Ford Global Technolgoies, LLC

Richard Twumasi-Boakye

James Fishelson

Archak Mittal

Xiaolin Cai

Andrea Broaddus

Dhanush Harihar

Mohammed Fayaj Navaz

Amit Bhagwan

Eric H Wingfield

Shan Chy Chueh

Girish Yadav

Jon Coleman

The disclosure generally pertains to systems and methods to classify a road based on a level of support offered by the road for autonomous driving operations. An example method may involve a computer receiving sensor data from a vehicle, the sensor data containing information about a current functional condition of a road. The computer may predict a future functional condition of the road by using a deterioration model to evaluate the sensor data. The computer may then determine a level of support offered by the road for autonomous driving operations based on the future functional condition of the road, and assign a classification to the road based on the level of support offered by the road for autonomous driving operations. The level of support offered by the road for autonomous driving operations may also be based on items such as road markings, traffic signs, traffic signals, and/or infrastructure elements.

Systems and methods to classify a road based on a level of suppport offered by the road for autonomous driving operations

Robot-assisted package delivery with integrated curbside parking monitoring and curb operations optimization is disclosed herein. An example method includes dispatching a delivery vehicle and delivery robot to a delivery location, the delivery location including a parking location for the delivery vehicle that allows for deployment of the delivery robot on a delivery mission, determining occupancy of the parking location, the delivery vehicle parking at the parking location when the parking location is unoccupied, the delivery robot being deployed upon parking of the delivery vehicle, and instructing the delivery vehicle to remain parked during the delivery mission or to leave the parking location and return at later point in time based on an estimated time of arrival of the delivery robot after the delivery mission.

Robot-assisted package delivery with integrated curbside parking monitoring and curb operations optimization

Systems and methods for predicting travel patterns of a vehicle or one or more occupants in the vehicle are provided. A transceiver may receive data indicative of at least one of passenger information, vehicle occupancy, vehicle status, and vehicle location from at least one of a voice recognition device, IoT sensors such as seat sensors, vehicle status sensors, or a GPS system. A processor may determine travel patterns based on the data, predict travel patterns based on the determined travel patterns, and generate a database comprising the predicted travel patterns. In addition, the processor may generate an alert if the determined travel pattern deviates from the predicted travel patterns.

Systems and methods for using in-vehicle voce recognition, IoT sensors and vehicle state data for augmenting car-generated GPS/location-based data for predicting travel patterns

Hybrid light personal electric vehicle analysis is disclosed herein. An example method includes determining empirical data from a light personal electric vehicle (LPEV) over a period of time, determining LPEV behaviors from the empirical data during the period of time, and building a hybrid simulation model over the period of time by switching between a pedestrian simulation model when the LPEV behaviors correspond to pedestrian behaviors and a vehicle simulation model when the LPEV behaviors correspond to vehicle behaviors.

Hybrid light personal electric vehicle analysis

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Taylor, MI, United States of America

Richard Twumasi-Boakye