Patent Grant
12037032
The invention relates to a method for detecting systematic deviations during determination of a movement variable of a ground-based, more particularly rail-based, vehicle.
The invention also relates to an arrangement for detecting systematic deviations during determination of a movement variable of a ground-based, in particular rail-based, vehicle, which is suitably configured for carrying out the method according to the invention.
The invention further relates to a ground-based, in particular, a rail-based vehicle with an arrangement of this type.
During the operation of a ground-based vehicle, both the dynamic location of the vehicle—that is, the determination of the movement status by determining at least one movement variable of the vehicle—as well as the reliable calculation of so-called confidence interval limits for the at least one movement variable have a particular significance.
In the context of the present invention, the movement variable should be understood to be a physical variable which characterizes the movement state of the ground-based vehicle itself and/or on the basis of which, for example, by suitable calculations another variable can be determined, which then characterizes the movement status of the ground-based vehicle. Therefore, in the context of the invention, for example, the distance covered by the vehicle, the velocity of the vehicle and the acceleration of the vehicle are to be understood as being the movement variables. However, the position of the vehicle is also to be understood as a movement variable since, on the basis thereof, the distance covered can be determined.
In practice, these movement variables are designated locating variables. The determination of a movement variable (locating variable) takes place in relation to a reference system and/or in relation to a reference point in a reference system, wherein the reference system can be, for example, local or global.
The acquisition or capture of measurement values of one of these movement variables takes place in a known manner by means of sensors. In practice location sensors, in particular, are used as sensors. Known types of location sensors—that is, known sensor types for determining a movement variable—are incremental position sensors, Doppler radar sensors (Doppler radars, for short) and satellite navigation system receivers.
The capture of the measurement values is subject to errors which can be subdivided into random and systematic errors.
In particular, a sensor has its own random errors—that is that its own random error characteristic which is, or at least can be, different from the random error characteristic of other sensors.
In addition, sensors of a respective sensor type differ with regard to their random error characteristics from the sensors of other sensor types.
In the case of location sensors, for example, location sensors for determining the momentary distance covered or the momentary velocity, the systematic errors that underlie these can affect to a different extent the locating variable to be determined. The influences of the systematic errors on the determination of the respective locating variable can thus be expressed to a differing extent and often act together.
A systematic error can have at least one error component in the form of a systematic deviation (of a systematic effect) which originates from the respective sensor principle of the sensor type. Such a systematic deviation is designated an internal systematic deviation.
Furthermore, a systematic error can also have at least one error component in the form of a systematic deviation which is caused by the surrounding environment. Such a systematic deviation is then designated an external systematic deviation.
An example of a systematic error is an error caused by slipping of the wheels of an axle of the vehicle—that is, the slipping of the wheels during acceleration, which is known as spinning or wheelslip, and the slipping of the wheels during braking, which is also known as sliding or gliding.
An incremental position sensor associated with an axle of the vehicle measures too long a distance during spinning and too short a distance during sliding. A systematic error component caused by an axle control device during driving of an axle of a vehicle forms, for example, an internal systematic deviation. In contrast, a systematic error component caused by an irregularity of the coefficient of friction between the wheel and the ground or, in the case of a rail-bound vehicle, between the wheel and the rail, forms an external systematic deviation.
Typical external error sources during the acquisition of measurement values by means of a Doppler radar sensor result from the substrate structure and due to snow or ice which adheres to the Doppler radar sensor.
Currently, in practice, uniform limit values are specified, for example, for different sensors for the detection of systematic deviations (systematic effects), the existence of which makes a recalculation of the confidence intervals necessary. Therein, the limit values are often tuned such that they function reliably for the different or newly added sensors. Often, measurement values of sensors are smoothed before the comparison with the uniformly specified limit values. The smoothing, as a low pass filtration, can have a negative effect on the detection of sudden acceleration or braking processes. For example, an undershoot or an overshoot can occur during a determination of the velocity of the vehicle.
The specification and/or adjustment of limit values that are uniformly specified for all sensors can therefore lead thereto that, for example, during measurement with an incremental position sensor, gliding is often falsely detected and the confidence interval limits must then be increased. If the confidence interval limits are not reduced in a timely manner thereafter, this leads to operational restrictions during operation of the ground-based vehicle. For example, the vehicle, in particular a rail-based vehicle, must travel slower for a longer time and becomes delayed or it does not stop exactly within a door range in a station, or sometimes even forced braking is triggered.
It is an object of the invention to optimize the operation of a ground-based vehicle, in particular, to minimize the aforementioned operational restrictions during operation of the ground-based vehicle.
This object is achieved with methods for detecting systematic deviations during determination of a movement variable of a ground-based, in particular a rail-based, vehicle,
According to the invention, for the making of the assumption regarding the existence of a systematic deviation, in particular, for the reliable demonstration of whether significant systematic deviations exist, can exist or do not exist, the previously determined statistical sensor accuracy value and thus a random error characteristic of the sensor used for measuring is taken into account. Thus, systematic deviations can advantageously be reliably differentiated from random deviations.
In an advantageous embodiment of the method according to the invention, it is provided that the operational state of a timer that has been started in the event of a previously made assumption that a systematic deviation exists is read out at the time point. On readout of the operational state of the timer, it is established whether it is running—that is, it is in its active operational state—or not—that is, it is in its passive operational state.
In an advantageous manner, it is then provided that the assumption regarding the existence of a systematic deviation is undertaken as follows:
Preferably, an additional test variable value is formed and, in the course of an additional comparison, is compared with a specified additional test bound in order to make the assumption regarding the existence of a systematic deviation dependent also upon an additional comparison result obtained during the additional comparison.
In an advantageous manner, it is then provided that the assumption regarding the existence of a systematic deviation is undertaken as follows:
In the event that at least one further sensor provides a further measurement value associated with the time point, it is advantageously provided that
In an advantageous manner, it is then provided that the assumption regarding the existence of a systematic deviation is undertaken as follows:
Preferably, an additional further test variable value is formed and, in the course of an additional comparison, is compared with the specified additional test bound in order to make the assumption regarding the existence of a systematic deviation dependent also upon an additional comparison result obtained during the additional further comparison.
In an advantageous manner, it is then provided that the assumption regarding the existence of a systematic deviation is made as follows:
By means of the method according to the invention, for the determination of the movement state of the ground-based vehicle, it can be assessed better than previously known in practice whether it is to be assumed that the measurement value associated with the time point is and/or could be subject to a significant systematic deviation or whether this can be precluded. This has the advantage that confidence interval limits can be calculated more optimally and reliably.
Preferably, the statistical sensor accuracy value is determined on the basis of a sensor characteristic curve or a sensor function determined for the at least one sensor.
For this purpose, it is advantageously provided that during a real or simulated test journey of the vehicle or of a test vehicle used in its place, by means of the at least one sensor or a test sensor of the same sensor type used in its place, test measurement values are captured and, on the basis of these test measurement values, test values of the movement variable are determined and that on the basis of the test values of the movement variable for the at least one sensor, a variation of its statistical sensor accuracy is determined dependent upon the movement variable in the form of the respective sensor characteristic and/or sensor function.
In order to keep the proportion of systematic deviations in the movement variable as minimal as possible during the test journey, it is regarded as advantageous if the movement variable of the vehicle and/or of the test vehicle used in its place during the test journey is changed, in particular, by means of careful acceleration and careful braking, such that a slipping of the wheels of the vehicle on the ground or the rails is substantially prevented.
Furthermore, it is regarded as advantageous if
Furthermore, it is regarded as advantageous if
It is then preferably provided that
It is then preferably provided that
Firstly, it is regarded as advantageous if one test bound is preferably determined as a quantile of the standard normal distribution of the order 1−α/2, wherein a value is specified for α as the probability of error.
Secondly, it is regarded as advantageous if the additional test bound is preferably determined as a quantile of the standard normal distribution of the order 1−α/2, wherein an additional value is specified for α as the probability of error.
The invention will now be described in greater detail by reference to the drawings.
In the exemplary embodiment shown, the detection of systematic deviations takes place using two sensors S1 and S2, wherein they are incremental position sensors of the same sensor type. The two sensors S1 and S2 can therefore be, for example, of the Hasler® OPG sensor type from Sécheron Hasler Group. Alternatively, the two sensors can be, for example, of the sensor type BMIV from Baumer Electric. It should be noted, however, that for the execution of the method according to the invention, a single sensor, for example, the sensor S1 is also sufficient, as will be shown below, in particular, also in relation to the descriptions regarding
One of the two sensors which is identified as S1 is associated with one axle A1 of one of the wheelsets RS of one wheel truck DG1 and the other sensor which is identified as S2 is associated with an axle A2 of one of the wheelsets RS of the further wheel truck DG2.
A computer unit RE is linked via communication paths K1 and K2 to the sensors S1 and S2. Furthermore, a timer is provided as a component of the computer unit RE. The timer T could however also be provided separately from the computer unit RE and connected via a suitable communication path to the computer unit RE.
The first sensor S1 serves for capturing measurement values nS1 of the rotary speed of the axle A1 of one wheel truck DG1 on the basis of which, in a known manner, specifically taking account of a diameter of a wheel R1 fastened to the axle A1, the computer unit RE determines velocity values vS1 of the vehicle F as values of the movement variable v. The velocity v of the vehicle F thus herein represents the movement variable.
In the same way, the second sensor serves for capturing further measurement values nS2 of the rotary speed of the axle A2 of the further wheel truck DG2 on the basis of which, taking account of a diameter of a wheel R2 fastened to the axle A2, the computer unit RE determines further velocity values vS2 of the vehicle F as further values of the movement variable v. The velocity v of the vehicle F thus herein represents the movement variable.
The measurement values nS1 of the first sensor pass via one communication path K1 to the computer unit RE and the further measurement values nS2 pass via the further communication path K2 to the computer unit.
It could, however, also be provided that a sensor computation unit of the sensor S1 itself determines the velocity values vS1 of the vehicle F from the measurement values nS1 of the rotary speed of the axle A1 and outputs them via the communication paths K1 to the computer unit RE and that, in a corresponding manner, a further sensor computation unit of the sensor S2 itself determines the velocity values vS2 of the vehicle F from the measurement values nS2 of the rotary speed of the axle A2 and outputs them via the further communication path K2 to the computer unit RE.
The sensors S1 and S2, the computer unit RE with its timer T and the communication paths K1 and K2 together form the arrangement A which is suitably configured for detecting systematic deviations sA in the determination of the movement variable v of the vehicle F. The arrangement is suitably configured, in particular, for carrying out the method described below which is subdivided into two partial methods.
By means of a first of the two partial methods, initially a typical random error characteristic is reliably determined for each of the sensors S1 and S2 used.
Following thereafter is the second partial method which uses the typical random error characteristic established once for each of the sensors S1 and S2, in order to reveal significant systematic error influences; thus in order to differentiate systematic deviations (effects) reliably from random deviations (effects).
Alternatively thereto, merely a typical random error characteristic of the sensor type used of the two sensors and on use of sensors of different sensor types, a typical random error characteristic could also be determined for each of the different sensor types.
In addition, the method also functions very efficiently if only one sensor or if any number of sensors are used simultaneously.
In addition, in place of the sensor S1, a test sensor of the same type as the sensor S1 and/or in place of the further sensor S2, a further test sensor of the same sensor type as the further sensor S2 could also be used.
According to
Firstly, during a real test journey of the vehicle F, in a method step V1, by means of one sensor S1, test measurement values nS1.1, nS1.2, nS1.3, . . . of the axle rotary speed nS1 and by means of the further sensor S2, further test measurement values nS2.1, nS2.2, nS2.3, . . . of the axle rotary speed nS2 are captured. During this test journey, the velocity, which here forms the movement variable v of the vehicle, is changed, in particular, by means of careful accelerations and careful braking such that slipping of the wheels of the vehicle F on the ground and/or here particularly on the rails S of the track G is substantially prevented.
The test journey could be a simulated test journey. Alternatively such test measurement values could also be captured during a real or simulated test journey of a test vehicle used in place of the vehicle F.
A sensor computation unit (not shown) of the sensor S1 or the computer unit RE then determines, in a method step identified here as V2, using the test measurement values nS1.1, nS1.2, nS1.3, . . . , test values vS1.1, vS1.2, vS1.3, . . . of the movement variable v. In addition, a further sensor computation unit (not shown) of the further sensor S2 or the computer unit RE determines, using the further test measurement values nS2.1, nS2.2, nS2.3, . . . , further test values vS2.1, vS2.2, vS2.3, . . . of the movement variable v.
Subsequently, the computer unit RE then determines from the test values vS1.1, vS1.2, vS1.3, . . . for one sensor S1, a variation of a statistical sensor accuracy σs_vS1 dependent upon the movement variable v in the form of a sensor characteristic curve KS1. Accordingly, the computer unit RE then determines from the further test values vS2.1, vS2.2, vS2.3, . . . for the further sensor S2, a variation of a statistical sensor accuracy σs_vS2 dependent upon the movement variable v in the form of a further sensor characteristic curve KS2.
In place of the sensor characteristic curves KS1, KS2, sensor functions which represent the sensor characteristic curves KS1, KS2 could also be determined.
For this purpose, in a method step denoted as V3 herein, the computer unit RE firstly checks whether the vehicle has passed through at least three acceleration and deceleration phases during the test journey and whether between each of these phases, it has made a stop of at least 3 s. If this is not the case, the test journey must be continued until these conditions are met. If this is the case, the computer unit carries out the next method step denoted as V4 here.
In the method step V4, by means of a low pass filtration, the computer unit RE initially forms low pass filter values from the test values vS1.1, vS1.2, vS1.3, . . . of the at least one sensor S1 or the test sensor used in its place. Subsequently, for each of the test values vS1.1, vS1.2, vS1.3, . . . , the computer unit RE determines a sliding standard deviation σs_vS1.1, σs_vS1.2, σs_vS1.3, . . . on the basis of the low pass filter values. Accordingly, in the method step V4, by means of a low pass filtration, the computer unit RE firstly forms further low pass filter values from the test values vS2.1, vS2.2, vS2.3, . . . of the at least one further sensor S2 and/or the further test sensor used in its place. Subsequently, for each of the further test values vS2.1, vS2.2, vS2.3, . . . , the computer unit RE determines a further sliding standard deviation σs_vS2.1, σs_vS2.2, σs_vS2.3, . . . on the basis of the further low pass filter values.
In a method step denoted here as V5, into a representation of the sliding standard deviations σs_vS1.1, σs_vS1.2, σs_vS1.3, . . . , the computer unit RE fits a regression line as the sensor characteristic curve KS1 over the moduli |vS1.1|, |vS1.2|, |vS1.3|, . . . of the test values vS1.1, vS1.2, vS1.3, . . . . The fitting of the regression line takes place, in particular, with the aid of the per se known least squares method. The regression line reveals the variation of the statistical sensor accuracy σs_vS1 of the one sensor S1, dependent upon the movement variable v. Accordingly, into a representation of the further sliding standard deviations σs_vS2.1, σs_vS2.2, σs_vS2.3, . . . , the computer unit RE fits a further regression line as the sensor characteristic curve KS2 over the moduli |vS2.1|, |vS1.2|, |vS2.3|, . . . of the test values vS2.1, vS2.2, vS2.3, . . . . The fitting of the further regression line takes place, in particular, with the aid of the per se known least squares method. This further regression line reveals the variation of the statistical sensor accuracy σs_vS2 of the further sensor S2, dependent upon the movement variable v.
In a method step denoted here as V6, the one sensor characteristic curve KS1 and the further sensor characteristic curve KS2 are stored along with additional details in a memory store of the computer unit RE.
The computer unit thus carries out the method steps V4, V5 and V6 simultaneously or temporally offset both for the test values vS1.1, vS1.2, vS1.3, . . . of the sensor S1 and also for the further test values vS2.1, vS2.2, vS2.3, . . . of the further sensor S2.
According to
In a method step denoted here as V11, the sensor S1 captures a measurement value nS1.t, associated with the time point t, of the axle rotary speed nS1 of the axle A1 of one wheel truck DG1.
In addition, in a method step, denoted here as V12, the operational state Z.t of the timer T at the time point t is read out. The timer T can assume two operational states. Once it has been started and is running, it is in an active operational state. If it is not running, then it is in a passive operational state. The timer is always started or restarted as soon as an assumption A1 has been made by the computer unit RE that a systematic deviation sA exists.
In the method step V12, a test bound TS is also specified. Preferably, this test bound TS is determined as a quantile of the standard normal distribution of the order 1−α/2. Herein, a value Wα is specified for α as the probability of error. This value Wα can be stored in a memory store (not shown here) of the computer unit RE.
In a method step denoted here as V13, the computer unit RE determines, on the basis of the measurement value nS1.t and, taking account of the diameter of the wheel R1 fastened to the axle A1, a value vS1.t of the movement variable v associated with the time point t.
In a method step denoted here as V14, on the basis of the sensor characteristic curve KS1 previously determined for the at least one sensor S1, a statistical sensor accuracy value σs_vS1.t is determined for the value vS1.t of the movement variable v in that this statistical sensor accuracy value σs_vS1.t is read out at the interface denoted here as P2 from the memory store (not shown) of the computer unit RE.
In addition, in the method step V14, dependent upon the value vS1.t of the movement variable v associated with the time point t and of the statistical sensor accuracy value σs_vS1.t of the sensor S1 determined for this value vS1.t, a test variable value TGS1.t associated with the time point t is formed.
The formation of the test variable value TGS1.t takes place according to the substeps Vi to Vvii of the method step V14 of
In a substep denoted here as Vi, on the basis of a movement model BM applied to a previous system state SZ.tp of the vehicle F, an expected system state SZ*.t of the vehicle F for the time point t is determined.
In a substep denoted here as Vii, on the basis of a transfer model TM applied to the expected system state SZ*.t, an expected value v*.t of the movement variable v is determined for the time point t.
In a substep denoted here as Viii, the difference between the value vS1.t of the movement variable v and the expected value v*.t of the movement variable v is determined as an innovation value dS1.t.
In a substep denoted here as Viv, on the basis of the movement model BM applied to a previous system state accuracy value σ_SZ.tp and a transmission model UM applied to a specified system noise SR, a system state accuracy value σ_SZ*.t of the expected system state SZ*.t is determined.
In a substep denoted here as Vv, on the basis of the system state accuracy value σ_SZ*.t of the expected system state SZ*.t and of the transfer model TM, an accuracy value σ_v*.t of the expected value v*.t of the movement variable v is determined.
In a substep denoted here as Vvi, the sum of the accuracy value σ_v*.t of the expected value v*.t of the movement variable v and the sensor accuracy value σs_vS1.t is determined as an innovation accuracy value σ_dS1.t.
And in a substep denoted here as Vvii, the quotient is determined from the modulus |dS1.t| of the innovation value dS1.t and the innovation accuracy value σ_dS1.t as the test variable value TGS1.t.
After the formation of the test variable value TGS1.t, in the method step denoted as V15 in
In the method step denoted as V16, it is tested whether the timer T is running.
The assumption regarding the existence of a systematic deviation (sA) is made as follows:
According to
In a method step denoted here as V111, the sensor S1 captures a measurement value nS1.t, associated with the time point t, of the axle rotary speed nS1 of the axle A1 of one wheel truck DG1.
In addition, in a method step, denoted here as V112, the operational state Z.t of the timer T at the time point t is read out. The timer T can assume two operational states. Once it has been started and is running, it is in an active operational state. If it is not running, then it is in a passive operational state. The timer is always started or restarted as soon as an assumption A1 has been made by the computer unit RE that a systematic deviation sA exists.
In the method step V112, a test bound TS and an additional test bound TS′ are specified. Preferably, the test bound TS is again determined as a quantile of the standard normal distribution of the order 1−α/2 and for α, a value Wα is specified as the probability of error. The additional test bound TS′ is preferably determined as a quantile of the standard normal distribution of the order 1−α/2, wherein an additional value W′a is specified for a as the probability of error. The values Wα and W′α can again be stored in a memory store of the computer unit RE.
In the method step denoted here as V113, the computer unit RE determines, on the basis of the measurement value nS1.t and, taking account of the diameter of the wheel R1 fastened on the axle A1, a value vS1.t of the movement variable v associated with the time point t.
In the method step denoted here as V114, on the basis of the sensor characteristic curve KS1 previously determined for the at least one sensor S1, a statistical sensor accuracy value σs_vS1.t for the value vS1.t of the movement variable v is determined in that this statistical sensor accuracy value σs_vS1.t is read out at the interface denoted here as P2 from the memory store (not shown) of the computer unit RE.
In addition, in the method step V114, dependent upon the value vS1.t of the movement variable v associated with the time point t and of the statistical sensor accuracy value σs_vS1.t of the sensor S1 determined for this value vS1.t, a test variable value TGS1.t associated with the time point t is formed.
The formation of the test variable value TGS1.t again takes place according to the substeps Vi to Vvii of
Additionally, in the method step V114 an additional test variable value TG′S1.t which is associated with the time point t is formed.
According to
In the substep denoted here as Vviii, a residual value d′S1.t is determined as the product of the innovation value dS1.t and a specified weighting factor GF.
In the substep denoted here as Vix, a residual accuracy value σ_d′S1.t is determined as the product of the weighting factor GF multiplied by minus one and the sensor accuracy value σs_vS1.t.
And in the substep denoted here as Vx, the quotient is determined from the modulus |d′S1.t| of the residual value d′S1.t and the residual accuracy value s_d′S1.t as the additional test variable value TG′S1.t.
After the formation of the test variable value TGS1.t and the additional test variable value TG′S1.t, in the method step denoted as V115 in
In the method step denoted as V116, it is tested again whether the timer T is running.
The assumption regarding the existence of a systematic deviation sA is made as follows:
Then the following method steps are carried out:
In the method step denoted here as V211, the sensor S1 captures a measurement value nS1.t, associated with the time point t, of the axle rotary speed nS1 of the axle A1 of the wheel truck DG1. And the sensor S2 captures a further measurement value nS1.t, associated with the time point t, of the axle rotary speed nS2 of the axle A2 of the wheel truck DG2.
In a method step denoted here as V212, the operational state Z.t of the timer T at the time point t is read out. The timer T can again assume two operational states. Once it has been started and is running, it is in an active operational state. If it is not running, then it is in a passive operational state. Here also, the timer is always started or restarted as soon as an assumption A1 has been made by the computer unit RE that a systematic deviation sA exists.
In the method step V212, a test bound TS is again also specified. Preferably, this test bound TS is determined as a quantile of the standard normal distribution of the order 1−α/2. Herein, a value Wα is specified for α as the probability of error. This value Wα can be stored in a memory store (not shown here) of the computer unit RE.
In the method step V213, the computer unit RE determines, on the basis of the measurement value nS1.t and taking account of the diameter of the wheel R1 fastened to the axle A1, a value vS1.t of the movement variable v associated with the time point t. In addition, the computer unit RE determines, on the basis of the further measurement value nS2.t and taking account of the diameter of the wheel R2 fastened to the axle A2, a further value vS2.t of the movement variable v associated with the time point t.
In a method step denoted here as V214, on the basis of the sensor characteristic curve KS1 previously determined for the one sensor S1, a statistical sensor accuracy value σs_vS1.t is determined for the value vS1.t of the movement variable v in that this statistical sensor accuracy value σs_vS1.t is read out at the interface denoted here as P2 from the memory store (not shown) of the computer unit RE. In addition, in the method step denoted here as V214, on the basis of the sensor characteristic curve KS2 previously determined for the further sensor S2, a further statistical sensor accuracy value σs_vS2.t for the further value vS2.t of the movement variable v is determined in that this further statistical sensor accuracy value σs_vS2.t is read out at the interface P2 from the memory store (not shown) of the computer unit RE.
In addition, firstly, in the method step V214, dependent upon the value vS1.t of the movement variable v associated with the time point t and of the statistical sensor accuracy value σs_vS1.t of the sensor S1 determined for this value vS1.t, a test variable value TGS1.t associated with the time point t is formed. Secondly, dependent upon the further value vS2.t of the movement variable v associated with the time point t and of the further statistical sensor accuracy value σs_vS2.t of the further sensor S2 determined for this further value vS2.t, a further test variable value TGS2.t associated with the time point t is formed.
The formation of the test variable value TGS1.t takes place in the substeps Vi to Vvii of
The formation of the further test variable value TGS2.t takes place according to
In the substep Viii, the difference is determined from the further value vS2.t of the movement variable v and the expected value v*.t of the movement variable v as a further innovation value dS2.t.
In the substep denoted here as Vvi, the sum of the accuracy value σ_v*.t of the expected value v*.t of the movement variable v and the further sensor accuracy value σs_vS2.t is determined as a further innovation accuracy value σ_dS2.t.
And in the substep Vvii, the quotient is determined from the modulus |dS2.t| of the further innovation value dS2.t and the further innovation accuracy value σ_dS2.t as the further test variable value TGS2.t.
After the formation of the test variable value TGS1.t and the further test variable value TGS2.t, in the method step denoted as V215 in
In the method step denoted as V216, it is tested again whether the timer T is running.
The assumption regarding the existence of a systematic deviation sA is made as follows:
The assumption regarding the existence of a systematic deviation sA is then made as follows:
Summarizing, it can therefore be established that during the first of the two partial methods which can also be designated the calibration method, at least the following steps are provided if the velocity of the vehicle forms the movement variable:
The result of the first partial method is the accuracies of the respective sensors.
The second of the two partial methods, which can also be designated the spinning and sliding detection method, then comprises, for example, the following further steps:
If the test variable value is larger than the test bound, then a systematic effect, for example, spinning or sliding exists.
With the use of the timer in the manner described, a false assertion that no slippage or no spinning has occurred when it is actually the case is prevented.
The following can be regarded as substantial advantages of the method according to the invention:
| Number | Date | Country | Kind |
|---|---|---|---|
| 10 2019 204 519.7 | Mar 2019 | DE | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2020/055246 | 2/28/2020 | WO |
| Publishing Document | Publishing Date | Country | Kind |
|---|---|---|---|
| WO2020/200593 | 10/8/2020 | WO | A |
| Number | Name | Date | Kind |
|---|---|---|---|
| 20050187698 | Arai | Aug 2005 | A1 |
| 20080288192 | Kumar | Nov 2008 | A1 |
| 20120118735 | Calder | May 2012 | A1 |
| 20120239297 | Yokota | Sep 2012 | A1 |
| 20130103225 | Kim | Apr 2013 | A1 |
| 20200023869 | Poesel | Jan 2020 | A1 |
| Number | Date | Country |
|---|---|---|
| 102014226612 | Jun 2016 | DE |
| 102014226612 | Jun 2016 | DE |
| 102017212179 | Jan 2019 | DE |
| WO 2017121579 | Jul 2017 | WO |
| WO 2018177677 | Oct 2018 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 20220185348 A1 | Jun 2022 | US |
