Ford Global Technolgoies, LLC

University of Michigan

Johannes Geir Kristinsson

Fling Finn Tseng

Ryan Abraham McGee

Hai Yu

Shiqi Qiu

Dimitar Petrov Filev

Himanshu Verma

Fazal Urrahman Syed

Kalyani Purushottam Sonawane

Jason Meyer

David Anthony Hatton

Krishnaswamy Venkatesh Prasad

Hussein F Nasrallah

Bhagyashri Satyabodha Katti

Aed M Dudar

Ming Lang Kuang

Shane Elwart

Thomas Edward Pilutti

Mathew Alan Boesch

Yonghua Li

Hsin-Hsiang Yang

Patrick Maloney

Anthony Mark Phillips

Thomas Nelson

Helen Elizabeth Kourous-Harrigan

Joseph Wisniewski

Alex Perkins

David Alan Hamilton

Payam Naghshtabrizi

Swati Johar Rawat

Nikhil Kailas Jamdade

Kwaku O Prakah-Asante

Imad Hassan Makki

Yifan Chen

Devesh Upadhyay

Daryl Joseph Martin

Victoria Leigh Schein

Venkatapathi Raju Nallapa

Basavaraj Tonshal

Timothy Mark Feldkamp

Joseph Gerald Supina

Vishal Vinayak Nageshkar

Arun Dutta

Nithya Somanath

John Michael Galan Ferrer

Tom Nelson

Mahmoud A Abou-Nasr

Padma Aiswarya Kolisetty

Ryan Lee Baker

Aiswarya Kolisetty

Jessamyn Smallenburg

Jonathan Niemi

Wenduo Wang

Jungme Park

Yi Murphey

Chen Fang

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 profile for an occupant is stored. The profile includes a plurality of clusters of sitting positions for the occupant in a seat and classifications of respective clusters as preferred or nonpreferred. Respective clusters are classified as preferred or nonpreferred based on sitting scores for the respective sitting positions in that cluster. A higher sitting score increases a likelihood of the classification being preferred. A series of pressure maps indicating a respective series of sitting positions of the occupant in the seat are received. The pressure maps include a current pressure map. One of the sitting positions is assigned to one of the clusters that is classified as preferred in response to the sitting score of that sitting position being greater than a threshold score. A physical configuration of the seat is adjusted in response to the current sitting position being in one of the clusters that is classified as nonpreferred.

Seat adjustment

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 vehicle may determine that erratic vehicle behavior has been sensed, based on comparison of a sensed vehicle behavioral characteristic at a given location compared to a predefined expected value of the characteristic. The vehicle may further determine whether an environmental anomaly has been detected in association with the given location and classify the sensed erratic behavior based on whether the environmental anomaly was detected. Responsive to classifying the behavior as erratic based on determining no environmental anomaly was detected, the vehicle may report the erratic behavior to a remote server, along with the given location. The remote server may receive a plurality of such reports for a given location and update a classification of the behavior based on data indicated in the plurality of reports.

Vehicular anomaly detection, reporting, and dynamic response

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

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.

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

An indication of a physical location specified as a name of a geoshortcut is received. The name is identified as corresponds to the geoshortcut according to a format of the name matching a format of geoshortcut name identifiers. The name is sent, to a cloud server maintaining a geoshortcut service, in a request for geoshortcut information regarding the geoshortcut. The geoshortcut information regarding the geoshortcut is received from the cloud server responsive to the request, the geoshortcut information including data identifying the physical location in a native format as received, data identifying the physical location in a predefined location format available across geoshortcuts, and expiration data indicative of for how long the data identifying the physical location is deemed valid. The geoshortcut information is utilized to determine the physical location referenced by the name of the geoshortcut.

Geoshortcuts

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

A device that includes a processor and a memory, the memory storing instructions executable by the processor such that the device is programmed to identify a wearable device and a set of vehicle data from a vehicle. The user device sends a human machine interface (HMI) message to the wearable device. The HMI message is based at least in part on the set of vehicle data.

Customizable reporting with a wearable device

A vehicle includes a controller programmed to identify tag phrases for locations that express a relationship between the location and a learned location tag. The tag phrases are based on words and word sequences learned from prior selections and inputs of a user. The controller is programmed to communicate the tag phrases to the user and provide trip assistance based on a selected tag phrase chosen by the user. The locations and tag phrases are accessible to devices such that locations are identified with common tag phrases on each of the devices.

Location tagging for ubiquitous access

An example method of crowd-sourcing energy consumption data for vehicles includes controlling a vehicle in response to a predicted energy consumption that is continually updated based on a difference between a previous predicted energy consumption and a previous actual base energy consumption. Another example method of routing a vehicle includes changing a route for a vehicle in response to a predicted energy consumption for the vehicle when travelling the route, the predicted energy consumption based on a difference between a previous predicted energy consumption and a previous actual base energy consumption.

Crowd sourcing to predict vehicle energy consumption

An adjustable vehicle traction battery charge setpoint strategy is disclosed which enables the adjustment of the maximum battery State of Charge (SOC) setpoint used for battery charging from an electric utility grid. Based on knowledge of the upcoming route and related driving behavior, the vehicle calculates a setpoint less than the maximum battery SOC charging setpoint so that when the vehicle begins operation, it can utilize regenerative braking and historical driving behavior to allow the battery charge to be maximized during the trip.

Vehicle battery charge setpoint control

A vehicle includes a horn, a plurality of exterior lights, and at least one controller. The at least one controller is configured to grant vehicle system access to a nomadic device paired to the vehicle, and in response to receiving a message that contains data identifying a source of the message while the vehicle is in a key-off state from a nomadic device not paired to the vehicle, activate the horn and exterior lights according to respective patterns.

Vehicle alert from a non-paired device

Growing community of inventors

Ann Arbor, MI, United States of America

Johannes Geir Kristinsson