Robert Boschgmbh

Michael Pfeiffer

Laura Beggel

Martin Schiegg

Kilian Rambach

Oliver Lange

Jochen Marx

Thomas Pfeil

Jasmin Ebert

Kanil Patel

Alexander Kugele

Bernhard Kausler

A method for ascertaining a physical property of an object. The method includes detecting, for each input point in time of a sequence of input points in time, sensor data including information about a physical object, using an event-based sensor; for each subsequence of a breakdown of the sequence of input points in time into multiple subsequences including: feeding the sensor data detected for the input points in time of the subsequence to a pulsed neural network which generates a first processing result of the subsequence; feeding the processing result of the subsequence to a non-pulsed neural network; and processing the processing result of the subsequence by non-pulsed neurons of one or multiple first layer(s) of the non-pulsed neural network for generating a second processing result of the subsequence; and feeding the second processing results of the multiple subsequences to one or multiple second layer(s) of the non-pulsed neural network.

Device and method for ascertaining a physical property of a physical object

A method for identifying potentially hazardous or at-risk objects in the surroundings of a vehicle. The method includes detecting an area of the surroundings using at least one event-based sensor, the event-based sensor including light-sensitive pixels, and a relative change of the light intensity incident upon a pixel by at least a predefined percentage prompting the sensor to output an event assigned to this pixel. The method also includes assigning events output by the sensor to objects in the area; analyzing, for at least one object to which events are assigned, the events assigned to the object with respect to present movements of the object; and ascertaining an impending movement of the object, and/or an impending change of state, of the object from the present movements. An associated computer program is also described.

Quick identification of hazardous or at-risk objects in the surroundings of a vehicle

A generator for generating two- or multi-dimensional frequency representations of synthetic radar signals from a set of radar signals measured by a physical radar sensor. The generator includes a random number generator and a first AI module, which, as input, receives vectors or tensors of random values from the random number generator and uses an internal processing chain to map each such vector, respectively each such tensor, onto a two- or multi-dimensional frequency representation of a synthetic radar signal. The internal processing chain of the first AI module is parameterized by a multiplicity of parameters which are set in such a way that the two- or multi-dimensional frequency representation of the radar signal and/or at least one characteristic derived therefrom have the same distribution for the synthetic radar signals as for the measured radar signals.

Generation of synthetic radar signals

A method for detecting an anomalous image among a dataset of images using an Adversarial Autoencoder includes training an Adversarial Autoencoder in a first training with a training dataset of images, with the Adversarial Autoencoder being optimized such that a distribution of latent representations of images of the training dataset of images approaches a predetermined prior distribution and that a reconstruction error of reconstructed images of the training dataset of images is minimized. Subsequently, anomalies are detected in the latent representation and the Adversarial Autoencoder is trained in a second training with the training dataset of images, but taking into account the detected anomalies. The anomalous image among the first dataset of images is detected by the trained Adversarial Autoencoder dependent on at least one of the reconstruction error of the image and a probability density under the predetermined prior distribution.

Method for detecting an anomalous image among a first dataset of images using an adversarial autoencoder

A method is described for locating and/or classifying at least one object, a radar sensor that is used including at least one transmitter and at least one receiver for radar waves. The method includes: the signal recorded by the receiver is converted into a two- or multidimensional frequency representation; at least a portion of the two- or multidimensional frequency representation is supplied as an input to an artificial neural network, ANN that includes a sequence of layers with neurons, at least one layer of the ANN being additionally supplied with a piece of dimensioning information which characterizes the size and/or absolute position of objects detected in the portion of the two- or multidimensional frequency representation; the locating and/or the classification of the object is taken from the ANN as an output.

Locating and/or classifying objects based on radar data, with improved reliability at different distances

A method for ascertaining whether a series of sensor values contains an anomaly, including the following steps: providing a shapelet and at least one training data series; measuring in each case a distance between the shapelet and the training data series at a plurality of different predefinable positions of the training data series; ascertaining at least one minimal distance from the measured distances and ascertaining at least one change variable for at least one predefinable data point of the shapelet the change variable being ascertained as a function of at least one of the measured distances. A computer program, a device for carrying out the method, and a machine-readable memory element, on which the computer program is stored are also provided.

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Boeblingen, Germany

Michael Pfeiffer