Robert Boschgmbh

Robert Bosch Gmbh

Jan Stellet

Wolfgang Branz

Michael Egert

Stefan Aulbach

Nils Tiemann

Maxim Dolgov

Thomas Michalke

Matthias Woehrle

Matthias Schleicher

Ulrich Baumann

Jan Schumacher

A method for checking the performance of a prediction task by a neural network. The method includes: supplying image data to a feature extraction network that is trained to determine a representation of the image data; supplying the determined representation to a prediction network; a first determination of an optical flow between the image data and further image data from the representation; a second determination of the optical flow between the image data and the further image data from the image data using a different calculation path; and comparing the result of the first determination of the optical flow with the result of the second determination of the optical flow and accepting a result of the prediction task as correct if the result of the first determination of the optical flow agrees with the result of the second determination of the optical flow within a predefined tolerance.

Method for checking the performance of a prediction task by a neural network

A computer-implemented method for safeguarding a system against false negatives. The method includes: receiving a time series of a criticality, the system including a functionality that is triggered when the criticality meets a first predetermined criterion; computing a time series of a reference, the reference being a comparison criticality for triggering the functionality; computing a time series of an error measure based on the time series of the criticality and the time series of the reference, a non-triggering of the functionality being classified as a false negative when a portion of the time series of the error measure meets a second predetermined criterion; and identifying at least one near-false negative, a non-triggering of the functionality of the system being classified as a near-false negative when a portion of the time series of the error measure meets a third predetermined criterion, but not the second predetermined criterion.

Safeguarding a system against false negatives

A computer-implemented method for safeguarding a system against false positives. The method includes: receiving a time series of a criticality, the system including a functionality that is triggered when the criticality meets a first predetermined criterion; computing a time series of a reference, the reference being a comparison criticality for a triggering of the functionality; computing a time series of an error measure at least based on the time series of the criticality and the time series of the reference, a triggering of the functionality being classified as a false positive when a portion of the time series of the error measure meets a second predetermined criterion; and identifying at least one near-false positive, a non-triggering of the functionality being classified as a near-false positive when a portion of the time series of the error measure meets a third predetermined criterion, but not the second predetermined criterion.

Safeguarding a system against false positives

A method for selecting a main object for an assistance function or automated driving function of a driver assistance system or driving system of a motor vehicle, the system containing object selection branches which include a first rule-based object selection branch for selecting a main object for the function and a second object selection branch, the second object selection branch including an artificial neural network for selecting a main object for the function, having the following steps: Aggregating of sensor data of the at least one sensor to form one or more object data records; Evaluating a novelty of a traffic situation characterized by the aggregated object data records in relation to training data of the artificial neural network; Switching between the object selection branches of the system, a rule-based object selection branch being used when the novelty of the traffic situation exceeds a threshold value.

Selection of a main object for an assistance function or automated driving function of a driver assistance system or driving system of a motor vehicle

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Stuttgart, Germany

Jan Stellet