Patents by Inventor Laura Fieback

Laura Fieback has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20240067204
    Abstract: A method for setting up a virtual ad hoc network for transmitting at least one piece of information to a first transportation vehicle as a first network subscriber. The method includes the information being provided based on first data from at least one second network subscriber, and at least one second network subscriber being a second transportation vehicle or an infrastructure component; wherein a motor-vehicle-external, central data processing device receives second data from multiple potential second network subscribers and takes the second data and at least one space-time parameter as a basis for determining the second network subscribers and setting up the virtual ad hoc network with the first transportation vehicle, as the first network subscriber, and the determined second network subscribers, the space-time parameter defining a specific area of relevance that has a predetermined physical and temporal correlation with the current position of the first transportation vehicle.
    Type: Application
    Filed: June 28, 2023
    Publication date: February 29, 2024
    Inventors: Christian BRUNS, Jakob SPIEGELBERG, Jan SONNENBERG, Bernd LEHMANN, Laura FIEBACK, Marius SPIKA, Tatjana KRUSCHA, Fabian GALETZKA, Benjamin GROß, Jan KEMPA, Christoph KÖNIG, Marvin SCHRÖDER
  • Publication number: 20230373498
    Abstract: The present disclosure relates to a method, to a computer program comprising instructions, and to a device for environment sensing in a vehicle. For the environment sensing, environment data are recorded by means of at least one vehicle sensor. A prediction is calculated by means of a trained machine learning model based on the recorded environment data, wherein the prediction includes a measure of uncertainty for the prediction. Equally, a deviation value is determined for the recorded environment data that provides a measure of how significantly the recorded environment data deviate from training data for the machine learning model. At least one conformity score is determined based on the measure of uncertainty and the deviation value and then a prediction set is determined based on the at least one determined conformity score. Then, a control signal is generated depending on the determined prediction set.
    Type: Application
    Filed: May 16, 2023
    Publication date: November 23, 2023
    Applicant: Volkswagen Aktiengesellschaft
    Inventors: Marvin Schröder, Tatjana Kruscha, Fabian Galetzka, Benjamin Groß, Jan Kempa, Christoph König, Jakob Spiegelberg, Jan Sonnenberg, Christian Bruns, Bernd Lehmann, Laura Fieback, Marius Spika