EP3768568B1 - Véhicule ferroviaire comprenant un dispositif de commande - Google Patents

Véhicule ferroviaire comprenant un dispositif de commande Download PDF

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Publication number
EP3768568B1
EP3768568B1 EP19721218.6A EP19721218A EP3768568B1 EP 3768568 B1 EP3768568 B1 EP 3768568B1 EP 19721218 A EP19721218 A EP 19721218A EP 3768568 B1 EP3768568 B1 EP 3768568B1
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EP
European Patent Office
Prior art keywords
rail vehicle
control
rail
trained
rail vehicles
Prior art date
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Active
Application number
EP19721218.6A
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German (de)
English (en)
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EP3768568A1 (fr
EP3768568C0 (fr
Inventor
Laurentiu GOGA
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
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Siemens Mobility GmbH
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Publication of EP3768568C0 publication Critical patent/EP3768568C0/fr
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    • 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
    • B61L15/0081—On-board diagnosis or maintenance
    • 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
    • B61L27/50—Trackside diagnosis or maintenance, e.g. software upgrades
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L2210/00—Vehicle systems
    • B61L2210/02—Single autonomous vehicles

Definitions

  • the invention relates to a rail vehicle with a control device for controlling the rail vehicle.
  • rail vehicles are controlled via higher-level monitoring devices in the form of signal boxes.
  • Such control or monitoring by the signal box requires a permanent communication connection to the rail vehicles in the track section controlled by the signal box.
  • the disclosure document DE 10 2006 007 788 A1 discloses a method for computer-aided monitoring of the operation of a rail vehicle.
  • the invention is based on the object of specifying a rail vehicle that enables more independent driving than before.
  • the control device is based on artificial intelligence and has been trained to control the rail vehicle on at least a predetermined route.
  • the rail vehicle also has a communication device for direct communication with other rail vehicles.
  • a significant advantage of the rail vehicle according to the invention can be seen in the fact that it is based on artificial intelligence and, in view of the computing power available today, can be very easily enabled to make a railway track system completely self-sufficient, in particular, for example, without Interlocking monitoring, or at least largely to drive independently in a safe manner.
  • a signal box can also be used to increase security, but this is not always necessary.
  • control device has been trained using machine learning on the basis of external data.
  • control device has been trained on the basis of measurement data that was previously recorded during previous journeys of the rail vehicle or another rail vehicle with the same driving behavior on the specified route.
  • control device can be or have been trained in an advantageous manner on the basis of route data which describes the predetermined route.
  • control device records measurement data while driving on the specified route, stores it and uses it for subsequent machine learning for the purpose of improving its control behavior, i.e. improves its control behavior with the recorded measurement data within the framework of machine learning.
  • the control device has been trained in such a way that when controlling the rail vehicle, it takes into account information from other rail vehicles traveling on the route, in particular a rail vehicle in front, in particular location information, speed information and / or timetable information from other rail vehicles.
  • the control device is preferably trained in particular in such a way that it maintains a predetermined minimum distance from a rail vehicle in front when controlling the rail vehicle.
  • control device For routes with an available, higher-level monitoring device, in particular a signal box, it is advantageous if the control device has been trained in such a way that it takes into account, in particular executes, control commands from the higher-level monitoring device or the signal box when controlling the rail vehicle.
  • the control device has preferably been trained for an ATO driving operation, in particular an ATO driving operation with automation level 4, and/or for a driving operation according to ETCS Level 3.
  • the invention also relates to a method for operating rail vehicles according to claim 9.
  • the rail vehicles are controlled by the vehicle's own control devices, which are based on artificial intelligence and have been trained to control the respective rail vehicle on the route to be traveled, whereby the rail vehicles communicate directly with each other.
  • the rail vehicles preferably drive autonomously.
  • control devices when controlling their rail vehicle, take into account information from other rail vehicles traveling on the route, in particular a rail vehicle in front, in particular location information, speed information and / or timetable information from other rail vehicles.
  • control devices record measurement data while driving on the specified route, store them and use them for subsequent machine learning, thereby increasing their artificial intelligence.
  • a higher-level monitoring device in particular a signal box, preferably takes over the control of the rail vehicles in whole or in part. For this purpose, it preferably transmits control commands to the control devices of the rail vehicles.
  • the Figure 1 shows a railway track system 5, which is driven by three rail vehicles 10, 11, 12.
  • the rail vehicles 10, 11 and 12 each have a control device 100 which is based on artificial intelligence and has been trained to control their respective rail vehicle on at least a predetermined route.
  • control devices 100 of the rail vehicles 10, 11 and 12 are each programmed in such a way that the rail vehicles 10, 11 and 12 move from a starting point 20 of the railway track system 5 to an upper destination point 21 in Figure 1 and/or a lower one Can drive to destination point 22 and can return to the starting point 20 from the two destination points 21 and 22 in a corresponding manner.
  • the rail vehicles 10, 11 and 12 communicate with one another by means of communication devices (not shown in detail), which can be arranged in or outside the control devices 100.
  • Such communication can be based, for example, on radio signals F, as exemplified in the Figure 1 is shown.
  • Radio communication can based on the GSM-R mobile radio system known in railway technology or on another mobile radio system.
  • the rail vehicles 10, 11 and 12 communicate with each other directly via radio, i.e. without using a higher-level communication network.
  • the Figure 2 shows a second exemplary embodiment of a railway track system 5, which is driven by three rail vehicles 10, 11 and 12.
  • the rail vehicles 10, 11 and 12 each have control devices 100 which are based on artificial intelligence and have been trained to control the respective rail vehicle with a view to driving at least a predetermined route, here between the starting point 20 and the two destination points 21 and 22 are.
  • an interlocking 30 is provided, which forms a higher-level monitoring device and can transmit control commands SB to the rail vehicles 10, 11 and 12, which are to be taken into account by the rail vehicles 10, 11 and 12.
  • Such monitoring or additional control of rail vehicle traffic on the railway track 5 by the signal box 30 is carried out in particular when the signal box 30 determines that the rail vehicles 10, 11 and 12 traveling on the railway track 5 are in a dangerous situation or are approaching a dangerous situation .
  • the control device 100 has been trained on the rail vehicles 10, 11 and 12 by machine learning in such a way that the rail vehicles 10, 11 and 12 can drive in an automatic driving mode, also known in technical terms as ATO driving mode. Particularly preferably, completely autonomous driving according to automation level 4 is achieved.
  • the rail vehicles 10, 11 and 12 communicate with one another by means of their control devices 100;
  • control devices 100 In this regard, reference is made to the above explanations in connection with FIG.
  • the signal box 30 will transmit any control commands SB for controlling or influencing the driving behavior of the rail vehicles 10, 11 and 12, preferably via radio. It is advantageous if the signal box 30 uses the same radio system as the rail vehicles 10, 11 and 12 as part of their communication with one another.
  • the Figure 3 shows an exemplary embodiment of a control device 100, which is used in the rail vehicles 10, 11 and 12 according to the Figures 1 and 2 can be used.
  • the control device 100 according to Figure 3 has a computer 110 and a memory 120.
  • a control program module SPM is stored in the memory 120, which determines the behavior of the computer 110 and thus the control behavior of the control device 100 as a whole.
  • the control program module SPM is in accordance with the exemplary embodiment Figure 3 been generated by machine learning using an external training device 200.
  • the external training device 200 preferably uses external data De, which is supplied by external sources, to train the control program module SPM or to form the control program module SPM.
  • the external data can include, for example, driving behavior data that describes the driving behavior of the respective rail vehicle 10, 11 or 12.
  • the training device uses 200 according to Figure 3 For training the control program SPM, preferably measurement data Dm, which were obtained from previous journeys of the respective rail vehicle or another rail vehicle with the same or similar driving behavior on the specified route or the railway track system 5 according to Figures 1 and 2 have been previously recorded.
  • the training device 200 will advantageously use route data Ds to train the control program SPM, which shows the routes to be traveled between the starting point 20 and the two destination points 21 and 22 or the railway track system 5 according to Figures 1 and 2 describe completely.
  • the training device 200 For training the control device 100, the training device 200 preferably has a training program TPM stored in a memory 220, which is executed by a computing device 210 of the training device 200.
  • control program module SPM and thus the control behavior of the control device 100 is completely determined before the rail vehicles 10, 11 and 12 are put into operation and remains unchanged afterwards.
  • Such a design of the control device 100 is particularly advantageous in view of the fact that it is possible to understand at any time on which data set the artificial intelligence of the control device 100 is based or on what level of knowledge the control device 100 works on in its control behavior.
  • the Figure 4 shows a second exemplary embodiment of a control device 100, which is used in the rail vehicles 10, 11 and 12 according to the Figures 1 and 2 can be used.
  • the control device 100 according to Figure 4 has been trained using an external training device 200 or an external training program TPM, as described in connection with Figure 3 has already been explained above.
  • the external training using the external training device 200 can be based on external data De, measurement data Dm and route data Ds; In this context, please refer to the above explanations in connection with Figure 3 referred.
  • the memory 120 additionally contains its own training program TPMe, which enables further training of the control device 100 and thus a further improvement of the artificial intelligence of the control device 100.
  • the further training of the control device 100 using the own or internal training program TPMe is preferably based on current measurement data Dma, which is recorded and processed during current journeys of the respective rail vehicle 10, 11 or 12, i.e. after completion of the external training by the external training device 200 become.
  • An advantage of the exemplary embodiment according to Figure 4 is that a further improvement of the artificial intelligence and thus the control behavior of the control device 100 can be achieved by additionally using current measurement data Dma for training after the external training has been completed by the external training device 200, so that the control behavior and thus the Driving behavior of the rail vehicle can be improved on further journeys compared to the original level of knowledge or the original learning success.
  • the Figure 5 shows a third exemplary embodiment of a control device 100, which is used in the rail vehicles 10, 11 and 12 according to the Figures 1 and 2 can be used.
  • the control device 100 has a computer 110 and a memory 120 and in this respect corresponds to the exemplary embodiments according to Figures 3 and 4 .
  • a training program TPMe is stored, which carries out the machine learning and thus the generation of the artificial intelligence of the SPM control program entirely on its own.
  • the control device's own Training program TPMe preferably use external data De, measurement data Dm, route data Ds and current measurement data Dma, as is the case in connection with the Figures 3 and 4 has been explained above.
  • An advantage of the embodiment variant according to Figure 5 is that an external training device 200, as in the exemplary embodiments according to Figures 3 and 4 is provided, can be dispensed with since the control program module SPM can be generated solely on the basis of the own training program TPMe in the memory 120 of the control device 100.

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  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • General Health & Medical Sciences (AREA)
  • Mechanical Engineering (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)
  • Train Traffic Observation, Control, And Security (AREA)

Claims (12)

  1. Véhicule (10, 11, 12) ferroviaire comprenant un dispositif (100) de commande embarqué pour la commande du véhicule (10, 11, 12) ferroviaire, dans lequel le dispositif (100) repose sur l'intelligence artificielle et a subi un apprentissage pour commander le véhicule (10, 11, 12) ferroviaire au moins sur un parcours donné à l'avance, dans lequel le véhicule (10, 11, 12) ferroviaire est constitué pour, au moyen du dispositif (100) de commande, emprunter une installation de chemin de fer d'une façon sûre d'une manière autonome complètement ou au moins dans une grande mesure,
    caractérisé en ce que
    le véhicule (10, 11, 12) ferroviaire a un dispositif de communication pour une communication directe avec d'autres véhicules ferroviaires et le dispositif (100) de commande a subi un apprentissage, de manière à ce que, lors de la commande du véhicule (10, 11, 12) ferroviaire, il prenne en compte des informations d'autres véhicules (10, 11, 12) ferroviaires empruntant le parcours, en particulier d'un véhicule (10, 11, 12) ferroviaire précédent, en particulier des informations de lieu, des informations de vitesse et/ou des informations de plan de circulation d'autres véhicules (10, 11, 12) ferroviaires.
  2. Véhicule (10, 11, 12) ferroviaire suivant la revendication 1,
    caractérisé en ce que
    le dispositif (100) de commande a subi un apprentissage par enseignement par machine sur la base de données (De) extérieures.
  3. Véhicule (10, 11, 12) ferroviaire suivant l'une des revendications précédentes,
    caractérisé en ce que
    le dispositif (100) de commande a subi un apprentissage sur la base de données (Dm) de mesure, qui ont été prises auparavant sur le parcours donné à l'avance, lors de trajets antérieurs du véhicule (10, 11, 12) ferroviaire ou d'un autre véhicule (10, 11, 12) ferroviaire ayant le même comportement de circulation.
  4. Véhicule (10, 11, 12) ferroviaire suivant l'une des revendications précédentes,
    caractérisé en ce que
    le dispositif (100) de commande a subi un apprentissage sur la base de données de parcours, qui décrivent le parcours donné à l'avance.
  5. Véhicule (10, 11, 12) ferroviaire suivant l'une des revendications précédentes,
    caractérisé en ce que
    le dispositif (100) de commande détecte, pendant des trajets sur le parcours donné à l'avance, des données (Dma) de mesure en cours, les met en mémoire et les utilise pour l'enseignement par machine à suivre, afin d'améliorer son comportement de commande.
  6. Véhicule (10, 11, 12) ferroviaire suivant l'une des revendications précédentes,
    caractérisé en ce que
    le dispositif (100) de commande a subi un apprentissage, de manière à ce qu'il maintienne, lors de la commande du véhicule (10, 11, 12) ferroviaire, une distance minimum donnée à l'avance à un véhicule (10, 11, 12) ferroviaire le précédent.
  7. Véhicule (10, 11, 12) ferroviaire suivant l'une des revendications précédentes,
    caractérisé en ce que
    le dispositif (100) de commande a subi un apprentissage, de manière à prendre en compte, en particulier à exécuter, lors de la commande du véhicule (10, 11, 12) ferroviaire, des instructions (SB) de commande d'un dispositif de contrôle supérieur hiérarchiquement, en particulier d'un poste (30) d'aiguillage.
  8. Véhicule (10, 11, 12) ferroviaire suivant l'une des revendications précédentes,
    caractérisé en ce que
    le dispositif (100) de commande a subi un apprentissage pour un fonctionnement en circulation ATO, en particulier en fonctionnement en circulation ATO ayant le stade d'automatisation 4, et/ou pour un fonctionnement en circulation suivant ETCS level 3.
  9. Procédé pour faire fonctionner des véhicules ferroviaires, dans lequel on commande les véhicules (10, 11, 12) ferroviaires par des dispositifs (100) de commande embarqués, qui reposent sur l'intelligence artificielle et qui ont subi un apprentissage pour la commande du véhicule (10, 11, 12) ferroviaire respectif sur le parcours à emprunter, dans lequel les véhicules (10, 11, 12) ferroviaires sont constitués pour, au moyen des dispositifs (100) de commande, emprunter d'une façon sûre, de manière autonome complètement ou du moins dans une grande mesure, une installation de chemin de fer,
    caractérisé en ce que
    les véhicules (10, 11, 12) ferroviaires communiquent entre eux directement et les dispositifs (100) de commande ont subi un apprentissage de manière à ce que, lors de la commande de leur véhicule (10, 11, 12) ferroviaire, ils prennent en compte des informations d'autres véhicules (10, 11, 12) ferroviaires empruntant le parcours, en particulier d'un véhicule (10, 11, 12) ferroviaire précédent, en particulier des informations de lieu, des informations de vitesse et/ou des informations de plan de circulation d'autres véhicules (10, 11, 12) ferroviaires.
  10. Procédé suivant la revendication 9,
    caractérisé en ce que
    les véhicules (10, 11, 12) ferroviaires circulent d'une manière autonome.
  11. Procédé suivant l'une des revendications 9 à 10 précédentes,
    caractérisé en ce que
    les dispositifs (100) de commande détectent, pendant des trajets sur le parcours donné à l'avance, des données (Dma) de mesure en cours, les mettent en mémoire et les utilisent pour l'enseignement par machine à suivre, afin d'améliorer leur comportement de commande respectif.
  12. Procédé suivant l'une des revendications 9 à 11 précédentes,
    caractérisé en ce que
    dans le cas de la détection d'une situation de danger, un dispositif de contrôle supérieur hiérarchiquement, en particulier un poste (30) d'aiguillage, prend en charge en tout ou partie, la commande des véhicules (10, 11, 12) ferroviaires en transmettant en particulier des instructions (SB) de commande aux dispositifs (100) de commande des véhicules (10, 11, 12) ferroviaires.
EP19721218.6A 2018-05-16 2019-04-17 Véhicule ferroviaire comprenant un dispositif de commande Active EP3768568B1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102018207634.0A DE102018207634A1 (de) 2018-05-16 2018-05-16 Schienenfahrzeug mit Steuereinrichtung
PCT/EP2019/059937 WO2019219319A1 (fr) 2018-05-16 2019-04-17 Véhicule ferroviaire comprenant un dispositif de commande

Publications (3)

Publication Number Publication Date
EP3768568A1 EP3768568A1 (fr) 2021-01-27
EP3768568B1 true EP3768568B1 (fr) 2023-10-11
EP3768568C0 EP3768568C0 (fr) 2023-10-11

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Application Number Title Priority Date Filing Date
EP19721218.6A Active EP3768568B1 (fr) 2018-05-16 2019-04-17 Véhicule ferroviaire comprenant un dispositif de commande

Country Status (4)

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EP (1) EP3768568B1 (fr)
DE (1) DE102018207634A1 (fr)
ES (1) ES2967226T3 (fr)
WO (1) WO2019219319A1 (fr)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102019218611A1 (de) * 2019-11-29 2021-06-02 Siemens Mobility GmbH Fahrzeug und Verfahren zum Betreiben eines Fahrzeugs

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH04133601A (ja) * 1990-09-21 1992-05-07 Toshiba Corp 保安機能付自動運転制御装置
AUPR221900A0 (en) * 2000-12-20 2001-01-25 Central Queensland University Vehicle dynamics prediction system and method
DE102006007788A1 (de) * 2006-02-20 2007-08-30 Siemens Ag Verfahren zur rechnergestützten Überwachung des Betriebs eines einen vorgegebenen Streckenverlauf fahrenden Fahrzeugs, insbesondere eines spurgebundenen Schienenfahrzeugs
DE102014219817A1 (de) * 2014-09-30 2016-03-31 Siemens Aktiengesellschaft Verkehrssicherung
CA3027360C (fr) * 2016-06-13 2022-04-12 Siemens Industry, Inc. Systeme et procede destines a l'optimisation d'itineraire de train comprenant un systeme d'apprentissage automatique

Also Published As

Publication number Publication date
EP3768568A1 (fr) 2021-01-27
ES2967226T3 (es) 2024-04-29
DE102018207634A1 (de) 2019-11-21
WO2019219319A1 (fr) 2019-11-21
EP3768568C0 (fr) 2023-10-11

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