Volkswagen Ag

Dspace Gmbh

Peter Schlicht

Fabian Hüger

Nico Maurice Schmidt

Nikhil Kapoor

Ruby Moritz

Tim Fingscheidt

Serin Varghese

Yasin Bayzidi

Sascha Saralajew

Florens Fabian Gressner

Jens Schneider

Stephan Scholz

Jan David Schneider

Feix Assion

John Serin Varghese

Lars Holdijk

Robin Chan

Maike Rees

A method, system and computer program for processing image data, to a vehicle comprising such a system, and to a method, system and computer program for generating a filter. Image data processing may include obtaining the image data, and applying a filter on the image data to generate filtered image data, the filter being configured to suppress adversarial perturbations within the image data. The filtered image data is processed using a machine-learning model.

Methods, systems and computer programs for processing image data for generating a filter

Disclosed is a method for automatically executing a vehicle function of a vehicle based on spatially resolved raw sensor data for environment perception generated by at least one sensor of the vehicle. The method comprises

Method for automatically executing a vehicle function, method for evaluating a computer vision method and evaluation circuit for a vehicle

The invention relates to a method for generating disturbed input data for a neural network for analyzing sensor data, in particular digital images, of a driver assistance system, in which a first metric is defined which indicates how the magnitude of a change in sensor data is measured, a second metric is defined which indicates where a disturbance of sensor data is directed, an optimization problem is generated from a combination of the first metric and second metric, the optimization problem is solved by means of at least one solution algorithm, wherein the solution indicates a target disturbance of the input data, and disturbed input data is generated from sensor data for the neural network by means of the target disturbance.

Method and generator for generating disturbed input data for a neural network

The invention relates to a method for operating a machine learning model, comprising the following steps during a training phase: receiving selected multidimensional training data; selecting subsets from the received training data; generating a training data set, wherein the training data set includes data set elements which are generated on the basis of the selected subsets; training the machine learning model using the training data set; and/or comprising the following steps during an inference phase: receiving sensor data of at least one sensor; selecting subsets from the received sensor data; generating a data stack, wherein the data stack includes the respective selected subsets as stack elements; applying the, or a, machine learning model trained according to the steps of the training phase to every stack element of the data stack, wherein the application occurs simultaneously, and deducing an inference result; and outputting the inference result.

Method and device for optimized operation of a machine learning model with a data stack for a driving assistance function of a vehicle

A method for operating a driver assistance system of a vehicle is disclosed, wherein sensor data are recorded from the surroundings of the vehicle, the recorded sensor data are verified, the verified sensor data are analyzed by a neural network and analyzed sensor data are generated. Based on the analyzed sensor data, control data are generated for controlling the vehicle. During verification of the sensor data, at least first sensor data, which were recorded at a first, earlier point in time, are compared with second sensor data, which were recorded at a second, later point in time, the result of the comparison is cross-checked with a database in which data on perturbations to input data of a neural network are stored, wherein it is checked whether the second sensor data were generated at least in part by a perturbation to the first sensor data that is stored in the database.

Method for operating a driver assistance system of a vehicle and driver assistance system for a vehicle

A method for detecting an adversarial perturbation in input data of a neural network, wherein a conditional generative adversarial network is trained during a training phase, wherein a generator network of the conditional generative adversarial network is trained to generate adversarial perturbations conditioned on input data of the neural network, and wherein a discriminator network of the conditional generative adversarial network is trained at least to detect an adversarial perturbation in the input data generated by the generator network, and wherein, during an application phase, the trained discriminator network detects an adversarial perturbation in input data of the neural network and provides a detection result. Also disclosed is a backend server, a detection device and a system.

Method for recognizing an adversarial disturbance in input data of a neural network

A method for automated driving of a vehicle, comprising: provide function modules, wherein the function modules each comprise at least one subfunction for automated driving, and wherein the function modules are each assigned at least one module validity attribute that defines the context of the surroundings in which the particular function module is valid, provide at least one system configuration, wherein the at least one system configuration comprises at least one of the function modules, and wherein the at least one system configuration is assigned at least one configuration validity attribute that defines the context of the surroundings in which the at least one system configuration is valid, select one of the at least one system configurations, wherein the selection depends on a context of the surroundings and the at least one associated configuration validity attribute, control the vehicle, wherein controlling is based on the selected system configuration.

Method and device for the automated driving of a vehicle

The invention relates to a method for checking an AI-based information processing system used in the partially automated or fully automated control of a vehicle, wherein at least one sensor of the vehicle provides sensor data, the captured sensor data are evaluated by an AI-based information processing system arranged in a first control circuit of the vehicle and, from the evaluated sensor data, at least one output for controlling the vehicle is generated. The AI-based information processing system is checked by a testing circuit arranged in a second control circuit of the vehicle using at least one testing method, and wherein a test result of the at least one testing method is stored, with a reference to the tested AI-based information processing system and to the at least one testing method used, in a multi-dimensional data structure in a database arranged in the vehicle.

Method and device for checking an AI-based information processing system used in the partially automated or fully automated control of a vehicle

The invention relates to a method for updating a control model for automatic control of at least one mobile unit. A central control unit generates a detection task and transmits same to the mobile unit. The mobile unit comprises sensors, and the detection task comprises conditions for detecting sensor data sets by means of the sensors. The mobile unit detects the sensor data sets by means of the sensors using the detection task, generates transmission data using the detected sensor data sets, and transmits the transmission data to the central control unit. The central control unit receives the transmission data and generates an updated control model using the received transmission data. The system according to the invention for updating a control model for automatic control of at least one mobile unit comprises a central control unit by means of which a detection task may be generated and transmitted to the mobile unit. The mobile unit comprises sensors, and the detection task comprises conditions for detecting sensor data sets by means of the sensors. The sensor data sets may be detected by means of the sensors of the mobile unit using the detection task, transmission data may be generated using the detected sensor data sets, and the transmission data may be transmitted to the central control unit. In doing so, the central control unit may receive the transmission data, and an updated control model may be generated using the received transmission data.

Method and system for updating a control model for automatic control of at least one mobile unit

A method, a computer program with instructions, and an apparatus for configuring a control system for an at least partially autonomous transportation vehicle. Data relating to the driving situation are captured, selection criteria are then determined from the available data, two or more AI modules are selected from a library of AI modules during driving based on selection criteria, and combined execution of the selected two or more AI modules by the control system is initiated.

Configuration of a control system for an at least partially autonomous transportation vehicle

The invention relates to a method for abstracting a data record, wherein the data record is provided for machine learning at least one function, comprising the following steps: Training a complex neural network structure comprising different neural networks in the at least one function by way of machine learning based on the data record by means of a machine learning control apparatus, wherein the neural networks and the complex neural network structure are optimized with respect to maximum representativity of the data record, providing the trained complex neural network structure in the form of a data-record-specific knowledge module so that knowledge contained in the data record can be further used in a manner compliant with data protection. The invention further relates to an associated device.

Method and device for abstracting a data record

The present disclosure relates to a method for generating control signals to assist occupants in a vehicle, wherein a context of the vehicle is determined, a rule of a rule-based data system is selected depending on the determined context, wherein the rule-based data system comprises a plurality of rules, wherein each rule has a condition part and a result part, wherein the condition part comprises conditions for the context of the vehicle, a confidence value associated with the selected rule is determined, wherein the confidence value indicates the probability with which the result of the rule corresponds with the preference of the user, a result of the selected rule is generated, a control signal is generated and output depending on the generated rule result, wherein the control signal automates a vehicle function with a degree of automation, wherein the degree of automation depends on the confidence value of the selected rule. The disclosure likewise relates to a device for executing this method.

Method and device for generating control signals to assist occupants in a vehicle

A method, an apparatus, and a computer-readable storage medium having instructions for cancelling a redundancy of two or more redundant modules. Results of the two or more redundant modules are received; reliabilities of the results are ascertained; and, based on the ascertained reliabilities, an overall result is determined from the results. The overall result is output for further processing.

Method, apparatus, and computer-readable storage medium having instructions for cancelling a redundancy of two or more redundant modules

A method for processing sensor data in a number of controllers in a controller complex. The controllers are connected to at least one sensor via at least one communication bus, wherein the sensor data of the at least one sensor are processed by at least two different controllers in stages. At least one processing stage is concordant in the two controllers or is equivalent to the other stage at least in so far as the results of the processing are converted into one another by a conversion. Provision is made for a preprocessing unit to which the sensor data of the at least one sensor are supplied, wherein the processing of the sensor data in the at least one concordant processing stage is performed in the preprocessing unit, and the processed sensor data are forwarded to the at least two different controllers for individual further processing.

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Wolfsburg, Germany

Peter Schlicht