公开(公告)号：US11893004B2

公开(公告)日：2024-02-06

申请号：US17003088

申请日：2020-08-26

Applicant: Ford Global Technologies, LLC

Inventor： Gaurav Pandey ,  Brian George Buss ,  Dimitar Petrov Filev

IPC: G06F16/23 ,  G06F17/16 ,  G05D1/02 ,  G05D1/00 ,  G01D3/032

CPC classification number: G06F16/2365 ,  G01D3/032 ,  G05D1/0088 ,  G05D1/0223 ,  G06F17/16 ,  G05D1/027 ,  G05D1/0246 ,  G05D1/0257 ,  G05D1/0278 ,  G05D2201/0213

Abstract: A computer includes a processor and a memory storing instructions executable by the processor to receive a time series of vectors from a sensor, determine a weighted moving mean of the vectors, determine an inverse covariance matrix of the vectors, receive a current vector from the sensor, determine a squared Mahalanobis distance between the current vector and the weighted moving mean, and output an indicator of an anomaly with the sensor in response to the squared Mahalanobis distance exceeding a threshold. The squared Mahalanobis distance is determined by using the inverse covariance matrix.

公开(公告)号：US11829131B2

公开(公告)日：2023-11-28

申请号：US17083469

申请日：2020-10-29

Applicant: Ford Global Technologies, LLC

Inventor： Iman Soltani Bozchalooi ,  Francois Charette ,  Dimitar Petrov Filev ,  Ryan Burke ,  Devesh Upadhyay

IPC: G05D1/02 ,  G06T7/73 ,  G06V10/25 ,  G06F18/22 ,  G06F18/214 ,  G06F18/2113 ,  G06N3/045

CPC classification number: G05D1/0221 ,  G06F18/214 ,  G06F18/2113 ,  G06F18/22 ,  G06N3/045 ,  G06T7/73 ,  G06V10/25 ,  G05D2201/0213 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30252

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to train a neural network included in a memory augmented neural network based on one or more images and corresponding ground truth in a training dataset by transforming the one or more images to generate a plurality of one-hundred or more variations of the one or more images including variations in the ground truth and process the variations of the one or more images and store feature points corresponding to each variation of the one or more images in memory associated with the memory augmented neural network. The instructions can include further instructions to process an image acquired by a vehicle sensor with the memory augmented neural network, including comparing a feature variance set for the image acquired by the vehicle sensor to the stored processing parameters for each variation of the one or more images, to obtain an output result.

公开(公告)号：US20230195093A1

公开(公告)日：2023-06-22

申请号：US17558804

申请日：2021-12-22

Applicant: Ford Global Technologies, LLC

Inventor： Harshal Maske ,  Devesh Upadhyay ,  Jim Birley ,  Dimitar Petrov Filev ,  Justin Miller ,  Robert Bennett

IPC: G05B19/418

CPC classification number: G05B19/4189 ,  G05B2219/32391

Abstract: A method includes defining a sensor layout of a digital twin based on one or more sensor parameters and one or more routing control locations of the digital twin. The method includes simulating a manufacturing routine of a plurality of pallets and a plurality of workstations based on one or more pallet parameters associated with the plurality of pallets and one or more workstation parameters associated with the plurality of workstations and calculating, for each routing control location from among the one or more routing control locations, a transient production value and a steady state production value based on the manufacturing routine. The method includes iteratively adjusting the sensor layout of the digital twin until each transient production value is less than or equal to a threshold transient production value and each steady state production value is less than or equal to a threshold steady state production value.

公开(公告)号：US20220063651A1

公开(公告)日：2022-03-03

申请号：US17004342

申请日：2020-08-27

Applicant: Ford Global Technologies, LLC

Inventor： Qi Dai ,  Jinhong Wang ,  Wen Guo ,  Xunnong Xu ,  Suzhou Huang ,  Dimitar Petrov Filev

IPC: B60W60/00 ,  B60W50/06 ,  G01C21/34 ,  G08G1/052 ,  G08G1/01 ,  G08G1/056 ,  B60W30/18 ,  B60W30/085 ,  B60W10/20 ,  B60W10/18 ,  G01C21/36 ,  G08G1/00

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to calibrate utility functions that determine optimal vehicle actions based on an approximate Nash equilibrium solution for multiple agents by determining a difference between model-predicted future states for the multiple agents to observed states for the multiple agents. The instructions can include further instructions to determine a vehicle path for a vehicle based on the optimal vehicle actions.

公开(公告)号：US20210248468A1

公开(公告)日：2021-08-12

申请号：US17241513

申请日：2021-04-27

Applicant: Ford Global Technologies, LLC

Inventor： Gaurav Kumar Singh ,  Pavithra Madhavan ,  Bruno Jales Costa ,  Gintaras Vincent Puskorius ,  Dimitar Petrov Filev

IPC: G06N3/08 ,  G06N3/04 ,  G06K9/00 ,  G06T7/246

Abstract: The present invention extends to methods, systems, and computer program products for classifying time series image data. Aspects of the invention include encoding motion information from video frames in an eccentricity map. An eccentricity map is essentially a static image that aggregates apparent motion of objects, surfaces, and edges, from a plurality of video frames. In general, eccentricity reflects how different a data point is from the past readings of the same set of variables. Neural networks can be trained to detect and classify actions in videos from eccentricity maps. Eccentricity maps can be provided to a neural network as input. Output from the neural network can indicate if detected motion in a video is or is not classified as an action, such as, for example, a hand gesture.

公开(公告)号：US11055859B2

公开(公告)日：2021-07-06

申请号：US16108127

申请日：2018-08-22

Applicant: Ford Global Technologies, LLC

Inventor： Bruno Sielly Jales Costa ,  Gintaras Vincent Puskorius ,  Gaurav Kumar Singh ,  Dimitar Petrov Filev

IPC: G06T7/11 ,  G06T7/254 ,  G05D1/02

Abstract: A computing system can determine moving objects in a sequence of images based on recursively calculating red-green-blue (RGB) eccentricity 249 k based on a video data stream. A vehicle can be operated based on the determined moving objects. The video data stream can be acquired by a color video sensor included in the vehicle or a traffic infrastructure system.

公开(公告)号：US20200307577A1

公开(公告)日：2020-10-01

申请号：US16778444

申请日：2020-01-31

Applicant: Ford Global Technologies, LLC

Inventor： Subramanya Nageshrao ,  Bruno Sielly Jales Costa ,  Dimitar Petrov Filev

IPC: B60W30/14 ,  G06N3/08 ,  G06K9/00 ,  G06K9/62 ,  G05D1/00

Abstract: The present disclosure describes systems and methods that include calculating, via a reinforcement learning agent (RLA) controller, a plurality of state-action values based on sensor data representing an observed state, wherein the RLA controller utilizes a deep neural network (DNN) and generating, via a fuzzy controller, a plurality of linear models mapping the plurality of state-action values to the sensor data.

公开(公告)号：US20200065665A1

公开(公告)日：2020-02-27

申请号：US16111550

申请日：2018-08-24

Applicant: Ford Global Technologies, LLC

Inventor： Subramanya Nageshrao ,  Hongtei Eric Tseng ,  Dimitar Petrov Filev ,  Ryan Lee Baker ,  Christopher Cruise ,  Leda Daehler ,  Shankar Mohan ,  Arpan Kusari

IPC: G06N3/08 ,  G06N3/04 ,  B60W30/10

Abstract: A computing system can determine a vehicle action based on inputting vehicle sensor data to a first neural network including a first safety agent that can determine a probability of unsafe vehicle operation. The first neural network can be adapted, at a plurality of times, by a periodically retrained deep reinforcement learning agent that includes a second deep neural network including a second safety agent. A vehicle can be operated based on the vehicle action.

公开(公告)号：US10570839B2

公开(公告)日：2020-02-25

申请号：US13689280

申请日：2012-11-29

Applicant: FORD GLOBAL TECHNOLOGIES, LLC

Inventor： Steven Joseph Szwabowski ,  John Ottavio Michelini ,  Dimitar Petrov Filev ,  Craig Thomas Hodorek ,  Eric Hongtei Tseng ,  Davor Hrovat

IPC: F02D45/00 ,  F02D41/02 ,  F02D41/24 ,  F02D11/10 ,  F02D41/10

Abstract: Methods and systems for adjusting vehicle operation in response to vehicle weight are described. In one example, an adaptive driver demand correction is adjusted in response to vehicle weight. The methods and systems may provide for more consistent powertrain response and lower vehicle emissions at lower vehicle weights.

公开(公告)号：US10152037B2

公开(公告)日：2018-12-11

申请号：US13938080

申请日：2013-07-09

Applicant: Ford Global Technologies, LLC

Inventor： Dimitar Petrov Filev ,  Michio Sugeno ,  Yan Wang ,  Luka Eciolaza ,  Tadanari Taniguchi ,  John Ottavio Michelini

IPC: G05B13/04 ,  F02D41/14 ,  F02D41/00

Abstract: A method for operating an actuator is disclosed. The actuator may be a linear or non-linear actuator. In one example, a pseudo-inverse piecewise bilinear model is adapted in response to output of a feedback controller to improve feed forward control.