WO2020249558A1 - Procédé pour détecter un signal de chemin de fer - Google Patents

Procédé pour détecter un signal de chemin de fer Download PDF

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Publication number
WO2020249558A1
WO2020249558A1 PCT/EP2020/065968 EP2020065968W WO2020249558A1 WO 2020249558 A1 WO2020249558 A1 WO 2020249558A1 EP 2020065968 W EP2020065968 W EP 2020065968W WO 2020249558 A1 WO2020249558 A1 WO 2020249558A1
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WO
WIPO (PCT)
Prior art keywords
image
signal
signals
train
region
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.)
Ceased
Application number
PCT/EP2020/065968
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English (en)
Inventor
Clemens Reisner
Omair SAWAR
Michael Kreilmeier
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Mission Embedded GmbH
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Mission Embedded GmbH
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Mission Embedded GmbH filed Critical Mission Embedded GmbH
Priority to IL288651A priority Critical patent/IL288651B2/en
Publication of WO2020249558A1 publication Critical patent/WO2020249558A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • 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
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L15/00—Indicators provided on the vehicle or train for signalling purposes
    • B61L15/0058—On-board optimisation of vehicle or vehicle train operation

Definitions

  • the invention relates to a method and a system for the
  • the invention relates to a method and system for automatically detecting, whether a signal is present and relevant for the rail track that is actually used by a train. If needed, determination of the actual state of the signal, e.g. a stop (typically red), a go (typically green) , or another state, is possible after the relevant signal has been detected.
  • a stop typically red
  • a go typically green
  • signal is defined and shall be understood as railway signal.
  • the overall method can be used to assist a train driver to find the relevant signal for the respective train and to avoid human failure by misinterpretation of signals.
  • misinterpretation can occur in particular on railway networks having many signals for different rail tracks that are visible at the same time.
  • the method can also be used to provide derived and relevant signal information for
  • one objective of the invention is to
  • a standard two-dimensional camera e.g. an area scan camera as image sensor, which produces two-dimensional images.
  • image sensors are widely available, easy to assemble and in general simpler and more reliable than other systems for three-dimensional environmental perception, e.g. LIDAR- sensors .
  • the invention solves this problem using a method of claim lfor detecting a signal, which is associated with the rail track that is used by a train; this method comprises the following steps :
  • a common projection plane in particular a ground plane, preferably estimating the position of a ground point of the respective signal within the image
  • the criteria for determining said association may be governed by regulatory constraints and/or constraints defined by the railway network operator.
  • the invention further solves this problem using a system of claim 3.
  • Such a system is designed to detect a railway signal being associated with the rail track that is used by a train.
  • the system comprises:
  • an image sensor mounted or mountable in or on the train that is arranged to take at least one two-dimensional image of the on-coming rail tracks and signals
  • the criteria for determining said association may be governed by regulatory constraints and/or constraints defined by the railway network operator.
  • the invention can be used when a train is on a railtrack.
  • respective rail track are typically placed at predefined positions with respect to the rail track. These positions are usually determined by the rules of a signal regulation and/or the operator of the railway network. For example, these predefined positions of the signals may be on the right-hand- side of said rail track. Therefore, the respective states of these signals being located on this predefined position with respect to the rail track are valid for the respective train.
  • signals which are not placed at predefined positions and are not matching other association criteria as defined in the governing regulations for a given rail track, are not relevant for trains on this rail track.
  • human failure is avoided or minimized.
  • the system according to the invention can be improved by a geolocation unit connected to the processing unit, wherein
  • the processing unit further comprises a memory for a map of the railway network including the positions of rail tracks and signals, wherein each signal is associated with a rail track, and
  • processing unit is further programmed to execute the following steps :
  • Fig. 1 depicts a typical image taken from a train.
  • Fig. 2 schematically shows the detection of rail tracks and signals .
  • Fig. 3 schematically shows one example for the calculation of the position of the ground points of the signals within the image .
  • Fig. 4 schematically depicts a perspective image
  • Fig. 5 shows a map as used in an advantageous second
  • an image sensor which is located in or on the train and which is at least partly directed in the driving direction of the train, i.e. the direction of travel or movement of the train, takes two-dimensional images I t of the surrounding area, in particular of the rail track and of the signals.
  • image sensors can be simply mounted in the front part of the train.
  • FIG. 1 One example of such an image I t is shown in Fig. 1.
  • the image contains rail track-regions T 1 , ..., T 4 each of which depicts one rail track.
  • the image I t contains signal-regions Si, ..., S 4 ,eachof which shows one signal.
  • the image of the image sensor is transmitted to a processing unit, which is used to further execute the following steps (b) to (g) .
  • a classification method is used in order to automatically detect the rail track-regions T 1 , ... , T 4 and the signal-regions Si, ..., S 4 .
  • Railtracks can be detected either using classical approaches, e.g. shown in Rui Fan and Naim Dahnoun "Real-Time Stereo Vision-Based Lane Detection System", 2018 or Han Ma et el. "Multiple Lane Detection
  • machine learning algorithms which e.g. train an end-to-end neural network to segment the railtracks from the background; such methods are known from the state of the art, in particular from Davy
  • the position of the actual rail track within the image I t is determined.
  • the rail track T c used by the train and the respective region of the rail track T c within the image I t can be found by searching one of the rail track regions T t containing rail tracks already identified in step (b) that goes through or overlaps with a predefined image area A p as shown in Fig. 2.
  • This image area A p usually depends on the orientation of the image sensor within the train. If an image sensor is heading straight in the direction of movement, the predefined image area A p is in the center of the lower part of the image I t .
  • the image area A p can be easily defined after mounting the image sensor in or on the train by searching for a region of the image, which is close to the train and which shows a part of the rail track, independently of the curvature of the rail track .
  • the rail track identified in step (c) is represented by a center path P c .
  • the positions of the ground points G 1 , ... , G 4 of the signals S T are estimated with respect to the image I t .
  • the ground point G 1 , ... , G 4 is the point within the image that shows the base point of the pole. More generally, the ground point is the point within the image I t that depicts a surface point, which is directly underneath the respective signal S, even if the signal is not mounted on a pole, but e.g. mounted on overhead infrastructure .
  • the size, i.e. height and width, of the panels of the signals are known. If there are different types of signals available, said type has to be firstly estimated by the shape and/or content of the image region of the signal.
  • a typical kind of signal could have a
  • the signal could typically be mounted in a height of 4.85-meters to 6.95-meters (see Fig. 3) .
  • the image sensor is mounted on the train in a manner that its y-axis is - at least
  • the height h t and width w t of the image region of the signal can be obtained by different methods, such as detection of an axis-parallel bounding box, etc.
  • ground point is at a position between 194 px and 278 px under the lower edge of the signal's image region S t .
  • an average pole height for example of 6-meters can be used, which would lead to a y- distance d t of 240 px.
  • position y 1 t can be defined as the highest or least y- position, depending on the orientation of the y-axis of the image sensor.
  • the horizontal center x m , t of the signal region can be defined as the x-coordinate of the centroid of the signal region.
  • the position of the ground point G t can be defined as:
  • ground point G t can be determined by
  • the ground point G t can be determined by calculating width and height of a rectangular bounding box that is
  • the ground point of a signal there could be other possible ways to determine the ground point of a signal. For example, using the known gauge-width of the rail track and the signal panel's dimensions (i.e. real height and width) , one could estimate the ground point of the signal in the two-dimensional image by comparing the signal panel's pixel-width to the gauge's pixel-width of the gauge of the rail track, both scaled in an appropriate manner. In order to determine correctly scaled pixel-heights and/or pixel- widths it is advantageous to consider the image's perspective distortion and the orientation of the rail tracks.
  • ground points G t were determined within the image I t , it is still not possible to determine the signal which is closest to the actual rail track directly from the image, because the image as taken by the image sensor is distorted, i.e. distances within the image are not directly related to the distances of the objects depicted in the image. As shown, the position of a ground point G t underneath the signal was estimated from the image by projection of the signal within the image I t to the ground plane.
  • a perspective transformation R is determined, by which it is possible to transform the sensor image I t into the projection plane; as this projection plane is - in this very example - parallel to the ground plane, the transformation returns a bird' s eye view image I B .
  • the transformation R can be a
  • perspective transformation that is based on visual properties of the image and/or mounting properties of the image sensor.
  • One particular method to determine the transformation R is to find a trapezoidal region R T within the region Tc of the actual rail track. Based on the mounting properties of the image sensor and the assumption of a sufficiently flat and smooth track bed it is clear, that this trapezoidal region R T
  • the perspective transformation R is in a further step (f) applied to the selected rail track, in particular to a central path Pi, ... , P 4 within the rail track, and to the ground points that were determined in step (d) .
  • a ground point G t and its respective signal S t should be associated with the actual rail track only if the distance from the ground point G t to the region, in particular the path P c , of the selected rail track S t under said perspective transformation R is below a
  • a regulation sets out more complicated rules relating to the association of signals with rail tracks. These regulations can rely on the determined distances and/or relative placement, e.g. right or left from the rail track, only. However, it is also possible that there are more complex rules relating to the association of rail tracks and signals that are based on a sequence of detections or the position of the train and the signal in the railway network. These rules may rely on distances, relative placement and sequences of signals on the rail track.
  • An advanced second embodiment of the invention uses the method as described above in order to determine images of signals that are associated with the actual rail track.
  • this embodiment also relies on a geolocation-map, containing the location of the rail tracks t A , .. ⁇ , t G as well as the position S A , ..., S L of the signals (Fig. 5) .
  • a geolocation-unit is used, the geolocation-unit being connected or integrated to the
  • the geolocation-unit provides the actual geolocation of the train which is further processed by the processing unit as follows.
  • GNSS Global Navigation Satellite System
  • SLAM Simultaneous Localization And Mapping
  • the method for detecting image regions of signals, which are associated with the actual rail track as shown in the first embodiment of the invention only needs to be carried out, if such a signal is expected based on the geolocation method as explained above. If, such as for the position e 3 , there is no on-coming signal within the associated region E 3 , the visual search for signals is turned off in order to reduce the likelihood for false positive matches.
  • the associated region Ei contains many ground points S F , S G , Si, S . However, none of these ground points are associated with the actual rail track t c so that there is no on-coming signal; the method for detecting image regions of signals is turned off.
  • the method for detecting image regions of signals is carried out in order to find a visual representation of said signal in the image I t .
  • the information relating to the map positions of signals can be used in order to further avoid false positive detections of signals with respect to both further described embodiments of the invention.
  • a matching algorithm is used in order to match the rail tracks t A , ... , t G of the map to the rail tracks as contained in bird' s eye view image I B . If a selected ground point G t matches the geolocation of a signal of the predefined map, which is associated with the actual path as determined by the geolocation, the overall reliability of the result will be increased.
  • any other event can be any other event.
  • the information automatically retrieved from the image such as simple audible, visual, or haptic warning.

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  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Train Traffic Observation, Control, And Security (AREA)

Abstract

L'invention concerne un procédé pour détecter un signal de chemin de fer qui est associé à la voie ferrée qui est utilisée par un train, lequel procédé comprend : a) la prise d'au moins une image en deux dimensions (It) à partir d'un train avec un capteur d'image situé dans ou sur le train, qui génère une image des voies ferrées et des signaux à l'approche, b) l'utilisation d'un procédé de classification pour identifier des régions d'image (Tt) correspondant à des voies ferrées et des régions d'image (St) correspondant à des signaux, à l'intérieur de l'image (It), c) parmi lesdites régions d'image identifiées (Tt) correspondant à des voies ferrées, la sélection de la région d'image (Tc), qui correspond à la voie ferrée qui est utilisée par le train, en particulier par l'identification de la voie ferrée traversant une région prédéfinie de l'image, et/ou ayant la surface ou la taille de pixel maximale parmi les voies ferrées identifiées en étape b), d) pour chacune des régions d'image (St) correspondant à un signal : - l'estimation de points projetés des signaux et/ou du trajet projeté de la voie ferrée sélectionnée (Tc) dans un plan de projection commun, en particulier un plan de sol, l'estimation, de préférence, de la position d'un point de sol (G t ) du signal respectif à l'intérieur de l'image (I t ), e) l'estimation d'une transformation de perspective (R) pour transformer l'image (It) dans le plan de projection commun, de préférence en une image en vue aérienne (I B ), sur la base de propriétés visuelles de l'image et/ou de propriétés de montage du capteur d'image, f) l'application de la transformation de perspective à la région d'image sélectionnée (Tc) ou à des parties de celle-ci, en particulier à un trajet central à l'intérieur de la voie ferrée, et aux points projetés, et g) la détermination d'un ou de plusieurs points projetés (Gt) qui ont une distance minimale et/ou une distance inférieure à une distance seuil prédéfinie et/ou un placement relatif par rapport à la région d'image sélectionnée (Tc) sous ladite transformation de perspective (R), et l'identification des signaux et/ou des régions d'image (St) correspondant à des signaux associés à ces points projetés à l'intérieur de l'image (It).
PCT/EP2020/065968 2019-06-12 2020-06-09 Procédé pour détecter un signal de chemin de fer Ceased WO2020249558A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
IL288651A IL288651B2 (en) 2019-06-12 2020-06-09 Method and System for Detecting a Railroad Signal

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
EP19179792.7A EP3750776B1 (fr) 2019-06-12 2019-06-12 Procédé et système de détection d'un signal de chemin de fer
EP19179792.7 2019-06-12

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WO2020249558A1 true WO2020249558A1 (fr) 2020-12-17

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PCT/EP2020/065968 Ceased WO2020249558A1 (fr) 2019-06-12 2020-06-09 Procédé pour détecter un signal de chemin de fer

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EP (1) EP3750776B1 (fr)
ES (1) ES2930674T3 (fr)
IL (1) IL288651B2 (fr)
WO (1) WO2020249558A1 (fr)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR3142983A1 (fr) * 2022-12-08 2024-06-14 Alstom Holdings Procédé pour déterminer la position d’un véhicule ferroviaire
DE102024203289A1 (de) * 2024-04-10 2025-10-16 Siemens Mobility GmbH Verfahren und Einrichtungen zum Betreiben eines Schienenfahrzeugs

Citations (2)

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Publication number Priority date Publication date Assignee Title
EP3048559A1 (fr) * 2015-01-21 2016-07-27 RindInvest AB Procédé et système de détection d'une voie ferrée
US20180057030A1 (en) * 2013-11-27 2018-03-01 Solfice Research, Inc. Real Time Machine Vision and Point-Cloud Analysis For Remote Sensing and Vehicle Control

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180057030A1 (en) * 2013-11-27 2018-03-01 Solfice Research, Inc. Real Time Machine Vision and Point-Cloud Analysis For Remote Sensing and Vehicle Control
EP3048559A1 (fr) * 2015-01-21 2016-07-27 RindInvest AB Procédé et système de détection d'une voie ferrée

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
DAVY NEVENBERT DE BRABANDERESTAMATIOS GEORGOULISMARC PROESMANSLUC VAN GOOL, TOWARDS END-TO-END LANE DETECTION: AN INSTANCE SEGMENTATION APPROACH, 2018
HAN MA, MULTIPLE LANE DETECTION ALGORITHM BASED ON OPTIMISED DENSE DISPARITY MAP ESTIMATION, 2018
MIN BAI, DEEP MULTI-SENSOR LANE DETECTION, 2018
RUI FANNAIM DAHNOUN, REAL-TIME STEREO VISION-BASED LANE DETECTION SYSTEM, 2018
SINA AMINMANSOUR: "Video Analytics for the Detection of Near-miss Incidents at Railway Level Crossings and Signal Passed at Danger Events", QUEENSLAND UNIVERSITY OF TECHNOLOGY, 10 October 2017 (2017-10-10), https://eprints.qut.edu.au/112765/1/Sina_Aminmansour_Thesis.pdf, XP055640309 *

Also Published As

Publication number Publication date
EP3750776B1 (fr) 2022-08-24
IL288651B2 (en) 2026-06-01
IL288651B1 (en) 2026-02-01
ES2930674T3 (es) 2022-12-20
EP3750776A1 (fr) 2020-12-16
IL288651A (en) 2022-02-01

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