same configuration as the first embodiment will be omitted.

FIG. 7 is a schematic block diagram of the arithmetic device according to the second embodiment. FIG. 8 is a schematic diagram showing an example of the positional relationship between the position sensor and the wheels. As shown in FIG. 7, the control unit 24 of the arithmetic device 14a according to the second embodiment further includes a relational information acquisition unit 44, a wheel position calculation unit 46, and a traveling direction acquisition unit 48.

In the second embodiment, the relationship information acquisition unit 44 acquires relationship information indicating the relative position between the position sensor 10A and the wheel TR. The related information includes the position of the position sensor 10A in the vehicle 10 (the position of the position sensor 10A in a coordinate system with the vehicle 10 as a reference) and the position of the wheel TR in the vehicle (the position of the wheel TR in the coordinate system with the vehicle 10 as a reference). ) can be said to be information indicating the positional relationship with In this embodiment, the related information acquisition unit 44 acquires the position of the position sensor 10A in the vehicle 10, the width of the vehicle 10 (vehicle width), and the wheelbase of the vehicle 10 (from the center of the front wheels when the vehicle is viewed from the side). (distance to the center of the rear wheel) is acquired as relationship information. The relational information acquisition unit 44 may obtain the relational information using any method. For example, the relationship information may be set (measured) in advance, and the relationship information acquisition unit 44 may acquire the set relationship information.

In the second embodiment, the position information acquisition unit 36 acquires the position information of the position sensor 10A detected by the position sensor 10A while the vehicle is traveling on the road R. The wheel position calculation unit 46 calculates the position information of the wheel TR based on the position information of the position sensor 10A and related information. The position information of the wheel TR is, for example, information indicating the position of the wheel TR in the earth coordinate system. For example, the wheel position calculating unit 46 calculates, as the position of the wheel TR, a position shifted from the position of the position sensor 10A by the relative position of the wheel TR with respect to the position sensor 10A indicated by the related information. In the example of FIG. 8, the wheel position calculation unit 46 determines the position P of the position sensor 10A in the coordinate system of the vehicle 10 based on the position of the position sensor _10A in the vehicle 10, the width of the vehicle 10, and the wheel base. Position _PTR1 (relative position of wheel TR1), position _PTR2 (relative position of wheel _{TR2) of wheel TR2 with respect to position P10A of position sensor 10A} , position PTR3 of wheel _TR3 with respect to position P10A of position sensor _10A ( The relative position of the wheel TR3) and the position P of the wheel TR4 with respect to the position _P10A of the position sensor _10A (the relative position of the wheel TR4) are calculated. Then, the wheel position calculation unit 46 calculates a position that is shifted by the relative position of the wheel TR1 from the position of the position sensor 10A as the position of the wheel TR1, and a position that is shifted by the relative position of the wheel TR2 from the position of the position sensor 10A. , the position of the wheel TR2 is calculated as the position of the wheel TR2, and the position that is shifted by the relative position of the wheel TR3 from the position of the position sensor 10A is calculated as the position of the wheel TR3, and the position that is shifted by the relative position of the wheel TR4 from the position of the position sensor 10A is calculated as the position of the wheel TR3. , is calculated as the position of wheel TR4.

In the present embodiment, the wheel position calculation unit 46 uses, in addition to the position information of the position sensor 10A and the related information, the traveling direction of the vehicle 10 (orientation of the vehicle 10 in the earth coordinate system) acquired by the traveling direction acquisition unit 48. The position information of the wheel TR is calculated based also on the information. That is, the relative position of the position sensor 10A and the wheel TR in the coordinate system of the vehicle 10 is constant regardless of the traveling direction of the vehicle 10, but the position of the wheel TR with respect to the position sensor 10A in the earth coordinate system is Depends on the direction of travel. Therefore, the wheel position calculation unit 46 can calculate the position information of the wheels TR with high accuracy by calculating the position information of the wheels TR also using the traveling direction of the vehicle 10. The traveling direction acquisition unit 48 may acquire the traveling direction of the vehicle 10 using any method, but for example, the steering angle of the vehicle 10 at the timing when the position information of the position sensor 10A is detected, or the position information of the position sensor 10A. Information on the traveling direction detected by the gyro sensor at the timing when the gyro sensor is detected may be used.

However, it is not essential to calculate the position information of the wheels TR using the traveling direction of the vehicle 10. For example, if the position sensor 10A is provided for each wheel TR, the position of the wheel TR with respect to the position sensor 10A in the earth coordinate system is constant, so the traveling direction of the vehicle 10 is not required.

In the second embodiment, the position information of the wheels TR calculated in this way is treated as the position information of the vehicle 10. Specifically, in the second embodiment, when the learning model is made to learn the behavior information and the road surface condition in the non-overlapping section S3 as teacher data, the position information of the wheels TR is used as the position information of the vehicle 10. . That is, in the second embodiment, the learning unit 40 determines the behavior information of the vehicle 10 and the non-overlapping section S3 based on the position information of the wheels TR (position information of the vehicle 10) that is associated with the behavior information of the vehicle 10. and the road surface condition. That is, for example, the learning unit 40 extracts a non-overlapping section S3 that is within a predetermined distance from the position of the wheel TR (preferably at a position that overlaps with the position of the wheel TR). Then, the learning unit 40 associates the behavior information associated with the position of the wheel TR with the extracted road surface condition of the non-overlapping section S3, and creates a data set of teacher data.

The subsequent learning process and the process of calculating the road surface condition using the machine-learned learning model are the same as in the first embodiment, so a detailed explanation will be omitted. In the case of calculation, the wheel position calculation unit 46 calculates the position information of the wheel TR based on the position information of the position sensor 10A detected by the vehicle 10 moving on a road with an unknown road surface condition and related information. do. The calculation unit 42 uses the position information of the wheels TR as the position information of the vehicle 10, and inputs the behavior information when the position information of the wheels TR (position information of the position sensor 10A) is detected into the learning model. The road surface condition at the position of the wheel TR (position of the vehicle 10) when the behavior information is detected is calculated. However, the calculation unit 42 is not limited to calculating the road surface condition by inputting behavior information into a learning model, and may not use a learning model. That is, based on the behavior information when the position information of the wheel TR (position information of the position sensor 10A) is detected, the calculation unit 42 uses any method to determine the position (of the wheel TR) when the behavior information is detected. The road surface condition at the location of the vehicle 10 may also be calculated.

In this way, in the second embodiment, the behavior information and the road surface condition in the non-overlapping section S3 are associated using the position information of the wheels TR. Therefore, it becomes possible to set the position where the behavior information is detected with higher precision, and the correspondence between the behavior information and the road surface condition can be made more precise, and the calculation accuracy of the learning model can be improved. In addition, in the second embodiment, like the first embodiment, the behavior information and the road surface condition in the non-overlapping section S3 are used as the teacher data, but the present invention is not limited to this. Road surface conditions may be used as training data. For example, in the second embodiment, IRI in a 20 m section may be used as training data.

However, the position information of the wheels TR in the second embodiment is not limited to the use of associating the behavior information for teacher data with the road surface condition or the use of calculating the road surface condition at the position where the behavior information is detected, but can be used for any purpose. May be used for. For example, the calculation device 14 may output position information of the wheels TR, may perform arbitrary processing based on the position information of the wheels TR, or may output position information of the wheels TR to another device. It may be transmitted or may be displayed on a display device of the arithmetic device 14 (not shown).

As explained above, the calculation device 14a according to the second embodiment includes the position information acquisition unit 36 that acquires the position information of the position sensor 10A detected by the position sensor 10A mounted on the vehicle 10 moving on the road. , a relationship information acquisition unit 44 that acquires relationship information indicating the positional relationship between the position of the position sensor 10A in the vehicle 10 and the position of the wheel TR in the vehicle 10, and based on the position information and the relationship information of the position sensor 10A, A wheel position calculation unit 46 that calculates position information of the wheel TR is included. According to the present embodiment, by calculating the position information of the wheels TR, it is possible to grasp the position of the vehicle 10 in more detail. Therefore, by using the position information of the wheels TR, the road surface condition for each position can be determined in detail. can be grasped. For example, in this embodiment, similarly to the first embodiment, in order to calculate the road surface condition of the non-overlapping section S3, the road surface condition for each position is calculated in detail (for example, in units of tens of centimeters). Further, according to the present embodiment, the position where the behavior information is detected can be determined in detail (for example, in units of tens of centimeters) based on the position information of the wheel TR. In other words, according to the present embodiment, it is possible to associate behavior information with road surface conditions using the position information of the wheels TR whose positions can be grasped in detail, so that the road surface conditions for each position can be calculated in detail and with high precision. It becomes possible to do so.

Furthermore, the wheel position calculation unit 46 calculates position information of the wheels TR based also on the traveling direction of the vehicle 10. Thereby, the position of the wheel TR can be calculated with higher accuracy.

Additionally, the arithmetic device 14a includes a learning section 40. The learning unit 40 associates the behavior information with the road surface condition of the road R based on the position information of the wheels TR, and uses the correlated behavior information and road surface condition as training data to add the behavior information and the road surface condition to the learning model. machine learning the correspondence relationship. Thereby, it is possible to make the correspondence between the behavior information and the road surface condition more accurate, and improve the calculation accuracy of the learning model.

Furthermore, the calculation device 14a includes a behavior information acquisition section 38 and a calculation section 42. The behavior information acquisition unit 38 acquires behavior information indicating the behavior of the vehicle 10 moving on the road. The calculation unit 42 calculates the road surface condition of the road at the position indicated by the position information of the wheel TR based on the behavior information. More preferably, the calculation unit 42 calculates the road surface condition of the road at the position indicated by the position information of the wheel TR by inputting the acquired behavior information into a learning model that performs machine learning on the correspondence between the behavior information and the road surface condition. Calculate. In this manner, in the second embodiment, the positions of the wheels TR calculated by the wheel position calculation unit 46 are used when calculating the road surface condition. That is, in the second embodiment, by treating the position where behavior information is detected as the position of the wheel TR and calculating the road surface condition at the position of the wheel TR, it is possible to calculate the road surface condition for each position with high accuracy. It becomes possible.

Although the embodiments and examples of the present invention have been described above, the embodiments are not limited by the contents of these embodiments. Furthermore, the above-mentioned components include those that can be easily assumed by those skilled in the art, those that are substantially the same, and those that are in a so-called equivalent range. Furthermore, the aforementioned components can be combined as appropriate. Furthermore, various omissions, substitutions, or changes of the constituent elements can be made without departing from the gist of the embodiments described above.

1 Detection System 10 Vehicle

10A Position Sensor

10B Behavior Sensor 14 Arithmetic Unit 30 Road Surface Information Acquisition Unit 32 IRI Calculation Unit 34 Road Condition Calculation Unit 36 Position Information Acquisition Unit 38 Behavior Information Acquisition Unit 40 Learning Unit 42 Calculation Unit 44 Related Information Acquisition Unit 46 Wheel position calculation unit S1 First section S2 Second section S3 Non-overlapping section TR Wheel

Claims

a position information acquisition unit that acquires position information of the position sensor detected by a position sensor mounted on a vehicle moving on a road;
a relationship information acquisition unit that acquires relationship information indicating a positional relationship between the position of the position sensor in the vehicle and the position of a wheel in the vehicle;
a wheel position calculation unit that calculates position information of the wheel based on the position information of the position sensor and the relationship information;
including,
Computing device.
The arithmetic device according to claim 1, wherein the wheel position calculation unit calculates position information of the wheels based on a traveling direction of the vehicle.
a behavior information acquisition unit that acquires behavior information indicating the behavior of a vehicle moving on the road;
The behavior information and the road surface condition of the road are associated with each other based on the wheel position information, and the behavior information and the road surface condition are added to a learning model using the correlated behavior information and the road surface condition as training data. 3. The arithmetic device according to claim 1, further comprising a learning unit that performs machine learning on the correspondence relationship between the two.
a behavior information acquisition unit that acquires behavior information indicating the behavior of a vehicle moving on the road;
a calculation unit that calculates a road surface condition of the road at a position indicated by the wheel position information by inputting the behavior information into a learning model that machine-learns a correspondence relationship between the behavior information and the road surface condition of the road; The arithmetic device according to claim 1, further comprising: .
a behavior information acquisition unit that acquires behavior information indicating the behavior of a vehicle moving on the road;
The computing device according to claim 1, further comprising: a computing unit that computes a road surface condition of the road at a position indicated by the wheel position information based on the behavior information.
obtaining position information of the vehicle detected by a position sensor mounted on the vehicle moving on the road;
acquiring relationship information indicating a positional relationship between the position of the position sensor in the vehicle and the position of a wheel in the vehicle;
calculating position information of the wheel based on the position information and the relationship information;
including,
Calculation method.
obtaining position information of the vehicle detected by a position sensor mounted on the vehicle moving on the road;
acquiring relationship information indicating a positional relationship between the position of the position sensor in the vehicle and the position of a wheel in the vehicle;
calculating position information of the wheel based on the position information and the relationship information;
make the computer execute
program.