Patents by Inventor Shreyasha Paudel

Shreyasha Paudel 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).

  • Patent number: 11455565
    Abstract: Original sensor data is received from one or more sensors of a vehicle. Free space around the vehicle is identified according to the sensor data, such as by identifying regions where data points have a height below a threshold. A location for an object model is selected from the free space. A plane is fitted to sensor data around the location and the object model is oriented according to an orientation of the plane. Sensing of the object model by a sensor of the vehicle is simulated to obtain simulated data, which is then added to the original sensor data. Sensor data corresponding to objects that would have been obscured by the object model is removed from the original sensor data. Augmented sensor data may be used to validate a control algorithm or train a machine learning model.
    Type: Grant
    Filed: August 31, 2017
    Date of Patent: September 27, 2022
    Assignee: Ford Global Technologies, LLC
    Inventors: Daniel Bogdoll, Shreyasha Paudel, Tejaswi Koduri
  • Publication number: 20220234617
    Abstract: A system comprising a computer including a processor and a memory, the memory including instructions such that the processor is programmed to: process vehicle sensor data with a deep neural network to generate a prediction indicative of one or more objects based on the data and determine an object uncertainty corresponding to the prediction and when the object uncertainty is greater than an uncertainty threshold, segment the vehicle sensor data into a foreground portion and a background portion. Classify the foreground portion as including an unseen object class when a foreground uncertainty is greater than a foreground uncertainty threshold; classify the background portion as including unseen background when a background uncertainty is greater than a background uncertainty threshold; and transmit the data and a data classification to a server.
    Type: Application
    Filed: January 26, 2021
    Publication date: July 28, 2022
    Applicant: Ford Global Technologies, LLC
    Inventors: Gautham Sholingar, Sowndarya Sundar, Jinesh Jain, Shreyasha Paudel
  • Publication number: 20220207348
    Abstract: A system comprising a computer including a processor and a memory, the memory including instructions such that the processor is programmed to: determine whether a difference between a friction coefficient label and a determined friction coefficient corresponding to an image depicting a surface is greater than a label threshold; modify the determined friction coefficient to equal the friction coefficient label when the difference is greater than the label threshold; and retrain a neural network using the image and the friction coefficient label.
    Type: Application
    Filed: December 29, 2020
    Publication date: June 30, 2022
    Applicant: Ford Global Technologies, LLC
    Inventors: Sara Dadras, Jinesh Jain, Shreyasha Paudel
  • Publication number: 20220101053
    Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to determine a second convolutional neural network (CNN) training dataset by determining an underrepresented object configuration and an underrepresented noise factor corresponding to an object in a first CNN training dataset, generate one or more simulated images including the object corresponding to the underrepresented object configuration in the first CNN training dataset by inputting ground truth data corresponding to the object into a photorealistic rendering engine and generate one or more synthetic images including the object corresponding to the underrepresented noise factor in the first CNN training dataset by processing the simulated images with a generative adversarial network (GAN) to determine a second CNN training dataset.
    Type: Application
    Filed: September 30, 2020
    Publication date: March 31, 2022
    Applicant: Ford Global Technologies, LLC
    Inventors: Artem Litvak, Xianling Zhang, Nikita Jaipuria, Shreyasha Paudel
  • Patent number: 11209831
    Abstract: A vehicle system includes a processor and a memory. The memory stores instructions executable by the processor to identify an area of interest from a plurality of areas on a map, to determine that a detected sound is received in a vehicle audio sensor upon determining that a source of the sound is within the area of interest and not another area in the plurality of areas, and to operate the vehicle based at least in part on the detected sound.
    Type: Grant
    Filed: May 3, 2019
    Date of Patent: December 28, 2021
    Assignee: FORD GLOBAL TECHNOLOGIES, LLC
    Inventors: Shreyasha Paudel, Jinesh Jain, Gaurav Pandey
  • Patent number: 11188089
    Abstract: Systems, methods, and devices for determining a location of a vehicle or other device are disclosed. A method includes receiving sensor data from a sensor and determining a prior map comprising LIDAR intensity values. The method includes extracting a sub-region of the prior map around a hypothesis position of the sensor. The method includes extracting a Gaussian Mixture Model (GMM) distribution of intensity values for a region of the sensor data by expectation-maximization and calculating a log-likelihood for the sub-region of the prior map based on the GMM distribution of intensity values for the sensor data.
    Type: Grant
    Filed: June 21, 2018
    Date of Patent: November 30, 2021
    Assignee: Ford Global Technologies, LLC
    Inventors: Sarah Houts, Praveen Narayanan, Graham Mills, Shreyasha Paudel
  • Publication number: 20210300356
    Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to, based on sensor data in a vehicle, determine a database that includes object data for a plurality of objects, including, for each object, an object identification, a measurement of one or more object attributes, and an uncertainty specifying a probability of correct object identification, for the object identification and the object attributes determined based on the sensor data, wherein the object attributes include an object size, an object shape and an object location. The instructions include further instructions to determine a map based on the database including the respective locations and corresponding uncertainties for the vehicle type and download the map to a vehicle based on the vehicle location and the vehicle type.
    Type: Application
    Filed: March 25, 2020
    Publication date: September 30, 2021
    Applicant: Ford Global Technologies, LLC
    Inventors: Shreyasha Paudel, Marcos Paul Gerardo Castro, Sandhya Bhaskar, Clifton K. Thomas
  • Patent number: 10928834
    Abstract: A method for autonomous vehicle localization. The method may include receiving, by an autonomous vehicle, millimeter-wave signals from at least two 5G transmission points. Bearing measurements may be calculated relative to each of the 5G transmission points based on the signals. A vehicle velocity may be determined by observing characteristics of the signals. Sensory data, including the bearing measurements and the vehicle velocity, may then be fused to localize the autonomous vehicle. A corresponding system and computer program product are also disclosed and claimed herein.
    Type: Grant
    Filed: May 14, 2018
    Date of Patent: February 23, 2021
    Assignee: FORD GLOBAL TECHNOLOGIES, LLC
    Inventors: Sarah Houts, Shreyasha Paudel, Lynn Valerie Keiser, Tyler Reid
  • Patent number: 10849543
    Abstract: Data from sensors of a vehicle is captured along with data tracking a driver's gaze. The route traveled by the vehicle may also be captured. The driver's gaze is evaluated with respect to the sensor data to determine a feature the driver was focused on. A focus record is created for the feature. Focus records for many drivers may be aggregated to determine a frequency of observation of the feature. A machine learning model may be trained using the focus records to identify a region of interest for a given scenario in order to more quickly identify relevant hazards.
    Type: Grant
    Filed: June 8, 2018
    Date of Patent: December 1, 2020
    Assignee: FORD GLOBAL TECHNOLOGIES, LLC
    Inventors: Shreyasha Paudel, Jinesh Jain, Clifton Thomas
  • Publication number: 20200348687
    Abstract: A vehicle system includes a processor and a memory. The memory stores instructions executable by the processor to identify an area of interest from a plurality of areas on a map, to determine that a detected sound is received in a vehicle audio sensor upon determining that a source of the sound is within the area of interest and not another area in the plurality of areas, and to operate the vehicle based at least in part on the detected sound.
    Type: Application
    Filed: May 3, 2019
    Publication date: November 5, 2020
    Applicant: Ford Global Technologies, LLC
    Inventors: Shreyasha Paudel, Jinesh Jain, Gaurav Pandey
  • Publication number: 20190391268
    Abstract: Systems, methods, and devices for determining a location of a vehicle or other device are disclosed. A method includes receiving sensor data from a sensor and determining a prior map comprising LIDAR intensity values. The method includes extracting a sub-region of the prior map around a hypothesis position of the sensor. The method includes extracting a Gaussian Mixture Model (GMM) distribution of intensity values for a region of the sensor data by expectation-maximization and calculating a log-likelihood for the sub-region of the prior map based on the GMM distribution of intensity values for the sensor data.
    Type: Application
    Filed: June 21, 2018
    Publication date: December 26, 2019
    Inventors: Sarah Houts, Praveen Narayanan, Graham Mills, Shreyasha Paudel
  • Publication number: 20190374151
    Abstract: Data from sensors of a vehicle is captured along with data tracking a driver's gaze. The route traveled by the vehicle may also be captured. The driver's gaze is evaluated with respect to the sensor data to determine a feature the driver was focused on. A focus record is created for the feature. Focus records for many drivers may be aggregated to determine a frequency of observation of the feature. A machine learning model may be trained using the focus records to identify a region of interest for a given scenario in order to more quickly identify relevant hazards.
    Type: Application
    Filed: June 8, 2018
    Publication date: December 12, 2019
    Inventors: Shreyasha Paudel, Jinesh Jain, Clifton Thomas
  • Publication number: 20190346860
    Abstract: A method for autonomous vehicle localization. The method may include receiving, by an autonomous vehicle, millimeter-wave signals from at least two 5G transmission points. Bearing measurements may be calculated relative to each of the 5G transmission points based on the signals. A vehicle velocity may be determined by observing characteristics of the signals. Sensory data, including the bearing measurements and the vehicle velocity, may then be fused to localize the autonomous vehicle. A corresponding system and computer program product are also disclosed and claimed herein.
    Type: Application
    Filed: May 14, 2018
    Publication date: November 14, 2019
    Inventors: Sarah Houts, Shreyasha Paudel, Lynn Valerie Keiser, Tyler Reid
  • Publication number: 20190065637
    Abstract: Original sensor data is received from one or more sensors of a vehicle. Free space around the vehicle is identified according to the sensor data, such as by identifying regions where data points have a height below a threshold. A location for an object model is selected from the free space. A plane is fitted to sensor data around the location and the object model is oriented according to an orientation of the plane. Sensing of the object model by a sensor of the vehicle is simulated to obtain simulated data, which is then added to the original sensor data. Sensor data corresponding to objects that would have been obscured by the object model is removed from the original sensor data. Augmented sensor data may be used to validate a control algorithm or train a machine learning model.
    Type: Application
    Filed: August 31, 2017
    Publication date: February 28, 2019
    Inventors: Daniel Bogdoll, Shreyasha Paudel, Tejaswi Koduri
  • Publication number: 20190065933
    Abstract: Original sensor data is received from one or more sensors of a vehicle. Free space around the vehicle is identified according to the sensor data, such as by identifying regions where data points have a height below a threshold. A location for an object model is selected from the free space. A plane is fitted to sensor data around the location and the object model is oriented according to an orientation of the plane. Sensing of the object model by a sensor of the vehicle is simulated to obtain simulated data, which is then added to the original sensor data. Sensor data corresponding to objects that would have been obscured by the object model is removed from the original sensor data. Augmented sensor data may be used to validate a control algorithm or train a machine learning model.
    Type: Application
    Filed: August 31, 2017
    Publication date: February 28, 2019
    Inventors: Daniel Bogdoll, Shreyasha Paudel, Tejaswi Koduri