Ford Global Technolgoies, LLC

Ford Motor Company Limited

The Toro Company

Scott Vincent Myers

Ashley Elizabeth Micks

Harpreetsingh Banvait

Alexandru Mihai Gurghian

Venkatapathi Raju Nallapa

Lisa Scaria

Sneha Kadetotad

Mohamed Ahmad

Shane Elwart

Nikhil Nagraj Rao

Alexandro Walsh

Vidya Nariyambut Murali

Francois Charette

David A Herman

Parsa Mahmoudieh

Jinesh J Jain

Ramchandra Ganesh Karandikar

Bibhrajit Halder

Ashok E Rodrigues

Jonathan Thomas Mullen

Walter Joseph Talamonti

Brielle Reiff

Zachary David Nelson

Maryam Moosaei

Madeline Jane Schrier

Madeline Jane Goh

Mark Crawford

Tory Smith

Nithika Sivashankar

Fazal Urrahman Syed

Praveen Narayanan

Wei Xu

Gerald L Mock

Alexander Groh

Jason Lester Hill

Kyu Jeong Han

Joshua Scott Smith

Paul A Standerfer

Connie Zeng

John F Fuller

Stanley C Campbell

Russ Huffman

David Silver

Utah Iev

Tu Minh Nguyen

Stephan Crain

Steven A Snow

Harpreestsingh Banvait

The disclosure relates to methods, systems, and apparatuses for autonomous driving vehicles or driving assistance systems and more particularly relates to vehicle radar perception and location. The vehicle driving system disclosed may include a storage media, a radar system, a location component and a driver controller. The storage media stores a map of roadways. The radar system is configured to generate perception information from a region near the vehicle. The location component is configured to determine a location of the vehicle on the map based on the radar perception information and other navigation related data. The drive controller is configured to control driving of the vehicle based on the map and the determined location.

Vehicle radar perception and localization

A method for package delivery includes identifying a plurality of delivery locations for package delivery. The method includes determining a driving route for an automated ground vehicle to optimize delivery to the delivery locations using one or more automated aerial vehicles. The method includes controlling the automated ground vehicle to navigate the delivery route. The method further includes determining timing for release of the one or more automated aerial vehicles during navigation of the delivery route to deliver packages to the plurality of delivery locations.

Systems, methods, and devices for automated vehicle and drone delivery

Example user identification systems and methods are described. In one implementation, a method determines a first step pulse associated with a user carrying a key fob and detects a user device proximate the key fob. The method further identifies a second step pulse measured by the user device and determines whether the first and second step pulses match. If the step pulses match, the user carrying the key fob is identified as the owner of the user device.

User identification systems and methods

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

A method is disclosed for using an autonomous vehicle to teach a student how to drive. The method may include generating, by the autonomous vehicle during a training session, a plan for navigating a section of road. During the training session, the autonomous vehicle may receive control instructions from the student regarding how the student would like to navigate the section of road. If the autonomous vehicle determines that implementing the instructions would be safe and legal, it may implement those instructions. However, if the autonomous vehicle determines that implementing the instructions would not be safe and legal, it may execute the plan. During a training session, an autonomous vehicle may provide on a head-up display to the student certain visual feedback regarding a driving performance of the student.

Autonomous vehicle providing driver education

A method for generating training data is disclosed. The method may include executing a simulation process. The simulation process may include traversing a virtual, forward-looking sensor over a virtual road surface defining at least one virtual railroad crossing. During the traversing, the virtual sensor may be moved with respect to the virtual road surface as dictated by a vehicle-motion model modeling motion of a vehicle driving on the virtual road surface while carrying the virtual sensor. Virtual sensor data characterizing the virtual road surface may be recorded. The virtual sensor data may correspond to what a real sensor would have output had it sensed the road surface in the real world.

Virtual sensor-data-generation system and method supporting development of algorithms facilitating navigation of railway crossings in varying weather conditions

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 UAV includes a fork lift system and a length component. The forklift system includes one or more elongated members extending at least partially in a horizontal direction. The forklift system also includes an extension mechanism configured to selectively retract and extend the one or more elongated members relative to an opposing surface. For example, the elongated members may include the tines of the fork or supporting member of the forklift system. The length component is configured to control the extension mechanism to adjust a distance between the opposing surface and the one or more elongated members to accommodate a payload.

Drone forklift

The disclosure relates generally to methods, systems, and apparatuses for automated or assisted driving and more particularly relates to identification, localization, and navigation with respect to bollard receivers. A method for detecting bollard receivers includes receiving perception data from one or more perception sensors of a vehicle. The method includes determining, based on the perception data, a location of one or more bollard receivers in relation to a body of the vehicle. The method also includes providing an indication of the location of the one or more bollard receivers to one or more of a driver and component or system that makes driving maneuver decisions.

Bollard receiver identification

A method for processing audible sounds using ultrasonic sensors. The method includes passively monitoring, via ultrasonic sensors, an external environment for a audible sounds. An audible sound may be detected and used to produce a sound signal. The sound signal may be filtered to determine one or more features corresponding thereto, including a class, a position, and a velocity. A priority may be assigned to the sound signal based on the sound signal to determine an appropriate response. A corresponding system and computer program product are also disclosed and claimed herein.

Passive sound source classification and localization

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

A vehicle includes one or more laterally mounted microphones and a controller programmed to detect a signature of an unoccupied position adjacent the vehicle in outputs of the microphones. The signature may be identified using a machine learning algorithm. In response to detecting an unoccupied position, the controller may invoke autonomous parking, store the location of the unoccupied position for later use, and/or report the unoccupied position to a server, which then informs other vehicles of the available parking. The unoccupied position may be verified by evaluating whether map data indicates legal parking at that location. The unoccupied position may also be confirmed with one or more other sensors, such as a camera, LIDAR, RADAR, SONAR, or other type of sensor.

System and methods for identifying unoccupied parking positions

A method for determining lane information includes receiving perception data from at least two sensors, the at least two sensors including a rear facing camera of a vehicle. The method includes determining, based on the perception data, a number of lanes on a roadway within a field of view captured by the perception data using a neural network. The method includes providing an indication of the number of lanes to an automated driving system or driving assistance system.

Rear camera lane detection

Example systems and methods for detecting an animal proximate a vehicle are described. In one implementation, a wearable device carried by an animal is activated when the wearable device is within a predetermined distance of a vehicle. The vehicle receives a signal from the wearable device and determines an approximate distance between the wearable device and the vehicle. An alert is generated to warn a driver that an animal is near the vehicle. The alert has an intensity level that corresponds to the approximate distance between the wearable device and the vehicle.

Detecting an animal proximate a vehicle

Growing community of inventors

Camarillo, CA, United States of America

Scott Vincent Myers