Ford Global Technolgoies, LLC

Madeline Jane Goh

Vidya Nariyambut Murali

Yi Zhang

Bruno Sielly Jales Costa

Maryam Moosaei

Scott Vincent Myers

Oleg Yurievitch Gusikhin

Jinesh J Jain

Venkatapathi Raju Nallapa

Parsa Mahmoudieh

Casey Bryan Feldman

Example vehicle occupancy management systems and methods are described. In one implementation, a method receives a current vehicle image representing a current interior of a vehicle. An occupancy management system detects at least one passenger in the vehicle based on the current vehicle image and determines a seating location of the passenger. A seat map is generated that identifies the seating location of the passenger in the vehicle.

Vehicle occupancy management systems and methods

In one implementation, a method activates an unmanned aircraft inside a vehicle to capture images of the vehicle interior. The method accesses a flight path for the unmanned aircraft and receives data associated with the vehicle's current movement. The method adjusts the flight path of the unmanned aircraft to compensate for the vehicle's current movement.

Vehicle inspection systems and methods

A method is disclosed for detecting and classifying one or more traffic lights. The method may include converting an RGB frame to an HSV frame. The HSV frame may be filtered by at least one threshold value to obtain at least one saturation frame. At least one contour may be extracted from the at least one saturation frame. Accordingly, a first portion of the RGB may be cropped in order to encompass an area including the at least one contour. The first portion may then be classified by an artificial neural network to determined whether the first portion corresponds to a not-a-traffic-light class, a red-traffic-light class, a green-traffic-light class, a yellow-traffic-light class, or the like.

Traffic-light detection and classification using computer vision and deep learning

A method and an apparatus for background subtraction highly applicable in autonomous driving scenarios are described. The method involves a reduction of illumination effects by constructing a normality background image from a normality model based on a plurality of baseline images taken under different illuminating conditions. A subtracted image is obtained by subtracting the normality background image from a scene image pixel-wise (i.e., pixel-by-pixel). The scene image may contain one or more foreground objects. The foreground objects are identified by highlighting the pixels in the subtracted image whose intensity is more than a predetermined standard deviation in the normality model. An illumination-invariant color space transformation algorithm may optionally be utilized to further reduce the variant illumination effects.

Images background subtraction for dynamic lighting scenarios

The disclosure relates to systems and methods for estimating or determining the motion of a vehicle and/or the distance to objects within view of a rear camera. A method for rear obstacle detection using structure from motion includes identifying image features in a first frame corresponding to features in a second frame, wherein the first frame and the second frame comprise adjacent image frames captured by a rear-facing camera of a vehicle. The method includes determining parameters for a non-planar motion model based on the image features. The method includes determining camera motion based on the parameters for the non-planar motion model.

Rear obstacle detection and distance estimation

An autonomous vehicle includes interior sensors including an IR camera and a visible light camera. Images of an interior of the vehicle are captured using the cameras both before and after a passenger rides in the vehicle. The IR images from before and after are subtracted to obtain a difference image. Pixels above a threshold intensity a clustered. Clusters having an above-threshold size are determined to be anomalies. Portions of images from the visible light camera corresponding to the anomalies are sent to a dispatcher, who may then clear the vehicle to pick up another passenger or proceed to a cleaning station. Anomalies may be identified based on a combination of the IR images and visible light images.

System and method for assessing the interior of an autonomous vehicle

A system for providing media content in a vehicle includes a content component, a noise component, and a closed captioning component. The content component is configured to receive content from a content provider, wherein the content is configured for rendering by the media system of a vehicle. The noise component is configured to determine a noise level within a cabin of the vehicle. The closed captioning component is configured to, in response to determining that the noise level exceeds a threshold, trigger display of closed captioning for the content.

Systems, methods, and devices for rendering in-vehicle media content based on vehicle sensor data

A vehicle includes a pair of cameras disposed on the vehicle and oriented to view an entrance to the vehicle. The vehicle also includes a processor configured to compare a profile image of a passenger's facial feature from one of the pair with a frontal image of the passenger's facial feature from the other of the pair. The processor is configured to lock the entrance to the vehicle in response to the facial features being different.

Object detection for vehicles

Systems, methods, and devices for pedestrian detection are disclosed herein. A method includes receiving one or more images from a rear-facing camera on a vehicle. The method further includes determining that a pedestrian is present in the one or more images, predicting future motion of the pedestrian, and notifying a driver-assistance or automated driving system when a conflict exists between forward motion of the vehicle and the predicted future motion of the pedestrian.

Growing community of inventors

Palo Alto, CA, United States of America

Madeline Jane Goh