Patents by Inventor Shubham Shrivastava

Shubham Shrivastava 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: 20220374274
    Abstract: Some embodiments of the present application include obtaining first data from a data feed to be provided to a plurality of machine learning models and detecting a changepoint in the first data. In response to the changepoint being detected, a first machine learning model may be executed on the first data to obtain first output datasets. A first performance score for the first machine learning model may be computed based on the first output datasets. A second machine learning model may be caused to execute on the first data based on the first performance score satisfying a first condition.
    Type: Application
    Filed: May 24, 2021
    Publication date: November 24, 2022
    Applicant: Capital One Services, LLC
    Inventors: Xinyu Chen, Abhishek Shrivastava, Boryana Manz, Yingbo Li, Katelyn Ge, Jason Redd, Shubham Chitkara, Pothuraju Tallapaneni
  • Patent number: 11482007
    Abstract: A computer-implemented method for estimating a vehicle pose for a moving vehicle is described includes obtaining, via a processor disposed in communication with a monochromatic camera, a monochromatic image of an operating environment, and detecting in the monochromatic image an event patch showing a plurality of pixels associated with the moving vehicle. The method further includes generating an optical flow map using an unsupervised optical flow prediction network to predict an optical flow for each pixel in the monochromatic image. The optical flow map includes a Red-Green-Blue (RGB) patch having color information associated with a velocity for the moving vehicle. The system generates a pixel-level event mask that includes the RGB patch, and estimates the vehicle pose for the moving vehicle.
    Type: Grant
    Filed: February 10, 2021
    Date of Patent: October 25, 2022
    Assignee: Ford Global Technologies, LLC
    Inventor: Shubham Shrivastava
  • Publication number: 20220335647
    Abstract: A first six degree-of-freedom (DoF) pose of an object from a perspective of a first image sensor is determined with a neural network. A second six DoF pose of the object from a perspective of a second image sensor is determined with the neural network. A pose offset between the first and second six DoF poses is determined. A first projection offset is determined for a first two-dimensional (2D) bounding box generated from the first six DoF pose. A second projection offset is determined for a second 2D bounding box generated from the second six DoF pose. A total offset is determined by combining the pose offset, the first projection offset, and the second projection offset. Parameters of a loss function are updated based on the total offset. The updated parameters are provided to the neural network to obtain an updated total offset.
    Type: Application
    Filed: April 7, 2021
    Publication date: October 20, 2022
    Applicant: Ford Global Technologies, LLC
    Inventors: Shubham Shrivastava, Punarjay Chakravarty, Gaurav Pandey
  • Publication number: 20220253634
    Abstract: A computer-implemented method for estimating a vehicle pose for a moving vehicle is described includes obtaining, via a processor disposed in communication with a monochromatic camera, a monochromatic image of an operating environment, and detecting in the monochromatic image an event patch showing a plurality of pixels associated with the moving vehicle. The method further includes generating an optical flow map using an unsupervised optical flow prediction network to predict an optical flow for each pixel in the monochromatic image. The optical flow map includes a Red-Green-Blue (RGB) patch having color information associated with a velocity for the moving vehicle. The system generates a pixel-level event mask that includes the RGB patch, and estimates the vehicle pose for the moving vehicle.
    Type: Application
    Filed: February 10, 2021
    Publication date: August 11, 2022
    Applicant: Ford Global Technologies, LLC
    Inventor: Shubham Shrivastava
  • Publication number: 20220219708
    Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to capture, from a camera, one or more images, wherein the one or more images include at least a portion of a vehicle, receive a plurality of keypoints corresponding to markers on the vehicle and instantiate a virtual vehicle corresponding to the vehicle. The instructions include further instructions to determine rotational and translation parameters of the vehicle by matching a plurality of virtual keypoints to the plurality of keypoints and determine a multi-degree of freedom (MDF) pose of the vehicle based on the rotational and translation parameters.
    Type: Application
    Filed: January 14, 2021
    Publication date: July 14, 2022
    Applicant: Ford Global Technologies, LLC
    Inventors: Punarjay Chakravarty, Shubham Shrivastava
  • Publication number: 20220214692
    Abstract: Present embodiments use deep reinforcement learning (DRL) algorithms and use one or more path planning approaches to create a path using a deep learning approach using a reinforcement learning algorithm, trained using traditional learning algorithms such as A-Star. The reinforcement learning algorithm takes in a forward-facing camera operative as part of a computer vision system for a robot, and utilizes training the algorithm to train the robot to traverse from point A to point B in an operating environment using a sequence of waypoints as a breadcrumb trail. The system trains the robot to learn the path section by section by the waypoints, which prevents requiring the robot to solve the entire path.
    Type: Application
    Filed: January 5, 2021
    Publication date: July 7, 2022
    Applicant: Ford Global Technologies, LLC
    Inventors: Punarjay Chakravarty, Kaushik Balakrishnan, Shubham Shrivastava
  • Publication number: 20220180106
    Abstract: A plurality of temporally successive vehicle sensor images are received as input to a variational autoencoder neural network that outputs an averaged semantic birds-eye view image that includes respective pixels determined by averaging semantic class values of corresponding pixels in respective images in the plurality of temporally successive vehicle sensor images. From a plurality of topological nodes that each specify respective real-world locations, a topological node closest to the vehicle, and a three degree-of-freedom pose for the vehicle relative to the topological node closest to the vehicle, is determined based on the averaged semantic birds-eye view image. A real-world three degree-of-freedom pose for the vehicle is determined by combining the three degree-of-freedom pose for the vehicle relative to the topological node and the real-world location of the topological node closest to the vehicle.
    Type: Application
    Filed: December 7, 2020
    Publication date: June 9, 2022
    Applicant: Ford Global Technologies, LLC
    Inventors: Mokshith Voodarla, Shubham Shrivastava, Punarjay Chakravarty
  • Patent number: 11348278
    Abstract: A computing device is programmed to generate a plurality of raw 3D point clouds from respective sensors having non-overlapping fields of view, scale each of the raw point clouds including scaling real-world dimensions of one or more features included in the respective raw 3D point cloud, determine a first transformation matrix that transforms a first coordinate system of a first scaled 3D point cloud of a first sensor to a second coordinate system of a second scaled 3D point cloud of a second sensor, and determine a second transformation matrix that transforms a third coordinate system of a third scaled 3D point cloud of a third sensor to the second coordinate system of the second scaled 3D point cloud of the second sensor.
    Type: Grant
    Filed: September 10, 2020
    Date of Patent: May 31, 2022
    Assignee: FORD GLOBAL TECHNOLOGIES, LLC
    Inventors: Punarjay Chakravarty, Shubham Shrivastava, Gaurav Pandey, Xue Iuan Wong
  • Publication number: 20220164582
    Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to receive a monocular image and provide the image to a variational autoencoder neural network (VAE), wherein the VAE has been trained in a twin configuration that includes a first encoder-decoder pair that receives as input unlabeled real images and outputs reconstructed real images, and a second encoder-decoder pair that receives as input synthetic images and outputs reconstructed synthetic images and wherein the VAE includes third and fourth decoders that are trained using labeled synthetic images, segmentation ground truth and depth ground truth. The instructions can include further instructions to output from the VAE a segmentation map and a depth map based on inputting the monocular image.
    Type: Application
    Filed: November 24, 2020
    Publication date: May 26, 2022
    Applicant: Ford Global Technologies, LLC
    Inventors: Nithin Raghavan, Shubham Shrivastava, Punarjay Chakravarty
  • Publication number: 20220076441
    Abstract: A computing device is programmed to generate a plurality of raw 3D point clouds from respective sensors having non-overlapping fields of view, scale each of the raw point clouds including scaling real-world dimensions of one or more features included in the respective raw 3D point cloud, determine a first transformation matrix that transforms a first coordinate system of a first scaled 3D point cloud of a first sensor to a second coordinate system of a second scaled 3D point cloud of a second sensor, and determine a second transformation matrix that transforms a third coordinate system of a third scaled 3D point cloud of a third sensor to the second coordinate system of the second scaled 3D point cloud of the second sensor.
    Type: Application
    Filed: September 10, 2020
    Publication date: March 10, 2022
    Applicant: Ford Global Technologies, LLC
    Inventors: Punarjay Chakravarty, Shubham Shrivastava, Gaurav Pandey, Xue Iuan Wong
  • Publication number: 20210405168
    Abstract: A calibration device and method of calculating a global multi-degree of freedom (MDF) pose of a camera affixed to a structure is disclosed. The method may comprise: determining, via a computer of a calibration device, a calibration device MDF pose with respect to a global coordinate system corresponding to the structure; receiving, from an image system including the camera, a camera MDF pose with respect to the calibration device, wherein a computer of the image system determines the camera MDF pose based on an image captured by the camera including at least a calibration board affixed to the calibration device; calculating the global MDF pose based on the calibration device MDF pose and the MDF pose; and transmitting the global MDF pose to the image system such that a computer of the image system can use the global MDF pose for calibration purposes.
    Type: Application
    Filed: June 29, 2020
    Publication date: December 30, 2021
    Applicant: Ford Global Technologies, LLC
    Inventors: Sagar Manglani, Punarjay Chakravarty, Shubham Shrivastava
  • Patent number: 11189049
    Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to determine a plurality of topological nodes wherein each topological node includes a location in real-world coordinates and a three-dimensional point cloud image of the environment at the location of the topological node and process an image acquired by a sensor included in a vehicle using a variational auto-encoder neural network trained to output a semantic point cloud image, wherein the semantic point cloud image includes regions labeled by region type and region distance relative to the vehicle.
    Type: Grant
    Filed: October 16, 2020
    Date of Patent: November 30, 2021
    Assignee: Ford Global Technologies, LLC
    Inventors: Punarjay Chakravarty, Shubham Shrivastava
  • Patent number: 11107228
    Abstract: The present disclosure discloses a system and a method. In example implementations, the system and the method can include receiving an image having a first perspective; generating, via a deep neural network, a depth map corresponding to the image having the first perspective; generating, via the deep neural network, a point cloud representation based on the depth map; projecting the point cloud representation onto a point cloud representation corresponding to an image having a second perspective; generating a depth map corresponding to the image having the second perspective; and generating a synthetic image having the second perspective based on the depth map corresponding to the image having the second perspective and a semantic segmentation map corresponding to the image having the first perspective, wherein the second perspective is different from the first perspective.
    Type: Grant
    Filed: April 2, 2020
    Date of Patent: August 31, 2021
    Assignee: Ford Global Technologies, LLC
    Inventor: Shubham Shrivastava