Monitoring and detection of sensor errors in an inertial measurement system - Patents.com

Abstract

The present invention uses the angular velocity (ω A ,ω B ) and specific force (f A ,f B The present invention relates to a method for detecting a failure (F) of two inertial measurement units (4, 5) used in a vehicle (22) for measuring a vehicle speed (V) and a vehicle position (V), the method comprising the steps of: detecting a failure (F) of two inertial measurement units (4, 5) used in a vehicle (22) for measuring a vehicle speed ... In order to detect a fault (F) by comparing the sensor signals of the master inertial measurement unit (4), a fault model parameter is estimated with respect to the master inertial measurement unit (4) and is used as a reference value for correcting the measurement values ​​of the slave inertial measurement unit (5), and in an object detection unit (17), one of the two inertial measurement units (4, 5) whose motion estimation is causing an erroneous prediction of the calculated position of an object around the vehicle (22) is detected as a fault, among the motion estimations calculated by Kalman filters (13, 14) arranged downstream of each of the two inertial measurement units (4, 5).

Description

[Technical Field]

[0001] The present invention relates to a method for detecting faults in an inertial measurement unit according to the preamble of claim 1 . [Background technology]

[0002] Inertial Measurement Units (IMUs) are an arrangement of accelerometers and gyroscopes that are used to measure the specific force (acceleration) and rotational velocity of an object in space. IMUs play a key role in estimating the vehicle's motion state, which is required for vehicle control systems, driver assistance systems, and autonomous driving to correctly interpret information from ambient sensors (cameras, LiDAR, radar, etc.). The term Vehicle Motion Observer (VMO) has been established for an arrangement and method for calculating the vehicle's specific motion from IMU data combined with additional vehicle sensor systems (Non-Patent Document 5). Such an arrangement is shown in Figure 1. Figure 1 illustrates an application example of an IMU 1 for motion estimation and navigation based on the prior art. The IMU 1 transmits rotational rate signals from the gyroscopes and specific force signals from the accelerometers (Non-Patent Document 9). In the sensor integration unit 2, motion variables such as the position angle α, the velocity v and / or the position P are calculated by integration from these values ​​and corrected with the aid of other sensors or sensor units 3 (odometer, magnetometer, barometric altimeter, steering angle, vehicle level and / or GNSS receiver, etc.). In the following, the result of the described data fusion is called estimated motion state or motion estimation for short. Where the vehicle motion can be confused with the motion of objects around the vehicle, the terms proper motion state or proper motion estimation should be used for the vehicle.

[0003] An IMU sensor is said to be faulty if the defined (and therefore predetermined by design) relationship between the physical measurement value and the output sensor signal is lost within the sensor, causing the specified measurement behavior to not occur. In this case, the IMU sensor in question becomes unusable. If a fault within the sensor goes undetected and is propagated to the motion calculations, it can lead to significant errors or grossly erroneous values ​​in the useful signals, thus posing a safety risk in safety-related applications.

[0004] Vehicle control systems, driver assistance systems, and autonomous driving require extremely high safety standards. For this reason, it is necessary to automatically detect and locate IMU sensor failures and remove the inaccurate sensor from motion calculations, for example by switching to a redundant sensor (backup solution), within a set time period (FTTI: Fault Tolerant Time Interval).

[0005] The class of automotive IMU systems considered here (automotive-grade IMUs) already offers rudimentary monitoring capabilities, especially with regard to electrical faults. This type of monitoring is referred to below as monolateral monitoring, since it is based on information from only one sensor each. Monolateral methods have low sensitivity and can only detect errors with relatively large amplitudes. This means that the monitoring threshold is generally too high for driver assistance systems and autonomous driving. Similarly, they also offer limited diagnostic coverage for minor errors that could pose a safety risk. This diagnostic coverage can be significantly improved by introducing redundant sensor elements.

[0006] Non-Patent Document 4 summarizes the current state of the art for simple redundant sensor placement. A related solution is shown in Figure 2. In this case, two redundant inertial measurement units 4 (IMU A) and 5 (IMU B) are first calibrated to each other with respect to their systematic errors (bias, sensitivity, misalignment, etc.), and then the calibrated sensor values ​​corresponding to each other are compared. If the difference in measurements exceeds a threshold, a fault is considered within the respective sensor pair. However, it is not yet possible to assign the fault to one of the two sensors, because additional reference values ​​would be required. Non-Patent Document 4 attempts to solve this problem by monitoring the innovations of each Kalman filter for estimating the proper motion. In the two Kalman filters, the motion state predicted with the assistance of the IMU is fused with the remaining vehicle sensor systems (wheel rotation speed, steering angle), making the vehicle sensor systems a third, independent source of information. By evaluating the innovations (weighted differences between predictions and measurements), the IMU error can be localized. A drawback of this procedure is that the monitoring procedure described in [4] is not equally selective for all IMU degrees of freedom of motion, since measurements derived from the vehicle sensor system are primarily limited to translational velocity. For example, it is less sensitive to faults that cause deviations in rotational degrees of freedom. Furthermore, the detection of small-amplitude faults is limited by the resolution of the wheel speed sensor system. In particular, in the case of fusion of estimated eigenmotion states with ambient information, as occurs in object tracking, there are high requirements, in part, for detecting IMU faults through lower detection thresholds and shorter error detection times, which the described method likely cannot meet.

[0007] Although non-patent literature 7 and non-patent literature 6 make a general claim that faults in proper motion estimation can be detected when fusing ambient sensors, the sources do not include specific examples of this. Furthermore, this claim generally focuses on faults that result in physically unrealistic information about the vehicle's motion state. Nothing is said about faults that result in physically possible, but incorrect, motion states. Furthermore, the solutions proposed therein do not include redundancy in the inertial measurement unit and in the calculation of the vehicle's own motion. However, redundancy in the inertial sensors is necessary to shorten the error detection time and increase the sensitivity of error diagnosis through a low error threshold. Summary of the Invention [Problem to be solved by the invention]

[0008] The object of the present invention is to propose a new method for detecting faults in an inertial measurement unit. [Means for solving the problem]

[0009] This problem is solved according to the invention by a method having the features of claim 1.

[0010] Advantageous embodiments of the invention are the subject matter of the dependent claims.

[0011] A method according to the present invention for detecting faults in two inertial measurement units used in a vehicle to measure angular velocity and specific force uses two inertial measurement units each equipped with multiple sensors including an acceleration sensor and a gyro sensor, for example using a multi-hypothesis approach.

[0012] According to the present invention, a first inertial measurement unit is used as a master inertial measurement unit, and a second inertial measurement unit, which may have lower performance than the first inertial measurement unit, is used as a slave inertial measurement unit. Measurement values ​​of the master inertial measurement unit are used as reference values ​​for correcting measurements of the slave inertial measurement unit by estimating fault model parameters for the master inertial measurement unit to detect a fault by comparing two corresponding sensor signals from each of the two inertial measurement units. In the object tracking unit, one of the two inertial measurement units, which is performing motion estimation using a Kalman filter located downstream of the inertial measurement unit, is detected as faulty, causing an erroneous prediction of the calculated position of an object around the vehicle.

[0013] In one embodiment, an error state from a monitoring unit supplied with sensor values ​​from an inertial measurement unit is transmitted to the object tracking unit, and in the event of a fault, multiple hypotheses regarding the motion state can be checked, while in the event of normal operation, one motion state is used.

[0014] In one embodiment, the vehicle's proper motion is described by the vehicle pose and transmitted to the object tracking unit.

[0015] In one embodiment, in the object tracking unit, a new object position of an object or new object positions of multiple objects is predicted in vehicle-fixed coordinates from the calculated hypothesis about the vehicle pose and a previously calculated object position of the object or multiple object positions of multiple objects.

[0016] In one embodiment, the object positions of one or more objects measured by the vehicle's ambient sensors are compared to predicted object positions from the vehicle's inherent motion hypotheses identified using a Kalman filter, and inherent motion hypotheses in which the deviation between the predicted object positions and the measured object positions exceeds a threshold are detected as incorrect.

[0017] In one embodiment, if a failure occurs in one of the inertial measurement units, the failure is detected by comparison with the redundant sensors of the other inertial measurement unit, and a switchover occurs to the other inertial measurement unit if the proper motion hypotheses calculated using the inaccurate sensor values ​​are rejected as inaccurate.

[0018] In one embodiment, the monitoring results of two inertial measurement units are incorporated into the fault detection of the object tracking unit, where a fault is detected only if the deviation of the predicted object position for one of the hypotheses exceeds a threshold value but not for the other hypothesis, and at the same time an error is detected by the sensor comparison of the IMU fault detection unit.

[0019] In one embodiment, fault detection includes only one object detected as stationary or multiple objects detected as stationary. Faults are detected for a hypothesis where the predicted object position changes significantly, while the other hypothesis is that the object position remains constant in location.

[0020] The present invention appropriately exploits the higher sensitivity of the object tracking method with respect to errors in proper motion estimation for error detection, thereby achieving detection of even very small amplitude IMU faults.

[0021] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. [Brief explanation of the drawings]

[0022] [Figure 1] FIG. 1 is a schematic diagram of a typical use case of an inertial measurement unit (IMU) for estimating vehicle motion variables. [Figure 2] FIG. 1 is a schematic diagram of IMU channel monitoring with simple redundancy. [Figure 3] 1 is a schematic diagram of a solution according to the invention, including egomotion (proper motion) solution evaluation and error detection within the scope of object tracking. [Figure 4]1A and 1B are schematic diagrams for explaining the basic concept of the present invention in two-dimensional space. DETAILED DESCRIPTION OF THE INVENTION

[0023] In all the drawings, the same reference numerals are used to designate corresponding parts.

[0024] A simple redundant method for detecting a fault F in an inertial measurement unit uses two inertial measurement units 4 and 5, each equipped with multiple accelerometer and gyroscope sensors, as shown in FIG.

[0025] In the figure, the two inertial measurement units 4 and 5 are also designated as IMU A and IMU B. Each of the inertial measurement units has a vector with three angular rate signals:

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[0026] In FIG. 2, a first inertial measurement unit 4 is used as a master inertial measurement unit, and a second inertial measurement unit 5, which may have lower performance than the first inertial measurement unit 4, is used as a slave or backup inertial measurement unit 12. In this case, the monitoring unit 12 estimates systematic fault parameters (fault model parameters, error model parameters) in the model of the slave inertial measurement unit 5 relative to the master inertial measurement unit 4 and detects signal deviations between the corrected signals. An excessively large signal deviation may be caused by a potential fault F in either one of the two inertial measurement units 4 or 5. However, the detection by the monitoring unit 12 alone does not allow detection of which of the two inertial measurement units 4 or 5 is affected by the malfunction (failure) F. However, it is possible to detect which specific sensor pair is affected.

[0027] To calculate the vehicle's motion state, the angular velocity (vector ω A , vector ω B ) and specific force signal (vector f A , vector f B ) are provided as inputs to one Kalman filter 13, 14 (Vehicle Motion Observer, abbreviated as VMO), where they are fused with measurements of other vehicle sensors (such as wheel speeds and steering angle). Each of the two Kalman filters 13, 14 then uses the same measurements. The Kalman filters 13, 14 determine the respective motion states BZ A, BZ B for each inertial measurement unit 4, 5. As can be seen from Figure 1, the aforementioned data fusion of the signals of the inertial measurement unit 1 with the remaining vehicle sensor systems is a standard approach for determining vehicle motion variables.

[0028] The solution proposed in Non-Patent Document 4 as prior art is intended to monitor the innovations of two Kalman filters 13 and 14 in a monitoring unit 15, as shown in FIG. 2, and to use them together with the information sent from the monitoring unit 12 for error detection (fault detection) IF in a fault detection unit 16.

[0029] In the following description, the following mathematical notations are used:

[0030] t0: time point u , u′ : Vehicle attitude estimated from IMU and vehicle sensor system Δq eb n : Vehicle position angle (vehicle attitude, vehicle direction) increment Δp eb n : Vehicle position increment C(Δq eb n ): Rotation matrix calculated from position angle increments H( u ): Homogeneous transformation from Earth-fixed coordinates to vehicle-fixed coordinates x :Object position z : Object location measurement using ambient sensor systems K: Kalman filter gain ν Innovation S: measurement error covariance matrix P - : State error covariance matrix (a priori) before correction by measurement R: Covariance matrix of measurements κ: threshold Ψ: Vehicle yaw angle relative to the Earth-fixed coordinate system ΔΨ: Yaw angle increment ω z :Yaw rate δω z :Yaw rate error v x : Vehicle longitudinal speed v y : Vehicle lateral speed Δt: time interval between two measurements l r : Distance from the rear axle of the vehicle to the vehicle-fixed coordinate system

[0031] Unlike the prior art shown in FIG. 2, according to the present invention, as shown in FIG. 3, the calculation result of the vehicle motion state (correct vehicle posture) u and incorrect vehicle posture u′ The information (error state) from the monitoring unit 12 is fed (transmitted) to the object tracking unit 17. Here, the motion state is the position of the object around the vehicle. x - is used in the prediction block 18 to predict the predicted position x - In the object position correction unit 19, the object measurements from the surrounding sensor system are z and corrected accordingly (corrected positions of objects around the vehicle x + ). If there is a failure F in the inertial measurement units 4 and 5, the predicted position x - and the measured or corrected position x + This, in combination with independent IMU error detection in the monitoring unit 12, is then used to detect IMU faults F, identify their location (assign the corresponding IMU) and, if necessary, isolate the IMU errors by switching to a redundant solution, i.e., the respective error-free inertial measurement unit 4, 5. If neither of the inertial measurement units 4, 5 has a fault F, the predicted position x - and the measured or corrected position x + There will be good agreement between

[0032] The detection problem is solved by propagating two redundant proper motion estimates to the object tracking unit 17, where these estimates can be used as hypotheses for the vehicle's proper motion.

[0033] The object tracking unit 17 typically receives data from various sensor sources (cameras, radar, lidar, ultrasound, etc.) about objects around the vehicle. Typically, this data is fused and tracked over time. The object tracking unit 17 thereby extracts object information, tracks the motion of these objects over time, and predicts their future motion. In this way, dynamic objects such as vehicles and pedestrians can be tracked. Detecting static objects, such as small obstacles on the road surface, is another example of a task of the object tracking unit 17.

[0034] When fusing objects around the vehicle, the vehicle's proper motion needs to be corrected, allowing the correct position and velocity of these objects to be calculated. For this purpose, accurate and reliable motion estimates are used as input from the VMO 13 or 14 to the object tracking unit 17, where the proper motion states are used to predict the positions of surrounding objects during observation by the surrounding sensor system. Inaccurate proper motion states can lead to incorrectly predicted positions of these objects. For example, this can cause the position of a stationary object to change from one observation to the next. All stationary objects being tracked are considered to be affected by this simultaneously. This leads to the following basic idea of ​​the present invention: if a sudden motion is predicted for an object around the vehicle that was previously classified as a stationary object (e.g., a wall), there is a high possibility that there is an anomaly in the motion calculation.

[0035] The solution according to the present invention is to calculate two inherent motion estimates as hypotheses for the two redundant inertial measurement units 4 and 5 in their respective Kalman filters 13 and 14 or VMOs. At the same time, the sensor values ​​of the inertial measurement units 4 and 5 are fed to the monitoring unit 12. When the two inertial measurement units 4 and 5 are working properly, they transmit similar sensor signals, the difference between which is below the threshold of the monitoring unit 12. The inherent motion state calculated therefrom is also the same, and it corresponds to the object around the vehicle being observed by the object tracking unit 17. On the other hand, if a fault F occurs in one of the IMU sensors 4 and 5, this result is detected by the monitoring unit 12. On the other hand, differences in the hypotheses of the vehicle inherent motion state, resulting in an inaccurate solution, and a discrepancy between the predicted and measured positions of the object. On the other hand, the error-free motion state calculated from the error-free IMU corresponds to the observed object. By combining the two approaches, both the inaccurate IMU 4 and 5 and the sensor with the fault F are clearly located, and the system can switch to the backup solution.

[0036] In one embodiment, the motion states BZ A, BZ B calculated from the two redundant inertial measurement units 4, 5 may be provided to an object tracking unit 17 for further processing.

[0037] In one embodiment, the error state from the IMU monitoring unit 12, as determined by comparing the corresponding IMU sensors of the two inertial measurement units 4, 5, can be provided to the object tracking unit 17, so that only if a fault F occurs do the two hypotheses about the motion state need to be checked.

[0038] Here, in one embodiment, the proper motion of the vehicle 22 is calculated as the vehicle attitude u It is described by:

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[0039] where Δq ebn is x n axis and y n is the position increment (angular increment) of the vehicle 22 relative to a local Earth-fixed (tangential) coordinate system 20 with axes Δp eb n is the position increment of the corresponding vehicle 22 in the Earth-fixed coordinate system 20. Also, C(Δq eb n ) represents the rotation matrix that can be calculated from the position increments. The overall transformation (rotation and displacement) of the object from Earth-fixed coordinates to vehicle-fixed coordinates, H( u ) can be written as follows by a homogeneous matrix (homogeneous transformation from Earth-fixed coordinates to vehicle-fixed coordinates) (Non-Patent Document 8, Non-Patent Document 10) well known from practical use:

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[0040] In one embodiment, predictions for the positions of objects around the vehicle can be calculated from two conditions: the vehicle's proper motion and previous object positions.

[0041] Δp o n represents the object's position in Earth-fixed coordinates, and hence its position in vehicle-fixed coordinates, Δp o b is given by the following formula:

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[0042] In one embodiment, the object position of the object in Earth-fixed coordinates x denotes the state of the object for object tracking.

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[0043] Object location measurement using ambient sensor systems z is the measurement sent by the surrounding sensors (object position measurement by the surrounding sensor system).

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[0044] These measurements need to be fused in a Kalman filter 13, 14 to determine the object's location. For simplicity, we will assume that the object has already been classified as a static object. In this case, x must be constant, and the fusion task in the Kalman filter13,14 is to z Estimation of object state using x^ It consists of a correction (innovation) of (the estimated position of the object). z k represents a series of measurements of an object at discrete time instants k=0,1,2,..., u k is the relevant vehicle attitude, x^ k - is the uncorrected (a priori) estimate of the object position, and then the improved (a posteriori) position estimate x^ k + The correction to is performed using the following formula:

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[0045] The difference between the measurement and the predicted measurement determined from the vehicle eigenmotion and a priori position estimation is then the innovation ν k This is called (Non-Patent Document 2, Non-Patent Document 3, Non-Patent Document 1) and is expressed by the following formula:

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[0046] where P k - is the covariance matrix of the a priori state estimation error, and R k is the covariance matrix of the measurement errors. The latter includes basic statistical assumptions about the accuracy (error distribution) of the measurement errors and proper motion estimation. The innovation vector ν k Each component ν ik satisfies the following formula:

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[0047] That is, the difference between the measured and predicted measurements is the diagonal component of the covariance, s iik (For κ=3, 99.73% of the innovations in the statistical mean must be in the range described by equation (9). For κ=4, it would be 99.994%). If this is not the case, there is either an error in the measurement (outlier) or an error in the prediction (e.g. due to incorrectly calculated proper motion), where u k A and u k B are the two hypotheses of proper motion based on inertial measurement units A and B, 4 and 5, respectively; x^ k - Let,be the a priori estimated position of the object around the vehicle that is classified as static.,Faults in IMU,A,4,and the resulting u k A Due to the error in the calculation, the following applies to the innovation component allocated:

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[0048] Also, the hypothesis u k B The following applies to the elements of innovation allocated to:

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[0049] In this case, hypothesis A is clearly false and it is detected that there is an error in proper motion A. In this way, the error detected by the IMU monitoring of the monitoring unit 12 can be unambiguously assigned to one of the two IMUs 4, 5.

[0050] This procedure will be explained using a simple example in two-dimensional space, with reference to Figures 4(A) and 4(B). Figure 4(A) shows the Earth-fixed coordinate system 20 in two-dimensional space for a vehicle 22, and the x b axis and y b The relationship between the two coordinate systems is shown in Fig. 2. The yaw angle Ψ represents the rotation between the two coordinate systems. The corresponding rotation matrix is ​​as follows:

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[0051] In the example shown in FIG. 4(B), the vehicle 22 moves at a constant speed v x , the vehicle 22 is moving straight towards an object position 25 directly in front of it, which is represented by the vector x It is described by:

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[0052] At time t0, which corresponds to the discrete time k=0, the vehicle 22 moves in a yaw angle increment ΔΨ0=0 and a position increment Δp b0 n It has the following characteristics.

[0053]

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[0054] The motion of the vehicle 22 is described by the following three equations:

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[0055] rotation speed ω z is also called yaw rate in this context and is measured by the inertial measurement units 4, 5. x is the fixed longitudinal velocity of the vehicle 22, which can be calculated by the VMO 13, 14 by fusing wheel rotation sensor and IMU data. In this example, a common simplification of the calculation is that the lateral velocity of the vehicle 22 is calculated from the following equation:

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[0056] As a result, we obtain the following for the measured position of the object in vehicle coordinates:

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[0057] Here, at time t0, a constant systematic error δω z appears at the IMU yaw rate sensor, the actual yaw rate ω z=0 instead of an inaccurate yaw rate ω z =0+δω z (twenty one) is measured by the sensor. Therefore, integrating equation (15) leads to the following incorrect yaw angle at time t1: ΔΨ1=δω z Δt (22)

[0058] This error propagates into the position calculation, resulting in an incorrect calculated attitude 24 of the vehicle 22. To be more specific, the error in the yaw angle is small, and the trigonometric functions use small-angle approximations.

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[0059] Rotation matrix obtained from small-angle approximation

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[0060] As a result, the following equation is obtained for the measurement prediction from the vehicle eigenmotion:

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[0061] This result can best be explained by a numerical example. v x =30m / s (28) l r=1.5m (29)

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[0062] For the yaw rate case without fault F, we obtain the following accurate measurement predictions:

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[0063] For a yaw rate with fault F, the following occurs:

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[0064] That is, due to the incorrect proper motion, the position of object 25 in vehicle coordinates is predicted to be shifted more than 0.4 m to the right in the y-component, while the measured object position 26 is always exactly in front of vehicle 22, even in the case of fault F. The associated standard deviation is e.g.

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[0065] In one embodiment, two hypotheses for predicted object positions can be compared to the object positions 25, 26 measured by the ambient sensor system. If one of the hypotheses results in the measured object position 25, 26 exceeding a threshold relative to the predicted object position 25, 26, but the other hypothesis does not, then an error in the corresponding inertial measurement unit 4, 5 can be inferred.

[0066] In one embodiment, the results of monitoring the two IMU sensors 4, 5 can be incorporated into the error detection of the object tracking unit 17. In this case, a fault F is detected only if the deviation of the predicted object position 25, 26 exceeds a threshold value for one of the hypotheses but not for the other hypothesis, while at the same time an error is detected by the sensor comparison of the IMU fault detection unit 16.

[0067] In one embodiment, fault detection involves one object detected as stationary or multiple objects detected as stationary, and a fault F can be detected for a hypothesis where the predicted object position 25, 26 changes significantly, while for the other hypothesis the object position remains constant.

[0068] The present invention allows for fault-tolerant identification of vehicle 22 motion variables in the event of a failure F in an IMU with simple redundancy (continuous operation on failure), without imposing special requirements on geometry and sensor quality. Rather, the present invention is applicable to low-cost heterogeneous IMU hardware that is already partially present in vehicle 22.

[0069] Taking into account the increasing safety requirements during automated driving, the method according to the present invention can protect the signals of the master inertial measurement unit 4 against sensor errors as a measure to ensure functional safety (continued operation in the event of a failure) of the estimation of the vehicle's inherent motion.

[0070] The key point here is the use of dissimilar (not similar) inertial measurement units (IMUs) 4, 5, particularly mounted at different points on the vehicle 22.

[0071] The solution according to the present invention achieves fault tolerance in the calculation of vehicle motion variables, using systems and methods for object detection and fusion that already exist in driver assistance systems and automated vehicles. [Prior art documents] [Non-patent literature]

[0072] [Non-Patent Document 1] Berman, Z.: Outliers Rejection in Kalman Filtering-Some New Observations.In: IEEE / ION Position,Location and Navigation Symposium-PLANS 2014,S.1008-1013 9 [Non-patent document 2] Grewal,MS;Adrews,AP:Kalman Filtering.New York:John Wiley,2001 9 [Non-patent document 3] Groves,PD:Principles of GNSS,Inertial and Multisensor integrated Navigation systems.Artech House,2013 9 [Non-patent document 4] Kalkkuhl,J.;Bergmann,M.;Engelhardt,T.;Bleimund,F.:DE 10 2021 004 103 A1:Verfahren und Anordnung zur Uberwachung und Detektion von Sensorfehlern in Inertial-Mess-Systemen 3,17 [Non-Patent Document 5] Klier,W.;Reim,A.;Stapel,D.:Robust Estimation of Vehicle Sideslip Angle-An Approach w / o Vehicle and Tire Models.In:SAE Technical Paper Series (2008),Nr.2008-01-0582 1 [Non-patent document 6] Mercep,L.;Pollach,M.:US10553044B2:Self-diagnosis of faults with a secondary system in an autonomous driving system.4

Non-licensed Document 7

Non-licensed literature 9

Non-licensed literature 10

Claims

1. Angular velocity (vector ω Ａ , vector ω Ｂ ) and acceleration (vector f Ａ , vector f Ｂ 1. A method for detecting a failure (F) of two inertial measurement units (4, 5) used in a vehicle (22) for measuring a vehicle speed, comprising: each of the two inertial measurement units (4, 5) includes a plurality of sensors including an acceleration sensor and a gyro sensor; a first inertial measurement unit (4) is used as a master inertial measurement unit and a second inertial measurement unit (5), which may have lower performance than the first inertial measurement unit (4), is used as a slave inertial measurement unit; the measurements of the master inertial measurement unit (4) are used as reference values ​​for correcting the measurements of the slave inertial measurement unit (5) by estimating fault model parameters for the master inertial measurement unit (4) in order to detect a fault (F) by comparing two corresponding sensor signals from each of the two inertial measurement units (4, 5); In the object tracking unit (17), a malfunction (F) is detected in the inertial measurement unit of the motion estimation that causes an erroneous prediction of the calculated position of an object around the vehicle (22) among the motion estimations calculated by the Kalman filters (13, 14) arranged downstream of the two inertial measurement units (4, 5). A method characterized by:

2. an error state from a monitoring unit (12) supplied with sensor values ​​of the inertial measurement units (4, 5) is transmitted to the object tracking unit (17), and in the event of a fault (F) several hypotheses regarding the movement state (BZ A, BZ B) can be checked, while in the event of normal operation one of the movement states (BZ A, BZ B) is used, The method of claim 1.

3. the proper motion of the vehicle (22) is described by a vehicle pose (u, u') and transmitted to the object tracking unit (17), 3. The method according to claim 1 or 2.

4. In the object tracking unit (17), a new object position (25, 26) of one object or new object positions (25, 26) of multiple objects is predicted in vehicle-fixed coordinates from the calculated hypothesis regarding the vehicle pose (u, u') and a previously calculated object position (25, 26) of one object or multiple previously calculated object positions (25, 26) of multiple objects. The method of claim 3.

5. the object positions (25, 26) of one or more objects measured by the surrounding sensors of the vehicle (22) are compared with the object positions (25, 26) predicted from the proper motion hypotheses of the vehicle (22) determined using the Kalman filter (13, 14), and the proper motion hypotheses in which the deviation between the predicted object positions (25, 26) and the measured object positions (25, 26) exceeds a threshold are detected as incorrect.

3. The method according to claim 1 or 2.

6. In the event of a failure (F) in one of the inertial measurement units (4, 5), the failure (F) is detected by comparison with the redundant sensor of the other inertial measurement unit (4, 5), and if the proper motion hypothesis calculated using the inaccurate sensor value is rejected as inaccurate, a switchover to the other inertial measurement unit (4, 5) is performed. The method of claim 1.

7. the monitoring results of the two inertial measurement units (4, 5) are incorporated into the fault detection of the object tracking unit (17), in which a fault (F) is detected only if the deviation of the predicted object position (25, 26) for one of the hypotheses exceeds a threshold value but not for the other hypothesis, and at the same time an error is detected by the sensor comparison of the IMU fault detection unit (16). The method of claim 2.

8. characterized in that the fault detection involves one object detected as stationary or multiple objects detected as stationary, and a fault (F) is detected for a hypothesis in which the predicted object position (25, 26) changes significantly, while for another hypothesis the object position remains constant, The method of claim 5.

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