JP2001088683A - Road surface friction coefficient estimating device for vehicle - Google Patents

Abstract

PROBLEM TO BE SOLVED: To reduce computing load related to the estimation of a road surface and to quickly and accurately estimate the road surface by positively utilizing data of traveling environment. SOLUTION: This road surface friction coefficient estimating device 1 is so constituted as providing an adaptive control road surface μestimation part 2 and a real value comparison road surface μestimation part 3, and the adaptive control road surface μestimation part 2 estimates the road surface μ based on the motion state and a motion equation of the vehicle using the adaptive control. Signals operated under low μ state such as a wiper switch 12, a low outer temperature judgment part 13, a traction controller 14, etc., are inputted in the adaptive control road surface μestimation part 2 so that the accurate road surface μ is estimated even under the low μ. The real value comparison road surface μestimation part 3 estimates the road surface μbased on a yaw rate estimated by an observer based on the motion state and the motion equation of the vehicle. When a traveling environment recognition part 4 detects a state recognized to be completely covered with snow, the real value comparison road surface μ estimation part 3 approaches to the estimate value of the adaptive control road surface μ estimation part 2 superior in the estimation of the low μ.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an apparatus for estimating a road friction coefficient of a vehicle, which can appropriately estimate a road surface μ according to a traveling environment.

[0002]

2. Description of the Related Art In recent years, various control techniques for traction control, braking force control, torque distribution control and the like have been proposed and put to practical use in vehicles. In many of these techniques, the road surface μ is used for calculation or correction of necessary control parameters, and it is necessary to accurately estimate the road surface μ in order to execute the control reliably.

In order to estimate the road surface μ, a reference value of a predetermined parameter obtained by inputting a driving state of a vehicle to a vehicle motion model set in advance based on a vehicle motion equation is compared with an actual value (sensor value). Various techniques have been proposed for estimating the road surface μ by comparing values or values estimated by an observer. For example, in Japanese Patent Application No. 10-242030, the present applicant also compares a vehicle slip angle estimated by an observer with a vehicle slip angle reference value on a high μ road and a low μ road based on a vehicle motion model to compare a road surface μ. We propose a technique for estimating.

[0004]

On the other hand, in recent years, it has become possible to detect the traveling environment of a vehicle with a camera or the like, and the road surface μ can be clearly predicted from the traveling environment. In such a case, if these data are actively utilized, the calculation load related to the estimation of the road surface μ can be reduced, and the estimation of the road surface μ can be performed quickly and accurately.

SUMMARY OF THE INVENTION The present invention has been made in view of the above circumstances, and it is possible to actively utilize data of a driving environment to reduce a calculation load related to estimation of a road surface μ, and to quickly and accurately estimate a road surface μ. It is an object of the present invention to provide an apparatus for estimating a road surface friction coefficient of a vehicle that can perform the method.

[0006]

According to a first aspect of the present invention, there is provided an apparatus for estimating a road surface friction coefficient of a vehicle, comprising: a road surface μ estimating unit for estimating a road surface μ based on a vehicle motion state. Road friction coefficient estimating device
Traveling environment recognition means for detecting and recognizing a traveling environment; low μ road traveling state detecting means for detecting a low μ road traveling state; a vehicle motion state; and a low μ road traveling state from the low μ road traveling state detecting means. A low μ side road surface μ estimating means for estimating the road surface μ according to the road surface μ estimating means, wherein the road surface μ estimating means sets the road surface μ to the low μ side The estimation is performed according to the road surface μ estimated by the road surface μ estimating means.

That is, the traveling environment recognition means detects and recognizes the traveling environment, the low μ road traveling state detecting means detects the low μ road traveling state, and the low μ road surface μ estimating means detects the motion state of the vehicle and the low μ state. The road surface μ is estimated according to the low μ road traveling state from the road traveling state detecting means. Then, the road surface μ estimating means estimates the road surface μ based on the motion state of the vehicle, but when the traveling environment recognition means detects a state that can be regarded as one-sided snow, the road surface μ is estimated by the low μ side road surface μ estimating means. Is estimated according to the road surface μ estimated by

According to a second aspect of the present invention, there is provided an apparatus for estimating a road surface friction coefficient of a vehicle, wherein the road surface μ estimating means calculates an actual value of the yaw rate by an observer. An actual value estimating section for calculating, a high μ road reference value estimating section for calculating a yaw rate reference value on a predetermined high μ road by a vehicle motion model, and a yaw rate reference value on a predetermined low μ road by a vehicle motion model A low μ road reference value estimating unit that compares the actual value of the yaw rate with the high μ road reference value and the low μ road reference value to estimate the current road surface μ.
And an estimating unit.

That is, the road surface μ estimating means calculates an actual value of the yaw rate by an observer in an actual value estimating section,
The high μ road reference value estimating unit calculates the reference value of the yaw rate on the predetermined high μ road by the vehicle motion model, and the low μ road reference value estimating unit calculates the reference value of the yaw rate on the predetermined low μ road by the vehicle motion model. Is calculated. Then, the yaw rate comparison road surface μ estimating section compares the actual value of the yaw rate with the high μ road reference value and the low μ road reference value to estimate the current road surface μ.

Further, according to a third aspect of the present invention, in the vehicle road surface friction coefficient estimating apparatus according to the first or second aspect, the low μ side road surface μ estimating means includes: The present invention is characterized in that the road surface μ is estimated by adaptive control according to the vehicle motion state and the low μ road traveling state from the low μ road traveling state detecting means, and the road surface μ is accurately estimated by adaptive control.

According to a fourth aspect of the present invention, there is provided an apparatus for estimating a road surface friction coefficient according to the present invention.
A road friction coefficient estimating device for a vehicle including a road surface μ estimating unit for estimating the road surface, comprising a driving environment recognizing unit that detects and recognizes a driving environment. When a state that can be regarded is detected, the estimated road surface μ is set to a preset value.

That is, the driving environment is detected and recognized by the driving environment recognition means. The road surface μ estimating means estimates the road surface μ based on the motion state of the vehicle. However, when the driving environment recognizing means detects a state that can be regarded as one-sided snow, the estimated road surface μ
Is a preset value.

Further, according to a fifth aspect of the present invention, an apparatus for estimating a road surface friction coefficient of a vehicle includes a road surface μ estimating means for estimating a road surface μ by adaptive control based on a motion state of the vehicle. The apparatus has a traveling environment recognition means for detecting and recognizing a traveling environment, and the road surface μ estimating means detects an initial state of the road surface μ to be estimated by adaptive control when the traveling environment recognizing means detects a state that can be regarded as one-sided snow. The value is set to a preset value.

That is, the driving environment is detected and recognized by the driving environment recognition means. Then, the road surface μ estimating means estimates the road surface μ by adaptive control based on the motion state of the vehicle, but when the driving environment recognition means detects a state that can be regarded as one-sided snow,
The initial value of the road surface μ estimated by the adaptive control is set to a preset value.

[0015]

Embodiments of the present invention will be described below with reference to the drawings. 1 to 4 show an embodiment of the present invention, FIG. 1 is a functional block diagram showing a configuration of a road friction coefficient estimating device, FIG. 2 is an explanatory diagram showing an equivalent two-wheel vehicle model of a four-wheel vehicle, FIG. 3 is a functional block diagram showing a configuration of an actual value comparison road surface μ estimating unit, and FIG. 4 is an explanatory diagram showing a configuration of an observer.

In FIG. 1, reference numeral 1 is mounted on a vehicle,
1 shows a road friction coefficient estimating device for estimating a road surface μ. This road surface friction coefficient estimating device 1 mainly includes two road surface μ estimating units, an adaptive control road surface μ estimating unit 2 and an actual value comparison road surface μ estimating unit 3. These two road surface μ estimating units 2
A signal based on the recognition result from the traveling environment recognition unit 4 is input to 3.

The traveling environment recognizing section 4 serves as traveling environment recognizing means, and a pair of cameras 5 provided on the left and right sides of the ceiling in the passenger compartment take stereo images of an object outside the vehicle from different viewpoints. The image pair is subjected to a process of obtaining distance information over the entire image from the corresponding positional deviation amount based on the principle of triangulation to generate a distance image representing a three-dimensional distance distribution. Then, the distance image is processed based on various stored data to recognize a road condition ahead of the vehicle, a three-dimensional object (preceding vehicle), and the like.

Further, as described in detail in Japanese Patent Application No. 11-216191 by the present applicant, the traveling environment recognizing unit 4 monitors a monitoring area set in a predetermined area in a captured image obtained by a pair of cameras 5. It has a fail-safe function of outputting an image halt signal when detecting a state that can be regarded as snow on the entire road surface based on image data in the area. Specifically, the number of luminance edges in the horizontal direction of the monitoring area,
The magnitude of the overall luminance of the monitoring area is calculated, and when the number of luminance edges is smaller than the determination value and the overall luminance level is greater than the determination value, it is determined that the surface can be regarded as one-sided snow. . Then, when the traveling environment is in a state in which the traveling environment can be regarded as one-sided snow, the traveling environment recognizing unit 4 outputs a signal indicating this (hereinafter, referred to as a snow condition signal) to the two road surface μ estimating units 2 and 3.

The adaptive control road surface μ estimating unit 2 is connected to a front wheel steering angle sensor 6, a vehicle speed sensor 7, and a yaw rate sensor 8 in addition to the running environment recognizing unit 4, and the front wheel steering angle δfs, the vehicle speed Vs, and the vehicle speed Vs, respectively. Yaw rate (yaw angular velocity) (d
セ ン サ / dt) s are inputted. The suffix s of each parameter is used to distinguish that the parameter is a sensor value.

The vehicle has a wiper switch 12,
At least one of the low outside air temperature determination unit 13, the traction control device 14, the antilock brake (ABS) control device 15, the braking force control device 16, the slip detection device 17, and the transmission control device 18 is mounted. An operation signal is input to the estimating unit 2. Here, the wiper switch 12, the low outside air temperature judging unit 13, the traction control device 14, the antilock brake (ABS) control device 15, and the braking force control device 1
6. The slip detecting device 17 and the transmission control device 18 output an operation signal to the adaptive control road surface μ estimating section 2 when the vehicle is estimated to be traveling on a low μ road, and It serves as state detection means.

In brief, the wiper switch 12 outputs an operation signal to the adaptive control road surface μ estimation unit 2 when the wiper is operated.

The low outside air temperature judging section 13 judges, for example, whether or not the outside air temperature is low outside air temperature (for example, 0 ° C. or less), and when the outside air temperature is low, sends an operation signal to the adaptive control road surface μ estimating section 2. Output.

The traction control device 14 detects the slip ratio of each wheel based on, for example, the wheel speeds of the four wheels,
When the slip ratio exceeds a set value, a predetermined control signal is output to the brake drive system and the engine control system to perform braking or torque reduction of the engine. The signal is also output to adaptive control road surface μ estimation unit 2.

The ABS control unit 15 can control the speed, acceleration / deceleration, and pseudo calculation vehicle speed of each wheel based on, for example, the wheel speeds of four wheels and a signal from a brake switch. If the deceleration is equal to or greater than a predetermined value, it is determined that the vehicle is suddenly braked, the wheel speed at that time is set as the initial speed, and thereafter, a value calculated by decelerating at the predetermined deceleration) is calculated, and a pseudo calculation is performed. Judging from the comparison between the vehicle speed and the wheel speed, the degree of acceleration and deceleration of the wheels, etc., three hydraulic modes of pressure increase, hold, and pressure reduction are selected when the antilock brake is activated, and the selected predetermined brake control Outputs a signal to the brake drive system. The operation signal of the ABS control device 15 is also output to the adaptive control road surface μ estimating unit 2.

The braking force control unit 16 calculates the differential value of the target yaw rate, the differential value of the predicted yaw rate for low μ road running, and both differential values based on, for example, the four-wheel speed, the front wheel steering angle, the yaw rate, and the vehicle specifications. Calculate the deviation between the actual yaw rate and the target yaw rate, calculate the target braking force for correcting the understeer tendency or the oversteer tendency of the vehicle based on these values, and calculate the understeer of the vehicle. To correct the tendency, select the inside rear wheel in the turning direction as the braking wheel to apply braking force, and to correct the oversteering tendency, select the front outside wheel in the turning direction as the braking wheel to apply braking force, and output a control signal to the brake drive system to select the wheel To the target braking force to control the braking force. The operation signal of the braking force control device 16 is also output to the adaptive control road surface μ estimation unit 2.

The slip detecting device 17 determines whether or not the vehicle is in a slip state, for example, based on whether or not a rotational speed ratio between the left-right average of the front wheel speed and the right-left average of the rear wheel speed exceeds a preset threshold value. Then, an operation signal is output to the adaptive control road surface μ estimating unit 2 in the slip state.

The transmission control device 18 includes:
An operation signal is output to the adaptive control road surface μ estimating unit 2 mainly when one range for enhancing traction, running performance, and escapeability on a low μ road is selected.

Then, the adaptive control road surface μ estimating section 2
The road surface μ is estimated and calculated, and the estimated road surface μ is output to the power distribution control device 21. On the other hand, when the snow condition signal is input from the driving environment recognition unit 4, the actual value comparison road surface μ estimation unit 3 calculates the road surface μ. Estimates are read. The power distribution control device 21 controls front and rear power distribution by controlling a differential limiting torque of a center differential (not shown) according to the estimated road surface μ.

The adaptive control road surface μ estimating section 2 calculates the road surface μ.
For example, the present applicant uses the method using the adaptive control disclosed in Japanese Patent Application Laid-Open No. H8-2274 to calculate the front wheel steering angle δfs,
Using the vehicle speed Vs and the yaw rate (dψ / dt) s, the cornering power of the front and rear wheels is extended to a nonlinear region and estimated based on the equation of motion of the lateral motion of the vehicle, and the equivalent cornering power of the front and rear wheels on a high μ road is estimated. Road surface μ according to the road surface condition based on the ratio of the cornering power of the front and rear wheels estimated above
(Road surface μ estimated value E).

In the method for estimating the road surface μ, the yaw rate response based on the equation of motion of the vehicle is compared with the actual yaw rate, and the value is estimated online using the equivalent cornering power of the tire as an unknown parameter. Specifically, it is calculated by a parameter adjustment rule based on the following adaptive control theory.

Using the vehicle motion model shown in FIG. 2, the equations of motion relating to the translational motion in the lateral direction of the vehicle are as follows: the cornering forces (one wheel) of the front and rear wheels are Ff and Fr, the body mass is M, and the lateral acceleration is (d ² y). / Dt ² ) is given by M · (d ² y / dt ² ) = 2 · Ff + 2 · Fr (1)

On the other hand, the equation of motion relating to the rotational motion about the center of gravity is represented by the distances from the center of gravity to the front and rear wheel axes Lf, Lf
r, the yaw moment of inertia of the vehicle body is Iz, and the yaw angular acceleration is (d ² ψ / dt ² ), and Iz · (d ² ψ / dt ² ) = 2 · Ff · Lf−2 · Fr · Lr (2) Indicated by

If the vehicle slip angle is β and the vehicle slip angular velocity (dβ / dt), the lateral acceleration (d ² y / d)
t ² ) is represented by (d ² y / dt ² ) = V · ((dβ / dt) + (dψ / dt)) (3)

The cornering force has a response close to the first-order delay with respect to the tire slip angle. However, ignoring this response delay, and further linearizing the suspension characteristics by using an equivalent cornering power incorporating the tire characteristics. It is as follows. Ff = −Kf · βf (4) Fr = −Kr · βr (5) Here, Kf and Kr are equivalent cornering powers of the front and rear wheels, and βf and βr are side slip angles of the front and rear wheels.

In consideration of the effects of the roll and suspension in the equivalent cornering power Kf, Kr,
Using the equivalent cornering powers Kf and Kr, the front and rear wheelslip angles βf and βr can be simplified as follows by setting the front wheel steering angle to δf. βf = β + Lf · (dψ / dt) / V−δf (6) βr = β−Lr · (dψ / dt) / V (7)

Therefore, various parameters are estimated by showing the above-mentioned equation of motion in a state variable expression, setting a parameter adjustment rule and developing an adaptive control theory. Next, the cornering power of the actual vehicle is obtained from the estimated parameters. The parameters of the actual vehicle include a vehicle body mass, a yawing moment of inertia, and the like. These parameters are assumed to be constant, and only the cornering power of the tire changes.
Factors that change the cornering power of the tire include:
There are non-linearity of the lateral force on the slip angle, the influence of the road surface μ, the influence of the load transfer, and the like. The cornering powers Kf and Kr of the front and rear wheels are obtained from a parameter p estimated by a change in the yaw rate (identified by the change in the yaw rate) and a parameter q estimated by the front wheel steering angle δf (identification proceeds by the input of the steering angle). And, for example, Kf = (q · Iz · n) / (2 · Lf) (8) Kr = (p · Iz + Lf · Kf) / Lr (9)

Accordingly, the front wheel steering angle δfs,
By calculating the vehicle speed Vs and the yaw rate (d / dt) s, the cornering powers Kf and Kr of the front and rear wheels in the nonlinear region are estimated. And the estimated cornering power K of the front and rear wheels
For example, the road surface μ is calculated by comparing f and Kr with those of a high μ road for each of the front and rear wheels, and a road surface μ estimated value E in a nonlinear region is set with high accuracy based on the road surface μ.

That is, the reference equivalent cornering power on the front wheel side and the rear wheel side (equivalent cornering power on a high μ road).
Are Kf0 and Kr0, respectively, the estimated road surface μ values Ef and Er on the front wheel side and the rear wheel side are as follows: Ef = Kf / Kf0 (10) Er = Kr / Kr0 (11) And the front wheel side and the rear wheel The average value of the road surface μ estimated values Ef and Er on the side is defined as the final road surface estimated value E. E = (Ef + Er) / 2 (12)

In the road surface μ estimating method to which the above adaptive control theory is applied, control is performed using the estimated road surface μ (current estimated value), and as a result, it is calculated how much the actual road surface μ is displaced. The calculated deviation is added to the current estimated value, that is, an integration operation is performed based on whether it is higher or lower than the current estimated value, and an accurate value is obtained.

A wiper operation signal of the wiper switch 12, a low outside temperature determination signal of the low outside temperature determination unit 13,
The operation signal of the traction control device 14, the operation signal of the ABS control device 15, the operation signal of the braking force control device 16, the slip detection signal of the slip detection device 17, and the signal by selection of one range of the transmission control device 18 are adaptive control road surface μ estimation. When the road surface μ exceeding the set value is calculated by the road surface μ estimating method, the road surface μ is forcibly initialized to a low μ (for example, 0.3 corresponding to snow compaction) when input to the unit 2, The road surface μ is calculated again from the low μ value.

That is, the above-described road surface μ estimation method in the adaptive control road surface μ estimation unit 2 is performed by an integration operation based on whether the road surface μ is higher or lower than the current estimated value.
If the initial road surface μ estimation value (initial value) is significantly different from the actual road surface μ when the road surface μ changes, the time required to obtain an appropriate road surface μ estimation result becomes long. For this reason, when the above-mentioned signals generated in at least one of the slip traveling state of the vehicle and the traveling state on the traveling road with a low road surface μ are input, a value closer to the low μ, that is, the actual road surface μ, The responsiveness is improved by calculating the road surface μ from a close value. Therefore, the adaptive control road surface μ estimation unit 2 calculates
It is provided as low μ side road surface μ estimating means for accurately estimating the road surface μ even on a low μ road. Since the road surface μ can be accurately estimated even on a low μ road, the power distribution control device 21 is expected to operate sufficiently on a low μ road.
Then, the estimated value of the road surface μ is output. When a snow condition signal is input from the traveling environment recognition unit 4, the road value μ estimation value E is read by the actual value comparison road surface μ estimation unit 3.

The adaptive control road surface μ estimating section 2 receives the value of the road surface μ in the middle region between the high μ region and the low μ region when the judgment signal indicating that the vehicle starts after a long-term stop is input. (For example, μ = 0.5), the road surface μ is calculated from the intermediate value. Here, the long term of the start after the long term stop is, for example, a time period required for exchanging units at the time of vehicle shipment or at a dealer or the like. When the engine is normally restarted, the road surface μ is estimated based on the previously estimated cornering power by the backup power supply.

On the other hand, the actual value comparison road surface μ estimation unit 3
The traveling environment recognition unit 4 serves as a road surface μ estimation unit.
In addition, a front wheel steering angle sensor 6, a vehicle speed sensor 7, a yaw rate sensor 8, and a lateral acceleration sensor 9 are connected, and a front wheel steering angle δfs, a vehicle speed Vs, and a yaw rate (d / d), respectively.
t) s, each sensor value of the lateral acceleration ^{(d 2 y / dt 2)} s is adapted to be input.

Then, the actual value comparison road surface μ estimating section 3
Calculates the road surface μ, outputs the estimated road surface μ to the driving support control device 21,
Performs control related to cornering of a curve existing ahead using the road surface μ and assists the driver. Here, when the snow condition signal is input from the driving environment recognizing unit 4, the actual value comparison road surface μ estimating unit 3 adapts when the condition continues for a preset time (for example, 90 seconds). The road surface μ estimation value E is read from the control road surface μ estimation unit 2, and the road surface μ to be estimated is asymptotically approached to the road surface μ estimation value E.

As shown in FIG. 3, the actual value comparison road surface μ estimating unit 3 includes a high μ road reference value estimating unit 3a, a low μ road reference value estimating unit 3b, an actual value estimating unit 3c, a yaw rate comparison road surface μ estimating unit. It is mainly composed of a part 3d.

The high μ road reference value estimating section 3a calculates the vehicle speed Vs
And the front wheel steering angle δfs are input, and the detected vehicle speed Vs and the yaw rate corresponding to the front wheel steering angle δfs are changed to the high μ road reference yaw rate by a vehicle motion model based on a preset vehicle motion equation on the high μ road. dψ / dt) H and outputs it to the yaw rate comparison road surface μ estimation unit 3d.

From the high μ road reference value estimating section 3a, in addition to the high μ road reference yaw rate (dψ / dt) H, the yaw angular acceleration (d ² ψ / dt ² ) H of the high μ road reference is obtained. The lateral acceleration (d ² y / dt ² ) H is output to the yaw rate comparison road surface μ estimation unit 3d. The subscript H of each parameter output from the high μ road reference value estimation unit 3a indicates that the parameter is a high μ road reference parameter.

The low μ road reference value estimating unit 3b calculates the vehicle speed Vs
And the front wheel rudder angle δfs are input, and the detected vehicle speed Vs and the yaw rate corresponding to the front wheel rudder angle δfs are reduced by the vehicle motion model based on the vehicle equation of motion on the low μ road set in advance. dψ / dt) L and outputs it to the yaw rate comparison road surface μ estimation unit 3d.

From the low μ road reference value estimating section 3b, in addition to the low μ road reference yaw rate (dψ / dt) L, the low μ road reference yaw angular acceleration (d ² ψ / dt ² ) L
Is output to the yaw rate comparison road surface μ estimation unit 3d.
The subscript L of each parameter output from the low μ road reference value estimating unit 3b indicates that the parameter is a low μ road reference parameter.

The vehicle motion model used in the high μ road reference value estimating section 3a and the low μ road reference value estimating section 3b and the calculation of each parameter will be described with reference to FIG. That is, with respect to the vehicle motion model of FIG.
The following state equation is obtained from the equation (7). (Dx (t) / dt) = A · x (t) + B · u (t) ... (13) x (t) = [β (dψ / dt)] T u (t) = [δf 0] T a11 = −2 · (Kf + Kr) / (MV) a12 = −1-2 · (Lf · Kf−Lr · Kr) / (M · V)
^{V 2) a21 = -2 · (} Lf · Kf-Lr · Kr) / Iz a22 = -2 · (Lf 2 · Kf + Lr 2 · Kr) / (Iz
· V) b11 = 2 · Kf / (MV ·) b21 = 2 · Lf · Kf / Iz b12 = b22 = 0

In the high μ road reference value estimating section 3a, for example, equivalent cornering powers Kf and Kr when the road surface μ is 1.0 are set in advance in the above equation (13), and the vehicle motion state (vehicle speed) at that time is set. Vs, (dx (t) / dt) = [(dβ / dt) (d ²に お け る) at the front wheel steering angle δfs)
/ Dt ² )] By calculating ^T , the vehicle slip angular velocity (dβ / dt) H and the yaw angular acceleration (d ² ψ /
dt ² ) Calculate H. Then, the calculated vehicle slip angular velocity (dβ / dt) H and yaw angular acceleration (d
By integrating the ^{2 ψ} / dt ²⁾ H, the vehicle body slip angle βH and the yaw rate of the high-μ road reference (dψ / dt) H are obtained. By substituting the vehicle slip angle βH based on the high μ road and the yaw rate (d （/ dt) H into the above equation (6), the front wheel slip angle βfH based on the high μ road is calculated. Furthermore,
By substituting the vehicle slip angular velocity (dβ / dt) H and the yaw rate (dψ / dt) H based on the high μ road into the above equation (3), the high μ road reference lateral acceleration (d ² y / dt ² ) H is calculated. Is calculated.

Similarly, in the low μ road reference value estimating section 3b, for example, equivalent cornering powers Kf and Kr when the road surface μ is 0.3 are set in advance in the above equation (13), and the vehicle motion at that time is set. (Dx (t) / dt) = [(dβ / dt) in the state (vehicle speed Vs, front wheel steering angle δfs)
(D ² ψ / dt ² )] By calculating ^T , the vehicle slip angular velocity (dβ / dt) L and yaw angular acceleration (d ² ψ / dt ² ) L based on the low μ road are calculated. Then, by integrating the calculated vehicle slip angular velocity (dβ / dt) L and yaw angular acceleration (d ² ψ / dt ² ) L based on the low μ road, the vehicle slip angle βL based on the low μ road and the yaw rate (dμ) are integrated. / D
t) L is obtained. Also, the vehicle slip angle β based on a low μ road
By substituting L and the yaw rate (dψ / dt) L into the above equation (6), the low μ road reference front wheel slip angle βfL is calculated.

The actual value estimating section 3c calculates the vehicle speed Vs and the front wheel.
Steering angle δfs, lateral acceleration (d^Twoy / dt ^Two) S and Yawley
(Dψ / dt) s is input, and the actual vehicle behavior is
Actual yaw rate (dψ / dt) while feeding back
The object formed by the vehicle motion model for estimating O
It is a server. The yaw estimated by the actual value estimating unit 3c
The rate (dψ / dt) O is the yaw rate comparison road surface μ estimation
Output to the unit 3d. Also, the actual value estimating unit 3c
And the like, in addition to the above yaw rate (dψ / dt) O,
Acceleration (d^Twoψ / dt^Two) O is the yaw rate comparison road surface μ
Output to the fixed part 3d. The actual value estimating unit 3
The subscript O of each parameter output from c is the observer
Indicates that the parameter is from

Here, the structure of the observer according to the present embodiment will be described with reference to FIG. When the output that can be measured (detected by the sensor) is shown below, y (t) = Cx (t) (14) The configuration of the observer is as follows. (Dx ′ (t) / dt) = (A−K · C) · x ′ (t) + K · y (t) + B · u (t) (15)

When this observer is applied to a vehicle motion model, x (t) becomes a state variable vector (x ′ (t)
“′” Indicates an estimated value), u (t) is an input vector, A and B are coefficient matrices of a state equation, and these correspond to those described above. Further, y (t) is an observable sensor output vector, y (t) = [βs (dψ / dt) s] ^T , and the vehicle body slip angle βs by the sensor is expressed by the above (3).
From the relation of the equation, the lateral acceleration (d ² y / d
It is obtained by integrating the vehicle slip angular velocity (dβ / dt) s obtained by the sensor based on t ² ) s and the yaw rate (dψ / dt) s. Further, C is a matrix (a unit matrix in the present embodiment) indicating the relationship between the sensor output and the state variable, and K is a feedback gain matrix that can be set arbitrarily, and is shown as follows.

From these relationships, the observer uses the observer to determine the yaw angular acceleration (d ² ψ / dt ² ) O and the vehicle slip angular velocity (d
β / dt) O is estimated by the following equations (16) and (17). (D ² ψ / dt ² ) O = a11 ・ (dψ / dt) O + a12 ・ βO + b11 ・ δfs + k11 ・ ((dψ / dt) s- (dψ / dt) O) + k12 ・ (βs-βO) 16) (dβ / dt) O = a21 · (dψ / dt) O + a22 · βO + k21 · ((dψ / dt) s− (dψ / dt) O) + k22 · (βs−βO) (17)

The yaw angular acceleration (d ² ψ / dt ² ) O and the vehicle slip angular velocity (dβ /
dt) O, the yaw rate (dψ / d
t) Calculate O and the vehicle slip angle βO. Further, the vehicle slip angle βO and the yaw rate (dψ / dt) O are calculated according to the above (6).
By substituting into the equation, the front wheel slip angle βfO is calculated.

The calculation in the high μ road reference value estimating unit 3a, the low μ road reference value estimating unit 3b, and the actual value estimating unit 3c is a division by 0 at the vehicle speed Vs = 0, and cannot be performed. Therefore, at an extremely low speed (for example, a speed that does not reach 10 km / h), the yaw rate and the lateral acceleration are sensor values. That is, (dψ / dt) H = (dψ / dt) L = (dψ / dt)
O = (dψ / dt) s (d 2 y / dt 2) O = a ^{(d 2 y / dt 2)} s. Also, the slip angle of the vehicle body is set to βH = βL = βO = δfs · Lr / (Lf + Lr) from the geometrical relationship of steady circular turning. At this time, since no cornering force is generated, the front wheel slip angles are all zero.

ΒfH = βfL = βfO = 0

The yaw rate comparison road surface μ estimation unit 3d
Are the sensor values of the vehicle speed Vs and the front wheel steering angle δfs,
Yaw rate (dψ / dt) H, yaw angular acceleration (d^Twoψ
/ Dt ^Two) H, lateral acceleration (d^Twoy / dt^Two) H and low μ
Road-based yaw rate (dψ / dt) L, yaw angular acceleration
(D^Twoψ / dt^Two) L and yaw estimated as actual value
Rate (dψ / dt) O and yaw angular acceleration (d^Twoψ / d
t^Two) O is input. Then, the execution condition described later is satisfied.
High yaw rate (dψ / dt)
H, low μ road reference yaw rate (dψ / dt) L and yaw rate
(Dψ / dt) O, based on the following equation (18)
A new road surface friction coefficient μγn is calculated.

Here, μH is the high μ road reference value estimating unit 3a
Road friction coefficient assumed in advance (for example, 1.0)
And μL respectively indicate a road surface friction coefficient (for example, 0.3) assumed in advance by the low μ road reference value estimating unit 3b. μγn = ((μH-μL) ・ (dψ / dt) O + μL ・ (dψ / dt) H-μH ・ (dψ / dt) L) / ((dψ / dt) H- (dψ / dt) L) (18)

That is, in equation (18), a linear function is formed from the high μ road reference yaw rate (dψ / dt) H and the low μ road reference yaw rate (dψ / dt) L, and the yaw rate (dψ / d dt) The road surface μ is obtained by substituting O, and is set as a new road surface friction coefficient μγn. The new road friction coefficient μγn is limited between a predetermined upper limit value (for example, 1.0) and a lower limit value (for example, another road friction coefficient value accurately obtained particularly on a low μ road). And

Then, as shown in equation (19), the road surface friction coefficient μγ is calculated by a yaw rate comparison by performing a weighted average of the road surface friction coefficient μn-1 estimated last time and the current road surface friction coefficient μγn. The road surface friction coefficient μγ by comparison is output as the finally estimated road surface μ.

Μγ = μn−1 + κ1 · (μγn−μn−1) (19) where the weight function κ1 is a high μ road reference yaw rate (dψ
The larger the difference between / dt) H and the low μ road reference yaw rate (dL / dt) L, the higher the reliability, the higher the estimated value. κ1 = 0.3 · | (dψ / dt) H− (dψ / dt) L | / | (dψ / dt) H | (20)

The yaw rate comparison road surface μ estimating section 3
At d, the following conditions are set in advance as conditions for calculating the road surface friction coefficient μγ based on the yaw rate comparison.

(1-1) A vehicle which is originally a multi-degree-of-freedom system is approximated by two degrees of freedom around a horizontal axis and a vertical axis, and a two-wheel model is used. No calculation is performed during low-speed running and large turning. For example,
If the vehicle speed Vs does not reach 10 km / h or if the absolute value of the steering angle is greater than 500 deg, no calculation is performed.

(1-2) Electric noise of sensor input value,
In consideration of the influence of disturbance or the like not taken into account at the modeling stage, the calculation is not performed when the absolute value of the yaw rate at which the ratio of the influence of noise or disturbance is relatively large is small.
For example, the absolute value of the yaw rate (dψ / dt) O is 1.5
If it does not reach deg / s, no calculation is performed.

(1-3) Since the road surface friction coefficient is estimated based on the fact that a difference in the cornering force appears due to the road surface friction coefficient, the ratio of the influence of noise or disturbance to the influence of the road surface friction coefficient is relatively large. If the increasing cornering force is small, that is, if the absolute value of the lateral acceleration proportional to the cornering force is small, the calculation is not performed. For example, a high μ road reference lateral acceleration (d ² y / dt)
² ) If the absolute value of H does not reach 0.15G, no calculation is performed.

(1-4) The yaw rate response to the steering angle input changes depending on the road surface friction coefficient, and may cause a delay. If the road surface friction coefficient is estimated when this delay occurs, the error increases. Therefore, if the delay is large,
If it can be determined that the error due to the delay increases, no calculation is performed. For example, if it is determined that the error due to a delay other than when the yaw rate rises (a setting range from the start of the yaw rate generation to the transition to the convergence side) becomes large, the calculation is not performed. Here, the rise of the yaw rate is determined by (yaw rate) · (yaw angular acceleration).

(1-5) If the absolute value of the difference between the high μ road reference yaw rate and the low μ road reference yaw rate does not have a sufficient magnitude against the influence of noise, disturbance, etc., no calculation is performed. For example, if the absolute value of the difference between the high μ road reference yaw rate (dψ / dt) H and the low μ road reference yaw rate (dψ / dt) L does not reach 1 deg / s, no calculation is performed.

(1-6) In order to avoid division by zero in the above equation (20), for example, a high μ road reference yaw rate (dψ / dt)
If the absolute value of H does not reach 1 deg / s, no calculation is performed.

Further, a signal indicating the driving environment is input from the driving environment recognition unit 4 to the yaw rate comparison road surface μ estimation unit 3d. When the snow condition signal is input from the driving environment recognizing unit 4 and the condition continues for a set time (for example, 90 seconds), the road surface μ estimation value E is read from the adaptive control road surface μ estimation unit 2. , The road surface friction coefficient μγ estimated by the yaw rate comparison road surface μ estimation unit 3d
Is asymptotic to the road-surface estimated value E from the adaptive control road-surface estimating unit 2, and this value is output as the finally estimated road surface friction coefficient μ. When the snow condition signal from the traveling environment recognition unit 4 is lost, the yaw rate comparison road surface μ estimation unit 3d returns to the normal road surface μ estimation after a predetermined time (for example, 180 seconds) has elapsed. ing.

That is, when the snow condition signal is input from the driving environment recognizing unit 4, the vehicle is likely to be traveling on a low μ road, and therefore, the actual value comparison road surface μ estimating unit 3 particularly outputs the low μ.
The road surface μ estimation value E from the adaptive control road surface μ estimation unit 2 that can accurately estimate the road surface μ even on a road is adopted.

In this embodiment, two road surface μ estimating units, ie, the adaptive control road surface μ estimating unit 2 and the actual value comparing road surface μ estimating unit 3 are provided, and when the traveling environment can be regarded as one-sided snow, The road surface μ estimation value of the actual value comparison road surface μ estimation
Is asymptotic to the estimated value from the adaptive control road surface μ estimating unit 2 which is particularly excellent in estimating the above. However, the following application is of course possible.

The road surface friction coefficient estimating device calculates the adaptive control road surface μ.
When only one estimating unit, the actual value comparison road surface μ estimating unit, or the road surface μ estimating unit of another method is used, when the traveling environment can be regarded as one-sided snow, a preset road surface A fixed value of μ (for example, 0.35) is set.

If the road surface friction coefficient estimating device is only the adaptive control road surface μ estimating unit, the wiper switch 12, the low outside air temperature judging unit 13, and the traction control device 14 described in the adaptive control road surface μ estimating unit 2 described above. , ABS control device 15
Similarly to the operation signal from the above, when the traveling environment can be regarded as one-sided snow, the initial value of the adaptive control is set to a preset value (for example, 0.35).

Further, in the present embodiment, a description has been given of the combination of the road surface μ estimation method by adaptive control and the road surface μ estimation method by the observer. However, the present invention can be applied to a combination of other road surface μ estimation methods. Needless to say.

[0078]

As described above, according to the present invention,
When detecting and recognizing the driving environment and detecting a state that can be considered as one-sided snow, the road surface μ according to this driving environment can be quickly set, so the data of the driving environment is actively utilized to estimate the road surface μ. Related computing load can be reduced and the road surface μ
Can be quickly and accurately estimated.

[Brief description of the drawings]

FIG. 1 is a functional block diagram showing a configuration of a road surface friction coefficient estimating apparatus.

FIG. 2 is an explanatory view showing an equivalent two-wheeled vehicle model of a four-wheeled vehicle;

FIG. 3 is a functional block diagram showing a configuration of an actual value comparison road surface μ estimation unit;

FIG. 4 is an explanatory diagram showing a configuration of an observer.

[Explanation of symbols]

DESCRIPTION OF SYMBOLS 1 Road surface friction coefficient estimation apparatus 2 Adaptive control road surface μ estimation unit (low μ side road surface μ estimation unit) 3 Actual value comparison road surface μ estimation unit (road surface μ estimation unit) 3a High μ road reference value estimation unit 3b Low μ road reference value Estimating unit 3c Actual value estimating unit 3d Yaw rate comparison road surface μ estimating unit 4 Running environment recognition unit (driving environment recognition means) 6 Front wheel steering angle sensor 7 Vehicle speed sensor 8 Yaw rate sensor 9 Lateral acceleration sensor 12 Wiper switch (Low μ road running state detection 13) Low outside air temperature determination section (low μ road running state detecting means) 14 Traction control device (low μ road running state detecting means) 15 ABS control device (low μ road running state detecting means) 16 braking force control apparatus (low μ road traveling state detecting means) 17 Slip detecting device (low μ road traveling state detecting means) 18 Transmission control device (low μ road traveling state detecting means)

Continued on the front page (51) Int.Cl. ⁷ Identification symbol FI Theme coat II (reference) F02D 45/00 314 G01N 19/02 B G01N 19/02 B60R 21/00 624C G01P 15/00 G01P 15/00 J F term (Ref.) DB11 DB17 DB18

Claims (5)

[Claims] An apparatus for estimating road friction coefficient of a vehicle, comprising: a road friction coefficient estimating means for estimating a road friction coefficient based on a motion state of a vehicle; a driving environment recognition means for detecting and recognizing a driving environment; A low μ road traveling state detecting means for detecting a state, and a low μ side road surface μ estimating means for estimating a road surface μ according to the motion state of the vehicle and the low μ road traveling state from the low μ road traveling state detecting means. Wherein the road surface μ estimating means estimates a road surface μ according to the road surface μ estimated by the low μ side road surface μ estimating means when the driving environment recognizing means detects a state that can be regarded as one-sided snow. Road friction coefficient estimating device for a vehicle. 2. The road surface μ estimating means calculates an actual value of a yaw rate by an observer, and a high μ road reference value estimator calculates a reference value of a yaw rate on a predetermined high μ road by a vehicle motion model. A low μ road reference value estimating unit for calculating a reference value of the yaw rate on a predetermined low μ road by a vehicle motion model; and comparing the actual value of the yaw rate with the high μ road reference value and the low μ road reference value. 2. The vehicle road surface friction coefficient estimating device according to claim 1, further comprising a yaw rate comparison road surface μ estimating unit for estimating a current road surface μ. 3. The low μ side road surface μ estimating means estimates the road surface μ by adaptive control in accordance with the vehicle motion state and the low μ road running state detection means from the low μ road running state detecting means. The apparatus for estimating a road surface friction coefficient of a vehicle according to claim 1. 4. A vehicle road surface friction coefficient estimating device comprising a road surface μ estimating device for estimating a road surface μ based on a vehicle motion state, comprising: a driving environment recognizing device for detecting and recognizing a driving environment; The road friction coefficient estimating device for a vehicle, wherein the μ estimating means sets a road surface μ to be estimated to a preset value when the running environment recognizing means detects a state that can be regarded as one-sided snow. 5. A vehicle road surface friction coefficient estimating device comprising a road surface μ estimating device for estimating a road surface μ by adaptive control based on a motion state of a vehicle, comprising a driving environment recognizing device for detecting and recognizing a driving environment. The road surface μ estimating means detects a road surface μ to be estimated by adaptive control when the driving environment recognizing means detects a state that can be regarded as one-sided snow.
A friction coefficient estimating device for a vehicle, characterized in that an initial value of (i) is set to a preset value.