Robert Boschgmbh

Jasmin Ebert

Sebastian Muenzner

Claudius Glaeser

Fabian Timm

Florian Faion

Florian Drews

Lars Rosenbaum

Alexandru Paul Condurache

Thomas Gumpp

Gor Hakobyan

Kilian Rambach

Michael Pfeiffer

Michael Ulrich

Chun Yang

Rainer Stal

Zoltan Karasz

Laszlo Anka

A method for monitoring surroundings of a first sensor system. The method includes: providing a temporal sequence of data of the first sensor system for monitoring the surroundings; generating an input tensor including the temporal sequence of data of the first sensor system, for a trained neural network; the neural network being configured and trained to identify, on the basis of the input tensor, at least one subregion of the surroundings, in order to improve the monitoring of the surroundings with the aid of a second sensor system; generating a control signal for the second sensor system with the aid of an output signal of the trained neural network, in order to improve the monitoring of the surroundings in the at least one subregion.

Method for monitoring surroundings of a first sensor system

A method for determining the spatial orientation of an object from at least one measuring signal which includes the response of the object to electromagnetic interrogation radiation. A method for predicting the trajectory of at least one object from at least one measuring signal which includes the response of the object to electromagnetic interrogation radiation, in conjunction with a scalar velocity of the object. A method for training a classifier and/or a regressor.

Determining the orientation of objects using radar or through the use of electromagnetic interrogation radiation

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 training a generator. The generator is supplied with at least one actual signal that includes real or simulated physical measured data from at least one observation of the first area. The actual signal is translated by the generator into a transformed signal that represents the associated synthetic measured data in a second area. Using a cost function, an assessment is made concerning to what extent the transformed signal is consistent with one or multiple setpoint signals, at least one setpoint signal being formed from real or simulated measured data of the second physical observation modality for the situation represented by the actual signal. Trainable parameters that characterize the behavior of the generator are optimized with the objective of obtaining transformed signals that are better assessed by the cost function. A method for operating the generator, and that encompasses the complete process chain are also provided.

Translation of training data between observation modalities

A method for the detection of an object in an environment of a vehicle as a function of sensor signals of a sensor for acquiring the environment of the vehicle. The method includes: processing the sensor signals using a region proposal network to obtain at least one object hypothesis per anchor, the object hypothesis including an object probability and a bounding box; selecting the best object hypothesis on the basis of a quality model, the quality model being a function of the anchor and the bounding box of the object hypothesis; identifying redundant object hypotheses relative to the selected object hypothesis, the redundant object hypotheses being identified as a function of the anchors of the redundant object hypotheses, using a target function assigned to the region proposal network; and fusing the selected object hypothesis with the identified redundant object hypotheses for the object detection.

Method, device, computer program, and machine-readable storage medium for the detection of an object

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Heidelberg, Germany

Jasmin Ebert