Ford Global Technologies, LLC

Ford Motor Company Limited

Honeywell International Inc.

Other

University of Michigan

Ford Global Tech., Inc.

Dimitar Petrov Filev

Jianbo Lu

Fling Finn Tseng

Kwaku O Prakah-Asante

Steven Joseph Szwabowski

John Ottavio Michelini

Fazal Urrahman Syed

Imad Hassan Makki

Perry Robinson Macneille

Oleg Yurievitch Gusikhin

Davor Hrovat

Yifan Chen

Yan Wang

Anthony Mario D'Amato

Finn Finn Tseng

Basavaraj Tonshal

Johannes Geir Kristinsson

Bruno Sielly Jales Costa

Gary Steven Strumolo

Mark Schunder

Jeffrey Allen Greenberg

Thomas John Giuli

Erica Klampfl

Aed M Dudar

Ryan Abraham McGee

Diana Yanakiev

Devesh Upadhyay

Eric Hongtei Tseng

Gintaras Vincent Puskorius

Hsin-Hsiang Yang

Pankaj Kumar

Sanghyun Hong

Krishnaswamy Venkatesh Prasad

Jonathan Thomas Mullen

Robert Roy Jentz

Ilya V Kolmanovsky

Thomas Edward Pilutti

Ryan J Skaff

Venkatapathi Raju Nallapa

Paul Stephen Bryan

Uwe Hoffmann

Shane Elwart

Stefano Di Cairano

Hongtei Eric Tseng

Hassene Jammoussi

Jaroslav Pekar

Gaurav Pandey

Paul Aldighieri

Shiqi Qiu

Smruti Panigrahi

Michael Casedy

Ondrej Santin

Jeffrey Allen Doering

Pietro Buttolo

Michael James Uhrich

Adam Nathan Banker

Gregory Michael Pietron

Ali Hassani

Hong Jiang

Dennis C Reed

Felix Nedorezov

William David Treharne

Hai Yu

James Stewart Rankin, Ii

Li Xu

Raj Sohmshetty

Zhengyu Dai

Douglas Scott Rhode

Peter A Friedman

John Shutko

Brent Edward Sealy

Roger Lyle Huffmaster

Francois Charette

Sunil Bharitkar

Anthony Mark Phillips

Christopher John Teslak

Vijay K Nagasamy

Jeffrey Dan Rupp

David Michael Russell

Vladimir V Kokotovic

Eric A Luehrsen

Lee Albert Feldkamp

Justin Miller, Jr

Shie Mannor

Kevin Richard John Ellwood

Medville Jay Throop

Michael Seemann

Eric H Wingfield

Shankar Narayan Mohan

Himanshu Verma

Peter Douglas Kuechler

Elizabeth Therese Hetrick

Mary Catherine Farmer

Louis Tijerina

Brian David Tillman

Thomas E Pearson

Cynthia Mowery Flanigan

Peter W A Zegelaar

David Scott Rohweder

Enrique Corona

Methods and systems are provided for monitoring a health of a vehicle component. In one embodiment, a method is provided, comprising dividing a population of vehicles of a connected vehicle population into a plurality of vehicle classes; for each vehicle class of the plurality of vehicle classes, training a class-specific model of the vehicle class to predict a health status variable of a vehicle component included in the vehicle class based on labelled data from historic databases and calibration data; and for each vehicle class of the plurality of vehicle classes, using a first Federated Learning strategy to request local model data from each vehicle of a plurality of vehicles of the vehicle class; receive the local model data from the plurality of vehicles; update the class-specific model based on the received local model data; and send updated parameters of the class-specific model to vehicles included in the vehicle class.

Methods and systems for estimating a remaining useful life of an asset

Systems and methods for personalized route prediction including receiving, at a first time, first input data associated with a first route; populating a first database with the input data; receiving, at a third time, second input data associated with a second route; comparing the second input data to the first input data included within the first database; determining, based on the comparison, a first cluster including the first data and the second input data or a second cluster including the second input data; populating a second database based on the first cluster or the second cluster; determining, using the first database and at a second time, at least one of: predicted departure data, predicted destination data, and/or predicted route data; and causing, based on the predicted departure data, predicted destination data, and/or predicted route data, to perform an action in association with a vehicle.

Systems and methods for personalized route prediction

The present invention extends to methods, systems, and computer program products for path planning for autonomous moving devices. Aspects of the invention include planning a path for a mobile robot to move autonomously in an environment that includes other static and moving obstacles, such as, for example, other mobile devices and pedestrians, without reference to a prior map of the environment. A planned path for a mobile robot can be determined, adjusted, and adapted using diffusion maps to avoid collisions while making progress towards a global destination. Path planning can include using transition probabilities between grid points to find a feasible path through parts of the environment to make progress towards the global destination. In one aspect, diffusion maps are used in combination with a receding horizon approach, including computing diffusion maps at specified time intervals.

Path planning for autonomous moving devices

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.

Vehicle path planning

Embodiments describe a system configured with a brain machine interface (BMI) system implemented in a vehicle for performing vehicle functions using electrical impulses from motor cortex activity in a user's brain. The system uses fuzzy states for increased robustness. The fuzzy states are defined by sets of Gaussian kernel-type membership functions that are defined for steering and velocity action function states. The membership functions define fuzzy states that provide overlapping control tiers for increasing and decreasing vehicle functionality. An autonomous vehicle may perform control and governance of transitions between membership functions that may overlap, resulting in smooth transitioning between the states.

Analog driving feature control brain machine interface

Methods and systems are provided for increasing an accuracy of anomaly detection in assets such as vehicle components. In one example, a method provides for continuous health monitoring of connected physical assets, comprising adapting thresholds for anomaly detection and root cause analysis algorithms for the connected assets based on an aggregation of new connected data using machine learning; updating and ranking advanced statistical and machine learning models based on their performance using connected data until confirming a best performing model; and deploying the best performing model to monitor the connected physical assets.

Methods and systems for anomaly detection of a vehicle

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.

Anomaly detection in multidimensional sensor data

Operation data from one or more vehicle subsystems are input to a vehicle dynamics model. Predicted operation data of the one or more vehicle subsystems are output from the vehicle dynamics model. The operation data and the predicted operation data are input to an optimization program that is programmed to output control directives for the one or more vehicle subsystems. One or more vehicle subsystems are operated according to the output control directives.

Enhanced vehicle operation

A method includes obtaining state information from one or more sensors of a digital twin. The method includes determining an action at the first routing control location based on the state information, where the action includes one of a pallet merging operation and a pallet splitting operation, and determining a consequence state based on the action. The method includes calculating a transient production value based on the consequence state and a transient objective function, calculating a steady state production value based on the consequence state and a steady state objective function, and selectively adjusting one or more reinforcement parameters of the reinforcement learning system based on the transient production value and the steady state production value.

Systems and methods for training a reinforcement learning system for pallet routing in a manufacturing environment

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.

Vehicle neural network enhancement

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.

Classifying time series image data

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.

Interpreting data of reinforcement learning agent controller

A system comprises a processor that is programmed to define a plurality of vehicle groups based on vehicle specification data and define a plurality of sub-groups for each of the vehicle groups based on environmental data and sensor data received from each of a plurality of vehicles. The processor is programmed to adjust fuel tank leak detection classifiers for the sub-groups based on ground truth data. The ground truth data include, for each of the plurality of vehicles, a leak detection status and a leak test result.

Adaptive diagnostic parametrization

A computer, including a processor and a memory, the memory including instructions to be executed by the processor to determine optimal vehicle actions based on a modified version of a Nash equilibrium solution to a multiple agent game, wherein the Nash equilibrium solution is modified by performing an adaptive grid search optimization technique based on calculating rewards and penalties for the agents to determine optimal vehicle actions, wherein the agents include one or more of autonomous vehicles, non-autonomous vehicles, stationary objects, and non-stationary objects including pedestrians and wherein the rewards and the penalties for the agents are determined by simulating behavior of the agents to determine possible future states for the agents to determine the optimal vehicle actions. The instructions can include further instructions to determine a vehicle path based on the optimal vehicle actions and download the vehicle path to the vehicle.

The method of monitoring an operation includes acquiring data from sensors including images of a workspace in which the operation is to be performed, identifying a human operator and a controlled element within the workspace using the acquired images, determining whether the operation has initiated based on a known activation trigger, estimating pose of the human operator using the images, monitoring state of the controlled element based on acquired data, and determining whether an abnormality occurred based on the estimated pose, the state of the controlled element, a duration of the operation, or a combination thereof.

Method and system for monitoring manufacturing operations using computer vision for human performed tasks

Growing community of inventors

Novi, MI, United States of America

Dimitar Petrov Filev