Patents by Inventor Julio Fernando Jarquin Arroyo

Julio Fernando Jarquin Arroyo 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: 20220242452
    Abstract: System and techniques for vehicle occupant monitoring are described herein. Sensor data, that includes visual image data, is obtained from a sensor array of the vehicle. An object carried by the vehicle is detected from the visual image data. A safety event for the vehicle may be identified based on the object detection and an operational element of the vehicle is altered in response to detecting the safety event.
    Type: Application
    Filed: September 23, 2021
    Publication date: August 4, 2022
    Inventors: Fabian Oboril, Cornelius Buerkle, Frederik Pasch, Bernd Gassmann, Javier Turek, Maria Soledad Elli, Javier Felip Leon, David Gonzalez Aguirre, Ignacio Javier Alvarez Martinez, Julio Fernando Jarquin Arroyo
  • Publication number: 20220114805
    Abstract: The automated driving perception systems described herein provide technical solutions for technical problems facing navigation sensors for autonomous vehicle navigation. These systems may be used to combine inputs from multiple navigation sensors to provide a multimodal perception system. These multimodal perception systems may augment raw data within a development framework to improve performance of object detection, classification, tracking, and sensor fusion under varying external conditions, such as adverse weather and light, as well as possible sensor errors or malfunctions like miss-calibration, noise, and dirty or faulty sensors. This augmentation may include injection of noise, occlusions, and misalignments from raw sensor data, and may include ground-truth labeling to match the augmented data. This augmentation provides improved robustness of the trained perception algorithms against calibration, noise, occlusion, and faults that may exist in real-world scenarios.
    Type: Application
    Filed: December 22, 2021
    Publication date: April 14, 2022
    Inventors: Julio Fernando Jarquin Arroyo, Ignacio J. Alvarez, Cornelius Buerkle, Fabian Oboril
  • Publication number: 20220111864
    Abstract: Disclosed herein are systems and methods for cross-domain training of sensing-system-model instances. In an embodiment, a system receives, via a first application programming interface (API), an input-dataset selection identifying an input dataset, which includes a plurality of dataframes that are in a first dataframe format and that have annotations corresponding to one or more sensing tasks performed with respect to the dataframes. The system executes a plurality of dataframe-transformation functions to convert the plurality of dataframes of the input dataset into a predetermined dataframe format. The system trains an instance of a first machine-learning model using the converted dataframes of the input dataset to perform at least a subset of the one or more sensing tasks. The system outputs, via the first API, one or more model-validation metrics pertaining to the training of the instance of the first machine-learning model.
    Type: Application
    Filed: December 22, 2021
    Publication date: April 14, 2022
    Inventors: Julio Fernando Jarquin Arroyo, Ignacio Javier Alvarez Martinez