Ford Global Technolgoies, LLC

Robert Boschgmbh

Jonathan Diedrich

Adil Nizam Siddiqui

Ali Hassani

Brian Bennie

Cynthia M Neubecker

Mark Gehrke

Eric L Reed

Somak Datta Gupta

Haron Abdel-raziq

Mahmoud Yousef Ghannam

Brad Alan Ignaczak

Cheri Lyn Hansen

Hamid M Golgiri

Hemanth Yadav Aradhyula

Hafiz Malik

Jeffrey Allen Bochenek

John Robert Van Wiemeersch

Kenneth James Miller

Ryan Edwin Hanson

Laura Viviana Hazebrouck

Robert Parenti

David Hiskens

Charles James Miller

Devesh Upadhyay

Thomas Michael McQuade

Linsheng Chen

Nicholas Alexander Scheufler

Scott Howard Gaboury

Justin Miller, Jr

Alexandra Taylor

Bradley Warren Smith

Jake S Schwartz

John Moore

Gunter Rottner

Alireza Rahimpour

Eric Robert Krieger

Joshua Wheeler

Robert D Klosek

Terry Haggerty

Dhivya Srinivasan

Martin Van Hoeckel

A vehicle having a vehicle camera, a detection unit, and a processor is disclosed. The vehicle camera may be configured to capture a video feed in a camera field of view. The detection unit may be configured to measure vibrations caused by malicious events associated with the vehicle. The processor may be configured to obtain a trigger signal, and activate the detection unit responsive to obtaining the trigger signal. The processor may obtain input from the detection unit responsive to activating the detection unit, and detect a malicious event associated with the vehicle based on the input. The processor may further control a vehicle camera operation responsive to detecting the malicious event.

Vehicle security systems and methods

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 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

Systems and methods for ranking routes based on driving complexity are provided. The systems and methods may generate routes for a user based on factors indicative of difficulty levels of the routes (for example, based on the types of road features that exist on the route, among various other factors). The difficulty levels of the generated routes may then be used to automatically select, by a navigation system of a vehicle, a route that is suitable for the user based on their driving experience level. Alternatively, the routes may be presented to the user via the vehicle or a mobile device for selection by the user. The systems and methods may also be dynamic in that the difficulty levels of the routes may be adjusted as the user gains more driving experience.

Systems and methods for ranking routes based on driving complexity

A plurality of thermal images forward of a vehicle are collected. Thermal data in the plurality of thermal images is normalized based on an ambient air temperature to generate a plurality of normalized thermal images. The plurality of normalized thermal images are input to a machine learning program trained to output an identification of an object based on the ambient air temperature and a risk of collision between the vehicle and the object. A vehicle component is actuated based on the identification of the object and the risk of collision with the object.

Object identification with thermal imaging

A computer, including a processor and a memory, the memory including instructions to be executed by the processor to acquire a first image with a visible and NIR light camera and acquire a second image with an infrared camera. The instructions can include further instructions to determine whether the second image includes a live human face by comparing a first infrared profile included in the second image with second infrared profile included in a previously acquired third image acquired with the infrared camera; and when the second image includes the live human face, output the first image.

Counterfeit image detection

A vehicle characterizes a sunload on the vehicle using an imaging system including at least one camera capturing image data (e.g., without using an ambient light sensor). The image data includes at least a portion of a passenger cabin. A region of interest (ROI) overlay receives the image data and extracts selected image data according to predetermined scene elements of the vehicle environment. An occupant overlay is configured to detect a vehicle occupant represented in the selected image data and configured to generate truncated image data by subtracting image data corresponding to the vehicle occupant from the selected image data. An ambient light model uses environmental parameters including a sun position to estimate an expected sunload range. A mapper generates a sunload map comprising respective sunload values for a plurality of locations on the vehicle according to the truncated image data and the expected sunload range.

Camera-based vehicle sunload mapping

A computer, including a processor and a memory, the memory including instructions to be executed by the processor to acquire a first image from a first camera by illuminating a first object with a first light and determine an object status as one of a real object or a counterfeit object by comparing a first measure of pixel values corresponding to the first object to a threshold.

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, determine a first measure of pixel values in the image and 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. The instructions include further instructions to, 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.

A computer, including a processor and a memory, the memory including instructions to be executed by the processor to determine a first measure of pixel values in the first image, acquire a second image, estimate ambient light illuminating the first object based on the second image and modify the first measure of pixel values based on a second measure of pixel values corresponding to estimated ambient light based on the second image. The instructions include further instructions to perform a comparison of the modified first measure of pixel values to a third measure of pixel values determined from a third image of a second object, wherein the third 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 third measure of pixel values within a tolerance, determine whether the first object and the second object are a same object.

A computer, including a processor and a memory, the memory including instructions to be executed by the processor to divide each of one or more images acquired by a camera into a plurality of zones, determine respective camera noise values for respective zones based on the one or more images, determine one or more zone expected values for the one or more images by summing camera noise values multiplied by scalar coefficients for each zone and normalizing the sum by dividing by a number of zones in the plurality of zones, and determine a source of the camera as being one of the same camera or an unknown camera based on comparing the one or more zone expected values to previously acquired expected zone values.

Camera identification

Vision sensor dynamic watermarking via noise characterization are disclosed herein. An example device can capture an image by a first device, where the image comprising a pixel group has baseline noise characterization caused by a base noise profile for the first device. The device can produce a noise watermark to create a watermarked image, where the noise watermark can be produced by altering the baseline noise characterization to produce modified noise characterization. The device can also transmit the watermarked image to a receiver.

Vision sensor dynamic watermarking via noise characterization

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, determine a first measure based on a first number of pixels corresponding to the first object in the first image and determine a relative distance measure by comparing the first measure based on the first number of pixels corresponding to the first object to a second measure based on a second number of pixels corresponding to a second object in the first image. The instructions can include instructions to modify the first measure of pixel values based on the relative distance measure, perform a comparison of the modified first measure of pixel values to a third measure of pixel values determined from a second image of a second object, and when the comparison determines that the first measure is equal to the third measure of pixel values within a tolerance, determine that the first object and the second object are a same object.

A computing device can be programmed to, upon determining that a location of a vehicle is within a set of boundaries associated with a wireless network and that a user device is authenticated to the network, authorize the user device to control the vehicle. The computing device can be further programmed to, upon determining that the vehicle is not within the boundaries, authorize the user device to control the vehicle based on transmitting an authentication credential to a server and receiving an authorization certificate.

Multimode vehicle proximity security

Real-time wind data for a location is determined based on a detected movement of an object relative to a vehicle. The real-time wind data includes a wind speed and a wind direction. Upon receiving stored wind data for the location from a remote computer, a crosswind risk is determined based on the real-time wind data and the stored wind data. A vehicle component is actuated to compensate for the crosswind risk.

Growing community of inventors

Carleton, MI, United States of America

Jonathan Diedrich