Robert Boschgmbh

Matthias Woehrle

Konrad Groh

Christoph Gladisch

Christian Heinzemann

Roland Norden

Erik Tonner

Jens-Holger Barth

Andreas Hopf

Frank Kowol

Mona Meister

Ulrich Seger

Oliver Willers

Jens Oehlerking

Martin Herrmann

Michael Rittel

Nadja Schalm

A computer-implemented method for evaluating an image classifier, in which a classifier output of the image classifier is provided for the actuation of an at least semi-autonomous robot. The evaluation method includes: ascertaining a first dataset including image data and annotations being assigned to the image data, the annotations including information about the scene imaged in the respective image and/or about image regions to be classified and/or about movement information of the robot; ascertaining regions of the scenes that are reachable by the robot based on the annotations; ascertaining relevance values for image regions to be classified by the image classifier; classifying the image data of the first image dataset with the aid of the image classifier; evaluating the image classifier based on image regions correctly classified by the image classifier and incorrectly classified image regions, as well as the calculated relevance values of the corresponding image regions.

Method and device for evaluating an image classifier

A computer-implemented method for training a data-based time determining model for determining an opening or closing time of an injection valve based on a sensor signal. The method includes: providing an unlabeled analysis point time series by sampling the sensor signal of a sensor of the injection valve; training the data-based time determining model to assign a time specification which represents a specific opening or closing duration to an analysis point time series, the training process being carried out using a first shifting function to time-shift the analysis point time series and a second shifting function in order to time-shift the time specification. A consistency loss function is used for the training process.

Method and device for training a data-based time determining model for determining an opening or closing time of an injection valve using a machine learning method

A computer-implemented method for analysing the relevance of visual parameters for training a computer vision model. Upon adjusting the set of visual parameters to increase their relevance, a new set of visual data and corresponding groundtruth results that can be used in (re)training and/or testing the computer vision model.

Computer-implemented method for analyzing relevance of visual parameters for training a computer vision model

A computer-implemented method for testing conformance between images generated by a synthetic image generator and images obtained from authentic visual data. A conformance test result results from comparing results of global sensitivity analyses used to rank the effect of visual parameters on the computer vision model both for synthetic and authentic visual data.

Computer-implemented method for testing conformance between real and synthetic images for machine learning

A method determines a distance metric for determining a distance to a data point having heterogeneous classes of variables. The method includes providing training records each assigning a label to a data point, the training records partitioned into training data points of a training amount and validation data points of a validation amount, and training a data-based system model with the training amount, such that the data-based system model associates data points with a model output, respectively. The method further includes for each validation data point of the validation amount, determining a quality level of the data-based system model and a distance value to a nearest training data point for each of the heterogeneous classes of variables. The distance value to the nearest training data point is determined separately with respect to a respective class of variables.

Method and apparatus for determining a distance metric for determining a distance dimension of heterogeneous data points

A method for operating an injection valve by determining an opening or closing time of the injection valve based on a sensor signal. The method includes: providing an evaluation point time series by sampling a sensor signal of a sensor of the injection valve; using a non-linear data-based first sub-model to obtain a first output vector based on the evaluation point time series, wherein each element of the first output vector is associated with a specific time; using a linear, data-based second sub-model to obtain a second output vector based on the evaluation point time series, wherein each element of the second output vector is associated with a specific time; limiting the time determined by the first output vector depending on the second output vector in order to obtain the opening or closing time.

Method and device for operating a fuel injection valve with the aid of machine learning methods

A method for training a data-based evaluation model to determine an opening or closing time of an injection valve based on a sensor signal. The method includes: measuring an operation of the injection valve in order to determine at least one sensor signal and an associated opening or closing time; sampling the sensor signal at a sampling rate in order to obtain a sensor signal time series with sensor signal values; determining a plurality of training data sets by assigning a plurality of evaluation point time series generated from a sensor signal time series to the opening or closing time associated with the sensor signal, wherein the evaluation point time series has a lower temporal resolution than the sensor signal time series; training the data-based evaluation model depending on the determined training data sets.

Method and device for operating a fuel injection valve

Modifying a visual parameter specification characterising the operational design domain of the computer vision model by improving the visual parameter specification according to a sensitivity analysis of the computer vision model.

Modifying parameter sets characterising a computer vision model

Reducing the number of parameters in a visual parameter set based on a sensitivity analysis of how a given visual parameter affects the performance of a computer vision model to provide a verification parameter set having a reduced size.

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Bietigheim-Bissingen, Germany

Matthias Woehrle