Ford Global Technolgoies, LLC

Ford Motor Company Limited

Other

Livio, Inc.

John Robert Van Wiemeersch

Stuart C Salter

Vivekanandh Elangovan

Ali Hassani

Erick Michael Lavoie

Robert Bruce Kleve

Pietro Buttolo

Brendan F Diamond

Paul Kenneth Dellock

Kevin Thomas Hille

Ryan Edwin Hanson

Patrick Kevin Holub

Hamid M Golgiri

Hussein H Berry

Michael Hrecznyj

Aaron Matthew DeLong

Brian Bennie

David Brian Glickman

Steven Yellin Schondorf

Dilip B Patel

Ronald Patrick Brombach

Laura Viviana Hazebrouck

Annette Lynn Huebner

Keith Weston

Mahmoud Yousef Ghannam

Thomas Lee Miller

Kristopher Karl Brown

Thomas Joseph Hermann

Timothy Thivierge, Jr

Daniel M King

Eric L Reed

Lisa Therese Boran

Tarik Safir

Karl Wojcik

Anthony Dwayne Cooprider

Kosta Papanikolaou

Donald Paul Bilger

Sam Harris

Adil Nizam Siddiqui

Brad Alan Ignaczak

Howard Paul Tsvi Linden

Jeffrey Allen Greenberg

Todd Ansbacher

Cynthia M Neubecker

Zeljko Deljevic

Justin Miller, Jr

Jayanthi Rao

Joshua R Wheeler

Ritesh Pandya

Peter Phung

John Thomas Ricks

Scott Alan Watkins

Oliver Lei

Aaron Lewis Mills

Ryan J O'Gorman

Paul Aldighieri

Scott Howard Gaboury

Gregory Paul Thomas

Edward Andrew Pleet

Brian Petersen

Shiqi Qiu

Ross Dykstra Pursifull

Cornel Lewis Gardner

Clay Wesley Maranville

Jonathan Diedrich

Robert Spahl

Richard William Kautz

Michael Alan Mcnees

Lawrence Daniel Cepuran

Mark Larry

Walter Joseph Talamonti

Aed M Dudar

Mark Anthony Cuddihy

Gary Steven Strumolo

Oleg Yurievitch Gusikhin

Aaron Bradley Johnson

Mahendra Somasara Dassanayake

Andrew Denis Lewandowski

Ronald Hugh Miller

David James Tippy

Jordan Barrett

Darrel Alan Recker

Scott Andrew Amman

David Celinske

Anthony Ligi, Jr

Pramita Mitra

Rajesh K Patel

Mark Douglas Malone

Joey Ray Grover

Scott Smereka

Patrick Lawrence Jackson Van Hoecke

Ahmed Benmimoun

David Anthony Symanow

William Najib Mansur

Danielle Rosenblatt

Jonathan Hair

Tuan Anh Be

Anthony Melatti

Christopher Charles Hunt

A vehicle including a first detection unit, a second detection unit, and a processor is disclosed. The first detection unit may be configured to detect presence of an occupant inside the vehicle. The second detection unit may be configured to detect that the vehicle may be in a double-lock mode. The processor may be configured to obtain inputs from the first detection unit and the second detection unit. Responsive to obtaining the inputs, the processor may determine that a predetermined condition is met. The predetermined condition may be met when the occupant may be present inside the vehicle and the vehicle may be in the double-lock mode. The processor may be further configured to output a notification comprising instructions for the occupant to perform a predetermined action responsive to a determination that the predetermined condition is met to enable the vehicle to exit the double-lock mode.

Systems and methods to enable a vehicle to exit double-lock mode

A system communicatively coupled with a vehicle is disclosed. The system may include a detection unit configured to measure sound in proximity to a vehicle tire, and a processor communicatively coupled with the detection unit. The processor may be configured to obtain inputs from the detection unit, and identify a slow leakage in the vehicle tire based on the inputs. The processor may be further configured to output a notification responsive to a slow leakage identification. The system may be a part of the vehicle or may be part of a user device communicatively coupled to the vehicle.

Slow leak detection systems and methods

A vehicle is configured to be connected to a trailer. The vehicle is configured to detect connections of the trailer to utility infrastructure. Movement of the vehicle is moderated if physical utility connections are present.

Pre-departure trailer based attachment detection system

An unmanned aerial vehicle (UAV) is disclosed. The UAV may include a UAV transceiver configured to receive course information associated with a course. The UAV may further include a UAV processor that may be configured to obtain the course information from the UAV transceiver. Responsive to obtaining the course information, the UAV processor may determine a UAV position in the course, and identify a course marker, from a plurality of course markers, in proximity to the UAV position based on the course information. The UAV processor may then determine navigation instructions associated with the course marker for a vehicle to navigate the course based on the course information. The UAV may be configured to move in proximity to a vehicle front portion and display the navigation instructions.

Unmanned aerial vehicle for course marking

A vehicle with sound monitoring, having a cabin interior configured to transport one or more occupants, an interior microphone located in the cabin interior for detecting interior sound, a navigation device for detecting a geographic location of the vehicle, and a controller to determine sound ordinance rules with a sound limit based on the detected location, the controller further determining an exterior sound level based on the detected interior sound with the interior microphone, wherein the controller further generates an output indicative of the detected exterior sound level relative to the sound limit.

Vehicle sound ordinance control and method

A computer includes a processor and a memory, and the memory stores instructions executable to actuate a cleaning system to spray a sensor in accordance with a cleaning plan stored in the memory, actuate the cleaning system to spray the sensor in response to an input from an operator, and adjust the cleaning plan based on the input from the operator to actuate the cleaning system.

Vehicle sensor cleaning

Output can be provided from a selected biometric analysis task that is one of a plurality of biometric analysis tasks based on an image provided from an image sensor. The selected biometric analysis task can be performed in a deep neural network that includes a common feature extraction neural network, a plurality of biometric task-specific neural networks and a plurality of expert pooling neural networks that perform the plurality of biometric analysis tasks by inputting the image to the common feature extraction network to determine latent variables. The latent variables can be input to the plurality of biometric task-specific neural networks to determine a plurality of first outputs. Concatenated first output results can be formed and the concatenated plurality of first result outputs and the latent variables can be input to the plurality of expert pooling neural networks to determine one or more biometric analysis task outputs.

Biometric task network

A method for managing occupant interaction with components of a vehicle includes generating, via a time-of-flight sensor, a point cloud representing a compartment of the vehicle, the point cloud including three-dimensional positional information about the compartment. The method further includes identifying at least one body segment of a user based on the point cloud. The method further includes determining, via processing circuitry in communication with the time-of-flight sensor, a position of at least one feature of the at least one body segment. The method further includes calculating an orientation of the at least one body segment based on the at least one feature. The method further includes estimating an intended task of the user based on the orientation of the at least one body segment. The method further includes generating, via the processing circuitry, a response signal in response to estimation of the intended task.

Systems and methods for managing occupant interaction using depth information

An approach to managing mismatched controller alerts is disclosed. The approach includes storing, to a component database of a cloud system having one or more hardware computing devices, mismatched controller alerts received from a plurality of vehicles, each of the mismatched controller alerts indicating an identifier of a current vehicle that the mismatched controller is installed into, and a location of current vehicle. The approach further includes performing clustering of the mismatched controller alerts by the cloud system to correlate the mismatched controller alerts by location. The approach further includes indicating clustered locations of the mismatched controller alerts by the cloud system.

TCU replacement snitch mode

A vehicle includes a plurality of RADAR modules arranged at a common side of a plurality of sides of the vehicle and configured to detect an obstruction in a region exterior to the vehicle. A first RADAR module is on a left portion of the common side and has a first field of view. A second RADAR module is on a right portion of the common side and has a second field of view. A third RADAR module is between the first and second RADAR modules and is spaced from the first RADAR module by a first distance and spaced from the second RADAR module by a second distance. The third RADAR module has a third field of view at least partially overlapping each of the first and second fields of view. Control circuitry triangulates a position of the obstruction between a pair of the plurality of RADAR modules.

Detection system for a vehicle

A method for determining spatial availability for a compartment of a vehicle includes generating, via at least one time-of-flight sensor, a first point cloud representing the compartment of the vehicle, the first point cloud including three-dimensional positional information of the compartment. The method further includes calculating, via processing circuitry in communication with the at least one time-of-flight sensor, spatial information corresponding to a target configuration for the compartment. The method further includes comparing, via the processing circuitry, the spatial information to the first point cloud. The method further includes determining an availability of the target configuration based on the comparison of the spatial information to the first point cloud.

Vehicle space morphing

A method to prevent intoxicated operation is described. The method includes providing instructions to a vehicle user to position a vehicle user body portion posture in a predefined alignment. The method further includes obtaining the vehicle user body portion posture from a first vehicle detector, and determining whether the vehicle user body portion posture is in the predefined alignment. The method includes activating a plurality of vehicle visual indicators to illuminate in a predefined manner when the vehicle user body portion posture is in the predefined alignment. The method further includes providing instructions to the vehicle user to move vehicle user eyes to track the plurality of vehicle visual indicators, and obtaining a vehicle user eye movement from a second vehicle detector. The method further includes determining whether the vehicle user eye movement meets a predetermined condition, and actuating a control action accordingly.

Inebriation test system

A vehicle including a detection unit and a processor is disclosed. The detection unit may be configured to capture inputs associated with an external light being illuminated in proximity to the vehicle. The processor may be configured to obtain the inputs from the detection unit, and determine an illumination characteristic associated with the external light based on the inputs obtained from the detection unit. The processor may further actuate a vehicle light to illuminate based on the illumination characteristic.

Systems and methods for actuating vehicle lights based on external light show

Systems and methods for implementing protections against catalytic converter theft. A vehicle may comprise Ultra-Wideband (UWB) anchors or sensors. The UWB sensors of a vehicle may be used to detect the relative distance and/or location of UWB-capable devices in close proximity to the vehicle. An assessment may be performed using the UWB anchors and/or other sensors to determine whether the presence of the unknown device is indicative of a threat to the vehicle. If a threat is detected, one or more protection steps may be taken.

Systems and methods for detecting/preventing vehicle parts theft

A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive range data from a range sensor of a vehicle, detect a person in proximity to the vehicle based on the range data, activate a camera of the vehicle upon detecting the person, set a parameter of one of the camera or a facial recognition algorithm based on the range data of the person, and perform the facial recognition algorithm on the person using data from the camera.

Activation of facial recognition

Growing community of inventors

Novi, MI, United States of America

John Robert Van Wiemeersch