Ford Global Technolgoies, LLC

Medha Karkare

John Robert Van Wiemeersch

Ali Hassani

Robert Parenti

Jake S Schwartz

Faizan Shaik

Justin Miller, Jr

Mackenzie Martinez

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 first computer and a second computer are each connected to a communication network. User data is generated for a user by the first computer based on receiving, from the second computer, initial enrollment biometric data for the user. The user is authorized by the first computer based on a confidence score for challenge biometric data exceeding a first confidence threshold. Then the user data is updated with updated enrollment biometric data by the first computer based on a confidence score for the updated enrollment biometric data exceeding a second confidence threshold. The second confidence threshold is greater than the first confidence threshold. The initial enrollment biometric data is deleted by the second computer after a predetermined time of receiving the initial enrollment biometric data.

Sensor fusion is performed for efficient deep learning processing. A camera image is received from an image sensor and supplemental sensor data is received from one or more supplemental sensors, the camera image and the supplemental sensor data including imaging of a cabin of a vehicle. Regions of interest in the camera image are determined based on one or more of the camera image or the supplemental sensor data, the regions of interest including areas of the camera image flagged for further image analysis. A machine-learning model is utilized to perform object detection on the regions of interest of the camera image to identify one or more objects in the camera image. The objects are placed into seating zones of the vehicle.

Sensor fusion area of interest identification for deep learning

An acceleration of a head of an occupant of a vehicle relative to the vehicle is determined based on data describing motion of the vehicle and respective poses of the head from a plurality of consecutive frames of image data. A vehicle suspension is adjusted based on the determined acceleration.

Enhanced vehicle suspension

Upon authorizing a user via challenge biometric data, user data is determined to be updated based on detecting a trigger. Upon detecting the trigger prior to authenticating the user, the user data is updated with the challenge biometric data based on authenticating the user. Upon detecting the trigger after authenticating the user, the user data is updated with updated challenge biometric data based on a confidence score for the second biometric data exceeding a threshold. Structure components are controlled based on the updated user data.

Performing object localization inside a cabin of a vehicle is provided. A camera image is received of a cabin of a vehicle by an image sensor at a first location with respect to a plurality of seating zones of a vehicle. Object detection on the camera image is performed to identify one or more objects in the camera image. A machine-learning model trained on images taken at the first location is utilized to place the one or more objects into the seating zones of the vehicle according to a plurality of bounding boxes corresponding to the plurality of seating zones for the first location.

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Canton, MI, United States of America

Medha Karkare