CN110087970A - For method, equipment and the rolling stock of progress obstacle recognition, especially rail vehicle in railway traffic, especially in rail traffic - Google Patents
For method, equipment and the rolling stock of progress obstacle recognition, especially rail vehicle in railway traffic, especially in rail traffic Download PDFInfo
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- CN110087970A CN110087970A CN201780075914.6A CN201780075914A CN110087970A CN 110087970 A CN110087970 A CN 110087970A CN 201780075914 A CN201780075914 A CN 201780075914A CN 110087970 A CN110087970 A CN 110087970A
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- 238000005096 rolling process Methods 0.000 title claims abstract description 45
- 238000000034 method Methods 0.000 title claims abstract description 30
- 230000004888 barrier function Effects 0.000 claims abstract description 32
- 238000010191 image analysis Methods 0.000 claims abstract description 13
- 241001465754 Metazoa Species 0.000 claims abstract description 5
- 238000004458 analytical method Methods 0.000 claims description 46
- 238000012545 processing Methods 0.000 claims description 43
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 claims description 10
- 238000005259 measurement Methods 0.000 claims description 6
- 238000001931 thermography Methods 0.000 claims description 6
- 239000013256 coordination polymer Substances 0.000 claims description 5
- 238000001514 detection method Methods 0.000 claims description 5
- 238000007689 inspection Methods 0.000 claims description 5
- 229910052742 iron Inorganic materials 0.000 claims description 5
- 239000000463 material Substances 0.000 claims description 4
- 238000005036 potential barrier Methods 0.000 claims description 4
- 238000003672 processing method Methods 0.000 claims description 4
- 230000008859 change Effects 0.000 claims description 3
- 230000000694 effects Effects 0.000 claims description 3
- 230000000007 visual effect Effects 0.000 claims description 2
- 238000004891 communication Methods 0.000 description 3
- 230000004069 differentiation Effects 0.000 description 2
- 230000004927 fusion Effects 0.000 description 2
- 238000009434 installation Methods 0.000 description 2
- 230000004044 response Effects 0.000 description 2
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- 230000008901 benefit Effects 0.000 description 1
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Classifications
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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
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- Train Traffic Observation, Control, And Security (AREA)
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Abstract
For the barrier when rolling stock (BFZ) is on the railway section (BST) in the railway network (BNE) in automatic identification railway traffic (BVE) or the barrier when rail vehicle (SFZ) is located on the track section (SST) in rail network (SNE) in automatic identification rail traffic (SVE), it proposes, multiple images (BI based on the running section region (FSB) being located in front of the rolling stock (BFZ, SFZ)FSB), mark respectively in the picture image-region (BIB) --- it shows by rolling stock (BFZ, SFZ the lane (FS, GL)) used --- in be identified by by image analysis and described mark the lane that visually positions and be compared by it with the known image metamessage (BMI) stored or with the known image metamessage (BMI) stored and additional information (ZI) and in the image-region segment (BIB of the image-region markedAS) in identified by object identifying method: in the lane (FS, GL it whether there is object (OBJ) on), such as people, animal, the tree to fall down, etc., wherein, when recognizing the object (OBJ) by object identifying method, in image-region (BIB), preferably in image-region segment (BIBAS) in mark barrier.
Description
Technical field
The present invention relates to a kind of preambles according to Patent right requirement 1 in railway traffic, especially track
Carried out in traffic the method for obstacle recognition, a kind of preamble according to Patent right requirement 10 in railway traffic
In, the equipment of obstacle recognition and a kind of being used for for preamble according to Patent right requirement 24 are especially carried out in rail traffic
Rolling stock, the track particularly for carrying out obstacle recognition in rail traffic that obstacle recognition is carried out in railway traffic
Vehicle.
Background technique
Rolling stock is traffic and the means of transport of track binding as the component part of modern traffic infrastructure,
Such as it suspends rollably, above or below magnetic field on or below one or two guide rail (rail) and ground or is suspended on cable wire
Shangdi travels forward.In the traffic and means of transport of the track binding being previously mentioned, the rail vehicle based on wheel rail system is the widest
General propagation, the rail vehicle or by itself running driving device (motor-car) or by locomotive traction or promotion, and
In the rail vehicle, the steel wheel with wheel rim is mainly guided on two rail or rail.
Summary of the invention
The task that the present invention is based on is, illustrates a kind of for carrying out in railway traffic, especially in rail traffic
Method, equipment and the rolling stock of obstacle recognition, especially rail vehicle, by it, when rolling stock is located in the railway network
Barrier when on railway section in automatic identification railway traffic, alternatively, when rail vehicle is located at the track section in rail network
Barrier when upper in automatic identification rail traffic.
Barrier in railway traffic, especially in rail traffic automatic identification --- it is this international patent application
(application number PCT/...；Publication number WO ...) and can generate priority German patent application (application number:
102016224344.6) theme --- the future of the rail vehicle in rolling stock or rail traffic in railway traffic
It is automatically indispensable certainty in terms of the driving of (autonomous) or auxiliary.
Therefore, automatic for rolling stock/rail vehicle or auxiliary driving is it is necessary that identification lane/rail
Movement in region or static object or people.Simultaneously it is necessary that the object of difference permission and people are (for example, in lane/iron
Buffer on rail, the maintenance workers by lane/rail) with the object that does not allow and people (such as the tree uprooped or
The child that person plays).
The problem of automatic or auxiliary driving, is so far by arriving section infrastructure such as inductance loop, along road
The computer of section and expensive additional investment in communications facility between train and section component are realized.In addition, using
Special protection fence is for avoiding enter into rail (such as known to airport).
But not only the aspect of automatic obstacle recognition is for automatic (independently) or the driving of auxiliary in future
Important, and subsequent technical aspect is important, and the technical aspect all should be more or less and at present patent application
In technology correlation and therefore it is listed and its content should pay attention to and when necessary even should be by before the background surfaces
Including.
It is related to following aspect:
1) according to international patent application (application number PCT/EP2016/057804；2017/174155 A1 of publication number WO) He Qi
Disclosed in technical teaching, signal in railway traffic/rail traffic automatic identification.
2) according to German patent application (application number 102016224358.6) and international patent application (application number PCT/...；It is open
Number WO ...) and in wherein disclosed technical teaching, unsafe condition in railway traffic/rail traffic automatic knowledge respectively
Not.
3) according to German patent application (application number 102016224335.7) and international patent application (application number PCT/...；It is open
Number WO ...) and wherein respectively disclosed technical teaching, the automatic knowledge of lane/rail in railway traffic/rail traffic
Not.
4) according to German patent application (application number 102016224355.1) and international patent application (application number PCT/...；It is open
Number WO ...) and wherein respectively disclosed technical teaching, when traditional position supported by satellite determine it is unsuccessful or not
The determination of the substitution of position when sufficiently in rail traffic.
5) according to German patent application (application number 102016224331.4) and international patent application (application number PCT/...；It is open
Number WO ...) and wherein respectively disclosed technical teaching, in railway traffic/rail traffic based on lane/rail figure
As the execution of analysis.
Previously mentioned, context-sensitive task barrier defined in the preamble in Patent right requirement 1
Recognition methods is set out, is solved by the feature illustrated in the characteristic of Patent right requirement 1.
In addition, previously mentioned, context-sensitive task hinders defined in the preamble in Patent right requirement 8
Object identification equipment is hindered to set out, solve by the feature illustrated in the characteristic of Patent right requirement 8.
In addition, previously mentioned, context-sensitive task is defined in the preamble in Patent right requirement 20
Rolling stock, especially rail vehicle set out, are solved by the feature illustrated in the characteristic of Patent right requirement 20.
It is according to the ideas based on the present invention of independent claims 1,8 and 20, based in front of rolling stock
Running section region multiple images, the image-region marked respectively in the picture --- described image region is substantially shown
The lane used by rolling stock --- in, be identified by by image analysis it is described mark the lane that visually positions and
It is compared with the known image metamessage stored or with the known image metamessage and additional information stored
Compared with and being identified in the image-region segment of the image-region marked by object identifying method: whether being deposited on lane
In object, such as people, animal, the tree to fall down, etc., wherein when recognizing the object by object identifying method, scheming
As marking barrier in region, preferably in image-region segment.Image metamessage includes herein from running section according to the meaning of word
The characteristic and performance data of the image of region detection.
Basic principle of the invention is herein, with the sensing mechanism and calculating and analysis Processing Algorithm in rolling stock
Metadata of the utilized in combination about section, such as section trend, to improve the identification of object and people and to can be realized fair
Perhaps the differentiation of object and people and do not allow.
Target in this is to can be realized to facilitate automatically without additional put into the infrastructure of section
It drives.
In order to identify on lane/rail or in the key area by lane/rail object or people, briefly
For running section region --- " in vehicle front " region (in driving direction) use pattern recognizer or mode ratio
Compared with algorithm (so-called pattern matching algorithm).
1. for, in the object of distant location or the identification of people, these efficiency of algorithm low lands work on rail, because
The only only a fraction of of image is significant correlation.
2. these algorithms can not distinguish the object of permission and people with the object and people not allowed.
In focusing on the automotive environment on road, it is known that analysis processing is so-called according to 6,405,128 B1 of US
" electronic horizon ".
Object and lane/rail automatic identification and differentiation between object/people that is permission and not allowing can be with
It is realized in an advantageous manner at least partially through following steps:
A. in the first step, (such as video camera, laser sensor, infrared taken the photograph in rolling stock using different type
Camera, thermal imaging camera, radar installations, other image capture devices, etc.) multiple images recording equipment (such as sense
Device), to generate railway section/track section image or other information in front of rolling stock/rail vehicle.
Even if it may be thus possible, for example, to the radar and the view of people for identification that will be used to identify metal object under the weather of difference
Frequency video camera and image capture device, such as thermal imaging camera combine.
B. in the second step, in corresponding image, pass through image analysis by external metamessage
To mark the rail of current driving.
Scheme 1:
In the image of video image or video class, this can be gone out by edge identifying algorithm from the track directly in vehicle front
Hair ground carries out.By using additional information, such as metro planning, map material or similar, this knowledge can be more robustly executed
Not.Here, with reference to according to German patent application (application number 102016224331.4) and international patent application (application number
PCT/...；Publication number WO ...) and wherein respectively disclosed technical teaching in rolling stock/rail traffic based on
The execution of lane/rail image analysis.
Scheme 2:
In the image based on radar, this can be generally based on the understanding in travelled section, and to carry out, (lane/rail moves towards phase
It is known for geographical location).
C. in third step, the rail in travelled lane/travelled is focused on, for each used image
Recording equipment (such as image capture device) is identified by object identifying method: on lane/rail with the presence or absence of object or
People.It means that only considering the rail with travelled lane/travelled and the key area on its left side and the right
Image segments.Here, using following one or two of method for mode matching according to image capture device.
Here, improving identification quality also by integrating exterior information or additional information.
C1. n- matching
Check: whether there is following mode in the image segments of significant correlation: the mode is, for example, people or object, such as falls down
Tree or rolling stock in traveling ahead, such as rail vehicle, train.If it is, label barrier or potential obstacle
Object.
C2.-matching is born
It checks: whether recognizing expected mode, such as by the rail walked in an image or multiple images or logical
Cross the rail carrier of rule.If this is not the case, passes through image data base inspection: whether anticipating scrambling
(information can be carried out for example in the initialization by motor-car driver when driving).If it is not expected that scrambling,
Mark potential barrier.
D. in four steps, the result of obstacle tag is merged by different image capture devices.For example also lead to herein
It crosses using the image processing method in relation to probability theory, combines different information sources, such as hidden Markov model to minimize
It is vicious identification and exclude " false is negative " namely vicious hypothesis --- in lane/rail region there is no pair
As although its necessary being.
It may be implemented by the analysis of the foregoing summary of the image in the section in front of rolling stock:
Compare the object in the lane/rail region for more efficiently identifying significant correlation so far and people；
Can distinguish in the region in front of rolling stock/rail vehicle (but equally in the lane that is travelled or travelled
Except rail and the key area on its left side and the right) permission object and people on the lane travelled or travelled
Rail in or the object not allowed and people in the key area on the left side and the right；
Under the conditions of the unfavorable visual field object and people can be more reliably identified compared to by motor-car driver；
It no longer needs motor-car driver to carry out cognitive disorders object, allows to independently of its availability identify travelled vehicle
The rail in road/travelled.
During an advantageous expansion scheme of the invention, know about barrier according to claim 8
Other equipment can also use following additional component --- a) to c) for image recorder (such as image capture device):
A. correcting unit according to claim 15, the correcting unit will be used for the day of the analysis processing of iconography
Gas is included with brightness data.Therefore, the analysis processing of video image can be restricted to iron for example in the case where thick fog
The first 50 meters speed for going up and accordingly decreasing vehicle in front of road vehicles or rail vehicle.
B. focal length according to claim 16 changes component, according to ambient enviroment (such as railway station, urban area, township
Village, etc.) and speed select correct shooting angle, therefore optimally to support the analysis processing of image.For example, can not
Only suitably operate on free section shooting situation (it is required from remote image, so as to be based on speed
Make a response in time) and can suitably to operate the shooting situation in the region of railway station (required with high width
Image).Region of special interest can be additionally focused by fusion image data and section data, such as railway intersects
Mouthful.
C. illuminace component according to claim 17, such as the headlight work inside or outside the visible region of people
Make, by its improvement in the iconography shot under the weather of night or difference by image recorder or image capture device
Quality.
D. in the analysis treating stations of land side, pass through mobile wireless radio connections and receive from image memory device
Image, for described image, only with high uncertain factor analysis processing be possible.These images then can
To be handled by human expert's analysis, and then the information can be fed back in image memory device again, and described image is deposited
(option " A ") or obstacle is arranged in the obstacle recognition equipment that storage device can be arranged in rolling stock/rail vehicle
Except object identification equipment, such as the memory data library in rolling stock/rail vehicle such as data cloud
Distribute to obstacle recognition equipment.
1. in the case where enough communication bandwidths are with that arbitrarily can employ human expert, this can even be carried out in real time,
So that the result of analysis processing can be used for controlling rolling stock/rail vehicle.
2. can compare and distribute the track of a fleet or multiple fleets in addition, passing through the analysis treating stations of land side
The iconography of vehicle.
E. the mobile device of train driver or similar railway operators, the train driver or similar iron
Road staff travels jointly on the rail vehicle originally for serve passengers purpose and similarly assessment has with following d)
There is the image of high uncertain factor.
Furthermore, it is possible to which obstacle recognition equipment is configured to and serves as " rail traffic system, software definition letter
Number identification " in the sense that virtual machine.
Detailed description of the invention
Other advantage of the invention obtains the description of the embodiment of the present invention according to Fig. 1 and 2 by subsequent.Wherein:
Fig. 1 shows the identification based on rolling stock of the barrier of the tree-like formula to be poured on railway section,
Fig. 2 shows the barriers of the obstacle recognition based on rolling stock of the tree-like formula for being poured on railway section according to Fig. 1
Hinder the theory structure of object identification equipment.
Specific embodiment
Fig. 1 show when on the railway section BST shown in section district by district of the railway network BNE rolling stock BFZ in railway section
Close to the object OBJ being located on the FS of lane as barrier on the lane FS of BST --- it is to pour into vehicle in the illustrated case
The identification based on rolling stock of barrier when tree on road FS in railway traffic BVK.
According to the present embodiment, railway network BNE, the relevant railway section BST in lane be rail network SNE track section
SST, on it for the obstacle recognition based on rail vehicle in rail traffic SVK, rail vehicle SFZ is located on rail GL
And close to the object OBJ on rail GL is located at as barrier, the tree on rail GL is poured into the illustrated case.It replaces
For the shown rail traffic SVK with the rail vehicle SFZ travelled on the track section SST of rail network SNE, herein again
Based on the discussion carried out at the beginning, it is also contemplated that being arbitrarily based on shorted segment with any other x of imagination or being based on long section
Railway traffic system as another embodiment of the present invention.Therefore, it is also contemplated that for example with basic as respective class
The magnetic suspended railway traffic system (Stw.: magnetic-levitation train, Maglev etc.) of facility, by the railway network, railway section and railway
Vehicle composition.
In Rail Transit System shown in FIG. 1, in the motor-car TRW of rail vehicle SFZ, in order to barrier based on
The identification of rail vehicle, is mounted with obstacle recognition equipment HEV, and the motor-car has motor-car position of driver TFS and integrated
The operating position of display device AZE, vehicle driver FZF are located in the motor-car position of driver.Obstacle recognition equipment HEV
It thus include the image recorder BAZG for being preferably configured as sensor, described image recording equipment is for example configured to common view
Frequency video camera, laser sensor, thermal imaging camera, radar installations, thermal camera, etc. and acquisition due to image
Referred to as image capture device.
If the close object being located on rail GL as barrier of the rail vehicle SFZ travelled on rail GL
OBJ --- it is in the illustrated case the tree to fall down, then from rail vehicle SFZ, such as from the view of motor-car driver FZF
Angle in the motor-car position of driver TFS of motor-car TRW and/or from vehicle SFZ or on position is fixed, position of observation lane
Set, from be located at the rail vehicle SFZ in front of, herein preferably conform to the rail vehicle SFZ speed running section area
Domain FSB, by the detectable multiple images BI for representing the running section region FSB of image recorder BAZGFSB。
The image BI of FSB in running section regionFSBIn comprising have image-region segment BIBASImage-region BIB, institute
It states image-region segment and represents used rail GL and the region for rail traffic SVK key, pass through the key
Region, the image BI by running section region FSB of FSB in running section regionFSBImage-region BIB shown in part
Middle explanation is in the substantially left side of rail GL and the ambient enviroment for rail traffic SVK key on the right.
That is, running section region FSB also includes the region for rail traffic SVK key.
It is illustrated below by the description of Fig. 2, how having based on running section region FSB is included in now
Image-region BIB and described image region segments BIBASImage BIFSBExecute obstacle recognition.
Fig. 2 shows the obstacle recognitions of the obstacle recognition according to Fig. 1 based on rail vehicle for rail vehicle SFZ to set
The theory structure of standby HEV, the rail vehicle is on rail GL and approaches the object being located on rail GL as barrier
OBJ, the in the illustrated case tree to fall down.
According to the implementation about Fig. 1, image recorder BAZG constitutes the starting point of obstacle recognition, described image record
The image BI of equipment detection running section region FSBFSBFor obstacle recognition.
Image recorder BAZG is swingably constructed preferably for the orientation towards image object thus.
Be further possible that and may for detection technique reason also significantly, identical structural shape it is multiple
Image recorder BAZG, such as the equipment of multiple video cameras or different structural shapes, such as multiple video cameras
Machine, laser sensor, the sensor based on the positioning based on radio and distance measurement, thermal camera based on radar
And/or thermal imaging camera is included in obstacle recognition equipment HEV, the obstacle recognition sets shooting image BIFSB.Image
Furthermore record or Image Acquisition such multiple implementation can be important redundancy purpose.
In order to further improve the quality by image recorder BAZG record or acquisition image, in image recording
It is preferably comprised in equipment BAZG with lower component:
1. correcting unit KOK, by correcting unit by the weather for the analysis processing for being used for iconography and brightness data include
It is interior.By the component for instance it can be possible that in the case where thick fog, before the analysis processing of video image is restricted to rail vehicle
First 50 meters of side are upper and accordingly decrease the speed of rail vehicle.
2. focal length change component, according to ambient enviroment (such as railway station, urban area, rural area, etc.) and speed come
Correct shooting angle is selected, therefore optimally to support the analysis processing of image.Thus, it is possible to not only suitably operate certainly
By the shooting situation (required to come from remote image, so as to be made a response in time based on speed) on section and
The shooting situation (the required image with high width) in the region of railway station can suitably be operated.Can additionally it lead to
Fusion image data and section data are crossed to focus along the region of special interest track section SST in rail network SNE, it is all
Such as railroad grade crossing.
3. illuminace component BLK, such as it is configured to headlight, the headlight works inside or outside the visible region of people,
And by the illuminace component improve by image recorder or image capture device BAZG in night or the weather of difference
The quality of the iconography of lower shooting.
The image so shot is stored from image recorder BAZG into image memory device BSPE.Image storage dress
Set BSPE or correspondingly connected as the component and image recorder BAZG of obstacle recognition equipment HEV according to option " A "
Or according to option " B " except obstacle recognition equipment HEV for example as memory data library in motor-car or data cloud
In distribute to image recorder BAZG or can be connect with image recorder BAZG.
In order to analyze processing is recorded or acquired image to identify object --- the object is for along rail
It is barrier for the rail traffic of road segment segment, for example according to the tree of Fig. 1 poured on rail, image recorder BAZG and meter
Calculation/analysis processing device BAWE connection, the calculating/analysis processing device are equally a components of obstacle recognition equipment HEV.
For this purpose, calculating/analysis processing device BAWE such as image recorder BAZG, or filled according to option " A " and image storage
It sets BSPE connection or image memory device BSPE is distributed to according to option " B " or can be connect with it.By this method, generate by
The funtion part unit that calculating/analysis processing device BAWE, image recorder BAZG and image memory device BSPE are formed,
In, the component being previously mentioned of obstacle recognition equipment HEV is in order to calculate/analyze the obstacle recognition that processing is supported and partial function
Ground collective effect.
It is complete for calculating/analyzing the functional unit for the obstacle recognition that processing is supported in order to constitute --- wherein participate in
The unit of obstacle recognition functionally collective effect is mentioned by another part unit, i.e. information database IDB extension
The funtion part unit arrived.Information database IDB herein can be for example integrated as structural unit with image memory device BSPE
In common storage equipment.The storage equipment being not explicitly shown in Fig. 2 can be in its side for another example image memory device
BSPE is such, or according to option " A " as obstacle recognition equipment HEV component and image recorder BAZG and calculating/
Analysis processing device BAWE is correspondingly connected or in the external motor-car of obstacle recognition equipment HEV or is counted according to option " B "
According to distributed in cloud image recorder BAZG and calculating/analysis processing device BAWE or with image recorder BAZG and
Calculating/analysis processing device BAWE can be connected.Here, with reference to German patent application (application number 102016224355.1) He Yuqi
Corresponding international patent application (application number PCT/...；Publication number WO...) in information-storing device, for when traditional
Position is alternatively determined when determining unsuccessful or insufficient by the position that satellite is supported in rail traffic.
In information database IDB, other than image metamessage BMI, additional information ZI, such as running section are also stored
Planning or map material, etc., described image metamessage is included in image BI according to the meaning of wordFSBThe running section region of middle detection
The characteristic and speciality data of FSB.Diagram according to fig. 2, information database IDB distribute to obstacle recognition in the following manner and set
Standby HEV can be connect with obstacle recognition equipment HEV: that is, calculating/analysis processing device BAWE is in order to calculate/analysis processing
The obstacle recognition of support and access the image metamessage BMI and additional information ZI stored in information database IDB.Information
Database IDB for example in motor-car or is constructed as database layout preferably except obstacle recognition equipment HEV thus
For data cloud.
In order to calculate/analyze the obstacle recognition that processing is supported, calculating/analysis processing device BAWE preferably has non-easy
The memory SP that can be read and processor PZ for the property lost are stored with the program module of control obstacle recognition in the memory
PGM, control program command (CP command) that processor is readable, the processor implement the control program command (CP command) of described program module PGM with
The obstacle recognition supported for calculating/analyzing processing.For this purpose, processor PZ is in addition to the image in access information database IDB
Except metamessage BMI and additional information ZI, in order to control purpose and in order to read data also access images recording equipment BAZG
With image memory device BSPE.
Calculating/analysis processing device BAWE or program module PGM is together with the control program command (CP command) for executing program module PGM
To calculate/analyze the barrier that the processor PZ for the obstacle recognition that processing is supported is supported now concerning calculating/analysis processing
Identification is configured to, so that in image BIFSBMiddle to mark following image-region BIB respectively, described image region is shown by the rail
The rail GL that road vehicle SFZ is used, wherein rail vehicle SFZ's marks the rail GL visually positioned to pass through figure by described
It identifies as analysis and believes either with the known image metamessage BMI stored or with the known image primitive stored
Breath MMI and additional information ZI is compared.
Image analysis is executed preferably by edge identifying algorithm and is therefore marked, wherein from running section region FSB
In the rail GL that detects set out, in the BIB of described image region, by rail GL relative to being examined in image detected
The image share of the whole image variation of survey is identified by the trend of the rail vehicle SFZ rail GL used.
Moreover it is preferred that when by sensing based on radar, based on the positioning based on radio and distance measurement
Device shoots image BIFSBWhen, the understanding based on used rail GL executes image analysis and therefore marks, because used
The trend of rail GL is known relative to geographical location.
If rail vehicle SFZ by it is described mark the rail GL that visually positions identified by image analysis and
With the known image metamessage BMI stored otherwise with the known image metamessage BMI and additional information that are stored
ZI is compared, then for the image-region segment BIB of the image-region BIB markedASIt is identified by object identifying method:
It whether there is object OBJ on rail GL, such as people, animal, the tree to fall down, etc., described image region segments, which represent, to be made
Rail GL and region for rail traffic SVK key, wherein when identifying object OBJ by object identifying method
When, such as when barrier is located at image-region segment BIBASWhen middle and/or when barrier is potential barrier, in image district
The barrier is marked in the BIB of domain.
By object identifying method, based on n- comparison and/or bear-compares execution pattern and compare, wherein in n- comparison
In the case of check: in image-region segment BIBASIn whether include the specific mode of object, and it is negative-relatively in the case where examine
It looks into: in image-region segment BIBASIn whether comprising expected mode, its be the rail GL used by rail vehicle SFZ walked
The systematicness either formed by the lane carrier or rail carrier of the lane FS between the rail GL that extends in parallel.
If this inspection of result is terminated when bear-comparing with "No", and identified scrambling is pre- about its
Phase is compared with section image that is using as reference information and shooting in advance in running section initialization operation,
In, when it is not expected that when scrambling, in image-region BIB, for example in image-region segment BIBASMiddle label barrier and/
Or as potential obstacle tag barrier.
It is preferred that about by different image sources be combined by image processing method, such as hidden Markov model
Image procossing merges for all images BIFSBRespectively in image-region BIB or in image-region segment BIBASIn carried out
Obstacle tag.Thus it for example may be implemented: minimizing the probability of vicious identification and prevent " false is negative "
Namely vicious hypothesis --- object (OBJ) is not present in lane or rail region, although its necessary being.
In addition, for have for following such image in image memory device BSPE integrate or institute
The analysis treating stations AWS of the obstacle recognition equipment HEV setting land side of the image memory device BSPE of distribution: for the figure
Picture, only the analysis processing with high uncertain factor is possible, and the analysis treating stations are electrically connected by mobile wireless
It is connected on image memory device and receives the image stored at that from the image memory device and handled for modified analysis.
These images then processing can be analyzed by human expert and then the information can be fed back to image memory device again
In BSPE.
1. in the case where enough communication bandwidths are with that arbitrarily can employ human expert, this can even be carried out in real time,
So that the result of analysis processing can be used for controlling rolling stock/rail vehicle.
2. can compare and distribute the track of a fleet or multiple fleets in addition, passing through the analysis treating stations of land side
The iconography of vehicle.
For for image (for described image, only the analysis processing with high uncertain factor is possible)
Modified analysis processing analysis treating stations AWS alternatively, it is also possible to, for serve passengers purposes originally in track
The train chief or similar railroader travelled jointly on vehicle has high uncertainty by mobile device assessment
The image of factor, as human expert about image analysis treating stations AWS done in.
By the obstacle recognition equipment HEV described as before, can not have additional base along running section
It is assisted in the case where Infrastructure or even realizes that rolling stock BFZ's or rail vehicle SFZ is automatic (independently) or auxiliary
The driving helped.This especially as obstacle recognition equipment HEV as following virtual machine to realize when obtain, the virtual machine is in " iron
It constructs and works in the sense that rail traffic system, software definition signal identification ".
Claims (20)
1. one kind is used in railway traffic (BVK), the method for the especially middle progress obstacle recognition of rail traffic (SVK), special
Sign is,
A) from rolling stock (BFZ), especially rail vehicle (SFZ), the especially view from motor-car driver (FZF, TFS, TRW)
Angle and/or from vehicle (BFZ, SFZ) or on position is fixed, position of observation lane, from being located at the rolling stock
Detect generation in the running section region (FSB) of speed in front of (BFZ, SFZ), especially meeting the rolling stock (BFZ, SFZ)
Multiple images (the BI of running section region (FSB) described in tableFSB),
B) in described image (BIFSB) in mark following image-region (BIB) respectively, described image region is shown by the railway
Lane (FS) that vehicle (BFZ, SFZ) uses, especially rail (GL), wherein by image analysis identify rolling stock (BFZ,
Marking the lane (FS, GL) that visually positions by described and believe by it or with the known image primitive stored SFZ)
It ceases (BMI) or is planned with the known image metamessage (BMI) stored and additional information (ZI), such as running section or ground
Figure material is compared,
C) for the image-region segment (BIB of the image-region (BIB) markedAS), described image region segments, which represent, to be made
Lane (FS, GL) and region for the railway traffic (BVK, SVK) key, are known by object identifying method
It is other: to whether there is object (OBJ) on the lane (FS, GL), such as people, animal, the tree to fall down, etc., wherein when passing through
When object identifying method recognizes the object (OBJ), in image-region (BIB), preferably in image-region segment (BIBAS)
Middle label barrier and/or barrier is marked as potential barrier.
2. the method according to claim 1, wherein by the multiple images recording equipment of different structural shapes
(BAZG), for example, by video camera, laser sensor, based on radar with based on radio positioning and distance measurement be
Sensor, thermal camera and/or the thermal imaging camera on basis shoot described image (BIFSB).
3. method according to claim 1 or 2, which is characterized in that
Image analysis is executed by edge identifying algorithm, wherein the lane detected from the running section region (FSB)
(FS, GL) sets out, in described image region (BIB), by the lane (FS, GL) in image detected relative to institute
The image share of the whole image variation of detection identifies the lane (FS, GL) used by the rolling stock (BFZ, SFZ)
Trend.
4. according to the method described in claim 2, it is characterized in that, by it is based on radar, with based on radio positioning and
Sensor based on distance measurement shoots described image (BIFSB), the understanding based on used lane (FS, GL) executes figure
As analysis, because the trend of used lane (FS, GL) is known relative to geographical location.
5. method according to claim 1 to 4, which is characterized in that
It is based on n- comparison by object identifying method and/or bear-compares execution pattern to compare, wherein
A) it is checked in n- compare: in described image region segments (BIBAS) in whether include the specific mode of object, and
B) when bear-comparing
B1 it) checks: in described image region segments (BIBAS) in whether comprising expected mode, preferably walk by rolling stock
The lane carrier in the lane (FS) between lane (FS, GL) that (BFZ, SFZ) is used or the rail by extending in parallel (GL)
Or the systematicness that rail carrier is formed,
Identified scrambling is expected and as reference by the case where b2) being terminated for the inspection of result with "No" about it
Section image that is that information uses and shooting in advance in running section initialization operation is compared,
B3) for it is not expected that the case where scrambling, in image-region (BIB), preferably in image-region segment
(BIBAS) in label barrier and/or as potential obstacle tag barrier.
6. the method according to any one of claims 1 to 5, which is characterized in that
For by different image sources be combined by image processing method, such as hidden Markov model image at
Reason merges for all images (BIFSB) respectively in image-region (BIB) or image-region segment (BIBAS) in carried out
Obstacle tag, so as to minimize it is vicious identification and exclude " false is negative " namely vicious it is assumed that i.e. in vehicle
Object (OBJ) is not present in road or rail region, although its necessary being.
7. method according to any one of claim 1 to 6, which is characterized in that
By the method in the case where not having additional infrastructure along running section, the rolling stock is assisted
Automatic (independently) or the driving of auxiliary of (BFZ, SFZ).
8. one kind is used in railway traffic (BVK), the equipment of the especially middle progress obstacle recognition of rail traffic (SVK), special
Sign is,
A) at least one image recorder (BAZG), by described image recording equipment, from rolling stock (BFZ), especially rail
Road vehicle (SFZ) sets out, especially from the visual angle of motor-car driver (FZF, TFS, TRW) and/or from vehicle (BFZ, SFZ) or on
Position is fixed, position of observation lane, from be located at it is in front of the rolling stock (BFZ, SFZ), especially meet the iron
The running section region (FSB) of the speed of road vehicles (BFZ, SFZ) represents the multiple images of the running section region (FSB)
(BIFSB) can be detected and be storable in image memory device (BSPE),
B) calculating/analysis processing device (BAWE), the calculating/analysis processing device and described image recording equipment (BAZG),
Described image storage device (BSPE) and information database (IDB) connection and functionally collective effect, wherein preferably two
Person, i.e. image memory device (BSPE) and information database (IDB) are integrated in common storage equipment as structural unit, especially
It is configured with non-volatile memory (SP) and processor (PZ) that can be read, and is stored with control obstacle in the memory
The program module (PGM) of object identification, control program command (CP command) that processor is readable, the processor implement described program module
(PGM) obstacle recognition of the control program command (CP command) to be supported for calculating/analyzing processing, so that in image (BIFSB) in respectively
Mark following image-region (BIB), described image region show the lane (FS) used by the rolling stock (BFZ, SFZ),
Especially rail (GL), wherein rolling stock (BFZ, SFZ) marks the lane (FS, GL) visually positioned to pass through by described
Image analysis come identify and either with the known image metamessage (BMI) stored otherwise with the known image that is stored
Metamessage (BMI) and additional information (ZI), such as running section planning or map material are compared,
C) calculating/analysis processing device (BAWE) is configured to, so that for the image of the image-region (BIB) marked
Region segments (BIBAS), described image region segments represent used in lane (FS, GL) and for railway traffic (BVK,
SVK) crucial region, is identified by object identifying method: with the presence or absence of object (OBJ) on lane (FS, GL), such as
People, animal, the tree to fall down, etc., wherein when recognizing the object (OBJ) by object identifying method, in image-region
(BIB) in, preferably in image-region segment (BIBAS) in label barrier and/or mark obstacle as potential barrier
Object.
9. equipment (HEV) according to claim 8, which is characterized in that the multiple images note comprising different structural shapes
Recording apparatus (BAZG), for example, multiple video cameras, laser sensor, based on radar with positioning and spacing based on radio
Sensor, thermal camera and/or thermal imaging camera based on measurement, they shoot described image (BIFSB).
10. equipment (HEV) according to claim 8 or claim 9, which is characterized in that the calculating/analysis processing device (BAWE)
It is configured to, so that executing image analysis by edge identifying algorithm, wherein from the inspection in the running section region (FSB)
The lane (FS, GL) measured is set out, in described image region (BIB), by the lane (FS, GL) in image detected
In relative to the image share of whole image detected variation identify the lane used by the rolling stock (BFZ, SFZ)
The trend of (FS, GL).
11. equipment (HEV) according to claim 9, which is characterized in that
Calculating/the analysis processing device (BAWE) is configured to so that when by it is based on radar, to be determined based on radio
Sensor based on position and distance measurement shoots described image (BIFSB) when, it is based on the understanding of used lane (FS, GL)
Described image analysis is executed, because the trend of used lane (FS, GL) is known relative to geographical location.
12. the equipment according to any one of claim 8 to 11 (HEV), which is characterized in that
Calculating/the analysis processing device (BAWE) is configured to so that by object identifying method be based on n- comparison and/or
Bear-compare execution pattern to compare, wherein
A) it is checked in n- compare: in image-region segment (BIBAS) in whether include the specific mode of object, and
B) when bear-comparing
B1 it) checks: in image-region segment (BIBAS) in whether comprising expected mode, preferably walk by rolling stock
The lane carrier in the lane (FS) between lane (FS, GL) that (BFZ, SFZ) is used or the rail by extending in parallel (GL)
Or the systematicness that rail carrier is formed,
Identified scrambling is expected and as reference by the case where b2) being terminated for the inspection of result with "No" about it
Section image that is that information uses and shooting in advance in running section initialization operation is compared,
B3) for it is not expected that the case where scrambling, in image-region (BIB), preferably in image-region segment
(BIBAS) in label barrier and/or as potential obstacle tag barrier.
13. the equipment according to any one of claim 8 to 12 (HEV), which is characterized in that
Calculating/the analysis processing device (BAWE) is configured to so that for by different image sources be combined by
The image procossing of image processing method, such as hidden Markov model merges for all images (BIFSB) respectively in image district
Domain (BIB) or image-region segment (BIBAS) in the obstacle tag that has carried out, to minimize vicious identification simultaneously
And exclude " false is negative " namely it is vicious it is assumed that i.e. in lane or rail region be not present object (OBJ), although its
Necessary being.
14. the equipment according to any one of claim 8 to 13 (HEV), which is characterized in that
Described image recording equipment (BAZG) swingably constructs.
15. the equipment according to any one of claim 8 to 14 (HEV), which is characterized in that
Described image recording equipment (BAZG) has correcting unit (KOK), and the correcting unit is by weather together with brightness data
Including in the analysis processing to iconography.
16. the equipment according to any one of claim 8 to 15 (HEV), which is characterized in that
There is described image recording equipment (BAZG) focal length to change component (BVK), the focal length change component according to the vehicle
The spacing in road (FS, GL) selects correct shooting angle, therefore optimally to support multiple analysis processing.
17. the equipment according to any one of claim 8 to 16 (HEV), which is characterized in that
Described image recording equipment (BAZG) have illuminace component (BLK), especially headlight, the headlight the visible region of people it
It is interior or except work.
18. the equipment according to any one of claim 8 to 17 (HEV), it is characterised in that virtual machine, the virtual machine exist
It constructs and works in the sense that " rail traffic system, software definition signal identification ".
19. the equipment according to any one of claim 8 to 13 (HEV), which is characterized in that by the equipment (HEV)
In the case where not having additional infrastructure along running section, automatic (oneself of the rolling stock (BFZ, SFZ) is assisted
It is main) or auxiliary driving.
20. one kind in railway traffic (BVK), especially in rail traffic (SVK) for carrying out the obstacle in rail traffic (SVK)
The rolling stock (BFZ) of object identification, which is characterized in that
Equipment (HEV) according to any one of claim 8 to 19 for obstacle recognition is integrated into the railcar
In (BFZ, SFZ).
Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| DE102016224344.6 | 2016-12-07 | ||
| DE102016224344 | 2016-12-07 | ||
| PCT/EP2017/081834 WO2018104454A2 (en) | 2016-12-07 | 2017-12-07 | Method, system and track-bound vehicle, in particular rail vehicle, for recognizing obstacles in track-bound traffic, in particular in rail traffic |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110087970A true CN110087970A (en) | 2019-08-02 |
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ID=61017881
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| CN201780075914.6A Pending CN110087970A (en) | 2016-12-07 | 2017-12-07 | For method, equipment and the rolling stock of progress obstacle recognition, especially rail vehicle in railway traffic, especially in rail traffic |
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|---|---|
| EP (1) | EP3523177A2 (en) |
| CN (1) | CN110087970A (en) |
| RU (1) | RU2719499C1 (en) |
| WO (1) | WO2018104454A2 (en) |
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| Publication number | Publication date |
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| EP3523177A2 (en) | 2019-08-14 |
| WO2018104454A2 (en) | 2018-06-14 |
| WO2018104454A3 (en) | 2018-08-23 |
| RU2719499C1 (en) | 2020-04-20 |
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