Device for estimating the coefficient of friction of a vehicle against a road surface

Abstract

To provide a vehicle estimation device capable of accurately estimating the slipperiness of a road surface in front in a traveling direction in advance.SOLUTION: A vehicle estimation device determines a state of a road surface to determine which of a plurality of preset road surface states the state of the road surface belongs to, performs primary identification of a first range of a friction coefficient corresponding to the determined road surface state on the basis of friction coefficient information that is sectioned in advance for each of the plurality of road surface states and held in memory, and the determined road surface state, and narrows down a range of the friction coefficient from the first range to a narrower second range on the basis of the identified first range of the friction coefficient to perform secondary identification of the friction coefficient of the road surface.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to an estimation device capable of non-contactly estimating the coefficient of friction (μ value) of a vehicle (specifically, a tire) with respect to a road surface. [Background technology]

[0002] Vehicles are a convenient means of transportation, and for example, people can drive cars to various places. In order to further improve driving safety, it is important to accurately estimate the road surface conditions in the direction of vehicle travel in advance.

[0003] For example, Patent Document 1 proposes a technology that uses a polarization camera to capture an image of the road surface in the direction of travel of a moving object, classifies the road surface condition into one of multiple categories, and estimates the friction coefficient of the road surface based on the classified category. Patent Document 2 proposes a technology that irradiates the road surface with first and second infrared light and classifies the road surface condition (dry, wet (low moisture), wet (high moisture), snow (low moisture content), and snow (high moisture content)) based on information from the reflected light. Patent Document 3 proposes a technology that measures microwave thermal noise from an object on the road surface and measures physical temperature from infrared rays emitted from the object, and determines the surface condition of the object (snow, frozen, wet, dry) based on the ratio between the microwave thermal noise and the physical temperature. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2020-180924 [Patent Document 2] Japanese Patent Application Laid-Open No. 2000-002772 [Patent Document 3] Japanese Patent Application Laid-Open No. 2011-053184 Summary of the Invention [Problem to be solved by the invention]

[0005] Current technologies, including those disclosed in the above-mentioned patent documents, do not adequately meet market needs, and the following problems exist. First, although the above-mentioned patent documents are useful in that they use cameras and electromagnetic waves to determine the condition of the road surface, the current situation is that all of these documents simply determine the above-mentioned type and condition of the road surface.

[0006] Although technologies for determining road surface conditions using non-contact methods have been proposed in the past, they can only roughly determine the slipperiness (friction coefficient) based on the determined road surface condition, and further improvements in accuracy are desired in order to apply them to more precise vehicle control.

[0007] The present disclosure has been made in consideration of the above-mentioned problems as an example, and aims to provide an estimation device for a vehicle that can accurately estimate in advance the slipperiness of the road surface ahead in the direction of travel. [Means for solving the problem]

[0008] In order to solve the above problem, an estimation device mounted on a vehicle according to an embodiment of the present disclosure includes one or more processors and one or more memories communicably connected to the one or more processors, and The vehicle is running An estimation device for estimating a friction coefficient of a road surface, the processor comprising: Based on the road surface condition detected by a sensor mounted on the vehicle and the road surface data that is defined in advance and stored in the memory, Determine the condition of the road surface By doing so, the condition of the road surface on which the vehicle is traveling A first range of friction coefficients corresponding to the determined road surface condition is primarily specified based on friction coefficient information classified in advance for each of the plurality of road surface conditions stored in the memory and the determined road surface condition, and a second range of friction coefficients corresponding to the determined road surface condition is primarily specified based on the first range of friction coefficients. a μ-value-dependent parameter value that is a parameter having a high degree of influence on the primary identified road surface condition and that is previously determined by an experiment or a simulation and stored in the memory is selected; and The range of the friction coefficient is narrowed from the first range to a second narrower range to secondarily identify the friction coefficient of the road surface. [Effects of the Invention]

[0009] According to the present disclosure, it is possible to accurately estimate in advance the slipperiness of the road surface ahead in the direction of travel using a non-contact method. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a block diagram showing an example of the configuration of a vehicle equipped with an estimation device according to an embodiment; [Figure 2] FIG. 2 is a block diagram showing an example of the configuration of an estimation device and peripheral devices. [Figure 3] 1 is a flowchart showing a method for estimating a friction coefficient of a road surface that can be performed by the estimation device according to the embodiment. [Figure 4] FIG. 10 is a schematic diagram showing a road surface condition determination map applicable to the primary determination. [Figure 5] FIG. 10 is a schematic diagram showing an example of a data table (range divisions) of friction coefficients corresponding to determined road surface conditions. [Figure 6] FIG. 10 is a schematic diagram showing another example of a data table (average value basis) of friction coefficients corresponding to determined road surface conditions. [Figure 7] FIG. 10 is a schematic diagram showing another example of a data table of friction coefficients corresponding to determined road surface conditions (range divisions based on average values). [Figure 8] FIG. 10 is a schematic diagram showing a data table for narrowing down the friction coefficient for DRY that can be applied to the secondary determination. [Figure 9] FIG. 10 is a schematic diagram showing a wet friction coefficient narrowing data table applicable to secondary determination. [Figure 10] FIG. 10 is a schematic diagram showing a friction coefficient narrowing data table for SNOW that can be applied to secondary determination. [Figure 11] FIG. 10 is a schematic diagram showing a friction coefficient narrowing data table for ICE applicable to secondary determination. DETAILED DESCRIPTION OF THE INVENTION

[0011] Next, preferred embodiments for carrying out the present disclosure will be described. Furthermore, configurations other than those described in detail below may be supplemented appropriately with known vehicle structures and on-board systems, including various known on-board sensors.

[0012] [Vehicle 200] 1 shows an example of the configuration of a vehicle 200 according to this embodiment. In the following, a four-wheel drive automobile is exemplified as a vehicle suitable for this embodiment, but the present invention may also be applied to automobiles other than four-wheel vehicles, such as motorcycles, as long as the purpose of this disclosure is not impaired.

[0013] <Overall vehicle configuration> Fig. 1 is a schematic diagram showing an example of the configuration of a vehicle 200 equipped with an estimation device 100 according to this embodiment. The vehicle 200 shown in Fig. 1 is configured as a four-wheel drive vehicle in which drive torque output from a drive force source 9 that generates drive torque for the vehicle is transmitted to a left front wheel 3LF, a right front wheel 3RF, a left rear wheel 3LR, and a right rear wheel 3RR (hereinafter collectively referred to as "wheels 3" unless a distinction is required). The drive force source 9 may be an internal combustion engine such as a gasoline engine or a diesel engine, a drive motor, or both an internal combustion engine and a drive motor.

[0014] Vehicle 200 may be an electric vehicle equipped with two drive motors, for example, a front-wheel drive motor and a rear-wheel drive motor, or may be an electric vehicle equipped with drive motors corresponding to each of wheels 3. Furthermore, if vehicle 200 is an electric vehicle or a hybrid electric vehicle, vehicle 200 is equipped with a secondary battery that stores power supplied to the drive motors, and a motor or a generator such as a fuel cell that generates power to charge the battery.

[0015] The vehicle 200 is equipped with a driving force source 9, an electric steering device 15, and a brake fluid pressure control unit 20 as devices used to control the driving of the vehicle. The driving force source 9 outputs driving torque that is transmitted to the front drive shaft 5F and the rear drive shaft 5R via a transmission, a front wheel differential mechanism 7F, and a rear wheel differential mechanism 7R (not shown). The driving of the driving force source 9 and the transmission is controlled by a vehicle control device 41 that includes one or more electronic control units (ECUs: Electronic Control Units).

[0016] The front-wheel drive shaft 5F is provided with an electric steering device 15. The electric steering device 15 includes an electric motor and a gear mechanism (not shown), and is controlled by a vehicle control device 41 to adjust the steering angles of the left front wheel 3LF and the right front wheel 3RF. During manual driving, the vehicle control device 41 controls the electric steering device 15 based on the steering angle of the steering wheel 13 operated by the driver.

[0017] The brake system of the vehicle 200 is configured as a hydraulic brake system. A brake fluid pressure control unit 20 adjusts the hydraulic pressure supplied to brake calipers 17LF, 17RF, 17LR, and 17RR (hereinafter collectively referred to as "brake calipers 17" unless a distinction is required) provided on the front, rear, left, and right drive wheels 3LF, 3RF, 3LR, and 3RR, respectively, to generate braking force. The drive of the brake fluid pressure control unit 20 is controlled by a vehicle control device 41. If the vehicle 200 is an electric vehicle or a hybrid electric vehicle, the brake fluid pressure control unit 20 is used in conjunction with regenerative braking using a drive motor.

[0018] The vehicle control device 41 includes one or more electronic control devices that control the drive of the driving force source 9 that outputs the driving torque of the vehicle 200, the steering wheel 13 or the electric steering device 15 that controls the steering angle of the steering wheels, and the brake fluid pressure control unit 20 that controls the braking force of the vehicle 200. The vehicle control device 41 may also have a function of controlling the drive of a transmission that changes the speed of the output output from the driving force source 9 and transmits it to the wheels 3. The vehicle control device 41 is configured to be able to acquire information transmitted from an estimation device 100, which will be described later, and is configured to be able to perform vehicle control based on the result of estimation of the friction coefficient of the road surface by the estimation device 100.

[0019] The vehicle 200 may also be equipped with an ambient environment sensor 31, an occupant monitoring sensor 33, a biometric sensor 34, a vehicle condition sensor 35, a GPS (Global Positioning System) sensor 37, a vehicle-to-vehicle communication unit 39, a navigation system 40, and an HMI (Human Machine Interface) 43, etc.

[0020] Among these, the ambient environment sensor 31 includes a road surface temperature sensor for detecting the temperature of the road surface, an unevenness detection sensor for detecting unevenness on the road surface, and a moisture sensor for detecting the amount of moisture on the road surface, as described below. The sensor for detecting the road surface temperature may be any of various known temperature sensors, such as those exemplified in JP 2015-038516 A. The unevenness detection sensor for detecting unevenness on the road surface may be, for example, the device (road surface unevenness detection sensor) disclosed in JP 2004-138549 A, or any of various known means or laser rangefinders described in JP 2013-61690 A. The moisture sensor for detecting the amount of moisture on the road surface may be, for example, any of various known moisture detection sensors, such as those exemplified in JP 2006-46936 A.

[0021] Furthermore, the surrounding environment sensor 31 in this embodiment may be configured to include front imaging cameras 31LF and 31RF, a rear imaging camera 31R, and a LiDAR (Light Detection And Ranging) 31S.

[0022] The front photographing cameras 31LF, 31RF, the rear photographing camera 31R, and the LiDAR 31S constitute a surrounding environment sensor for acquiring information about the surrounding environment of the vehicle 200. The front photographing cameras 31LF, 31RF and the rear photographing camera 31R photograph the front or rear of the vehicle 200 and generate image data. The front photographing cameras 31LF, 31RF and the rear photographing camera 31R are equipped with imaging elements such as CCDs (Charged-Coupled Devices) or CMOSs ​​(Complementary Metal-Oxide-Semiconductors), and can transmit the generated image data to the estimation device 100.

[0023] 1, the front imaging cameras 31LF, 31RF are configured as a stereo camera including a pair of left and right cameras, and the rear imaging camera 31R is configured as a so-called monocular camera, but each may be either a stereo camera or a monocular camera. In addition to the front imaging cameras 31LF, 31RF and the rear imaging camera 31R, the vehicle 200 may also be equipped with a known camera that is mounted on a side mirror, for example, to capture images of the left rear or right rear.

[0024] The LiDAR 31S transmits optical waves and receives reflected waves of the optical waves, and detects an object and the distance to the object based on the time between transmitting the optical waves and receiving the reflected waves. The LiDAR 31S can transmit the detection data to the estimation device 100. The vehicle 200 may be equipped with one or more known sensors, such as a radar sensor such as a millimeter-wave radar, or an ultrasonic sensor, instead of or in addition to the LiDAR 31S, as the surrounding environment sensor 31 for acquiring information about the surrounding environment.

[0025] The occupant monitoring sensor 33 may also be configured to include an in-vehicle camera 33c. The interior camera 33c is composed of one or more known sensors that detect information about the driver of the vehicle 200. The interior camera 33c is equipped with an imaging element such as a CCD or CMOS, captures images of the interior of the vehicle, and generates image data. The interior camera 33c can transmit the generated image data to the estimation device 100. In this embodiment, the interior camera 33c is positioned so that it can capture images of the driver of the vehicle 200. Only one interior camera 33c may be installed, or multiple interior cameras 33c may be installed.

[0026] The biosensor 34 can detect biometric information of the driver and transmit the detected data to the estimation device 100. The biosensor 34 can be any of various known sensors, such as a radio wave Doppler sensor for detecting the driver's heart rate or a non-wearable pulse sensor for detecting the driver's pulse rate. The biosensor 34 can also be a set of electrodes embedded in the steering wheel 13 for measuring the driver's heart rate or electrocardiogram.

[0027] The vehicle state sensor 35 is composed of one or more known sensors that detect the operation state and behavior of the vehicle 200. The vehicle state sensor 35 includes at least one of a steering angle sensor, an accelerator position sensor, a brake stroke sensor, a brake pressure sensor, and an engine speed sensor, and detects the operation state of the vehicle 200, such as the steering angle of the steering wheel 13 or the steering wheels, the accelerator position, the brake operation amount, and the engine speed. The vehicle state sensor 35 also includes at least one of a vehicle speed sensor, an acceleration sensor, and an angular velocity sensor, and detects the vehicle behavior, such as the vehicle speed, longitudinal acceleration, lateral acceleration, and yaw rate. The vehicle state sensor 35 also includes a sensor that detects the operation of a turn signal, and detects the operation state of the turn signal. The vehicle state sensor 35 also includes a sensor that detects the inclination state of the vehicle 200, and detects the inclination state of the road. The vehicle state sensor 35 can transmit a sensor signal including the detected information to the estimation device 100.

[0028] The vehicle-to-vehicle communication unit 39 is an interface for communicating with vehicles traveling around the vehicle 200 (hereinafter also referred to as "other vehicles"). The navigation system 40 is a known navigation system that sets a driving route to a destination set by the occupant and notifies the driver of the driving route. A GPS sensor 37 is connected to the navigation system 40, and receives satellite signals from GPS satellites via the GPS sensor 37 to obtain position information of the vehicle 200 on map data. Note that instead of the GPS sensor 37, an antenna that receives satellite signals from another satellite system that identifies the position of the vehicle 200 may be used.

[0029] The HMI 43 is driven by the estimation device 100 and presents various information to the driver by means of image display, audio output, etc. The HMI 43 includes, for example, a display device provided in the instrument panel and a speaker provided in the vehicle. The display device may have the function of the display device of the navigation system 40. The HMI 43 may also include a head-up display that displays an image on the front window of the vehicle 200.

[0030] [Estimation device 100] Next, a specific configuration example of the estimation device 100 according to this embodiment that estimates the friction coefficient of the road surface ahead of the traveling vehicle 200 will be described.

[0031] FIG. 2 is a block diagram showing an example of the configuration of the estimation device 100 according to this embodiment. The estimation device 100 is connected to sensors SR (such as an ambient environment sensor 31, an occupant monitoring sensor 33, a biological sensor 34, a vehicle state sensor 35, and a GPS sensor 37) via a dedicated line or communication means such as a CAN (Controller Area Network) or a LIN (Local Internet). The estimation device 100 is also connected to the above-mentioned vehicle-to-vehicle communication unit 39, a navigation system 40, a vehicle control device 41, and an HMI 43 via a dedicated line or communication means such as a CAN or a LIN. The estimation device 100 is also configured to be connectable to an external network NET, such as the Internet, via a known communication means 45.

[0032] The estimation device 100 of this embodiment includes a control unit 50 and a known storage unit (memory 60 and database 70). The control unit 50 is configured with one or more processors such as CPUs (Central Processing Units). Part or all of the control unit 50 may be configured with updatable firmware or the like, or may be a program module or the like executed by instructions from the CPU or the like. Of the storage units, the memory 60 is configured with a known memory device such as RAM (Random Access Memory) or ROM (Read Only Memory).

[0033] Furthermore, the database 70 of the storage unit described above is configured with a known updatable recording medium, such as a solid state drive (SSD), a hard disk drive (HDD), a USB flash drive, or a storage device. However, the number and type of the storage units described above are not particularly limited in this embodiment. The storage unit of this embodiment may be configured to record information such as computer programs executed by the control unit 50, various parameters used in arithmetic processing, detection data, and arithmetic results. In this embodiment, the database 70 is composed of a primary identification database 71 for primary identification of the friction coefficient on the road surface, and a secondary identification database 72 for further narrowing down the friction coefficient for each road surface condition (type).

[0034] One or all of the primary identification database 71 and the secondary identification database 72 in the database 70 may be mounted on the vehicle 200, or may be stored in an external server that can communicate with the estimation device 100 via wireless communication means such as mobile communication. Also, some or all of the databases may be configured as a single database.

[0035] As shown in FIG. 2, the control unit 50 of this embodiment includes a road surface condition detection unit 51, a μ value parameter acquisition unit 52, a μ value calculation unit 53, and a vehicle control unit 56. The road surface condition detection unit 51, as the road surface condition detection means or friction coefficient range identification means (primary identification means) of the present disclosure, has a function of detecting the condition of the road surface on which the vehicle 200 is traveling. More specifically, the road surface condition detection unit 51 can determine the road surface condition from the matrix data shown in Fig. 4 based on information received from, for example, a well-known road surface temperature sensor, a road surface unevenness detection sensor, and a road surface moisture content sensor.

[0036] That is, as shown in Fig. 4, the road surface condition detection unit 51 has a function of detecting whether the state (type) of the road surface on which the vehicle is traveling is one of four states (DRY, WET, SNOW, ICE) based on a pre-matrixed relationship (matrix data) between road surface temperature, road surface unevenness, and road surface moisture content. The matrix data of road surface temperature, road surface unevenness, and road surface moisture content shown in Fig. 4 can be stored in the primary identification database 71. Therefore, the road surface condition detection unit 51 can refer to the matrix data stored in the primary identification database 71 and identify the road surface condition based on the data of road surface temperature, road surface unevenness, and road surface moisture content that are actually measured from each of the sensors.

[0037] As described above, the road surface condition detection unit 51 of this embodiment determines the road surface condition based on the road surface temperature, road surface unevenness, and road surface moisture content, but the present disclosure is not limited to this. That is, when determining the above-mentioned four conditions, for example, known determination techniques including those using image analysis as exemplified in Patent Document 1 may be applied as long as they do not conflict with the spirit of this embodiment. Furthermore, when determining the road surface condition, other known road surface conditions may be applied without being limited to the above-mentioned four conditions (DRY, WET, SNOW, ICE).

[0038] Furthermore, the road surface condition detection unit 51 of this embodiment has the function of detecting whether the road surface condition is one of the four conditions (DRY, WET, SNOW, ICE) and then identifying the primary range of the friction coefficient based on this detected road surface condition. That is, the road surface condition detection unit 51 identifies the range of the friction coefficient corresponding to the detected road surface condition based on a friction coefficient range data table in which the range of the friction coefficient is defined for each condition as shown in Fig. 5. More specifically, for example, when the road surface condition detection unit 51 identifies the road surface condition as "DRY" based on the matrix data shown in Fig. 4, it can further refer to the friction coefficient range data table shown in Fig. 5 and identify the range of the friction coefficient as "0.66 to 0.99".

[0039] The range data table of the friction coefficient shown in FIG. 5 is an example, and can be defined based on, for example, experiments or simulations, and can be stored in the primary identification database 71 described above. Furthermore, in this embodiment, a range data table defined for each road surface condition is used as the primary database of friction coefficients, but the present disclosure is not limited to this. For example, the primary database of friction coefficients may use average data of general friction coefficients defined for each road surface condition, as shown in Fig. 6, or range data that further takes into account the standard deviation of the average value of friction coefficients (e.g., a range of the average value ±0.2σ), as shown in Fig. 7. These average data and average-value-based range data can be defined based on experiments or simulations, similar to the friction coefficient range data table shown in Fig. 5, and can be stored in the primary identification database 71.

[0040] The μ value parameter acquisition unit 52, as a friction coefficient narrowing means (secondary identification means) of the present disclosure, has the function of acquiring μ value-related parameters to further narrow down the range of friction coefficients primarily identified by the road surface condition detection unit 51. Here, the "μ value-related parameters" in this embodiment refer to parameters that contribute (have a relatively high degree of influence) to the μ value defined for each road surface condition (type).

[0041] That is, the μ value on the road surface is a friction phenomenon that occurs between the road surface and the tires of the vehicle 200, and in this embodiment, it is divided into (α) rubber friction force, (β) snow column shear force, and (γ) edge effect. For example, when the road surface condition ahead of the vehicle 200 is "DRY," the parameter that contributes to the μ value is dominated by (α) rubber friction force (the force that causes rubber to deform due to road surface irregularities, which can be considered as hysteresis friction), and therefore "road surface irregularities" and the like are selected as μ value-attributing parameters.

[0042] Similarly, when the road surface condition ahead of the vehicle 200 is "WET," the frictional force of the rubber (α) (which is the force generated when the rubber tries to adhere to the road surface and can be regarded as adhesive friction) becomes dominant as the μ-value-driven parameter described above, and therefore in this embodiment, for example, "water film thickness" is selected as the μ-value-driven parameter.

[0043] Similarly, when the road surface condition ahead of the vehicle 200 is "SNOW," the dominant μ-value-driven parameter is (β) snow column shear force (the force that tries to break through compacted snow columns), and therefore, in this embodiment, for example, "density," "road surface temperature," and "moisture content" are selected as μ-value-driven parameters.

[0044] Similarly, when the road surface condition ahead of the vehicle 200 is "ICE," the above-mentioned μ-value-driven parameters are dominated by (α) the frictional force of the rubber (adhesion friction) and (γ) the edge effect (the force that scratches the road surface), and therefore, in this embodiment, for example, "road surface temperature" and "water film thickness" are selected as μ-value-driven parameters. The selection of the μ-value-dependent parameters corresponding to the road surface conditions described above is just one example, and in addition to the μ-value-dependent parameters described above, other parameters that contribute to the μ-value can be appropriately selected based on experiments, simulations, etc.

[0045] The μ value calculation unit 53 has a calculation function for further narrowing down the range of the friction coefficient detected corresponding to the road surface condition, based on the μ value-induced parameters acquired by the μ value parameter acquisition unit 52. More specifically, the μ value calculation unit 53 refers to μ value narrowing down tables corresponding to the road surface conditions, as exemplified in Figures 8 to 11, and secondarily identifies the friction coefficient (μ value) using the μ value-induced parameters acquired by the μ value parameter acquisition unit 52.

[0046] For example, when the road surface condition detection unit 51 detects that the road surface condition is "WET," the μ value calculation unit 53 refers to the μ value narrowing down table shown in FIG. 9 based on the amount of water (water film thickness) acquired by the μ value parameter acquisition unit 52, and further narrows down (secondarily identifies) the coefficient of friction (μ value) on the road surface on which the vehicle is traveling. Note that in this embodiment, the μ value is narrowed down to one point as the secondary identification, but the present invention is not limited to this form, and the range may be further narrowed down from the primarily identified range.

[0047] In this way, the control unit 50 of this embodiment has the function of selecting μ value parameters corresponding to the rubber friction force, snow column shear force, and edge effect between the tires of the vehicle 200 and the road surface in the secondary determination of the range of the friction coefficient, and narrowing down the range from the first range to the second range to estimate the friction coefficient of the road surface.

[0048] The vehicle control unit 56 has a function of controlling the vehicle 200 based on the value of the friction coefficient of the road surface narrowed down by the μ value calculation unit 53. Examples of vehicle control based on such narrowed down values ​​of the friction coefficient of the road surface include control to alert the occupants via the HMI 43, and control to adjust the drive torque of the vehicle 200 via the vehicle control device 41.

[0049] <Method for estimating the friction coefficient of the road surface> Next, a method for estimating a friction coefficient of a road surface ahead on which the vehicle 200 is traveling according to this embodiment will be described with reference to Fig. 3. The method for estimating a friction coefficient described below is executed by the above-described estimation device 100 in a non-contact manner.

[0050] First, in step 10, the road surface condition detection unit 51, which serves as the road surface condition detection means described above, detects the road surface condition ahead of the vehicle 200 based on information from the ambient environment sensor 31 described above. This determines which of a plurality of predetermined road surface conditions (e.g., DRY, WET, SNOW, ICE, etc.) the road surface condition belongs to. As an example, it is assumed that the road surface condition detection unit 51 determines that the road surface condition ahead of the vehicle 200 is "WET."

[0051] Next, a μ value range (primary μ value) defined for each road surface condition is identified in step 11. That is, the road surface condition detection unit 51 primarily identifies the first range of the friction coefficient corresponding to the detected condition (road surface condition is "WET") as "0.45 to 0.70" based on the determined road surface condition ("WET" in this example) and the friction coefficient range data table illustrated in Fig. 5, for example.

[0052] As mentioned above, the range data table applicable to this example is not limited to a data table that defines the range of friction coefficients corresponding to each road surface condition as shown in FIG. 5, but may also be a form that uses the average value of friction coefficients for each road surface condition (average value of general friction coefficients that can be statistically derived for each road surface) as shown in FIG. 6 or FIG.

[0053] Next, in steps 12 and 13, based on the first range of friction coefficients identified in the previous step, the range of friction coefficients is narrowed from the first range to a narrower second range, and the friction coefficient of the road surface is secondarily identified. That is, first, in step 12, the μ value parameter acquisition unit 52 acquires μ value-related parameters corresponding to the road surface condition identified by the road surface condition detection unit 51. In this example, since the road surface condition is determined to be "WET" in the previous step, the μ value parameter acquisition unit 52 acquires data such as "water film thickness" and "moisture content" as the μ value-related parameters based on the detection information of the ambient environment sensor 31, for example.

[0054] There are no particular limitations on the method for acquiring the "water film thickness" data; for example, it may be detected by a known method using a near-infrared sensor, or the water film thickness on the road surface may be detected using the salinity concentration sensor proposed in JP 2021-092412 A as the ambient environment sensor 31. There are also no particular limitations on the method for acquiring the "moisture content" data; for example, it may be possible to use a known moisture content sensor such as the sensor disclosed in WO 2019 / 044252.

[0055] In this way, the μ value parameter acquisition unit 52 of this embodiment can select the “water film thickness” as the μ value-induced parameter corresponding to the frictional force of the rubber when the determined road surface condition is in the “WET” category. Furthermore, when the determined road surface condition is in the "DRY" category, the μ parameter acquisition unit 52 can select "road surface unevenness" as a μ value-induced parameter corresponding to the frictional force of the rubber. Note that there are no particular limitations on the method for acquiring the "road surface unevenness" data, and for example, it can be detected by a known method using a near-infrared sensor as the ambient environment sensor 31.

[0056] Furthermore, when the determined road surface condition is classified as "SNOW," the μ-value parameter acquisition unit 52 can select at least one of "density," "road surface temperature," and "moisture content" as the μ-value-attributing parameter corresponding to the snow column shear force. There are no particular limitations on the method for acquiring the "density" data and "moisture content" data; for example, they can be detected by a known method using a near-infrared sensor as the ambient environment sensor 31.

[0057] Next, in step 13, the range of the friction coefficient that was primarily determined is narrowed down again depending on the road surface condition, thereby secondarily determining the friction coefficient (μ value) on the road surface on which the vehicle is traveling. More specifically, since the road surface condition is "wet" in this example, the μ value calculation unit 53 can determine the friction coefficient (μ value) on the road surface in a wet state on which the vehicle is traveling to be "0.6" based on the "water film thickness" obtained by the μ value parameter acquisition unit 52 and the μ value narrowing table illustrated in FIG. 9. Note that in this example, the friction coefficient value is determined to be one point, but it is not necessary to narrow down the friction coefficient value to one point; it is sufficient to narrow down the friction coefficient value to at least a secondary range that is narrower than the primary range of the friction coefficient that was primarily determined.

[0058] After the value of the friction coefficient of the road surface ahead on which the vehicle is traveling is narrowed down as described above, in step 14, this narrowed down value of the friction coefficient is reflected in the vehicle control of the traveling vehicle 200. More specifically, the drive torque of the vehicle 200 may be adjusted, for example, via the vehicle control device 41, based on the value of the friction coefficient narrowed down with high precision (secondary range).

[0059] In the next step 15, it is determined whether the system of the vehicle 200 has been stopped, and if it is determined that the system is OFF, the process is completed, whereas if the vehicle 200 is still traveling, the process returns to step 1 and the above-described process is repeated. Therefore, for example, if the weather changes from rainy to sunny while the vehicle 200 is traveling, the above-described estimation device 100 again determines that the road surface condition is "DRY", and the subsequent processes are repeated.

[0060] According to the estimation device 100 of the vehicle 200 and the road surface friction coefficient estimation method in this embodiment described above, it is possible to accurately estimate in advance the slipperiness (value of the friction coefficient) of the road surface ahead in the direction of travel according to various driving environments. While the preferred embodiments of the present disclosure have been described above with reference to the accompanying drawings, the present disclosure is not limited to such examples. In other words, it is clear that a person skilled in the art would attempt further modifications to the above-described embodiments, and it is understood that these modifications also fall within the technical scope of the present disclosure.

[0061] That is, the road surface condition detection unit 51 classifies the road surface into four states in advance based on the relationships between the temperature, unevenness, and moisture content described above, and determines which state it is in, but the method for determining the road surface condition is not limited to this. For example, the ambient environment sensor 31 may be mounted on the vehicle 200 as a light source that irradiates three types of near-infrared light with different wavelengths, and the road surface condition detection unit 51 may identify the road surface condition based on the three types of near-infrared light reflected and received by the road surface.

[0062] Furthermore, when the determined road surface condition spans multiple road surface conditions (for example, "WET" and "SNOW"), the estimation device 100 may weight the road surface conditions to narrow down the friction coefficient values ​​and perform secondary identification. As a specific example of such weighting, for example, weighting may be given to the road surface condition with a lower friction coefficient value ("SNOW" in the above example) among the multiple road surface conditions determined with an emphasis on safety, and the secondary range of friction coefficients may be set closer to "SNOW" than the intermediate value between "WET" and "SNOW" (for example, 4:6). [Explanation of symbols]

[0063] 200 vehicles 100 Estimator 31 Ambient environment sensor 41 Vehicle control device 50 Control device 51 Road surface condition detection unit 52 μ-value parameter acquisition section 53 μ value calculation unit 53 56 Vehicle control unit

Claims

1. one or more processors; one or more memories communicatively coupled to the one or more processors; An estimation device for estimating a friction coefficient of a road surface on which a vehicle travels in a non-contact manner, comprising: The processor: determining the road surface state based on the road surface state detected by a sensor mounted on the vehicle and the road surface data that is predetermined and stored in the memory, thereby determining which of a plurality of predetermined road surface states the state of the road surface on which the vehicle is traveling belongs; a first range of friction coefficients corresponding to the determined road surface condition is primarily identified based on friction coefficient information pre-classified for each of the plurality of road surface conditions stored in the memory and the determined road surface condition; based on the first range of the friction coefficient, selecting a μ-value-dependent parameter value that is a parameter having a high degree of influence on the primarily identified road surface condition and that has been previously determined by experiment or simulation and stored in the memory, and narrowing the range of the friction coefficient from the first range to a narrower second range based on the μ-value-dependent parameter value to secondarily identify the friction coefficient of the road surface. Estimation device.

2. The μ-value-attributed parameter values ​​are respectively predetermined in correspondence with the rubber friction force between the tire of the vehicle and the road surface, the snow column shear force, and the edge effect, The processor: When the road surface condition is in the DRY category, road surface unevenness is selected as the μ-value-attributed parameter value corresponding to the friction force of the rubber; When the road surface condition is in the WET category, a water film thickness is selected as the μ-value-attributed parameter value corresponding to the frictional force of the rubber; When the road surface condition is in the SNOW category, at least one of density, road surface temperature, and moisture content is selected as the μ-value-attributed parameter value corresponding to the snow column shear force; When the road surface condition is classified as ICE, the road surface temperature or the water film thickness is selected as the μ-value-attributed parameter value corresponding to the frictional force of the rubber and the edge effect. The estimation device according to claim 1 .

3. One or more processors; one or more memories communicatively coupled to the one or more processors; An estimation device for estimating a friction coefficient of a road surface on which a vehicle travels in a non-contact manner, comprising: The processor: determining the state of the road surface based on the road surface temperature, road surface unevenness, and road surface moisture content detected by the sensors mounted on the vehicle and matrix data of the road surface temperature, road surface unevenness, and road surface moisture content stored in advance in the memory, thereby determining which of a plurality of predetermined road surface states the state of the road surface on which the vehicle is traveling belongs to; a first range of friction coefficients corresponding to the determined road surface condition is primarily identified based on friction coefficient information pre-classified for each of the plurality of road surface conditions stored in the memory and the determined road surface condition; Based on the specified first range of the friction coefficient, select μ-value-dependent parameter values ​​that are parameters that have a high degree of influence on the primarily specified road surface condition and that are respectively defined in advance in correspondence with the rubber friction force between the vehicle tire and the road surface, the snow column shear force, and the edge effect and are stored in the memory, and secondarily identify the friction coefficient of the road surface by narrowing the range of the friction coefficient from the first range to a narrower second range based on the μ-value-dependent parameter values. Estimation device.

4. a sensor for detecting the condition of the road surface; The estimation device according to any one of claims 1 to 3; A vehicle having:

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