Patents by Inventor Florian Faion

Florian Faion 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: 20230419649
    Abstract: A method for the chronological correction of multimodal data includes: receiving a first data set from a reference sensor with measurements at different measurement timepoints, receiving a second data set of a second sensor with measurements at different measurement timepoints, each not exactly matching those of the reference sensor, reading the first and the second data sets by a neural network and identifying a respective plurality of feature vectors for the first and second data set at the respective measurement timepoints, merging and comparing the respective feature vectors, which refer to corresponding, not exactly matching measurement timepoints, by the neural network so that parameters of a chronological correction are identified, and identifying a chronological offset between the respective measurement timepoints of the reference sensor and the second sensor, and/or a corrected data set from the second sensor based on the measurement timepoints of the reference sensor.
    Type: Application
    Filed: June 19, 2023
    Publication date: December 28, 2023
    Inventors: Claudius Glaeser, Fabian Timm, Florian Drews, Michael Ulrich, Florian Faion, Lars Rosenbaum
  • Publication number: 20230406298
    Abstract: Learning extraction of movement information from sensor data includes providing a time series of frames of sensor data recorded by physical observation of an object, providing a time series of object boundary boxes each encompassing the object in sensor data frames, supplying the object boundary box at a time t, as well as a history of sensor data from the sensor data time series, and/or a history of object boundary boxes from the time series of object boundary boxes, prior to time t to a trainable machine learning model which predicts an object boundary box for a time t+k, comparing the predicted object boundary box with a comparison box obtained from the time series of object boundary boxes for the time t+k, evaluating a deviation between the predicted object boundary box and the comparison box using a predetermined cost function, and optimizing parameters which characterize the behavior of the model.
    Type: Application
    Filed: June 19, 2023
    Publication date: December 21, 2023
    Inventors: Claudius Glaeser, Fabian Timm, Florian Drews, Michael Ulrich, Florian Faion, Lars Rosenbaum
  • Publication number: 20230394757
    Abstract: A method of generating input data for a machine learning model includes determining, for a sensor, a point cloud with points detected by the sensor from surfaces in the environment of the sensor, generating a preliminary target sensor point cloud for a target sensor by transforming, for the sensor, points of the determined point cloud into points from the perspective of the target sensor according to the relative position of the target sensor to the sensor, generating a target sensor point cloud for the target sensor by using the preliminary target sensor point cloud, wherein points which, due to one or more surfaces for which points exist in the preliminary target sensor point cloud, are not detectable by the target sensor are eliminated in the target sensor point cloud, and using the target sensor point cloud as input for the machine learning model.
    Type: Application
    Filed: May 31, 2023
    Publication date: December 7, 2023
    Inventors: Thomas Nuernberg, Florian Faion, Thomas Michalke
  • Publication number: 20230358879
    Abstract: 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.
    Type: Application
    Filed: November 3, 2021
    Publication date: November 9, 2023
    Inventors: Sebastian Muenzner, Alexandru Paul Condurache, Claudius Glaeser, Fabian Timm, Florian Drews, Florian Faion, Jasmin Ebert, Lars Rosenbaum, Michael Ulrich, Rainer Stal, Thomas Gumpp
  • Publication number: 20230234610
    Abstract: A method is for training an object detector configured to detect objects in sensor data of a sensor. The method includes providing first sensor data of the sensor, providing an object representation assigned to the first sensor data, and transmitting the object representation to a sensor model. The method further includes imaging object representations onto the first sensor data of the sensor with the sensor model, assigning the object representation to second sensor data with the sensor model, and training the object detector based on the second sensor data.
    Type: Application
    Filed: January 20, 2023
    Publication date: July 27, 2023
    Inventors: Claudius Glaeser, Fabian Timm, Florian Drews, Michael Ulrich, Florian Faion, Lars Rosenbaum
  • Patent number: 11455791
    Abstract: 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.
    Type: Grant
    Filed: July 10, 2020
    Date of Patent: September 27, 2022
    Assignee: Robert Bosch GmbH
    Inventors: Florian Faion, Alexandru Paul Condurache, Claudius Glaeser, Florian Drews, Jasmin Ebert, Lars Rosenbaum, Rainer Stal, Sebastian Muenzner, Thomas Gumpp
  • Publication number: 20220083820
    Abstract: A method for creating a training dataset, a validation dataset, and/or a test dataset for an AI module from measurement data includes dividing the measurement data into divided portions based on time periods, applying a mathematical function to the divided portions of the measurement data in order to obtain signatures representing the divided portions, determining a measure of a frequency of occurrence of a respective signature of the obtained signatures, and creating the training dataset, the validation dataset, and/or the test dataset from the measurement data based on the determined measure of the frequency.
    Type: Application
    Filed: September 15, 2021
    Publication date: March 17, 2022
    Inventors: Mark Schoene, Alexandru Paul Condurache, Claudius Glaeser, Florian Faion, Florian Drews, Jasmin Ebert, Lars Rosenbaum, Michael Ulrich, Rainer Stal, Sebastian Muenzner, Thomas Gumpp
  • Publication number: 20210027082
    Abstract: 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.
    Type: Application
    Filed: July 10, 2020
    Publication date: January 28, 2021
    Inventors: Florian Faion, Alexandru Paul Condurache, Claudius Glaeser, Florian Drews, Jasmin Ebert, Lars Rosenbaum, Rainer Stal, Sebastian Muenzner, Thomas Gumpp