EP4363981A1 - Procédé et dispositif de reconfiguration d'une architecture de système d'un véhicule autonome - Google Patents

Procédé et dispositif de reconfiguration d'une architecture de système d'un véhicule autonome

Info

Publication number
EP4363981A1
EP4363981A1 EP22737569.8A EP22737569A EP4363981A1 EP 4363981 A1 EP4363981 A1 EP 4363981A1 EP 22737569 A EP22737569 A EP 22737569A EP 4363981 A1 EP4363981 A1 EP 4363981A1
Authority
EP
European Patent Office
Prior art keywords
configuration
generated
training data
machine learning
vehicle
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
EP22737569.8A
Other languages
German (de)
English (en)
Inventor
Tobias Kain
Jan David Schneider
Serin VARGHESE
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.)
Volkswagen AG
Original Assignee
Volkswagen AG
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 Volkswagen AG filed Critical Volkswagen AG
Publication of EP4363981A1 publication Critical patent/EP4363981A1/fr
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
    • G06F11/3013—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system is an embedded system, i.e. a combination of hardware and software dedicated to perform a certain function in mobile devices, printers, automotive or aircraft systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/445—Program loading or initiating
    • G06F9/44505—Configuring for program initiating, e.g. using registry, configuration files
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5061—Partitioning or combining of resources
    • G06F9/5066—Algorithms for mapping a plurality of inter-dependent sub-tasks onto a plurality of physical CPUs
    • 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

Definitions

  • the invention relates to a method and a device for reconfiguring a system architecture of an automated vehicle. Furthermore, the invention relates to a method for training a machine learning method.
  • an automated vehicle requires a large number of application instances that are executed on calculation nodes, for example to process acquired sensor data and, based on the processed sensor data, to generate and provide control signals for controlling and/or regulating the vehicle.
  • the application instances have specific requirements here, e.g. in relation to computing power, working memory requirements, storage space etc. as well as redundancy conditions.
  • the application instances must be distributed to the calculation nodes in such a way that these requirements are met.
  • a respective configuration is dependent on a context in which the vehicle is located.
  • the invention is based on the object of improving a method and a device for reconfiguring a system architecture of an automated vehicle.
  • a method for reconfiguring a system architecture of an automated vehicle is provided, the system architecture having a large number of application instances and a large number of calculation nodes, the application instances being distributed over the calculation nodes according to a configuration, with at least some of the application instances being detected
  • Sensor data are supplied to at least one sensor and control signals for controlling the vehicle are generated and provided by at least some of the application instances, wherein at least one item of context information of a current context in which the vehicle is operated is recorded and/or obtained, the recorded and /or received at least one item of context information is supplied to a trained machine learning method, wherein the trained machine learning method estimates a configuration based on the at least one piece of context information nt, and wherein the configuration is adjusted according to the estimated configuration.
  • a device for reconfiguring a system architecture of an automated vehicle having a large number of application instances and a large number of calculation nodes, the application instances being distributed over the calculation nodes according to a configuration, with at least some of the application instances containing recorded sensor data at least one sensor and wherein at least some of the application instances generate and provide control signals for controlling the vehicle, comprising a context detection device and a reconfiguration device, wherein the context detection device is set up to receive at least one item of context information from a current context in which the vehicle is being operated , to detect and/or to obtain, and wherein the reconfiguration device is set up to provide a trained machine learning method ellen to supply the acquired and/or received at least one item of context information to the trained machine learning process and to allow the trained machine learning process to estimate a configuration based on the at least one item of context information, and to adapt the configuration according to the estimated configuration.
  • the context detection device is set up to receive at least one item of context information from a current context in which the vehicle is being operated , to detect and/or to
  • a method for training a machine learning method for use in the method for reconfiguring a system architecture of an automated vehicle is provided, with training data being generated from a simulation in which the vehicle and a vehicle environment are realistically simulated, with context information being generated for this purpose are being generated for Context information is generated in each case a configuration and within the framework of the simulation, in which the generated configuration is used, at least one performance indicator for evaluating the generated configuration is determined, wherein the context information is used as input data of the machine learning method to train the machine learning method, the associated generated Configuration and the associated at least one performance indicator are used as the basic truth during training, a training data set being generated from such training data, the machine learning method being trained with the generated training data set, and the machine learning method being provided.
  • a device for training a machine learning method comprising a data processing device with at least one computing device and at least one memory, the data processing device being set up to execute the method for training a machine learning method.
  • the method and device for reconfiguring a system architecture of an automated driving vehicle makes it possible to provide a configuration for a given context. Since a context in which the vehicle is operated can vary greatly and can have a wide range of different characteristics, this means that a configuration can also be provided, in particular estimated, for rarely occurring or previously unknown contexts.
  • a trained machine learning method is used for this purpose. At least one detected and/or received piece of context information is fed to the trained machine learning method as input data. Based on this, the machine learning method estimates a configuration. A current configuration is adjusted according to the estimated configuration.
  • the method for training a machine learning method for use in the method for reconfiguring a system architecture of an automated vehicle also makes it possible to provide a broad base of training data. This is done by generating training data from a simulation in which the vehicle and a vehicle environment are realistically simulated. For this purpose, context information is generated. For example, contexts can be randomly selected within the simulation, with the at least one item of context information then being derived from the randomly selected context in each case. A configuration is generated for each of the generated context information. As part of the simulation using the generated configuration at least one key performance indicator for evaluating the generated configuration is determined. This triple of context information, configuration and performance metric is used to train the machine learning process.
  • the context information is supplied to the machine learning method as input data, the associated configuration generated and the associated at least one performance indicator being used as basic truth during training.
  • a training data record is generated from such training data, in particular from such triples.
  • the machine learning method is trained and made available in a manner known per se using the generated training data set. After the training, the trained machine learning method can estimate a configuration and the at least one performance indicator based on at least one item of context information.
  • the vehicle and the vehicle environment can be simulated, for example, using at least one of the following simulation tools:
  • the configuration generated for the at least one item of context information can be generated randomly, that is, in particular the required application instances can be distributed randomly to the calculation nodes. In this case, only the requirements specified by the at least one piece of context information need to be observed and fulfilled.
  • the configuration is generated using more complex methods. Methods can be used, for example, which take into account previously defined limitations (constraints). For example, redundancy requirements, hardware segregation requirements (these define how many different computing nodes a redundant function must be executed on) and/or resource requirements (eg memory, CPU and/or network resources) could be defined as constraints. These constraints are then used as input to a solver, which attempts to find a configuration that satisfies all constraints.
  • a previously determined configuration eg according to the method described above
  • this configuration is used as input for the simulation.
  • an application is provided by at least one application instance.
  • an application instance is a process that provides a specific functionality and that is executed on at least one compute node.
  • an application instance can provide one of the following functionalities in connection with automated driving: environment perception, localization, navigation, trajectory planner or a forecast of one's own behavior and/or the behavior of objects in the vicinity of the vehicle, etc.
  • at least some of the application instances receive sensor data , which were detected by at least one sensor and/or data from other application instances.
  • At least some of the application instances provide control signals for the vehicle.
  • the application instances can be operated in an active and in at least one passive operating state. In the active operating state, the application instance has a direct influence on the control of the vehicle.
  • an application instance runs redundantly next to a similar active application instance, receives the same input data and generates the same output data or control signals, but has no influence on the control of the vehicle.
  • Various levels of the passive state can be provided, which differ only, for example, in how quickly a passive application instance can be transferred to the active operating state. In particular, it is provided that both the active and the passive application instances are monitored.
  • the fault can be isolated by shutting down the faulty application instance and by switching to a redundant application instance the functionality of the faulty application instance can be maintained, additionally starting a new passive application instance to restore redundancy conditions.
  • an affected passive application instance can only be terminated and replaced by a newly started passive application instance with the same functionality, so that redundancy conditions in particular are restored.
  • a configuration includes in particular an assignment of, in particular active and passive, application instances to individual calculation nodes.
  • the configuration lays in particular, which application instance is executed on which calculation node, as well as the associated operating states of the application instances (e.g. active or passive, etc.).
  • the configuration depends in particular on specified redundancy conditions and/or segregation conditions, which are specified or are specified in each case as a function of the functionalities of the application instances. For example, it can be provided that the redundancy condition prescribes a simple redundancy. An active application instance and a passive application instance are then operated for an application or a functionality.
  • different redundancy conditions can be provided for the same functionalities, eg single (eg pedestrian detection on a freeway) or multiple redundancy (eg quadruple redundancy for pedestrian detection in a play street).
  • a segregation condition is in particular a specification for a number of different calculation nodes on which an application must be executed using redundant application instances.
  • a context refers in particular to a description of at least parameters and/or properties that are characteristic of a situation in which the vehicle is located.
  • a context includes, for example, information about the surroundings of the vehicle, a time of day, driver and/or passenger requests (e.g. comfort requests, entertainment requests, etc.) and/or an error status (faulty application instances, failed calculation nodes, faulty sensors, etc.).
  • a context can in particular include at least one item of error information, the at least one item of error information describing errors that occur in application instances and/or calculation nodes. For example, application instances that are working incorrectly or that were stopped unexpectedly and/or calculation nodes that are faulty or defective can be described using error information.
  • the at least one item of context information then also includes the at least one item of error information.
  • the configuration can then be adjusted, for example, in such a way that the affected application instances are restarted, if necessary on a different calculation node, and/or by shutting down a faulty calculation node and application instances that were running on the deactivated calculation node are distributed to other calculation nodes.
  • a machine learning method is in particular a neural network.
  • the neural network is in particular a deep neural network, which in particular has a number of hidden layers.
  • the machine learning method corresponds in particular to a mapping from the input data to a configuration and at least one associated performance indicator.
  • the vehicle is in particular a motor vehicle.
  • the vehicle can also be another land, water, air, rail or space vehicle, for example a drone or an air taxi.
  • the detected error is isolated by means of a switching device, in particular by switching to application instances that are redundant to the application instances concerned. Redundancy conditions and/or segregation conditions specified for the application instances are restored by reconfiguring the configuration using an application placement device. It is provided in particular that the modified configuration is estimated and made available using the method described in this disclosure.
  • Parts of the device in particular the context detection device and/or the reconfiguration device and/or the at least one monitor device, can be designed individually or together as a combination of hardware and software, for example as program code that runs on a microcontroller or microprocessor. However, it can also be provided that parts are designed individually or combined as an application-specific integrated circuit (ASIC) and/or as a field-programmable gate array (FPGA).
  • ASIC application-specific integrated circuit
  • FPGA field-programmable gate array
  • at least one performance indicator is determined during the application of the customized configuration and stored as part of the customized configuration, with training data being generated from the recorded and/or received at least one piece of context information, the customized configuration and the determined at least one performance indicator wherein a training data set compiled from such training data is provided as a field generated training data set.
  • a key performance indicator (also referred to as a key performance indicator, KPI) can in particular include reliability. Reliability describes the probability that the vehicle will not break down within a specified period of time (a reliability value of 100% is therefore particularly desirable here).
  • KPI key performance indicator
  • Reliability describes the probability that the vehicle will not break down within a specified period of time (a reliability value of 100% is therefore particularly desirable here).
  • an autonomous vehicle failure could be defined as a complete failure of the autonomous driving function (failure of both a primary system and a backup system).
  • Another key performance indicator is, for example, the number of accidents that occurred in the simulation or almost caused accidents. Resource utilization of the overall system and/or a range that the vehicle still has at the end of the simulation run could also be considered as a key performance indicator.
  • the training data record generated in the field is transmitted to a central server.
  • training data obtained in the field from a number of vehicles, in particular from a vehicle fleet can be collected on the central server and made usable for training the machine learning method.
  • the trained machine learning method is loaded into a memory of a reconfiguration device of at least one vehicle for provision.
  • the trained machine learning method can be placed directly for use in the field.
  • One embodiment of the method for training provides that at least one training data record generated in the field is obtained and added to the training data record.
  • the machine learning method can be (post-)trained, taking into account training data generated or collected in the field under real application conditions.
  • the training data record is only generated from such training data and/or only such training data is added to the training data record whose at least one performance indicator meets at least one predetermined selection criterion.
  • a training data record that has already been optimized with regard to the predefined selection criterion can be generated and made available.
  • this allows the machine learning method trained using the already preselected training data set to estimate configurations that have already been optimized with regard to the at least one predetermined selection criterion, based on the recorded and/or received context information. Not only is any possible configuration available here, but an improved configuration whose at least one performance indicator already satisfies the at least one selection criterion.
  • the at least one selection criterion can contain, for example, at least one predefined threshold value and/or value range for at least one key performance indicator.
  • One embodiment of the training method provides for the simulation and training to be carried out on a central server.
  • significantly larger calculation resources can be made available than would be possible in a vehicle, for example.
  • training on a central server makes it possible to take into account training data from vehicles in a vehicle fleet obtained in the field.
  • a vehicle comprising at least one device according to one of the described embodiments.
  • Fig. 1 is a schematic representation of an embodiment of the device for
  • FIG. 2 shows a schematic flow chart of an embodiment of the method for training a machine learning method
  • FIG. 3 shows a schematic representation to clarify an interaction of a device in a vehicle in the field with a central server.
  • 1 shows a schematic representation of an embodiment of the device 1 for reconfiguring a system architecture 51 of a vehicle 50 driving in an automated manner.
  • the device 1 performs the method described in this disclosure for reconfiguring the system architecture 51 of the automated driving vehicle 50 .
  • the system architecture 51 comprises a multiplicity of application instances 20-x and a multiplicity of calculation nodes 21-x, the application instances 20-x being distributed over the calculation nodes 21-x according to a configuration 22. At least some of the application instances 20-x are supplied with detected sensor data from at least one sensor, and at least some of the application instances 20-x generate and provide control signals for controlling the vehicle 50.
  • the device 1 comprises a context detection device 2 and a reconfiguration device 3.
  • the context detection device 2 and the reconfiguration device 3 can be implemented, for example, as a combination of hardware and software on a computing node 21-x.
  • the context detection device 2 is set up to detect and/or obtain at least one item of context information 10 of a current context in which the vehicle 50 is being operated.
  • a context includes, for example, information about the surroundings of the vehicle 50, a time of day, driver and/or passenger requests (e.g. comfort requests, entertainment requests, etc.) and/or an error status (faulty application instances, failed calculation nodes, faulty sensors, etc.).
  • the at least one item of context information 10 is derived or determined by the context detection device 2, for example from detected sensor data, and/or received from a service provider (e.g. weather data, etc.).
  • the reconfiguration device 3 provides a trained machine learning method 3-1, in particular in the form of a trained neural network.
  • the trained machine learning method 3-1 in particular the trained neural network, is trained to estimate a configuration 22x based on the at least one item of context information 10.
  • the reconfiguration device 2 also includes a
  • the configuration adjustment device 3-2 is set up to adjust the configuration 22 according to the estimated configuration 22x.
  • the configuration adjustment device 3-2 configures the application instances 20-x on the calculation nodes 21-x according to the estimated configuration 22x.
  • the advantage of the device 1 is that a configuration 22x can also be estimated for rare or unknown contexts, so that a configuration 22 can always be made available regardless of a specific characteristic of the context in which the vehicle 50 is currently located.
  • the trained machine learning method 3-1 in particular the trained neural network, associated with the estimated configuration 22x also estimates at least one performance indicator 23x.
  • the sequence described is in particular continuously repeated, so that a context-dependent configuration 22x is continuously and repeatedly estimated. It can be provided here that the context detection device 2 continuously detects and/or receives the current context and only transmits the at least one (changed) piece of context information to the reconfiguration device 3 when the context changes.
  • the at least one key performance indicator 23 is determined, for example, by means of a monitor device 4.
  • the training data set 40f generated in the field is transmitted to a central server 30 (FIG. 3).
  • FIG. 2 shows a schematic flow chart of an embodiment of the method for training a machine learning method for use in the device or in the associated method.
  • training data are generated based on a simulation in which the vehicle and a vehicle environment are realistically simulated.
  • context information is generated in a measure 100a (vehicle environment, driver requests, error states of application instances and/or of calculation nodes and/or by a sensor etc.).
  • a configuration is generated for the generated context information.
  • the configuration is generated randomly.
  • the configuration created must meet the specified requirements.
  • the simulated vehicle is configured according to the configuration generated, that is to say the application instances are distributed to (simulated) calculation nodes according to the configuration generated.
  • the vehicle is simulated in the vehicle environment in the associated context.
  • at least one key performance indicator is determined for assessing the configuration generated.
  • training data are created from the context information, the generated configuration and the at least one performance indicator determined and fed to a training data set.
  • Measures 100a to 100f are repeated for a predetermined number of training data sets.
  • the machine learning method in particular a neural network, is trained using the training data set.
  • the context information is used as input data for the machine learning method.
  • the respectively associated configurations generated and the associated at least one performance indicator are used as ground truth in training.
  • the machine learning method is otherwise trained in a manner known per se.
  • the trained machine learning method is provided in a measure 102, in particular in the form of a data packet that describes and/or contains a structure and parameters of the trained machine learning method.
  • the trained machine learning method, in particular the trained neural network is transmitted to vehicles in a vehicle fleet and loaded into the respective memory of the reconfiguration of the individual vehicles.
  • a measure 103 at least one training data record generated in the field is obtained and added to the training data record. It can be provided that the training data record is only generated from such training data and/or only such training data is added to the training data record whose at least one performance indicator meets at least one predetermined selection criterion.
  • a measure 100g checks whether the at least one key performance indicator meets the at least one selection criterion or not. If the at least one performance indicator meets the at least one selection criterion, the triple of context information, configuration and at least one performance indicator is added to the training data record as training data in measure 10Of, otherwise the triple in measure 100h is discarded and no longer considered.
  • FIG. 3 shows a schematic representation to clarify an interaction of a device 1 in a vehicle 50 in the field with a central server 30 .
  • the device 1 and the vehicle 50 are basically configured as in the embodiment shown in FIG. 1 .
  • the same reference symbols designate the same features and terms.
  • the method for training the machine learning method is executed on the central server 30 , for example in an embodiment as was described with reference to FIG. 2 .
  • training data are generated in a measure 200 by simulating the vehicle and the vehicle environment, as has already been described above.
  • the training data includes triples of at least one item of context information 10, a configuration 22 and at least one performance indicator 23 and form a training data record 40.
  • a machine learning method in particular a neural network, is trained using the training data record 40.
  • the trained machine learning method 3-1 in particular the trained neural network
  • the central server 30 is transmitted from the central server 30 to at least one vehicle 50, for example via a wireless communication interface (not shown).
  • this takes place in the form of a structural description and parameters of the trained machine learning method, in particular the trained neural network.
  • the at least one vehicle 50 receives the trained machine learning method 3-1, in particular the trained neural network, and loads it into a memory of the reconfiguration device 3.
  • the trained method is then applied Machine learning method 3-1, as already described with reference to the embodiment in FIG.
  • a training data record 40f generated in the field by means of the monitor device 4 is transmitted to the central server 30 and inserted into the training data record 40 by the central server 30 . This can be done in particular for training data sets 40f of vehicles 50 of a vehicle fleet generated in the field. As a result, the training data record 40 can be continuously expanded and updated.
  • Monitor setup 0 context information 0-x application instances 1-x computation node 2 configuration 2x estimated configuration 3 key performance indicator 3x estimated key performance indicator 0 central server 0 training data set 0f field generated training data set 0 vehicle 1 system architecture 00-102 measures of the procedure (training) 00, 201 measures

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Abstract

L'invention concerne un procédé de reconfiguration d'une architecture de système (51) d'un véhicule autonome (51), l'architecture de système (51) comprenant une pluralité d'entités d'application (20-x) et une pluralité de nœuds de calcul (21-x). Les entités d'application (20-x) sont exécutées de manière distribuée à travers les nœuds de calcul (21-x) conformément à une configuration (22, 22x), des données de capteur capturées par au moins un capteur étant fournies à au moins certaines des entités d'application (20-x), et au moins certaines des entités d'application (20-x) générant et fournissant des signaux de commande pour commander le véhicule (50). Au moins une information contextuelle (10) d'un contexte actuel est détectée et/ou obtenue, contexte dans lequel le véhicule (50) est actionné, ladite au moins une information contextuelle (10) qui a été détectée et/ou obtenue étant fournie à un procédé d'apprentissage machine entraîné (3-1). Le procédé d'apprentissage machine entraîné (3-1) estime une configuration (22x) sur la base de l'au moins une information contextuelle (10), et la configuration (22) est adaptée en fonction de la configuration estimée (22x). L'invention concerne également un dispositif (1) associé et un procédé d'entraînement d'un procédé d'apprentissage machine (3-1).
EP22737569.8A 2021-07-01 2022-06-16 Procédé et dispositif de reconfiguration d'une architecture de système d'un véhicule autonome Pending EP4363981A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102021206943.6A DE102021206943A1 (de) 2021-07-01 2021-07-01 Verfahren und Vorrichtung zum Rekonfigurieren einer Systemarchitektur eines automatisiert fahrenden Fahrzeugs
PCT/EP2022/066488 WO2023274745A1 (fr) 2021-07-01 2022-06-16 Procédé et dispositif de reconfiguration d'une architecture de système d'un véhicule autonome

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EP4363981A1 true EP4363981A1 (fr) 2024-05-08

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CN (1) CN117651937A (fr)
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Publication number Priority date Publication date Assignee Title
US10248410B2 (en) 2017-07-25 2019-04-02 Toyota Jidosha Kabushiki Kaisha Implementation decision to provide ADAS function update for a vehicle
US20190244136A1 (en) 2018-02-05 2019-08-08 GM Global Technology Operations LLC Inter-sensor learning
US11423254B2 (en) 2019-03-28 2022-08-23 Intel Corporation Technologies for distributing iterative computations in heterogeneous computing environments
US11640342B2 (en) * 2019-10-30 2023-05-02 Ghost Autonomy Inc. Fault state transitions in an autonomous vehicle
DE102019220162A1 (de) * 2019-12-19 2021-06-24 Volkswagen Aktiengesellschaft Verfahren zur dynamischen kontextbasierten Verteilung von Software in einem Steuerungssystem eines Fahrzeugs sowie ein Steuerungssystem
DE102019220160A1 (de) * 2019-12-19 2021-06-24 Volkswagen Aktiengesellschaft Verfahren zur dynamischen kontextbasierten Verteilung von Software in einem Steuerungssystem eines Fahrzeugs sowie ein Steuerungssystem

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