Ford Global Technologies, LLC

Ford Motor Company Limited

Fling Finn Tseng

Dimitar Petrov Filev

Kwaku O Prakah-Asante

Aed M Dudar

Imad Hassan Makki

Johannes Geir Kristinsson

Jianbo Lu

Himanshu Verma

Shiqi Qiu

Robert Roy Jentz

Hsin-Hsiang Yang

Steven Joseph Szwabowski

Jason Meyer

Fazal Urrahman Syed

Perry Robinson Macneille

Ryan Abraham McGee

Sangeetha Sangameswaran

Pankaj Kumar

Bhagyashri Satyabodha Katti

Kalyani Purushottam Sonawane

Swati Johar Rawat

John Ottavio Michelini

Gary Steven Strumolo

Xiaoming Wang

Shane Elwart

Hai Yu

Yonghua Li

Patrick Maloney

Hassene Jammoussi

Jason Michael Miller

Joseph F Stanek

Qing Wang

Anthony Mario D'Amato

Timothy Mark Feldkamp

Helen Elizabeth Kourous-Harrigan

Medville Jay Throop

Fakhreddine Landolsi

Joseph Wisniewski

Daniel Joseph Madrid

Alex Perkins

Ryan Lee Baker

Nikhil Kailas Jamdade

Erick Michael Lavoie

Mahmoud Yousef Ghannam

Ilya V Kolmanovsky

Roger Arnold Trombley

Oleg Yurievitch Gusikhin

Dennis Seung-Man Yang

Russell Randall Pearce

Thomas Edward Pilutti

William David Treharne

Devesh Upadhyay

Daryl Joseph Martin

Victoria Leigh Schein

Jeffrey Allen Greenberg

Anthony Mark Phillips

Christopher John Teslak

Vishal Vinayak Nageshkar

Taylor Hawley

David Scott Rohweder

Brian David Tillman

John Michael Galan Ferrer

Payam Naghshtabrizi

Szushen Peter Ho

Prashant Rao

Chingpo Liu

Paul Eugene Coffman, Jr

Jessamyn Smallenburg

Jonathan Niemi

Kelly Trost

Samuel Laidlaw

Jóhannes Geir Kristinsson

Daniel Haakenson

David Starr

Electric vehicle battery monitoring systems and methods are disclosed herein. An example method includes displaying a state of charge for a battery of an electric vehicle on a display screen of the electric vehicle, determining a frequency of how often a user views the state of charge, inferring that the user has anxiety over a range for the electric vehicle based on the frequency, and presenting the user with options to extend the state of charge.

Electric vehicle battery monitoring systems and methods

An example method executed by processor of a vehicle payload assistance system may include obtaining information about a first laden weight of a vehicle. The processor determines a weight difference between the first laden weight and a payload capacity of the vehicle, and further determines, based on the weight difference, a payload margin available for loading the vehicle. A vehicle payload assistance operation may then be executed that includes detecting a change in the first laden weight due to introduction of an object into the vehicle, obtaining information about a second laden weight of the vehicle after introduction of the object into the vehicle, determining that the second laden weight exceeds the payload margin available for loading the vehicle, and issuing an advisory to reduce the second laden weight by removing the object from the vehicle.

Systems and methods for providing vehicle payload assistance

A vehicle charging management method is disclosed. The method may include obtaining a first input associated with a vehicle. The first input may include a state of charge (SOC) level of a vehicle battery, a distance travelled by the vehicle since last charge, and time since the last charge. The method may further include obtaining a second input associated with historical vehicle battery charging information. Responsive to obtaining the first input and the second input, the method may include determining a vehicle user intent to charge the vehicle battery. The method may further include comparing the vehicle user intent with a threshold value, and transmitting a notification to a communication device when the vehicle user intent is greater than the threshold value.

Vehicle charge intent prediction and recommendation systems and methods

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

A computer includes a processor and a memory storing instructions executable by the processor to identify a first scenario in which a vehicle is operating from a plurality of scenarios, prompt an operator to activate an adaptive cruise control of the vehicle in response to a preference score for the first scenario being above a threshold, refrain from prompting the operator to activate the adaptive cruise control in response to the preference score for the first scenario being below the threshold, and activate the adaptive cruise control in response to receiving an input to activate the adaptive cruise control from the operator. The scenarios indicate at least one characteristic of a road on which the vehicle is traveling. The preference score indicates a preference of the operator for activating the adaptive cruise control in the first scenario.

Adaptive cruise control activation

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

The disclosure is generally directed to systems and methods for facilitating travel over a last leg of a journey. In one example embodiment, an unmanned aerial vehicle (UAV) captures an image of an area to be traversed by an individual during the final leg of the journey. The image is evaluated to identify a hindrance that may be encountered by the individual in the area. A pre-emptive action may be taken to address the hindrance before reaching the area. The pre-emptive action, may, for example, include using the UAV and/or a terrestrial robot to assist the individual traverse the area. The assistance may be provided in various ways such as by use of the terrestrial robot to transport the individual and/or a personal item of the individual through the area, and/or by use of the UAV to provide instructions to guide the individual through the area.

Systems and methods for facilitating a final leg of a journey

The disclosure generally pertains to recommending an alternative off-road track to a driver of a vehicle. In an example method, a processor receives a sensor signal that is generated in response to an operating condition of the vehicle on an off-road track. The operating condition can include, for example, an angular orientation of a chassis portion of the vehicle with respect to a ground surface (yaw, pitch, roll, etc.), steering characteristics, speed characteristics, and/or braking characteristics. The processor evaluates the sensor signal to identify an attribute of the off-road track, such as, for example, a difficulty factor and/or a challenge level. The processor may assign a track score to the off-road track based on the attribute and may provide to the driver, a recommendation for use of another off-road track based on comparing the first track score to a second track score associated with the other off-road track.

Recommending an alternative off-road track to a driver of a vehicle

The disclosure is generally directed to in-vehicle advertisement systems and methods. An example method executed by processor of a vehicle may include determining an attention availability index of a driver of a vehicle and determining a first presentation duration of a first advertisement based on the attention availability index. In one scenario, the attention availability index of the driver may be determined by applying an inverse relationship to a concentration index associated with driving the vehicle. The first advertisement is presented over the first presentation duration, via an advertisement presentation device in the vehicle. In another scenario, the attention availability index of the driver may be compared to a threshold value. Based on the comparison, a second presentation duration for a second advertisement may be determined and the second advertisement presented via the advertisement presentation device in the vehicle.

In-vehicle advertisement systems and methods

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, comprising a computer including a processor and a memory storing instructions executable by the processor to determine a first lateral boundary for movement of a vehicle. The first lateral boundary is parallel to a longitudinal axis of the vehicle and is based on at least a size of the vehicle. Based on at least the size of the vehicle and a detected yaw rate of the vehicle, the instructions include to determine a wind condition at a location. The instructions include to update a distance of the first lateral boundary from the longitudinal axis to obtain an updated first lateral boundary, based on the wind condition.

Vehicle boundary control

The disclosure generally pertains to systems and methods for providing a personalized advertisement service. In an example method, first information associated with a vehicle can be identified. The first information can include at least a present location of the vehicle, a present speed of the vehicle, and traffic information associated with a present route of the vehicle. Second information associated with a driver of the vehicle can also be identified. An advertisement interaction score can be calculated based at least in part on the first information and the second information. A nature of an advertisement to be displayed at a vehicle infotainment system of the vehicle can be subsequently determined. The nature of the advertisement can comprise a viewable characteristic or a clickable characteristic. The advertisement can then be displayed at the vehicle infotainment system.

Systems and methods for providing a personalized advertisement service

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 puddle light system projects content-bearing images onto a ground surface adjacent a vehicle using a puddle light projector unit having a multicolor light source configured to project the content-bearing images using at least one selected color from a plurality of available colors. An image sensor directed at the ground surface and mounted on the vehicle is configured to capture colored images. A controller is configured to (A) construct one of the content-bearing images as an image representation with an illumination region to be projected onto the ground, (B) analyze a first colored image of the ground surface with the light source deactivated to identify a dominant surface color, (C) select a complementary color relative to the dominant surface color, (D) colorize the illumination region using the selected complementary color, and (E) command the puddle light projector unit to project the image representation using the colorized illumination region.

Adaptive color selection for vehicle puddle lights

Growing community of inventors

Ann Arbor, MI, United States of America

Fling Finn Tseng