Ford Global Technologies, LLC

Ford Motor Company Limited

Ford Global Technology, LLC

Oleg Yurievitch Gusikhin

Perry Robinson Macneille

Omar Makke

Yimin Liu

Dimitar Petrov Filev

David Allen Kowalski

Erica Klampfl

Basavaraj Tonshal

Yifan Chen

Ayush Shah

Patrick Lawrence Jackson Van Hoecke

Jeffrey Yeung

Fazal Urrahman Syed

Gary Steven Strumolo

Thomas John Giuli

Mark Schunder

Haysam M Kadry

Joe Phillip Pierucci

Dehua Cui

Krishnaswamy Venkatesh Prasad

Edward Andrew Pleet

Kacie Alane Theisen

David Anthony Hatton

John Matthew Ginder

Jeremy Lerner

Michael Edward Loftus

Jeffrey Allen Greenberg

Paul Aldighieri

John William Schmotzer

John Proietty

Randal Henry Visintainer

Kevin Lee Helpingstine

Manpreet Singh Bajwa

John Robert Van Wiemeersch

Erick Michael Lavoie

Kwaku O Prakah-Asante

Ryan Abraham McGee

Ilya V Kolmanovsky

Fling Finn Tseng

Hamid M Golgiri

Cynthia M Neubecker

Steven Joseph Szwabowski

Bruno Sielly Jales Costa

Patrick Kevin Holub

David A Herman

Christopher John Teslak

Brian Petersen

James Martin Lawlis

Julius Marchwicki

Mark John Jennings

Madeline Jane Goh

Edward K Krause

Melinda Kaye Hunsaker

Yu-Ning Liu

Dave A Herman

Joseph Carl Beiser

Giuseppe Rossi

Panduranga Kondoju

Aziz Makkiya

Sandeep Sasidharan

Elizabeth Halash

Harald C Martinez

Paul Eugene Coffman, Jr

John Howard Schneider

David Kramer

Andrew Gliesman

Fei Wang

Paul Duncan McCarthy

Alfredo Cateriano

Vadim Valerievich Glazunov

Mikhail Aleksandrovich Kurochkin

Thomas J Giuii

Celestine Chukwuemeka Aguwa

Terrence Martinak

Jing Chen

William Finkenstaedt

Eduardo Andres Garcia Magraner

Bardia Madani

Sina Fakhraee

Doug VanDagens

Yifan Chon

Daniel Reich

Leonid Mikhailovich Kurochkin

Sergei Gennadevich Popov

A system includes a computer on board a vehicle and a server remote from the vehicle. The computer and the server each store a network graph of actions performable by components of the vehicle that includes costs of transitioning between the actions. The computer is programmed to log a sequence of the actions; upon determining that a sub-sequence of the sequence constitutes a lowest-cost path from the beginning action to the final action based on the network graph, store a reduced sequence including a beginning action and a final action of the sub-sequence ordered immediately after the beginning action, the reduced sequence excluding at least one intermediate action from the sub-sequence; and transmit the reduced sequence to a server remote from the vehicle. The server is programmed to determine the sequence from the reduced sequence based on the network graph.

Vehicle data transmission

Managing data transfer and privacy via a multi-channel transfer of information is provided. A query is received from a client device, the query indicating an access identifier corresponding to an entity for which data is stored. A consented data pool is accessed to identify a random value corresponding to the access identifier. A hash value is computed using a combination of both the access identifier and the random value. An anonymous data pool of stored data is queried to identify results from the stored data tagged with the hash value. The results are returned to the client device responsive to the query.

Privacy aware multi channel data transfer

Intelligent ticketing and data offload planning is provided. A data center receives a ticket request from a vehicle requesting to perform a data upload of vehicle data over a communications network. An optimizer is utilized to generate a ticket, the ticket specifying a time and a location for the vehicle to perform the data upload. The ticket is received from the vehicle. The ticket is validated to ensure that the vehicle should still perform the data upload. The vehicle is indicated to perform the data upload over the communications network responsive to the optimizer confirming the data upload to proceed. The data upload is stored to a storage of the data center.

Intelligent ticketing and data offload planning for connected vehicles

Systems and methods are provided herein for creating and building a model of a unique infrastructure, such as a parking structure. The model may be created through the identification of various triggers by a vehicle traversing the infrastructure. The triggers may include, for example, sensors or other devices capable of sensing the vehicle within the infrastructure. The triggers may also be information received from the vehicle itself. Based on the triggers, it may be inferred that various places exist throughout the infrastructure. For example, it may be inferred through two subsequent triggers that a place exists between the two triggers. The place may be added to the model of the infrastructure in between the two triggers. The model may continue to grow in this manner.

Systems and methods for creating infrastructure models

An audio sample including speech and ambient sounds is transmitted to a vehicle computer. Recorded audio is received from the vehicle computer, the recorded audio including the audio sample broadcast by the vehicle computer and recorded by the vehicle computer and recognized speech from the recorded audio. The recognized speech and text of the speech are input to a machine learning program that outputs whether the recognized speech matches the text. When the output from the machine learning program indicates that the recognized speech does not match the text, the recognized speech and the text are included in a training dataset for the machine learning program.

Speech recognition in a vehicle

A computer includes a processor and a memory storing instructions executable by the processor to predict a time to failure of a vehicle component according to a damage model generated from vehicle operating data, based on the predicted time to failure, retrieve a schematic of the vehicle component and input the schematic to an additive printer to manufacture a replacement vehicle component, and replace the vehicle component with the replacement vehicle component according to maintenance instructions that, based at least in part on the predicted time to failure, specify a time and location to replace the vehicle component.

Enhanced vehicle maintenance

Harvesting the high performance computing capabilities of vehicle processors while the vehicles are parked and/or charging may provide use to otherwise unused resources as well as offer a way to discount parking and/or charging costs for the vehicle owner/driver. To harvest the computing capabilities, a dispatcher is utilized that receives incoming complex processing requests and parses the requests into tasks. The dispatcher dispatches the tasks to vehicle processors that are within, for example, a parking garage to which the dispatcher has access. The dispatcher may prioritize requests and/or prioritize the vehicle processors to distribute the tasks to optimize the power consumption, time, and processing capabilities used. When the vehicle processors complete the tasks and return results to the dispatcher, the dispatcher finalizes the job results and provides the results to the requestor. The dispatcher may discount the parking and/or charging costs for the vehicles that performed the computational tasks.

Harvesting vehicle hardware accelerators and GPU computing capabilities when idle

Devices, systems, and methods for remote control of autonomous vehicles are disclosed herein. A method may include receiving, by a device, first data indicative of an autonomous vehicle in a parking area, and determining, based on the first data, a location of the autonomous vehicle. The method may include determining, based on a the location, first image data including a representation of an object. The method may include generating second image data based on the first data and the first image data, and presenting the second image data. The method may include receiving an input associated with controlling operation of the autonomous vehicle, and controlling, based on the input, the operation of the autonomous vehicle.

Enhanced remote control of autonomous vehicles

A system that includes a computer and an unmanned aerial vehicle (UAV) is described. The computer may be programmed to receive, from the UAV, image data of a vehicle parking region; to process the data by identifying an actuation of a UAV indicator that occurs within a predetermined interval of a response of a vehicle in the region; and based on the identification, to determine a location of the vehicle.

Locating a vehicle using a drone

Methods and apparatus for vehicle climate control using distributed sensors are disclosed. A disclosed example method includes receiving data from sensors distributed within a vehicle at a first controller, processing the data at the first controller to identify an event associated with the interior of the vehicle and sending an instruction based on the event from the first controller to a second controller of the vehicle to affect an operation of a climate control system of the vehicle.

Methods and apparatus for vehicle climate control using distributed sensors

Autonomous vehicles may utilize neural networks for image classification in order to navigate infrastructures and foreign environments, using context dependent transfer learning adaptation. Techniques include receiving a transferable output layer from the infrastructure, which is a model suitable for the infrastructure and the local environment. Sensor data from the autonomous vehicle may then be passed through the neural network and classified. The classified data can map to an output of the transferable output layer, allowing the autonomous vehicle to obtain particular outputs for particular context dependent inputs, without requiring further parameters within the neural network.

Context dependent transfer learning adaptation to achieve fast performance in inference and update

A user destination is identified based on received location information from a user. A first seat in a vehicle is assigned to the user based on the user destination. At least one of the first seat and a second seat in the vehicle is moved upon user entry until a distance between the first seat and the second seat is greater than a distance threshold.

Vehicle seat operation

Methods and systems for vehicle localization are provided herein. An example method can include obtaining a map within an operating area. A location within the operating area is associated with a pattern of speed bumps that is configured to produce a vehicle pitch response from the vehicle when the vehicle travels over the pattern of speed bumps. The method can include obtaining motion sensor information from a vehicle sensor, determining when the motion sensor information matches the vehicle pitch response, and determining that the vehicle is in the location when the motion sensor information corresponds to the vehicle pitch response of the location.

Methods and systems for vehicle localization

A system includes a processor configured to wirelessly broadcast a message obtained from a first originating vehicle BUS or controller, following a determination that the message was on a pre-approved list for broadcast and having encrypted the message utilizing a temporary random key generated for a message session. The system may include vehicle controllers, a gateway module, and vehicle BUSSES connecting the system controllers to the gateway module. The gateway module may include a memory storing a list of pre-approved message types and corresponding source types, and a processor configured to receive a message from one of the vehicle controllers over one of the vehicle BUSSES to determine if a message type and source type of the received message matches an element of the list.

Method and apparatus for secure wireless vehicle bus communication

A system communicates with a plurality of vehicles onsite at a parking garage. The system determines an eventual intended destination for each respective vehicle of the plurality of vehicles and determines a path through the garage for each respective vehicle, to the destination for the respective vehicle. The system may segment the path into a plurality of shorter tasks for the respective vehicle, wherein each task moves the respective vehicle along the path until a point where the respective vehicle encounters an obstruction and instruct the respective vehicle to execute the shorter tasks for the respective vehicle.

Vehicle and network assisted parking

Growing community of inventors

West Bloomfield, MI, United States of America

Oleg Yurievitch Gusikhin