EP4509382A1 - Procédé de détermination d'une perturbation d'une infrastructure ferroviaire au moyen d'un dispositif de détection d'un train, produit programme informatique, support de stockage lisible par ordinateur et dispositif de détection - Google Patents

Procédé de détermination d'une perturbation d'une infrastructure ferroviaire au moyen d'un dispositif de détection d'un train, produit programme informatique, support de stockage lisible par ordinateur et dispositif de détection Download PDF

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Publication number
EP4509382A1
EP4509382A1 EP24190880.5A EP24190880A EP4509382A1 EP 4509382 A1 EP4509382 A1 EP 4509382A1 EP 24190880 A EP24190880 A EP 24190880A EP 4509382 A1 EP4509382 A1 EP 4509382A1
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EP
European Patent Office
Prior art keywords
train
detection device
fault
computing device
electronic computing
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP24190880.5A
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German (de)
English (en)
Inventor
Reiner Schmid
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Siemens Mobility GmbH
Original Assignee
Siemens AG
Siemens Corp
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Publication date
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Publication of EP4509382A1 publication Critical patent/EP4509382A1/fr
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L23/00—Control, warning or like safety means along the route or between vehicles or trains
    • B61L23/04—Control, warning or like safety means along the route or between vehicles or trains for monitoring the mechanical state of the route
    • B61L23/041—Obstacle detection

Definitions

  • the invention relates to a method for determining a disruption of a railway infrastructure by means of a detection device of a train according to the applicable patent claim 1. Furthermore, the invention relates to a computer program product, a computer-readable storage medium and a detection device.
  • the object of the present invention is to provide a method, a computer program product, a computer-readable storage medium and a detection device by means of which a disruption of a railway infrastructure can be determined in an improved manner by means of the detection device.
  • One aspect of the invention relates to a method for determining a fault in a railway infrastructure using a detection device of a train.
  • the surroundings of the train are detected using the detection device.
  • An anomaly associated with the railway infrastructure is determined depending on the detected surroundings using an electronic computing device of the detection device and the fault is determined depending on the determined anomaly using the electronic computing device.
  • the invention takes advantage of the fact that detection devices already installed on the train can be used to detect and determine corresponding disturbances in the railway infrastructure.
  • the train is equipped with a system for automatic obstacle detection, for example, a series of sensors or detection devices are present to perceive the surroundings of the train. These provide image data, lidar or radar data about the surroundings.
  • the detection device can be designed as a camera, lidar sensor, radar sensor or even ultrasonic sensor.
  • Automatic obstacle detection involves evaluating the sensor data to identify possible obstacles and hazards in the train's clearance space. This data is also used to detect other situations that affect rail operations.
  • These may include, for example, hazards on adjacent tracks, anomalies that require closer inspection, such as vegetation overgrowing the track, unexpected changes in the environment, or the like.
  • the relevant sensor data is stored, particularly about the exact location and time of origin, and then either examined using anomaly detection methods, for example based on an object recognition algorithm, or searched for reasons for more detailed monitoring using detection mechanisms specifically tailored to specific situations.
  • anomaly detection methods for example based on an object recognition algorithm
  • visual detection algorithms can be used for this.
  • the determination of the fault may already include an analyzed classification of the fault, but raw data collected by the train's detection device on which the corresponding derivation is based may also be stored.
  • the anomaly is determined depending on the current position of the train.
  • the determined anomaly can thus be saved with the position.
  • corresponding virtual data or digital maps can be available, which, for example, contain corresponding information regarding the objects to be expected on site.
  • the anomaly can then be determined when compared with this map.
  • an expected detected environment is specified depending on the current position and the expected detected environment is compared with the detected environment and the anomaly is determined depending on this.
  • an anomaly can be determined.
  • a database can be available in particular which contains the expected detected environment, whereby the detection device in particular can thus access this data and carry out a corresponding evaluation.
  • a track and/or a track bed and/or a switch and/or a railway signal and/or a station is checked for a fault as railway infrastructure.
  • other infrastructure objects can also be checked. This makes it possible for the entire railway infrastructure in particular to be checked for a fault.
  • a specific fault is transmitted to a central electronic computing device.
  • the central electronic computing device can be used, for example, to collect a large number of faults accordingly. This can then alert the central electronic computing device that a corresponding fault is present. This can, for example, initiate additional measures, such as appropriate repair measures, so that the fault can be rectified in the future.
  • the fault is verified by the central electronic computing device.
  • this can be done, for example, if historical data on this anomaly is already available.
  • a The corresponding fault must be verified. Only a verified fault leads to further measures.
  • information obtained about the fault caused by a single train is usually incomplete, this requires confirmation or verification in order to obtain data specifically tailored to the situation. This means that the fault can be determined with the utmost reliability.
  • a verification order for verifying the fault is transmitted to at least one other train by means of the central electronic computing device.
  • the other train can then also drive past a corresponding location of the potentially determined fault and record it accordingly.
  • the fault can be recorded using the same type of recording device or a different type of recording device.
  • the verification order is generated by the central electronic computing device and transmitted to another train. This means that the other train can confirm the fault independently of the train. This means that the fault can be determined with the utmost reliability.
  • next train is selected depending on the route of the next train. If, for example, the next train passes the anomaly, and in particular if there is a corresponding route plan that takes the train past the disruption, this train can be instructed to carry out a verification. This makes it easy to verify the disruption.
  • a further advantageous embodiment provides that the additional train is selected depending on the type of detection device present on the additional train. In particular, if, for example, a train does not have If the central electronic computing device does not have a detection device, it cannot be instructed to carry out a corresponding verification. In particular, the electronic computing device can decide which type of detection device is to be used to detect or verify the fault. For example, it can be provided that the central electronic computing device requires confirmation of a different detection type and thus selects a train that has a different detection type than the train that reported the fault. The fault can thus be reliably determined.
  • next train is selected depending on a different angle of detection of the potential disruption.
  • the next train can be selected based on an oncoming track or oncoming direction of travel, so that the disruption can be detected from a different angle. This makes it possible to have different angles of view of the disruption and thus to carry out reliable verification of the disruption.
  • next train is selected depending on the time of day when the potential disruption is detected. For example, it can be provided that only trains that pass the disruption during the day receive a corresponding verification order. This can prevent incorrect recording or recording with a low data information density, for example due to darkness.
  • a warning message is generated for at least one other train in the event of a specific fault.
  • the warning message can be generated by the train itself. Should For example, it can be provided that the fault must first be verified by the central electronic computer, and the corresponding warning message can then be generated by the central electronic computer. For example, it can then also be provided that, should the fault be verified accordingly, trains are instructed to drive past the fault at a reduced speed.
  • the warning message can also be generated for the relevant personnel, so that people can be sent to the fault to repair it.
  • a further aspect of the invention relates to a computer program product with program code means which causes an electronic computing device to carry out a method according to the previous aspect when the program code means are processed by the electronic computing device.
  • the invention also relates to a computer-readable storage medium with the computer program product.
  • the invention also relates to a detection device for determining a disruption of a railway infrastructure, with at least one electronic computing device, wherein the detection device is designed to carry out a method according to the preceding aspect. In particular, the method is carried out by means of the detection device.
  • Yet another aspect of the invention relates to a train with a detection device according to the preceding aspect.
  • Advantageous embodiments of the method are advantageous embodiments of the computer program, the computer-readable storage medium, the detection device and the train.
  • the detection device and the train have particular physical features to enable the corresponding process steps to be carried out.
  • an object detection algorithm can be understood as a computer algorithm that is able to identify and localize one or more objects within a provided input data set, for example an input image, for example by defining corresponding bounding boxes or regions of interest, ROI (English: "region of interest"), and in particular by assigning a corresponding object class to each of the bounding boxes, wherein the object classes can be selected from a predefined set of object classes.
  • the assignment of an object class to a bounding box can be understood in such a way that a corresponding confidence value or probability that the object identified within the bounding box belongs to the corresponding object class is provided.
  • the algorithm can provide such a confidence value or probability for each of the object classes for a given bounding box.
  • the assignment of the object class can, for example, include selecting or providing the object class with the greatest confidence value or the greatest probability.
  • the algorithm can simply define the bounding boxes without assigning a corresponding object class.
  • a computing unit/electronic computing device can be understood in particular as a data processing device that contains a processing circuit.
  • the computing unit can therefore in particular process data to carry out computing operations. This may also include operations to carry out indexed access to a data structure, for example a conversion table, LUT (English: "look-up table").
  • the computing unit can in particular contain one or more computers, one or more microcontrollers and/or one or more integrated circuits, for example one or more application-specific integrated circuits (ASICs), one or more field-programmable gate arrays (FPGAs), and/or one or more single-chip systems (SoCs).
  • the computing unit can also contain one or more processors, for example one or more microprocessors, one or more central processing units (CPUs), one or more graphics processing units (GPUs) and/or one or more signal processors, in particular one or more digital signal processors (DSPs).
  • the computing unit can also contain a physical or virtual network of computers or other of the aforementioned units.
  • the computing unit includes one or more hardware and/or software interfaces and/or one or more memory units.
  • a memory unit can be a volatile data storage device, for example a dynamic random access memory (DRAM) or a static random access memory (SRAM), or a non-volatile data storage device, for example a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory or flash EEPROM, a ferroelectric random access memory (FRAM), magnetoresistive random access memory (MRAM) or phase-change random access memory (PCRAM).
  • DRAM dynamic random access memory
  • SRAM static random access memory
  • ROM read-only memory
  • PROM programmable read-only memory
  • EPROM erasable programmable read-only memory
  • EEPROM electrically erasable programmable read-only memory
  • flash memory or flash EEPROM a ferroelectric random access memory (FRAM), magnetoresistive random access memory (MRAM) or phase-change random
  • An environmental sensor system/detection device can be understood, for example, as a sensor system that is able to generate sensor data or sensor signals that map, represent or reproduce an environment.
  • the ability to detect electromagnetic or other signals from the environment is not sufficient to consider a sensor system as an environmental sensor system.
  • cameras, radar systems, lidar systems or ultrasonic sensor systems can be understood as environmental sensor systems.
  • Algorithms for automatic visual perception which may also be referred to as computer vision algorithms, machine vision algorithms or machine vision algorithms, can be considered as computer algorithms for automatically performing a visual perception task.
  • a visual perception task which is also referred to as a computer vision task, can be understood, for example, as a task for extracting visual information from image data.
  • the visual perception task can, in some cases, in principle be performed by a human who is able to visually perceive an image corresponding to the image data. In the present context, however, visual perception tasks are also performed automatically without the need for human assistance.
  • a computer vision algorithm for example to detect the disturbance, can, for example,
  • Image processing algorithm or an algorithm for image analysis that is or was trained by machine learning and can be based, for example, on an artificial neural network, in particular a convolutional neural network.
  • the computer vision algorithm can, for example, comprise an object recognition algorithm, an obstacle detection algorithm, an object tracking algorithm, a classification algorithm, a semantic segmentation algorithm, and/or a depth estimation algorithm.
  • Corresponding algorithms can also be carried out analogously based on input data other than images that can be visually perceived by a human. For example, point clouds or images from infrared cameras, lidar systems, etc. can also be evaluated using appropriately adapted computer algorithms. Strictly speaking, the corresponding algorithms are not algorithms for visual perception, since the corresponding sensors can work in areas that are not visually perceptible to the human eye, for example in the infrared range. For this reason, such algorithms are referred to as algorithms for automatic perception in the context of the present invention. Algorithms for automatic perception therefore include algorithms for automatic visual perception, but are not limited to these with regard to human perception.
  • an algorithm for automatic perception can contain a computer algorithm for automatically carrying out a perception task, which is or has been trained, for example, by machine learning and can be based in particular on an artificial neural network.
  • Such generalized algorithms for automatic perception can also include object detection algorithms, object tracking algorithms, classification algorithms and/or segmentation algorithms, for example semantic segmentation algorithms.
  • an artificial neural network is used to implement an automatic visual perception algorithm
  • a commonly used architecture is that of a convolutional neural network, CNN.
  • a 2D CNN can be applied to corresponding 2D camera images.
  • CNNs can also be used for other automatic perception algorithms.
  • 3D CNNs, 2D CNNs or 1D CNNs can be applied to point clouds, depending on the spatial dimensions of the point cloud and the details of the processing.
  • an automatic perception algorithm depends on the specific underlying perception task.
  • the output of an object detection algorithm may include one or more bounding boxes defining a spatial position and optionally an orientation of one or more corresponding objects in the environment and/or corresponding object classes for the one or more objects.
  • An output of a semantic segmentation algorithm applied to a camera image may include a pixel-level class for each pixel of the camera image.
  • an output of a semantic segmentation algorithm applied to a point cloud may include a corresponding point-level class for each of the points.
  • the pixel-level or point-level classes may, for example, define an object type to which the respective pixel or point belongs.
  • an error message and/or a request to enter user feedback is issued according to the method. and/or a default setting and/or a predetermined initial state is set.
  • FIG. 1 shows a schematic block diagram according to an embodiment of the method.
  • FIG. shows a schematic block diagram according to an embodiment of a train 10 with an embodiment of a detection device 12.
  • the detection device 12 is for detecting a purely schematically shown Environment 14 of the train 10.
  • an anomaly 16 on a railway infrastructure can be detected in particular in the environment 14.
  • the detection device 12 can be designed to determine a fault 18 in the railway infrastructure 16.
  • the environment 14 of the train 10 is detected by means of the detection device 12.
  • An anomaly 16 associated with the railway infrastructure is determined as a function of the detected environment 14 by means of an electronic computing device 20 of the detection device 12.
  • the fault 18 is then determined as a function of the determined anomaly 16 by means of the electronic computing device 20.
  • the anomaly 16 is determined depending on the current position of the train 10. It can also be provided that an expected detected environment 14 is specified depending on the current position and the expected detected environment 14 is compared with the detected environment 14 and the anomaly 16 is determined depending thereon.
  • Railway infrastructure can in particular be a track and/or track bed and/or a switch and/or a railway track and/or a station that is checked for a fault 18.
  • the FIG. further shows that the specific fault 18 can be transmitted to a central electronic computing device 22.
  • the fault 18 is verified by the central electronic computing device 22.
  • a verification order 24 is transmitted to at least one further train 26, 28 by means of the central electronic computing device 22.
  • the further train 26, 28 can then be selected depending on a route of the further train 26, 28.
  • the central electronic computing device 22 can in particular be equipped with a route planner 30, which shows the routes of the trains 10, 26, 28.
  • the further train 26, 28 is selected depending on an existing detection device 32, 34 of the further train 26, 28.
  • a first further train 26 has a first detection device 32 and a second further train 28 has a second detection device 34.
  • corresponding observations 36 of the further trains 26, 28 are passed on to a situation assessment 38, which in turn is transmitted to the central electronic computing device 22.
  • the further train 26, 28 is selected depending on a time of the potential detection of the potential disturbance 18.
  • a warning message is generated for at least one other train 26, 28.
  • the central electronic computing device 22 can, for example, exchange potential train candidates 40 and location information 42 with the route planner 30.
  • a series of sensors are provided for detecting the surroundings 14 of the train 10. These provide image data, lidar data and radar data on the surroundings 14.
  • the sensor data is evaluated with regard to possible obstacles and hazards in the clearance space of the train 10. In addition, this data is also used to record other situations that affect rail operations.
  • These may be, for example, hazards on adjacent tracks, anomalies 16 that require closer inspection, such as vegetation on the track system, unexpected changes in the environment or the like.
  • sensor data about the exact location and time of origin are stored and then either examined using anomaly detection methods or searched for reasons for more detailed monitoring using detection mechanisms specifically tailored to certain situations.
  • the train 10 transmits this in one embodiment to the central electronic computing device 22, in particular in the form of a control center, in the form of an observation report. Based on the decision of the control center, the other trains 26, 28, whose routes also pass the observation point, can then be requested to collect further observation data for the affected area and within an affected area and to forward it to the central electronic computing device 22.
  • the observation report records the analyzed classification of the anomaly 16, but also the raw data collected by the detection device 12 of the train 10, on which the corresponding derivation is based.
  • a reliable situation assessment 38 can then be carried out in an advantageous embodiment and the necessary action can be derived. This is then issued in the form of a message to the control center staff, for example, or in combination with other electronic systems.
  • the situation assessment 38 takes into account in particular the available data on the normal condition of the route, Observation data from the other trains 26, 28 in closer temporal context and rules for assessing the situation, which can be specified by the operations management.
  • the essential difference from the prior art is the integration of the observations from the "first person" perspective of the train 10 equipped with the detection device 12 with the observations of other trains, in particular the other trains 26, 28, including the possibility of active research through targeted evaluation of the sensor data by the other trains 26, 28 in the event of anomalies 16.
  • the additional use of the sensors present in the other trains 26, 28 and intended for other purposes for the purposes described here is also characteristic of the invention and is not described in this way in the prior art.

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  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Train Traffic Observation, Control, And Security (AREA)
EP24190880.5A 2023-08-16 2024-07-25 Procédé de détermination d'une perturbation d'une infrastructure ferroviaire au moyen d'un dispositif de détection d'un train, produit programme informatique, support de stockage lisible par ordinateur et dispositif de détection Pending EP4509382A1 (fr)

Applications Claiming Priority (1)

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DE102023207856 2023-08-16

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EP4509382A1 true EP4509382A1 (fr) 2025-02-19

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EP24190880.5A Pending EP4509382A1 (fr) 2023-08-16 2024-07-25 Procédé de détermination d'une perturbation d'une infrastructure ferroviaire au moyen d'un dispositif de détection d'un train, produit programme informatique, support de stockage lisible par ordinateur et dispositif de détection

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2005120924A1 (fr) * 2004-06-11 2005-12-22 Stratech Systems Limited Procede et systeme de balayage de voie ferree et de detection d'objet etranger
WO2018104454A2 (fr) * 2016-12-07 2018-06-14 Siemens Aktiengesellschaft Procédé, dispositif et véhicule sur voie, notamment véhicule ferroviaire, pour la détection d'obstacle dans le transport sur voie, en particulier le transport ferroviaire
DE102020215754A1 (de) * 2020-12-11 2022-06-15 Siemens Mobility GmbH Optische Schienenwegerkennung

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2005120924A1 (fr) * 2004-06-11 2005-12-22 Stratech Systems Limited Procede et systeme de balayage de voie ferree et de detection d'objet etranger
WO2018104454A2 (fr) * 2016-12-07 2018-06-14 Siemens Aktiengesellschaft Procédé, dispositif et véhicule sur voie, notamment véhicule ferroviaire, pour la détection d'obstacle dans le transport sur voie, en particulier le transport ferroviaire
DE102020215754A1 (de) * 2020-12-11 2022-06-15 Siemens Mobility GmbH Optische Schienenwegerkennung

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