Patents by Inventor Jan Nikolaus Fritsch

Jan Nikolaus Fritsch 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).

  • REAL-TIME DETECTION OF LANES AND BOUNDARIES BY AUTONOMOUS VEHICLES
    Publication number: 20230267701
    Abstract: In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.
    Type: Application
    Filed: May 1, 2023
    Publication date: August 24, 2023
    Inventors: Yifang Xu, Xin Liu, Chia-Chin Chen, Carolina Parada, Davide Onofrio, Minwoo Park, Mehdi Sajjadi Mohammadabadi, Vijay Chintalapudi, Ozan Tonkal, John Zedlewski, Pekka Janis, Jan Nikolaus Fritsch, Gordon Grigor, Zuoguan Wang, I-Kuei Chen, Miguel Sainz
  • Real-time detection of lanes and boundaries by autonomous vehicles
    Patent number: 11676364
    Abstract: In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.
    Type: Grant
    Filed: April 5, 2021
    Date of Patent: June 13, 2023
    Assignee: NVIDIA Corporation
    Inventors: Yifang Xu, Xin Liu, Chia-Chih Chen, Carolina Parada, Davide Onofrio, Minwoo Park, Mehdi Sajjadi Mohammadabadi, Vijay Chintalapudi, Ozan Tonkal, John Zedlewski, Pekka Janis, Jan Nikolaus Fritsch, Gordon Grigor, Zuoguan Wang, I-Kuei Chen, Miguel Sainz
  • REAL-TIME DETECTION OF LANES AND BOUNDARIES BY AUTONOMOUS VEHICLES
    Publication number: 20210224556
    Abstract: In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.
    Type: Application
    Filed: April 5, 2021
    Publication date: July 22, 2021
    Inventors: Yifang Xu, Xin Liu, Chia-Chih Chen, Carolina Parada, Davide Onofrio, Minwoo Park, Mehdi Sajjadi Mohammadabadi, Vijay Chintalapudi, Ozan Tonkal, John Zedlewski, Pekka Janis, Jan Nikolaus Fritsch, Gordon Grigor, Zuoguan Wang, I-Kuei Chen, Miguel Sainz
  • Real-time detection of lanes and boundaries by autonomous vehicles
    Patent number: 10997433
    Abstract: In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.
    Type: Grant
    Filed: February 26, 2019
    Date of Patent: May 4, 2021
    Assignee: NVIDIA Corporation
    Inventors: Yifang Xu, Xin Liu, Chia-Chih Chen, Carolina Parada, Davide Onofrio, Minwoo Park, Mehdi Sajjadi Mohammadabadi, Vijay Chintalapudi, Ozan Tonkal, John Zedlewski, Pekka Janis, Jan Nikolaus Fritsch, Gordon Grigor, Zuoguan Wang, I-Kuei Chen, Miguel Sainz
  • REAL-TIME DETECTION OF LANES AND BOUNDARIES BY AUTONOMOUS VEHICLES
    Publication number: 20190266418
    Abstract: In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.
    Type: Application
    Filed: February 26, 2019
    Publication date: August 29, 2019
    Inventors: Yifang Xu, Xin Liu, Chia-Chih Chen, Carolina Parada, Davide Onofrio, Minwoo Park, Mehdi Sajjadi Mohammadabadi, Vijay Chintalapudi, Ozan Tonkal, John Zedlewski, Pekka Janis, Jan Nikolaus Fritsch, Gordon Grigor, Zuoguan Wang, I-Kuei Chen, Miguel Sainz
  • Adaptive driver assistance systems with robust estimation of object properties
    Patent number: 8175782
    Abstract: A computer-implemented system and method for estimating properties of objects represented in digital images, comprising the steps of (a) encoding input data from a sensor in a neural map comprising neurons having numerical activation values, wherein the activation values in the neural maps have continuous time dynamics defined by an update scheme; (b) creating, adapting and deleting weights of the neural map in unsupervised, incremental manner; (c) transmitting data from an input map to an output map, based on the values of the weights; wherein each weight between the input map (IM) and a neural output map (OM) has a unique source and destination neuron; and wherein data transmission is directed; and (d) detecting correlations between the input map (IM).
    Type: Grant
    Filed: November 21, 2008
    Date of Patent: May 8, 2012
    Assignee: Honda Research Institute Europe GmbH
    Inventors: Alexander Gepperth, Jan Nikolaus Fritsch
  • Adaptive Driver Assistance Systems With Robust Estimation Of Object Properties
    Publication number: 20090138167
    Abstract: A computer-implemented system and method for estimating properties of objects represented in digital images, comprising the steps of (a) encoding input data from a sensor in a neural map comprising neurons having numerical activation values, wherein the activation values in the neural maps have continuous time dynamics defined by an update scheme; (b) creating, adapting and deleting weights of the neural map in unsupervised, incremental manner; (c) transmitting data from an input map to an output map, based on the values of the weights; wherein each weight between the input map (IM) and a neural output map (OM) has a unique source and destination neuron; and wherein data transmission is directed; and (d) detecting correlations between the input map (IM).
    Type: Application
    Filed: November 21, 2008
    Publication date: May 28, 2009
    Inventors: Alexander Gepperth, Jan Nikolaus Fritsch