公开(公告)号：US20230112471A1

公开(公告)日：2023-04-13

申请号：US17497169

申请日：2021-10-08

Applicant: Ford Global Technologies, LLC

Inventor： Dominique Meroux ,  Sandhya Bhaskar ,  Zhilai Shen ,  Divya Juneja ,  Shubh Gupta ,  Feng Jin

IPC: G01C21/34 ,  B60K35/00 ,  B60W40/08 ,  G06Q30/02

Abstract: A system for providing a gaming platform for a shared ride hail trip includes a processor programmed to determine a user game objective for a user requesting the shared ride hail trip, determine a ride hail provider objective associated with the shared ride hail trip, generate a ride hail route option based on the user game objective and the ride hail provider objective, and award game points based on user actions such as selecting routes that use transportation modes that mitigate deviation from faster roads or longer routes, or reduce need for operation of a larger vehicle during peak demand times. The system also awards points for user route choices that supports other passengers’ needs and goals such as time constraints, physical limitations, or conservation of time or vehicle emissions. The system may encourage user behaviors that enrich user experience, and achieve user physical fitness and societal goals.

公开(公告)号：US20220405573A1

公开(公告)日：2022-12-22

申请号：US17351404

申请日：2021-06-18

Applicant: Ford Global Technologies, LLC

Inventor： Sandhya Bhaskar ,  Shreyasha Paudel ,  Nikita Jaipuria ,  Jinesh Jain

IPC: G06N3/08 ,  G06N3/04 ,  H04L29/08

Abstract: A first computer can operate a first instance of a neural network, receive a first data set input to the first instance of the neural network, determine a first calibration parameter for the neural network in the first instance of the neural network based on the first data set, and send the first calibration parameter to a server computer. A second computer can operate a second instance of the neural network, receive a second data set input to the second instance of the neural network, determine a second calibration parameter for the neural network in the second instance of the neural network based on the second data set, and send the second calibration parameter to the server computer. A server computer can aggregate the first and second calibration parameters to update a model of the neural network and update the neural network model for the first and second instances of the neural network at the first and second computers based on the aggregated first and second calibration parameters.

公开(公告)号：US20210300356A1

公开(公告)日：2021-09-30

申请号：US16829207

申请日：2020-03-25

Applicant: Ford Global Technologies, LLC

Inventor： Shreyasha Paudel ,  Marcos Paul Gerardo Castro ,  Sandhya Bhaskar ,  Clifton K. Thomas

IPC: B60W30/095 ,  G01C21/34 ,  G01C21/32 ,  G05B13/02 ,  G06N3/08 ,  G06N3/04

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.

公开(公告)号：US11851068B2

公开(公告)日：2023-12-26

申请号：US17509227

申请日：2021-10-25

Applicant: Ford Global Technologies, LLC

Inventor： Sandhya Bhaskar ,  Nikita Jaipuria ,  Jinesh Jain ,  Vidya Nariyambut Murali ,  Shreyasha Paudel

IPC: B60W40/04 ,  G06T7/50 ,  G06T7/20 ,  B60W30/09

CPC classification number: B60W40/04 ,  B60W30/09 ,  G06T7/20 ,  G06T7/50 ,  B60W2554/4042 ,  B60W2554/4043 ,  B60W2554/4044 ,  B60W2554/4045 ,  B60W2554/801 ,  B60W2554/802 ,  B60W2554/803 ,  B60W2554/804 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30261

Abstract: Image data are input to a machine learning program. The machine learning program is trained with a virtual boundary model based on a distance between a host vehicle and a target object and a loss function based on a real-world physical model. An identification of a threat object is output from the machine learning program. A subsystem of the host vehicle is actuated based on the identification of the threat object.

公开(公告)号：US20230368541A1

公开(公告)日：2023-11-16

申请号：US17745141

申请日：2022-05-16

Applicant: Ford Global Technologies, LLC

Inventor： Daniel Goodman ,  Sandhya Bhaskar ,  Nikita Jaipuria ,  Jinesh Jain ,  Vidya Nariyambut Murali

IPC: G06V20/58 ,  G06V20/40 ,  G06V10/82

CPC classification number: G06V20/58 ,  G06V20/41 ,  G06V10/82 ,  G06V20/46 ,  B60W60/0027

Abstract: A computer that includes a processor and a memory can predict future status of one or more moving objects by acquiring a plurality of video frames with a sensor included in a device, inputting the plurality of video frames to a first deep neural network to determine one or more objects included in the plurality of video frames, and inputting the objects to a second deep neural network to determine object features and full frame features. The computer can further input the object features and full frame features to a third deep neural network to determine spatial attention weights for the object features and full frame features, input the object features and full frame features to a fourth deep neural network to determine temporal attention weights for the object features and full frame features, and input the object features, full frame features, spatial attention weights and temporal attention weights to a fifth deep neural network to determine predictions regarding the one or more objects included the plurality of video frames.

公开(公告)号：US12139136B2

公开(公告)日：2024-11-12

申请号：US16829207

申请日：2020-03-25

Applicant: Ford Global Technologies, LLC

Inventor： Shreyasha Paudel ,  Marcos Paul Gerardo Castro ,  Sandhya Bhaskar ,  Clifton K. Thomas

IPC: B60W30/00 ,  B60W30/095 ,  G01C21/32 ,  G01C21/34 ,  G05B13/02 ,  G06N3/04 ,  G06N3/08

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.

公开(公告)号：US20240202503A1

公开(公告)日：2024-06-20

申请号：US18080799

申请日：2022-12-14

Applicant: Ford Global Technologies, LLC

Inventor： Sandhya Bhaskar ,  Jinesh Jain ,  Nikita Jaipuria ,  Shreyasha Paudel

IPC: G06N3/048 ,  G06N3/08

CPC classification number: G06N3/048 ,  G06N3/08

Abstract: A system and method to identify a data drift in a trained object detection deep neural network (DNN) includes receiving a dataset based on real world use, wherein the dataset includes scores associated with each class in an image, including a background (BG) class, measuring an intersection-over-union (IoU) conditioned expected calibration error (ECE) IoU-ECE by calculating an ECE under a white-box setting with detections from the dataset prior to non-maximum suppression (pre-NMS detections) that are conditioned on a specific IoU threshold, upon a determination of the IoU-ECE being greater than a preset first threshold, performing a white-box temperature scaling (WB-TS) calibration on the pre-NMS detections of the dataset to extract a temperature T, and identifying that the data drift has occurred upon a determination that temperature T exceeds a preset second threshold.

公开(公告)号：US20240112454A1

公开(公告)日：2024-04-04

申请号：US17944398

申请日：2022-09-14

Applicant: Ford Global Technologies, LLC

Inventor： Sandhya Bhaskar ,  Nikita Jaipuria ,  Jinesh Jain ,  Shreyasha Paudel

IPC: G06V10/776 ,  G06V10/764 ,  G06V10/774 ,  G06V10/82 ,  G06V10/98

CPC classification number: G06V10/776 ,  G06V10/764 ,  G06V10/774 ,  G06V10/82 ,  G06V10/98

Abstract: A system and method includes determining uncertainty estimation in an object detection deep neural network (DNN) by retrieving a calibration dataset from a validation dataset that includes scores associated with all classes in an image, including a background (BG) class, determining background ground truth boxes in the calibration dataset by comparing ground truth boxes with detection boxes generated by the object detection DNN using an intersection over union (IoU) threshold, correcting for class imbalance between ground truth boxes and background ground truth boxes in a ground truth class by updating the ground truth class to include a number of background ground truth boxes based on a number of ground truth boxes in the ground truth class, estimating uncertainty of the object detection DNN based on the class imbalance correction, and updating output data sets of the object detection DNN based on the class imbalance correction.

公开(公告)号：US20230128947A1

公开(公告)日：2023-04-27

申请号：US17509227

申请日：2021-10-25

Applicant: Ford Global Technologies, LLC

Inventor： Sandhya Bhaskar ,  Nikita Jaipuria ,  Jinesh Jain ,  Vidya Nariyambut Murali ,  Shreyasha Paudel

IPC: B60W40/04 ,  B60W30/09 ,  G06T7/50 ,  G06T7/20

Abstract: Image data are input to a machine learning program. The machine learning program is trained with a virtual boundary model based on a distance between a host vehicle and a target object and a loss function based on a real-world physical model. An identification of a threat object is output from the machine learning program. A subsystem of the host vehicle is actuated based on the identification of the threat object.