Ford Global Technolgoies, LLC

Levasseur Tellis

Gerald H Engelman

Timothy D Zwicky

M Nabeel Tarabishy

John Vincent Bond, Iii

Jonas Ekmark

Alex Maurice Miller

Richard Lee Stephenson

Sarra Awad Yako

Jonas L Z Jansson

Shane Elwart

Brian Robert Spahn

John Patrick Joyce

Anthony Mario D'Amato

Christopher Scott Nave

Ahmad Khalifeh

Gyu Myeong Joh

Shadi Jammoul

Andy Chuan Hsia

S M Iskander Farooq

Jianbo Lu

Manoharprasad K Rao

Thomas Edward Pilutti

Jialiang Le

Mathew Alan Boesch

Douglas Scott Rhode

Mark Douglas Malone

Michael Hafner

Jeffrey Dan Rupp

David J Schmidt

Matt Y Rupp

Colm Peter Boran

Xingping Chen

Christopher Semanson

Stephen Varghese Samuel

Tyler Glenn

Daniel Domek Eisele

Henrik Johannesson Lind

Reid James Steiger

Kyle Richard Post

Greg Blaisdell Stevens

Jonas Lars Zakarias Jansson

Sophia Dancel

Nigel John Clarke

Paul James Novak

Jiyi Liu

Robert E Williams

Ju Chen

Alexander Modigsson

Jeffrey Daniel Rupp

Roger A Trombley, Jr

A method to manage a vehicle is disclosed. The method may include obtaining vehicle inputs that may include an image captured by a vehicle camera. The method may further include estimating a time delay in teleoperation communication with the vehicle, and generating an augmented reality image based on the time delay and the vehicle inputs. The method may further include rendering the augmented reality image on a user interface to manage the vehicle.

Augmented reality for teleoperation

A computer, including a processor and a memory, the memory including instructions to be executed by the processor to identify patterns in first high anticipation scenarios based on user identification, wherein high anticipation scenarios include video sequences wherein a first vehicle will be within a specified distance of a first object in a first environment around the first vehicle, wherein user identification is determined by viewing portions of a respective video sequence. A first model including a first deep neural network can be trained to determine second high anticipation scenarios based on the patterns identified in the first high anticipation scenarios and a second model including a second deep neural network can be trained to modify locations and velocities of second objects in the first high anticipation scenarios and output modified high anticipation scenarios. The computer can be further programmed to train a third model including a third deep neural network to operate a vehicle based on the modified high anticipation scenarios output by the second model.

Vehicle operation labeling

Apparatuses and methods for automated parking of autonomous vehicles are provided herein. An example method includes receiving, by an automated driving system of an autonomous vehicle, a pull over request during a trip to a destination; initiating, by the automated driving system, a pull over sequence, the pull over sequence including determining if the autonomous vehicle is within a pre-determined distance from the destination; stopping the autonomous vehicle when the autonomous vehicle is within the pre-determined distance; and when the autonomous vehicle is not within the pre-determined distance, initiating a parking sequence to park the autonomous vehicle at a nearest available parking location.

Systems and methods for automated stopping and/or parking of autonomous vehicles

A vehicle system includes an autonomous mode controller and a processor. The autonomous mode controller is programmed to control a host vehicle in a partially autonomous mode. The processor is programmed to identify a driver, determine whether the driver is authorized to operate the host vehicle in the partially autonomous mode, and disable the partially autonomous mode if the driver is not authorized to operate the host vehicle in the partially autonomous mode.

Permissions for partially autonomous vehicle operation

First and second sets of data are collected in a vehicle from respective data sources. Each of the first and second sets of data are provided for determinations respectively selected from first and second categories of autonomous vehicle operations. A determination is made to take an autonomous action selected from the first category of autonomous vehicle operations. Data is used from each of the first and second data sets relating respectively to the first and second categories of autonomous vehicle operations to determine the autonomous action.

Autonomous driving sensing system and method

A controller includes a processor and a memory storing processor-executable instructions. The processor is programmed to control steering and at least one of propulsion and braking of a vehicle while following a route segment and, during the route segment, to activate a user interface at a frequency based on at least one of an elapsed duration of the route segment, an age of a user, a time of day, and a route-segment topology.

Vehicle user-communication system and method

A computer is programmed to score a requested vehicle lane change and actuate vehicle components to perform the lane change upon determining that the score is less than a predetermined threshold within the predetermined time.

Vehicle lane changing

A vehicle includes: (a) a rotatable door including: a door stop assembly configured to arrest opening of the door; a rotatable handle; a haptic feedback assembly arranged to vibrate the handle; (b) processor(s) configured to simultaneously activate the door stop and haptic feedback assemblies. The haptic feedback assembly is disposed within the rotatable handle. The processor(s) are configured to: compute a time-to-collision, and run the haptic feedback assembly at a level based on the time-to-collision.

Systems and methods for door collision avoidance

A controller includes a processor and a memory storing processor-executable instructions. The processor is programmed to activate a first autonomy mode to control steering and at least one of propulsion and braking. The processor is further programmed to, while in the first autonomy mode, disregard a command from an occupant to perform an action interfering with an ability of the occupant to one of see a field of travel of a vehicle and provide input to the vehicle.

Semiautonomous vehicle control system

A vehicle system includes a user interface, an autonomous mode controller, and a processor. The autonomous mode controller controls a host vehicle in a partially autonomous mode. The processor receives a first user input requesting the partially autonomous mode and command the user interface to present a tutorial associated with the partially autonomous mode. The processor receives a second user input indicating that a driver completed the tutorial and activates the partially autonomous mode in response to receiving the second user input.

Advanced autonomous vehicle tutorial

A vehicle system includes at least one autonomous driving sensor programmed to output environment signals representing an environment around an autonomous vehicle and object detection signals representing an object or person on an exterior surface of the autonomous vehicle. The vehicle system further includes an autonomous mode controller programmed to autonomously control the autonomous vehicle in accordance with the environment signals output by the autonomous driving sensor, detect the object or person on the exterior surface of the autonomous vehicle in accordance with the object detection signals, and limit autonomous operation of the autonomous vehicle in response to detecting the object or person on the exterior surface of the autonomous vehicle.

Vehicle exterior surface object detection

A control system for a vehicle includes a computer. The computer is programmed to command application of one of up to a predetermined steering torque value and up to a predetermined net asymmetric braking force value. Each predetermined force is selected to achieve a predetermined vehicle yaw torque that is at most the lesser of a first maximum yaw torque resulting from actuating a steering system and a second maximum yaw torque resulting from actuating a brake system.

Steering and braking control system

A vehicle has a restraint system with at least two restraint devices. A sensor detects a potential collision object and outputs a signal representing a relative position of the potential collision object to the vehicle. A processing device suppresses deployment of at least one of the restraint devices based at least in part on the signal output by the at least one sensor.

Adaptive suppression of vehicle restraint system

A vehicle includes an autonomous driving sensor configured to detect a road condition and output at least one road condition signal representing the road condition, an autonomous mode controller configured to control the vehicle according to the at least one road condition signal, and a communication module configured to broadcast the road condition signal.

Autonomous vehicle handling and performance adjustment

A vehicle may be steered without a driver's hands being on a vehicle steering control mechanism. A presence of an object within a predetermined distance of the vehicle may be detected using data from at least one object detection sensor that provides data to at least one of a passive safety system, a lane control system, a speed control system, and a brake control system. A steering control mechanism hands-on mode can then be enabled based at least in part on the presence of the object.

Hands-off steering wheel governed by pedestrian detection

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Southfield, MI, United States of America

Levasseur Tellis