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

Abstract

To prevent the decline in road surface condition estimation accuracy even when the motion of the vehicle's sprung weight portion is regulated by a motion control device.SOLUTION: A road surface condition estimation device includes: a data acquisition unit for acquiring driving state data representing the driving state of a vehicle and operation data representing physical parameters associated with the operation of a motion control device, which is installed in the vehicle and either manages or affects the movement of the vehicle's sprung weight portion with respect to the road surface; and a road surface condition estimation unit for estimating the condition of the road surface based on the driving state data and the operation data.SELECTED DRAWING: Figure 4

Description

[Technical field]

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

[0002] Conventionally, there are techniques for estimating the road surface condition on which a vehicle is traveling. For example, a technique is known in which the vertical acceleration, roll angle, or pitch angle of a vehicle is estimated using the detection results of a vertical acceleration sensor, the detection results of a vehicle speed sensor, and the transfer characteristics of a shock absorber according to the vehicle speed, and the road surface condition is estimated using the estimated values ​​(for example, see Patent Document 1). In this technique, a vertical acceleration sensor is fixed to the vehicle body in the sprung portion.

[0003] There is also a technique for improving the ride comfort of a vehicle by controlling the motion (posture) of the vehicle's sprung mass using a motion control device in accordance with road conditions. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2008-183971 A Summary of the Invention [Problem to be solved by the invention]

[0005] In a vehicle in which the motion of the sprung mass is controlled by a motion control device in response to road surface conditions as described above, the following problem occurs when estimating road surface conditions using a vertical acceleration sensor or the like provided in the sprung mass. That is, since the vertical acceleration sensor is fixed to the sprung mass, when the motion control device operates to reduce the vertical acceleration of the sprung mass, the vertical acceleration sensor detects the reduced vertical acceleration. Therefore, if the road surface condition is estimated based on the output value of the vertical acceleration, the accuracy of the estimation of the road surface condition decreases.

[0006] Therefore, one of the objectives of the present disclosure is to provide a road surface condition estimation device, a road surface condition estimation method, and a program that can suppress a decrease in the accuracy of estimating road surface conditions even when the movement of the sprung part of a vehicle is controlled by a motion control device. [Means for solving the problem]

[0007] A road surface condition estimation device as an example of the present disclosure includes a data acquisition unit that acquires driving condition data indicating the driving condition of a vehicle and operation data indicating physical quantities related to the operation of a motion control device that is mounted on the vehicle and controls or affects the movement of the sprung part of the vehicle relative to the road surface, and a road surface condition estimation unit that estimates a road surface condition, which is the state of the road surface, based on the driving condition data and the operation data.

[0008] According to such a road surface condition estimation device, the road surface condition estimation unit estimates the road surface condition, which is the state of the road surface, based on driving condition data and operation data, so that the accuracy of the estimation of the road surface condition can be prevented from decreasing even when the motion control device controls the movement of the vehicle's sprung part or affects the movement of the sprung part.

[0009] In the road surface condition estimation device, for example, the motion control device is a shock absorber provided in a suspension system and capable of changing the damping force according to an input current, and the physical quantity is at least one of a physical quantity related to the speed of expansion and contraction of the shock absorber and a current value indicating the value of the current input to the shock absorber.

[0010] According to such a road surface condition estimating device, even when the motion of the sprung portion of the vehicle is controlled by the shock absorber, it is possible to suppress a decrease in the accuracy of the estimation of the road surface condition.

[0011] In the road surface condition estimation device, for example, the motion control device is a stabilizer device that can change the amount of torsion depending on the input current, and the physical quantity is at least one of the operating amount of the stabilizer device and a current value indicating the value of the current input to the stabilizer device.

[0012] According to such a road surface condition estimating device, even when the motion of the sprung portion of the vehicle is controlled by the stabilizer device, it is possible to suppress a decrease in the accuracy of the estimation of the road surface condition.

[0013] In the road surface condition estimation device, for example, the motion control device is a steering device capable of changing the steering angle according to an input current, and the physical quantity is at least one of an operating amount of the steering device and a current value indicating the value of the current input to the steering device.

[0014] According to such a road surface condition estimating device, even if the steering device affects the motion of the sprung mass of the vehicle, it is possible to suppress a decrease in the accuracy of the estimation of the road surface condition.

[0015] In the road surface condition estimation device, for example, the motion control device is an electric drive device that can change the driving force applied to the wheels of the vehicle depending on an input current, and the physical quantity is at least one of an operating amount of the electric drive device and a current value indicating the value of the current input to the electric drive device.

[0016] Such a road surface condition estimating device can suppress a decrease in the accuracy of the estimation of the road surface condition even when the electric drive device affects the motion of the sprung portion of the vehicle.

[0017] In the road surface condition estimating device, for example, the road surface condition estimating unit estimates the road surface condition based on the traveling condition data and relationship information indicating a relationship of the road surface condition to the operation data.

[0018] According to such a road surface condition estimating device, it is possible to estimate the road surface condition based on the relationship information indicating the relationship of the road surface condition to the traveling condition data and the operation data.

[0019] In the road surface condition estimating device, for example, the related information is any one or a combination of a neural network, linear discrimination information for performing linear discrimination, and an equation of motion.

[0020] Such a road surface condition estimating device can estimate the road surface condition based on one or a combination of a neural network, linear discrimination information for performing linear discrimination, and an equation of motion.

[0021] In the road surface condition estimating device, for example, the road surface condition estimating unit determines an attitude change of the vehicle based on the operation data, and estimates the road surface condition based on the traveling state data and the attitude change. Such a road surface condition estimating device can estimate the road surface condition based on the driving condition data and the attitude variation of the vehicle.

[0022] In the road surface condition estimating device, for example, the road surface condition is roughness of the road surface.

[0023] With such a road surface condition estimation device, it is possible to prevent a decrease in the accuracy of estimating the roughness of the road surface even when the motion control device controls the motion of the vehicle's sprung mass or affects the motion of the sprung mass.

[0024] In the road surface condition estimating device, for example, the road surface condition is a friction coefficient of the road surface.

[0025] With such a road surface condition estimation device, it is possible to prevent a decrease in the accuracy of estimating the friction coefficient of the road surface even when the motion control device controls the motion of the vehicle's sprung mass or affects the motion of the sprung mass.

[0026] In the road surface condition estimating device, for example, the road surface condition is an inclination state of the road surface.

[0027] With such a road surface condition estimation device, it is possible to prevent a decrease in the accuracy of estimating the road surface inclination condition even when the motion control device controls the movement of the vehicle's sprung mass or affects the movement of the sprung mass.

[0028] In a road surface condition estimation method as an example of the present disclosure, a data acquisition unit acquires driving condition data indicating the driving condition of a vehicle and operation data indicating physical quantities related to the operation of a motion control device mounted on the vehicle and controlling or influencing the motion of the sprung part of the vehicle relative to the road surface, and a road surface condition estimation unit estimates a road surface condition, which is the state of the road surface, based on the driving condition data and the operation data.

[0029] According to this road surface condition estimation method, the road surface condition estimation unit estimates the road surface condition, which is the state of the road surface, based on driving condition data and operation data, so that the accuracy of the road surface condition estimation can be prevented from decreasing even when the motion control device controls the movement of the vehicle's sprung part or affects the movement of the sprung part.

[0030] A program as an example of the present disclosure causes a computer mounted on a vehicle to function as a data acquisition unit that acquires driving condition data indicating the driving condition of the vehicle and operation data indicating physical quantities related to the operation of a motion control device mounted on the vehicle that controls the motion of the sprung part of the vehicle relative to the road surface or affects the motion of the sprung part, and a road surface condition estimation unit that estimates the road surface condition, which is the state of the road surface, based on the driving condition data and the operation data.

[0031] According to such a program, the road surface condition estimation unit estimates the road surface condition, which is the state of the road surface, based on driving condition data and operation data, so that the accuracy of the estimation of the road surface condition can be prevented from decreasing even when the motion control device controls the movement of the vehicle's sprung part or affects the movement of the sprung part. [Brief description of the drawings]

[0032] [Figure 1] FIG. 1 is an exemplary schematic diagram showing a configuration of a vehicle interior of a vehicle according to an embodiment. [Diagram 2] FIG. 2 is an exemplary schematic diagram showing the external appearance of the vehicle according to the embodiment as viewed from above. [Diagram 3] FIG. 3 is an exemplary schematic block diagram showing a system configuration of a vehicle according to an embodiment. [Figure 4] FIG. 4 is an exemplary schematic block diagram showing a functional configuration of the ride comfort control device according to the embodiment. [Diagram 5] FIG. 5 is an exemplary schematic diagram for explaining an example of ride comfort control of a vehicle according to an embodiment. [Figure 6] FIG. 6 is an illustrative schematic diagram showing an example of a road surface pattern according to the embodiment. [Figure 7] FIG. 7 is an exemplary schematic diagram showing an example of the sprung acceleration with and without the motion control according to the embodiment. [Figure 8] FIG. 8 is an illustrative schematic diagram showing an example of the estimation results of the road surface height in the technique according to the embodiment and the technique according to the comparative example. [Figure 9] FIG. 9 is an illustrative schematic diagram showing an example of a road surface pattern according to the embodiment. [Figure 10] FIG. 10 is an exemplary schematic diagram showing an example of the sprung acceleration with and without the motion control according to the embodiment. [Figure 11] FIG. 11 is an illustrative schematic diagram showing an example of the estimation results of the road surface height in the technique according to the embodiment and the technique according to the comparative example. [Figure 12] FIG. 12 is an exemplary schematic diagram for explaining a method for estimating road surface roughness according to the technology according to the embodiment. [Figure 13]FIG. 13 is an exemplary schematic diagram for explaining a method for estimating road surface roughness according to the technology according to the embodiment. [Figure 14] FIG. 14 is a flowchart showing an example of a road surface condition estimation method executed by the ride comfort control device according to the embodiment. [Figure 15] FIG. 15 is a schematic diagram showing an example of the relationship between a portion including a suspension device in a vehicle according to an embodiment and parameters of an equation of motion. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0033] Hereinafter, an embodiment of the present disclosure will be described with reference to the drawings. The configuration of the embodiment described below and the actions and effects brought about by the configuration are merely examples and are not limited to the contents described below.

[0034] <Embodiment> First, a schematic configuration of a vehicle 1 according to an embodiment will be described with reference to FIGS.

[0035] FIG. 1 is an exemplary and schematic diagram showing the configuration inside a vehicle compartment 2a of a vehicle 1 according to an embodiment, and FIG. 2 is an exemplary and schematic diagram showing the exterior of a vehicle 1 according to an embodiment as viewed from above.

[0036] 1, a vehicle 1 according to an embodiment has a cabin 2a in which passengers including a driver as a user ride. A braking operation unit 4, an acceleration operation unit 5, a steering operation unit 6, a gear shift operation unit 7, and the like are provided in the cabin 2a so as to be operable by a user from a seat 2b.

[0037] The braking operation unit 4 is an operation input device that accepts an operation input by the driver to a braking mechanism for generating a braking force in the vehicle 1. The acceleration operation unit 5 is an operation input device that accepts an operation input by the driver to an acceleration mechanism for generating an acceleration force in the vehicle 1. The steering operation unit 6 is an operation input device that accepts an operation input by the driver to a steering mechanism for steering the steered wheels of the vehicle 1. The gear shift operation unit 7 is an operation input device that accepts an operation input by the driver to a gear shift mechanism for switching the gear ratio of the vehicle 1.

[0038] For example, in the example shown in Fig. 1, the braking operation unit 4 is a brake pedal provided under the driver's feet, the acceleration operation unit 5 is an accelerator pedal provided under the driver's feet, the steering operation unit 6 is a steering wheel protruding from the dashboard (instrument panel), and the gear shift operation unit 7 is a shift lever protruding from the center console.

[0039] A monitor device 11 having a display unit 8 capable of outputting various images and an audio output unit 9 capable of outputting various sounds is provided in the vehicle interior 2a. The monitor device 11 is provided, for example, in the center of the width direction (left-right direction) of the dashboard in the vehicle interior 2a. The display unit 8 is configured, for example, with an LCD (liquid crystal display) or an OELD (organic electroluminescence display).

[0040] Here, the display screen as the area where the image is displayed on the display unit 8 is provided with an operation input unit 10. The operation input unit 10 is configured as a touch panel capable of detecting the coordinates of a position where an indicator such as a finger or a stylus approaches (including contacts) the display unit 8. This allows the user (driver) to visually recognize the image displayed on the display screen of the display unit 8, and also allows the user to perform various operation inputs by performing a touch (or tap) operation using the indicator on the operation input unit 10.

[0041] In the embodiment, the operation input unit 10 may be various physical interfaces such as a switch, a dial, a joystick, a push button, etc. In the embodiment, another audio output device may be provided at a position different from the position of the monitor device 11 in the vehicle interior 2a. In this case, various sound information can be output from both the audio output unit 9 and the other audio output device. In the embodiment, the monitor device 11 may be configured to be capable of displaying information on various systems such as a navigation system and an audio system.

[0042] 1 and 2, the vehicle 1 according to the embodiment is configured as a four-wheeled vehicle having two front wheels 3F (left and right) and two rear wheels 3R ​​(left and right). For simplification, the front wheels 3F and the rear wheels 3R ​​may be collectively referred to as the wheels 3 below. In the embodiment, the sideslip angles of some or all of the four wheels 3 change (steer) in response to the operation of the steering unit 303a, etc.

[0043] The vehicle 1 is also equipped with a plurality of on-board cameras 15a to 15d (four in the example shown in FIG. 1 and FIG. 2) as imaging units for periphery monitoring. The on-board camera 15a is provided at the rear end 2e of the vehicle body 2 (for example, below the door 2h of the rear trunk) and images the area behind the vehicle 1. The on-board camera 15b is provided at the door mirror 2g at the right end 2f of the vehicle body 2 and images the area to the right of the vehicle 1. The on-board camera 15c is provided at the front end 2c of the vehicle body 2 (for example, the front bumper) and images the area in front of the vehicle 1. The on-board camera 15d is provided at the door mirror 2g at the left end 2d of the vehicle body 2 and images the area to the left of the vehicle 1. Hereinafter, the on-board cameras 15a to 15d may be simply referred to as on-board cameras 15 when there is no particular need to distinguish between them.

[0044] The vehicle-mounted camera 15 is a so-called digital camera having an imaging element such as a CCD (charge-coupled device) or a CIS (CMOS (complementary metal-oxide semiconductor) image sensor). The vehicle-mounted camera 15 captures images of the surroundings of the vehicle 1 at a predetermined frame rate and outputs image data of the captured images obtained by the imaging. The image data obtained by the vehicle-mounted camera 15 can also be used as frame images to form a moving image.

[0045] The vehicle body 2 is also provided with distance measuring units 16 and 17 that can be used as distance measuring sensors for detecting the distance to objects such as people and objects (including the road surface) present around the vehicle 1. The distance measuring units 16 and 17 are configured as sonar or laser radar that detects the distance to an object based on the transmission and reception of waves. In the example shown in Fig. 2, the distance measuring unit 16 is configured with four distance measuring units 16a to 16d, and the distance measuring unit 17 is configured with eight distance measuring units 17a to 17h.

[0046] The distance measuring unit 16 is provided to detect objects to the side of the vehicle 1, and the distance measuring unit 17 is provided to detect objects in front of and behind the vehicle 1. In addition, the distance measuring unit 16 is configured to detect objects that are relatively far away from the vehicle 1, for example, and the distance measuring unit 17 is configured to detect objects that are relatively close to the vehicle 1, for example.

[0047] Next, a system configuration provided for implementing various controls in the vehicle 1 according to the embodiment will be described with reference to Fig. 3. Note that the system configuration shown in Fig. 3 is merely an example and can be modified in various ways.

[0048] FIG. 3 is an exemplary schematic block diagram showing a system configuration of the vehicle 1 according to the embodiment.

[0049] As shown in Fig. 3, the vehicle 1 according to the embodiment includes a braking system 301, an acceleration system 302, a steering system 303, a gear shift system 304, an obstacle sensor 305, a driving state sensor unit 306, an in-vehicle camera 15, a monitor device 11, a vehicle control device 310, and an in-vehicle network 350. The vehicle 1 further includes a suspension system 401, a rear wheel steering system 402, a stabilizer system 403, an electric drive system 404, and a ride comfort control device 410. The suspension system 401 and the stabilizer system 403 are each an example of a motion control device that controls the motion of the sprung part 1a of the vehicle 1. The rear wheel steering system 402 and the electric drive system 404 are each an example of a motion control device that affects the motion of the sprung part 1a of the vehicle 1.

[0050] The braking system 301 controls the deceleration of the vehicle 1. The braking system 301 includes a braking unit 301a, a braking control unit 301b, and a braking unit sensor 301c.

[0051] The braking unit 301a is, for example, an actuator for driving a braking mechanism of the vehicle 1. The braking unit 301a may operate in a manner to assist the operation of the brake operating unit 4, or may operate separately from the operation of the brake operating unit 4.

[0052] The braking control unit 301b is configured as, for example, a microcomputer having a hardware processor such as a CPU (Central Processing Unit) etc. The braking control unit 301b controls the deceleration rate of the vehicle 1 by operating the braking unit 301a based on, for example, an instruction input via the in-vehicle network 350.

[0053] The braking unit sensor 301c is a sensing device for detecting the state of the braking unit 301a (and the braking operation unit 4). The detection result by the braking unit sensor 301c is output to the in-vehicle network 350 and used by each device on the in-vehicle network 350.

[0054] The acceleration system 302 controls the acceleration of the vehicle 1. The acceleration system 302 includes an acceleration unit 302a, an acceleration control unit 302b, and an acceleration sensor 302c.

[0055] The acceleration unit 302a is, for example, an actuator for driving an acceleration mechanism of the vehicle 1. The braking unit 301a may operate in a manner to assist the operation of the acceleration operation unit 5, or may operate separately from the operation of the acceleration operation unit 5.

[0056] The acceleration control unit 302b is configured as, for example, a microcomputer having a hardware processor such as a CPU. The acceleration control unit 302b controls the acceleration rate of the vehicle 1 by operating the acceleration unit 302a based on, for example, an instruction input via the in-vehicle network 350.

[0057] The acceleration sensor 302c is a sensing device for detecting the state of the acceleration unit 302a (and the acceleration operation unit 5). The detection result by the acceleration sensor 302c is output to the in-vehicle network 350 and used by each device on the in-vehicle network 350.

[0058] The steering system 303 controls the traveling direction of the vehicle 1. The steering system 303 has a steering unit 303a, a steering control unit 303b, and a steering unit sensor 303c.

[0059] The steering unit 303a is, for example, an actuator for driving a steering mechanism of the vehicle 1. The steering unit 303a may operate in a manner to assist the operation of the steering operation unit 6, or may operate separately from the operation of the steering operation unit 6.

[0060] The steering control unit 303b is configured as, for example, a microcomputer having a hardware processor such as a CPU. The steering control unit 303b controls the traveling direction of the vehicle 1 by operating the steering unit 303a based on, for example, an instruction input via the in-vehicle network 350.

[0061] The steering sensor 303c is a sensing device for detecting the state of the steering unit 303a (and the steering operation unit 6). The detection result by the steering sensor 303c is output to the in-vehicle network 350 and used by each device on the in-vehicle network 350.

[0062] The gear shift system 304 controls the gear ratio of the vehicle 1. The gear shift system 304 has a gear shift section 304a, a gear shift control section 304b, and a gear shift section sensor 304c.

[0063] The shifting unit 304a is, for example, an actuator for driving a transmission mechanism of the vehicle 1. The shifting unit 304a may operate in a manner to assist the operation of the shifting operation unit 7, or may operate separately from the operation of the shifting operation unit 7.

[0064] The gear shift control unit 304b is configured as, for example, a computer having a hardware processor such as a CPU. The gear shift control unit 304b controls the gear ratio of the vehicle 1 by operating the gear shift unit 304a based on, for example, an instruction input via the in-vehicle network 350.

[0065] The gear shifting section sensor 304c is a sensing device for detecting the state of the gear shifting section 304a (and the gear shifting operation section 7). The detection result by the gear shifting section sensor 304c is output to the in-vehicle network 350 and can be used by each device on the in-vehicle network 350.

[0066] The suspension system 401 can control the attitude of the vehicle body 2 and the input of force to the vehicle body 2. The suspension system 401 has a suspension device 401d, a suspension control unit 401b, and a current sensor 401c.

[0067] The suspension 401d connects the vehicle body 2 and the wheels 3. In the vehicle 1, the portion of the suspension 401d on the vehicle body 2 side is the sprung portion 1a, and the portion of the suspension 401d on the wheel 3 side is the unsprung portion 1b. The suspension 401d has a shock absorber 401a and a spring (not shown). The shock absorber 401a is capable of electrically controlling (adjusting) the damping force (damping characteristics). Specifically, the shock absorber 401a has an actuator 401aa that operates according to an input current, and an expandable shock absorber body 401ab. The actuator 401aa can change the opening degree of an orifice provided in a piston of the shock absorber body 401ab, and can change the opening degree between a valve body and a valve seat. This controls the amount of lubricating oil flowing between two oil chambers separated by a piston in shock absorber body 401ab, thereby adjusting the damping force of shock absorber 401a. Suspension device 401d is provided for each of four wheels 3 (two front wheels 3F and two rear wheels 3R), and the damping force of each of the four wheels 3 can be controlled independently. Shock absorber 401a is also called a damper.

[0068] The suspension control unit 401b is configured as a computer having a hardware processor such as a CPU, for example. The suspension control unit 401b controls the damping force of the shock absorber 401a by operating the actuator 401aa based on an instruction input via the in-vehicle network 350, for example.

[0069] The current sensor 401c is a sensing device for detecting a current value indicating the value of a current input to the actuator 401aa. The detection result by the current sensor 401c is output to the in-vehicle network 350 and can be used in each device on the in-vehicle network 350. In addition, a physical quantity (speed, acceleration, displacement, etc.) related to the speed of expansion and contraction of the shock absorber main body 401ab can be estimated using the detection result of the acceleration sensor 306B.

[0070] The rear wheel steering system 402 steers the rear wheels 3R. The rear wheel steering system 402 has a rear wheel steering device 402a, a rear wheel steering control unit 402b, a current sensor 402c, and an angle sensor 402d. The rear wheel steering device 402a is an example of a steering device.

[0071] The rear wheel steering device 402a steers the rear wheel 3R in response to an input current. The rear wheel steering device 402a has an actuator 402aa to which a current is input, and a rear wheel steering mechanism 402ab that changes the steering angle of the rear wheel 3R by the driving force of the actuator 402aa. The actuator 402aa is a motor.

[0072] The rear wheel steering control unit 402b is configured as, for example, a computer having a hardware processor such as a CPU, etc. The rear wheel steering control unit 402b controls the steering angle of the rear wheels 3R ​​by operating the actuator 402aa based on, for example, an instruction input via the in-vehicle network 350.

[0073] The current sensor 402c is a sensing device for detecting a current value indicating the value of a current input to the actuator 402aa. The angle sensor 402d is a sensing device for detecting a rotation angle (amount of rotation, amount of control) of the actuator 402aa as an operation of the rear wheel steering device 402a. The detection results by the current sensor 402c and the angle sensor 402d are output to the in-vehicle network 350 and can be used by each device on the in-vehicle network 350.

[0074] The stabilizer system 403 controls the roll generated in the vehicle 1. The stabilizer system 403 includes a stabilizer device 403a, a stabilizer control unit 403b, a current sensor 403c, and an angle sensor 403d. The stabilizer device 403a is provided for each of the front and rear suspension devices 401d of the vehicle 1. The front stabilizer device 403a connects the two front suspension devices 401d, left and right, and the rear stabilizer device 403a connects the two rear suspension devices 401d, left and right. The stabilizer control unit 403b, the current sensor 403c, and the angle sensor 403d are provided for each of the stabilizer devices 403a.

[0075] The stabilizer device 403a can change the amount of twist according to the input current. The stabilizer device 403a is a so-called active stabilizer device. The stabilizer device 403a has an actuator 403aa to which a current is input, and a stabilizer bar 403ab that connects the two suspension devices 401d on the left and right and is twisted by the actuator 403aa. The actuator 403aa is a motor.

[0076] The stabilizer control unit 403b is configured as, for example, a computer having a hardware processor such as a CPU, etc. The stabilizer control unit 403b controls the amount of twist of the stabilizer bar 403ab by operating the actuator 403aa based on, for example, an instruction input via the in-vehicle network 350.

[0077] The current sensor 403c is a sensing device for detecting a current value indicating the value of a current input to the actuator 403aa. The angle sensor 403d is a sensing device for detecting a rotation angle (amount of rotation, amount of control) of the actuator 403aa as an operation of the stabilizer device 403a. The detection results by the current sensor 403c and the angle sensor 403d are output to the in-vehicle network 350 and can be used in each device on the in-vehicle network 350.

[0078] The electric drive system 404 drives the wheels 3. The electric drive system 404 has an electric drive device 404a, an electric drive control unit 404b, a current sensor 404c, and an angle sensor 404d.

[0079] The electric drive device 404a can change the driving force output to the wheels 3 according to the input current. The electric drive device 404a has a motor 404aa to which a current is input, and a reducer 404ab that reduces the rotation of the motor 404aa and transmits it to the wheels 3. The electric drive device 404a is provided integrally with, for example, an axle (not shown).

[0080] The electric drive control unit 404b is configured as a computer having a hardware processor such as a CPU, for example. The electric drive control unit 404b controls the driving force by operating the motor 404aa based on an instruction input via the in-vehicle network 350, for example.

[0081] The current sensor 404c is a sensing device for detecting a current value indicating the value of a current input to the motor 404aa. The angle sensor 404d is a sensing device for detecting a rotation angle (amount of rotation, amount of control) of the motor 404aa as the operation of the electric drive device 404a. The detection results by the current sensor 404c and the angle sensor 404d are output to the in-vehicle network 350 and can be used by each device on the in-vehicle network 350.

[0082] The obstacle sensor 305 is a sensing device for detecting data related to objects (obstacles) that may exist around the vehicle 1. The obstacle sensor 305 includes distance measuring units 16 and 17 as distance measuring sensors that obtain distances to objects existing around the vehicle 1. The obstacle sensor 305 may also include an in-vehicle camera 15 that obtains an image of the situation around the vehicle 1. The detection results by the obstacle sensor 305 are output to the in-vehicle network 350 and can be used in each device on the in-vehicle network 350.

[0083] The running state sensor unit 306 is a device for detecting the running state of the vehicle 1. The running state sensor unit 306 includes, for example, a vehicle speed sensor 306A for detecting the vehicle speed, which is the speed of the vehicle 1, an acceleration sensor 306B for detecting the acceleration in the vertical direction, the front-rear direction, and the left-right direction of the vehicle 1, a wheel speed sensor for detecting the wheel speed of the vehicle 1, and a gyro sensor for detecting the turning speed (angular velocity) of the vehicle 1. The vehicle speed sensor 306A and the acceleration sensor 306B are fixed to, for example, the vehicle body 2. The detection results by the running state sensor unit 306 are output to the in-vehicle network 350 and can be used in each device on the in-vehicle network 350.

[0084] The acceleration sensor 306B is provided corresponding to each of the two front wheels 3F and the two rear wheels 3R. That is, the acceleration sensor 306B is provided on the vehicle body 2 corresponding to each of the four wheels 3 and the suspension device 401d. The acceleration sensor 306B detects the acceleration of the vehicle body 2 in the vehicle 1. In detail, each acceleration sensor 306B is provided at an acceleration detection target location located directly above each wheel 3 (directly above the wheel) on the vehicle body 2. That is, in this embodiment, there are four acceleration detection target locations. In this embodiment, as an example, the acceleration sensor 306B can acquire the acceleration in the up-down direction of the vehicle body 2 (sprung part 1a), the acceleration in the front-rear direction (longitudinal direction) of the vehicle body 2 (sprung part 1a), and the acceleration in the width direction (vehicle width direction, short side direction, left-right direction) of the vehicle body 2 (sprung part 1a).

[0085] The vehicle control device 310 is configured as, for example, an ECU (electronic control unit) that comprehensively controls various systems provided in the vehicle 1 to realize various functions.

[0086] The vehicle control device 310 includes a CPU 310a, a ROM (read only memory) 310b, a RAM (random access memory) 310c, a solid state drive (SSD) 310d, a display control unit 310e, and a sound control unit 310f.

[0087] The CPU 310a is a hardware processor that performs overall control of the vehicle control device 310. The CPU 310a reads out various control programs (computer programs) stored in the ROM 310b or the like, and realizes various functions according to instructions defined in the various control programs.

[0088] The ROM 310b is a non-volatile main storage device that stores parameters and the like required for executing the various control programs described above.

[0089] The RAM 310c is a volatile main storage device that provides a working area for the CPU 310a.

[0090] The SSD 310d is a rewritable non-volatile auxiliary storage device. In the vehicle control device 310 according to the embodiment, a hard disk drive (HDD) may be provided as the auxiliary storage device instead of the SSD 310d (or in addition to the SSD 310d).

[0091] Of the various types of processing that can be executed by the vehicle control device 310, the display control unit 310e is primarily responsible for image processing of images acquired from the vehicle-mounted camera 15 and generation of image data to be output to the display unit 8 of the monitor device 11.

[0092] Of the various processes that can be executed by the vehicle control device 310, the audio control unit 310f mainly controls the generation of audio data to be output to the audio output unit 9 of the monitor device 11 and the like.

[0093] The ride comfort control device 410 controls the ride comfort of the vehicle 1. The ride comfort control device 410 is configured as an ECU (electronic control unit) that realizes various functions by comprehensively controlling, for example, the suspension system 401, the rear wheel steering system 402, the stabilizer system 403, and the electric drive system 404. Although details will be described later, the ride comfort control device 410 according to the embodiment is configured to be able to execute a road surface condition estimation process that estimates the road surface condition and a ride comfort control process that controls the ride comfort of the vehicle 1.

[0094] The ride comfort control device 410 includes a CPU 410a, a ROM 410b, a RAM 410c, and an SSD 410d.

[0095] The CPU 410a is a hardware processor that performs overall control of the ride comfort control device 410. The CPU 410a reads out various control programs (computer programs) stored in the ROM 410b or the like, and realizes various functions according to instructions defined in the various control programs. The control programs mentioned here include a road surface condition estimation program and a ride comfort control processing program for executing the above-mentioned road surface condition estimation processing and ride comfort control processing.

[0096] The ROM 410b is a non-volatile main storage device that stores parameters and the like required for executing the various control programs described above.

[0097] The RAM 410c is a volatile main storage device that provides a working area for the CPU 410a.

[0098] The SSD 410d is a rewritable non-volatile auxiliary storage device. In the ride comfort control device 410 according to the embodiment, a HDD may be provided as the auxiliary storage device instead of the SSD 410d (or in addition to the SSD 410d).

[0099] The in-vehicle network 350 communicatively connects the braking system 301, the acceleration system 302, the steering system 303, the gear shift system 304, the obstacle sensor 305, the driving condition sensor unit 306, the operation input unit 10 of the monitor device 11, and the vehicle control device 310.

[0100] Fig. 4 is an exemplary schematic block diagram showing a functional configuration of the ride comfort control device 410 according to the embodiment. As shown in Fig. 4, in the present embodiment, as an example, the ride comfort control device 410 can function (operate) as a data acquisition unit 421, a road surface condition estimation unit 422, a ride comfort control unit 423, and the like, as shown in Fig. 4, by cooperation between hardware and software (programs). That is, as an example, the program may include modules corresponding to each block except for the storage unit 430 shown in Fig. 3. The storage unit 430 corresponds to, for example, the ROM 410b, the SSD 410d, and the like.

[0101] The data acquisition unit 421 acquires driving state data and operation data from various sensors. The driving state data indicates the driving state of the vehicle 1. For example, the driving state data is the vehicle speed detected by the vehicle speed sensor 306A and the acceleration in the vertical direction (vertical acceleration) of the vehicle 1 detected by the acceleration sensor 306B. The data acquisition unit 421 acquires at least one of the vehicle speed and the vertical acceleration. The operation data indicates a physical quantity related to the operation of a motion control device mounted on the vehicle 1 and capable of controlling the motion of the sprung part 1a of the vehicle 1 relative to the road surface F1 (FIG. 5), or a motion control device that affects the motion of the sprung part 1a. The motion control device is, for example, the shock absorber 401a, the rear wheel steering device 402a, the stabilizer device 403a, and the electric drive device 404a.

[0102] The physical quantity of the operation data acquired by the data acquisition unit 421 is one or more physical quantities from a group of physical quantities including a physical quantity (speed, acceleration, displacement, etc.) related to the speed of expansion and contraction of the shock absorber 401a, a current value indicating the value of the current input to the actuator 401aa of the shock absorber 401a, a rotation angle (operation amount) of the actuator 402aa of the rear wheel steering device 402a, a current value indicating the value of the current input to the actuator 402aa, a rotation amount (operation amount) of the actuator 403aa of the stabilizer device 403a and a current value indicating the value of the current input to the actuator 403aa, a rotation angle (operation amount) of the motor 404aa of the electric drive device 404a, and a current value indicating the value of the current input to the motor 404aa. The physical quantity acquired by the data acquisition unit 421 may be one, a plurality, or all of the above physical quantity groups.

[0103] The road surface condition estimation unit 422 receives, as an example, predetermined data (hereinafter also referred to as input data) acquired by the data acquisition unit 421. The input data is at least one of the driving state data and at least one of the above-mentioned operation data (physical quantity group), and as an example, all of them. The input data may include one of the driving state data and one of the above-mentioned operation data (physical quantity group). The road surface condition estimation unit 422 estimates the road surface condition based on the driving state data and the relational information indicating the relation of the road surface condition to the operation data. For example, the road surface condition estimation unit 422 uses the neural network 440 to estimate the change in height of the road surface F1 on which the vehicle 1 runs (hereinafter also referred to as the road surface height change amount) according to the input data. The neural network 440 is included in the relational information indicating the relation of the road surface condition to the driving state data and the operation data. The change in road surface height represents the unevenness that is the shape of the road surface F1. The neural network 440 is pre-stored in the storage unit 430.

[0104] The neural network 440 is configured as a recurrent neural network (RNN) that has been trained in advance to estimate the amount of change in height of the road surface F1 on which the vehicle 1 travels in response to input of input data. When the neural network 440 is trained, the input data is the above-mentioned input data acquired by the data acquisition unit 421, and the output data as teacher data is, for example, an actual measurement value (true value) of the amount of change in height of the road surface F1 measured by a road surface height detection sensor (not shown). The neural network 440 according to the embodiment is configured as, for example, a Seq2Seq (Sequence to Sequence) model.

[0105] Then, the road surface condition estimation unit 422 estimates the roughness of the road surface F1 based on the road surface height change amount estimated using the neural network 440. For example, the road surface condition estimation unit 422 estimates the road surface roughness based on information indicating the relationship between the road surface height change amount and the road surface roughness and the estimated road surface height change amount. Here, the road surface roughness is, for example, the roughness defined in ISO8608 of the ISO standard. The roughness of the road surface F1 represents the shape of the road surface F1. The road surface roughness and the road surface height change amount are examples of the road surface condition. Note that the neural network 440 may be configured to estimate the roughness of the road surface F1.

[0106] The ride comfort control unit 423 controls the ride comfort motion control device (shock absorber 401a, rear wheel steering device 402a, stabilizer device 403a, and electric drive device 404a) based on the road surface roughness (road surface condition) estimated by the road surface condition estimation unit 422 so that physical quantities related to the ride comfort of the vehicle 1 approach target values.

[0107] Next, with reference to Figs. 5 to 13, the estimation of road surface conditions when the ride comfort control unit 423 controls the motion control device (for example, the shock absorber 401a) will be described.

[0108] Fig. 5 is an exemplary schematic diagram for explaining an example of ride comfort control of the vehicle 1 according to the embodiment. As shown in Fig. 5, when the vehicle 1 goes over a step D on a road surface F1, the ride comfort control unit 423 controls the shock absorber 401a so as to reduce the inclination of the vehicle body 2 (sprung portion 1a).

[0109] Fig. 6 is an exemplary schematic diagram showing an example of a road surface pattern according to the embodiment. Fig. 7 is an exemplary schematic diagram showing an example of detection results of the acceleration sensor according to the embodiment and the acceleration sensor according to the comparative example. Fig. 8 is an exemplary schematic diagram showing an example of estimation results of road surface height according to the technology according to the embodiment and the technology according to the comparative example.

[0110] FIG 6 shows an example of data on the change in height of the road surface F1 with which the wheels 3 of the vehicle 1 traveling on the road surface F1 in FIG 5 come into contact. FIG 7 shows the difference in sprung acceleration with and without motion control for such a road surface F1. As can be seen from FIG 7, when motion control is performed, the sprung acceleration detected by the acceleration sensor 306B hardly changes when going over the step D. Therefore, if the road surface condition is detected only from the output value of the acceleration sensor 306B, the detection accuracy of the road surface condition will be low.

[0111] Fig. 8 shows the results of a simulation in which the change in height of the road surface F1 is estimated using the above-described technology of this embodiment and a technology of a comparative example. In the comparative example, the change in height of the road surface F1 is estimated only from the measurement results of the vertical acceleration of the vehicle 1. As can be seen from Fig. 8, the technology of this embodiment can accurately estimate the change in height of the road surface F1. In contrast, in the comparative example, the change in height of the step D on the road surface F1 cannot be confirmed, and therefore the accuracy of estimating the change in height of the road surface F1 is low.

[0112] Fig. 9 is an exemplary and schematic diagram showing an example of a road surface pattern according to the embodiment. Fig. 10 is an exemplary and schematic diagram showing an example of a sprung acceleration with and without motion control according to the embodiment. Fig. 11 is an exemplary and schematic diagram showing an example of an estimation result of the road surface height of the technology according to the embodiment and the technology according to the comparative example.

[0113] 9 to 11 are views corresponding to FIGS. 6 to 8, and show the patterns of the road surface F1 different from those in the examples of FIGS.

[0114] 12 and 13 are exemplary and schematic diagrams for explaining a method for estimating road surface roughness by the technology according to the embodiment. Specifically, FIG. 12 is a diagram showing the roughness of a road surface defined in ISO8608. The road surface roughness is set according to the change (frequency characteristic) of the road surface. Also, FIG. 13 is a diagram for explaining a linear discrimination method based on FIG. 12. The horizontal axis of FIG. 12 and FIG. 13 indicates the spatial frequency with respect to the road surface displacement, and the vertical axis of FIG. 12 and FIG. 13 indicates the power spectrum density with respect to the road surface displacement. Also, line L1 of FIG. 13 indicates the discriminant (Z=W1×X1+W2×X2). X1 is the power spectrum density, and X2 is the spatial frequency. Also, W1 and W2 are coefficients. Also, line B of FIG. 13 1 ~Line B n (n is a natural number) indicates the boundary between road surface classes. 1 ~Line B n Line B is used as a general term for all of the above.

[0115] The road surface condition estimation unit 422 estimates the road surface displacement in FIG. 11 from the acceleration data in FIG. 10 as the driving state data and the operation data, and calculates the first data shown by the line D1 in FIG. 12 by frequency analyzing the signal of the road surface displacement in FIG. 11. The road surface condition estimation unit 422 performs linear discrimination shown in FIG. 13 on this first data to calculate which road surface class the road surface roughness is. The linear discrimination is a general linear discriminant analysis that discriminates which class range the position of the first data with respect to the discrimination boundary is in based on the discriminant (line L1) and the discrimination boundary (line B). At this time, for example, the discriminant (Z1=W11×X1+W12×X2) can be used for classifying the line B1, the discriminant (Z2=W21×X1+W22×X2) can be used for classifying the line B2, and the discriminant (Zn=Wn1×X1+Wn2×X2) can be used for classifying the line Bn. The discriminant and the discriminant boundary are an example of discriminant information, and are included in the relationship information.

[0116] Next, an example of a road surface condition estimation method executed by the ride comfort control device 410 will be described with reference to Fig. 14. Fig. 14 is a flowchart showing an example of a road surface condition estimation method executed by the ride comfort control device 410 according to the embodiment.

[0117] 14, first, the data acquisition unit 421 acquires the above-mentioned input data (data) (S101). Next, the road surface condition estimation unit 422 estimates the amount of change in the road surface based on the input data acquired by the data acquisition unit 421 and the neural network 440 (S102). Then, the road surface condition estimation unit 422 estimates the road surface condition (road surface roughness as an example) based on the estimated amount of change in the road surface (S103).

[0118] As described above, in the road surface condition estimation method of this embodiment, the data acquisition unit 421 acquires driving condition data indicating the driving condition of the vehicle 1, and operation data indicating physical quantities related to the operation of motion control devices (one or more of the shock absorber 401a and the stabilizer device 403a) mounted on the vehicle 1 and controlling the motion of the sprung part 1a of the vehicle 1 relative to the road surface F1, and motion control devices (one or more of the rear wheel steering device 402a and the electric drive device 404a) that affect the motion of the sprung part 1a. The road surface condition estimation unit 422 estimates the road surface condition, which is the state of the road surface F1, based on the driving condition data and the operation data.

[0119] As described above, the ride comfort control device 410 (road surface condition estimation device) according to the embodiment includes a data acquisition unit 421 and a road surface condition estimation unit 422. The data acquisition unit 421 acquires driving condition data and operation data. The driving condition data indicates the driving condition of the vehicle 1. The operation data indicates physical quantities related to the operation of a motion control device (one or more of the shock absorber 401a and the stabilizer device 403a) mounted on the vehicle 1 and controlling the motion of the sprung part 1a of the vehicle 1 relative to the road surface F1, and a motion control device (one or more of the rear wheel steering device 402a and the electric drive device 404a) that affects the motion of the sprung part 1a. The road surface condition estimation unit 422 estimates the road surface condition, which is the condition of the road surface F1, based on the driving condition data and the operation data.

[0120] According to such a ride comfort control device 410, the road surface condition estimation unit 422 estimates the road surface condition, which is the state of the road surface F1, based on the driving condition data and operation data, so that even when the movement of the sprung part 1a of the vehicle 1 is controlled by the movement control device (shock absorber 401a, rear wheel steering device 402a, stabilizer device 403a, electric drive device 404a), the accuracy of the estimation of the road surface condition can be prevented from decreasing.

[0121] The motion control device is a shock absorber 401a provided in a suspension 401d and capable of changing a damping force according to an input current. The physical quantity is at least one of a physical quantity (speed, acceleration, displacement, etc.) related to the speed of expansion and contraction of the shock absorber 401a and a current value indicating the value of the current input to the shock absorber 401a.

[0122] According to such a ride comfort control device 410, even when the motion of the sprung portion 1a of the vehicle 1 is controlled by the shock absorber 401a, it is possible to suppress a decrease in the accuracy of the estimation of the road surface condition.

[0123] The motion control device is a rear wheel steering device 402a (steering device) capable of changing the steering angle according to an input current. The physical quantity is at least one of an operation amount of the rear wheel steering device 402a and a current value indicating a value of a current input to the rear wheel steering device 402a.

[0124] According to such a ride comfort control device 410, even if the rear wheel steering device 402a affects the movement of the sprung portion 1a of the vehicle 1, it is possible to suppress a decrease in the accuracy of the estimation of the road surface condition.

[0125] The motion control device is a stabilizer device 403a capable of changing the amount of twist in response to an input current. The physical amount is at least one of the amount of movement of the stabilizer device 403a and a current value indicating the value of the current input to the stabilizer device 403a.

[0126] According to such a ride comfort control device 410, even when the motion of the sprung portion 1a of the vehicle 1 is controlled by the stabilizer device 403a, it is possible to suppress a decrease in the accuracy of estimation of the road surface condition.

[0127] The motion control device is an electric drive unit 404a that can change the driving force applied to the rear wheels 3R ​​(wheels 3) of the vehicle 1 according to an input current. The physical amount is at least one of an operating amount of the electric drive unit 404a and a current value indicating the value of the current input to the electric drive unit 404a.

[0128] According to such a ride comfort control device 410, even if the electric drive device 404a affects the motion of the sprung portion 1a of the vehicle 1, it is possible to suppress a decrease in the accuracy of the estimation of the road surface condition.

[0129] In addition, in the ride comfort control device 410, the road surface condition is the roughness of the road surface F1.

[0130] Such a ride comfort control device 410 can suppress a decrease in the accuracy of estimating the roughness of the road surface F1 even when the motion control device (shock absorber 401a, rear wheel steering device 402a, stabilizer device 403a, electric drive device 404a) controls the motion of the sprung part 1a of the vehicle 1 or affects the motion of the sprung part 1a.

[0131] <Modification> Next, a modified example will be described.

[0132] In the first modified example, the road surface condition estimated by the road surface condition estimation unit 422 is the friction coefficient of the road surface F1. In this example, for example, during learning of the neural network 440, the input data is the above-mentioned input data acquired by the data acquisition unit 421, and the output data as the teacher data is, for example, the actual measurement value (true value) of the friction coefficient of the road surface F1 measured by a friction coefficient sensor (not shown) that detects the friction coefficient of the road surface F1. Therefore, the road surface condition estimation unit 422 can estimate the friction coefficient of the road surface F1 by inputting the running state data to the neural network 440.

[0133] An example of a specific method for calculating the friction coefficient of the road surface F1 will be described. Using the vehicle speed and steering amount of the steering wheel (not shown) of the driving state data, the yaw rate and lateral acceleration of the vehicle 1 are estimated from the vehicle speed during driving and the steering amount of the steering wheel. That is, the estimated values ​​of the yaw rate and lateral acceleration of the vehicle 1 are obtained. In the real world, while resistance is generated in the driving of the vehicle 1 due to the friction coefficient of the road surface F1, the yaw rate and lateral acceleration can be acquired from a sensor as driving state data. That is, the measured values ​​of the yaw rate and lateral acceleration can be acquired. A deviation occurs between the above estimated value and the measured value. The friction coefficient can be calculated by using the relationship between this deviation amount and the friction coefficient of the road surface F1. However, when the rear wheel steering device 402a performs motion control by steering the rear wheels 3R, the motion characteristics of the vehicle 1 change, and the estimated values ​​of the yaw rate and lateral acceleration of the vehicle 1 cannot be calculated with high accuracy. Therefore, by taking into account the amount of motion control due to steering of the rear wheel steering device 402a, it becomes possible to calculate the friction coefficient of the road surface F1 with high accuracy.

[0134] Such a ride comfort control device 410 can suppress a decrease in the accuracy of estimating the friction coefficient of the road surface F1 even when the motion of the sprung part 1a of the vehicle 1 is controlled by the motion control device (shock absorber 401a, rear wheel steering device 402a, stabilizer device 403a, electric drive device 404a).

[0135] In the second modified example, the road surface condition estimated by the road surface condition estimation unit 422 is the inclination state of the road surface F1. In this example, for example, during learning of the neural network 440, the input data is the above-mentioned input data acquired by the data acquisition unit 421, and the output data as teacher data is, for example, the actual measurement value (true value) of the inclination state (inclination angle) of the road surface F1 measured by an angle sensor (not shown) that detects the inclination state (inclination angle) of the road surface F1. Therefore, the road surface condition estimation unit 422 can estimate the inclination state (inclination angle) of the road surface F1 by inputting the running state data to the neural network 440.

[0136] Such a ride comfort control device 410 can suppress a decrease in the accuracy of estimating the inclination state of the road surface F1 even when the motion of the sprung part 1a of the vehicle 1 is controlled by the motion control device (shock absorber 401a, rear wheel steering device 402a, stabilizer device 403a, electric drive device 404a).

[0137] In the third modified example, the road surface condition estimation unit 422 determines the attitude variation of the vehicle 1 based on the operation data, and estimates the road surface condition based on the running state data and the attitude variation. For example, instead of the neural network 440, the road surface condition estimation unit 422 estimates the road surface condition using information for estimation determined in advance. The information for estimation is information indicating the relationship between the running state data and the operation data and the road surface condition, and is, for example, an equation (equation of motion). The running state data and the operation data are input as variables (input data) into the equation, and a result indicating the road surface condition (amount of change in road surface height, friction coefficient, inclination angle) is determined.

[0138] For example, the road surface condition estimation unit 422 uses an equation to estimate the amount of change in roll of the vehicle 1 (hereinafter also referred to as roll change amount) from the rotation amount (operation amount) of the actuator 403aa of the stabilizer device 403a or a current value indicating the value of the current input to the actuator 403aa. The road surface condition estimation unit 422 also uses an equation to estimate the amount of change in pitch of the vehicle 1 (hereinafter also referred to as pitch change amount) from the rotation angle (operation amount) of the motor 404aa of the electric drive device 404a or a current value indicating the value of the current input to the motor 404aa. The road surface condition estimation unit 422 also uses an equation to estimate the change in roll amount of the vehicle 1 from the rotation amount (operation amount) of the actuator 403aa of the stabilizer device 403a or a current value indicating the value of the current input to the actuator 403aa. Furthermore, the road surface condition estimation unit 422 uses an equation to estimate the amount of change in road surface height from the driving state data (vehicle speed, vertical acceleration), the amount of roll change, and the amount of pitch change.The road surface condition estimation unit 422 then uses an equation to estimate the road surface roughness from the amount of change in road surface height.The roll change and pitch change are examples of an attitude change of the vehicle 1, and the roll change and pitch change are examples of an attitude change of the vehicle 1.

[0139] FIG. 15 is a schematic diagram showing an example of the relationship between the part including the suspension device 401d in the vehicle 1 according to the embodiment and the parameters of the equation of motion. In the fourth modified example, the road surface condition estimating unit 422 estimates the road surface condition using the neural network 440 and the equation of motion. In addition, in this modified example, the sprung acceleration, which is the acceleration of the sprung part 1a, is used as an example of the running state data. The neural network 440 is configured as a recurrent neural network (RNN) that has previously learned to estimate the unsprung acceleration, which is the acceleration of the unsprung part 1b, in response to the input of the sprung acceleration as input data. In addition, the following equations of motion (1) and (2) of the suspension device 401d are used as an example of the equation of motion. The relationship between the parameters of the following equations of motion (1) and (2) and the part including the suspension device 401d in the vehicle 1 and the parameters of the equation of motion is shown in FIG. The equations of motion (1) and (2) are an example of the relationship information.

number

[0140] For example, the road surface condition estimation unit 422 estimates the unsprung acceleration from the sprung acceleration using the neural network 440. Next, the road surface condition estimation unit 422 calculates the amount of change in height of the road surface F1 (road surface displacement) from the sprung acceleration and the unsprung acceleration (estimated value) using the equations of motion (1) and (2).

[0141] According to this configuration, in the relationship between the estimation of the road surface F1 based on the driving condition data and the operation data, the parts of the behavior of the vehicle 1 that are clear according to the laws of physics are supplemented by the equations of motion (1) and (2), thereby improving the accuracy of the estimation of the state of the road surface F1.

[0142] The method of estimating the state of the road surface F1 is not limited to the above. The state of the road surface F1 may be estimated using only one of the neural network 440, the linear discrimination information for performing linear discrimination, and the equation of motion. The state of the road surface F1 may also be estimated using a combination of multiple of the neural network 440, the linear discrimination information for performing linear discrimination, and the equation of motion.

[0143] In the above-described embodiment and modified examples, the CPU 410a reads and executes programs stored in a storage device such as the ROM 410b or the SSD 410d, thereby realizing various functional modules such as a data acquisition unit 421, a road surface condition estimation unit 422, and a ride comfort control unit 423.

[0144] In the above embodiment and modified example, various functional modules such as the data acquisition unit 421, the road surface condition estimation unit 422, the ride comfort control unit 423, etc. are realized by a processor such as the CPU 410a reading and executing a program stored in a storage device such as the ROM 410b or the SSD 410d. However, this is not limited to this. For example, various functional modules such as the data acquisition unit 421, the road surface condition estimation unit 422, the ride comfort control unit 423, etc. can also be realized by independent hardware.

[0145] The vehicle control programs executed by the vehicle control device 310 and the ride comfort control device 410 in the above-described embodiment and modified example are provided in a state that they are pre-installed in a ROM or the like.

[0146] The programs executed by the vehicle control device 310 and the ride comfort control device 410 in the above-described embodiments and modified examples may be configured to be provided by being recorded in an installable or executable format on a computer-readable recording medium such as a CD-ROM, a flexible disk (FD), a CD-R, or a DVD (Digital Versatile Disk).

[0147] Furthermore, the programs executed by the vehicle control device 310 and the ride comfort control device 410 of the above-mentioned embodiment and modified examples may be stored on a computer connected to a network such as the Internet and provided by being downloaded via the network. Also, the programs executed by the vehicle control device 310 and the ride comfort control device 410 of the above-mentioned embodiment and modified examples may be provided or distributed via a network such as the Internet.

[0148] The program executed by the ride comfort control device 410 of the above embodiment and modified example has a modular structure including each of the above-mentioned units (data acquisition unit 421, road surface condition estimation unit 422, ride comfort control unit 423, etc.), and in terms of actual hardware, the CPU reads out the vehicle control program from the above-mentioned ROM and executes it, thereby loading each of the above-mentioned units onto the main memory device, and the data acquisition unit 421, road surface condition estimation unit 422, ride comfort control unit 423, etc. are generated on the main memory device.

[0149] Although some embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These novel embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope and spirit of the invention, and are included in the scope of the invention and its equivalents described in the claims. [Explanation of symbols]

[0150] 1. Vehicle 1a…Upper part of spring 3...wheels 3F…Front wheel 3R…Rear wheel 401a...Shock absorber (motion control device) 401d…Suspension device 402a...Rear wheel steering device (motion control device, steering device) 403a…Stabilizer device (motion control device) 404a...Electric drive unit (motion control unit) 410...Ride comfort control device (road surface condition estimation device) 421…Data Acquisition Section 422…Road surface condition estimation unit F1…road surface

Claims

1. a data acquisition unit that acquires running state data indicating a running state of a vehicle and operation data indicating a physical quantity related to an operation of a motion control device that is mounted on the vehicle and controls or influences the motion of the sprung mass of the vehicle relative to a road surface; a road surface condition estimating unit that estimates a road surface condition, which is a condition of the road surface, based on the running state data and the operation data; A road surface condition estimation device comprising:

2. the motion control device is a shock absorber provided in a suspension device and capable of changing a damping force in response to an input current, The physical quantity is at least one of a physical quantity related to a speed of expansion and contraction of the shock absorber and a current value indicating a value of the current input to the shock absorber. The road surface condition estimating device according to claim 1 .

3. the motion control device is a stabilizer device capable of changing an amount of torsion in response to an input current, the physical quantity is at least one of an amount of movement of the stabilizer device and a current value indicating a value of the current input to the stabilizer device; The road surface condition estimating device according to claim 1 .

4. the motion control device is a steering device capable of changing a steering angle in response to an input current, The physical quantity is at least one of an operation amount of the steering device and a current value indicating a value of the current input to the steering device. The road surface condition estimating device according to claim 1 .

5. the motion control device is an electric drive device capable of changing a drive force applied to wheels of the vehicle in response to an input current; the physical quantity is at least one of an operating quantity of the electric drive device and a current value indicating a value of the current input to the electric drive device; The road surface condition estimating device according to claim 1 .

6. the road surface condition estimating unit estimates the road surface condition based on relationship information indicating a relationship of the road surface condition to the traveling state data and the operation data; The road surface condition estimating device according to claim 1 .

7. The relationship information is one or more combinations of a neural network, linear discrimination information for performing linear discrimination, and a motion equation. The road surface condition estimating device according to claim 6.

8. the road surface condition estimating unit determines an attitude change of the vehicle based on the operation data, and estimates the road surface condition based on the traveling state data and the attitude change. The road surface condition estimating device according to claim 1 .

9. The road surface condition is the roughness of the road surface. The road surface condition estimating device according to claim 1 .

10. The road surface condition is a friction coefficient of the road surface. The road surface condition estimating device according to claim 1 .

11. The road surface condition is a slope condition of the road surface. The road surface condition estimating device according to claim 1 .

12. a data acquisition unit acquires running state data indicating a running state of a vehicle and operation data indicating a physical quantity related to an operation of a motion control device that is mounted on the vehicle and controls or influences a motion of a sprung mass of the vehicle relative to a road surface; a road surface condition estimating unit estimating a road surface condition, which is a condition of the road surface, based on the running state data and the operation data; A method for estimating road surface conditions.

13. The computer installed in the vehicle a data acquisition unit that acquires running state data indicating a running state of the vehicle and operation data indicating a physical quantity related to an operation of a motion control device that is mounted on the vehicle and controls or influences the motion of the sprung mass of the vehicle relative to a road surface; a road surface condition estimating unit that estimates a road surface condition, which is a condition of the road surface, based on the running state data and the operation data; A program that functions as a

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