Ford Global Technolgoies, LLC

Joseph Wisniewski

Perry Robinson MacNeille

Aiswarya Kolisetty

Perry Robinson Macneille

Fling Finn Tseng

Omar Makke

Johannes Geir Kristinsson

Nunzio Decia

Nicholas Colella

Nithya Somanath

David Alan Hamilton

Bhagyashri Satyabodha Katti

Kevin Lee Helpingstine

Matthew S Blackburn

Aed M Dudar

Dimitar Petrov Filev

Kwaku O Prakah-Asante

Imad Hassan Makki

Taylor Hawley

Finn Finn Tseng

Shiqi Qiu

Andrea Bowes Chowanic

Ayush Shah

John Michael Galan Ferrer

Prashant Rao

Harald C Martinez

A system for accessing vehicle sensor data. The system includes receiving vehicle sensor data and a vehicle location associated with the vehicle sensor data from a publisher vehicle. A subscriber request for remote data is received from a service subscriber. The subscriber request includes a request location. A match condition is determined. The match condition includes a vehicle location match between the request location and the vehicle location. The vehicle sensor data is transmitted to the service subscriber upon the occurrence of the match condition.

Remote sensor access

A method for controlling a vehicle includes identifying a user identity associated with a user riding in the vehicle, determining a destination associated with the user identity, creating a detection zone proximate to the vehicle when the vehicle is localized in a first drop-off location associated with the destination, causing a sensory system to identify a hazard within the detection zone and generate a localization of the hazard, determining that a first probability of realizing a risk associated with the hazard is less than a first probability threshold, and generating a vehicle door actuation that permits the user to exit the vehicle based on the first probability of realizing the risk.

Autonomous vehicle rider drop-off sensory systems and methods

A system is disclosed that may comprise: one or more processors of a center node, wherein the center node is a vehicle computer; and memory of the center node, wherein the memory stores instructions executable by the one or more processors, the instructions comprising to: determine a first network comprising the center node and a plurality of member nodes based on a mesh network of the center node and the plurality of member nodes; add at least one virtual node to the first network; and using the first network, exchange cryptographic data between the at least one virtual node, the center node, and the plurality of member nodes.

Vehicle blockchain transactions

A system includes a processor configured to determine traffic density over a road segment. The processor is also configured to model traffic parameters to maximize traffic flow over the road segment, based on the traffic density and travel characteristic data received from a plurality of vehicles exiting the road segment. The processor is further configured to determine a speed to density curve, using the model, that would maximize traffic flow and send the speed to density curve to a vehicle entering the road segment.

Method and apparatus for congestion reduction through cooperative adaptive cruise control

A system includes a processor configured to wirelessly instruct fuel dispensation initiation over a direct wireless connection between a vehicle and a refueling truck, responsive to a wireless request made by the refueling truck, the request including a valid token and a refueling truck MAC ID with which the wireless connection is established.

Method and apparatus for secure processing of fuel delivery requests

A vehicle includes a vehicle computing system (VCS) and an adaptive cruise control module, a lane departure warning (LDW) module, a navigation system, or an alert mechanism (e.g., audible, visual or tactile). The VCS communicates with an activity tracking device worn by a vehicle driver. The activity tracking device detects and records biometric characteristics of the driver, and is capable of exchanging data with the VCS. The VCS generates at least one output signal based on data from the activity tracking device. An adaptive cruise control module may increase the distance between the vehicle and a tracking vehicle based on the signal. A navigation system may generate a recommended parking location based on a difference between step goals and current steps detected by the activity tracking device.

Vehicle operation based on activity tracking

Apparatus and methods are disclosed for vehicle-to-vehicle cooperation to marshal traffic. An example disclosed cooperative vehicle includes an example vehicle-to-vehicle communication module and an example cooperative adaptive cruise control module. The example cooperative adaptive cruise control module determines a location of a traffic cataract. The example cooperative adaptive cruise control module also coordinates with other cooperative vehicles to form a platoon of standard vehicles. Additionally, the example cooperative adaptive cruise control module coordinates with other the cooperative vehicles to move the formed platoon through the traffic cataract at a constant speed.

Vehicle-to-vehicle cooperation to marshal traffic

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.

Vehicle mode determination

Growing community of inventors

Royal Oak, MI, United States of America

Joseph Wisniewski