EP3523175A1 - Procédé, dispositif et véhicule sur voie, notamment véhicule ferroviaire, pour l'analyse d'image basée sur la voie de circulation en trafic sur voie, notamment pour l'analyse d'image basée sur le rail en trafic ferroviaire - Google Patents

Procédé, dispositif et véhicule sur voie, notamment véhicule ferroviaire, pour l'analyse d'image basée sur la voie de circulation en trafic sur voie, notamment pour l'analyse d'image basée sur le rail en trafic ferroviaire

Info

Publication number
EP3523175A1
EP3523175A1 EP17829135.7A EP17829135A EP3523175A1 EP 3523175 A1 EP3523175 A1 EP 3523175A1 EP 17829135 A EP17829135 A EP 17829135A EP 3523175 A1 EP3523175 A1 EP 3523175A1
Authority
EP
European Patent Office
Prior art keywords
lane
image
track
fsb
rail
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
EP17829135.7A
Other languages
German (de)
English (en)
Inventor
Andreas Schaefer-Enkeler
Andreas Schönberger
Christopher Drexler
Wolfgang Trumler
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 Mobility 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 Siemens Mobility GmbH filed Critical Siemens Mobility GmbH
Publication of EP3523175A1 publication Critical patent/EP3523175A1/fr
Pending legal-status Critical Current

Links

Classifications

    • 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
    • B61L15/0072—On-board train data handling
    • 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
    • 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
    • B61L23/042—Track changes detection
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L25/00—Recording or indicating positions or identities of vehicles or trains or setting of track apparatus
    • B61L25/02—Indicating or recording positions or identities of vehicles or trains
    • B61L25/025—Absolute localisation, e.g. providing geodetic coordinates
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L25/00—Recording or indicating positions or identities of vehicles or trains or setting of track apparatus
    • B61L25/02—Indicating or recording positions or identities of vehicles or trains
    • B61L25/026—Relative localisation, e.g. using odometer
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
    • B61L27/04—Automatic systems, e.g. controlled by train; Change-over to manual control
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
    • B61L27/20—Trackside control of safe travel of vehicle or train, e.g. braking curve calculation
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
    • B61L27/70—Details of trackside communication
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/13—Edge detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30248—Vehicle exterior or interior
    • G06T2207/30252—Vehicle exterior; Vicinity of vehicle
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30248—Vehicle exterior or interior
    • G06T2207/30252—Vehicle exterior; Vicinity of vehicle
    • G06T2207/30256—Lane; Road marking

Definitions

  • the invention relates to a method for lane-based image analysis in rail traffic, in particular for track-based image analysis in rail traffic according to the preamble of patent claim 1, an apparatus for lane-based image analysis in rail traffic, especially for track-based image analysis in rail traffic according to the preamble of claim 11 and a railway vehicle for lane-based image analysis in rail traffic, in particular rail vehicle for track-based image analysis in rail traffic, according to the preamble of claim 26.
  • Rail vehicles are part of a modern traffic 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-bound transport and transport means are rail vehicles based on a wheel-rail system which are either pulled or pushed by their own traction drive (railcar) or by a locomotive and predominantly steel wheels with a wheel flange on two steel rails railways are the most widespread.
  • the underlying the invention object is to provide a method, a device and a railway vehicle, insbesonde ⁇ re rail vehicle, to specify the lane based image analysis in rail traffic, in particular for track-based image analysis in rail transport, with or automated (autonomous) or assisted driving the railway vehicles Rail vehicles without additional infrastructure along a route is improved.
  • the above-mentioned context-related object is achieved on the basis of the image analysis method defined in the preamble of patent claim 1 by the features specified in the characterizing part of patent claim 1.
  • the above-mentioned contextual object is achieved on the basis of the image analysis device defined in the preamble of patent claim 11 by the features specified in the characterizing part of claim 11.
  • Edge detection algorithms to recognize the course of a lane / track, which is used by the railway vehicle by a changing in the captured image portion of the lane / track to the entire picture captured and compared with stored known image metadata, these both lane-related or track ⁇ related primary metadata as well as railway-related res ⁇ pective rail-track related secondary metadata enthal ⁇ th.
  • the image meta information includes feature and characteristic data of the lane / track area recorded in the images.
  • the basic principle of the invention is, by means of an automated evaluation of the images, a part of the traffic lane / traffic lane that is visible in the respective image Identify tracks so that preferably (in advantageous developments) the following objectives are achieved:
  • a first step (a, for example, or more Rickak- quisitions confuse such as video cameras, laser sensors, Infrarotka ⁇ ra, thermal imaging cameras, radar devices, other Rickak- quisitions confuse, etc.) with the aid of at least one image recording apparatus a plurality of images from the web ⁇ vehicle from, for example, from the perspective of the railcar driver, he ⁇ summarized or recorded.
  • a first step (a, for example, or more Rickak- quisitionsetti such as video cameras, laser sensors, Infrarotka ⁇ ra, thermal imaging cameras, radar devices, other Schmak- quisitionstechnik, etc.) with the aid of at least one image recording apparatus a plurality of images from the web ⁇ vehicle from, for example, from the perspective of the railcar driver, he ⁇ summarized or recorded.
  • extension of the invention but also images of several image recording devices or image acquisition devices of the same type (eg, two video cameras) for mutual validation and synthesis of the results can be used.
  • extension of the invention but also images of a plurality of image recording devices and image acquisition devices of different type (eg, a video camera and a réelle brieflyka ⁇ ra) may be used for mutual validation and synthesis of the results.
  • non-imaging sensors such as radar, ultrasound or laser can be used to validate the image information.
  • the area at the near bottom of the screen is considered, because the lanes / tracks there are within We ⁇ sentlichen at the same location, while it is preferably the case that the image recording apparatus is fixedly mounted in the railway vehicle / rail vehicle.
  • edge detection algorithms which are used in a standard image processing, the profile of the lane traveled / busy of the track can be detected on the proportions lane / the track at the bottom of starting. a.
  • the maximum degree of bending of tracks / tracks can be taken into account in order to avoid false identifications of tracks).
  • the traveled lane / track can also be detected across points, in which the continuous guide rail of the track is detected.
  • metadata about the lane / track known is used, in particular the distance between the parallel laid rails and the width of the rail head.
  • Metadata about the route which are known, are used, for example at which geo ⁇ graphical positions railroad crossings, points or signal systems are.
  • Metadata about the distance of the signal system can then be used as follows.
  • the busy lane / the busy track is detected from step 2).
  • ii. An imaginary lane / a planned track GERADEAUS can easily be generated artificially, since the well-known busy lane / the well-known track at the bottom of the picture must be updated only linearly.
  • the busy lane / track LINKS is from the imaginary "STRAIGHT SEE lane” / the imaginary "STRAIGHT SEE track”, then it is a left turn. From the Ab ⁇ state of the traffic track of the intended "straight ahead lane” / a "STRAIGHT-track” and the distance from the vehicle (imaginary be calculated from the width of the lane / the track at the lower edge and the width of the lane / the track in each relevant image section), the degree of left curvature can be calculated.
  • the busy lane / track in the picture and a critical area to the left and right of the lane / track can be divided into several analysis sections with increasing distance from the vehicle.
  • the critical loading can ⁇ rich depending on the environment of the lane / the track (town ⁇ area vs. Country) different be broadly defined).
  • a standard for analysis section a standard for analysis section.
  • Pattern algorithm used to identify persons who ⁇ is a pattern algorithm used to identify persons who ⁇ .
  • the analysis sections of several consecutive images can be related to detect movement of persons and track their movement to predict potential collisions. IV. Detecting how far a railway vehicle is from an object
  • the distance to the object can be calculated.
  • the regular pattern of the lane / between the tracks is formed by a lane carrier / track carrier, used as a basis for the detection of irregularities. If an irregularity is detected (eg Beautyse or
  • the permissibility of the irregularity can take place by comparison with an image database. For example, In an initialization run, all irregularities can be recorded over all images of a route and then provided for adjustment.
  • the left or right curvature of a lane / a track can be detected.
  • the distance of a railway vehicle from objects / persons on a lane / track or in the critical area to the left or next to the lane / track can be calculated.
  • the following additional components - a) to c) for the image recording device may be used in relation to the image analysis device according to claim 11:
  • a correction component according to claim 21, weather ⁇ and brightness data for the evaluation of the photographic material with includes.
  • 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.
  • both shooting scenes on the open road can then (need pictures from a distance to view reagie ⁇ ren due to the speed to be able to) and shooting situations in Vietnamesesbe- rich (need images with a high width) be operated suitable.
  • a lighting component according to claim 23 ⁇ example, a headlight of the inside or outside of the human visible range lent operates, through which the Qua ⁇ formality of ons réelle from the image recording device or Profakquisiti- at night or in bad weather captured image material improves.
  • an image analysis device is formed as a virtual machine in the sense of "Software Defined signal recognition of Rail Traffic Systems" ⁇ and working.
  • FIG. 1 shows a lane-based image analysis for the recognition of route installations on a railway line and route
  • FIGURE 2 is a lane-based image analysis for object detection of a residing on a lane of a railway person
  • FIGURE 3 shows a basic structure of an image analysis device for the lane-based according to FIGS 1 and 2 ⁇ image analysis.
  • FIG. 1 shows a lane-based image analysis for the recognition of a route system SAL, such as e.g. a railroad crossing BÜG,
  • the lane ⁇ related railway line BST of the rail network BNE is a rail track SST of a rail network SNE, on which a rail vehicle SFZ on a rail GL and traveling in the rails ⁇ traffic SVK for a track-based image analysis to detect the track system SAL is the track system SAL on the track GL approaches.
  • a rail vehicle SFZ on a rail GL and traveling in the rails ⁇ traffic SVK for a track-based image analysis to detect the track system SAL is the track system SAL on the track GL approaches.
  • Rail network SNE moving rail vehicle SFZ is here again due to the discussion at the outset any other arbitrary short- or long-distance based rail transport system as a further embodiment of the invention conceivable and conceivable.
  • a maglev system (Stw .: Transrapid, Maglev, etc.) would also have a comparable infrastructure. consisting of railway network, railway line and railway vehicle, in question.
  • a picture analysis device BAV is provided 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 track-based image analysis for recognizing the route system SAL accommodated.
  • the image analysis device BAV includes for this purpose a preferably as a sensor formed image on ⁇ drawing device BAZG, which for example is formed as an ordinary video camera, laser sensor, thermal imager, radar equipment, infrared camera, etc., and because of acquisition of pic- tures is also referred to as an image acquisition device.
  • the rail vehicle SFZ traveling on the track GL approaches the signal system SI, AL, it starts from the rail vehicle SFZ, eg from the perspective of the railcar driver FZF in the driver's seat TFS of the railcar TRW and / or from a stationary, lane-based position in or on the vehicle SFZ, from a track of the rail vehicle SFZ, thereby preferably oriented to the speed of the rail vehicle SFZ, track area GLB a plurality of the track area GLB representing images BI GLB detectable.
  • a plurality of images BI FSB representing the traffic lane area FSB can be detected by a lane area FSB which is located upstream of the rail vehicle BFZ and preferably orientates itself to the speed of the rail vehicle BFZ.
  • the images BI FSB , BI GLB of the traffic lane area FSB, GLB each contain an image area BIB, in which lanes FS, GL detected in relation to the depicted traffic lane area FSB, GLB are substantially at the same location based on these portions of the detected lanes FS, GL with the aid of edge detection algorithms, the course of a lane FS, GL used by the railway vehicle BFZ, SFZ is detected by an image portion of the lane FS, GL changing in the acquired image to form the captured overall image.
  • a partial picture region BTB in the images BI FSB, BI GLB is for a currently determined geographical position of the railway vehicle BFZ, SFZ, in which the signal SI of the plant AL can be seen as a striking abutment feature of the routes ⁇ plant SAL.
  • this partial picture region BTB has a first image portion BAS1, by the used of the railway vehicle BFZ, SFZ lane FS, GL throughput leads, and a second image portion BAS2 wherein letz ⁇ more excellent for the route-detection, that is, the respective detecting a STRAIGHT-EAUS direction, LEFT-direction or
  • GL is relevant.
  • BI FSB BI glb of Fahrspurbe ⁇ richs FSB
  • GLB image area contained therein BIB
  • the image subregion BTB with the two image sections BAS 1, BAS 2
  • the image analysis for distance system detection and the detection of the signal system SI, AL and track detection is explained below with the description of FIG.
  • FIGURE 2 shows a lane-based image analysis for personnel ⁇ identifier in the rail BVK if on the off again cut as shown railway line BST of the rail network BNE the railway vehicle BFZ on the lane FS of the railway line BST at least one object OBJ, preferably a per ⁇ son, an animal, a movable or stationary object, is located in the area of the lane foreign and actually have to look for anything, so are there inadmissible.
  • object OBJ preferably a per ⁇ son, an animal, a movable or stationary object
  • the lane-related railway BST of the railway network BNE is here again the rail line SST of the rail network SNE, on which rail transport company SVK travels on track GL for rail-based image analysis for object recognition and approaches the object OGJ.
  • rail transport SVK illustrated with the vehicle running on the railway line SST of the rail network SNE rail vehicle SFZ any other x-any short or long distance based web transport system is here also conceivable as a white ⁇ teres embodiment of the invention, because of a ⁇ gangs guided discussion and imaginable.
  • a maglev system (Stw .: Transrapid, Maglev, etc.) with a correspondingly comparable infrastructure consisting of rail network, railway line and railway vehicle would also be suitable.
  • the image analysis device BAV is again provided in the railcar TRW of the rail vehicle SFZ with the driver's seat TFS and the integrated display device AZE, in which the workstation of the vehicle driver FZF is located, for the track-based image analysis for object recognition accommodated.
  • the image analysis apparatus includes BAV this again preferably formed as a sensor Bil ⁇ daufconces réelle BAZG which, for example as ordinary Videoka ⁇ ra, laser sensor, thermal imager, radar device Inf- rarotski etc., is formed.
  • the image area BIB is again contained, in which lanes FS, GL detected in relation to the depicted traffic lane area FSB, GLB lie substantially at the same location and starting from these proportions of the detected lanes FS, GL using the Kantenerkennungsalgo ⁇ algorithms of the course of the loading took advantage of the railway vehicle BFZ, SFZ lane FS, GL is detected by a changing in the captured image image portion of the lane FS, GL to the detected picture ,
  • a third image section BAS3 is contained in the images BI FSB , BI GLB , in which the object OBJ is ⁇ .
  • FIGURE 3 shows the basic structure of the Consul analysesvor- direction BAV for the lane-based image analysis for Stre ⁇ ckenstrom- and route-detection according to the FIGURE 1 and for object recognition according to the FIGURE 2, when the rail vehicle BFZ, SFZ, which on the lane FS , GL is on the road, according to the FIGURE 1 of the track system SAL or the signal giving or leading system AL on the railway line
  • the image recording device BAZG is preferably designed to be pivotable for alignment with the image object.
  • image recording devices BAZG the same design, eg multiple video cameras, or devices un ⁇ ferent type, eg multiple video cameras, laser sensors, RADAR-based, based on radio-based location and distance ⁇ measurement sensors, infrared cameras and / or thermal imaging cameras, are included in the image analysis device BAV, which receive the images BI FSB, BI GLB.
  • Such a multiple execution of the image recording or Profakquirie- tion may be relevant for redundancy purposes, among others.
  • a correction component KOK be included with the weather and Hellig ⁇ keitschal for the evaluation of the photographic material.
  • this component it is possible, for example, 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 variation component BVK in depen ⁇ dependence on the environment (eg station, city, country, etc.) and the speed chooses the right angle, so the evaluation of the image optimally under support alarm ⁇ zen.
  • ⁇ to additionally particularly interesting areas along the railway track in the rail network SST SNE can be focused by fusion of image data and link data, such as a railway crossing.
  • a lighting component BLK which is formed for example as a spotlight operating inside or outside the human visual range, and by which the quality of the captured from the image recording device or the image acquisition apparatus BAZG at night or in bad Witte ⁇ tion picture material 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 image analysis device BAV with the image recording device BAZG or according to option "B" outside the image analysis device BAV, eg as a storage database, in the railcar or in a data cloud associated with the image recording device BAZG or .with this connectable.
  • the image recorder BAZG is connected to a calculation / evaluation BAWE, which is also a component of the image analysis device BAV is.
  • the calculation / Ausenseeinrich- tung BAWE as the image recording apparatus BAZG either ge ⁇ Telss with said image memory means BSPE option "A" connected to or assigned according to option "B” of the image memory means BSPE or connectable therewith.
  • an information database IDB For the formation of a complete functional unit, in which the subunits involved functionally cooperate, said functional subunit is extended by a further subunit, an information database IDB.
  • the information database IDB can thereby be beispielswei ⁇ se integrated with the image storage device BSPE as a structural unit in a common storage device.
  • memory ⁇ apparatus may in turn, as the image storage device BSPE, either in accordance with option "A” as a component of the image analysis device BAV with the image recording apparatus BAZG and the calculation / evaluation BAWE connected accordingly or in accordance with option " B "outside the image analysis device BAV in the railcar or in a data cloud the image recording device BAZG and the calculation / evaluation device BAWE assigned to or be connected to this.
  • option "A" a component of the image analysis device BAV with the image recording apparatus BAZG and the calculation / evaluation BAWE connected accordingly
  • option " B outside the image analysis device BAV in the railcar or in a data cloud the image recording device BAZG and the calculation / evaluation device BAWE assigned to or be connected to this.
  • image meta information BMI which in the literal sense includes feature and property data of the traffic lane area FSB, GLB detected in the images BI FSB , BI GLB , is stored.
  • the Informationsda ⁇ tenbank IDB of the image analysis apparatus BAV is assigned in such a way and with this connected, as the calculation / evaluation BAWE ausensegeSysteme for the calculation / and lane-based image analysis for Streckenstrom- / Streckenverlauf- / Obj ect recognition accesses the image meta information BMI stored in the information database IDB.
  • the information database IDB is preferably outside the Marsh analysesvorrich ⁇ tion BAV, eg as a database, arranged in the railcar or is designed as a data cloud.
  • the calculation / evaluation device BAWE preferably has a non-volatile, readable memory SP in which processor-readable control program instructions of a picture analysis for route / route / Obj ect recognition-controlling program module PGM are stored, and a processor PZ, which executes the control program commands of the program module PGM for the calculation / evaluation-based and lane-based image analysis for track / route / Obj ekt recognition performs.
  • the processor accesses in addition - in addition to the accesses to the image-BMI meta information in the information database IDB - for control purposes and for reading out data to the image recording device and the image BAZG spoke pure Rich ⁇ tung BSPE to.
  • the lane-based image analysis in the calculation / evaluation BAWE advantageously such that in the image BI FSB , BI GLB is recognized where the Signaling system SI, AL or the track system SAL can be found by
  • BI FSB BI GLB is identified, in which a distinctive Anlagenmerk ⁇ times the track system SAL or the signal SI of the system AL can be seen, 2) the first image section BAS1 is determined in the image subregion BTB, through which the traffic lane FS, GL used by the railway vehicle BFZ, SFZ performs,
  • Meta information BMI and a standard edge detection algorithm is attempted to detect the outer edges of the track system SAL or the signal system SI, AL in the distance 3) determined by the lane FS, GL until the known and recognized outer edge substantially überde ⁇ bridges ,
  • the calculation / evaluation device BAWE in the lane-based image analysis continues to be such in an advantageous manner designed that in the image BI FSB / BI GLB is detected, in which direction, in particular LEFT, RIGHT or STRAIGHT ⁇ OFF, the railway vehicle BFZ, SFZ drives by for the detected from the detected course lane FS, GL
  • a pseudo-GERADEAUS lane in particular a pseudo-GERADEAUS track, is artificially generated by linear updating of the lane FS, GL used by the railway vehicle BFZ, SFZ in accordance with the image area BIB,
  • a LEFT direction eg a LEFT curve, is recognized when the used lane FS, GL is to the left of the pseudo-GERADEAUS lane,
  • a RIGHT direction e.g. a right-hand turn, is recognized when the used lane FS, GL is to the right of the pseudo-straight lane.
  • calculation / evaluation device BAWE in the case of lane-based image analysis is furthermore advantageously designed in such a way that in the image
  • BI FSB BI GLB is detected, whether at least one object OBJ, which may be, for example, a person, an animal, a movable or stationary object on the used lane FS, GL by
  • the calculation / evaluation device BAWE designed accordingly that in the image BI ⁇ SB ⁇ BI GLB is detected, how far the railway vehicle BFZ, SFZ is spaced from the object OBJ on the used lane FS, GL has been recognized by the one of a pixel width of the lane FS, G) in the image area BIB and a pixel width of the lane FS, GL in the third
  • Image section BAS3, in which the object OBJ is located are set in relation to each other, from this ratio and the obtained from the known third metadata width of the lane FS, GL of said distance is calculated.
  • image analyzing device BAV an automated (autonomous) or assisted driving of the railway vehicle or the railway vehicle BFZ SFZ without additional infrastructure along a route as may ⁇ be suspended or even realized. This is especially true if the image analysis device BAV 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)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Quality & Reliability (AREA)
  • Multimedia (AREA)
  • Image Analysis (AREA)
  • Image Processing (AREA)
  • Train Traffic Observation, Control, And Security (AREA)

Abstract

L'invention a pour objet d'améliorer la conduite automatisée (autonome) ou assistée de véhicules sur voie (BFZ) ou de véhicules ferroviaires (SFZ) sans infrastructure supplémentaire le long d'un tronçon de circulation. Selon l'invention, cet objet est réalisé en reconnaissant, à l'aide d'algorithmes de reconnaissance de bord, sur la base de plusieurs images (BIFSB, BIGLB) qui représentent une zone de voie de circulation/voie ferrée (FSB, GLB) dans une zone d'image (BIB) respectivement conditionnée dans les images et dans laquelle les voies de circulation/voies ferrées (FS, GL) détectées se trouvent sensiblement à une même position en référence à la zone de voie de circulation/voie ferrée (FSB, GLB) représentée de manière figurative, à partir des parts des voies de circulation/voies ferrées détectées dans la zone d'image (BIB), le tracé d'une voie de circulation/voie ferrée (FS, GL) qui est utilisée par le véhicule sur voie, par une part d'image de la voie de circulation/voie ferrée qui varie dans l'image acquise par rapport à l'image totale acquise, puis en le comparant avec des métainformations (MI) connues mémorisées. Celles-ci contiennent à la fois des métadonnées primaires en rapport avec la voie de circulation ou en rapport avec la voie ferrée ainsi que des métadonnées secondaires en rapport avec le tronçon de voie ou en rapport avec le tronçon de voie ferrée.
EP17829135.7A 2016-12-07 2017-12-07 Procédé, dispositif et véhicule sur voie, notamment véhicule ferroviaire, pour l'analyse d'image basée sur la voie de circulation en trafic sur voie, notamment pour l'analyse d'image basée sur le rail en trafic ferroviaire Pending EP3523175A1 (fr)

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DE102016224331 2016-12-07
PCT/EP2017/081845 WO2018104462A1 (fr) 2016-12-07 2017-12-07 Procédé, dispositif et véhicule sur voie, notamment véhicule ferroviaire, pour l'analyse d'image basée sur la voie de circulation en trafic sur voie, notamment pour l'analyse d'image basée sur le rail en trafic ferroviaire

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EP (1) EP3523175A1 (fr)
CN (1) CN110248858A (fr)
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CN110248858A (zh) 2019-09-17
RU2720303C1 (ru) 2020-04-28

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