Patents by Inventor Simon Roesler

Simon Roesler 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: 20240142608
    Abstract: A computer implemented method for determining a property of an object comprises the following steps carried out by computer hardware components: acquiring an image of a scene comprising the object; acquiring a plurality of lidar measurements of the scene; clustering the plurality of lidar measurements into a plurality of groups of lidar measurements; acquiring a radar measurement of the scene; identifying which of the plurality of groups of lidar measurements corresponds to the radar measurement; and determining the property of the object based on the image and the identified group of lidar measurements.
    Type: Application
    Filed: October 24, 2023
    Publication date: May 2, 2024
    Applicant: Aptiv Technologies AG
    Inventor: Simon ROESLER
  • Publication number: 20220402504
    Abstract: A computer-implemented method for generating ground truth data may include the following steps carried out by computer hardware components: for a plurality of points in time, acquiring sensor data for a respective point in time; and for at least a subset of the plurality of points in time, determining ground truth data of the respective point in time based on the sensor data of at least one present and/or past point of time and at least one future point of time.
    Type: Application
    Filed: June 17, 2022
    Publication date: December 22, 2022
    Inventors: Jan Siegemund, Jittu Kurian, Sven Labusch, Dominic Spata, Adrian Becker, Simon Roesler, Jens Westerhoff
  • Publication number: 20220308205
    Abstract: This document describes techniques and systems for a partially-learned model for speed estimates in radar tracking. A radar system is described that determines radial-velocity maps of potential detections in an environment of a vehicle. The model uses a data cube to determine predicted boxes for the potential detections. Using the predicted boxes, the radar system determines Doppler measurements associated with the potential detections that correspond to the predicted boxes. The Doppler measurements are used to determine speed estimates for the predicted boxes based on the corresponding potential detections. These speed estimates may be more accurate than a speed estimate derived from the data cube and the model. Driving decisions supported by the speed estimates may result in safer and more comfortable vehicle behavior.
    Type: Application
    Filed: December 15, 2021
    Publication date: September 29, 2022
    Inventors: Simon Roesler, Adrian Becker, Jan K. Schiffmann
  • Publication number: 20220214441
    Abstract: A computer implemented method for compressing radar data comprises the following steps carried out by computer hardware components: acquiring radar data comprising a plurality of Doppler bins; determining which of the plurality of Doppler bins represent stationary objects; and determining compressed radar data based on the determined Doppler bins which represent stationary objects.
    Type: Application
    Filed: December 30, 2021
    Publication date: July 7, 2022
    Inventors: Sven Labusch, Igor Kossaczky, Mirko Meuter, Simon Roesler
  • Publication number: 20220026568
    Abstract: A computer implemented method for detection of objects in a vicinity of a vehicle comprises the following steps carried out by computer hardware components: acquiring radar data from a radar sensor; determining a plurality of features based on the radar data; providing the plurality of features to a single detection head; and determining a plurality of properties of an object based on an output of the single detection head.
    Type: Application
    Filed: July 23, 2021
    Publication date: January 27, 2022
    Inventors: Mirko Meuter, Jittu Kurian, Yu Su, Jan Siegemund, Zhiheng Niu, Stephanie Lessmann, Saeid Khalili Dehkordi, Florian Kästner, Igor Kossaczky, Sven Labusch, Arne Grumpe, Markus Schoeler, Moritz Luszek, Weimeng Zhu, Adrian Becker, Alessandro Cennamo, Kevin Kollek, Marco Braun, Dominic Spata, Simon Roesler