Ford Global Technolgoies, LLC

Ford Motor Company Limited

Shiqi Qiu

Fling Finn Tseng

Johannes Geir Kristinsson

Jayanthi Rao

Bhagyashri Satyabodha Katti

John Robert Van Wiemeersch

Oliver Lei

Himanshu Verma

Kalyani Purushottam Sonawane

Dimitar Petrov Filev

Jason Meyer

Imad Hassan Makki

Sangeetha Sangameswaran

Helen Elizabeth Kourous-Harrigan

Nikhil Kailas Jamdade

Aed M Dudar

Devesh Upadhyay

Daryl Joseph Martin

Victoria Leigh Schein

Patrick Maloney

Pankaj Kumar

Hassene Jammoussi

Nicholas Alexander Scheufler

Vishal Vinayak Nageshkar

Arun Dutta

Fakhreddine Landolsi

Nithya Somanath

Joseph Wisniewski

Alex Perkins

Eric Marsman

Swati Johar Rawat

Jonathan Niemi

Matthew R Goulet

Samuel Laidlaw

Systems and methods for personalized route prediction including receiving, at a first time, first input data associated with a first route; populating a first database with the input data; receiving, at a third time, second input data associated with a second route; comparing the second input data to the first input data included within the first database; determining, based on the comparison, a first cluster including the first data and the second input data or a second cluster including the second input data; populating a second database based on the first cluster or the second cluster; determining, using the first database and at a second time, at least one of: predicted departure data, predicted destination data, and/or predicted route data; and causing, based on the predicted departure data, predicted destination data, and/or predicted route data, to perform an action in association with a vehicle.

Systems and methods for personalized route prediction

A system includes a computer programmed to, in response to receiving a parking transmission from a parked vehicle, transmit a parking authorization credential to the parked vehicle for rebroadcast from the parked vehicle, the parking authorization credential confirming that the parked vehicle is authorized for a parking location.

Vehicle parking control

An identifier for first set of display content on a vehicle display is input to a statistical model that outputs a plurality of probabilities that a user input will select each of a plurality of second set of display contents for display after the first set of display content is displayed. A first probability is identified for a predicted set of display content that is a highest probability in the plurality of probabilities. The plurality of probabilities are provided to at least one of an optimization model and a neural network upon determining an accuracy of the statistical model is below a threshold. Upon receiving, from the at least one of the optimization model and the neural network, a second probability for the predicted set of display content, the display content is selected based on the first and second probabilities. The vehicle display is updated based on the selected set of display content.

Vehicle display

A system comprises an image sensor with a field of view of an exterior of a vehicle; a human machine interface (HMI) in the vehicle arranged to display images based on data from the image sensor; and a first computer that includes a processor and a memory. The first computer is programmed to provide data from the image sensor to a machine learning program that outputs a bounding box around an object; cause the bounding box around the object to be displayed via the HMI; and transmit object data including an inputted label identifying the object to a second computer that is remote from the vehicle.

Machine learning

Vehicles can be equipped to operate in both autonomous and occupant piloted mode. Refueling stations can be equipped to refuel autonomous vehicles without occupant assistance. Refueling stations can be equipped with a fueling control computer that communicates with vehicles via wireless networks to move vehicles between waiting zones, service zones and served zones. Refueling stations can include liquid fuel, compressed gas and electric charging.

Autonomous vehicle fueling with centralized scheduling

A computer includes a processor and a memory storing instructions executable by the processor to receive a series of pressure maps indicating a respective series of sitting positions of an occupant in a seat, wherein the pressure maps include a current pressure map; update a profile of the occupant based on the pressure maps, wherein the profile includes a plurality of clusters of sitting positions and classifications of the clusters as preferred or nonpreferred, and updating the profile includes sorting one of the sitting positions into one of the clusters that is classified as preferred in response to the occupant remaining in that sitting position for greater than a threshold time; and adjust a physical configuration of the seat in response to the current sitting position being in one of the clusters that is classified as nonpreferred.

Seat adjustment

A vehicle may be configured to detect an object within an interior of the vehicle using a vehicle sensor. The vehicle may further determine whether the object should be removed, based on a determined object value, such as may be determined by comparison of an object characteristic to a database of objects. Also, responsive to determining the object should be removed, the vehicle may schedule removal at an automated object-removal center. The vehicle may also wirelessly notify the object-removal center of vehicle arrival when the vehicle arrives at the object-removal center, including sending identification of the object, receiving indication from the object-removal center that the object has been removed, and confirming removal of the object by attempting to detect the object using the vehicle sensor, the confirmation occurring based on non-detection of the object by the vehicle that originally detected the object and requested removal.

Method and apparatus for vehicle interior evaluation and situational servicing

A vehicle includes one or more vehicle-exterior displays and a processor. The vehicle receives indication of a second proximate vehicle, the indication including viewing preferences for an occupant of the second proximate vehicle. The vehicle selects content for display on at least one of the vehicle-exterior displays and selects at least one display having an exterior orientation determined to be viewable based on a heading of the second proximate vehicle. The vehicle then displays the selected content on the selected display.

Method and apparatus for messaging coordination

A vehicle includes a controller, programmed to responsive to user input to pick up an item at a shop, calculate an estimated time of arrival (ETA) to the shop and a predicted preparation time for the item by the shop; and responsive to a current time being within a grace period to the preparation time before the ETA, place an order for the item.

Adaptive ordering from a vehicle

Vehicles can be equipped to operate in both autonomous and occupant piloted mode. Refueling stations can be equipped to refuel autonomous vehicles without occupant assistance. Refueling stations can be equipped with wireless networks that communicate with vehicles to permit autonomous vehicles to self-schedule refueling at fueling stations serving a mixture of autonomous, semi-autonomous and occupant piloted vehicles.

Autonomous vehicle self-scheduled refueling

A computer determines that a rate of travel value for a route segment being traversed by a first vehicle exceeds a predetermined threshold. A maneuver is planned for the first vehicle to depart from the route segment based on collected vehicle operating data from one or more second vehicles previously traversing the route segment. The first vehicle is instructed to execute the maneuver to depart the route segment.

Vehicle route navigation

An exemplary electric vehicle includes a data storage module to hold a range of past energy consumption rates for an electric vehicle, and a controller to calculate a predicted energy consumption for the electric vehicle based on variations in the past energy consumption rates across the range. An exemplary vehicle assembly includes a data storage module to hold a range of past energy consumption rates for an electric vehicle, and a controller configured to calculate a predicted energy consumption for the electric vehicle based on variations in the range. An operation of the electric vehicle is configured to alter in response to the predicted energy consumption.

Predicting energy consumption for an electric vehicle using variations in past energy consumption

An operating mode is determined for a vehicle according to respective control states of each of a plurality of vehicle subsystems that include braking, steering, and propulsion. The operating mode is one of manual control, partial manual control, and no manual control. A route for the vehicle is determined based in part on the operating mode.

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Canton, MI, United States of America

Shiqi Qiu