Ford Global Technolgoies, LLC

Ford Motor Company Limited

Lisa Scaria

Scott Vincent Myers

Francois Charette

Praveen Narayanan

Ryan Burke

Alexandru Mihai Gurghian

Harpreetsingh Banvait

Nikhil Nagraj Rao

Mohamed Ahmad

Alexandro Walsh

Punarjay Chakravarty

Erick Michael Lavoie

Jinesh J Jain

Ashley Elizabeth Micks

Brielle Reiff

Sarah Houts

Iman Soltani Bozchalooi

Mark Crawford

Kaushik Balakrishnan

Henry Salvatore Savage

Francis Assadian

Candice Xu

Jinhyong Oh

Systems, methods, and devices for reserving a vehicle with a desired vehicle characteristic are disclosed herein. A system includes a receiver to receive a request to reserve a vehicle, wherein the request indicates a desired vehicle characteristic. The system includes a controller configured to determine a change to the vehicle that will satisfy the vehicle characteristic, and the system includes an implementation component configured to implement the change to the vehicle.

User input configured dynamic shuttle

A speech conversion system is described that includes a hierarchical encoder and a decoder. The system may comprise a processor and memory storing instructions executable by the processor. The instructions may comprise to: using a second recurrent neural network (RNN) (GRU1) and a first set of encoder vectors derived from a spectrogram as input to the second RNN, determine a second concatenated sequence; determine a second set of encoder vectors by doubling a stack height and halving a length of the second concatenated sequence; using the second set of encoder vectors, determine a third set of encoder vectors; and decode the third set of encoder vectors using an attention block.

Hierarchical encoder for speech conversion system

Techniques pertaining to vehicle occupancy indication and utilization thereof are described. A method may involve determining occupancy information regarding a number of occupants in a vehicle. The method may also involve indicating the occupancy information in either or both of a human-perceivable way and a machine-perceivable way.

Vehicle occupancy indication and utilization thereof

The present invention extends to methods, systems, and computer program products for using Unmanned Aerial Vehicles (UAVs) to inspect autonomous vehicles. An autonomous vehicle carries a UAV (or 'drone') in a protected area, for example, in a glove compartment, trunk, etc. Between rides, the UAV can be deployed to inspect the autonomous vehicle. Images from the UAV can be sent to other components for image analysis. When an inspection is completed, the UAV can return to the protected area. The UAV can inspect both the interior and exterior of an autonomous vehicle. When an inspection is passed, the autonomous vehicle can begin a new ride. When an inspection is failed, the autonomous vehicle can report for repairs or summon a tow vehicle.

Using unmanned aerial vehicles to inspect autonomous vehicles

A computer, including a processor and a memory, the memory including instructions to be executed by the processor to determine performance of a plurality of vehicle data sources used to operate a vehicle by evaluating each vehicle data source output data and train a deep neural network to determine reliability for each of the vehicle data sources based on the performance using reinforcement learning. The instructions can further include instructions to combine output data from the vehicle data sources based on the reliability including using the deep neural network to correlate output data from one or more vehicle data sources to the performance to determine how accurately the output data from each vehicle data source corresponds to vehicle performance and operate the vehicle based on combined output data.

Adaptive sensor fusion

A computing system can determine a vehicle command based on a received spoken language command and determined confidence levels. The computing system can operate a vehicle based on the vehicle command. The computing system can further determine the spoken language command by processing audio spectrum data corresponding to spoken natural language with an automatic speech recognition (ASR) system.

Vehicle language processing

Systems, methods, and devices for speech transformation and generating synthetic speech using deep generative models are disclosed. A method of the disclosure includes receiving input audio data comprising a plurality of iterations of a speech utterance from a plurality of speakers. The method includes generating an input spectrogram based on the input audio data and transmitting the input spectrogram to a neural network configured to generate an output spectrogram. The method includes receiving the output spectrogram from the neural network and, based on the output spectrogram, generating synthetic audio data comprising the speech utterance.

Neural network generative modeling to transform speech utterances and augment training data

A computing system can be programmed to receive a spoken language command in response to emitting a spoken language cue and process the spoken language command with a generalized adversarial neural network (GAN) to determine a vehicle command. The computing system can be further programmed to operate a vehicle based on the vehicle command.

A computing system can translate a spoken natural language command into an intermediate constructed language command with a first deep neural network and determine a vehicle command and an intermediate constructed language response with a second deep neural network based on receiving vehicle information. The computing system can translate the intermediate constructed language response into a spoken natural language response with a third deep neural network and operate a vehicle based on the vehicle command.

A vehicle is disclosed that includes systems for adjusting the transmittance of one or more windows of the vehicle. The vehicle may include a camera outputting images taken of an occupant within the vehicle. The vehicle may also include an artificial neural network running on computer hardware carried on-board the vehicle. The artificial neural network may be trained to classify the occupant of the vehicle using the images captured by the camera as input. The vehicle may further include a controller controlling transmittance of the one or more windows based on classifications made by the artificial neural network. For example, if the artificial neural network classifies the occupant as squinting or shading his or her eyes with a hand, the controller may reduce the transmittance of a windshield, side window, or some combination thereof.

Vehicle-window-transmittance-control apparatus and method

Example ride control systems and methods are described. In one implementation, a method receives a request to initiate a drive session to help a baby fall asleep in a vehicle. The method identifies a driving route for the drive session and implements the drive session by following the driving route. One or more vehicle-mounted sensors determine whether the baby in the vehicle has fallen asleep during the drive session.

Ride control systems and methods

The present invention extends to methods, systems, and computer program products for detecting hazards in anticipation of opening vehicle doors. Vehicle sensors (e.g., rear viewing cameras) can be used to detect and classify traffic, for example, as pedestrians, bicyclists, skateboarders, roller skaters, wheel chair, etc., approaching on the side of a vehicle. When there is a possibility of a vehicle occupant opening a door into approaching traffic, a warning can be issued in the vehicle cabin to alert vehicle occupants of the approaching traffic. In one aspect, a vehicle prevents a door from opening if opening the door would likely cause an accident.

Detecting hazards in anticipation of opening vehicle doors

The present invention extends to methods, systems, and computer program products for detecting physical threats approaching a vehicle. External sensors on a vehicle capture the environment around the vehicle. Approaching targets detected by the external sensors can be fed into a neural network to recognize and/or classify approaching targets as potential threats. Tracking mechanisms (e.g., Kalman filters, Particle filters, etc.) can leverage temporal information to determine if a threat is approaching a vehicle. When an approaching threat is detected, a vehicle can activate one or more counter measures to deter the threat. When a vehicle includes autonomous driving capabilities, counter measures can include automatically attempting to drive away from an approaching threat.

Growing community of inventors

Milpitas, CA, United States of America

Lisa Scaria