EP4022589A1 - Identification automatisée d'un comportement anormal d'un usager de la route - Google Patents

Identification automatisée d'un comportement anormal d'un usager de la route

Info

Publication number
EP4022589A1
EP4022589A1 EP20761802.6A EP20761802A EP4022589A1 EP 4022589 A1 EP4022589 A1 EP 4022589A1 EP 20761802 A EP20761802 A EP 20761802A EP 4022589 A1 EP4022589 A1 EP 4022589A1
Authority
EP
European Patent Office
Prior art keywords
neural network
artificial neural
vehicle
road user
data
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
EP20761802.6A
Other languages
German (de)
English (en)
Inventor
Ulrich Eberle
Sven Hallerbach
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.)
Stellantis Auto SAS
Original Assignee
PSA Automobiles SA
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 PSA Automobiles SA filed Critical PSA Automobiles SA
Publication of EP4022589A1 publication Critical patent/EP4022589A1/fr
Pending 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
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • 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
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/0116—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from roadside infrastructure, e.g. beacons
    • 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
    • G08G1/01—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
    • 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
    • G08G1/04—Detecting movement of traffic to be counted or controlled using optical or ultrasonic detectors
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/164—Centralised systems, e.g. external to vehicles
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U2101/00—UAVs specially adapted for particular uses or applications
    • B64U2101/30—UAVs specially adapted for particular uses or applications for imaging, photography or videography

Definitions

  • the object of the invention is to improve automated or autonomous driving with a vehicle in road traffic.
  • a first aspect of the invention relates to a method for training an artificial neural network with regard to the detection of abnormal behavior of road traffic participants, comprising the steps:
  • the external sensor unit is preferably arranged on an aircraft, in particular an unmanned aircraft such as a quadrocopter, a hexacopter, an octocopter, or on a fixed-wing drone.
  • the external sensor unit is arranged in a stationary manner in an area of a road on which the test vehicle is traveling, i.e. on a fixed device such as a street lamp, a warning beacon, a mast of a traffic sign, a traffic light, or other structures, in particular on a Intersection arranged.
  • the external sensor unit and the test vehicle sensor unit result in two sources of information in order to obtain data on the behavior of the first road user.
  • This advantageously facilitates the accurate training of the artificial neural network, since by means of the external sensor unit, which naturally looks down from above at the road and especially at the intersection at the first road user, approximately completely exact data about the behavior of the first road user are available Under certain circumstances, the mere observation by the test vehicle sensor unit would not be present to a sufficient extent, since it does not look down on the road and in particular the intersection with the first road user.
  • ground truth data can advantageously be obtained, this term being used in particular in the area of machine learning when complete and error-free data sets are used in the input for training purposes, so that an error in machine learning is due to incomplete or incorrect data sets can be practically ruled out and machine learning takes place in the best possible way.
  • An artificial neural network is essentially an adaptable function that can generate a mapping between the input data and the output data using input data and output data and using a cost function.
  • a large number of different neural network architectures are known in the prior art.
  • such artificial neural networks are used to establish a deterministic relationship between sensor-recorded data about the behavior of the first road user and a measure or a decision as to whether the behavior of the first road user is normal behavior or abnormal behavior Road user.
  • the behavior of the (first) road user is understood to mean, in particular, a trajectory guidance that preferably includes location information, a speed profile and an acceleration profile along the trajectory and optionally also preferably a lateral acceleration of the first road user.
  • What is normal behavior and what is abnormal behavior of the first road user is essentially a matter of free definition and it is the task of an engineer to define this.
  • Such a definition is part of the training of the artificial neural network and is assigned to the output of the neural network.
  • the neural network is fed for the purpose of training with the determined first data and the determined second data in the input channel of the neural network, so that the neural network calculates an output for these inputs, the output being a measure of normality or reflects the abnormality of the behavior of the first road user, and at least during the training of the artificial neural network, the output is assigned to a predetermined level or a decision on the normal or abnormal behavior of the first road user is assigned by default.
  • input values and output values are known, and in particular by minimizing a cost function that includes a difference between the output values calculated on the basis of the current parameters of the artificial neural network and the specified output values, the parameters are adjusted so that the artificial neural network, the relationship between the input values and output values during the training phase always better meets, until ultimately the neural network is trained to such an extent that it can reliably determine the extent of the normal or abnormal behavior of the first road user itself.
  • the usual methods of supervised and unsupervised learning are used.
  • the measure of normal or abnormal behavior can in particular be a numerical value or a symbolic value, whereupon a decision can be made, in particular by comparing the numerical value or the symbolic value with a limit value, whether normal or abnormal behavior of the first road user is present, or alternatively, the measure is preferably already a binary variable and directly contains the decision as to whether the behavior of the first road user is normal or abnormal, which is in particular due to a Activation function takes place, which maps the output of the artificial neural network to, in particular, 'O' or 'T'.
  • the training of the artificial neural network according to the first aspect of the invention is preferably carried out in a large number of repetitions in order to generate a sufficient number and sufficiently different types of first data and second data from the test vehicle sensor unit or the external sensor unit and a reliable mapping by the artificial generate neural network.
  • first data and second data are generated, in particular on a large number of first road users, and the artificial neural network is thus trained.
  • the artificial neural network is trained with practically complete and error-free data (so-called "ground truth” data) about the first road user, which significantly improves the quality of the training of the artificial neural network, whereas the trained artificial neural network in the vehicle can be operated reliably on the basis of current first data from the vehicle sensor unit.
  • Another advantage of the internal sensor unit is that, due to its natural arrangement above a road or on an aircraft, it is not evaluated directly by another road user, so that the training can go unnoticed by the other road user during the training phase of the artificial neural network determined data about the respective recorded road user result.
  • the method is also used to execute the artificial neural network in the vehicle and furthermore has the following steps:
  • an artificial neural network that has been trained according to the first aspect of the invention is used in another vehicle to to monitor the behavior of road users.
  • the additional vehicle is not necessarily the same as the test vehicle, but it can be. This explains why the vehicle can use the artificial neural network trained by means of the test vehicle, since the mapping which the artificial neural network reproduces is in principle independent of the carrier vehicle.
  • an artificial neural network, which was trained with a test vehicle and with an external sensor unit with respect to a first road user can advantageously be used on any vehicle in order to monitor the behavior of a second road user who is independent of the first road user .
  • Another aspect of the invention relates to a method for executing an artificial neural network in a vehicle, comprising the steps:
  • an artificial neural network the artificial neural network with first, one of which is arranged on a test vehicle
  • Test vehicle sensor unit determined, data about a first road user and with second, determined by an external and stationary sensor unit arranged in the area of a road or on an aircraft, data about the first road user has been trained as an input of the artificial neural network, and the artificial neural network Network has been trained with a predetermined level related to the behavior of the first road user for classification into normal or abnormal behavior of the first road user as a predetermined output of the artificial neural network,
  • the method furthermore has the step:
  • the reduction of the degree of autonomy with which the vehicle is currently operated can consist on the one hand of switching off some of the automating components of the vehicle, further up to handing over the complete and sole control authority over the vehicle to the driver of the vehicle.
  • the first data include kinematic information about the first road user and / or the second road user.
  • Kinematic information about the first road user relates in particular to information that reproduces a position or a time derivative of the position of the first road user.
  • Kinematic information can thus include a speed, an acceleration, a time derivative of the acceleration, in particular in the direction of travel, and furthermore in particular also at right angles to the current direction of travel.
  • the kinematic information can also be provided relative to restrictions and traffic rules, in particular speeds relative to a speed limit, steering movements relative to a lane, in particular to dashed or solid markings, and generally relative to other restrictions imposed by general rules or special traffic signs or illuminated displays.
  • the second data each include kinematic information about the first road user.
  • the respective kinematic data include at least one position and / or a trajectory of the respective road user.
  • the data about the first road user include information about a current lane in which the first road user is located and / or information about one or more lane changes by the first road user.
  • Another aspect of the invention relates to a vehicle having a vehicle sensor unit and a computing unit, the computing unit being designed to provide an artificial neural network, the artificial neural network having first data determined by a test vehicle sensor unit on a test vehicle via a first Traffic participant and with second, from an external sensor unit arranged stationary in the area of a road or arranged on an aircraft, data about the first traffic participant has been trained in each case as an input of the artificial neural network, and wherein the artificial neural network with a predetermined and based on the Behavior of the first road user related measure for classification into normal or abnormal behavior of the first road user has been trained as a predetermined output of the artificial neural network, the vehicle sensor unit for detection sen is executed from current first data about a second road user, and wherein the computing unit for executing the artificial neural network with the current first data as the only input of the artificial neural network and for deciding on the basis of the output of the artificial neural network whether an abnormal behavior of the second road user is present, is executed.
  • the vehicle sensor unit has at least one of the following:
  • Another aspect of the invention relates to a system with a test vehicle, with a calculation unit, and with an external sensor unit, for training an artificial neural network to detect abnormal behavior of road traffic participants, the test vehicle having a test vehicle sensor unit and the external sensor unit being stationary is arranged in the area of a road or is arranged on an aircraft, wherein
  • test vehicle sensor unit is designed to determine first data about a first road user
  • the external sensor unit is designed to determine second data about the first road user
  • the calculation unit for training the artificial neural network with the first data and with the second data in each case as an input of the artificial neural network and with a predetermined measure related to the behavior of the first road user for classification into normal or abnormal behavior of the road user as a predetermined output of the artificial neural network, and for storing the trained artificial neural network in a memory unit for later execution in the vehicle, the stored trained artificial neural network being designed to contain first data about a second road user currently recorded by a vehicle sensor unit of a vehicle to be executed as the only input of the artificial neural network.
  • the external sensor unit and / or the test vehicle sensor unit each have at least one of the following:
  • Another aspect of the invention relates to a computer program product, comprising instructions which, when the program is executed by a computer, cause the computer to execute the method as described above and below.
  • Another aspect of the invention relates to a computer-readable medium which comprises computer-controlled instructions for carrying out the method as described above and below.
  • FIG. 3 shows a system according to a further exemplary embodiment of the invention.
  • FIG. 2 shows a method for executing an artificial neural network in a vehicle 20, comprising the steps:
  • Provision H1 of an artificial neural network the artificial neural network with first data about a first road user 1 determined by a test vehicle sensor unit 11 arranged on a test vehicle 10 and with second data about the first road user 1 determined by an external sensor unit 30 has been trained in each case as an input of the artificial neural network, the external sensor unit 30 being arranged stationary in the area of a road or being arranged on an aircraft, and the artificial neural network with a predetermined level related to the behavior of the first road user 1 for Classification into normal or abnormal behavior of the first road user 1 has been trained as a predetermined output of the artificial neural network,
  • FIGS. 1 and 2 are reflected in the system of FIG. 3 and the description of the vehicle 20 from FIG. 4. The descriptions of the figures can therefore be used reciprocally.
  • Both the first data and the second data are sent to the external calculation unit 101, the training of the artificial neural network with the first data and with the second data each as an input of the artificial neural network and with a predetermined and based on the behavior of the first Road user 1-related measure for classification into normal or abnormal behavior of the road user takes over as a predetermined output of the artificial neural network.
  • the calculation unit 101 stores a plurality of second ones Road users 1 from the trained artificial neural network in a memory unit 3 for later execution in the vehicle 20 of FIG. 4.
  • the stored, trained artificial neural network is configured by the calculation unit 101 in such a way that it can execute first data currently recorded by a vehicle sensor unit 21 of a vehicle 20 about a second road user 2 as the only input of the artificial neural network. See the description of FIG. 4 in this regard.
  • FIG. 4 shows a vehicle 20 having a vehicle sensor unit 21 and a computing unit 22, the computing unit 22 providing the artificial neural network trained according to FIG. 3.
  • the vehicle sensor unit 21 records current first data about a second road user 2 with cameras arranged on the front of the vehicle and on the rear of the vehicle.
  • the computing unit 22 then executes the already completed trained artificial neural network with the current first data from the cameras of the vehicle sensor unit 21 as the only input of the artificial neural network.
  • a decision is made as to whether the behavior of the second road user 2 is abnormal. If an abnormal behavior of the second road user 2 is determined, a warning is output to the driver of the vehicle 20.

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Analytical Chemistry (AREA)
  • Chemical & Material Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Computational Linguistics (AREA)
  • General Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Health & Medical Sciences (AREA)
  • Traffic Control Systems (AREA)

Abstract

L'invention concerne un procédé d'entraînement d'un réseau neuronal artificiel qui présente les étapes suivantes : - Détermination (S1) de première données concernant un premier usager de la route (1) par une unité de détection de véhicule expérimental (11), - détermination de secondes données concernant le premier usager de la route (1) par une unité de détection (30) extérieure, entraînement (S3) du réseau neuronal artificiel avec les premières données et avec les secondes données, dans chaque cas comme entrée et avec une grandeur donnée pour le classement en comportement normal ou anormal de l'usager de la route comme sortie prédéfinie, et stockage (S4) du réseau neuronal artificiel dans une unité de mémoire (3) en vue d'une exécution ultérieure dans le véhicule (20), le réseau neuronal artificiel entraîné stocké en mémoire étant conçu de manière à être exécuté avec de premières données concernant un second usager de la route (2), détectées instantanément par une unité de détection de véhicule (21) d'un véhicule, comme seule entrée du réseau neuronal artificiel.
EP20761802.6A 2019-08-27 2020-08-25 Identification automatisée d'un comportement anormal d'un usager de la route Pending EP4022589A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102019212829.7A DE102019212829A1 (de) 2019-08-27 2019-08-27 Automatisierte Erkennung eines anormalen Verhaltens eines Verkehrsteilnehmers
PCT/EP2020/073724 WO2021037838A1 (fr) 2019-08-27 2020-08-25 Identification automatisée d'un comportement anormal d'un usager de la route

Publications (1)

Publication Number Publication Date
EP4022589A1 true EP4022589A1 (fr) 2022-07-06

Family

ID=72243116

Family Applications (1)

Application Number Title Priority Date Filing Date
EP20761802.6A Pending EP4022589A1 (fr) 2019-08-27 2020-08-25 Identification automatisée d'un comportement anormal d'un usager de la route

Country Status (3)

Country Link
EP (1) EP4022589A1 (fr)
DE (1) DE102019212829A1 (fr)
WO (1) WO2021037838A1 (fr)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113392920B (zh) * 2021-06-25 2022-08-02 北京百度网讯科技有限公司 生成作弊预测模型的方法、装置、设备、介质及程序产品
CN114944057B (zh) * 2022-04-21 2023-07-25 中山大学 一种路网交通流量数据的修复方法与系统

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102016212700A1 (de) * 2016-07-13 2018-01-18 Robert Bosch Gmbh Verfahren und System zur Steuerung eines Fahrzeugs
US10481044B2 (en) * 2017-05-18 2019-11-19 TuSimple Perception simulation for improved autonomous vehicle control
US10518729B2 (en) * 2017-08-02 2019-12-31 Allstate Insurance Company Event-based connected vehicle control and response systems
US20190101924A1 (en) * 2017-10-03 2019-04-04 Uber Technologies, Inc. Anomaly Detection Systems and Methods for Autonomous Vehicles
US10803746B2 (en) * 2017-11-28 2020-10-13 Honda Motor Co., Ltd. System and method for providing an infrastructure based safety alert associated with at least one roadway
US10950130B2 (en) * 2018-03-19 2021-03-16 Derq Inc. Early warning and collision avoidance

Also Published As

Publication number Publication date
WO2021037838A1 (fr) 2021-03-04
DE102019212829A1 (de) 2021-03-04

Similar Documents

Publication Publication Date Title
EP3970077B1 (fr) Procédé pour l'entraînement d'au moins un algorithme pour un appareil de commande d'un véhicule automobile, produit de programme informatique, véhicule automobile ainsi que système
DE102018128289B4 (de) Verfahren und vorrichtung für eine autonome systemleistung und zur einstufung
DE3820589C2 (de) Verfahren und Steuervorrichtung zum selbsttätigen Steuern eines Landfahrzeuges
DE102017214611B4 (de) Verfahren zum Überprüfen eines Reaktionssignals einer Fahrzeugkomponente sowie Überprüfungsvorrichtung und Kraftfahrzeug
EP3695244B1 (fr) Procédé et dispositif de génération d'un modèle de capteur inverse et procédé de détection d'obstacles
DE102010013943B4 (de) Verfahren und Vorrichtung für eine Funktionsprüfung einer Objekt-Erkennungseinrichtung eines Kraftwagens
DE102019215902A1 (de) Verfahren zur Bestimmung eines Gütegrades von Daten-Sätzen von Sensoren
WO2016150590A1 (fr) Procédé et dispositif pour surveiller une trajectoire de consigne à parcourir par un véhicule au sujet de l'absence de collision
DE102019105359B4 (de) Verfahren für ein Ultraschallmesssystem im Fahrzeug zur Erkennung und Klassifizierung von Objekten im Umfeld des Fahrzeugs mit Hilfe eines Deep-Learning Verfahrens und einem selbstlernenden Entscheider
EP4022589A1 (fr) Identification automatisée d'un comportement anormal d'un usager de la route
EP4073778A1 (fr) Procédé de création d'un algorithme d'usager de la route permettant la simulation informatique d'usagers de la route, procédé de formation d'au moins un algorithme pour une unité de commande d'un véhicule automobile, produit programme informatique et véhicule automobile
DE102019134408A1 (de) Selbstlernendes Netzwerk neuronaler Netzwerkmodelle für sicherheitsrelevante Anwendungen im Fahrzeug zur Erkennung und Klassifizierung von Objekten im Umfeld des Fahrzeugs mit Hilfe eines Deep-Learning Verfahrens
DE102020210789A1 (de) Analyse eines neuronalen Netzes für ein Fahrzeug
EP3748453A1 (fr) Procédé et dispositif de réalisation automatique d'une fonction de commande d'un véhicule
DE102017201796A1 (de) Steuervorrichtung zum Ermitteln einer Eigenbewegung eines Kraftfahrzeugs sowie Kraftfahrzeug und Verfahren zum Bereitstellen der Steuervorrichtung
WO2019211293A1 (fr) Procédé pour faire fonctionner un système d'aide à la conduite d'un véhicule autonome équipé d'au moins un capteur d'environnement pour détecter un environnement du véhicule autonome, support lisible par ordinateur, système et véhicule
WO2022251890A1 (fr) Procédé et système pour tester un système d'aide à la conduite d'un véhicule
WO2022122553A1 (fr) Comparaison de représentations numériques de situations de conduite d'un véhicule
DE102019212830A1 (de) Analyse und Validierung eines neuronalen Netzes für ein Fahrzeug
DE102020203829A1 (de) Verfahren zum Überwachen eines Fahrzeugsystems zum Erkennen einer Umgebung eines Fahrzeugs
DE102016208076A1 (de) Verfahren und vorrichtung zur auswertung eines eingabewerts in einem fahrerassistenzsystem, fahrerassistenzsystem und testsystem für ein fahrerassistenzsystem
DE102019209813A1 (de) Vorrichtung zum Navigieren eines Ist-Fahrzeugs durch zumindest einen Abschnitt einer Soll-Trajektorie, Verfahren und Verwendung
DE102022205174A1 (de) Kollektive Informationsbeschaffung für automatisierte Fahrsteuerfunktionen
WO2023193996A1 (fr) Test d'une fonction de commande de conduite automatique au moyen de données de circulation semi-réelles
DE102020117811A1 (de) Verfahren zum Bereitstellen einer Einrichtung zum Ermitteln von Interessenregionen für eine automatisierte Fahrzeugfunktion und Assistenzeinrichtung für ein Kraftfahrzeug

Legal Events

Date Code Title Description
STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: UNKNOWN

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: THE INTERNATIONAL PUBLICATION HAS BEEN MADE

PUAI Public reference made under article 153(3) epc to a published international application that has entered the european phase

Free format text: ORIGINAL CODE: 0009012

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: REQUEST FOR EXAMINATION WAS MADE

17P Request for examination filed

Effective date: 20220202

AK Designated contracting states

Kind code of ref document: A1

Designated state(s): AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR

DAV Request for validation of the european patent (deleted)
DAX Request for extension of the european patent (deleted)
RAP3 Party data changed (applicant data changed or rights of an application transferred)

Owner name: STELLANTIS AUTO SAS