JP2000055932A - Device for detecting anomary of sensor for detecting travelling state for vehicle - Google Patents

Abstract

PROBLEM TO BE SOLVED: To detect sensor anomalies speedily. SOLUTION: In a step S82, the estimated values of a yaw rate and lateral acceleration are computed from the output values of a yaw rate sensor, a lateral acceleration sensor, and a steering angle sensor. In a step S84, the estimated errors εr and εy between the output values and the estimated values are each computed on the yaw rate sensor and the lateral acceleration sensor. In a step S86, it is determined whether the estimated error εr of the yaw rate sensor is at least a predetermined threshold value εr0 or not. In the case that the estimated error εr of the yaw rate sensor is at least the predetermined estimated value εr0 in the step S86, it is determined that the yaw rate sensor is faulty in a step S88, SCS (the attitude of the vehicle) control is halted in a step S94, and a warning lamp is lighted up in a step S96 to notify an occupant of a sensor failure.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an abnormality detecting device such as a yaw rate sensor or a lateral acceleration sensor used for controlling the attitude of a vehicle for suppressing sideslip and spin of the running vehicle.

[0002]

2. Description of the Related Art Conventionally, a vehicle state quantity such as a yaw rate and a steering angle of a running vehicle is detected, and the vehicle skids or spins when cornering, avoiding an emergency obstacle, or suddenly changing road conditions. Numerous control devices have been proposed for suppression.

Japanese Patent Application Laid-Open No. Hei 6-115418 proposes a technique in which distribution control is executed only when it is really necessary by changing the start condition of distribution control according to the vehicle speed and the steering angle. .

Further, in the conventional failure diagnosis (fail-safe) of a sensor for controlling the attitude of a vehicle, when an abnormality of a system is detected, the abnormality is corrected so as not to cause a serious problem in performance and safety. Is common.

Japanese Patent Application Laid-Open No. 10-10152 discloses an abnormality of a yaw rate sensor based on a comparison between a fluctuation rate of a reference yaw rate calculated based on a motion parameter such as a steering angle other than a yaw rate and a fluctuation rate of an actual yaw rate. A method for determining the cause (for example, sticking or disconnection) is disclosed.

[0006]

However, in the conventional sensor failure diagnosis, it takes several seconds to several tens of seconds to determine the failure, so that there is little room for the delay in finding the sensor abnormality. In the case of, the vehicle behavior may suddenly change due to a sensor abnormality and fall into an unstable state.
Further, for example, since the yaw rate, the lateral acceleration, and the like frequently change abruptly depending on the behavior of the vehicle or do not show any change at all, it is accurately determined whether these sensors are abnormal or normal. It is very difficult.

SUMMARY OF THE INVENTION The present invention has been made in consideration of the above-described problems, and has as its object to prevent an erroneous determination due to the influence of an error included in a vehicle model itself, and to detect an abnormality in a vehicle running state detection sensor capable of quickly detecting a sensor abnormality. It is to provide a detection device.

[0008]

Means for Solving the Problems In order to solve the above problems and achieve the object, an abnormality detecting device for a vehicle running state detecting sensor according to the present invention has the following arrangement. That is, a first sensor for detecting a first traveling state amount of the vehicle and a second sensor for detecting a second traveling state amount related to the first traveling state amount.
A sensor for detecting an abnormality of the first sensor, wherein the abnormality detecting means sets a vehicle model for inputting an output value of the first sensor and estimating an output value of the second sensor. An abnormality of the first sensor is detected based on a deviation between an estimated value of the second sensor estimated by the vehicle model and an actual sensor output value, and the deviation always appears when the first sensor is abnormal. To correct the error included in the vehicle model.

[0009] Preferably, the first sensor detects at least one of a steering angle and a vehicle speed of the vehicle.

Preferably, the second sensor detects at least one of a yaw rate and a lateral acceleration of the vehicle.

[0011] Preferably, the abnormality detecting means includes:
A side slip angular velocity is calculated from an output value of the first sensor, an estimated value of the side slip angular velocity is calculated from the vehicle model, and an abnormality of the first sensor is detected based on a deviation between the side slip angular velocity and the estimated value.

Preferably, the abnormality detecting means corrects the vehicle model based on a road surface friction coefficient.

Preferably, the abnormality detecting means selects an optimum model from a preset vehicle model based on a road surface friction coefficient.

[0014] Preferably, the abnormality detecting means sets the vehicle model by a detection filter.

Further, the abnormality detecting device for a vehicle running state detecting sensor according to the present invention has the following configuration. That is, a first sensor for detecting a first traveling state amount of the vehicle and a second sensor for detecting a second traveling state amount related to the first traveling state amount.
A sensor and an output value of the first sensor,
Estimating means for estimating the output value of the sensor, and abnormality detecting means for detecting an abnormality of the first sensor based on a deviation between the estimated output value of the second sensor and the actual output value, wherein the abnormality detecting means comprises: The error of the estimating means is corrected based on the road surface friction coefficient so that the deviation always appears when the first sensor is abnormal.

[0016]

As described above, according to the first aspect of the present invention, a vehicle model for inputting the output value of the first sensor and estimating the output value of the second sensor is set, and is estimated by the vehicle model. Detecting the abnormality of the first sensor based on the deviation between the estimated value of the second sensor and the actual sensor output value,
By correcting an error included in the vehicle model so that a deviation always appears when the first sensor is abnormal, erroneous determination due to the influence of the error included in the vehicle model itself can be prevented, and the sensor abnormality can be detected quickly.

According to the second aspect of the present invention, the first sensor detects at least one of the steering angle and the vehicle speed of the vehicle, thereby preventing erroneous determination due to the influence of an error included in the vehicle model itself. As a result, the sensor abnormality can be quickly detected.

According to the third aspect of the present invention, the second sensor detects at least one of the yaw rate and the lateral acceleration of the vehicle, thereby preventing erroneous determination due to the influence of an error included in the vehicle model itself. As a result, the sensor abnormality can be quickly detected.

According to the fourth aspect of the present invention, the abnormality detecting means calculates a side slip angular velocity from an output value of the first sensor, calculates an estimated value of the side slip angular velocity from a vehicle model, and calculates the side slip angular velocity. By detecting the abnormality of the first sensor based on the deviation from the estimated value, the sensor abnormality can be quickly detected.

According to the fifth aspect of the present invention, the abnormality detecting means corrects the vehicle model based on the road surface friction coefficient, so that an error caused by a vehicle model error caused by a change in the road surface friction coefficient. Judgment can be prevented.

According to the sixth aspect of the present invention, the abnormality detecting means selects an optimal model from a preset vehicle model based on the road surface friction coefficient, thereby causing a change in the road surface friction coefficient. It is possible to prevent erroneous determination due to the influence of the error of the vehicle model.

According to the seventh aspect of the present invention, the abnormality detecting means sets the vehicle model by the detection filter, thereby preventing erroneous determination due to the influence of an error included in the vehicle model itself, and quickly. Sensor abnormality can be detected.

According to the present invention, the abnormality of the first sensor is detected based on the deviation between the estimated output value of the second sensor and the actual output value. By correcting the error of the estimating means based on the road surface friction coefficient so that the deviation always appears, it is possible to prevent erroneous determination due to the influence of the error of the estimating means caused by the change of the road surface friction coefficient.

[0024]

Embodiments of the present invention will be described below in detail with reference to the accompanying drawings.

A description will be given of a sensor abnormality detection method using a detection filter as the present embodiment.

FIG. 14 is a block diagram showing a sensor abnormality detection method using the detection filter of the present embodiment.

The abnormality detection by the detection filter is performed by designing an observer (state estimator) for the vehicle model and comparing the actual sensor output value with the estimated value based on the vehicle model to detect the abnormality of the sensor. This is a method in which a correction value (gain) is added to the input to the vehicle model so that the influence (offset) due to the failure of the sensor or the like appears independently of each other with a unique directionality (eigenvector) for each sensor. That is, as shown in FIG. 14, the difference between the actual sensor output value of the vehicle and the estimated value based on the vehicle model (estimated error ε) is monitored to detect a sensor abnormality, and a fail signal on the failed sensor is detected. By inputting the observer gain to the vehicle model so as to appear in the estimation error in proportion to the size, the sensor with the offset is detected as abnormal. Further, an estimation error 演算 calculated by weighting the difference between the actual sensor output value and the estimated value of the vehicle model in consideration of a change in the road surface friction coefficient μ or the like.
To detect sensor abnormality.

In this embodiment, a method in which a detection filter is applied to a linear two-degree-of-freedom model in which the road surface friction coefficient μ is set to 1 will be described.

First, the linear two-degree-of-freedom model is expressed by the following equations (1) and (2).
Is represented by

[0030]

(Equation 1)

With respect to the equations (1) and (2), the term n of the sensor failure is added to the right side of the state equation, and the dynamics of the virtual sensor (to be summarized by adding a first-order lag system) to facilitate the detection of the sensor failure. Expressions 3 to 5 are given below.

[0032]

(Equation 2)

Here, nr is a fail element on the output value of the yaw rate sensor, and ny is a fail element on the output value of the lateral acceleration sensor.

Next, an observer represented by the following equations (6) and (7) is constructed as the observer gain D.

[0035]

(Equation 3)

When equations 6 and 7 are subtracted from equation 5 to calculate the estimation error ε, the following equations 8 and 9 are obtained.

[0037]

(Equation 4)

From the equations (8) and (9), by setting the D matrix so that the eigenvector of (A-DC) coincides with f, the influence of the failure of each sensor can be independently observed. Specifically, the failure of the yaw rate sensor appears only in the direction of (0,0,1,0) T in the system state space,
On ε is observed as a deviation with a vector of (1,0) T. Similarly, the failure of the lateral acceleration sensor is (0,0,0,1) T on the state space and (0,1) T on ε. Therefore, the failure state of each sensor can be observed by observing each element of ε.

For example, the vehicle characteristics are set to typical values at a vehicle speed of 100 km / h, the virtual cutoff frequency of the sensor characteristics is set to λr = λy = 20π, and the eigenvalues of (A-DC) are both -1.
When the parameters are set so that 0 and the eigenvector become f, each matrix is represented by the following Expressions 10 to 12.

[0040]

(Equation 5)

When the estimation error ε is calculated from the observer assuming that a yaw rate sensor failure as shown in FIG. 12 occurs, the results shown in FIGS. 1 and 2 are obtained. FIG. 1 shows the estimated error εr of the yaw rate sensor when the yaw rate sensor fails,
FIG. 2 shows the estimated error εy of the lateral acceleration sensor when the yaw rate sensor fails.

In this embodiment, the front wheel steering angle is input to the vehicle model having two degrees of freedom, ie, the yaw rate r and the lateral acceleration y, as shown in FIG. The maximum speed is 180 deg / sec, and the vehicle speed is 100 km / h. 12 and 13 show output values of the yaw rate sensor and the lateral acceleration sensor,
It is assumed that the output value of the yaw rate sensor doubles in 13 seconds.

As can be seen from FIGS. 1 and 2, the effect of the failure of the yaw rate sensor does not appear in the estimation error εy of the lateral acceleration sensor, and the failure of the yaw rate sensor can be detected by observing the estimation error εr of the yaw rate sensor. . Further, 13 seconds in the steering pattern shown in FIG.
When a detection filter is applied assuming that an abnormality has occurred in the lateral acceleration sensor in the vicinity and the sensor output value has doubled, the results shown in FIGS. 3 and 4 are obtained.
FIG. 3 shows the estimated error εr of the yaw rate sensor when the lateral acceleration sensor fails, and FIG. 4 shows the estimated error εy of the lateral acceleration sensor when the lateral acceleration sensor fails.

As can be seen from FIGS. 3 and 4, the influence of the failure of the lateral acceleration sensor does not appear in the estimation error εr of the yaw rate sensor, but by observing the estimation error εy of the lateral acceleration sensor, Failure can be detected. Therefore, an abnormal sensor can be specified by comparing the estimation error of each sensor.

However, the detection filter is expressed by the following equation (10).
Since the observer based on the vehicle model illustrated in FIG. 12 is set, if an error exists in the vehicle model, accuracy deteriorates. Therefore, for example, when a detection filter based on Equations 10 to 12 is applied to a vehicle model in which the road surface friction coefficient μ is reduced to 0.7 times, the results shown in FIGS. 5 shows the estimation error εr of the yaw rate sensor when the yaw rate sensor fails, FIG. 6 shows the estimation error εy of the lateral acceleration sensor when the yaw rate sensor fails, and FIG. 7 shows the estimation of the yaw rate sensor when the lateral acceleration sensor fails. FIG. 8 shows the estimated error εy of the lateral acceleration sensor when the lateral acceleration sensor fails.

As can be seen from FIGS. 5 to 8, even if a model error is present, a deviation appears in the estimation error due to the sensor failure due to the detection filter. Will occur.

In order to reduce the influence of the model error, a method of updating the vehicle model while separately identifying the road surface friction coefficient μ or the like, or a method of preparing a vehicle model for each of several road surface friction coefficients μ and using a sensor A method of selecting an appropriate vehicle model from the output values and the like can be considered. Further, the estimation error ε may be weighted as in the following Expression 13. Equation 1 below
In FIG. 3, ξ corresponds to an estimation error of the sideslip angular velocity.

[0048]

(Equation 6)

FIG. 9 shows the estimated error of the sideslip angular velocity calculated from ξ when the yaw rate sensor shown in FIGS. FIG. 10 shows a comparative example of the response of the side slip angular velocity.

As can be seen from FIGS. 9 and 10, ξ is not affected by the road surface friction coefficient μ, and a correct deviation occurs when the sensor fails. As can be seen from FIG. 10, a relatively large deviation occurs when the steering angle is input (3-4 sec, 6-7 sec) only with the sideslip angular velocity, and it is not possible to capture only the offset that occurs when the sensor fails, but the vehicle model is used. By taking the difference from the estimated value, the S / N ratio increases, and even if a model error exists, a failure of any of the sensors can be detected.

In this embodiment, the abnormality detection of the yaw rate sensor and the lateral acceleration sensor has been described. However, the present invention is applicable to a steering angle sensor and other sensors. In this example, the gain of the detection filter is
Although set to 10 rad / sec, from the viewpoint of absorbing the model error, it is preferable to set the actual vehicle to about 100 to 200 rad / sec in consideration of the dynamic characteristics of the vehicle. <Sensor Abnormality Detection> Next, a sensor abnormality detecting method using a detection filter will be described.

FIG. 15 is a flowchart for explaining a sensor abnormality detection method using a detection filter. In the following, as an exemplary embodiment, description will be given of detection of abnormality of a yaw rate sensor and a lateral acceleration sensor used for vehicle attitude control (SCS control). However, abnormality of other sensors can be detected by a similar method. Needless to say.

As shown in FIG. 15, in step S80, the output values of the yaw rate sensor, the lateral acceleration sensor, and the steering angle sensor are read. Step S8
In step 2, the estimated values of the yaw rate and the lateral acceleration are calculated by the above equations 3 to 7.

In step S84, the estimated errors εr and εy between the output values and the estimated values for the yaw rate sensor and the lateral acceleration sensor are calculated from the above equations 8 and 9.

In step S86, it is determined whether or not the estimated error εr of the yaw rate sensor is equal to or larger than a predetermined threshold εr0. If the estimated error εr of the yaw rate sensor is equal to or larger than the predetermined threshold εr0 in step S86 (YES in step S86), it is determined that the yaw rate sensor has failed in step S88, the SCS control is stopped in step S94, and the warning lamp is turned on in step S96. Lights to notify the occupant of a sensor failure.

On the other hand, if the estimated error εr of the yaw rate sensor is not equal to or larger than the predetermined threshold εr0 in step S86 (NO in step S86), it is determined in step S90 whether the estimated error εy of the lateral acceleration sensor is equal to or larger than the predetermined threshold εy0. I do. In step S90, the estimation error ε of the lateral acceleration sensor
If y is equal to or greater than the predetermined threshold value εy0 (YE
S), it is determined that the lateral acceleration sensor has failed in step S92, the SCS control is stopped in step S94, and a warning lamp is turned on in step S96 to notify the occupant of the sensor failure.

If the estimated error εy of the lateral acceleration sensor is not equal to or larger than the predetermined threshold εy0 in step S90 (NO in step S90), the process returns to step S80. <Other Embodiment> As another embodiment, the vehicle model may be corrected based on the road surface friction coefficient μ in step S82 in FIG. 15 using the weighting ξ shown in Expression 13.

FIG. 16 is a flowchart for explaining a sensor abnormality detection method using a detection filter according to another embodiment.

In FIG. 16, step S80 in FIG.
After that, in step S81a, a road surface friction coefficient μ is estimated based on a change in the rotation speed of each wheel when an acceleration / deceleration torque due to a brake or a driving force acts on each wheel. In step S81b, a vehicle model is determined according to the road surface friction coefficient μ. Is corrected, and then the process of step S82 is performed.

As described above, according to this embodiment, the influence of the deviation between the output value of the sensor and the estimated value can be reduced, and the abnormality of the sensor can be detected immediately. Further, by setting a vehicle model in consideration of the influence of the road surface friction coefficient μ, the detection accuracy of the sensor abnormality can be improved. [Regarding SCS Control] Next, S to which this embodiment is applied
The CS control will be described.

FIG. 17 is a diagram showing the overall configuration of a control block of the SCS control device of this embodiment.

As shown in FIG. 17, the SCS of this embodiment is
The control device controls the front, rear, left and right wheels to suppress skidding and spin of the running vehicle, for example, when the running state of the vehicle is cornering, when avoiding an emergency obstacle, or when the road surface condition is suddenly changed, and the like. It controls power. Each wheel is provided with an FR (right front wheel) brake 31 such as a hydraulic disc brake, an FL (left front wheel) brake 32, an RR (right rear wheel) brake 33, and an RL (left rear wheel) brake 34. These FR, FL, RR, RL brakes 31 to 3
Reference numerals 4 are respectively connected to the hydraulic control unit 30. The hydraulic control unit 30 includes the FR, FL, RR, and RL brakes 3
1 to 34 are connected to each wheel cylinder (not shown),
A braking force is applied to each wheel by introducing hydraulic pressure to the wheel cylinder of each of the brakes 31 to 34. The hydraulic control unit 30 includes the pressurizing unit 36 and the master cylinder 3
7 is connected. The master cylinder 37 generates a primary hydraulic pressure in accordance with the depression force of the brake pedal 38. The primary hydraulic pressure is introduced into the pressurizing unit 36, is pressurized to the secondary hydraulic pressure by the pressurizing unit 36, and is introduced into the hydraulic control unit 30. The hydraulic control unit 30 includes the SCSECU 1
0, and electrically controls hydraulic pressures applied to the FRs, FLs, RRs, and RL brakes 31 to 34 in response to a braking control signal from the ECU 10 to control a braking force applied to each wheel.

SCS (STABILITY CONTROLLED SYSTEM)
The ECU (ELECTRONIC CONTROLLED UNIT) 10 controls the braking of the front, rear, left and right wheels as the attitude control device of the present embodiment, and controls the well-known ABS (anti-lock brake system) and traction control system (hereinafter referred to as traction control system). , Traction control). The SCS / ECU 10 includes an FR wheel speed sensor 11, an FL wheel speed sensor 12, and an RR wheel speed sensor 1.
3, RL wheel speed sensor 14, vehicle speed sensor 15, steering angle sensor 16, yaw rate sensor 17, lateral acceleration sensor 18, longitudinal acceleration sensor 19, brake pedal pressure sensor 35, EGIECU 20, traction off switch 40 are connected. I have.

An outline of the ABS control and the traction control will be described. The ABS control automatically controls the braking force on the wheels when a sudden braking operation is performed while the vehicle is running and the wheels are likely to lock on the road surface. This is a system that controls the wheels to stop while suppressing the lock of the wheels. Traction control suppresses the phenomenon that the wheels slip on the road surface while the vehicle is running by controlling the driving force or braking force on each wheel. This is a system that runs while traveling.

The FR wheel speed sensor 11 outputs a detection signal v1 of the right front wheel speed to the SCS / ECU 10. The FL wheel speed sensor 12 outputs a detection signal v2 of the wheel speed of the front left wheel to the SC.
Output to S · ECU 10. The RR wheel speed sensor 13 outputs a detection signal v3 of the wheel speed of the right rear wheel to the SCS / ECU 10. The RL wheel speed sensor 14 outputs a detection signal v4 of the wheel speed of the left rear wheel to the SCS / ECU 10. The vehicle speed sensor 15 outputs a detection signal V of the traveling speed of the vehicle to the SCS / ECU 1
Output to 0. The steering angle sensor 16 outputs a steering rotation angle detection signal θs to the SCS ECU 10. The yaw rate sensor 17 outputs a detection signal rs of the yaw rate actually generated in the vehicle body to the SCS / ECU 10. The lateral acceleration sensor 18 outputs a detection signal ys of a lateral acceleration actually generated in the vehicle body to the SCS / ECU 10. The longitudinal acceleration sensor 19 outputs a detection signal Z of the longitudinal acceleration actually generated in the vehicle body to the SCS / ECU 10. The brake depression force sensor 35 includes a pressure unit 36
And a detection signal PB of the depression force of the brake pedal 38
Is output to the SCS / ECU 10. The traction-off switch 40 is a switch for forcibly stopping wheel spin control (traction control), which will be described later, and outputs this switch operation signal S to the SCS-ECU 10. E
GI (ELECTRONIC GASOLINE INJECTION) ECU20
Means Engine 21, AT (AUTOMATIC TRANSMISSION) 2
2. Connected to the throttle valve 23, the output control of the engine 21, the shift control of the AT 22, the throttle valve 23
Is responsible for opening and closing control.

SCS ECU 10 and EGI ECU 2
Reference numeral 0 includes a CPU, a ROM, and a RAM, and executes a posture control program and an engine control program stored in advance based on the input detection signals. <Schematic Description of Posture Control> The posture control according to the present embodiment is to apply a turning moment and a deceleration force to the vehicle body by controlling the braking of each wheel, thereby suppressing the sideslip of the front wheels or the rear wheels. For example, when the rear wheel is likely to skid while the vehicle is turning (spin), a brake is mainly applied to the front outer wheel to apply an outward moment to suppress the entrainment behavior inside the turn. When the front wheels are likely to skid to the outside of the turn (drift out), an appropriate amount of brake is applied to each wheel to apply an inward moment, and the turning radius is reduced by suppressing the engine output and adding a deceleration force. Is suppressed.

Although the details of the attitude control will be described later, in general, the SCS / ECU 10 includes the vehicle speed sensor 15, the yaw rate sensor 17, and the lateral acceleration sensor 18 described above.
From the detected signals V, rs, and ys, the actual sideslip angle (hereinafter referred to as actual sideslip angle) βact and the actual yaw rate (hereinafter referred to as actual yaw rate) ract generated in the vehicle are calculated, and from the actual sideslip angle βact The reference value βref referred to in the calculation of the estimated sideslip angle βcont actually used for the SCS control is calculated. Also, the SCS / ECU 10
Calculates a target side slip angle βTR and a target yaw rate rTR as a posture to be a target of the vehicle from a detection signal of a steering steering angle sensor or the like, and calculates a difference between the estimated side slip angle βcont and the target side slip angle βTR or an actual yaw rate ract and a target yaw rate rT.
When the difference in R exceeds the predetermined threshold values β0, r0, attitude control is started, and control is performed so that the estimated actual skid angle βcont or the actual yaw rate ract converges to the target skid angle βTR or the target yaw rate rTR.

It should be noted that the present invention is applicable to modifications or variations of the above embodiment without departing from the spirit thereof.

[0069]

[Brief description of the drawings]

FIG. 1 is a diagram showing a change in an estimation error of a yaw rate sensor when a yaw rate sensor fails.

FIG. 2 is a diagram showing a change in an estimation error of a lateral acceleration sensor when a yaw rate sensor fails.

FIG. 3 is a diagram showing a change in an estimation error of a yaw rate sensor when a lateral acceleration sensor fails.

FIG. 4 is a diagram illustrating a change in an estimation error of the lateral acceleration sensor when the lateral acceleration sensor fails.

FIG. 5 is a diagram showing a change in an estimation error of the yaw rate sensor when the yaw rate sensor fails when the road surface friction coefficient μ is reduced.

FIG. 6 is a diagram showing a change in an estimation error of the lateral acceleration sensor when the yaw rate sensor fails when the road surface friction coefficient μ is reduced.

FIG. 7 is a diagram showing a change in an estimation error of the yaw rate sensor when the road surface friction coefficient μ decreases and the lateral acceleration sensor fails.

FIG. 8 is a diagram showing a change in an estimation error of the lateral acceleration sensor when the road surface friction coefficient μ decreases and the lateral acceleration sensor fails.

FIG. 9 is a diagram illustrating a change in an estimation error of a side slip angular velocity when the estimation error is weighted.

FIG. 10 is a diagram showing a change in a side slip angular velocity in the case of FIG. 31;

FIG. 11 is a diagram illustrating an input state of a front wheel steering angle according to the present embodiment.

FIG. 12 is a diagram showing a change in an output value when a yaw rate sensor fails.

FIG. 13 is a diagram showing a change in an output value of a lateral acceleration sensor.

FIG. 14 is a block diagram of a detection filter of the present embodiment.

FIG. 15 is a flowchart illustrating a sensor abnormality detection method according to the present embodiment.

FIG. 16 is a flowchart illustrating an abnormality detection method according to another embodiment.

FIG. 17 is a block diagram of a vehicle attitude control device.

──────────────────────────────────────────────────の Continued on the front page (51) Int.Cl. ⁷ Identification symbol FI Theme coat ゛ (Reference) G01P 3/42 G01P 3/42 K 15/00 15/00 Z (72) Inventor Tomohiko Adachi Aki-gun, Hiroshima 3-1 Fuchucho Shinchi Mazda Co., Ltd. F-term (reference) 2F029 AA02 AC12 2F069 AA83 AA86 AA93 BB21 DD02 EE04 GG63 HH30 NN12 QQ03 2F077 AA07 AA23 2F105 AA02 BB07 BB17 BB20

Claims (8)

[Claims] 1. A first method for detecting a first traveling state quantity of a vehicle.
A sensor, a second sensor for detecting a second traveling state quantity related to the first traveling state quantity, and abnormality detecting means for detecting an abnormality of the first sensor. A vehicle model for inputting an output value of the first sensor and estimating an output value of the second sensor is set, and based on a deviation between an estimated value of the second sensor estimated by the vehicle model and an actual sensor output value. Detecting an abnormality of the first sensor and correcting an error included in the vehicle model such that the deviation always appears when the first sensor is abnormal. . 2. The apparatus according to claim 1, wherein the first sensor detects at least one of a steering angle and a vehicle speed of the vehicle. 3. The abnormality detection device according to claim 2, wherein the second sensor detects at least one of a yaw rate and a lateral acceleration of the vehicle. 4. The abnormality detecting means calculates a skid angular velocity from an output value of the first sensor, calculates an estimated value of the skid angular velocity from the vehicle model, and based on a deviation between the skid angular velocity and the estimated value. The abnormality detection device for a vehicle traveling state detection sensor according to claim 1, wherein an abnormality of the first sensor is detected. 5. The abnormality detection device for a vehicle running state detection sensor according to claim 1, wherein the abnormality detection means corrects the vehicle model based on a road surface friction coefficient. . 6. The vehicle traveling according to claim 1, wherein said abnormality detecting means selects an optimal model from a preset vehicle model based on a road surface friction coefficient. Abnormality detection device for state detection sensor. 7. The abnormality detection device for a vehicle running state detection sensor according to claim 1, wherein the abnormality detection means sets the vehicle model by a detection filter. 8. A first method for detecting a first traveling state quantity of a vehicle.
A sensor, a second sensor for detecting a second traveling state quantity related to the first traveling state quantity, and an estimating means for inputting an output value of the first sensor and estimating an output value of the second sensor. And an abnormality detecting means for detecting an abnormality of the first sensor based on a deviation between an estimated output value of the second sensor and an actual output value, wherein the abnormality detecting means comprises: An abnormality detection device for a vehicle running state detection sensor, wherein an error of the estimating means is corrected based on a road surface friction coefficient so that a deviation always appears.