WO2018104454A2 - Procédé, dispositif et véhicule sur voie, notamment véhicule ferroviaire, pour la détection d'obstacle dans le transport sur voie, en particulier le transport ferroviaire - Google Patents

Procédé, dispositif et véhicule sur voie, notamment véhicule ferroviaire, pour la détection d'obstacle dans le transport sur voie, en particulier le transport ferroviaire Download PDF

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Publication number
WO2018104454A2
WO2018104454A2 PCT/EP2017/081834 EP2017081834W WO2018104454A2 WO 2018104454 A2 WO2018104454 A2 WO 2018104454A2 EP 2017081834 W EP2017081834 W EP 2017081834W WO 2018104454 A2 WO2018104454 A2 WO 2018104454A2
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WO
WIPO (PCT)
Prior art keywords
image
lane
bib
sfz
track
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PCT/EP2017/081834
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German (de)
English (en)
Other versions
WO2018104454A3 (fr
Inventor
Andreas Schönberger
Christopher Drexler
Andreas Schaefer-Enkeler
Jan Winhuysen
Original Assignee
Siemens Aktiengesellschaft
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.)
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Publication date
Application filed by Siemens Aktiengesellschaft filed Critical Siemens Aktiengesellschaft
Priority to CN201780075914.6A priority Critical patent/CN110087970A/zh
Priority to EP17832743.3A priority patent/EP3523177A2/fr
Priority to RU2019119851A priority patent/RU2719499C1/ru
Publication of WO2018104454A2 publication Critical patent/WO2018104454A2/fr
Publication of WO2018104454A3 publication Critical patent/WO2018104454A3/fr

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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

Definitions

  • Method, device and railway vehicle in particular rail vehicle, for obstacle detection in rail traffic, in particular in rail transport
  • the invention relates to a method for obstacle detection in rail traffic, especially in rail traffic, according to the preamble of claim 1, an apparatus for obstacle detection in rail traffic, especially in rail traffic, according to the preamble of claim 10 and a railway vehicle for obstacle detection in rail traffic, in particular a Rail vehicle for obstacle detection in rail traffic, according to the preamble of claim 24.
  • Rail vehicles are part of a modern transport infrastructure track-bound transport and transport, for example, rolling on or under of one or two rails (tracks), floating above or below a magnetic field or hanging on steel cables move.
  • rail-based means of transport and transport mentioned are rail vehicles, which are based on a wheel-rail system, either a own traction drive (railcar) or pulled by a locomotive and are predominantly steel wheels with a flange on two steel rails railways are the most widespread.
  • the problem underlying the invention is to specify a method, a device and a rail vehicle, in particular a rail vehicle, for obstacle detection in rail traffic, in particular in rail traffic, with the obstacle or obstacles in rail traffic when rail vehicles are traveling on railway lines in the railway network , respectively obstacles in rail traffic, when rail vehicles are traveling on rail tracks in the rail network, are automatically detected.
  • the automatic detection of obstacles in rail traffic, in particular in rail traffic which is the subject of the present International Patent Application (Application No. PCT / ..., Publication No. WO ...) and the DE patent application (Application no. 102016224344.6) is an indispensable MUST with regard to future automated (autonomous) or assisted driving of rail vehicles in railway traffic or rail vehicles in rail traffic.
  • Protective fences used to prevent access to the track e.g., known from airports.
  • the aforementioned contextual object is achieved on the basis of the obstacle detection method defined in the preamble of claim 1 by the features specified in the characterizing part of claim 1. Moreover, the aforementioned contextual object is achieved on the basis of the obstacle recognition device defined in the preamble of patent claim 8 by the features specified in the characterizing part of claim 8.
  • Image area which essentially shows a lane used by the railway vehicle, to recognize by image analysis the lane figuratively positioned by the marker and to compare it with stored known image meta information or with stored known image meta information and additional information and in an image area excerpt of the marked image area
  • Object recognition method to detect whether an object, such as a person, an animal, a fallen tree, etc., is located on the lane, wherein an obstacle is marked in the image area, preferably in the image area excerpt, when the object is recognized by the object recognition method.
  • the image meta information includes feature and property data of the images captured by the route area.
  • the basic principle of the invention is to use metadata about the route, eg the route, in combination with sensors in the railway vehicle as well as calculation and evaluation algorithms to improve the recognition of objects and people and the distinction between permissible and impermissible objects and persons to enable.
  • the aim is to make a contribution to fully automated driving possible without additional investments in the route infrastructure.
  • pattern recognition algorithms or pattern matching algorithms are generalized for the route area, the area "in front of the vehicle” (in FIG Direction of travel).
  • image recording devices eg sensors
  • a radar for detecting metallic objects, even in bad weather, can be combined with video cameras and image acquisition devices such as thermal imaging cameras to detect people.
  • this can be done approximately on the basis of the knowledge of the route traveled (the lane).
  • a third step focused on the traffic lane / track used per imaging device used (eg, an image acquisition device), it is detected by object recognition methods whether an object or a person is on the lane / track. This means that only the image section with the traffic lane / track and the critical area to the left and right of it are considered. Depending on the image acquisition device, one or both of the following pattern matching methods are used. Again, the recognition quality is increased by the integration of external information or additional information. Cl. Positive matching
  • an expected pattern is recognized, e.g. through a solid track or through regular rail supports in the picture or the pictures. If this is not the case, it is checked via an image database whether the irregularity was expected (this information can be made, for example, during initialization trips with a railcar driver). If the irregularity was not expected, a potential obstacle is marked.
  • the result of the obstacle marking is merged from the different image acquisition devices.
  • probabilistic image processing techniques such as Hidden Markov models
  • the different sources of information are combined to minimize misrecognition and "false negatives", i. E. the erroneous assumption that there is no object in the lane / track area, although it is actually present, to exclude.
  • the following additional components - a) to c) for the image recording device may be used in relation to the obstacle recognition device according to claim 8:
  • the evaluation of video images can be limited to the first 50 meters in front of the rail vehicle or rail vehicle and the speed of the vehicle can be throttled accordingly.
  • a focal length variation component according to claim 16 which selects the correct recording angle depending on the environment (eg, train station, city area, land, etc.) and the speed so as to optimally support the evaluation of the image.
  • both recording situations on the open road requiring images from a great distance in order to be able to react quickly due to the speed
  • recording situations in the station area requiring images with a high latitude
  • a land-side evaluation station which is connected via mobile radio, and receives images from an image storage device for which an evaluation is only possible with a high uncertainty factor. These images can then be evaluated by a human expert and this information can then in turn be stored in the image storage device, which is an obstacle detection device in the web.
  • the image material of rail vehicles of a fleet or several fleets can be matched and distributed via the land-based evaluation station.
  • FIG. 1 shows a railway-vehicle-based detection of an obstacle in the form of a tree overturned on a railroad
  • FIG. 2 shows a basic structure of an obstacle detection device for the obstacle detection based on the railway vehicle in the form of the tree overturned on the railway line.
  • FIG. 1 shows a railway-vehicle-based detection of an obstacle in rail traffic BVK when a rail vehicle BFZ is located on a lane FS of the railway line BST on a section of track BST of a railway network BNE located as an obstacle on the lane FS object OBJ, in the illustrated case one on the Lane FS overturned tree, approaching.
  • the lane-related railway BST of the rail network BNE is a railway track SST of a rail network SNE on which a rail vehicle SFZ is traveling on a track GL in rail traffic SVK for rail vehicle-based obstacle detection and is the object OBJ which is located on the track GL , in the case shown approaches the tree overturned on track GL.
  • the illustrated railway track SST of a rail network SNE on which a rail vehicle SFZ is traveling on a track GL in rail traffic SVK for rail vehicle-based obstacle detection and is the object OBJ which is located on the track GL , in the case shown approaches the tree overturned on track GL.
  • Rail transport SVK with the on the railway line SST of the rail network SNE moving rail vehicle SFZ is also here again due to the discussion at the outset any other arbitrary short- or long-haul rail transport system as a further embodiment of the invention conceivable and conceivable.
  • a magnetic levitation traffic system (Stw.: Transrapid, Maglev, etc.) would also have a correspondingly comparable infrastructure. consisting of railway network, railway line and railway vehicle, in question.
  • an obstacle recognition device HEV is accommodated in a railcar TRW of the rail vehicle SFZ with a driver's cab TFS and an integrated display device AZE in which the workstation of the vehicle driver FZF is located for the rail vehicle-based detection of an obstacle.
  • HEV for this purpose includes a preferably designed as a sensor image recording device BAZG, the e.g. is designed as an ordinary video camera, laser sensor, thermal imaging camera, radar device, infrared camera, etc., and is also referred to as an image acquisition device for acquiring images.
  • a sensor image recording device BAZG the e.g. is designed as an ordinary video camera, laser sensor, thermal imaging camera, radar device, infrared camera, etc., and is also referred to as an image acquisition device for acquiring images.
  • the images BI FSB of the route area FSB contain an image area BIB with an image area excerpt BIB AS , which represents the used track GL and an area critical for rail traffic SVK, through which a critical radius for rail traffic SVK lies substantially to the left and right of the track GL is indicated in the part of the travel route area FSB shown by the image area BIB of the images BI FSB of the travel route area FSB. That is, the route area FSB also includes the area critical for rail traffic SVK.
  • FIG. 2 shows the basic structure of the obstacle detection device HEV for the obstacle detection of the rail vehicle SFZ according to FIG. 1, which travels on the track GL and approaches the obj object on the track GL, in the illustrated case the overturned tree.
  • the starting point for the obstacle detection forms according to the comments on the FIGURE 1, the image recording device BAZG, which detects the images BI FSB of the route range FSB for obstacle detection.
  • the image recording device BAZG is preferably designed to be pivotable for alignment with the image object. Furthermore, it is possible and possibly also reasonable for acquisition-related reasons that several image recording devices BAZG the same design, eg multiple video cameras, or devices of different types, eg multiple video cameras, laser sensors, RADAR-based, based on radio-based location and distance measurement sensors, infrared cameras and or thermal imaging cameras containing obstacle detection device HEV that take the images BI FSB . Such a multiple execution of the image recording or Jardinakquirie- tion may be relevant for redundancy purposes, among others.
  • BAZG imaging device preferably contain the following components in the image recording device BAZG:
  • a correction component KOK with which weather and brightness data are included for the evaluation of the image material.
  • this component it is e.g. it is possible to limit the evaluation of video images to the first 50 meters in front of the rail vehicle in heavy fog and to throttle the speed of the rail vehicle accordingly.
  • a focal length change component BVK that selects the correct shooting angle depending on the environment (e.g., railway station, city area, land, etc.) and the speed so as to optimally support the evaluation of the image.
  • the environment e.g., railway station, city area, land, etc.
  • by merging image data and route data particularly interesting areas along the railway track SST can be focused in the rail network SNE, such as e.g. a railroad crossing.
  • An illumination component BLK which is embodied, for example, as a headlight which operates inside or outside the human visible region, and through which the quality of the image material recorded by the image recording device or the image acquisition device BAZG at night or in bad weather improves ,
  • the images thus captured are stored by the image recording device BAZG in an image storage device BSPE.
  • This image storage device BSPE is either connected according to option "A” as a component of the obstacle recognition device HEV with the image recording device BAZG or according to option "B" outside the obstacle recognition device HEV, eg as a storage database, in the power plant. gene or in a data cloud associated with the image recording device BAZG or connectable to this.
  • the image recording device BAZG is connected to a calculation / evaluation BAWE, which is also a component of the obstacle detection device HEV.
  • the calculation / evaluation device BAWE such as the image recording device BAZG, either according to the option "A" connected to the image storage device BSPE or assigned according to option "B" of the image storage device BSPE or connectable to this.
  • a function subunit from the calculation / evaluation device BAWE, the image recording device BAZG and the image storage device BSPE, in which the components of the obstacle recognition device HEV cooperate in a partially functional manner for a calculation / evaluation-supported obstacle recognition, is created.
  • an information database IDB can be integrated, for example, with the image storage device BSPE as a structural unit in a common storage device.
  • This storage device which is not explicitly shown in FIG. 2, can in turn, like the image storage device BSPE, either be connected according to option "A" as component of the obstacle recognition device HEV with the image recording device BAZG and the calculation / evaluation device BAWE or according to option "B" outside the obstacle detection device HEV in the railcar or in a data cloud the image recorder BAZG and the 5
  • information database IDB in addition to image meta information BMI, which in the literal sense include feature and property data of the route range FSB detected in the images BI FSB , additional information ZI, such as route maps or map material, etc., stored.
  • the information database IDB is assigned to the obstacle recognition device HEV in the manner or can be connected to it as the calculation / evaluation device BAWE for the calculation / evaluation-based obstacle recognition on the image meta information stored in the information database IDB BMI and additional information ZI accesses.
  • the information database IDB is for this purpose preferably outside the obstacle detection device HEV, e.g. as a database, in which railcar is arranged or is designed as a data cloud.
  • the calculation / evaluation device BAWE preferably has a non-volatile, readable memory SP in which process-readable control program instructions of a program that detect the obstacle detection are stored PGM, and a processor PZ which stores the control program instructions of the Program module executes PGM for calculation / evaluation-based obstacle detection, on.
  • the processor PZ additionally accesses - in addition to the accesses to the image meta information BMI and the additional information ZI in the information database IDB - for control purposes and for the readout of Data to the image recorder BAZG and the image storage device BSPE.
  • the calculation / evaluation device BAWE or the program module PGM with the control program instructions of the program module PGM for calculation / evaluation-based obstacle detection exporting processor PZ are now configured with respect to the calculation / evaluation-based obstacle detection such that in the images BI FSB respectively the image area BIB is marked, that of the rail vehicle
  • SFZ used track GL wherein the image-positioned by the mark rail GL of the rail vehicle SFZ detected by an image analysis and adjusted either with the stored known image metadata BMI or with the stored known image metadata MMI and the additional information ZI.
  • the image analysis and thus the marking is preferably carried out with the aid of edge detection algorithms, starting from the track GL detected in the route area FSB in the image area BIB, the course of the track GL used by the rail vehicle SFZ by an image portion changing in the acquired image of the track GL is detected to the total image.
  • the image analysis and hence the marking are preferably performed on the basis of the knowledge of the track GL in use, because the course of the track GL used is relative is known to a geographic location.
  • marked image area BIB which represents the used track GL and the area that is critical for rail traffic SVK, detected by an object recognition method, whether an object OBJ, such as a person, an animal, a fallen tree, etc., is on the track GL, wherein an obstacle in the image area BIB, such as when it is in the image area section BIB AS and / or if it is a potential obstacle, is marked when the object OBJ is recognized by the object recognition method.
  • a pattern matching is performed based on a positive comparison and / or a negative comparison in which, in the case of the positive comparison, it is checked whether object-specific patterns are included in the image area section BIB AS and checked in the case of the negative comparison whether in the image area section BIB AS an expected pattern, for example, the solid, used by the rail vehicle SFZ track GL or a regularity, which is formed by lane carrier of the lane FS or track carrier between the parallel tracks GL included.
  • this check ends with a "NO"
  • the determined irregularity is compared in terms of its expectation with path pictures used as reference information and previously recorded in route initialization runs, and if the irregularity was not expected, this is checked Obstacle in the image area BIB, for example, in the image area section BIB A s and / or as a potential obstacle is marked.
  • the obstacle markings made for all the images BI FSB in the image area BIB and the image area detail BIB A s, respectively, are preferably combined with the aid of image processing methods such as Hidden Markov Models with regard to image processing combining the different image sources.
  • image processing methods such as Hidden Markov Models with regard to image processing combining the different image sources.
  • a land-side evaluation station AWS is provided, which is connected via mobile radio to the image storage device and from this receives the images stored there for a modified evaluation. These images can then be evaluated by a human expert and this information can then be fed back into the image storage device BSPE.
  • the image material of rail vehicles of a fleet or several fleets can be compared and distributed via the onshore evaluation station AWS.
  • the obstacle recognition device HEV as described above, an automated (autonomous) or assisted driving of the railway vehicle BFZ or of the rail vehicle can be achieved. It is possible to assist or even realize SFZ without additional infrastructure along a route. This is especially true if the obstacle recognition device HEV is realized as a virtual machine, which is designed and functions in the sense of a "software-defined signal recognition of rail traffic system".

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

Abstract

L'objet de l'invention est la détection automatique d'obstacles dans le transport sur voie (BVE), lorsque des véhicules sur voie (BFZ) se déplacent sur des sections de voie (BST) dans le réseau de voie (BNE), ou d'obstacles dans le transport ferroviaire (SVE), lorsque des véhicules ferroviaires (SFZ) se déplacent sur des sections ferroviaires (SST) dans le réseau ferroviaire (SNE). À cet effet, sur la base de plusieurs images (BIFSB) d'une zone de trajet (FSB) située en amont d'un véhicule sur voie (BFZ, SFZ), dans une zone d'image (BIB) qui est marquée dans chaque cas dans les images et qui représente une voie de circulation (FS, GL) utilisée par le véhicule sur voie (BFZ, SFZ), la voie de circulation positionnée sur l'image est identifiée par le marquage à la suite d'une analyse d'image et comparée à des méta-informations d'image (BMI) connues mises en mémoire ou à des méta-informations d'image (BMI) connues mises en mémoire et à des informations supplémentaires (ZI) et, dans une partie (BIBAS) de la zone d'image marquée, une méthode de reconnaissance d'objet permet de repérer si un objet (OBJ), comme par exemple une personne, un animal, un arbre tombé etc., se situe sur la voie de circulation (FS, GL), un obstacle étant marqué dans la zone d'image (BIB), de préférence dans la partie (BIBAS) de la zone d'image, lorsque l'objet (OBJ) est reconnu par la méthode de reconnaissance d'objet.
PCT/EP2017/081834 2016-12-07 2017-12-07 Procédé, dispositif et véhicule sur voie, notamment véhicule ferroviaire, pour la détection d'obstacle dans le transport sur voie, en particulier le transport ferroviaire WO2018104454A2 (fr)

Priority Applications (3)

Application Number Priority Date Filing Date Title
CN201780075914.6A CN110087970A (zh) 2016-12-07 2017-12-07 用于在铁路交通中、尤其在轨道交通中进行障碍物识别的方法、设备和铁路车辆、尤其轨道车辆
EP17832743.3A EP3523177A2 (fr) 2016-12-07 2017-12-07 Procédé, dispositif et véhicule sur voie, notamment véhicule ferroviaire, pour la détection d'obstacle dans le transport sur voie, en particulier le transport ferroviaire
RU2019119851A RU2719499C1 (ru) 2016-12-07 2017-12-07 Способ, устройство и железнодорожное транспортное средство, в частности рельсовое транспортное средство, для распознавания препятствий в железнодорожном сообщении, в частности в рельсовом сообщении

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102016224344.6 2016-12-07
DE102016224344 2016-12-07

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WO2018104454A2 true WO2018104454A2 (fr) 2018-06-14
WO2018104454A3 WO2018104454A3 (fr) 2018-08-23

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EP (1) EP3523177A2 (fr)
CN (1) CN110087970A (fr)
RU (1) RU2719499C1 (fr)
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CN110087970A (zh) 2019-08-02

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