EP3750776A1 - Procédé de détection d'un signal de chemin de fer - Google Patents

Procédé de détection d'un signal de chemin de fer Download PDF

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Publication number
EP3750776A1
EP3750776A1 EP19179792.7A EP19179792A EP3750776A1 EP 3750776 A1 EP3750776 A1 EP 3750776A1 EP 19179792 A EP19179792 A EP 19179792A EP 3750776 A1 EP3750776 A1 EP 3750776A1
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EP
European Patent Office
Prior art keywords
image
rail track
signals
signal
train
Prior art date
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Granted
Application number
EP19179792.7A
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German (de)
English (en)
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EP3750776B1 (fr
Inventor
Clemens Reisner
Omair Sarwar
Michael Kreilmeier
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Mission Embedded GmbH
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Mission Embedded GmbH
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Priority to ES19179792T priority Critical patent/ES2930674T3/es
Priority to EP19179792.7A priority patent/EP3750776B1/fr
Priority to PCT/EP2020/065968 priority patent/WO2020249558A1/fr
Priority to IL288651A priority patent/IL288651B1/en
Publication of EP3750776A1 publication Critical patent/EP3750776A1/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
    • 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 detection of railway signals relevant for the rail track that is currently used by a train.
  • 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
  • another state is possible after the relevant signal has been detected.
  • 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. Such 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 automatic train operation.
  • the identification of the relative position of the signal with respect to the rail track as well as the determination of the state of the signal is of high importance also for automatic train operation.
  • one objective of the invention is to automatically detect the signals associated with the actual rail track currently being used by the train, in particular in order to assist the driver or for automatic train operation.
  • 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 1 for detecting a signal, which is associated with the rail track that is used by a train; this method comprises the following steps:
  • 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:
  • 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.
  • Signals which are assigned to or associated with the 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-handside 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.
  • situations in which the driver ignores a signal relevant to the train, or in which the driver incorrectly considers the signal of another rail track relevant can be avoided.
  • the derived and relevant signal information from the image region containing the signal is of particular use for the automatic train operation.
  • steps can be used to further avoid false positive detections of signals that are not relevant for the actual rail track. These steps comprise:
  • the system according to the invention can be improved by a geolocation unit connected to the processing unit, wherein
  • 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 S 1 , ..., S 4 , each of 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 S 1 , ..., 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 Algorithm Based on Optimised Dense Disparity Map Estimation", 2018 .
  • Classical approaches usually first extract the edges or lines from the rail track-regions and then apply curve fitting to refine the railtracks.
  • 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 Neven, Bert De Brabandere, Stamatios Georgoulis, Marc Proesmans and Luc Van Gool "Towards End-to-End Lane Detection: an Instance Segmentation Approach", 2018 or Min Bai et el. "Deep Multi-Sensor Lane Detection", 2018 .
  • the position of the actual rail track within the image I t is determined. Therefore, the regions of the current rail track, i.e. the rail track T c that is actually used by the train, is identified.
  • 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.
  • step (b) it is also possible to search for the regions T t having the maximum area or containing maximum pixels among the rail tracks identified in step (b).
  • the rail track currently used is close to the image sensor, it typically covers a bigger region within the image I t than the other rail track regions.
  • 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 following prerequisites are typically needed. Firstly, 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 rectangular panel of 1.4-meters height and 0.7-meters width. Moreover, 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 approximately - aligned vertically, and its x-axis is - at least approximately - aligned horizontally.
  • 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.
  • the lower edge position y l,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 ground point G t can be determined by calculating an offset from an image position within the region of the signal S t , wherein the offset is defined by the size, in particular by a length on the region of the signal S t .
  • the ground point G t can be determined by calculating width and height of a rectangular bounding box that is parallel to the image axes.
  • 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.
  • 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 typically covers a rectangular region R R within a bird's eye view image I B . Even if the rail track is curved, there is a region close to the train, which is sufficiently straight, to obtain an approximate trapezoid structure.
  • the perspective transformation is defined in such way, that it transforms at least a trapezoidal part of the region T c of the actual rail track to a rectangular region within the bird's eye view image I B .
  • the perspective transformation R is in a further step (f) applied to the selected rail track, in particular to a central path P 1 , ..., P 4 within the rail track, and to the ground points that were determined in step (d). From these data it is possible to determine real-world distances, or it is at least possible to compare distances of ground points to the actual rail track.
  • 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 predefined threshold distance.
  • 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 processing unit.
  • 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 E 1 contains many ground points S F , S G , S I , S J . 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.
  • the image region containing an image of the identified signal it is possible to display the image region containing an image of the identified signal to the train driver.
  • the procedure as described above is repeated regularly so as to provide a video in order to inform the train driver about relevant on-coming signals.
  • any other event can be triggered by 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)
EP19179792.7A 2019-06-12 2019-06-12 Procédé et système de détection d'un signal de chemin de fer Active EP3750776B1 (fr)

Priority Applications (4)

Application Number Priority Date Filing Date Title
ES19179792T ES2930674T3 (es) 2019-06-12 2019-06-12 Método y sistema para detectar una señal ferroviaria
EP19179792.7A EP3750776B1 (fr) 2019-06-12 2019-06-12 Procédé et système de détection d'un signal de chemin de fer
PCT/EP2020/065968 WO2020249558A1 (fr) 2019-06-12 2020-06-09 Procédé pour détecter un signal de chemin de fer
IL288651A IL288651B1 (en) 2019-06-12 2020-06-09 Method and system for detecting a track signal

Applications Claiming Priority (1)

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

Publications (2)

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EP3750776A1 true EP3750776A1 (fr) 2020-12-16
EP3750776B1 EP3750776B1 (fr) 2022-08-24

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EP (1) EP3750776B1 (fr)
ES (1) ES2930674T3 (fr)
IL (1) IL288651B1 (fr)
WO (1) WO2020249558A1 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP4382392A1 (fr) * 2022-12-08 2024-06-12 ALSTOM Holdings Procédé pour déterminer la position d'un véhicule ferroviaire
EP4631824A1 (fr) * 2024-04-10 2025-10-15 Siemens Mobility GmbH Procédé et dispositifs pour faire fonctionner un véhicule ferroviaire

Citations (2)

* Cited by examiner, † Cited by third party
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 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP4382392A1 (fr) * 2022-12-08 2024-06-12 ALSTOM Holdings Procédé pour déterminer la position d'un véhicule ferroviaire
FR3142983A1 (fr) * 2022-12-08 2024-06-14 Alstom Holdings Procédé pour déterminer la position d’un véhicule ferroviaire
EP4631824A1 (fr) * 2024-04-10 2025-10-15 Siemens Mobility GmbH Procédé et dispositifs pour faire fonctionner un véhicule ferroviaire

Also Published As

Publication number Publication date
WO2020249558A1 (fr) 2020-12-17
IL288651B1 (en) 2026-02-01
EP3750776B1 (fr) 2022-08-24
IL288651A (en) 2022-02-01
ES2930674T3 (es) 2022-12-20

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