EP3673471A1 - Système et procédé de prédiction d'une signalisation future d'un élément d'infrastructure de transport - Google Patents

Système et procédé de prédiction d'une signalisation future d'un élément d'infrastructure de transport

Info

Publication number
EP3673471A1
EP3673471A1 EP18830182.4A EP18830182A EP3673471A1 EP 3673471 A1 EP3673471 A1 EP 3673471A1 EP 18830182 A EP18830182 A EP 18830182A EP 3673471 A1 EP3673471 A1 EP 3673471A1
Authority
EP
European Patent Office
Prior art keywords
traffic
artificial intelligence
infrastructure element
signaling
traffic infrastructure
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.)
Ceased
Application number
EP18830182.4A
Other languages
German (de)
English (en)
Inventor
Daniel HOBOHM
Felix Rudolph
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.)
Yunex GmbH
Original Assignee
Siemens Mobility GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Siemens Mobility GmbH filed Critical Siemens Mobility GmbH
Publication of EP3673471A1 publication Critical patent/EP3673471A1/fr
Ceased legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • G08G1/0141—Measuring and analyzing of parameters relative to traffic conditions for specific applications for traffic information dissemination
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/091—Traffic information broadcasting
    • G08G1/093—Data selection, e.g. prioritizing information, managing message queues, selecting the information to be output
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
    • G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
    • G08G1/096716—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control where the received information does not generate an automatic action on the vehicle control
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
    • G08G1/096766—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission
    • G08G1/096775—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission where the origin of the information is a central station
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]

Definitions

  • the invention relates to a system and a method for supporting a forecast of future signaling of a traffic infrastructure element and a computer program.
  • so-called artificial intelligence algorithms can be used, which derive from a switching process of a light signal system as a function of detector data from traffic detectors and internal programming a future, probable future circuit or a probable future signal image of the traffic signal.
  • This approach is used, for example, in traffic-dependent controlled traffic signal systems, since such traffic signal systems flexibly adapt their green and red periods to a current traffic situation.
  • the correspondingly predicted signal images or switching times can be made available to road users, for example. These can, for example, accordingly adjust their speed in order to passie the light signal system during a green phase without stopping.
  • the object underlying the invention is to be seen hen to provide a concept for efficiently supporting a traffic participant in a forecast of a future signaling tion of a traffic infrastructure element.
  • a system for assisting a road user in forecasting future signaling of a traffic infrastructure element comprising: a memory device in which an artificial intelligence is stored, wherein the artificial intelligence comprises a prognosis model for predicting future signaling of the traffic infrastructure element, and a communication device for transmitting the artificial intelligence to a road user via a communication network.
  • a method of assisting a road user in predicting future signaling of a traffic infrastructure element using artificial intelligence comprising:
  • a computer program comprising program code for performing the method of assisting a road user in predicting future signaling of a traffic infrastructure element when the computer program is on a computer, for example, the system for assisting a road user in a forecast a future Sig nalmaschine a traffic infrastructure element is executed.
  • a script is provided to the traffic participant, eg a motor vehicle, which includes or comprises this artificial intelligence, so that the road user, eg the motor vehicle, is then in the respective subsecond-precise situation ("real-time").
  • current and / or georeferenced can derive how the scaffoldinf ra Modellelement, eg a traffic signal, will soon switch.
  • the invention is based on the finding that the above task can be solved by the fact that the artificial Intelli genz, which includes a forecasting model for predicting a future signaling of the transport infrastructure element, is sent to the or the road users via a communi cation network.
  • the road user becomes or the road users themselves are enabled to predict the future signaling of the traffic infrastructure element using artificial intelligence.
  • the traffic participants can advantageously consider their own boundary conditions. For example, the road user be able to take into account, from which direction he approaches the light signal system or where the road user wants to go or where the road user is relative to the traffic signal.
  • the concept according to the invention is based on providing the artificial intelligence comprising the prognosis model, which is used to forecast the future signaling of the traffic infrastructure element, to the traffic participant (s) via a communication network.
  • the road user is a motor vehicle.
  • the motor vehicle is is.
  • the motor vehicle is is is.
  • the motor vehicle comprises a driver assistance system which uses the artificial intelligence for the prognosis of the future signaling of the traffic infrastructure element.
  • the driver assistance system controls For example, a lateral and / or a longitudinal guidance of the motor vehicle based on the forecast of the future signaling of the traffic infrastructure element.
  • the prediction model is designed to predict the future signaling of the traffic infrastructure element based on traffic data, wherein the communication device is formed to transmit the traffic data to the road user via the communication network.
  • the technical advantage means that the future signaling of the traffic infrastructure element can be efficiently predicted.
  • the same traffic data are sent to the road user via the communication network, which uses the traffic infrastructure element itself for its own determination of the future signaling.
  • the traffic infrastructure element e.g. stored an algorithm that uses the traffic data to determine a future signaling, which is then displayed by means of the traffic infrastructure element.
  • the information may be processed directly by the motor vehicle or an associated element, e.g. in highly automated or even autonomous driving applications.
  • the same input data (in this case the traffic data) is available to the road user as the traffic infrastructure element in order to predict future signaling using the forecasting model.
  • the prognosis model is designed to provide the future signaling of the traffic infrastructure element based on signaling data which provides a current signaling of the traffic information. describe and / or based on
  • Switching time data that describe a current switching time of the traffic infrastructure element to predict, wherein the communication device is designed to send the signaling data and / or the switching time data to the traffic participants via the communication network.
  • the traffic information is made available to the road user, current information, the signaling data or the switching time data, of the traffic infrastructure element.
  • the signaling data or the switching time data are in this case determined in particular by means of the traffic infrastructure element itself.
  • Switching time data in particular comprise the information about one or more times at which a change in the signaling has taken place or will take place.
  • the communication device comprises a first communication interface for sending the artificial intelligence to the road user.
  • the communication device comprises a second communication interface for transmitting the signaling data and / or switching time data and / or traffic data to the road user.
  • the second communication interface is e.g. from the traffic infrastructure element.
  • the communication device is designed to send the artificial intelligence only to the road user when one or more predetermined conditions are met.
  • This causes the technical advantage that the sending of the artificial intelligence can be carried out efficiently.
  • it can be determined exactly under which conditions the artificial intelligence should be sent to the road user.
  • the one or more predetermined conditions are each an element selected from the following group of conditions: road user is within a predetermined distance to the traffic infrastructure element, receiving by the communication device a request of the road user to send the artificial Intelligence.
  • the technical advantage has the effect of ensuring efficiently that the artificial intelligence is sent only to road users who are within a predetermined distance from the traffic infrastructure element.
  • This distance can refer to a immedi direct light signal system, or even for example for an entire city or region.
  • the technical advantage is caused that an available bandwidth of the communication network is used efficiently, for example, insofar as the artificial intelligence is sent only when prompted.
  • a condition is, for example, that a prognosis quality falls below a predetermined minimum threshold. Then, that is, when falling below the predetermined minimum threshold value, it is provided, for example, that the prognosis model is retrained or trained and then sent to the traffic participant.
  • a condition is, for example, that a prognosis quality becomes larger or better and thus exceeds a predetermined maximum threshold value. Then, when crossing of the predetermined maximum threshold value, the artificial intelligence comprising the forecasting model is sent to the traffic participant.
  • a continuous improvement is possible in particular, because more and more data is available for model training.
  • One condition is, for example, that a rescheduling of the traffic infrastructure element, e.g. the light signal system, has taken place.
  • the traffic infrastructure element e.g. the light signal system
  • a computing device which is adapted to train an artificial intelligence template or the artificial intelligence stored in the memory device both based on the data that the traffic infrastructure element itself uses to determine future signaling, and based on the by means of the traffic infrastructure element itself determined and signaled signaling to train, the computing device is designed to save the trained artificial intelligence template as the artificial intelligence in the memory device res pektive the stored in the memory device venezli che intelligence based on the training to ren upgraded Ren.
  • the technical advantage means that the artificial intelligence can be efficiently created or updated.
  • the traffic infrastructure element is an element selected from the following group of traffic infrastructure elements: Lichtsignalan situation, variable message signs, speed limit traffic signs.
  • the traffic infrastructure element is a traffic signal
  • the signaling example a signal image of the traffic signal system.
  • the signaling is e.g. a specific type of traffic sign.
  • the signaling is e.g. an indication of a permissible maximum speed.
  • Traffic data in the sense of the description describe for example a traffic condition.
  • Traffic data in the sense of the description include, for example, detector data, e.g. Detector data of one or more detectors for detecting a traffic.
  • detectors are arranged, for example, on, above or in a roadway and include, for example, motor vehicles.
  • traffic data in the sense of the description includes in particular all data which includes the traffic infrastructure element, e.g. a traffic signal system, to receive future signaling based on this data, e.g. a signal image to determine, which is then displayed by means of the transport infrastructure element actually.
  • the traffic infrastructure element e.g. a traffic signal system
  • receive future signaling based on this data, e.g. a signal image to determine, which is then displayed by means of the transport infrastructure element actually.
  • an algorithm stored in the traffic infrastructure element is used.
  • Detector data are in particular not only loop data of embedded in a road induction loops (Detekto Ren), the motor vehicles detect, but also, for example, pedestrian buttons and arrival information of buses and trains, so-called public transport (public transport).
  • Artificial intelligence in the sense of the description includes, for example, methods and / or methods from the field of informatics which, for example, enable the automation of intelligent behavior.
  • the methods of artificial intelligence are characterized, for example, by the fact that with the means of mathematics, the ability to learn and the ability to deal with uncertainty and probabi listic information are enabled.
  • the following methods are attributed to artificial intelligence: machine learning, deep learning, neural networks, knowledge acquisition, knowledge representation, cognitive models, heuristic search, language processing.
  • artificial intelligence For the various methods of artificial intelligence are also a variety of KI (artificial intelligence) programming languages and so-called frameworks available, which allow the user to use so-called software libraries (software libraries) and these, for example, on cloud-based data centers to carry out.
  • software libraries software libraries
  • cloud-based data centers For the various methods of artificial intelligence are also a variety of KI (artificial intelligence) programming languages and so-called frameworks available, which allow the user to use so-called software libraries (software libraries) and these, for example, on cloud-based data centers to carry out.
  • software libraries software libraries
  • the system is designed or set up to support a road user in a forecast of a future signaling of a traffic infrastructure element, the method for supporting a road user in a forecast ei ner future signaling a transport infrastructure elements perform or perform.
  • Technical functionalities of the system also result from corresponding technical functionalities of the process and vice versa.
  • the system includes the traffic infrastructure element.
  • the system does not include the traffic infrastructure element.
  • the communication network comprises a mobile radio network and / or a WLAN communication network.
  • the prognosis model is designed to predict the future signaling of the traffic infrastructure element based on traffic data, wherein the traffic data are sent to the traffic participants via the communication network.
  • the prognosis model is designed to map the future signaling of the traffic infrastructure element based on signaling data describing a current signaling of the traffic infrastructure element, and / or based on
  • Switching time data describing a current switching time of the traffic infrastructure element to predict, the signaling data and / or the switching time data to the Road users are sent over the communication network.
  • the artificial intelligence is not sent to the road user until one or more predetermined conditions have been met.
  • FIG. 1 shows a first system for supporting a traffic participant in a forecast of a future signaling of a traffic infrastructure element
  • FIG. 2 shows a flowchart of a method for supporting a road user in a forecast of future signaling of a traffic infrastructure elements
  • 3 shows a second system for supporting a traffic participant in a forecast of a future signaling of a traffic infrastructure element.
  • FIG. 1 shows a system 101 for supporting a traffic participant in a forecast of a future signaling tion of a traffic infrastructure element, comprising:
  • a communication device 107 for sending the artificial intelligence 105 to a road user via a communication network.
  • FIG. 2 shows a flowchart of a method for supporting a road user in a forecast of future signaling of a traffic infrastructure element using artificial intelligence, the artificial intelligence comprising a prognosis model for forecasting a future signaling of the traffic infrastructure element, comprising:
  • FIG. 3 shows a second system 301 for assisting a road user in a forecast of a future signaling of a traffic infrastructure element.
  • the traffic infrastructure element is a traffic signal system 303.
  • the traffic signal 303 traffic data 305 embracege provides.
  • the traffic data 305 comprises detector data from three detectors, symbolically represented by three arrows with the reference Characters 307, 309 and 311 is shown.
  • Detector data are, in particular, not only loop data of induction loops embedded in a roadway, which detect motor vehicles, but also, for example, pedestrian buttons as well as arrival information of buses and trains, so-called public transport telegrams.
  • a non-GE indicated control unit of the traffic signal system 303 each determines a signal image for three signal groups 313, 315 and 317.
  • the controller uses, for example, an algorithm.
  • the signal image data corresponding to these signal images are symbolically marked as an ellipse with the reference numeral 319.
  • the system 301 comprises a memory device 323 in which an artificial intelligence 325 is stored.
  • the system 301 further includes a computing device 331.
  • the computing device 331 and the storage device 323 are part of a cloud infrastructure 321.
  • the traffic data 305 and the signal image data 319 are sent to the computing device 331 and in particular stored in the cloud infrastructure 321, e.g. in the storage device 323.
  • the computing device 331 trains the artificial intelligence 325 using the traffic data 305 and the signal image data 319.
  • the artificial intelligence is all the more performant, as it detects different switching behavior and detector data of the Lichtsig signal system 303 over an ever-increasing period of time, thus usually with data training for several months. It is thus provided here that an output of the traffic signal system 303, ie the signal images determined in addition to the traffic data 305 also the computing device 331 are provided to train the artificial intelligence 325 so that they can learn the behavior of the traffic signal system 303 to hie over it to perform a prognosis of the future signal image can.
  • the trained artificial intelligence 325 then includes a prognosis model for predicting a future signaling of the traffic signal system 303.
  • the system 301 includes a first wireless communication interface 327 for transmitting the trained artificial intelligence 325 comprising the predictive model to a traffic participant 329, e.g. a motor vehicle, via a wireless communication network.
  • a traffic participant 329 e.g. a motor vehicle
  • the system 301 includes a second wireless communication interface 333, which is included in the traffic signal system 303.
  • This second communication interface 333 transmits over the same wireless communication network and / or over another wireless communication network, e.g. via a WLAN communication network and / or via a mobile radio network, current traffic signal information, for example switching times and / or detector data 305 and / or signal image data 319, to the road user 329.
  • the first and second communication interfaces 327, 333 form a communication device of the system 301.
  • the road user 329 is thus advantageously enabled, using the artificial Intel ligenz 325 and the traffic signal information itself a prognosis of a future signaling of Lichtsig signaling system 303 perform.
  • the further traffic participant 329 can, for example, take into account its current location or its current direction of travel.
  • the road user 329 can interpret the results independently.
  • the artificial intelligence 325 is additionally or instead trained in the cloud infrastructure 321 in or on a controller.
  • the controller is the control unit of the traffic signal and be found in particular at the intersection at which the traffic signal system is arranged. This means, in particular, that the training of the model does not necessarily have to take place in the cloud infrastructure 321, but can also take place locally on or in the control unit. For this, e.g. a hardware extension in the controller in the form of one or more additional CPUs provided.
  • the trai artificial intelligence 325 trigger-based on traffic participants, for example, to the road user 329, transmitted or sent via the communication network.
  • trigger-based means in particular that the artificial intelligence 325 is only sent to the traffic participant or parties when one or more predetermined conditions have already been fulfilled, as already explained above.
  • a communication network includes a DSRC communication network.
  • DSRC stands for "dedicated short burst communication" and includes, for example, a WLAN communication network.
  • the current traffic signal information is sent in parallel to the transmission of the artificial intelligence 325 to the traffic participant.
  • the aktuel len traffic signal information to non-discriminatory the road user (s) are sent. Discrimination-free here means, in particular, that every transport participant receives the information on an equal footing without any preference being given to certain road users.
  • the computing device 331 updates the artificial intelligence 325, for example, updates it periodically.
  • the invention is based on the concept of training a trained artificial intelligence comprising a prognosis model for predicting future signaling of the traffic infrastructure element to road users.
  • the road users receive, for example, non-discriminatory traffic data, for example detector data, so that the road users can interpret the data themselves using the artificial intelligence or can predict the future signaling of the traffic infrastructure element itself.
  • the road user can create a prognosis that is, for example, dependent on his current location, direction or intended driving behavior.
  • the trained artificial intelligence (a software whose generation requires a high degree of traffic engineering knowledge) is present in the motor vehicles themselves.
  • the motor vehicles now interpret the information specifically for themselves, so that correspond de applications that run in the motor vehicle itself who can process this information.
  • the training of artificial intelligence usually requires a high computational effort and is therefore advantageously carried out on a central infrastructure.
  • the trained artificial intelligence is, however, in terms of file size, relatively small and can therefore quickly and easily be sent to terminals that are covered by the road users are carried by these, via a communication network.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Atmospheric Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Software Systems (AREA)
  • Mathematical Physics (AREA)
  • Artificial Intelligence (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Medical Informatics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Computational Linguistics (AREA)
  • Multimedia (AREA)
  • Traffic Control Systems (AREA)

Abstract

L'invention concerne un système (101) destiné à aider un usager de la route (329) lors d'une prédiction d'une signalisation future d'un élément d'infrastructure de trafic (303). Le système comprend : un moyen de mémorisation (103) dans lequel une intelligence artificielle (105) est mémorisée, l'intelligence artificielle (105) comprenant un modèle de prédiction de la signalisation future de l'élément d'infrastructure de trafic (303), et un moyen de communication (107) destiné à envoyer l'intelligence artificielle (105) à un usager de la route (329) via un réseau de communication. L'invention concerne en outre un procédé correspondant et un programme informatique correspondant.
EP18830182.4A 2017-12-21 2018-12-13 Système et procédé de prédiction d'une signalisation future d'un élément d'infrastructure de transport Ceased EP3673471A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102017223579.9A DE102017223579A1 (de) 2017-12-21 2017-12-21 System und Verfahren zum Unterstützen einer Prognose einer zukünftigen Signalisierung eines Verkehrsinfrastrukturelements
PCT/EP2018/084674 WO2019121283A1 (fr) 2017-12-21 2018-12-13 Système et procédé de prédiction d'une signalisation future d'un élément d'infrastructure de transport

Publications (1)

Publication Number Publication Date
EP3673471A1 true EP3673471A1 (fr) 2020-07-01

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EP18830182.4A Ceased EP3673471A1 (fr) 2017-12-21 2018-12-13 Système et procédé de prédiction d'une signalisation future d'un élément d'infrastructure de transport

Country Status (5)

Country Link
US (1) US11594125B2 (fr)
EP (1) EP3673471A1 (fr)
AU (1) AU2018389568A1 (fr)
DE (1) DE102017223579A1 (fr)
WO (1) WO2019121283A1 (fr)

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US12609905B1 (en) 2024-09-10 2026-04-21 T-Mobile Usa, Inc. Telecommunication using artificial intelligence (AI) agents and generative AI models

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DE102020202380A1 (de) 2020-02-25 2021-08-26 Siemens Mobility GmbH Verfahren zur Verkehrsbeeinflussung und Verkehrsbeeinflussungseinrichtung
DE102020202656A1 (de) * 2020-03-02 2021-09-02 Siemens Mobility GmbH Bestimmen eines zukünftigen Schaltverhaltens einer Anlageeinheit
US11634123B2 (en) * 2020-07-09 2023-04-25 Toyota Research Institute, Inc. Methods and systems for prioritizing computing methods for autonomous vehicles
DE102021001802A1 (de) 2021-04-08 2022-10-13 Abdullatif Alhaj Rabie Smart-Ampelsystem im Auto, Smart-Ampelsystem in der Straße

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US9253753B2 (en) * 2012-04-24 2016-02-02 Zetta Research And Development Llc-Forc Series Vehicle-to-vehicle safety transceiver using time slots
US8972145B2 (en) * 2013-03-15 2015-03-03 Bayerische Motoren Werke Aktiengesellscahft Systems and methods for predicting traffic signal information
US9396657B1 (en) * 2013-04-12 2016-07-19 Traffic Technology Solutions, LLC Prediction of traffic signal state changes
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Publication number Priority date Publication date Assignee Title
US12609905B1 (en) 2024-09-10 2026-04-21 T-Mobile Usa, Inc. Telecommunication using artificial intelligence (AI) agents and generative AI models

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AU2018389568A1 (en) 2020-05-28
DE102017223579A1 (de) 2019-06-27
US20200327803A1 (en) 2020-10-15
WO2019121283A1 (fr) 2019-06-27
US11594125B2 (en) 2023-02-28

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