Robert Boschgmbh

Thilo Strauss

Andrej Junginger

Markus Hanselmann

Holger Ulmer

Michael Johannes Oechsle

Katharina Dormann

Antonio La Marca

Jens Stefan Buchner

Sebastian Boblest

Melissa Lober

A method for synthetically generating a point cloud of radar or LIDAR reflections, a reflection indicating at least one location at which radar or LIDAR interrogating radiation has been reflected. In the method, distribution functions which according to a random distribution provide samples in each case for at least one of the variables contained in the radar or LIDAR reflections are provided; synthetic reflections are generated by drawing samples in each case from the distribution functions for variables contained in the radar or LIDAR reflections, one of multiple distribution functions being selected according to at least one selection random distribution in order to draw each sample; the synthetic reflections are combined to form the sought point cloud.

Synthetic generation of radar and LIDAR point clouds

A method is disclosed for converting source radar data of a source configuration of a radar system target radar data of a target configuration. The method comprises: providing a source array of grid cells for source reflex locations; determining, for each respective grid cell in the source array, a probability or frequency that source reflex locations are located in the respective grid cell; forming a source tensor including the source array populated with the probability or frequency for each grid cell; transforming the source tensor into a target tensor including a target array of grid cells for the target reflex locations and indicating the probabilities or frequencies of the target reflex locations for each respective grid cell; and generating the target radar data by sampling the location coordinates of the target reflex locations.

Augmentation and processing of radar data with machine learning

A device and a computer-implemented method for classifying data, in particular for a Controller Area Network or an automotive Ethernet network. A plurality of messages is received from a communications network. A message that has a predefined message type is selected for an input variable for an input model of a plurality of input models of an artificial neural network associated with the predefined message type. The input variable is determined as a function of the message, and in an output area of the artificial neural network a prediction is output that is usable for classifying the message as a function of the input variable, or a reconstruction of an input variable is output that is usable for classifying the message as a function of this input variable.

Device and method for classifying data in particular for a controller area network or an automotive ethernet network

An apparatus and a method for calibrating a system for recognizing attempts to penetrate into a computer network, in particular of a motor vehicle, at least one parameter being estimated on the basis of a data set, the data set encompassing values that characterize a detected occurrence of messages in the computer network; a distribution function being determined on the basis of the at least one parameter; an inverse of the distribution function being determined; and at least one limit for the values being calibrated, on the basis of the inverse, in a rule for rule-based recognition of attempts to penetrate into the computer network.

Method and apparatus for calibrating a system for recognizing attempts to penetrate a computer network

A method for ascertaining an anomaly in a communications network. In a first phase, a discriminator is trained to recognize whether messages transmitted over the communications network are indicative of the anomaly existing; during training, normal data and artificial data produced by a generator are fed to the discriminator, and, in response, the discriminator is trained to recognize that normal data being fed thereto connotes no anomaly, and artificial data being fed thereto connotes an anomaly. In a second phase, the generator is trained to produce artificial data which, when fed to the discriminator, are classified with the greatest possible probability as normal data. In a third phase, contents of messages received over the communications network are fed as an input variable to the discriminator; an output variable is ascertained, and the decision as to whether the anomaly exists or not being made as a function of the output variable.

Method and device for ascertaining anomalies in a communications network

A method for creating rules for recognizing anomalies in a data stream of data packets. The method includes: providing a reference time signal having successive reference points in time; for at least two data portions from one or multiple data packets determined by a selected data packet type in a data stream section, ascertaining a time series of successive values of the relevant data portion, the values of the time series corresponding to the values of the relevant data portion or being a function of these values, the values of the relevant data portion each being assigned to a respective reference point in time of the respective reference points in time; carrying out a correlation method in order to ascertain, in each case, one correlation value for at least two different time series; creating a rule for the rule-based anomaly recognition method as a function of the ascertained correlation values.

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Ludwigsburg, Germany

Thilo Strauss