METHOD AND APPARATUS FOR IDENTIFYING PROPERTIES OF A VEHICLE, COMPUTER PROGRAM PRODUCT AND COMPUTER-READABLE MEDIUM FOR STORING AND/OR PROVIDING THE COMPUTER PROGRAM PRODUCT

Abstract

A method for identifying properties of rail-guided vehicles uses an axle counter to detect vehicle measurement data as the vehicle crosses. The measurement data are analyzed in a computer and speed and distances between vehicle axles are ascertained. A property of the vehicle is ascertained in a computer based on the ascertained speed and distances between axles. A checking step ascertains a pattern of normal distances between axles in that a normal distance between axles calculated by considering a predefined normal speed is assigned to each ascertained distance between axles, and by considering their order, the normal distances between axles merge to form the pattern. The pattern is compared with reference patterns, and upon identified conformity of the pattern and reference pattern, a type linked to the reference pattern is assigned to the vehicle as a property. An apparatus and computer program determining properties of vehicles are also provided.

Claims

1. A method for identifying properties of a rail-guided vehicle, the method comprising: using an axle counter to detect measurement data as the rail-guided vehicle crosses the axle counter; analyzing the measurement data in a computer-assisted manner and ascertaining a speed and distances between axles of the vehicle; ascertaining a property of the rail-guided vehicle in a computer-assisted manner based on the ascertained speed and the ascertained distances between axles; carrying out a checking step including ascertaining a pattern of normal distances between axles by calculating a normal distance between axles by taking a predefined normal speed assigned to each of the ascertained distances between axles into account, and merging the normal distances between axles to form the pattern by taking an order of the ascertained distances between axles into account; comparing the pattern with reference patterns; and upon identifying a conformity of the pattern with a reference pattern, assigning a type linked to the reference pattern to the vehicle as a property.

2. The method according to claim 1, which further comprises ascertaining each of the distances between axles by taking an individual speed of the vehicle applicable to a relevant distance between axles into account.

3. The method according to claim 1, which further comprises calculating an individual speed of the vehicle as an average speed of the vehicle in a time period during which the axles defining the distance between axles pass the axle counter.

4. The method according to claim 1, which further comprises calculating an individual speed of the vehicle from two speeds of the axles calculated by the axle counter based on the axles defining the distance between axles.

5. The method according to claim 1, which further comprises smoothing a signal characteristic before ascertaining the distances between axles.

6. The method according to claim 1, which further comprises carrying out a further checking step by ascertaining an overall length of the vehicle as a further property of the vehicle.

7. The method according to claim 1, which further comprises carrying out a further checking step by subjecting measured speeds to a plausibility check with regard to at least one of direction or value or overall length, and outputting a result of the plausibility check.

8. The method according to claim 7, which further comprises evaluating a machine learning method for at least one of the checking step or the further checking step.

9. The method according to claim 8, which further comprises applying the machine learning method only when the result of the plausibility check is positive.

10. The method according to claim 1, which further comprises ascertaining probability densities for the properties from measurement data of a number of measurements.

11. An apparatus for determining properties of rail-guided vehicles, the apparatus comprising: at least one axle counter for detecting measurement data as the vehicles cross said at least one axle counter; a computer configured to analyze the measurement data and ascertain a speed and distances between axles of a vehicle; said computer configured to ascertain a property of the vehicle based on the ascertained speed and the ascertained distances between axles; and said computer configured to carry out the method according to claim 1 when ascertaining the property.

12. A non-transitory computer program product with program commands stored thereon that when executed on a computer carry out the method according to claim 1.

13. A non-transitory computer-readable medium for at least one of storing or providing the computer program product according to claim 12.

Description

BRIEF DESCRIPTION OF THE FIGURES

[0080] FIG. 1 is a diagrammatic, side-elevational view of an exemplary embodiment of the inventive apparatus with its interrelationships and with a computer infrastructure (computing environment) of the apparatus as a block diagram, with the individual functional units containing program modules, which in each case can run in one or more processor(s) and the interfaces which can accordingly be configured in terms of software or hardware;

[0081] FIGS. 2 and 3 are side-elevational views showing partly identical or similar patterns of distances between axles for a passenger train and a freight train;

[0082] FIG. 4 is a graph of the signal strength s as a function of the time t of a dual axle counter with two sensors n equal to 1 and 2 for an exemplary embodiment of the inventive method;

[0083] FIG. 5 is a graph which shows different signal characteristics n of an axle counter as a function of the time t as a graph for an exemplary embodiment of the inventive method,

[0084] FIG. 6 is a flowchart of an exemplary embodiment of the inventive method, with the functional units and interfaces in FIG. 1 being indicated by way of example and if possible for the individual method steps to be implemented individually or in groups by way of program modules and the functional units and interfaces in FIG. 2 being indicated by way of example; and

[0085] FIG. 7 is a diagram symbolically showing two normal distributions for ascertained measurement data in order to explain the principle of the invention.

DETAILED DESCRIPTION OF THE INVENTION

[0086] Referring now to the figures of the drawings in detail and first, particularly, to FIG. 1 thereof, there is seen a rail system with a rail GL, a control center LZ, which has a second computer CP2 and a second memory unit SE2 connected thereto by a seventh interface S7, and a signal box or tower SW. A vehicle FZ in the form of a train travels on the rail GL in the direction of a railroad crossing BU. A first axle counter AZ1 and a second axle counter AZ2 are installed on the rail GL, and these are adapted in a manner known per se to count the axles of the vehicle FZ.

[0087] The axle counter AZ1 is connected to the signal box or tower SW, strictly speaking to a first computer CP1 present in this signal box or tower, through a first interface S1, and the second axle counter AZ2 is connected to the first computer CP1 through a second interface S2. In addition, the first computer CP1 has a third interface S3 for the railroad crossing BU. In addition, the first computer CP1 is connected to a memory unit SE1 through a sixth interface S6.

[0088] The signal box or tower SW has a first antenna system A1, the control center LZ has a second antenna system A2, and the vehicle FZ has a third antenna system A3. Both the communication of the signal box or tower SW with the control center LZ through a fourth interface S4 and the communication of the vehicle FZ with the control center LZ through a fifth interface S5 is possible hereby. The fourth interface S4 and the fifth interface S5 are wireless interfaces in this regard. The first interface S1, the second interface S2 and the third interface S3 can be both wired as well as wireless interfaces, with the antenna technology, which would be necessary for forming wireless interfaces, not being illustrated for the latter case.

[0089] If the vehicle FZ moves on the rail GL in the direction of the railroad crossing BU, the axles of the vehicle FZ firstly pass the second axle counter AZ2 and then the first axle counter AZ1. The acquired measured values can be transmitted to the first computer CP1 through the first interface S1 and the second interface S2, with the first computer CP1 (and also the second computer CP2) being adapted for carrying out the inventive method. The first computer CP1 can also undertake the actuation of the railroad crossing BU directly. Another possibility lies in the first computer CP1 being connected to a further computer (not illustrated in FIG. 1) through the third interface S3, which computer is used through a further interface for actuation of the railroad crossing BU.

[0090] FIG. 2 illustrates a passenger train PZ travelling on the rail GL as the vehicle FZ in FIG. 1. This passenger train PZ is formed of a locomotive LK, a plurality of passenger cars PW and a power car TK at the end of the passenger train PZ opposite the locomotive LK.

[0091] The distances between the individual axles (indicated by wheels) are also schematically illustrated. It has been found that different distances between axles occur multiple times in the passenger train PZ, so the sequence of distances between axles can be examined for the existence of patterns. The distances between axles are marked by the uppercase letters A to G. The sequence of distances between axles is formed of FFEFFGABACABACABACADA.

[0092] If the locomotive LK and the power car TK are disregarded, since these differ in respect of their distances between axles from the passenger car PW, a sequence of distances between axles, which is continuously repeated, thus results for the successive passenger cars, which are identical in construction. In this respect they form a pattern MT, which is marked for the passenger car PW following the locomotive LK with a curly bracket. The sequence of distances between axles in the pattern MT illustrated in FIG. 2 is ABAC. This sequence of distances between axles also results for the two subsequent passenger cars.

[0093] The situation is different in the freight train GZ illustrated in FIG. 3 on the rail GL, which is composed of a locomotive LK and a first freight car GW1, a second freight car GW2 and a third freight car GW3. These have different lengths and numbers of axles, so a plurality of different distances between axles result, which are provided the uppercase letters A to H. In FIG. 3 it becomes clear that repeating patterns may in no way be discovered in the illustrated sequence ABACDEDFGFH, and this allows a freight train to be inferred.

[0094] FIG. 4 illustrates the signal characteristics of a dual axle counter as a function of the time t. Two individual sensors are installed in this counter, so there are two signal characteristics n, which are marked by 1 and 2 in FIG. 4.

[0095] It can be seen in FIG. 4 that the two individual sensors generate a first raw signal RS1 and a second raw signal RS2, which are transferred by a smoothing step of the method into a first smoothed signal GS1 and a second smoothed signal GS2. These are now available for a further evaluation.

[0096] The individual sensors 1 and 2 are installed one behind the other in the dual axle counter, so that when a wheel passes there is a time delay in the generation of the sensor signals. FIG. 4 illustrates this time delay in the first time period T1 and in the second time period T2. This time period can be used, for example, to calculate the speed of the wheel passing the sensor and thereby of the vehicle.

[0097] The first time period T1 is to be assigned to a first wheel and the second time period T2 to a second wheel, it being possible for these wheels to pertain, for example, to a truck or bogie of a vehicle. With known speed (calculated by way of the first time period T1 and/or the second time period T2), the distance A of the axles of the truck (cf. FIGS. 2 and 5) can be calculated from the resulting time period T12, which lies between the detection of the first wheel and the detection of the second wheel.

[0098] FIG. 5 illustrates the signal characteristics n=1 . . . 4 of in each case only one sensor of a dual axle counter on the basis of the pattern MT in FIG. 2. The different examples 1 to 4 represent different speed and acceleration states of the vehicle, which crosses the axle counter. The first signal indicating an axle (as also illustrated in FIG. 4 by a signal rise and subsequent signal drop) lies in all cases 1 to 4 at an instant t1. The different characteristics of the signal characteristics 1 to 4 will be explained on the basis of the further instants, which are illustrated in FIG. 5.

[0099] The signal characteristic 4 is a signal characteristic in which by way of calculation, the normal distances between axles A, B and C are calculated since the vehicle moves at the predefined normal speed in this case. In other words, it would not be necessary to convert this signal characteristic by taking into account the predefined normal speed since it could be compared directly with the reference patterns.

[0100] The signal characteristic 3 results when the vehicle crosses the axle counter at a constant speed, with this speed being higher than the normal speed. This may be seen in FIG. 5 in that, compared with the signal characteristic 4, the signal characteristic 3 is compressed on the time axis. While the signal characteristic 4 extends from instant T1 to instant T6, the signal characteristic 3 ends earlier at instant T3.

[0101] The signal characteristic 2 results when the vehicle is constantly accelerated as it crosses the axle counter. It can be seen in this case that the signal characteristic 2 is not compressed by a constant factor like the signal characteristic 3, instead the compression of the signal increases continuously. The second peak of the signal characteristic is therefore already shifted slightly at instant T7 compared with instant T2 in the second peak of the signal characteristic 4. It is assumed that the vehicle had the same speed at instant T1 as the vehicle in the case of signal characteristic 4, in other words, the normal speed. The instant T7 is therefore earlier than the instant T2. The signal characteristic thereby ends overall at instant T4 earlier than the signal characteristic 4, which ends at instant T6.

[0102] The signal characteristic 1 shows a non-constant acceleration behavior of the vehicle as it crosses the axle counter. On the basis of the instants T1 and T2 it may be seen that the vehicle is en route at normal speed at this instant. The instants T8 and T9 show that in this case there is a greater speed, so that an acceleration has taken place (t8 and T9 lie closer together than T1 and T2). In addition, T10 and T6 show that in this case there is a lower speed than at instant T1 and T2 (T10 and T6 have a greater distance than T1 and T2). It should be noted in this connection that the signals are merely representative and further axles could lie between the instants T2 and T8 and T1 and T10, which would generate further peaks (not illustrated).

[0103] According to the method described in relation to FIG. 4, a distance between axles may be calculated for all signal characteristics illustrated in FIG. 5 in each case by taking into account adjacent peaks. By taking into account the measured speeds and individual speeds calculated therefrom (for example, average speeds calculated as individual speeds, in respect of adjacent axles in each case), these may be normalized in each case, so that, irrespective of the speed or acceleration behavior of the vehicle, the normal distances A, B and C can be calculated. The method used in this case is described in relation to FIG. 6.

[0104] FIG. 6 illustrates the inventive method, which is being carried out in a divided manner, by way of example, by the first computer CP1 and the second computer CP2. The first memory unit SE1 and the second memory unit SE2 are used. The method starts both in the computer CP1 and in the computer CP2. In the computer CP2, a measurement step MSG is carried out, which can generate, by way of example, signal characteristics in FIGS. 4 and 5. A smoothing step GLT then follows for generating a smoothed signal GS1, GS2 from the respective raw signals RS1, RS2 (cf. FIG. 4). A plausibility step PLS is then carried out in which the particular properties of the signal characteristic can be used to be able to carry out an assessment in advance as to whether the signal characteristic represents a realistic operating state of the vehicle. Measuring errors, for example, can be identified hereby, but also adversarial attacks on the apparatus, it being possible for both to result in undesirable operating states and even accidents.

[0105] In a query step PLS? it is checked whether or not the plausibility check could be successfully carried out. If this is not the case, a standard step DFL is carried out, which can safely operate the system or transfers it into a safe state. Safe operation is possible, for example in a case of a railroad crossing, if for the closing time of the railroad crossing the most adverse case of a fast approaching passenger train is assumed and the barriers are activated early. An incident can thereby be ruled out for even the most adverse case that can be assumed. One example of a safety measure is the initiation of emergency braking for the vehicle.

[0106] If the plausibility check could be carried out successfully then the pattern MT generated with the measurement in the first computer CP1 carries out a normalization step NRM. This proceeds in accordance with the principles explained in FIG. 5, so that, as a result, the pattern MT with normal distances between axles is generated. This can be compared in a comparison step CMP with reference patterns RMT, which are stored in the first memory unit SE1 and are retrieved from there.

[0107] If a reference pattern RMT was identified, then in a selection step for the type of the vehicle ST_GT, for example a train type such as passenger train, local train or freight train can be selected. Subsequently in a control step CRL, control, for example of a train component, tailored to the identified train type can be carried out. For example, the closing time of a railroad crossing can be controlled as a function of the identified train type. Control commands CMD can be retrieved from the first memory unit SE1 for the purpose of modification of the control step CRL.

[0108] In addition, the result which has been checked for plausibility is transmitted to the second computer CP2. A machine learning step LRN is carried out in this computer, with the second computer CP2 being equipped with artificial intelligence, for example by applying a neural network. If the machine learning step was carried out successfully, a modification step MOD follows, which generates modified or new reference patterns RMT. For example, it was possible to identify that new high-speed trains with a greater number of cars are travelling on a particular track, on which the axle counter is installed. A corresponding reference pattern RMT is then stored accordingly in the second memory unit SE2.

[0109] In addition, suitable new control commands CMD can be generated on the basis of the learned modifications in a creation step for control commands ST_CMD for relevant reference patterns RMT. These are stored in the first memory unit SE1.

[0110] For the method in the first computer CP1 and the second computer CP2, a query STP? takes place as to whether the end of operation or the end of the process has been reached. If this is the case, the method is stopped. If this is not the case, the processes in the first computer CP1 begin with a renewed measurement step MSG and, as necessary, the process in the second computer CP2 begins with a renewed learning step LRN.

[0111] FIG. 7 illustrates, by way of example, two parameters, inventively measured or determinable by the axle counter, in a plane, which could also be referred to as the x-y plane and on which the measured value distribution MV of the measured values becomes clear. According to this, the speed GSW would be illustrated on the x-axis and distances between axles A . . . H on the Y-axis. The z-axis serves to illustrate (for example, estimated) probability densities.

[0112] For the parameters under consideration, in this example location-specific, representative data is gathered or measured and classified, for example passenger train as normal distribution NV2 and freight train as normal distribution NV1, as already described above. This is a finite number of integral or real-valued measurement data of the axle counters, for example this could be the speed and the distance between axles to give a clear two-dimensional example in this case. In other words, in principle, a classification task is obtained as is schematically illustrated in FIG. 7.

[0113] Where representative data exists it is known how such problems of pattern identification can be solved with methods of machine learning, for example by way of neural networks. With this application in the case of axle counters, there is greater scope in setting the classification limits because with low-dimensional problems of this kind it is possible to also estimate the probability densities for the two classes from the data (for example with density estimation). The error probabilities for an incorrect classification can be ascertained thereby (cf. for example Duda et al.: Pattern Classification, Wiley, 2001), FIG. 5 shows this symbolically for a first normal distribution NV1 and a second normal distribution NV2, but, in principle this also works for distributions other than normal distributions.

[0114] If it is assumed in the example that the small ellipse would be the first classification limit KG1 for freight trains and the large ellipse the classification limit KG2 for passenger trains, then the error probabilities could be calculated with the estimated distributions. If the error classification probability for freight trains were to be too high, the classification limits would be changed. In the example in FIG. 7, a smaller ellipse would then be obtained for the first classification limit KG1. There can also be applications, however, where the classification errors are asymmetric, in other words, the errors do not have the same significance. For example, considering safety, it would be irrelevant in the case of time-controlled activation of a railroad crossing if a slow freight train were to be classified as a fast passenger train, whereas this would be dangerous in the case of a ban on trains meeting in a tunnel. In other words, in each case the safety aspect has to be considered in the evaluation of the types or probabilities of error.

[0115] The following is a summary list of reference numerals and the corresponding structure used in the above description of the invention: [0116] GL rail [0117] FZ rail-guided vehicle (rail vehicle) [0118] BU railroad crossing [0119] LZ control center [0120] SW signal box or tower [0121] A1 . . . A3 antenna [0122] AZ1 . . . AZ2 axle counter [0123] S1 . . . S7 interface [0124] CP1 . . . CP2 computer [0125] SE1 . . . SE2 memory unit [0126] PZ passenger train [0127] LK locomotive [0128] PW passenger car [0129] TK power car [0130] GZ freight train [0131] GW1 . . . GW3 freight car [0132] A . . . H distance between axles [0133] MT pattern [0134] RMT reference pattern [0135] MSG measurement step [0136] GLT smoothing step [0137] PLS plausibility step [0138] PLS? query step: plausibility check taken place [0139] DFL standard step [0140] CRL control step [0141] NRM normalization step [0142] CMP comparison step [0143] ST_GT selection step for type [0144] CMD control commands [0145] LRN machine learning step [0146] MOD modification step [0147] ST_CMD creation step for control commands [0148] STP? query step: end of the process [0149] S measurement signal [0150] t time [0151] t1 . . . t10 instants [0152] T1 . . . T12 time periods [0153] RS1 . . . RS2 raw signal [0154] GS1 . . . GS2 smoothed signal