Method for detecting systematic deviations when determining a movement variable of a ground-based, particularly rail-based, vehicle, as well as corresponding device and vehicle

Description

[0001] The invention relates to a method for detecting systematic deviations when determining a movement variable of a ground-based, in particular rail-based, vehicle.

[0002] The invention also relates to an arrangement for detecting systematic deviations when determining a movement variable of a ground-based, in particular rail-based, vehicle, which arrangement is suitably designed for carrying out the method according to the invention.

[0003] Furthermore, the invention relates to a ground-based, in particular rail-based, vehicle with such an arrangement.

[0004] DE 10 2017 212 179 A1 discloses a method for correcting a measured position of a rail-bound vehicle. A detection unit determines position-relevant information based on signal data from various sensors and receiving devices. A test unit uses the position-relevant information to determine whether a previously measured position of the rail-bound vehicle is incorrect. The position-relevant information can directly indicate the correct current position of the rail-bound vehicle.

[0005] When operating a ground-based vehicle, both the dynamic positioning of the vehicle - i.e. the determination of the state of motion by determining at least one movement variable of the vehicle - and the reliable calculation of so-called confidence interval limits for the at least one movement variable (also referred to as confidence interval limits) are of particular importance.

[0006] Within the scope of the present invention, a movement variable is understood to be a physical quantity that characterizes the state of motion of the ground-based vehicle itself or on the basis of which, for example, through suitable calculations, another quantity can be determined that then characterizes the state of motion of the ground-based vehicle. Thus, within the scope of the invention, movement variables include, for example, the distance traveled by the vehicle, the speed of the vehicle, and the acceleration of the vehicle. However, the position of the vehicle can also be understood as a movement variable, since the distance traveled can be determined based on this.

[0007] In practice, these motion quantities are also referred to as location quantities. The determination of a motion quantity (location quantity) is carried out relative to a reference system or to a reference point within a reference system, where the reference system can be local or global, for example.

[0008] The recording or acquisition of measured values of each of these motion variables is carried out in a conventional manner using sensors. In practice, so-called positioning sensors are particularly used. Common types of positioning sensors—that is, sensors used to determine a motion variable—are displacement incremental encoders, Doppler radar sensors (Doppler radars for short), and satellite navigation system receivers.

[0009] The recording of measured values is subject to errors, which can be divided into random and systematic errors.

[0010] In particular, each sensor has its own random errors - i.e., its own random error characteristics, which differ, or at least can differ, from the random error characteristics of other sensors.

[0011] In addition, sensors of a particular sensor type differ from sensors of other sensor types in terms of their random error characteristics.

[0012] In the case of positioning sensors, for example, those used to determine the current distance traveled or the current speed, the systematic errors they are subject to can have varying degrees of impact on the positioning parameter to be determined. The influence of systematic errors on the determination of the respective positioning parameter can therefore vary in intensity and often interact.

[0013] A systematic error can have at least a component in the form of a systematic deviation (a systematic effect) that arises from the respective sensor principle of the sensor type. Such a systematic deviation is also referred to as an internal systematic deviation.

[0014] Furthermore, a systematic error can also contain at least a portion of the error in the form of a systematic deviation caused by the surrounding environment. Such a systematic deviation is then referred to as an external systematic deviation.

[0015] An example of a systematic error is an error caused by wheel slippage on one of the vehicle's axles - i.e., wheel slippage during acceleration, also known as skidding or skidding slip, and wheel slippage during braking, also known as sliding or sliding slip. A position incremental encoder assigned to a vehicle's axle measures a distance that is too long during skidding or too short during sliding. A systematic error component caused by an axle control unit when controlling a vehicle axle, for example, constitutes an internal systematic deviation. In contrast, a systematic error component caused by an irregularity in the coefficient of friction between wheel and ground or, in the case of a rail-bound vehicle, between wheel and rail, constitutes an external systematic deviation.

[0016] Typical external sources of error when recording measured values using a Doppler radar sensor result from the nature of the ground and from snow or ice adhering to the Doppler radar sensor.

[0017] Currently, in practice, uniform thresholds are defined for different sensors to detect systematic deviations (systematic effects), the presence of which requires a recalculation of the confidence interval limits. The thresholds are often tuned to ensure they work reliably for different or newly added sensors. Sensor readings are often smoothed before being compared with the uniformly defined thresholds. Smoothing, as a low-pass filter, can have a negative impact on the detection of sudden acceleration and braking events. For example, it can lead to undershooting or overshooting when determining the vehicle's speed.

[0018] The definition or setting of uniform limit values across all sensors can therefore lead to, for example, incorrectly detecting sliding slip when measuring with an incremental position encoder, and the confidence interval limits then having to be increased. If the confidence interval limits are not subsequently reduced in a timely manner, this leads to operational restrictions in the operation of the ground-based vehicle. For example, the vehicle, especially a rail-bound vehicle, may have to travel at a lower speed for an extended period and be delayed, or it may not stop precisely in the door area in a station area, or in some cases even trigger emergency braking.

[0019] The invention is based on the object of optimizing the operation of a ground-based vehicle, in particular minimizing the aforementioned operational restrictions during the operation of the ground-based vehicle.

[0020] This object is achieved based on a method for detecting systematic deviations when determining a movement variable of a ground-based, in particular rail-based, vehicle, in which a value of the movement variable assigned to a point in time is determined on the basis of a measured value assigned to a point in time by at least one sensor (DE 10 2017 212 179 A1), in that depending on the value of the movement variable assigned to the time and a statistical sensor accuracy value of the at least one sensor determined for this value, a test variable value assigned to the time is formed and compared with a predetermined test limit in the course of a comparison in order to make an assumption regarding the presence of a systematic deviation depending on a comparison result obtained in the comparison, wherein the test variable value is additionally formed depending on a probable value of the movement variable for the time, wherein a probable system state of the vehicle for the time is determined on the basis of a movement model applied to a previous system state of the vehicle and the probable value of the movement variable for the time is determined on the basis of a transfer model applied to the probable system state.

[0021] According to the invention, in order to make the assumption regarding the presence of a systematic deviation, in particular to reliably indicate whether significant systematic deviations exist, could exist, or are not present, the previously determined statistical sensor accuracy value and thus a random error characteristic of the sensor used for the measurement are taken into account. This advantageously allows systematic deviations to be reliably distinguished from random deviations.

[0022] In an advantageous embodiment of the method according to the invention, the operating state of a timer that was started in the event of a previously made assumption that a systematic deviation exists is read out at that time. When reading the operating state of the timer, it is determined whether it is running—i.e., in its active operating state—or not running—i.e., in its passive operating state.

[0023] It is then advantageous to provide that the assumption regarding the existence of a systematic deviation is made as follows: if the test statistic value assigned to the time point is greater than the specified test limit, then the presence of a systematic deviation at that time point is assumed; if the test statistic value is less than the specified test limit and, in addition, the timer is running, then the presence of a systematic deviation at that time point is not excluded; if the test statistic value is less than the specified test limit and, in addition, the timer is not running, then the presence of a systematic deviation at that time point is excluded.

[0024] Preferably, an additional test value is formed and compared with a predetermined additional test limit in the course of an additional comparison in order to make the assumption regarding the presence of a systematic deviation also depending on an additional comparison result obtained in the additional comparison.

[0025] It is then advantageous to provide that the assumption regarding the existence of a systematic deviation is made as follows: if the test statistic value assigned to the time point is greater than the specified test limit and / or if the additional test statistic value is greater than the specified additional test limit, then the presence of a systematic deviation at that time point is assumed, if on the one hand the test statistic value is less than the specified test limit and on the other hand the additional test statistic value is less than the specified additional test limit and in addition the timer is running, then the presence of a systematic deviation at that time point is not excluded, if on the one hand the test statistic value is less than the specified test limit and on the other hand the additional test statistic value is less than the specified additional test limit and in addition the timer is not running, then the presence of a systematic deviation at that time point is excluded.

[0026] In the case that at least one further sensor provides a further measured value associated with the time, it is advantageous that on the basis of this further measured value, a further value of the movement quantity assigned to the time is determined and, depending on the further value of the movement quantity assigned to the time and a statistical sensor accuracy value of the further sensor determined for this further value, a further test quantity value assigned to the time is formed and, in the course of a further comparison, is compared with the predetermined test limit in order to make the assumption regarding the presence of a systematic deviation, also depending on a further comparison result obtained in the further comparison.

[0027] It is then advantageous to provide that the assumption regarding the existence of a systematic deviation is made as follows: if the test statistic value assigned to the time point is greater than the specified test limit and / or if the further test statistic value assigned to the time point is greater than the specified test limit, then the presence of a systematic deviation at that time point is assumed, if the test statistic value assigned to the time point and the further test statistic value assigned to the time point are smaller than the specified test limit and, in addition, the timer is running, then the presence of a systematic deviation at that time point is not excluded, if the test statistic value assigned to the time point and the further test statistic value assigned to the time point are smaller than the specified test limit and, in addition, the timer is not running, then the presence of a systematic deviation at that time point is excluded.

[0028] Preferably, an additional further test value is also formed and compared with the specified additional test limit in the course of an additional further comparison in order to make the assumption regarding the presence of a systematic deviation also depending on an additional further comparison result obtained in the additional further comparison.

[0029] It is then advantageous to provide that the assumption regarding the existence of a systematic deviation is made as follows: if at least one of the following conditions applies: the test statistic value assigned to the time point is greater than the specified test limit, the further test statistic value assigned to the time point is greater than the specified test limit, the additional test statistic value assigned to the time point is greater than the specified additional test limit, the additional further test statistic value assigned to the time point is greater than the specified additional test limit, then the existence of a systematic deviation at the time point is assumed, if on the one hand the test statistic value and the further test statistic value are smaller than the specified test limit and on the other hand the additional test statistic value and the additional further test statistic value are smaller than the specified additional test limit and in addition the timer is running, then the existence of a systematic deviation at the time point is not excluded,If, on the one hand, the test statistic value and the additional test statistic value are smaller than the specified test limit and, on the other hand, the additional test statistic value and the additional additional test statistic value are smaller than the specified additional test limit and, in addition, the timer is not running, then the existence of a systematic deviation at that time is excluded.

[0030] Using the method according to the invention, it is therefore advantageously possible to assess, more effectively than previously known in practice, when determining the motion state of the ground-based vehicle, whether it is likely that the measured value assigned to a time point is or could be subject to a significant systematic deviation, or whether this can be ruled out. This, in turn, has the advantage that confidence interval limits can be calculated more optimally and reliably.

[0031] Preferably, the statistical sensor accuracy value is determined based on a sensor characteristic curve or sensor function previously determined for the at least one sensor.

[0032] For this purpose, it is advantageously provided that during a real or simulated test drive of the vehicle or a test vehicle used instead of it, test measurement values are recorded by means of the at least one sensor or by means of a test sensor of the same sensor type used instead of it, and test values of the movement variable are determined on the basis of these test measurement values, and that on the basis of the test values of the movement variable for the at least one sensor, a profile of its statistical sensor accuracy is determined as a function of the movement variable in the form of the respective sensor characteristic curve or sensor function.

[0033] In order to keep the proportion of systematic deviations in the test values of the movement quantity as low as possible during the test drive, it is considered advantageous if the movement quantity of the vehicle or the test vehicle used instead of it is changed during the test drive, in particular by careful acceleration and careful braking, in such a way that slipping of the wheels of the vehicle on the ground or the rails is essentially avoided.

[0034] Furthermore, it is considered advantageous if by means of low-pass filtering, low-pass filter values are formed from the test values, a sliding standard deviation is determined for each of the test values on the basis of the low-pass filter values, and a best-fit line, in particular using the least squares method, is fitted into a subsequently formed representation of the sliding standard deviations over the amounts of the test values, wherein the best-fit line indicates the course of the statistical sensor accuracy of the at least one sensor as a function of the movement variable.

[0035] In addition, it is considered advantageous if the difference between the value of the movement variable and the expected value of the movement variable is determined as the innovation value.

[0036] Then it is preferably provided that a system state accuracy value of the expected system state is determined based on the movement model applied to a previous system state accuracy value and a transfer model applied to a given system noise, an accuracy value of the expected value of the movement variable is determined based on the system state accuracy value of the expected system state and the transfer model, the sum of the accuracy value of the expected value of the movement variable and the sensor accuracy value is determined as the innovation accuracy value, and the quotient of the absolute value of the innovation value and the innovation accuracy value is determined as the test variable value.

[0037] And it is preferably intended that a residual value is determined as the product of the innovation value and a predetermined weighting factor, a residual accuracy value is determined as the product of the weighting factor multiplied by minus one and the sensor accuracy value, and the quotient of the absolute value of the residual value and the residual accuracy value is determined as the additional test statistic value.

[0038] On the one hand, it is considered advantageous if one test bound is preferably determined as a quantile of the standard normal distribution of order 1-α / 2, where a value is specified for α as the error probability.

[0039] On the other hand, it is considered advantageous if the additional test bound is preferably determined as a quantile of the standard normal distribution of order 1-α / 2, where an additional value is specified for α as the error probability.

[0040] The invention will be explained in more detail below with reference to the figures. Figure 1 a ground-based vehicle according to the invention with an arrangement according to the invention for detecting systematic deviations when determining a movement variable of the vehicle, Figure 2 a flowchart of a first sub-method of a method according to the invention for detecting systematic deviations when determining the movement size of the vehicle and Figures 3 to 6 Flow diagrams of four embodiments of a second sub-method of the method according to the invention and Figures 7 and 8 Substeps from in the Figures 3 to 6 shown process steps

[0041] The Figure 1shows a ground-based vehicle F in the form of a rail-bound vehicle. The vehicle F is, for example, a locomotive. The vehicle F can move in the usual way on the rails S of a track G in the directions of travel L and R. It has bogies DG1, DG2, each with two wheelsets RS.

[0042] In the illustrated embodiment, systematic deviations are detected using two sensors S1 and S2, which are position incremental encoders of the same sensor type. The two sensors S1 and S2 can therefore be, for example, the "Hasler ®< OPG" sensor type from the Sécheron Hasler Group. Alternatively, the two sensors can be, for example, the "BMIV" sensor type from Baumer Electric. It should be noted, however, that a single sensor, for example, sensor S1, is sufficient to carry out the method according to the invention, as will be explained below, particularly in connection with the explanations regarding the Figure 3 becomes apparent.

[0043] One of the two sensors, which is designated S1, is assigned to an axle A1 of one of the wheel sets RS of one bogie DG1 and the other sensor, which is designated S2, is assigned to an axle A2 of one of the wheel sets RS of the other bogie DG2.

[0044] A computing unit RE is connected to the sensors S1 and S2 via communication paths K1 and K2. A timer is also provided as a component of the computing unit RE. However, the timer T could also be implemented separately from the computing unit RE and connected to the computing unit RE via a suitable communication path.

[0045] The first sensor S1 is used to record measured values n S1 of the axle speed of axle A1 of one bogie DG1. Based on these values, the computing unit RE determines speed values v S1 of the vehicle F as values of the motion quantity v in a known manner, namely, taking into account the wheel diameter of a wheel R1 mounted on axle A1. The speed v of the vehicle F thus constitutes the motion quantity v here.

[0046] Similarly, the second sensor serves to record additional measured values n S2 of the axle speed of axle A2 of the other bogie DG2. Based on these values, the computing unit RE, taking into account the wheel diameter R2 of a wheel mounted on axle A2, determines additional speed values v S2 of the vehicle F as additional values of the motion quantity v. The speed v of the vehicle F thus also constitutes the motion quantity here.

[0047] The measured values n S1 of the first sensor reach the computing unit RE via one communication path K1 and the further measured values n S2 reach the computing unit RE via the further communication path K2.

[0048] However, it could also be provided that a sensor computing unit of the sensor S1 determines the speed values v S1 of the vehicle F itself from the measured values n S1 of the axle speed of the axle A1 and outputs these to the computing unit RE via the communication path K1 and that, in a corresponding manner, a further sensor computing unit of the sensor S2 determines the speed values v S2 of the vehicle F itself from the measured values n S2 of the axle speed of the axle A2 and outputs these to the computing unit RE via the further communication path K2.

[0049] The sensors S1 and S2, the computing unit RE with its timer T, and the communication paths K1 and K2 together form the arrangement A, which is designed to detect systematic deviations sA when determining the movement variable v of the vehicle F. The arrangement is designed in particular to carry out the method described below, which is divided into two sub-methods.

[0050] By means of a first of the two sub-procedures, a typical random error characteristic is reliably determined for each of the sensors S1 and S2 used.

[0051] This is followed by the second sub-procedure, which uses the typical random error characteristics established for each of the sensors S1 and S2 to detect significant systematic error influences; that is, to reliably distinguish systematic deviations (effects) from random deviations (effects).

[0052] Alternatively, only a typical random error characteristic of the sensor type used for the two sensors could be determined, and when using sensors of different sensor types, a typical random error characteristic for each of the different sensor types could be determined.

[0053] In addition, the process also works very efficiently when only one sensor or when any number of sensors are used simultaneously.

[0054] In addition, instead of the sensor S1, a test sensor of the same sensor type as the sensor S1 could be used and / or instead of the further sensor S2, another test sensor of the same sensor type as the further sensor S2 could be used.

[0055] According to Figure 2The first of the two sub-procedures, which is also referred to below as the calibration method or auto-calibration method, essentially comprises the following procedural steps: First, during a real test drive of the vehicle F, in a procedural step V1, test measured values n S1 .1, n S1 .2, n S1 .3, ... of the axle speed n S1 are recorded by means of one sensor S1 and further test measured values n S2 .1, n S2 .2, n S2 .3, ... of the axle speed n S2 are recorded by means of the further sensor S2. During this test drive, the speed, which here forms the movement variable v of the vehicle, is changed, in particular by careful acceleration and careful braking, in such a way that slipping of the wheels of the vehicle F on the ground or here specifically on the rails S of the track G is essentially avoided.

[0056] The test drive could also be a simulated test drive. Alternatively, such test measurements could also be recorded during a real or simulated test drive of a test vehicle used instead of vehicle F.

[0057] A sensor computing unit of the sensor S1 (not shown here) or the computing unit RE then determines, in a method step designated V2 here, test values v S1 .1, v S1 .2, v S1 .3, ... of the movement variable v based on the test measured values n S1 .1, n S1 .2, n S1 .3, ... In addition, a further sensor computing unit of the further sensor S2 (not shown here) or the computing unit RE determines further test values v S2 .1, v S2 .2, v S2 .3, ... of the movement variable v based on the further test measured values n S2 .1, n S2 .2, n S2 .3, ...

[0058] Subsequently, the computing unit RE uses the test values v S1 .1, v S1 .2, v S1 .3, ... to determine the profile of the statistical sensor accuracy σs_v S1 for one sensor S1 as a function of the movement variable v in the form of a sensor characteristic curve K S1. Correspondingly, the computing unit RE uses the further test values v S2 .1, v S2 .2, v S2 .3, ... to determine the profile of the statistical sensor accuracy σs_v S2 for the further sensor S2 as a function of the movement variable v in the form of a further sensor characteristic curve K S2.

[0059] Instead of the sensor characteristics K S1 , K S2 , sensor functions could also be determined which represent the sensor characteristics K S1 , K S2 .

[0060] To do this, the RE processing unit first checks, in a process step designated here as V3, whether the vehicle has completed at least three acceleration and deceleration phases during the test drive and whether it has stopped for at least 3 seconds between each of these phases. If this is not the case, the test drive must be continued until these conditions are met. If this is the case, the processing unit performs the next process step, designated here as V4.

[0061] In method step V4, the computing unit RE first forms low-pass filter values from the test values v S1 .1, v S1 .2, v S1 .3, ... of the at least one sensor S1 or the test sensor used in place of it by means of low-pass filtering. The computing unit RE then determines a sliding standard deviation σs_v s1 .1, σs_v S1 .2, σs_v S1 .3, ... for each of the test values v S1 .1, v S1 .2, v S1 .3, ... on the basis of the low-pass filter values. Accordingly, in method step V4, the computing unit RE first forms further low-pass filter values from the test values v S2 .1, v S2 .2, v S2 .3, ... of the at least one further sensor S2 or the further test sensor used in place of it by means of low-pass filtering. The computing unit RE then determines a further moving standard deviation σs_v S2 .1 , σs_v S2 .2, σs_v S2 .3, ... for each of the further test values v S2 .1, v S2 .2, v S2 .3, ... on the basis of the further low-pass filter values.

[0062] In a process step designated here as V5, the computing unit RE fits a best-fit line as the sensor characteristic curve K S1 into a representation of the sliding standard deviations σs_v S1 .1, σs_v S1 .2, σs_v S1 .3, ... over the amounts |v S1 .1|, |v S1 .2|, |v S1 .3|, ...; of the test values v S1 .1, v S1 .2, v S1 .3, ... The best-fit line is fitted in particular using the well-known method of least squares. The best-fit line indicates the course of the statistical sensor accuracy σs_v S1 of one sensor S1 as a function of the movement variable v. Accordingly, the computing unit RE fits a further best-fit line as a sensor characteristic curve K S2 into a representation of the further sliding standard deviations σs_v S2 .1, σs_v S2 .2, σ s _v S2 .3, ... over the amounts |v S2 .1|, |v S2 .2|, |v S2 .3|, ...; of the test values v S2 .1, v S2 .2, v S2 .3, ...The fitting of the additional best-fit line is also carried out using the well-known least-squares method. This additional best-fit line indicates the course of the statistical sensor accuracy σs_v S2 of the additional sensor S2 as a function of the motion quantity v.

[0063] In a process step designated here as V6, one sensor characteristic curve K S1 and the further sensor characteristic curve K S2 are stored in a memory of the computing unit RE along with additional information.

[0064] The computing unit therefore carries out the process steps V4, V5 and V6 simultaneously or with a time delay both for the test values v S1 .1, v S1 .2, v S1 .3, ... of the sensor S1 and for the further test values v S2 .1, v S2 .2, v S2 .3, ... of the further sensor S2.

[0065] According to Figure 3A first embodiment of the second of the two sub-methods essentially comprises the following method steps: In a method step designated here as V11, the sensor S1 detects a measured value n S1 .t of the axle speed n S1 of the axle A1 of one bogie DG1, assigned to the time t.

[0066] Furthermore, in a process step designated here as V12, the operating state Zt of timer T is read at time t. Timer T can assume two operating states. If it is started and running, it is in an active operating state. If it is not running, it is in a passive operating state. The timer is always started or restarted as soon as the computing unit RE has made an assumption A1 that a systematic deviation sA exists.

[0067] In process step V12, a test bound TS is also specified. This test bound TS is preferably determined as a quantile of the standard normal distribution of order 1-α / 2. A value Wα is specified for α as the error probability. This value Wα can be stored in a memory of the computing unit RE (not shown here).

[0068] In a process step designated here as V13, the computing unit RE determines a value v S1 .t of the movement quantity v assigned to the time t based on the measured value n S1 .t and taking into account the wheel diameter of the wheel R1 attached to the axle A1.

[0069] In a method step designated here by V14, a statistical sensor accuracy value σs_v S1 .t for the value v S1 .t of the movement quantity v is determined on the basis of the sensor characteristic curve K S1 previously determined for the at least one sensor S1 by reading this statistical sensor accuracy value σs_v S1 .t from the memory (not shown) of the computing unit RE at the interface designated here by P2.

[0070] In addition, in method step V14, a test variable value TG S1 .t assigned to the time t is formed as a function of the value v S1 .t of the movement variable v assigned to the time t and the statistical sensor accuracy value σs_v S1 .t of the sensor S1 determined for this value v S1 .t.

[0071] The test statistic value TG S1 .t is formed according to the Figure 7 in the following sub-steps Vi to Vvii of process step V14.

[0072] In a sub-step designated here by Vi, a probable system state SZ*.t of the vehicle F for the time t is determined based on a motion model BM applied to a previous system state SZ.tp of the vehicle F.

[0073] In a sub-step designated here as Vii, a probable value v*.t of the movement quantity v for the time t is determined using a transfer model TM applied to the probable system state SZ*.t.

[0074] In a sub-step designated here as VIII, the difference between the value v S1 .t of the movement variable v and the expected value v*.t of the movement variable v is determined as the innovation value d S1 .t.

[0075] In a sub-step referred to here as Viv, a system state accuracy value σ_SZ*.t of the expected system state SZ*.t is determined based on the motion model BM applied to a previous system state accuracy value σ_SZ.tp and a transfer model UM applied to a given system noise SR.

[0076] In a sub-step designated here as Vv, an accuracy value σ _ v*.t of the expected value v*.t of the movement quantity v is determined.

[0077] In a sub-step referred to here as Vvi, the sum of the accuracy value σ_ v*.t of the expected value v*.t of the movement quantity v and the sensor accuracy value σs_v S1 .t is determined as the innovation accuracy value σ_d S1 .t.

[0078] And in a sub-step designated here as Vvii, the quotient of the absolute value |d S1 .t| of the innovation value d S1 .t and the innovation accuracy value σ_d S1 .t is determined as the test statistic value TG S1 .t.

[0079] After the formation of the test statistic value TG S1 .t, the Figure 3 In the method step designated V15, the test variable value TG S1 .t is compared with the specified test limit TS in order to make an assumption A1, A2 or A3 regarding the existence of a systematic deviation sA depending on a comparison result obtained in the comparison.

[0080] In the process step designated V16, it is checked whether the timer T is running.

[0081] The assumption regarding the existence of a systematic deviation (sA) is made as follows: if the test variable value TG S1 .t assigned to the time t is greater than the specified test limit TS, then in the method step designated V17 the presence of a systematic deviation sA at the time t is assumed and the timer T is started or restarted, if the test variable value TG S1 .t is less than the specified test limit TS and, in addition, the timer T is running, then in the method step designated V18 the presence of a systematic deviation sA at the time t is not excluded, and if the test variable value TG S1 .t is less than the specified test limit TS and, in addition, the timer T is not running, then in the method step designated V19 the presence of a systematic deviation sA at the time t is excluded.

[0082] According to Figure 4A second embodiment of the second of the two sub-methods essentially comprises the following method steps: In a method step designated here as V111, the sensor S1 detects a measured value n S1 .t of the axle speed n S1 of the axle A1 of one bogie DG1, assigned to the time t.

[0083] Furthermore, in a process step designated here as V112, the operating state Zt of timer T at time t is read. Timer T can assume two operating states. If it is started and running, it is in an active operating state. If it is not running, it is in a passive operating state. The timer is always started or restarted as soon as the computing unit RE has made an assumption A1 that a systematic deviation sA exists.

[0084] In method step V112, a test bound TS and an additional test bound TS' are also specified. Preferably, the test bound TS is again determined as a quantile of the standard normal distribution of order 1-α / 2, and a value Wα is specified for α as the error probability. The additional test bound TS' is also preferably determined as a quantile of the standard normal distribution of order 1-α / 2, with an additional value W'α being specified for α as the error probability. The values Wα and W'α can again be stored in a memory of the computing unit RE.

[0085] In the process step designated here as V113, the computing unit RE determines a value V S1 .t of the movement quantity v assigned to the time t based on the measured value n S1 .t and taking into account the wheel diameter of the wheel R1 attached to the axle A1.

[0086] In the method step designated here by V114, a statistical sensor accuracy value σs_v S1 .t for the value V S1 .t of the movement quantity v is determined on the basis of the sensor characteristic curve K S1 previously determined for the at least one sensor S1 by reading this statistical sensor accuracy value σs_V S1 .t from the memory (not shown) of the computing unit RE at the interface designated here by P2.

[0087] In addition, in method step V114, a test variable value TG S1 .t assigned to the time t is formed as a function of the value v S1 .t of the movement variable v assigned to the time t and the statistical sensor accuracy value σs_v S1 .t of the sensor S1 determined for this value v S1 .t.

[0088] The formation of the test statistic value TG S1 .t is again carried out according to the Figure 7 in sub-steps Vi to Vvii.

[0089] In method step V114, an additional test variable value TG' S1 .t assigned to the time t is also formed.

[0090] According to the Figure 8 To form the additional test statistic value TG' S1 .t, the already Figure 7 known sub-steps Vi, Vii and Viii, followed by sub-steps Vviii, Vix and Vx.

[0091] In the sub-step designated here as Vviii, a residual value d' S1 .t is determined as the product of the innovation value d S1 .t and a given weighting factor GF.

[0092] In the sub-step referred to 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_v S1 .t.

[0093] And in the sub-step denoted here by Vx, the quotient of the magnitude |d' S1 .t| of the residual value d' S1 .t and the residual accuracy value σ_d' S1 .t is determined as the additional test statistic value TG' S1. t.

[0094] After the formation of the test statistic value TG S1 .t and the additional test statistic value TG' S1 .t, the Figure 4 In the process step designated V115, the test quantifier value TG S1 .t is compared with the specified test limit TS. Furthermore, in an additional comparison, the additional test quantifier value TG' S1 .t is compared with the specified additional test limit TS'.

[0095] In the process step designated V116, it is checked again whether the timer T is running.

[0096] The assumption regarding the existence of a systematic deviation sA is made as follows: if the test variable value TG S1 .t assigned to the time t is greater than the predetermined test limit TS and / or if the additional test variable value TG' S1 .t is greater than the predetermined additional test limit TS', then in method step V117 the presence of a systematic deviation sA at the time t is assumed and the timer is started or restarted if, on the one hand, the test variable value TG S1 .t is smaller than the predetermined test limit TS and, on the other hand, the additional test variable value TG' S1 .t is smaller than the predetermined additional test limit TS' and, in addition, the timer T is running, then in method step V118 the presence of a systematic deviation sA at the time t is not excluded if, on the one hand, the test variable value TG S1 .t is smaller than the predetermined test limit TS and, on the other hand, the additional test variable value TG' S1 .t ist is smaller than the specified additional test limit TS' and, in addition, the timer T is not running, then the presence of a systematic deviation sA at the time t is excluded in method step V119.

[0097] The Figure 5 shows a third embodiment of the second of the two sub-methods. This is applied when the additional sensor S2 provides a further measured value n S2 .t associated with time t.

[0098] Then, the following process steps are carried out: In the process step designated here as V211, the sensor S1 records a measured value n S1 .t of the axle speed n S1 of the axle A1 of one bogie DG1, assigned to time t. And the sensor S2 records another measured value n S2 .t of the axle speed n S2 of the axle A2 of the bogie DG2, assigned to time t.

[0099] In a process step designated here as V212, the operating state Zt of timer T is read at time t. Timer T can again assume two operating states. If it is started and running, it is in an active operating state. If it is not running, it is in a passive operating state. Here, too, the timer is always started or restarted as soon as the arithmetic unit RE has made an assumption A1 that a systematic deviation sA exists.

[0100] In process step V212, a test bound TS is again specified. This test bound TS is preferably determined as a quantile of the standard normal distribution of order 1-α / 2. A value Wα is specified for α as the error probability. This value Wα can be stored in a memory of the computing unit RE (not shown here).

[0101] In method step V213, the computing unit RE determines a value v S1 .t of the movement variable v assigned to time t based on the measured value n S1 .t and taking into account the wheel diameter of the wheel R1 attached to the axle A1. In addition, the computing unit RE determines a further value v S2 .t of the movement variable v assigned to time t based on the further measured value n S2 .t and taking into account the wheel diameter of the wheel R2 attached to the axle A2.

[0102] In a method step designated here by V214, a statistical sensor accuracy value σs_v S1 .t for the value v S1 .t of the movement variable v is determined on the basis of the sensor characteristic curve K S1 previously determined for one sensor S1 by reading this statistical sensor accuracy value σs_v S1 .t from the memory (not shown) of the computing unit RE at the interface designated here by P2. In addition, in method step V214, a further statistical sensor accuracy value σs_v S2 .t for the further value v S2 .t of the movement variable v is determined on the basis of the sensor characteristic curve K S2 previously determined for the further sensor S2 by reading this further statistical sensor accuracy value σs_v S2 .t from the memory (not shown) of the computing unit RE at the interface P2.

[0103] In method step V214, on the one hand, a test variable value TG S1 .t assigned to time t is formed depending on the value v S1 .t of the movement variable v assigned to time t and the statistical sensor accuracy value σs_v S1 .t of the sensor S1 determined for this value v S1 .t. On the other hand, a further test variable value TG S2 .t assigned to time t is formed depending on the further value v S2 .t of the movement variable v assigned to time t and the further statistical sensor accuracy value σs_v S2 .t of the further sensor S2 determined for this further value v S2 .t.

[0104] The formation of the test statistic value TG S1 .t takes place in the sub-steps Vi to Vvii according to Figure 7 .

[0105] The formation of the further test statistic value TG S2 .t is carried out according to the Figure 7as follows: In sub-step Viii, the difference between the further value v S2 .t of the movement variable v and the expected value v*.t of the movement variable v is determined as the further innovation value d S2 .t.

[0106] In the sub-step Vvi, the sum of the accuracy value σ_ v*.t of the expected value v*.t of the movement quantity v and the further sensor accuracy value σs_v S2 .t is determined as a further innovation accuracy value σ_d S2 .t.

[0107] And in sub-step Vvii, the quotient of the magnitude |d S2 .t| of the further innovation value d S2 .t and the further innovation accuracy value σ_d S2 .t is determined as the further test statistic value TG S2 .t.

[0108] After the formation of the test statistic value TG S1 .t and the further test statistic value TG S2 .t, the Figure 5In the process step designated V215, the test quantifier value TG S1 .t is compared with the specified test limit TS. In addition, the additional test quantifier value TG S2 .t is compared with the specified test limit TS in an additional comparison.

[0109] In the process step designated V216, it is checked again whether the timer T is running.

[0110] The assumption regarding the existence of a systematic deviation sA is made as follows: if the test variable value TG S1 .t assigned to the time t is greater than the predetermined test limit TS and / or if the further test variable value TG S2 .t assigned to the time t is greater than the predetermined test limit TS, then in method step V217 the presence of a systematic deviation sA at the time t is assumed, if the test variable value TG S1 .t assigned to the time t and the further test variable value TG S2 .t assigned to the time t are smaller than the predetermined test limit TS and, in addition, the timer T is running, then in method step V218 the presence of a systematic deviation sA at the time t is not excluded if the test variable value TG S1 .t assigned to the time t and the further test variable value TG S2 .t assigned to the time tt is smaller than the specified test limit TS and, in addition, the timer T is not running, then the presence of a systematic deviation sA at the time t is excluded in process step V219.

[0111] The Figure 6 shows a fourth embodiment of the second of the two sub-methods. This is used when the additional sensor S2 also provides a further measured value n S2 .t assigned to time t, and when, in addition to the one test variable value TG S1 .t, an additional test variable value TG' S1 .t and, in addition to the further test variable value TG S2 .t, a further additional test variable value TG' S2 .t are determined. Accordingly, in addition to the one test limit TS, an additional test limit is also specified.

[0112] The assumption regarding the existence of a systematic deviation sA is then made as follows: if at least one of the following conditions applies: the test statistic value TG S1 .t assigned to time t is greater than the specified test limit TS, the further test statistic value TG S2 .t assigned to time t is greater than the specified test limit TS, the additional test statistic value TG' S1 .t assigned to time t is greater than the specified additional test limit TS', the additional further test statistic value TG' S2 .t assigned to time t is greater than the specified additional test limit TS', then in process step V317 the existence of a systematic deviation sA at the time t is assumed, if, on the one hand, the test variable value TG S1 .t and the further test variable value TG S2 .t are smaller than the specified test limit TS and, on the other hand, the additional test variable value TG' S1 .t and the additional further test variable value TG' S2 .t are smaller than the specified additional test limit TS' and, in addition, the timer T is running, then the presence of a systematic deviation sA at the time t is not excluded in method step V318, if, on the one hand, the test variable value TG S1 .t and the further test variable value TG S2 .t are smaller than the specified test limit TS and, on the other hand, the additional test variable value TG' S1 .t and the additional further test variable value TG' S2 .t are smaller than the specified additional test limit TS' and, in addition, the timer T is not running, then the presence of a systematic deviation sA at the time t is excluded in method step V319.

[0113] In summary, it can be stated that the first of the two sub-procedures, which can also be referred to as the calibration method, involves at least the following steps if the speed of the vehicle is the movement variable: Driving at various speeds, carefully accelerating and braking the vehicle when changing speed, low-pass filtering the measured values, calculating the accuracies of the various sensors for the entire journey, and iteratively fitting a best-fit line using the least-squares method. Eliminating sliding and skidding phenomena through statistical tests, and repeating the calculation of the best-fit line. Continue this process until no systematic influences are present.

[0114] The result of the first sub-process is the accuracy of the respective sensors.

[0115] The second of the two sub-procedures, which can also be referred to as the slip and slide detection method, then includes, for example, the following additional steps: Calculating the expected speed of the vehicle at the time of arrival of a measured value (e.g. a position incremental encoder measured value) with the help of a motion model (e.g. using an extended Kalman filter), calculating the difference (here preferably the speed difference) between the calculated expected speed and the measured speed. Calculating the accuracy of the speed difference. Calculating the test limit for a given error probability alpha. Calculating the test variable value. Comparing the test variable value with the test limit.

[0116] If the test value is larger than the test limit, a systematic effect, e.g. skidding or sliding, is present.

[0117] Using the timer in the manner described prevents a false statement being made that there is no slippage or skidding when this is in fact the case.

[0118] The following are the main advantages of the method according to the invention: The process functions reliably regardless of the vehicle (vehicle type) and can therefore be flexibly offered for initial installation or retrofitting to a wide variety of vehicles (vehicle types). The system suitable for carrying out the process can be easily installed (implemented) or retrofitted. The system consists of very few components – making it very cost-effective in terms of initial procurement – and requires correspondingly low maintenance and care costs, including the software of the computing unit that carries out individual process steps. The process requires little computing power. A maximum of as many comparisons are performed as there are sensors. Any number of sensors can be dynamically switched on or off. In principle, the process already functions using one sensor. However, it is advantageous to use at least two sensors.The detection of slip or skidding is therefore preferably based on at least two sensors being involved. Dedicated test sensors are not required. For example, it is advantageous to attach at least two incremental displacement sensors to train axles that are controlled in sufficiently different ways or where the external influence of the environment in combination with the method according to the invention leads to sufficiently strong variation in the calculated displacement values of the sensors. In principle, however, the method can use any number of sensors, in particular incremental displacement sensors, such as those currently present on the train. Instead of or in addition to incremental displacement sensors, so-called absolute sensors such as GNSS or Doppler radars can also be integrated into the method according to the invention. However, Doppler radars or GNSS are not required as reference sensors for the method. The input variables for the method are the measured values and their statistics.Vehicle-specific characteristics do not need to be disclosed for the procedure. The procedure is characterized by a very high detection rate for sliding and skidding slip; previous tests show a detection rate of 100% and no false detection of nonexistent sliding or skidding slip. By reliably detecting sliding and skidding slip, confidence intervals can be narrowed in a timely manner. The first sub-procedure (the calibration method described above) for determining the sensor statistics (individual sensor accuracies) can be performed once for the sensor type at the factory or more frequently during operation and is essentially constant for the respective sensor type.

Claims

1. Method for detecting systematic deviations (sA) during determination of a movement variable (v) of a ground-based, in particular rail-based, vehicle (F), - wherein, on the basis of a measurement value (nS1.t), associated with a time point (t), of at least one sensor (S1), a value (vS1.t) of the movement variable (v) associated with the time point (t) is determined, characterised in that - dependent upon the value (vS1.t), associated with the time point (t), of the movement variable (v) and upon a statistical sensor accuracy value (σs_vS1.t) of the at least one sensor (S1) determined for this value (vS1.t), a test variable value (TGS1.t) associated with the time point (t) is formed and, in the course of a comparison, is compared with a specified test bound (TS), in order to make an assumption (A1, A2, A3), dependent upon a comparison result obtained from the comparison, with regard to an existence of a systematic deviation (sA), - wherein the test variable value (TGS1.t) is formed for the time point (t) additionally as a function of an expected value (v*.t) of the movement variable (v), - wherein 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) is determined for the time point (t) and - on the basis of a transfer model (TM) applied to the expected system state (SZ*.t), the expected value (v*.t) of the movement variable (v) for the time point (t) is determined.

2. Method according to claim 1, characterised in that the operational state (Z.t) of a timer (T) that has been started in the event of a previously made assumption (A1) that a systematic deviation (sA) exists is read out at the time point (t).

3. Method according to one of claims 1 or 2, characterised in that an additional test variable value (TG'S1.t) is formed and, in the course of an additional comparison, is compared with a specified additional test bound (TS') in order to make the assumption regarding the existence of a systematic deviation (sA) dependent also upon an additional comparison result obtained during the additional comparison.

4. Method according to one of claims 1 or 2, characterised in that in the event that at least one further sensor (S2) provides a further measurement value (nS2.t) associated with the time point (t), - on the basis of this further measurement value (nS2.t), a further value (vS2.t) of the movement variable (v) associated with the time point (t) is determined, and - dependent upon the further value (vS2.t), associated with the time point (t), of the movement variable (v) and upon a 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 and, in the course of a further comparison, is compared with the specified test bound (TS), in order to make the assumption (A1, A2, A3), dependent also upon a further comparison result obtained from the further comparison, with regard to an existence of a systematic deviation (sA).

5. Method according to claim 4, characterised in that an additional further test variable value (TG'S2.t) is formed and, in the course of an additional further comparison, is compared with the specified additional test bound (TS') in order to make the assumption regarding the existence of a systematic deviation (sA), dependent also upon an additional further comparison result obtained during the additional further comparison.

6. Method according to claim 2, characterised in that the assumption regarding the existence of a systematic deviation (sA) is made as follows: - if the test variable value (TGS1.t) associated with the time point (t) is larger than the specified test bound (TS), then the existence of a systematic deviation (sA) at the time point (t) is assumed, - if the test variable value (TGS1.t) is smaller than the specified test bound (TS) and the timer (T) is also running, then the existence of a systematic deviation (sA) at the time point (t) is not precluded, - if the test variable value (TGS1.t) is smaller than the specified test bound (TS) and the timer (T) is also not running, then the existence of a systematic deviation (sA) at the time point (t) is precluded.

7. Method according to claim 3, characterised in that the assumption regarding the existence of a systematic deviation (sA) is made as follows: - if the test variable value (TGS1.t) associated with the time point (t) is larger than the specified test bound (TS) and / or if the additional test variable value (TG'S1.t) is larger than the specified additional test bound (TS'), then the existence of a systematic deviation (sA) at the time point (t) is assumed, - if, firstly, the test variable value (TGS1.t) is smaller than the specified test bound (TS) and, secondly, the additional test variable value (TG'S1.t) is smaller than the specified additional test bound (TS') and the timer (T) is also running, then the existence of a systematic deviation (sA) at the time point (t) is not precluded, - if, firstly, the test variable value (TGS1.t) is smaller than the specified test bound (TS) and, secondly, the additional test variable value (TG'S1.t) is smaller than the specified additional test bound (TS') and the timer (T) is also not running, then the existence of a systematic deviation (sA) at the time point (t) is precluded,8. Method according to claim 4, characterised in that the assumption regarding the existence of a systematic deviation (sA) is made as follows: - if the test variable value (TGS1.t) associated with the time point (t) is larger than the specified test bound (TS) and / or if the additional test variable value (TGS2.t) associated with the time point (t) is larger than the specified test bound (TS), then the existence of a systematic deviation (sA) at the time point (t) is assumed, - if the test variable value (TGS1.t) associated with the time point (t) and the further test variable value (TGS2.t) associated with the time point (t) are smaller than the specified test bound (TS) and the timer (T) is also running, then the existence of a systematic deviation (sA) at the time point (t) is not precluded, - if the test variable value (TGS1.t) associated with the time point (t) and the further test variable value (TGS2.t) associated with the time point (t) are smaller than the specified test bound (TS) and the timer (T) is also not running, then the existence of a systematic deviation (sA) at the time point (t) is precluded.

9. Method according to claim 5, characterised in that the assumption regarding the existence of a systematic deviation (sA) is made as follows: - if at least one of the following conditions applies: • the test variable value (TGS1.t) associated with the time point (t) is larger than the specified test bound (TS), • the further test variable value (TGS2.t) associated with the time point (t) is larger than the specified test bound (TS), • the additional test variable value (TG'S1.t) associated with the time point (t) is larger than the specified additional test bound (TS'), • the additional further test variable value (TG'S2.t) associated with the time point (t) is larger than the specified additional test bound (TS'), then the existence of a systematic deviation (sA) at the time point (t) is assumed, - if, firstly, the test variable value (TGS1.t) and the further test variable value (TGS2.t) are smaller than the specified test bound (TS) and, secondly, the additional test variable value (TG'S1.t) and the additional further test variable value (TG'S2.t) are smaller than the specified additional test bound (TS') and the timer (T) is also running, then the existence of a systematic deviation (sA) at the time point (t) is not precluded, - if, firstly, the test variable value (TGS1.t) and the further test variable value (TGS2.t) are smaller than the specified test bound (TS) and, secondly, the additional test variable value (TG'S1.t) and the additional further test variable value (TG'S2.t) are smaller than the specified additional test bound (TS') and the timer (T) is also not running, then the existence of a systematic deviation (sA) at the time point (t) is precluded.

10. Method according to one of claims 1 to 9, characterised in that the statistical sensor accuracy value (σs_vS1.t) is determined on the basis of a sensor characteristic curve (KS1) or sensor function previously determined for the at least one sensor (S1).

11. Method according to claim 10, characterised in that during a real or simulated test journey of the vehicle (F) or of a test vehicle used in its place, by means of the at least one sensor (S1) or a test sensor of the same sensor type used in its place, test measurement values (nS1.1, nS1.2, nS1.3, ...) are captured and, on the basis of these test measurement values (nS1.1, nS1.2, nS1.3, ...), test values (vS1.1, vS1.2, vS1.3, ...) of the movement variable (v) are determined and in that on the basis of the test values (vS1.1, vS1.2, vS1.3, ...) of the movement variable (v) for the at least one sensor (S1), a variation of its statistical sensor accuracy (σs_vS1) dependent upon the movement variable (v) in the form of the respective sensor characteristic curve (KS1) and / or the sensor function is determined.

12. Method according to claim 11, characterised in that the movement variable (v) of the vehicle (F) and / or of the test vehicle used in its place during the test journey, in particular, by means of careful acceleration and careful braking, is changed such that a slippage of the wheels of the vehicle (F) on the ground or the rails (S) is substantially prevented.

13. Method according to one of claims 11 or 12, characterised in that - by means of a low pass filtration, low pass filter values are formed from the test values (vS1.1, vS1.2, vS1.3, ...), - for each of the test values on the basis of the low pass filter values, a sliding standard deviation (σs_vS1.1, σs_vS1.2, σs_vS1.3, ...) is determined, and - a regression line is fitted into a representation (D) formed thereafter of the sliding standard deviations over the moduli (lvS1.1|, |vS1.2|, |vS1.3|, ...;) of the test values (vS1.1, vS1.2, vS1.3, ...), in particular with the aid of the least squares method, wherein the regression line shows the variation of the statistical sensor accuracy (σs_vS1) of the at least one sensor (S1) dependent upon the movement variable (v).

14. Method according to claim 1, characterised in that 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).

15. Method according to claim 14, characterised in that - on the basis of the movement model (BM) applied to a previous system state accuracy value (σ_SZ.tp) and of 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, - on the basis of a 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, - 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 - 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).

16. Method according to claim 14, characterised in that - a residual value (d'S1.t) is determined as the product of the innovation value (dS1.t) and a specified weighting factor (GF), - a residual accuracy value (s_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 - the quotient is determined from the modulus (|d'S1.t|) of the residual value (d'S1.t) and the residual accuracy value (σ_d'S1.t) as the additional test variable value (TG'S1.t).

17. Method according to one of claims 1 to 16, characterised in that one test bound (TS) is determined as a quantile of the standard normal distribution of the order 1-α / 2, wherein a value (Wα) is specified for α as the probability of error.

18. Method according to one of claims 3 to 16, characterised in that 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'α) is specified for α as the probability of error.

19. Arrangement (A) for detecting systematic deviations (sA) during determination of a movement variable (v) of a ground-based, in particular rail-based, vehicle (F), which comprises at least one sensor (S1), having a computer unit (RE) upon which software can be executed, which causes the arrangement (A) to carry out a method according to one of claims 1 to 18.

20. Ground-based, in particular rail-based, vehicle (F) with an arrangement (A) according to claim 19.