Ford Global Technolgoies, LLC

Ali Hassani

John Robert Van Wiemeersch

Hamid M Golgiri

Vivekanandh Elangovan

Erick Michael Lavoie

Ryan Edwin Hanson

Jonathan Diedrich

Dilip B Patel

Alexandra Taylor

Adil Nizam Siddiqui

Justin Miller, Jr

Alyssa Chatten

Hafiz Malik

Daniel M King

David Melcher

Hemanth Yadav Aradhyula

Mark Anthony Cuddihy

Gary Steven Strumolo

Pramita Mitra

Thomas Joseph Hermann

Aaron Matthew DeLong

Jeffrey Allen Greenberg

Craig John Simonds

Brad Alan Ignaczak

Laura Viviana Hazebrouck

Walter Joseph Talamonti

Timothy Thivierge, Jr

Arun Dutta

Jake S Schwartz

Jakob Nikolaus Hoellerbauer

Lawrence Chikeziri Amadi

Aniruddh Ravindran

Medha Karkare

Zaid El Shair

Kirk Leonard

Stuart C Salter

Pietro Buttolo

Gintaras Vincent Puskorius

Scott Andrew Amman

Christopher W Bell

Francois Charette

Vijay K Nagasamy

Michael Hrecznyj

John Edward Huber

John William Schmotzer

Ranjani Rangarajan

Robert D Bedard

Robert Parenti

Cameron Smyth

Alexander Groh

Ryan Joseph Gorski

Tom Nelson

Aaron Matthew Delong

Michael Kruntovski

Brigitte Frances Mora Richardson

Clifford Anthony Bailey

Pol Llado

Gabrielle Jost

An Ji

Willie Ham

Lauren Peng

Paul Kenneth Dellock

Dimitar Petrov Filev

Kwaku O Prakah-Asante

Brian Bennie

Thomas Edward Pilutti

Yifan Chen

Cynthia M Neubecker

John Paul Gibeau

Kristopher Karl Brown

Joshua R Wheeler

Howard Paul Tsvi Linden

Kevin Thomas Hille

Patrick Kevin Holub

Thomas Nelson

Mark Larry

Nitendra Nath

Kenneth Michael Mayer

Devinder Singh Kochhar

Jochen Schubert

Jeffrey Yeung

Yuliya Aksyutina

Steven Wayne Friedlander

Yasmin Jawad

Armin Mueller-Lerwe

Jalal Jawany

David Hiskens

Darren Lee

Michael Andrew Simons

Shubham Sharma

Michael Hand

Patricia Seashore

Paul Theodosis

Christoph Hirner

Gerard Edward Szczepaniak

Dylan Verster

James Gilpin

Alyssa Hauler

Mohammad Talha

Jeff Dippel

A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive a plurality of radar data streams from a respective plurality of ultrawideband (UWB) radars in a passenger compartment of a vehicle, generate a combined radar data stream by averaging the radar data streams together, measure a biometric characteristic of an occupant of the passenger compartment based on the combined radar data stream, and actuate a component of the vehicle based on the biometric characteristic.

In-vehicle biometrics with ultrawideband radar

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 selected biometric analysis task is performed in a deep neural network that includes a common feature extraction neural network, a plurality of task-specific neural networks, a segmentation neural network, a landmark mesh neural network, a plurality of soft target segmentation 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 biometric analysis task outputs. The latent variables can be input to a landmark mesh neural network to determine a landmark mesh. The landmark mesh and the first biometric task outputs to a plurality of expert pooling neural networks to determine a plurality of second biometric task outputs.

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

Based on a measured intensities of first and second light sources while both the first and second light sources are actuated, and a default secondary illuminator output intensity, a computer may determine an adjusted secondary illuminator output intensity and actuate the secondary light source to output light at the adjusted secondary light source output intensity.

Synchronizing image frames by super sampling

A facial recognition authentication system and method include obtaining a current image in response to a request for facial recognition authentication of a user. A set of current image features are generated from the input image. Live image features and spoof image features are retrieved for the user. Then a determination is output that the input image is one of live or spoofed based on a comparison of the current image features to the live image features and the spoof image features.

Spoof images for user authentication

A biometric evaluation system for a vehicle includes a vision sensor configured to detect a biometric quality of a user. The biometric evaluation system also includes an operational system for the vehicle. At least one processor is in communication with the vision sensor. The processor is configured to execute a first classification algorithm that performs a first biometric validation based on the biometric quality to estimate a state of the user. The first classification algorithm requires a first service latency. In response to an outcome of the first biometric validation, the at least one processor is further configured to execute a second classification algorithm that performs a second biometric validation to confirm the state of the user. The second classification algorithm requires a second service latency greater than the first service latency. The processor is further configured to communicate a signal to modify operation of the vehicle.

Biometric service evaluation architecture for a vehicle

A device localization system for a vehicle and a mobile device configured as a Phone-as-a-Key (PaaK) is described. The system includes a fob-free mode for performing remote control vehicle features using a selective mobile device localization technique that reduces motive functionality of the vehicle, based location of a user performing the vehicle control during the remote-control operation. The system determines a geographic position of a user using a single Bluetooth® Low Energy (BLE) antenna, and establishes establish a high-fidelity zone based on a change in mobile device position. The system reduces a motive functionality of the vehicle, based on the selective mobile device localization, from a first motive mode having a full motive functionality to a second motive mode responsive to determining that the mobile device is not present in the high-fidelity zone.

Remote vehicle motive control with optimized mobile device localization

A computer, including a processor and a memory, the memory including instructions to be executed by the processor to acquire a first image by illuminating a first object with a first light beam, segment the first image of the first object to determine regions that correspond to a first surface material and determine a first measure of pixel values in regions of the first image that correspond to the first surface material. The instructions include further instructions to perform a comparison of the first measure of pixel values to a second measure of pixel values determined from a second image of a second object, wherein the second image is previously acquired by illuminating the second object with a second light beam and when the comparison determines that the first measure is equal to the second measure of pixel values within a tolerance, determine that the first object and the second object are a same object.

Material spectroscopy

A deep neural network can provide output from a selected biometric analysis task that is one of a plurality of biometric analysis tasks based on an image. 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, a plurality of segmentation mask neural networks and an expert pooling neural network 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 biometric analysis task outputs. The latent variables can be input to a segmentation neural network to determine a facial feature segmentation output. The facial feature segmentation output can be output to a plurality of segmentation mask neural networks.

A computer, including a processor and a memory, the memory including instructions to be executed by the processor to acquire one or more images from a camera and determine first camera noise values based on the one or more images by determining reactions of camera photo receptors to light. The instructions can include further instructions to compare the first camera noise values with second camera noise values determined based on previously acquired images from the camera and output a tamper determination for the camera based on whether the first camera noise values match, within a tolerance value, the second camera noise values determined based on the previously acquired images from the camera.

Camera tampering detection

Upon obtaining biometric data for a user of a structure while the user is outside the structure, user data for the user is updated based on a confidence score for the biometric data exceeding a first confidence threshold. Based on the confidence score for the biometric data not exceeding the first confidence threshold, instructions are provided to the user to provide updated biometric data. Then the user data is updated based on a confidence score for the updated biometric data exceeding a second confidence threshold. The second confidence threshold is greater than the first confidence threshold. Structure components are controlled based on the updated user data.

Enhanced biometric authorization

A system for secure sensor operation is provided. A sensor circuit includes a smart controller configured to provide trusted data. An operational circuit includes an operational controller configured to, responsive to receipt of a request from the sensor circuit, perform an identity verification using a cryptographic challenge to authenticate the sensor circuit before utilizing the trusted data.

Biometric authenticated vehicle start with paired sensor to key intrustion detection

A first frame of image data is captured at a first illumination intensity during a first time segment. A second frame of image data is captured at a second illumination intensity during a second time segment. The first frame and the second frame are combined and outputted as a compensated frame.

Image capture with varied illuminations

A system includes an illumination source operable at an adjustable duty cycle, a camera arranged to detect illumination from the illumination source, and a computer communicatively coupled to the illumination source and the camera. The duty cycle indicates a proportion of time that the illumination source is illuminated in a repeating pattern. The computer is programmed to operate the illumination source with the duty cycle at a baseline duty cycle in response to failing to detect an object having a preset type in image data from the camera, and operate the illumination source with the duty cycle at a high duty cycle in response to detecting the object having the preset type in the image data from the camera. The high duty cycle has the illumination source illuminated for a greater proportion of time than the baseline duty cycle.

Illumination control for vehicle sensors

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Ann Arbor, MI, United States of America

Ali Hassani