WO2018206481A1 - Procédé de génération de données d'apprentissage pour un procédé de reconnaissance de formes sur la base de l'apprentissage automatique pour un véhicule à moteur, véhicule à moteur, procédé pour faire fonctionner un dispositif de calcul ainsi que système - Google Patents

Procédé de génération de données d'apprentissage pour un procédé de reconnaissance de formes sur la base de l'apprentissage automatique pour un véhicule à moteur, véhicule à moteur, procédé pour faire fonctionner un dispositif de calcul ainsi que système Download PDF

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Publication number
WO2018206481A1
WO2018206481A1 PCT/EP2018/061659 EP2018061659W WO2018206481A1 WO 2018206481 A1 WO2018206481 A1 WO 2018206481A1 EP 2018061659 W EP2018061659 W EP 2018061659W WO 2018206481 A1 WO2018206481 A1 WO 2018206481A1
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WIPO (PCT)
Prior art keywords
pattern recognition
recognition method
motor vehicle
training data
local confidence
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PCT/EP2018/061659
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German (de)
English (en)
Inventor
Felix Friedmann
Oleksandr VOROBIOV
Original Assignee
Audi Ag
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Publication of WO2018206481A1 publication Critical patent/WO2018206481A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/217—Validation; Performance evaluation; Active pattern learning techniques

Definitions

  • the invention relates to a method for generating training data for a machine learning based pattern recognition method for a motor vehicle, a motor vehicle, a method for operating a computing device and a system according to the independent claims.
  • DE 10 2013 021 840 A1 describes a detection algorithm for calculating a confidence value for an object detected in an environment of a motor vehicle.
  • a surrounding area of the motor vehicle is recorded in an image by means of a camera of a driver assistance system and objects in the image are detected by means of an image processing device.
  • the image processing device determines at least one property for each detected object and, using a predetermined optimization algorithm, generates an environment model for the surrounding area of the motor vehicle. Subsequently, by means of the image processing device, the properties of the objects are compared with the environment model and, based on the comparison, incorrectly determined properties are detected.
  • DE 10 2009 048 699 A1 discloses a neural network for teaching a probability analysis for determining a confidence for a clear path for a vehicle.
  • a plurality of images which are generated by means of a camera device, are compared and analyzed with one another.
  • areas which indicate a free area on which a potential roadway can be estimated are separated from other areas of the respective image which do not indicate a potential roadway.
  • the free path can be based on the free area be determined and the vehicle operated in response to the free path by means of a driver assistance system.
  • DE 10 2010 020 298 A1 describes a method for determining traffic data from a sequence of partially overlapping, georeferenced digital aerial images.
  • the digital aerial images are successively provided with confidence values by means of classifiers trained on vehicle recognition, the confidence values indicating a probability that a vehicle was recognized at the respective location.
  • features of training data which in turn are extracted from a data set of example cars, are extracted from digital aerial images having the same resolution as the geo-referenced digital aerial images taken in flight.
  • the object of the present invention is to provide a method for generating training data for a machine learning based pattern recognition method for a motor vehicle, a motor vehicle, a method for operating a computing device, and a system by which training data for a machine learning based improvement process of Pattern recognition method can be generated particularly advantageous, so that the improvement process can be carried out particularly efficiently.
  • the object is achieved according to the invention by a method for generating training data for a machine learning based pattern recognition method for a motor vehicle, a motor vehicle, a method for operating a computing device and a system having the features of the independent claims.
  • Advantageous embodiments with expedient developments of the invention are specified in the respective dependent claims and in the description.
  • a first aspect of the invention relates to a method of generating training data for a machine learning based pattern recognition method for a motor vehicle.
  • the pattern recognition method and at least one reference system different from the pattern recognition method each provide at least one measured value during operation of the motor vehicle.
  • respective accuracies are determined as respective local confidence values of the measured values.
  • respective average recognition rates are determined as the basic confidence of the pattern recognition method and the reference system.
  • a local confidence comparison is carried out in which the respective basic confi- dences and the respective local confidence values are compared.
  • the method first selects the pattern recognition method and the reference system for the respective motor vehicle, determines the respective average recognition rate as basic confidence, and implements and integrates the pattern recognition method and the reference system into the motor vehicle.
  • the respective measured values and their accuracy are determined as the local confidence values by means of the pattern recognition method and by means of the reference system, the respective accuracy determining a deviation probability of the respective measured value from a corresponding includes real value.
  • the respective basic confidences and the respective local confidence values of the pattern recognition method and of the reference system are compared with one another in order to determine and compare a respective validity of the pattern recognition method and the reference system.
  • the respective validity comprises the respective average recognition rates and the respective accuracies of the pattern recognition method or of the reference system.
  • the respective measured values with the respective associated local confidence values are sent as the training data to the computing device.
  • the respective measurement data with the associated local confidence values are sent to the computing device as training data, if the local confidence comparison shows a higher validity of the reference system compared to the pattern recognition method in a respective measurement situation.
  • the measurement data with the associated local confidence values are not sent to the computing device as training data if the validity of the pattern recognition method which exceeds the validity of the reference system.
  • training data can be advantageously generated specifically for such situations, in which the pattern recognition method is inferior to the reference system in terms of validity.
  • the training data generated in the method it is thus possible with the training data generated in the method to particularly efficiently train situations in which the pattern recognition method is subject to the validity of the reference system. This allows for a particularly effective training of the pattern recognition method in the improvement process, since only training data which have turned out to be relevant in the course of the local confidence comparison are used for training.
  • the local confidence comparison is carried out for a locally limited area for simultaneously recorded respective measured values.
  • the local confidence comparison is carried out for respective measured values of the pattern recognition method and of the reference system, which have been recorded in the localized area on a timely basis, that means in particular in the same traffic situation.
  • the local confidence comparison for the locally restricted area for measured values recorded in the same traffic situation can thus be carried out between the pattern recognition method and the reference system.
  • first training data in which the local confidence values of the pattern recognition method have a high discrepancy with the local confidence values of the reference system
  • second training data in which the local confidence values of the pattern recognition method have a lower discrepancy compared to the high discrepancy to the local confidence values of the reference system, with a lower priority compared to the high priority.
  • the training data are determined as a function of the discrepancy, determined in the local confidence comparison, between the validities of the respective measured values of the patterner comprising the respective basic confidence and the respective local confidence value. identification system and the reference system with different priorities.
  • the respective priorities influence delivery of the training data to the machine learning based improvement process of the pattern recognition method.
  • Training data having the high priority is fed to the improvement process earlier than training data having the lower priority than the high priority.
  • the first training data in which the validity of the pattern recognition method has the high discrepancy with the validity of the reference system, are fed to the improvement process earlier than the second training data, which are provided with the lower priority, thus the pattern recognition method can be trained particularly effectively in the course of the improvement process.
  • the training data is generated by means of the pattern recognition method and the reference system, and then the pattern recognition method is trained with the prioritized training data in the improvement process.
  • the training of the pattern recognition method takes place by means of machine learning, for example using neural networks.
  • the pattern recognition method is sent in its improved version to the motor vehicle, wherein it can be used in its improved version together with the reference system for the generation of new training data.
  • the pattern recognition method can thus be continuously improved.
  • the computing device which is designed as an external component with respect to the motor vehicle, receives training data from a plurality of motor vehicles, prioritizes it, trains the pattern recognition method on the basis of the training data of the plurality of motor vehicles, the pattern recognition method via machine learning changed and sent to the respective motor vehicles.
  • the computing device is superordinate to the motor vehicle, in particular the plurality of motor vehicles, and is arranged at a distance from the motor vehicles. This advantageously makes it possible to obtain the training data for the training from a large number of measured values. Nieren the pattern recognition method are generated, whereby many different traffic situations can be detected by means of the measured values and reproduced in the training data.
  • the pattern recognition method can advantageously be trained by means of particularly extensive training data.
  • the computing device transmits the pattern recognition method, which is improved by the training, to the respective motor vehicles, so that new training data for further improving the pattern recognition method can be generated by means of the improved pattern recognition method.
  • a second aspect of the invention relates to a motor vehicle, which is designed to carry out the method described.
  • the pattern recognition method can thus be trained with the training data generated by the method.
  • the invention also includes a control device for the motor vehicle.
  • the control device has a processor device which is set up to carry out an embodiment of the method according to the invention.
  • the processor device can have at least one microprocessor and / or at least one microcontroller.
  • the processor device can have program code which is set up to execute the embodiment of the method according to the invention when executed by the processor device.
  • the program code may be stored in a data memory of the processor device.
  • a third aspect of the invention relates to a method of operating a computing device for training at least one machine learning based pattern recognition method for at least one motor vehicle.
  • the computing device receives respective measured values provided by the pattern recognition method and at least one reference system of the motor vehicle different from the pattern recognition method during operation of the motor vehicle.
  • the computing device receives respective local confidence values of the measured values.
  • the computing means receives the measurement values and the associated confidence values as training data for an improvement process of the pattern recognition method by means of the machine learning.
  • the computing device transmits the training data, for example, to a neural network by means of which the pattern recognition method is trained with the training data.
  • the computing device includes the neural network so that the pattern recognition process is trained by the computing device in the machine learning based improvement process.
  • the pattern recognition method can thereby be improved particularly efficiently and quickly.
  • a fourth aspect of the invention relates to a system for a machine learning based pattern recognition method for a motor vehicle.
  • the system comprises a reference system of the motor vehicle, wherein for the reference system a first average recognition rate is determined as the first basic confidence and wherein by means of the reference system during operation of the motor vehicle at least a first measured value and depending on temporal and local properties a first accuracy be determined as the first local confidence value of the first measured value.
  • the system comprises a detection device of the motor vehicle that is different from the reference system and designed to carry out the pattern recognition method.
  • a second average recognition rate is determined as a second basic confidence.
  • At least one second measured value is provided by means of the pattern recognition method during operation of the motor vehicle and a second accuracy is determined as a second local confidence value of the second measured value as a function of temporal and spatial properties.
  • the system has an arithmetic unit of the motor vehicle which is set up to perform a local confidence comparison in which the respective basic confidences and the respective local confidence values are compared, and depending on the local confidence comparison, at least the respective measured values with the associated local To send confidence values as training data to a calculating unit designed to train the pattern recognition method.
  • the system comprises the reference system for which the first basic confidence is determined and by means of which the at least one first measured value, in particular a plurality of measured values with associated first local confidence values, is determined during operation of the motor vehicle.
  • the average recognition rate of the pattern recognition method is determined on the basis of input data provided by the detector.
  • the input data are detected, on the basis of which the pattern recognition method moreover determines the at least one second measured value as well as respective associated, in Depending on temporal and spatial properties determined second local confidence values determined.
  • the local confidence comparison between the first basic confidence, the second basic confidence, the at least one first local confidence value and the at least one second local confidence value is performed.
  • training data for training the pattern recognition method can be generated by the system and provided for a computing device for carrying out a machine learning based improvement process of the pattern recognition method.
  • the computing device is superordinate and is adapted to receive training data from multiple vehicles, to prioritize them, to train the pattern recognition method on the basis of the training data of the plurality of motor vehicles, to change the pattern recognition method via machine learning and to this to send the respective motor vehicles.
  • the superordinate computing device is arranged externally to the motor vehicles and at a distance from the motor vehicles.
  • the system can thus generate the training data for training the pattern recognition method from a multiplicity of measurement data acquired with a plurality of motor vehicles and associated local confidence values. This enables a particularly efficient improvement process of the pattern recognition method by means of the computing device.
  • the detection device comprises a camera by means of which vehicle camera images can be recorded and evaluated in the detection device.
  • the pattern recognition method determines the second measured value and the associated second local confidence value of the second measured value on the basis of at least one vehicle camera image recorded by the camera.
  • the second measured value is a distance of the motor vehicle in which the detection device is arranged, to a road user who is at a distance from the motor vehicle.
  • the pattern recognition method by means of the pattern recognition method, the road user is classified and depending on the Classification and its size determined on the vehicle camera image, the distance between the motor vehicle and the road user. This results in the advantage that the second measured value can be determined particularly easily, for example with the aid of an image processing method, by means of the pattern recognition method.
  • the reference system detects a laser device and / or a radar device, by means of which a distance from the motor vehicle to a road user can be determined.
  • the distance from the motor vehicle to the road user is determined by means of the reference system by means of a laser device and / or with the aid of a radar device.
  • the laser device and the radar device are advantageously particularly accurate measuring systems, so that the at least one first measured value can be detected with particularly high accuracy by means of the reference system having the laser device and / or the radar device.
  • FIG. 1 is a process diagram for generating training data for a machine learning based pattern recognition method for a motor vehicle
  • FIG. 2 shows a schematic side view of a motor vehicle with a reference system, a detection device and a motor vehicle
  • Arithmetic unit and a higher-level computing device are considered Arithmetic unit and a higher-level computing device.
  • the training data are generated during operation of the motor vehicle 1 and transmitted to a computing device 6.
  • the pattern recognition method is trained by machine learning in an improvement process.
  • the pattern recognition method in the motor vehicle 1 is implemented in a first method step S1 1.
  • a first second method step S21 an average recognition rate of the pattern recognition method is determined as the second basic confidence of the pattern recognition method.
  • a second measured value and an associated second local confidence value are determined by means of the pattern recognition method. Both the second basic confidence and the second local confidence value are supplied to a local confidence comparison, which is carried out in a fourth method step S4.
  • a reference system 4 is implemented in the motor vehicle 1. Subsequently, a first basic confidence of the reference system 4 is determined in a second second method step S22, and in a second third method step S32, a first measured value and an associated first local confidence value for the first measured value are determined by means of the reference system 4. Both the first basic confidence and the first local confidence value are supplied to the local confidence comparison of the fourth method step S4.
  • the respective ones first method steps S1 1, S21 and S31 can each be carried out simultaneously or with a time delay to the second method steps S12, S22 and S32.
  • the fourth method step S4 the first basic confidence, the second basic confidence, the first local confidence value and the second local confidence value are compared in the course of the local confidence comparison.
  • training data are generated as a function of the local confidence comparison by filtering out respective measured values and respective associated local confidence values, in which the local confidence comparison has revealed a higher validity of the reference system 4 compared to the pattern recognition method. This means that the training data are generated from the measured values and the associated local confidence values in which the respective accuracy and / or the respective average recognition rate of the reference system 4 is above that of the pattern recognition method.
  • the respective measured values forming the training data and the respective local confidence values are prioritized in a sixth method step S6 as a function of a discrepancy between the respective local confidence values and the respective basic confidences of the pattern recognition method and the reference system 4.
  • a seventh method step S7 the pattern recognition method is trained with the aid of machine learning.
  • the improvement process of the pattern recognition method is performed by means of the prioritized training data.
  • a new, improved pattern recognition method is created on the basis of the improvement process in the seventh method step S7.
  • FIG. 2 shows a schematic side view of a system comprising a motor vehicle 1, a computing unit 2, a detection device 3 and a reference system 4, and the computing device 6.
  • both the arithmetic unit 2 and the detection device 3 and the reference system 4 is arranged in or on the motor vehicle 1.
  • the motor vehicle 1 comprises a pattern recognition process system which comprises the detection device 3 and the arithmetic unit 2.
  • the detection device 3 comprises detection sensors, such as a camera and / or a lidar.
  • the arithmetic unit 2 may comprise a pattern recognition control unit for carrying out the pattern recognition method, in particular a neuromorphic chip, a field programmable gate array (FPGA) and / or a graphics processor (GPU).
  • the motor vehicle 1 includes reference sensors and a reference controller on which the reference method for the pattern recognition method runs.
  • the arithmetic unit 2 has a module for comparing the basic confi- dences and the local confidence values.
  • this is a comparison control unit, on which the basic confidences and the respective local confidence values of the pattern recognition method system and the reference system 4 can be compared and on which a decision can be made as to whether the current measured values are used as training data for the pattern recognition method to a backend , that means the computing device 6, to be transmitted.
  • the vehicle has a mobile radio module, not shown in the figures.
  • the training data can be sent to the computer 6.
  • the computing device 6 comprises a mobile radio connection for receiving the training data, that is to say the measured values and the associated local confidence values for which the local confidence comparison has revealed that the pattern recognition method should be improved.
  • the pattern recognition method is re-trained, tested and then sent to the motor vehicle 1 or several motor vehicles 1.
  • a road user 5 can be detected.
  • the detection of the traffic participant 5 is evaluated in the arithmetic unit 2 of the motor vehicle 1.
  • the respective basic confidences of the pattern recognition method and the reference system 4 as well as the pattern recognition method are stored in the arithmetic unit 2.
  • the reference system 4 detects the road user 5, determines the first measured value and the associated first local confidence value and transmits them to the arithmetic unit 2.
  • the local confidence comparison is performed and the training data is generated based on the local confidence comparison.
  • training data are transmitted from the arithmetic unit 2 to the higher-level computer 6.
  • the higher-level computer 6 prioritizes the training data and performs the improvement process of the pattern recognition method. Subsequently, the computing device 6 transmits the new, improved pattern recognition method to the arithmetic unit 2 of the motor vehicle 1.
  • the first third method step S31 is performed by means of the detection device 3 and the arithmetic unit 2.
  • the second third method step S32 is performed by the reference system 4.
  • the local confidence comparison of the fourth method step S4 takes place in the arithmetic unit 2 of the motor vehicle 1.
  • the fifth method step S5, in which the training data are generated, takes place in the arithmetic unit 2 of the motor vehicle 1.
  • the prioritization of the training data of the sixth method step S6 takes place in the computing device 6, in which the improvement process and the creation of the new, improved pattern recognition method of the seventh method step S7 and the eighth method step S8 also take place.
  • the method described is based on the finding that software functions which fulfill pattern recognition tasks in the motor vehicle 1 are currently being developed as part of a development process and are being transmitted to respective motor vehicles 1, in particular customer vehicles.
  • the software functions are manually developed and evaluated by humans.
  • Machine learning makes it possible to train the pattern recognition process in the improvement process, which means to develop automatically.
  • improvement processes are trained in a development stage of the software function and the pattern recognition method does not change in the motor vehicle 1 or can be changed only with considerable effort.
  • Examples of methods of pattern recognition are recognition of at least one road user 5, in particular other vehicles, in vehicle camera images, a determination of a distance to the road user 5 and / or a spatial depth on the basis of the driving Toy camera images, a prediction of an intention of the road user 5 in an environment of the motor vehicle 1 and a prediction of a behavior of a driver of the motor vehicle.
  • a disadvantage of an exclusive training of the pattern recognition method in the development stage is that the pattern recognition method can not automatically respond to novel situations that occur after completion of the development process. Although it is possible in principle to collect measurement data for further training, to re-train the pattern recognition method and to distribute it by software updates to customer vehicles, a considerable effort is required when recording new measurement data by means of measuring vehicles.
  • the machine learning based pattern recognition method it is usually necessary to annotate input data, such as the vehicle camera images as well as reference data generated by the reference system 4 as first measurement data, such as a distance to the road user 5, in particular to compare.
  • input data such as the vehicle camera images
  • reference data generated by the reference system 4 as first measurement data, such as a distance to the road user 5, in particular to compare.
  • the reference system 4 installed in the motor vehicle 1, which in the present case comprises a radar device for a speed measurement and a laser scanner for a distance measurement, it is possible to automatically annotate the second measured values obtained in the motor vehicle 1 and determined by the pattern recognition method with the first measured values of the reference system 4 in particular, and thus to generate a data set, in particular the training data for a continuous training of the pattern recognition method.
  • advantageously training goals, in particular labels can be generated.
  • the machine learning based pattern recognition method and a reference method based on the reference system 4 are considered to be parallel systems each having at least one basic confidence and at least one local confidence value.
  • the respective basic confidence is determined by an average detection rate or error rate measured in tests. For example, this is a percentage of correctly recognized pedestrians, ie a correct classification of the road user 5.
  • the basic confidence depends on underlying individual elements, in particular hardware, due to a maximum resolution, a noise and / or a fault , and dependent of functions implemented on the individual elements.
  • the respective at least one local confidence value relates to a value distribution output by the pattern recognition method and by the reference method for an expected accuracy of the respective measured values per local and temporal unit.
  • the respective basic confidence and the respective local confidence values are used to decide how the respective measured values of the pattern recognition method and the reference method are to be used further, for example an emergency braking depending on the measured values can be triggered.
  • the local confidence comparison it is determined for local areas how strongly the respective measured values of the two methods are to be included in a decision of a driver assistance system. For a given spatial area, for example, respective first measured values of a Mobileye Passantenerkennung can be used and for other areas respective second measured values of the pattern recognition method.
  • the currently evaluated measured values are annotated together with their local confidence values, which means in particular stored, transmitted to the external computing device 6, also referred to as the backend, and there to train the pattern recognition method used in the improvement process.
  • the measured values annotated with the respective local confidence values, in particular assigned, are weighted in the backend as a function of the discrepancy within and between the basic confi- dences and the local confidence values in the training.
  • Measured values in which the pattern recognition method performs particularly poorly compared to the reference method are prioritized in training. This results in the advantage that the measured values in which the particularly high discrepancy occurs within and between the basic confi- dences and the local confidence values between the pattern recognition method and the reference method are used to give samples a higher priority during training for these particularly critical measured values To provide. hereby there is a significant increase in quality of the trained, improved pattern recognition method.
  • the described method it is possible to use the measured values for a continuous learning process and thus a continuous improvement and adaptation of the developed software functions, in particular of the pattern recognition method.
  • at least one customer's vehicle as the motor vehicle 1 for recording new measurement data, it is possible to achieve a particularly high coverage.
  • particularly unusual situations can be recorded in order to improve the pattern recognition method for these situations called Corner Cases.
  • the described automatic development process makes a particularly high speed of reaction possible, since interruptions occurring in manual development processes can be dispensed with. As a result, problems that arise in the at least one customer vehicle can be remedied particularly quickly.
  • an annotation, in particular the generation, of the training data is carried out automatically by the reference method, in particular by the local confidence comparison of the pattern recognition method with the reference method.
  • the priority ascertained via the discrepancy within and between the basic confidences and the local confidence values it is possible to train the pattern recognition method with new measured values arriving in the computing device 6 in accordance with the respective priority. This will resolve particularly critical issues first.
  • the respective local confidence values it is possible to specifically reduce weaknesses of the pattern recognition method in the case of certain patterns occurring by weighting the training data locally.
  • the method described selectively transfers the measured values only if they represent an added value for the training. In this way, a data volume can be kept particularly low during a transmission of the training data from the arithmetic unit 2 to the arithmetic unit 6.

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Abstract

L'invention concerne un procédé pour la génération de données d'apprentissage pour un procédé de reconnaissance de formes se basant sur l'apprentissage automatique pour un véhicule à moteur (1), selon lequel le procédé de reconnaissance de formes et au moins un système de référence (4) différent du procédé de reconnaissance de formes fournissent chacun au moins une valeur de mesure pendant que le véhicule à moteur (1) fonctionne. En outre, des précisions respectives sont déterminées en fonction des propriétés temporelles et locales en tant que valeurs de confiance locales respectives des valeurs de mesure et des taux de reconnaissance moyens respectifs sont définis comme confiances de base du procédé de reconnaissance de formes et du système de référence (4) pour le procédé de reconnaissance de formes et pour le système de référence (4). En outre, une comparaison de confiance locale est réalisée, selon laquelle les confiances de base respectives et les valeurs de confiance locales respectives sont comparées, et au moins les valeurs de mesure respectives sont envoyées avec les valeurs de confiance locales associées en tant que données d'apprentissage à un dispositif de calcul (6) conçu pour l'apprentissage du procédé de reconnaissance de formes en fonction de la comparaison de confiance locale. L'invention concerne en outre un véhicule à moteur (1), un procédé pour faire fonctionner un dispositif de calcul (6), ainsi qu'un système.
PCT/EP2018/061659 2017-05-11 2018-05-07 Procédé de génération de données d'apprentissage pour un procédé de reconnaissance de formes sur la base de l'apprentissage automatique pour un véhicule à moteur, véhicule à moteur, procédé pour faire fonctionner un dispositif de calcul ainsi que système WO2018206481A1 (fr)

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DE102017207958.4A DE102017207958B4 (de) 2017-05-11 2017-05-11 Verfahren zum Generieren von Trainingsdaten für ein auf maschinellem Lernen basierendes Mustererkennungsverfahren für ein Kraftfahrzeug, Kraftfahrzeug, Verfahren zum Betreiben einer Recheneinrichtung sowie System
DE102017207958.4 2017-05-11

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DE102019124257A1 (de) * 2019-09-10 2021-03-11 Bayerische Motoren Werke Aktiengesellschaft Verfahren, Vorrichtung, Computerprogramm und Computerprogrammprodukt zur Ermittlung von KI-Trainingsdaten in einem Fahrzeug und Verfahren, Vorrichtung, Computerprogramm und Computerprogrammprodukt zur Ermittlung von relevanten Situationsparametern zum Trainieren einer künstlichen Intelligenzeinheit eines automatisiert fahrbaren Fahrzeuges

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