JP2021142969A - Sensor error correction device - Google Patents

Abstract

To estimate a zero point error of a steering angle with good accuracy.SOLUTION: A calculation unit 14 compares a yaw rate value calculated on the basis of a roll angle and a pitch angle of a vehicle calculated from an angular speed detected by an IMU 26, a steering angle detected by a steering angle sensor 28, and a longitudinal speed of the vehicle detected by a vehicle speed sensor 24 with a yaw rate value detected by the IMU 26 according to behavior of the vehicle, and estimates a zero point error of the steering angle detected by the steering angle sensor 28.SELECTED DRAWING: Figure 1

Description

The present invention relates to a sensor error correction device, and more particularly to a sensor error correction device that estimates a zero point error of a steering angle detected by a steering angle sensor.

Conventionally, the steering angle value has been used in systems such as anti-slip function and power steering that do not require a minute amount of steering angle. However, in advanced safety functions such as automatic driving, it is necessary to measure a minute amount in consideration of the influence of the road surface inclination and perform accurate control.

Patent Document 1 discloses an invention of a correction device that corrects a steering angle zero point by using a slip angle sensor and a self-aligning torque sensor in addition to an acceleration sensor, a yaw rate sensor, and a steering angle sensor.

In Patent Document 2, the steering angle is converted into the yaw rate to calculate the normative yaw rate using the gain that changes depending on the vehicle speed, and the value of the normative yaw rate is compared with the value of the yaw rate sensor to correct the zero point of the steering angle. The invention of the zero point deviation amount calculation device of the steering angle sensor is disclosed.

International Publication 2010/140234 Japanese Unexamined Patent Publication No. 2010-120450

However, the invention described in Patent Document 1 has a problem that it is difficult to apply it to the current mass-produced vehicle because it is necessary to use a slip angle sensor and a self-aligning torque sensor that are not mounted on the mass-produced vehicle. there were. Further, the invention described in Patent Document 1 uses an equation of motion considering the influence of the road surface cant angle to improve the estimation accuracy, but does not consider the pitch angle (front-rear gradient) of the road surface and is a vehicle. During automatic operation, the correction accuracy of the steering angle zero point may deteriorate.

In the invention described in Patent Document 2, the road surface gradient is not taken into consideration in the gain for converting the steering angle into yaw rate, and the accuracy may deteriorate on a sloped road surface. Further, in the invention described in Patent Document 2, the gain for converting the steering angle into the yaw rate generally includes an error, and the error tends to be remarkable when the yaw rate value is large, and as a result, the steering angle sensor of the steering angle sensor There is a problem that the accuracy of error correction is lowered.

The present invention has been made in view of the above problems, and an object of the present invention is to realize a sensor error correction device capable of accurately estimating a zero point error of a steering angle.

The sensor error correction device according to the first aspect is an inertial measurement unit capable of detecting an angular velocity indicating the behavior of the vehicle during traveling, a vehicle speed detecting unit for detecting the front-rear speed of the vehicle, and a steering angle of the vehicle. Based on the steering angle detection unit, the attitude angle of the vehicle calculated from the angular velocity detected by the inertial measurement unit, the steering angle detected by the steering angle detection unit, and the front-rear speed detected by the vehicle speed detection unit. The calculated vehicle body yaw rate value and the inertial measurement yaw rate value included in the angular velocity detected by the inertial measurement unit are compared according to the behavior of the vehicle, and the zero point error of the steering angle detected by the steering angle detection unit is obtained. It includes an error estimation unit for estimation.

In the first aspect, the vehicle body yaw rate is calculated by comparing the vehicle body yaw rate value calculated based on the vehicle attitude angle, the vehicle steering angle, and the vehicle front-rear speed with the inertial measurement yaw rate value detected by the inertial measurement unit. Estimate the zero point error of the steering angle included in the value.

In the second aspect, in the first aspect, the inertial measurement unit outputs an error-corrected inertial measurement yaw rate value, and the error estimation unit outputs the error-corrected inertial measurement yaw rate value and the error-corrected inertial measurement yaw rate value. The zero point error of the steering angle is estimated by comparing with.

As a result, the inertial measurement yaw rate value output by the inertial measurement unit with improved accuracy in recent years is treated as an error-corrected value.

In the third aspect, in the first aspect or the second aspect, the error estimation unit estimates the zero point error of the steering angle when the vehicle body yaw rate value related to the behavior of the vehicle is equal to or less than the reference value. If the vehicle body yaw rate value exceeds the reference value, the zero point error of the steering angle is not estimated.

This suppresses the effects of scale factor errors that can occur on the yaw rate.

In the fourth aspect, the error estimation unit detects the attitude angle of the vehicle calculated from the angular velocity detected by the inertial measurement unit, the steering angle detected by the steering angle detection unit, and the vehicle speed detection unit. The vehicle body inertia rate value is calculated from the lateral speed of the vehicle estimated based on the front-rear speed.

Thereby, the vehicle body yaw rate value can be calculated from the lateral speed of the vehicle based on the fact that the lateral speed of the vehicle and the vehicle body yaw rate value are inextricably linked in the movement of the vehicle.

In a fifth aspect, the error estimation unit calculates a predicted value of a state quantity including the vehicle body yaw rate value, the lateral speed of the vehicle, and the zero point error of the steering angle detected by the steering angle detection unit. The pre-estimator that calculates the predicted value of the observed value detected by the inertial measuring device from the predicted value of the state quantity using the observation equation for the observed value of the inertial measurement yaw rate value detected by the inertial measuring device, and the inertia. Based on the difference between the observed value detected and output by the measuring device and the predicted value of the observed value calculated by the pre-estimated unit, the predicted value of the state quantity calculated by the pre-estimated unit is corrected. It includes a state estimation unit and a state estimation unit.

In the sixth aspect, the pre-estimation unit further uses the zero point error of the steering angle when the inertial measurement yaw rate value is equal to or less than the reference value or the front-rear speed exceeds the reference value. The system noise is reduced and the covariance matrix of the state quantity is updated, and the system noise is increased when the inertial measurement yaw rate value exceeds the reference value or when the front-rear velocity is equal to or less than the reference value. The covariance matrix of the state quantity is updated, and the state estimation unit detects and outputs the difference between the observed value detected and output by the inertial measurement unit and the predicted value of the observed value calculated by the pre-estimation unit. , And, based on the covariance matrix of the state quantity, the predicted value of the state quantity calculated by the pre-estimation unit is corrected.

According to the present invention, there is an effect that the zero point error of the steering angle can be estimated accurately.

It is a block diagram which showed an example of the sensor error correction apparatus which concerns on embodiment of this invention. It is the schematic which showed the coordinate system in embodiment of this invention. It is explanatory drawing which showed an example of the variable in embodiment of this invention. It is a block diagram which showed an example of the input / output relation of the algorithm using a linear Kalman filter. Explanation showing the relationship between the front-rear speed and lateral speed at the center of gravity of the vehicle, the front-rear speed and lateral speed at the center of the rear wheel axle, the actual steering angle of the front wheels, the distance from the center of gravity to the center of the front wheel axle, and the distance from the center of gravity to the center of the rear wheel axle. It is a figure.

Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. As shown in FIG. 1, the sensor error correction device 10 according to the present embodiment includes a storage device 18 that stores data necessary for the calculation of the calculation device 14 and a calculation result by the calculation device 14, and a vehicle detected by the vehicle speed sensor 24. An input device 12 for inputting the front-rear speed, the angular speed and acceleration of the vehicle attitude angle detected by the IMU 26, and the steering angle of the vehicle detected by the steering angle sensor 28, and the input data and storage device 18 input from the input device 12. A display composed of a computing device 14 composed of a computer or the like that calculates the estimation of the vehicle position based on the data stored in the computer, and a CRT or LCD that displays the vehicle position and the like calculated by the computing device 14. It is composed of a device 16.

Subsequently, the coordinate system related to the behavior of the vehicle 200 is defined as shown in FIG. The earth coordinate system 204 is a coordinate system in which the gravitational acceleration direction and z _e are parallel to each other with _{respect to the earth plane, and y e} points in the north direction. The road surface coordinate system 206 is a coordinate system in which z _r passes through the center of gravity of the vehicle 200 and is oriented in the direction perpendicular to the road surface, and x _r is oriented in the vehicle traveling direction. The vehicle body coordinate system 208 is a coordinate system fixed on the vehicle body spring, z _v faces the vehicle body vertically upward direction, and x _v faces the vehicle body traveling direction. Therefore, the front-rear direction of the vehicle 200 is a direction parallel to the x-axis of the vehicle body coordinate system 208. As will be described later, in the present embodiment, the reference point of the vehicle body coordinate system 208 is not the center of gravity of the vehicle 200, but the center of the axle of the rear wheel of the vehicle 200 in the vehicle width direction. Further, in the present embodiment, the vehicle body coordinate system 208 is used, and the earth coordinate system 204 is not used.

Further, the roll angle φ, the pitch angle θ, and the yaw angle ψ, which are Euler attitude angles, are defined as shown in FIG. 2 with respect to the earth coordinate system 204. For example, the roll angle φ is the rotation angle around the x-axis, the pitch angle θ is the rotation angle around the y-axis, and the yaw angle ψ is the rotation angle around the z-axis. Further, each of the roll angle φ, the pitch angle θ, and the yaw angle ψ shows positive values when rotated in the right-handed screw direction (in the direction of each arrow in FIG. 2). In the present embodiment, for convenience, the yaw angle deviation described later uses the road surface coordinate system 206 as the reference coordinate system, and further sets the oiler attitude angle originally defined for the earth coordinate system 204 with respect to the vehicle body coordinate system 208. It is defined as a roll angle φ _v , a pitch angle θ _v, and a yaw angle ψ _v. Hereinafter, when the roll angle φ, the pitch angle θ, and the yaw angle ψ are simply described, it is basically assumed that the posture angle is defined with respect to the vehicle body coordinate system 208.

FIG. 3 is an explanatory diagram showing an example of variables in the present embodiment. In the present embodiment, each of the front-rear speed U of the vehicle 200, the lateral speed V of the vehicle 200, and the vertical speed W of the vehicle 200 is defined. U is parallel to the x-axis, V is parallel to the y-axis, and W is parallel to the z-axis.

Further, the output values of the IMU 26 corresponding to the roll angle φ, the pitch angle θ, and the yaw angle ψ of the vehicle 200 are defined as the roll rate P, the pitch rate Q, and the yaw rate R, which are the angular velocities.

In the past, the accuracy of the IMU26, gyro sensor, etc. was insufficient, and the vehicle motion model was also used to estimate the yaw rate R. However, in recent years, the accuracy of the MEMS gyro used for the inexpensive IMU26 has been improved. In this embodiment, the yaw rate R _s value detected by the IMU 26 is used as the error-corrected yaw rate value.

Further, in the present embodiment, each of the roll angle φ and the pitch angle θ of the vehicle 200 is detected based on the roll rate P and the pitch rate Q output by the IMU 26, and is applied to the motion model of the vehicle 200 described later.

The principle of estimating the zero point error of the steering angle sensor 28 will be described below.

First, FIG. 4 is a block diagram showing an example of the input / output relationship of an algorithm using a linear Kalman filter. The linear Kalman filter is based on the steering angle detected by the steering angle sensor 28, the yaw rate detected by the IMU 26, the vehicle speed detected by the vehicle speed sensor 24, and the roll angle and pitch angle which are the attitude angles of the vehicle 200 detected by the IMU 26. The zero point error of the steering angle sensor 28 is estimated.

In the present embodiment, the steering angle detected by the steering angle sensor 28, the vehicle speed detected by the vehicle speed sensor 24, and the vehicle 200 are added to the vehicle motion model in which the influence of the roll angle φ and the pitch angle θ of the vehicle 200 on the vehicle motion is taken into consideration. The zero point error of the steering angle is estimated based on the comparison between the yaw rate value calculated by applying each of the attitude angles of the above and the yaw rate value detected by the IMU26. By estimating the zero point error of the steering angle when the yaw rate of the vehicle 200 is small, the influence of the scale factor error that may occur on each of the yaw rate detected by the IMU 26 and the yaw rate calculated by the vehicle motion model is suppressed.

In the vehicle motion model in which the influences of the roll angle φ and the pitch angle θ of the vehicle 200 on the vehicle motion in the present embodiment are taken into consideration, the equation of motion of the two-wheel model centered on the rear wheel axle is derived. When considering the movement of the vehicle 200, it is easy to assume the movement centered on the center of gravity of the vehicle 200. Therefore, in the present embodiment, the motion around the center of gravity axis of the vehicle 200 is first considered, and then the motion is converted to the motion based on the center of the rear wheel axis of the vehicle 200.

Assuming that the roll angle of the road surface attitude angle is φ _r and the pitch angle is θ _r , the equation of motion in the lateral direction is expressed by the following equation.

_{F y} in the above is a force acting in the y-axis direction, and is represented by the sum of _{the front tire lateral force F f} and the rear tire lateral force F _{r as shown in the following equation.}

The front tire lateral force F _f and the rear tire lateral force F _r are expressed by the following equations.

In relation to the above equation, the speed around the center of gravity CG of the vehicle 200 shown in FIG. 5 is v _c = (U _c , V _c , W _c ), the yaw rate is R _c , the front wheel actual steering angle is δ _w , and the center of gravity CG. The distance from the front wheel axle center 210 is l _f , the distance from the center of gravity CG to the rear wheel axle center 202 is l _r , the cornering stiffness of the front wheels and the rear wheels is K _f and K _r , respectively, and the vehicle mass is m.

Substituting the front wheel tire lateral force F _f and the rear wheel tire lateral force F _{r into F y of the} above equation of motion, the equations of motion for the lateral velocity V _c and the yaw rate R _c are as follows.

The last term related to the gravitational acceleration g in the equation of motion related to the lateral velocity V _c is a term expressing the influence of gravity due to the road surface attitude angle. Since the yaw rate is generated by the anteroposterior balance of the lateral velocity generated by the road surface attitude angle, the gravitational acceleration indirectly affects the _{yaw rate R c} even if the equation of motion related to _{the yaw rate R c does not include the term related to the gravitational acceleration.} do.

Consider three position vectors to transform the above equation into variables centered on the rear axle. Vector r _ew to the rear wheel axle center from the origin of the earth coordinate system, the vector r _ec from the origin of the earth coordinate system to the vehicle center of gravity CG, given the vector r _cw to the rear wheel axle center from the center of gravity of the vehicle CG, the following equation The relationship like this is established.

When the above equation is time-differentiated, the rotational angular velocity ω _{c at the center of gravity and the rotational angular velocity ω v} at the center of the rear axle are the same. Be organized.

As shown in FIG. 5, assuming that the front-rear speed of the rear wheel axle center 202 of the vehicle 200 is U _v and the lateral speed is V _{v , U v} = U _c and V _v = V _c -l _r from the above equations. It becomes R _v. When this is used in the equation of motion in the lateral direction, the following equation is derived. In this embodiment, the equation of state for constructing the Kalman filter is formulated based on the following equation.

Hereinafter, the state quantity x in the equation of state according to the present embodiment is defined as follows.

_{Δ e} included in the state quantity x is the zero point error of the steering angle. Furthermore, the input of the state transition equation is defined as follows.

_{By inputting the front-rear speed U v,} which is the vehicle speed of the vehicle 200, and the posture angle (roll angle φ _r and pitch angle θ _r ) of the vehicle 200, the vehicle motion model is linearized with respect to the vehicle lateral speed V _v and the yaw rate R _v. Can be transformed into. The equation of state in this embodiment is as follows, and the vehicle lateral velocity V _v and the yaw rate R _v are inextricably linked. In other words, it is also possible to calculate the yaw rate R _v from the attitude angle of the vehicle 200, the front wheel actual steering angle δ _{w , and the lateral speed V v} of the vehicle 200 estimated based on the front-rear speed U _{v of the vehicle 200.}

_{An error covariance matrix Q n} is defined in which each of the errors v included in each of the above equations of state is a diagonal component. Each element of Q _n is the system noise of each element of the state quantity x.

Of the state quantities x, the zero point error δ _e of the steering angle is a random walk model driven by white noise.

When describing the equation of state with a linear model, it is as follows. The A matrix represents a transition matrix based on the state quantity, and the B matrix is a matrix that considers the influence of the state transition due to the input.

By switching the noise of Q _{n depending on the magnitude of the yaw rate R v} , the influence of the scale factor error of the vehicle motion model can be suppressed. For example, when the yaw rate R _v is equal to or less than the reference value (2 deg / sec as an example), the system noise is suppressed and the zero point error δ _e of the steering angle is estimated. On the contrary, when the yaw rate R _v exceeds the reference value, the system noise is increased so that _{the estimation of the zero point error δ e} of the steering angle cannot be advanced. Further, _{the noise of Q n} may be switched _{depending on} the magnitude of the front-rear speed U v of the vehicle 200. Specifically, in the region where the front-rear speed U _v of the vehicle 200 is low, the system noise is increased so that _{the estimation of the zero point error δ e} of the steering angle is not advanced, and in the _{region where the front-rear speed U v} is high, the system noise. May be reduced to advance the estimation of the zero point error δ _{e of the steering angle.} Whether or not to estimate the zero point error δ _{e of the steering angle, the reference value of the yaw rate R v and} the reference value of the front-rear speed U _v are specifically determined through an actual vehicle test of the vehicle 200 or the like.

Next, the observables (observables) by IMU26 are defined as follows. The observable is only the yaw rate R _{s detected by IMU26.}

The observation equation Cx satisfying y = Cx defines the correspondence between the state quantity (right side) and the observation quantity (left side) as shown in the following equation. In this embodiment, since the observable is _{only the yaw rate R s} detected by the IMU 26, the observable equation is as follows, and the C matrix representing the relationship between the state quantity and the observed quantity is composed of only one variable.

_{W Rs} in the above equation is the observed noise. Therefore, the error covariance matrix of the observation equation is defined as follows.

Hereinafter, the calculation method of the linear Kalman filter will be described. The linear Kalman filter compares the difference between the predicted step of updating the state quantity based on the state equation, the observed amount corresponding to the pre-predicted value of the state quantity, and the actually observed observed quantity, and the predicted value of the state quantity. It is composed of a filtering step that performs a process of correcting the above.

As shown in the following equations (1) and (2), in the prediction step, each of the state quantity x _k-1 at the time k-1 and the covariance matrix P _k-1 of the state quantity x _k-1 is set. , The state quantity x _{k at} time k and the covariance matrix P _k of the state quantity x _k are updated respectively. _{U k} in the equation (1) is an input of the state transition equation at time k, and is the front-rear speed U _v of the vehicle 200 detected by the vehicle speed sensor 24, the front wheel actual steering angle δ _w detected by the steering angle sensor 28, and _{The roll angle φ r} and the pitch angle θ _r of the vehicle 200 detected by the IMU 26.

_{In the filtering step, the Kalman gain K k} is defined by the above-mentioned state equation, covariance matrix, C matrix, and observed noise as in the following equation (3).

Using the above Kalman gain K _k , the process of weighting the observed quantity y and the estimated value of the state quantity x is performed as in the following equations (4) and (5). _{Since the observed amount y includes only the yaw rate R s} detected by the IMU 26, the vehicle motion in consideration of the influence of _{the roll angle φ r} and the pitch angle θ _r of the vehicle 200 on the vehicle motion by the processing using the equation (4). The value of _{yaw rate R v} calculated based on the model is compared with the value of _{yaw rate R s detected by IMU26.}

By repeating the processes represented by the above equations (1) to (5) for each time step, the zero point error δ _e of the steering angle is estimated. The value of the zero point error δ _e of the steering angle is updated by the sequential processing represented by the equations (1) to (5).

In the present embodiment, the sequential processing described above, the value of the detected yaw rate R _s is performed by reducing the system noise if less than a predetermined reference value in IMU26 value predetermined reference yaw rate R _s detected by IMU26 If the value is exceeded, increase the system noise. As described above, the yaw rate R _s includes a scale factor error in addition to the error due to the zero point error δ _e. Since the scale factor error is _{added to the yaw rate R s} at a constant rate, it has a feature that the influence is larger when the _{yaw rate R s is larger than when it is small.} In the present embodiment, the estimation accuracy of the zero point error δ _e can be improved by estimating _{the zero point error δ e} of the steering angle only in the region where the influence of the scale factor is small and the yaw rate R _{s is small.}

According to the principle described above, the arithmetic unit 14 according to the present embodiment includes the vehicle body yaw rate value, the lateral speed of the vehicle, and the zero point error of the steering angle detected by the steering angle sensor 28 according to the above equation (1). A pre-estimation unit that calculates the predicted value of the quantity and calculates the predicted value of the observed value detected by the IMU26 from the predicted value of the state quantity using the observation equation for the observed value of the inertial measurement yaw rate value detected by the IMU26. Based on the difference between the observed value detected and output by the IMU26 and the predicted value of the observed value calculated by the pre-estimated unit, the predicted value of the state quantity calculated by the pre-estimated unit is corrected according to the above equation (4). Includes a state estimation unit.

The pre-estimation unit further reduces the system noise expressed by using the zero point error of the steering angle when the inertial measurement yaw rate value is less than or equal to the reference value or when the front-rear speed exceeds the reference value. The covariance matrix is updated, and when the inertial measurement yaw rate value exceeds the reference value or the front-rear velocity is less than the reference value, the system noise is increased and the state quantity covariance matrix is updated.

Further, the state estimation unit was calculated by the pre-estimation unit based on the difference between the observed value detected and output by the IMU26 and the predicted value of the observed value calculated by the pre-estimation unit, and the covariance matrix of the state quantity. Correct the predicted value of the state quantity.

Further, the arithmetic unit 14 estimates the position of the vehicle 200 by using the highly accurate steering angle δ _r obtained by removing the zero point error δ _{e from the front wheel actual steering angle δ w} detected by the steering angle sensor 28. The estimation result is displayed on the display device 16 as needed, and contributes to the control of the automatic driving of the vehicle 200.

As described above, according to the sensor error correction device 10 according to the present embodiment, the yaw rate can be calculated using the highly accurate steering angle calculated based on the vehicle motion model described above and the front-rear speed of the vehicle 200. It is possible, and by comparing the calculated yaw rate value with the yaw rate value detected by the IMU 26, the zero point error of the steering angle can be estimated accurately.

In advanced safety functions such as automatic driving, it is necessary to perform accurate control in consideration of the minute influence of the road surface inclination, but in this embodiment, the steering angle due to the road surface inclination is determined by estimation based on the vehicle motion model. Control that takes into account even minute effects becomes possible.

By correcting the zero point of the steering angle with high accuracy, for example, lane keep control with better followability becomes possible, and the accuracy of the autonomous navigation algorithm using the steering angle can be improved.

In this embodiment, the vehicle body posture angle is calculated using the output value of the IMU 26, but the present embodiment is not limited to this. For example, the vehicle body posture angle may be calculated using a geomagnetic sensor, GPS, or the like. Since the IMU26, the geomagnetic sensor, the GPS, and the like are general-purpose sensors, the sensor error correction device 10 according to the present embodiment can be widely mounted on various mass-produced vehicles.

10 Sensor error correction device 12 Input device 14 Arithmetic unit 16 Display device 18 Storage device 24 Vehicle speed sensor 26 IMU
28 Steering angle sensor 200 Vehicle

Claims (6)

An inertial measurement unit that can detect the angular velocity including the yaw rate of the vehicle while driving,
A vehicle speed detection unit that detects the front-rear speed of the vehicle and
A steering angle detection unit that detects the steering angle of the vehicle, and
The vehicle body attitude angle calculated from the angular velocity detected by the inertial measurement unit, the steering angle detected by the steering angle detection unit, and the vehicle body yaw rate value calculated based on the front-rear speed detected by the vehicle speed detection unit. An error estimation unit that estimates the zero point error of the steering angle detected by the steering angle detection unit by comparing the inertial measurement yaw rate value included in the angular velocity detected by the inertial measurement unit according to the behavior of the vehicle.
Sensor error correction device including. The inertial measurement unit outputs
The error estimation unit is calculated based on the attitude angle of the vehicle calculated from the angular velocity detected by the inertial measurement unit, the steering angle detected by the steering angle detection unit, and the front-rear speed detected by the vehicle speed detection unit. An error-corrected inertial measurement yaw rate value is calculated from the vehicle body yaw rate value, and the inertial measurement yaw rate value detected by the inertial measurement unit is compared with the error-corrected inertial measurement yaw rate value to achieve zero steering angle. The sensor error correction device according to claim 1, wherein the point error is estimated. When the inertial measurement yaw rate value is equal to or less than the reference value, the error estimation unit reduces the system noise expressed by using the zero point error of the steering angle, updates the zero point error of the steering angle, and updates the inertia. According to claim 1 or 2, when the measured yaw rate value exceeds the reference value, the system noise is increased and the zero point error of the steering angle is repeatedly updated to estimate the zero point error of the steering angle. The sensor error correction device described. The error estimation unit estimates based on the attitude angle of the vehicle calculated from the angular velocity detected by the inertial measurement unit, the steering angle detected by the steering angle detection unit, and the front-rear speed detected by the vehicle speed detection unit. The sensor error correction device according to any one of claims 1 to 3, wherein the vehicle body inertial rate value is calculated from the lateral speed of the vehicle. The error estimation unit
The predicted value of the state quantity including the vehicle body yaw rate value, the lateral speed of the vehicle, and the zero point error of the steering angle detected by the steering angle detection unit is calculated, and the inertial measurement yaw rate value detected by the inertial measurement unit is observed. A pre-estimation unit that calculates the predicted value of the observed value detected by the inertial measurement unit from the predicted value of the state quantity using the observation equation for the value.
The predicted value of the state quantity calculated by the pre-estimating unit based on the difference between the observed value detected and output by the inertial measurement unit and the predicted value of the observed value calculated by the pre-estimating unit. The state estimation unit that corrects
The sensor error correction device according to any one of claims 1 to 4. The pre-estimation unit further reduces the system noise expressed by using the zero point error of the steering angle when the inertial measurement yaw rate value is equal to or less than the reference value or the front-rear speed exceeds the reference value. Then, the covariance matrix of the state quantity is updated.
When the inertial measurement yaw rate value exceeds the reference value, or when the front-rear velocity is equal to or less than the reference value, the system noise is increased to update the covariance matrix of the state quantity.
The state estimation unit is based on the difference between the observed value detected and output by the inertial measurement unit and the predicted value of the observed value calculated by the pre-estimating unit, and the covariance matrix of the state quantity. The sensor error correction device according to claim 5, wherein the predicted value of the state quantity calculated by the pre-estimation unit is corrected.

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