Ford Global Technolgoies, LLC

Ford Motor Company Limited

Vimoc Technologies, Inc.

Alexandru Mihai Gurghian

Scott Vincent Myers

Harpreetsingh Banvait

Mohamed Ahmad

Vidya Nariyambut Murali

Ashley Elizabeth Micks

Lisa Scaria

Venkatapathi Raju Nallapa

Parsa Mahmoudieh

Sarah Houts

Alexandro Walsh

Tory Smith

Francois Charette

Nikhil Nagraj Rao

Mark Crawford

Natalie Muenster

Ravin Ratnakant Sardal

Praveen Narayanan

Tejaswi Koduri

Ramchandra Ganesh Karandikar

Kyle J Carey

Aaron James Hector

David Silver

According to one embodiment, a system for determining a position of a vehicle includes an image sensor, a top-down view component, a comparison component, and a location component. The image sensor obtains an image of an environment near a vehicle. The top-down view component is configured to generate a top-down view of a ground surface based on the image of the environment. The comparison component is configured to compare the top-down image with a map, the map comprising a top-down light LIDAR intensity map or a vector-based semantic map. The location component is configured to determine a location of the vehicle on the map based on the comparison.

Vehicle localization using cameras

Techniques and examples pertaining to virtual reality remote valet parking are described. A processor of a control system of a vehicle may establish a wireless communication with a remote controller. The processor may provide a stream of video images captured by a camera of the vehicle to the remote controller. The processor may receive a signal from the remote controller. The processor may maneuver the vehicle from one location to another according to the signal.

Virtual reality remote valet parking

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

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

The disclosed technology for building an edge convolutional neural network (CNN) system for IoT includes training on-site processors to analyze image data and identify motorized vehicles, bicycles and people in near-real-time, using a big cloud CNN, a small cloud CNN and an on-site CNN. At least five hundred site-specific images from cameras are analyzed using the big cloud CNN to produce a machine-generated training set that includes an image that has regions, and for each region, coordinates of bounding boxes for objects in the region, and classification of contents of the bounding boxes as a motorized vehicle, bicycle or person; and the training set. The machine-generated training set gets used to train the small cloud CNN; and coefficients from the trained small cloud CNN get transferred to the on-site CNN, thereby configuring the on-site CNN to recognize motorized vehicles, bicycles and people in images from the cameras in near-real-time.

System and method for building an edge CNN system for the internet of things

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

Methods and systems for opening an access point of a vehicle. A system and a method may involve receiving wirelessly a signal from a remote controller carried by a user. The system and the method may further involve receiving audio or video data indicating the user approaching the vehicle. The system and the method may also involve determining an intention of the user to access an interior of the vehicle based on the audio or video data. The system and the method may also involve opening an access point of the vehicle responsive to the determining of the intention of the user to access the interior of the vehicle.

Methods and systems for opening of a vehicle access point using audio or video data associated with a user

A method for detecting stubs or intersecting roadways includes receiving perception data from at least two sensors. The at least two sensors include a rear facing camera of a vehicle and another sensor. The perception data includes information for a current roadway on which the vehicle is located. The method includes detecting, based on the perception data, an intersecting roadway connecting with the current roadway. The method also includes storing an indication of a location and a direction of the intersecting roadway with respect to the current roadway.

Rear camera stub detection

Example sinkhole detection systems and methods are described. In one implementation, a method receives data from multiple sensors mounted to a vehicle and analyzes the received data to identify a sinkhole in a roadway ahead of the vehicle. If a sinkhole is identified, the method adjusts vehicle operations and reports the sinkhole to a shared database and/or another vehicle.

Sinkhole detection systems and methods

Example lane detection systems and methods are described. In one implementation, a method received an image from-facing vehicle camera and applies a geometric transformation to the image to create a birds-eye view of the image. The method analyzes the birds-eye view of the image using a neural network, which was previously trained using side-facing vehicl camera images, to determine a lane position associated with the birds-eye view of the image.

Lane detection systems and methods

Example inductive loop detection systems and methods are described. In one implementation, a method receives image data from a camera of a vehicle and determines a geographic position of the vehicle. Based on the image data and the geographic position of the vehicle, the method determines a location of an inductive loop in a roadway proximate the vehicle. The data associated with the location of the inductive loop is stored in a storage device within the vehicle. For a vehicle, a detectable zone may be determined based on actual or simulated outputs an inductive loop system at various locations relative to the vehicle. While driving, the vehicle is controlled to cause the detectable zone to pass over or stop over a known location of the inductive loop.

Inductive loop detection systems and methods

Techniques pertaining to an autonomous police vehicle are described. A method may involve a processor associated with an autonomous vehicle obtaining an indication of violation of one or more traffic laws by a first vehicle. The method may also involve the processor maneuvering the autonomous vehicle to pursue the first vehicle. The method may further involve the processor remotely executing one or more actions with respect to the first vehicle.

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Palo Alto, CA, United States of America

Alexandru Mihai Gurghian