EP4124542A1 - Procédé et dispositif de détection d'obstacles sur une route - Google Patents

Procédé et dispositif de détection d'obstacles sur une route Download PDF

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Publication number
EP4124542A1
EP4124542A1 EP21188893.8A EP21188893A EP4124542A1 EP 4124542 A1 EP4124542 A1 EP 4124542A1 EP 21188893 A EP21188893 A EP 21188893A EP 4124542 A1 EP4124542 A1 EP 4124542A1
Authority
EP
European Patent Office
Prior art keywords
route
objects
vehicle
obstacle
detected
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
EP21188893.8A
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German (de)
English (en)
Inventor
Jens Braband
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
Priority to EP21188893.8A priority Critical patent/EP4124542A1/fr
Priority to CN202210896928.7A priority patent/CN115690721A/zh
Publication of EP4124542A1 publication Critical patent/EP4124542A1/fr
Pending legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/165—Anti-collision systems for passive traffic, e.g. including static obstacles, trees
    • 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
    • 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

Definitions

  • the invention relates to a method for detecting obstacles on a route traveled by a vehicle, in which the vehicle uses a sensor device to detect objects in front of the vehicle in the direction of travel, the detected objects are computer-aided and computer-aided are evaluated to identify obstacles .
  • the invention also relates to an arrangement for detecting obstacles with a sensor device for detecting objects and a computer for detecting the detected objects.
  • the invention relates to a vehicle which has an arrangement for detecting obstacles with a sensor device for detecting objects in front of the vehicle in the direction of travel and a computer for detecting the detected objects.
  • the invention relates to a computer program product and a provision device for this computer program product, the computer program product being equipped with program instructions for carrying out this method.
  • obstacles are detected by using a suitable sensor system (optical sensors, radar, ultrasound) to detect objects in the area in front of the vehicle as such and to classify them with the aid of computers, preferably using artificial intelligence.
  • the result of the classification is the detection of those objects that are to be rated as obstacles.
  • an obstacle is an object that threatens to collide with the vehicle in question.
  • objects can also be detected that are outside the path of the vehicle, for example, so there is no risk of collision. These are also not classified as obstacles.
  • objects In the case of railway lines in particular, there are a large number of objects that line the line. Examples include signals, switches, track elements such as balises, overhead lines, tunnels, railway stations and the like. These must not be classified as obstacles because the vehicle can drive past them unhindered.
  • AI applications are currently not approved for safety applications (i.e. applications at a high safety level) in the railway sector, especially since their exact function is difficult to understand and the required distances that are necessary for sensors in the railway sector are not covered can. They can only be treated like a black box that needs to be monitored using specific methods. This is a particular problem in obstacle detection for automatic rail travel, which must be done with a high level of security.
  • AI it cannot be predicted to what extent the sensors can detect obstacles in the specific situation and whether they are sufficiently capable of detecting them at all, and it is not clear whether the clearance gauge of a route is free of obstacles.
  • Clearance profiles are standardized cross-sections of the infrastructure. They ensure the space that must be kept free for the unrestricted passage of vehicles and loads with certain properties and dimensions.
  • artificial intelligence also abbreviated to KI below
  • computer-assisted machine learning also abbreviated to ML below
  • ML computer-assisted machine learning
  • supervised learning the system is trained through the relationship between the input and the associated output of known data, and in this way it learns approximately functional relationships. It depends on the availability of suitable and sufficient data, because if the system is trained with unsuitable (e.g. non-representative) data, it learns faulty functional relationships.
  • unsupervised learning the system is also trained with sample data, but only with input data and without any connection to a known output. It learns how data groups are to be formed and expanded, what is typical for the relevant use case and where deviations or anomalies occur. This allows use cases to be described and error states to be discovered.
  • reinforcement learning the system learns through trial and error by proposing solutions to given problems and receiving a positive or negative evaluation of this suggestion via a feedback function. Depending on the reward mechanism, the AI system learns to perform corresponding functions.
  • ANN learns mainly by modifying the weights of the neurons.
  • An adaptation of the threshold value can be taken care of by an on-neuron.
  • ANN are able to learn complicated non-linear functions using a learning algorithm that attempts to determine all parameters of the function from existing input and desired output values by means of an iterative or recursive procedure.
  • ANN are a realization of the connectionist paradigm, since the function consists of many simple, similar parts. The behavior only becomes complex when they are added together.
  • the object of the invention is to specify a method for recognizing obstacles on a route that is traveled by a vehicle, which runs automatically and thereby meets high safety requirements.
  • the object of the invention is to specify a vehicle which can carry out such a method.
  • the object of the invention consists in specifying a computer program product and a provision device for this computer program product, with which the aforementioned method can be carried out.
  • a route atlas is used for evaluating the objects, which contains a large number of objects to be recognized and their position on or on the route, with location information being determined for the recognized objects are, the location information is compared with the route atlas, each recognized object, whose Location information matches the position of a corresponding object to be recognized, triggers an assessment that the route is free until an object to be recognized is contained in the route atlas within a predetermined safety distance in front of the vehicle, to which no recognized object has been assigned, and independently previously triggered assessments triggering the assessment that there is an obstacle on the route.
  • the method according to the invention for detecting obstacles thus reverses the known procedure for detecting obstacles.
  • the objects that can potentially be an obstacle are recognized, and the subject of the assessment is whether these objects actually constitute an obstacle. Uncertainty arises in the event that an object is not recognized and is therefore ignored even if it represents an obstacle.
  • a risk potential in the obstacle detection according to the prior art lies a risk potential in the obstacle detection according to the prior art.
  • the objects to be recognized according to the method according to the invention are those whose position is known from the route atlas. Therefore, in the event that these objects are not detected at the expected position, it can be concluded that they are covered by another unknown object. This is likely to be an obstacle, which is why the fact that an object cannot be detected can be used to initiate a safety measure, such as emergency braking.
  • This method has the advantage that only previously known objects have to be recognized. This can be done with greater procedural certainty than recognizing the obstacles themselves, the shape and location of which on or along the route are not known until they have been recognized as such.
  • a further advantage is that in the event that an object should have been recognized according to the method according to the invention but was not recognized, a safety measure which would not have been necessary is initiated. A Thus, failure to recognize the objects does not lead to an accident, but to a groundless safety measure. The consequences of such a misjudgment are therefore advantageously significantly less than if an accident were to occur. This shows that the method according to the invention can be operated with greater security even in the event of an error.
  • the safety distance must be dimensioned in such a way that the safety measure, which can preferably be emergency braking, can be effectively initiated in order to prevent an accident.
  • the safety measure which can preferably be emergency braking
  • the speed-dependent braking distance in the event of emergency braking of the vehicle plus the distance covered by the vehicle while the method according to the invention is being carried out for detecting the obstacle and up to the onset of the safety measure can be defined as the safety distance.
  • the idea according to the invention of supporting the AI with a model that is enriched with a priori information from a route atlas is used according to the invention for a route vacancy notification, in particular clearance clearance notification, i. H. spaces within the safety distance in front of the vehicle are to be explicitly identified in which there can be no obstacle because all expected objects (defined by the route atlas) have been detected. Plausibility checks can be used to determine which types of obstacles cannot be in the structure gauge.
  • the train locates itself on the track or is located on the track. Therefore, the track position is also known with regard to the route atlas. Using the route atlas and the inaccuracy, one can then determine the probability distribution of the position of the objects. In particular, the distance to the object can also be estimated.
  • the AI algorithm preferably supplies all recognized objects as well as their outlines.
  • the route atlas is characterized in that it depicts or describes objects to be recognized in relation to their position on the route. Different formats can be selected here.
  • the route atlas is designed one-dimensionally, for example as a table, containing the objects and their position on the route without considering the course of the route (expressed as route information, for example in meters). Such a representation is one-dimensional.
  • the route atlas can also be two-dimensional.
  • the course of the route on the earth's surface can be taken into account. This is a two-dimensional one Projection.
  • the course of the route can also be displayed three-dimensionally in the route outlet.
  • the height differences of the route can also be recorded here.
  • the position of the objects to be recognized can also be defined in terms of their height above the track.
  • the three-dimensional route atlas can in particular be mapped as virtual reality.
  • VR Virtual reality
  • reality also referred to as physical reality
  • How detailed the VR has to be generated depends on the individual application.
  • VR is generated in a three-dimensional space and depicts its physical properties, in particular topography, in a simplified manner.
  • a prerequisite for the use of VR is knowledge of the location and the viewing direction of the viewer. As a result, it must be located. If a VR is used for rail traffic, certain simplifications result. For example, the field of view of a train driver is clearly defined through the windscreen, so that the image section of the VR to be displayed can be determined by locating the train.
  • the safety distance is specified. With this specification, safe operation of the vehicle must be taken into account. Therefore, the safety distance can be determined variably, depending on the route situation and operating situations. For example, the safety distance can be increased as the speed of the vehicle increases, since the braking distance also increases with the speed. The safety distance can, for example, be reduced in curves due to the curvature of the route due to the geographical conditions. At the same time, however, you drive more slowly in curves.
  • “computer-aided” or “computer-implemented” can be understood to mean an implementation of the method in which at least one computer or processor executes at least one method step of the method.
  • Computers can be, for example, personal computers, servers, handheld computers, mobile phones and other communication devices that process computer-aided data, processors and other electronic devices for data processing, which can preferably also be combined to form a network.
  • a “processor” can be understood to mean, for example, a converter, a sensor for generating measurement signals, or an electronic circuit.
  • a processor can in particular be a main processor (Central Processing Unit, CPU), a microprocessor, a microcontroller, or a digital signal processor, possibly in combination with a memory unit for storing program instructions, etc.
  • CPU Central Processing Unit
  • a processor can also be understood to mean a virtualized processor or a soft CPU.
  • a “memory unit” can be understood to mean, for example, a computer-readable memory in the form of a random-access memory (RAM) or data memory (hard disk or data carrier).
  • RAM random-access memory
  • data memory hard disk or data carrier
  • the "interfaces" can be realized in terms of hardware, for example wired or as a radio connection, and/or software, for example as an interaction between individual program modules or program parts of one or more computer programs.
  • Cloud is to be understood as an environment for “cloud computing” (German computer cloud or data cloud). What is meant is an IT infrastructure that is made available via interfaces of a network such as the Internet. It usually includes storage space, computing power or software as a service, without these having to be installed on the local computer using the cloud.
  • the services offered as part of cloud computing cover the entire spectrum of information technology and include, among other things, infrastructure, platforms and software.
  • Program modules are to be understood as meaning individual functional units which enable a program sequence of method steps according to the invention. These functional units can be implemented in a single computer program or in several computer programs that communicate with one another. The interfaces implemented here can be implemented in terms of software within a single processor or in terms of hardware if multiple processors are used.
  • the assessment that there is an obstacle on the route is only triggered if the obstacle is at least partially in the clearance gauge.
  • the clearance gauge is of interest, which can be imagined as a virtual tunnel around the route.
  • Each sensor can now be mentally linked to the outline of any object by logical rays, just like ray tracing in computer graphics. When one sees an object with no obstructions or intersections, one knows that there can be no obstructions on the associated rays. On At any distance one can now think of a 2D geometric cut through the light space profile of the beam path with the cross-sectional area of the tunnel, in other words a slice of the tunnel at a certain distance. In the case of curves, it is also necessary to consider which panes are actually in the field of vision.
  • the covered parts of the clearance gauge are compared with patterns for specific types of obstacles and, if they match a pattern it is determined that the hidden parts of the structure gauge are assigned to the type of obstacle represented by the pattern.
  • traffic signs are detected as objects that provide information for carry the detection of the object.
  • additional traffic signs can be set up, which have as their main function the detection as an object. At least the function of being an object to be detected is fed into the method as information. At the same time, the traffic signs can also contain a code with which additional information can be recorded. This can be, for example, location coordinates.
  • the chronological progression in the detection of the objects is tracked, and time intervals in which the objects cannot be detected, taking into account the Route occasions are used to determine the geometry of the obstacle.
  • the vehicle uses the sensor device to detect the obstacle in front of the vehicle in the direction of travel and the detected obstacle is computer-aided.
  • obstacle detection is carried out in the sense that is known per se according to the prior art. In comparison to the method according to the prior art, however, it is already known at this point in time that there is an obstacle on the route. Safety measures can therefore already be initiated. However, the detection of the obstacle as such, in particular the detection of the type of obstacle, can produce additional information that can influence the decision on how to implement the safety measure (this has already been discussed).
  • the stated object is also achieved according to the invention with the initially specified subject of the claim (vehicle) in that the arrangement is set up to carry out a method according to one of Claims 1-6.
  • a provision device for storing and/or providing the computer program product.
  • the provision device is, for example, a storage unit that stores and/or provides the computer program product.
  • the provision device is, for example, a network service, a computer system, a server system, in particular a distributed, for example cloud-based computer system and/or virtual computer system, which stores and/or provides the computer program product preferably in the form of a data stream.
  • the provision takes place in the form of a program data block as a file, in particular as a download file, or as a data stream, in particular as a download data stream, of the computer program product.
  • this provision can also be made, for example, as a partial download consisting of several parts.
  • Such a computer program product is read into a system, for example using the provision device, so that the method according to the invention is executed on a computer.
  • the components described represent the Embodiments each represent individual features of the invention to be considered independently of one another, which also develop the invention independently of one another and are therefore also to be regarded as part of the invention individually or in a combination other than that shown. Furthermore, the components described can also be combined with the features of the invention described above.
  • FIG 1 a vehicle FZ is shown which is traveling in a direction of travel FR on a track GL.
  • This vehicle FZ has a first sensor SN1 on the front, which can detect objects OB1 .
  • the first object OB1 is a balise built into track GL.
  • the second object OB2 is a mast for an overhead line. This is shown as an example, with the route formed by the track GL naturally accompanying a large number of such masts, which can be detected by the first sensor SN1.
  • the third object OB3 is a signal.
  • the fourth to seventh objects OB4, OB5, OB6, OB7 are special traffic signs which, indicated by an X, carry an identifier that can be detected optically.
  • the eighth object OB8 is the entrance to a tunnel.
  • the objects OB1 ... OB6 are located at a safety distance SCA within which they must be reliably detected.
  • the vehicle FZ has an antenna AT and is connected wirelessly via a first interface S1 to an antenna AT of a control center LZ and via a second interface S2 to a satellite STL.
  • the satellite preferably belongs to a navigation system (for example GPS) so that the vehicle FZ can independently determine its position. For example, timetable data and a route atlas can be transmitted to the vehicle FZ via the control center LZ (more on this below).
  • first structure gauge LPR1 which in figure 1 is shown, it will not be possible to determine an image of the objects OB4, OB5, OB6, OB7 within this clearance profile.
  • the height of the traffic signs that represent these objects is selected such that they are preferably covered by obstacles close to the ground, such as the first obstacle HD1 in the form of a tree trunk.
  • the first structure gauge LPR1 is shown.
  • the first obstacle HD1 appears as a hatched area, the exact shape of which is not recognized in the method according to the invention.
  • the shape of the first obstacle HD1 can be inferred since it covers the fourth object OB4, the fifth object OB5, the sixth object OB6 and the seventh object OB7.
  • the third object can still be recognized in the form of a signal, as a result of which the height of the first obstacle HD1 can be inferred in the method according to the invention.
  • figure 2 to recognize that the extension of the first obstacle HD1 is only determined within the first clearance profile LRP1. Even if, for example, the fourth object OB4 is also blocked by the first obstacle HD1 (cf. figure 1 ) is covered, this is not relevant for the first structure gauge LPR1. In other words, an obstacle that is in the current line of sight BR, like this one in figure 1 is shown, only the fourth object OB4 covered, do not represent an obstacle because this would lie outside the clearance gauge.
  • figure 3 represents a second structure gauge LRP2, which is not included in figure 1 is marked. This contains, for example, a second obstacle HD2, which almost completely fills the structure clearance profile LRP2.
  • another vehicle is on track GL, which is why immediate action, preferably in the form of emergency braking, is required to avoid collisions.
  • FIG 4 a computer infrastructure is shown which is suitable for carrying out the method according to the invention.
  • Program modules can be processed by a first computer CP1 in the control center LZ, by a second computer CP2 in the vehicle FZ and within a cloud CLD.
  • the control center LZ has a first computer CP1, which is connected to a first storage device SE1 via a third interface S3.
  • the first computer CP1 is connected to the cloud CLD via an eighth interface S8.
  • the first computer is CP1 over a first interface S1 connected to a second computer CP2 of the vehicle FZ.
  • the second computer CP2 has a fourth interface S4 to a second storage device SE2.
  • a positioning module GPS is provided in the vehicle FZ, which is connected to the second computer CP2 via a seventh interface S7 and to the satellite SEL via the second interface S2.
  • the vehicle FZ also has a first sensor SN1, for example a camera, and a second sensor SN2, for example a radar.
  • the sensor data of the first sensor SN1 are transmitted to the second computer CP2 via a fifth interface S5 and the sensor data of the second sensor SN2 are transmitted via a sixth interface S6.
  • FIG 5 shows an exemplary embodiment of a process sequence of the method according to the invention, supplemented by some preparatory sequences.
  • the cloud CLD is shown, which provides a service for creating a route atlas STA.
  • a test drive is carried out by a measuring vehicle FZM, which, after starting the method, generates route data in a generation step for route data GEN STD, which are, for example, digital images of route sections.
  • route data STD_OT which can be transmitted to the cloud CLD via an interface S9 and stored there.
  • a service provider not shown in detail, is also connected to the Cloud CLD and creates a route atlas in a generation step for the route atlas GEN_STA.
  • This can in particular consist of a virtual reality VR, ie a three-dimensional representation of the environment of the route. Alternatively, it is possible to choose a two-dimensional or even one-dimensional representation.
  • the route atlas STA is stored in the cloud CLD.
  • the route atlas STA is made available via the eighth interface S8 in an input step for the route atlas STA_IN.
  • the timetable FPL_IN also loaded a schedule FPL from the cloud CLD via the eighth interface S8.
  • a subsequent query step FZ? it is clarified whether a vehicle is in use. If this is not the case, in a query step for the end of the procedure FTP? queried whether the operating procedure should be terminated in the control center LZ. If this is the case, the process is stopped. If this is not the case, the timetable data is renewed in a further input step FPL_IN and the process begins again.
  • a detection step for objects SEN_OB then takes place in the vehicle, in which sensor data for detecting objects are generated.
  • the sensor data are processed such that objects OB in front of the vehicle FZ are detected.
  • the objects found in the previous detection step for objects IDF_OB are compared with the objects to be expected based on an evaluation of the route atlas STA.
  • the knowledge of the vehicle position is included here in a manner that is not shown.
  • An obstacle is identified when an object whose detection would be expected based on knowledge of the route atlas cannot be detected in the vehicle's field of view BF.
  • the in figure 1 illustrated safety distance SCA taken into account, which is a measure of the smallest distance from the vehicle and thus also the latest point in time when an object must have been detected. So at the latest when this enters the safety distance SCA.
  • a query step for critical obstacles returns CRT? checked if the obstacle is a problem for the approaching vehicle. This is particularly the case if the obstacle protrudes into the structure gauge (cf Figure 1 and 2 Executed) . If the obstacle is not critical, it is determined in a query step for the end of the method whether the operation of the vehicle FZ has ended. If this is the case, the process is stopped. If this is not the case, the procedure described begins again with the detection step for objects SEN_OB.
  • the next step is to initiate emergency braking EBK.
  • a message is then sent via the first interface S1 to the control center LZ, which then makes a corresponding change to the timetable (in figure 5 only hinted at). In any case, the process in the vehicle FZ is stopped even after the emergency braking EBK.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Traffic Control Systems (AREA)
  • Train Traffic Observation, Control, And Security (AREA)
EP21188893.8A 2021-07-30 2021-07-30 Procédé et dispositif de détection d'obstacles sur une route Pending EP4124542A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
EP21188893.8A EP4124542A1 (fr) 2021-07-30 2021-07-30 Procédé et dispositif de détection d'obstacles sur une route
CN202210896928.7A CN115690721A (zh) 2021-07-30 2022-07-28 一种检测路线上障碍物的方法及设备

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
EP21188893.8A EP4124542A1 (fr) 2021-07-30 2021-07-30 Procédé et dispositif de détection d'obstacles sur une route

Publications (1)

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EP4124542A1 true EP4124542A1 (fr) 2023-02-01

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102023201797A1 (de) 2023-02-28 2024-08-29 Siemens Mobility GmbH Computer-implementiertes Verfahren zur Kennzeichenzuordnung für einen autonomen Zug

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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
DE102014206473A1 (de) * 2014-04-03 2015-10-08 Bombardier Transportation Gmbh Automatische Assistenz eines Fahrers eines fahrspurgebundenen Fahrzeugs, insbesondere eines Schienenfahrzeugs
DE102016224212A1 (de) * 2016-12-06 2018-06-07 Siemens Aktiengesellschaft Automatisierte Freiraumerkennung mittels Differenzanalyse für Fahrzeuge
WO2018104454A2 (fr) * 2016-12-07 2018-06-14 Siemens Aktiengesellschaft Procédé, dispositif et véhicule sur voie, notamment véhicule ferroviaire, pour la détection d'obstacle dans le transport sur voie, en particulier le transport ferroviaire
US20190370569A1 (en) * 2018-06-01 2019-12-05 Qualcomm Incorporated Techniques for sharing of sensor information

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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
DE102014206473A1 (de) * 2014-04-03 2015-10-08 Bombardier Transportation Gmbh Automatische Assistenz eines Fahrers eines fahrspurgebundenen Fahrzeugs, insbesondere eines Schienenfahrzeugs
DE102016224212A1 (de) * 2016-12-06 2018-06-07 Siemens Aktiengesellschaft Automatisierte Freiraumerkennung mittels Differenzanalyse für Fahrzeuge
WO2018104454A2 (fr) * 2016-12-07 2018-06-14 Siemens Aktiengesellschaft Procédé, dispositif et véhicule sur voie, notamment véhicule ferroviaire, pour la détection d'obstacle dans le transport sur voie, en particulier le transport ferroviaire
US20190370569A1 (en) * 2018-06-01 2019-12-05 Qualcomm Incorporated Techniques for sharing of sensor information

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102023201797A1 (de) 2023-02-28 2024-08-29 Siemens Mobility GmbH Computer-implementiertes Verfahren zur Kennzeichenzuordnung für einen autonomen Zug

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