Ford Global Technolgoies, LLC

Shant Tokatyan

Shounak Athavale

Francois Charette

Daniel Boccuccia

Zachary David Nelson

Jennifer L Brace

Lindsay Chan

Lakshmi Krishnan

A method for parcel delivery service that permits secure and timely delivery of items to unattended vehicles. The method includes receiving an order to deliver an item to a vehicle. A current location of the vehicle may be determined using vehicle identification information. After the vehicle has been located, the item may be authenticated at the vehicle, and access to the interior of the vehicle may be automatically provided to enable the vehicle to securely receive the item. A corresponding system is also disclosed and claimed herein.

Unattended parcel delivery service

Example natural speech data generation systems and methods are described. In one implementation, a natural speech data generator initiates a game between a first player and a second player and determines a scenario associated with the game. A first role is assigned to the first player and a second role is assigned to the second player. The natural speech data generator receives multiple natural speech utterances by the first player and the second player during the game.

Natural speech data generation systems and methods

Example stain and trash detection systems and methods are described. In one implementation, a method receives a clean image of a vehicle interior and a second image of the vehicle interior after a passenger has occupied the vehicle. The method quantizes the clean image and the second image to reduce noise and identifies differences between the clean image and the second image. Additionally, the method identifies at least one contour in the identified differences and classifies an area defined by the contour as one of a stain, dirt, trash, or another item.

Stain and trash detection systems and methods

A controller receives images from a rearward-facing camera having an inter-lane region in the field of view thereof. A region of interest of the images is identified corresponding to the inter-lane region. The region of interest is converted to a grayscale image having a drastically reduced number of intensity levels. A difference image is determined between the grayscale image for each image relative to the grayscale image of a preceding image. If the non-zero pixels in the difference image exceed a threshold and the area of non-zero pixels in the difference images for the input images are increasing with time, a lane-splitting vehicle will be determined to be present and an alert may be generated and/or autonomous lane changes may be suppressed.

Detection of lane-splitting motorcycles

Example vehicle stain and trash detection systems and methods are described. In one implementation, a method receives a clean image and clean Lidar data associated with a clean vehicle interior. The method also receives a second image and second Lidar data associated with the vehicle interior after a passenger has occupied the vehicle. Differences are identified between the clean image and the second image. Additionally, differences are identified between the clean Lidar data and the second Lidar data. The method determines whether the vehicle interior includes at least one of a stain, dirt, or trash based on the identified differences.

Vehicle stain and trash detection systems and methods

Example vehicle cleanliness detection systems and methods are described. In one implementation, a method positions a vehicle proximate an image capture station and receives multiple images from the image capture station. The multiple images include different views of the vehicle's exterior surfaces. A vehicle cleanliness detection system analyzes the multiple images to determine a cleanliness of the vehicle's exterior surfaces. Based on analyzing the cleanliness of the vehicle's exterior surfaces, the method determines whether the vehicle exterior needs to be cleaned.

Vehicle cleanliness detection systems and methods

A system receives candidate strings from a speech recognition engine. Where the speech recognition indicates success, the candidate string may be reported or otherwise used. Otherwise, candidate strings may be processed by comparing collapsed forms of the candidate strings to collapsed forms of a user's contacts. The collapsed form may replace contiguous consonants with a 1 and contiguous vowels with a 0. If a candidate string has a collapsed form matching a contact's collapsed form, that contact may be used. Where multiple candidate strings match the collapsed forms of multiple contacts, the contacts may be filtered according to textual similarity to the candidate strings, proximity to a typical usage location for the contacts, and a typical usage time for the contacts.

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Foster City, CA, United States of America

Shant Tokatyan