DE102017213806A1 - Calibration of vehicle sensors - Google Patents

Abstract

A method for self-calibration of vehicle sensors (31) is described. In the method, measured values (z _t , W _t ) are recorded on the basis of sensor data of the vehicle sensors (31). An optimal filter is applied to the measured values (z _t , W _t ). Furthermore, as part of the filtering process, an updated calibration value (c _t ) of a calibration variable (c) is determined as the updated value (x _t ) of the state variable (x) of the optimum filter. A calibration device (20) will also be described. Moreover, a rail vehicle (30) will be described.

Description

The invention relates to a method for self-calibration of vehicle sensors. Furthermore, the invention relates to a calibration device. Moreover, the invention relates to a rail vehicle.

Rail vehicles need exact sensor data and odometric data in order to operate safely. Rail vehicles rely on a variety of on-board sensors, such as encoders, Doppler radar, GPS and Balise detectors, to determine their position and speed. However, the odometrical data are correct only if the sensors used have been precisely calibrated. Often errors occur during such a calibration. Such errors can be directly due to errors in the calibration process or caused by a drifting process that begins after the calibration. In this way, the readings become inaccurate and unreliable.

Conventionally, a calibration is performed before the use of the rail vehicle or at regular time intervals. However, in the meantime, there may be significant deviations of the measured values from correct values, so that the accuracy and reliability of the odometric data is not always guaranteed.

There is therefore the task of developing an improved calibration method with which deviations from measured values, for example from odometric measured values, can be avoided or at least reduced over longer periods of time.

This object is achieved by a method for self-calibration of vehicle sensors according to claim 1, a calibration device according to claim 12 and a rail vehicle according to claim 13.

In the method according to the invention for self-calibration of vehicle sensors, measured values are recorded on the basis of sensor data of the vehicle sensors. Then an optimal filter is applied to the measured values. The optimal filter is to be understood as a filter which generates an estimated value, wherein an uncertainty of the estimated value, which is determined on the basis of uncertain measured values, is minimized.

As optimal filter, for example, a linearized optimal filter, such as a linearized Kalman filter can be used. Such a linearized optimal filter has a state transition model and / or observation model linearized by a previous value of a state variable.

Alternatively, however, non-linear filters, such as Unscented Kalman filters or so-called particle filters based on stochastic sequential Monte Carlo methods, may also be used.

In the filtering process, updated calibration values of a calibration quantity are obtained as updated values of the state quantity of the optimal filter. Advantageously, sensors are recalibrated automatically, since this process is automatically carried out during the filtering process, which is used for the evaluation and monitoring of sensor data.

The calibration device according to the invention has a data receiving unit for acquiring measured values on the basis of sensor data of the vehicle sensors. Part of the calibration device according to the invention is also a filter unit for applying an optimum filter to the measured values. The filter unit also serves to determine an updated calibration value of a calibration quantity as an updated value of a state variable of the optimal filter. The calibration device according to the invention shares the advantages of the method according to the invention for self-calibration of vehicle sensors of a rail vehicle.

The rail vehicle according to the invention comprises the calibration device according to the invention. The time estimating device according to the invention shares the advantages of the calibration device according to the invention.

Some components of the calibration device according to the invention can be formed predominantly in the form of software components. This concerns in particular parts of the filter unit. In principle, however, this component can also be partly realized, in particular when it comes to particularly fast calculations, in the form of software-supported hardware, for example FPGAs or the like. Likewise, the required interfaces, for example, if it is only about a transfer of data from other software components, be designed as software interfaces. However, they can also be configured as hardware-based interfaces, which are controlled by suitable software.

A largely software-based implementation has the advantage that already existing in a rail vehicle computer systems can be retrofitted in a simple way by a software update to work on the inventive way. In this respect, the problem is also solved by a corresponding computer program product with a computer program, which can be loaded directly into a memory device of such a computer system, with program sections in order to carry out all steps of the method according to the invention when the computer program is executed in the computer system.

Such a computer program product may contain, in addition to the computer program, additional components, e.g. a documentation and / or additional components, also hardware components, such as e.g. Hardware keys (dongles, etc.) for using the software include.

For transport to the storage device of the computer system and / or for storage on the computer system, a computer-readable medium, for example a memory stick, a hard disk or another portable or permanently installed data carrier can be used, on which the computer program readable and executable program units of a computer unit are stored. The computer unit may e.g. for this purpose have one or more cooperating microprocessors or the like.

The dependent claims and the following description each contain particularly advantageous embodiments and further developments of the invention. In this case, in particular the claims of a claim category can also be developed analogously to the dependent claims of another claim category and their description parts. In addition, in the context of the invention, the various features of different embodiments and claims can also be combined to form new embodiments.

In one embodiment of the method according to the invention for the self-calibration of vehicle sensors, the state variable comprises a vector variable. Such a state variable comprises as components both a calibration quantity and further information to be processed about the technical and / or dynamic state of a vehicle.

For example, the measurement data or sensor data can be generated by odometry sensors and odometry data can be determined as measured values. If the speed of a vehicle is to be determined as a state variable, the measured values of the state variable may include a rotational frequency of a rotary encoder of a tachometer. Alternatively, other speed measurement methods may be used with other metrics, such as time intervals between traversing two measurement points or the like. The state variable to be estimated comprises, in the case of speed measurement as vector components, the speed and the calibration variable which, in the case of a rotary encoder, indicates the wheel circumference as a speed measuring unit. Advantageously, not only velocity measurement values are checked for errors in the filter process, but also calibration measurement values are determined or updated at the same time.

wobei x_k ein aktueller Zustand zum Zeitpunkt k ist, F_k ein Zustandsübergangsmodell repräsentiert, welches den Übergang zwischen dem vorhergehenden Zustand x_k-1 zu dem aktuellen Zustand x_k beschreibt. B_k beschreibt die Dynamik einer deterministischen Störung u_k, w_k beschreibt den zufallsbedingten Anteil der Störung. Eine weitere Gleichung beschreibt ein lineares Beobachtungsmodell, d.h. eine lineare Beziehung zwischen dem wahren Zustand x_k und einem beobachteten Zustand z_k: $${\text{z}}_{\text{k}}={\text{H}}_{\text{k}}{\text{x}}_{\text{k}}+{\text{v}}_{\text{k}}.$$

In a preferred embodiment of the method according to the invention for the self-calibration of vehicle sensors, the optimum filter comprises an extended Kalman filter. A Kalman filter model describes the development of a true state from a previous state: $${\text{x}}_{\text{k}}={\text{F}}_{\text{k}}{\text{x}}_{\text{k}-1}+{\text{B}}_{\text{k}}{\text{u}}_{\text{k}}+{\text{w}}_k.$$

where x _{k is} a current state at time k, F _{k represents} a state transition model describing the transition between the previous state x _k-1 to the current state x _k . B _k describes the dynamics of a deterministic disturbance u _k , w _k describes the random part of the disturbance. Another equation describes a linear observation model, ie a linear relationship between the true state x _k and an observed state z _k : $${\text{z}}_{\text{k}}={\text{H}}_{\text{k}}{\text{x}}_{\text{k}}+{\text{v}}_{\text{k}},$$

Here, the matrix H _{k describes} the actual observation model representing the relationship between the true state and the observed state, and v _k represents a noise term.

In a variant of the method according to the invention for self-calibration of vehicle sensors, a Jakobi matrix is calculated on the basis of the previous values of the state variable to determine the linearized state transition model and observation model.

wobei $A={\frac{\partial f\left(x,u\right)}{\partial x}|}_{x_t,u_t}$

der Jakobi-Matrix entspricht zu einem Zeitpunkt t, und x= f(x,u) beschreibt ein nichtlineares Zustandsübergangsmodell, wobei x einen geschätzten Durchschnittswert der Zustandsgröße x repräsentiert. Der Wert 6t ist die Zeit zwischen zwei Filtervorgängen.If the observation model and the state transition model are initially non-linear, then using an extended Kalman filter method, a linearization of the two matrices F _k and H _k can first be carried out using a Jakobi matrix. When viewed continuously over time, the linearized matrix F (instead of F _k ) results $$F=e^{A\Delta t}.$$

in which $A={\frac{\partial f\left(x.u\right)}{\partial x}|}_{x_t.u_t}$

the Jakobi matrix corresponds at a time t, and x = f (x, u) describes a nonlinear state transition model, where x represents an estimated average value of the state quantity x. The value 6t is the time between two filtering operations.

wobei H wiederum die linearisierte Beobachtungsmatrix darstellt.The nonlinear observation model h ( x ) can be linearized as follows: $$H={\frac{\partial H\left(\overline{x}\right)}{\partial \overline{x}}|}_{{\overline{x}}_t}.$$

where H again represents the linearized observation matrix.

so ergibt sich für h(x) = v/c die linearisierte Beobachtungsmatrix zu $H={\frac{\partial H\left(\overline{x}\right)}{\partial \overline{x}}|}_{{\overline{x}}_t}=\left(\begin{array}{ccc}0& \frac{1}{c}& \frac{-\nu }{c^2}\end{array}\right).$

If a state x is represented by the distance traveled d, the speed v of the vehicle and the calibration factor c, ie $x=\left(\begin{array}{c}d\\ \nu \\ c\end{array}\right).$

for h (x) = v / c we get the linearized observation matrix $H={\frac{\partial H\left(\overline{x}\right)}{\partial \overline{x}}|}_{{\overline{x}}_t}=\left(\begin{array}{ccc}0& \frac{1}{c}& \frac{-\nu }{c^2}\end{array}\right),$

In the example given, the state transition model F is linear, so it does not have to be linearized. In this case, the matrix is $\text{F}=\left(\begin{array}{ccc}1& \Delta \text{t}& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right),$

From the linearized observation matrix H and the prediction value P the covariance P and the uncertainty of measurement R we can calculate the Kalman matrix K:

As usual, the superscript T signals a transposed matrix.

The auxiliary variable innovation (symbolized by "y") results in:

The size y describes how exactly the predicted mean x is able to describe the current measured value by means of the observation model H.

The prediction value x then results on the basis of the innovation y, the Kalman matrix K and the average value x as follows:

In one embodiment of the method according to the invention for self-calibration of vehicle sensors, the measured values relate to a wear process of a functional element of the rail vehicle and the state variable includes a wear state of the functional element.

For example, this variant can be applied to an opening operation of a vehicle door, wherein the state variable relates to a wear state of functional elements of the door. The calibration values serve in this embodiment of the information whether a functional element is worn and needs to be replaced or not. Advantageously, an automated function monitoring can be performed. The maintenance intervals can be based on the actual wear of the vehicle door, whereby unnecessary maintenance and personnel costs can be avoided and still the functionality of the vehicle door can be guaranteed.

Alternatively, a wear of a wheel of a rail vehicle can be monitored as a wear process. If a wheel diameter is determined which is below a predetermined threshold, then the wheel must be replaced. Advantageously, personnel can be saved for complex inspection views of technical components of a rail vehicle.

• 1 ein Flussdiagramm, welches ein Verfahren zur Selbstkalibration von Fahrzeugsensoren eines Schienenfahrzeugs gemäß einem ersten Ausführungsbeispiel der Erfindung veranschaulicht,
• 2 ein Blockdiagramm, welches eine Kalibrationseinrichtung gemäß einem Ausführungsbeispiel der Erfindung veranschaulicht,
• 3 ein Blockdiagramm, welche ein Schienenfahrzeug mit einer Kalibrationseinrichtung gemäß einem Ausführungsbeispiel der Erfindung veranschaulicht.

The invention will be explained in more detail below with reference to the accompanying figures with reference to embodiments. Show it:

• 1 a flowchart illustrating a method for self-calibration of vehicle sensors of a rail vehicle according to a first embodiment of the invention,
• 2 a block diagram illustrating a calibration device according to an embodiment of the invention,
• 3 a block diagram illustrating a rail vehicle with a calibration device according to an embodiment of the invention.

In 1 is a flowchart 100 which illustrates a method for self-calibration of a sensor system of a rail vehicle according to an embodiment of the invention. At the step 1.I be measured values first W _t the rotational frequency W received as sensor data from a rotary encoder. The measured values W _t indicate the height of the rotational frequency of the wheels of the rail vehicle. At the step 1.II become current values using an extended Kalman filter v _t . c _t determined for the speed v and the wheel circumference c. The wheel circumference c simultaneously represents the calibration value to be updated. The estimation takes place on the basis of older values v _t-Δt , c _t-Δt for the speed v and the wheel circumference c and the currently determined value W _t the rotational frequency W of the wheels of the rail vehicle. The determined new values c _t . v _t for the wheel circumference c and the speed v of the rail vehicle are at the step 1.III output to a controller and at least the current speed value v _t is displayed to a driver. Furthermore, the step becomes 1.I returned and a new measured value W _{t + At} the rotational frequency W of the rotary encoder and thus the wheels of the rail vehicle detected. Subsequently, in the filtering process in the step 1.II based on the new measured value W _{t + Δt} and the updated values c _t . v _t new values c _{t + Δt} , v _{t + Δt} determined. The determined values c _{t + .DELTA.t} , v _{t + .DELTA.t} are output to a controller and the speed value v _{t + .DELTA.t} is displayed to the driver.

In 2 a block diagram is shown which shows a calibration device 20 illustrated according to an embodiment of the invention. The calibration device 20 includes a measurement data reception interface 21 , with the measured data W _t received by one or more sensor units. The measured data W _t are sent to a filter unit 22 which is based on the measured data W _t and on the basis of older values v _t-Δt c _t-Δt for the velocity v and the wheel circumference c with the help of an extended Kalman filter new values v _t . c _t determined for the speed v and the wheel circumference c. The new speed value v _t is via an output interface 23 to a control device which outputs the speed value v _t For example, in a cab brings to the display. In addition, the new values v _t . c _t from the filter unit 22 at a later time, t + Δt is used to obtain again updated calibration values c _{t + Δt} and velocity values v _{t + Δt} , etc.

In 3 is a rail vehicle 30 illustrated. The rail vehicle 30 includes a sensor unit 31 , For example, an encoder unit, with the values W _t the rotational frequency W of the wheels of the rail vehicle 30 be determined. Based on these values W _t be with the help of a calibration device 20 According to one embodiment of the invention, calibration values c are updated and a speed v of the rail vehicle 30 determined. Corresponding updated speed values v _t are sent to a controller 32 which outputs these values v _t in a driver's cab (not shown) of the rail vehicle 30 brings to the display.

It is finally pointed out again that the above-described methods and devices are merely preferred embodiments of the invention and that the invention can be varied by a person skilled in the art without departing from the scope of the invention, as far as it is specified by the claims. For the sake of completeness, it is also pointed out that the use of indefinite articles does not exclude "a" or "one", that the characteristics in question can also be present multiple times. Similarly, the term "unit" does not exclude that it consists of several components, which may also be spatially distributed.

Claims (15)

Method for self-calibration of vehicle sensors (31), comprising the steps: - acquiring measured values (z _t , W _t ) on the basis of sensor data of the vehicle sensors (31), - applying an optimum filter to the measured values (z _t , W _t ), - Determining an updated calibration value (c _t ) of a calibration variable (c) as an updated value (x _t ) of a state variable (x) of the optimal filter. Method according to Claim 1 in which the optimal filter has a state transition model (F) and / or observation model (H) linearized by a previous value (x _t ) of the state variable (x). Method according to Claim 1 or 2 , where the self-calibration is done in real time. Method according to one of the preceding claims, wherein the state variable (x) comprises a vectorial state variable. Method according to one of the preceding claims, wherein the sensor data are generated by odometry sensors and odometry data are determined as measured values (z _t , W _t ). Method according to one of the preceding claims, wherein the measured values (z _t , W _t ) comprise a rotational frequency (W _t ) of a rotary encoder of a tachometer. Method according to one of Claims 5 or 6 , wherein the state variable (x) comprises the velocity (v) and the calibration variable (c). The method of any one of the preceding claims, wherein the optimal filter comprises an extended Kalman filter. Method according to Claim 8 , wherein for the determination of the linearized state transition model (F) and observation model (H) on the basis of the previous values (x _t ) of the state variable, a Jakobi matrix is calculated. Method according to one of Claims 1 to 4 wherein the measured values (z _t ) relate to a wear process of a functional element of the rail vehicle (30) and the state variable (x) comprises a wear state of the functional element. Method according to Claim 10 wherein the functional element comprises one of the following: - a door of a rail vehicle, - a wheel of a rail vehicle. Calibration device (20), comprising: - a data receiving unit (21) for acquiring measured values (z _t , W _t ) on the basis of sensor data from vehicle sensors (31), - a filter unit (22) for applying an optimum filter to the measured values (z _t , W _t ) and - for determining an updated calibration value (c _t ) of a calibration variable (c) as an updated value (x _t ) state variable (x) of the optimal filter. Rail vehicle (30), comprising a calibration device (20) according to Claim 12 , A computer program product comprising a computer program which can be loaded directly into a memory unit of a rail vehicle (30), with program sections for carrying out all the steps of a method according to one of Claims 1 to 11 when executing the computer program in the rail vehicle (30). Computer-readable medium on which program sections executable by a computer unit are stored in order to perform all the steps of the method according to one of Claims 1 to 11 execute when the program sections are executed by the computer unit.

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