Ford Global Technolgoies, LLC

Ford Motor Company Limited

Harpreetsingh Banvait

Ashley Elizabeth Micks

Jinesh J Jain

Scott Vincent Myers

Sneha Kadetotad

Venkatapathi Raju Nallapa

Alexandru Mihai Gurghian

Brielle Reiff

Kyu Jeong Han

Vidya Nariyambut Murali

Nikhil Nagraj Rao

Lisa Scaria

Bruno Sielly Jales Costa

Mohamed Ahmad

Mark Crawford

Francois Charette

Ashok E Rodrigues

Praveen Narayanan

Parsa Mahmoudieh

Maryam Moosaei

Peter Gyumyeong Joh

Casey Bryan Feldman

Alexandro Walsh

Joshua Scott Smith

Surjya Sarathi Ray

Scott Vincent Meyers

David Silver

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

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 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

Systems, methods, and devices for predicting driver intent and future movements of a human driven vehicles are disclosed herein. A computer implemented method includes receiving an image of a proximal vehicle in a region near a vehicle. The method includes determining a region of the image that contains a driver of the proximal vehicle, wherein determining the region comprises determining based on a location of one or more windows of the proximal vehicle. The method includes processing image data only in the region of the image that contains the driver of the proximal vehicle to detect a driver's body language.

Predicting vehicle movements based on driver body language

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

Methods and apparatus pertaining to an intelligent vehicle access point opening system are provided. A method may involve detecting a presence of an object in a vicinity of a cover of an access point of a vehicle. The method may also involve receiving a command to open the cover and activating a mechanism to open the cover responsive to receiving the command. The method may further involve determining whether the object is likely to fall as the cover is being opened. The method may additionally involve pausing opening of the cover responsive to a determination that the object is likely to fall as the cover is being opened.

Intelligent vehicle access point opening system

Systems, methods, and devices for detecting a vehicle's turn signal status for collision avoidance during lane-switching maneuvers or otherwise. A method includes detecting, at a first vehicle, a presence of a second vehicle in an adjacent lane. The method includes identifying, in an image of the second vehicle, a sub-portion containing a turn signal indicator of the second vehicle. The method includes processing the sub-portion of the image to determine a state of the turn signal indicator. The method also includes notifying a driver or performing a driving maneuver, at the first vehicle, based on the state of the turn signal indicator.

Vehicle turn signal detection

The disclosure relates to methods, systems, and apparatuses for virtual sensor data generation and more particularly relates to generation of virtual sensor data for training and testing models or algorithms to detect objects or obstacles. A method for generating virtual sensor data includes simulating, using one or more processors, a three-dimensional (3D) environment comprising one or more virtual objects. The method includes generating, using one or more processors, virtual sensor data for a plurality of positions of one or more sensors within the 3D environment. The method includes determining, using one or more processors, virtual ground truth corresponding to each of the plurality of positions, wherein the ground truth comprises a dimension or parameter of the one or more virtual objects. The method includes storing and associating the virtual sensor data and the virtual ground truth using one or more processors.

Virtual sensor data generation for wheel stop detection

A method for generating training data is disclosed. The method may include executing a simulation process. The simulation process may include traversing a virtual camera through a virtual driving environment comprising at least one virtual precipitation condition and at least one virtual no precipitation condition. During the traversing, the virtual camera may be moved with respect to the virtual driving environment as dictated by a vehicle-motion model modeling motion of a vehicle driving through the virtual driving environment while carrying the virtual camera. Virtual sensor data characterizing the virtual driving environment in both virtual precipitation and virtual no precipitation conditions may be recorded. The virtual sensor data may correspond to what a real sensor would have output had it sensed the virtual driving environment in the real world.

Virtual sensor-data-generation system and method supporting development of vision-based rain-detection algorithms

A system and method for assisted or autonomous parking of a vehicle is disclosed. The method may begin when the vehicle approaches a feeder lane within a parking lot. At that point, a computer system may decide whether the vehicle should enter the feeder lane. The computer system may use at least one of machine learning, computer vision, and range measurements to determining whether a condition precedent for entering the feeder lane exists. The condition precedent may include an in-bound arrow on the feeder lane or parking lines and/or a parked vehicle adjacent the feeder lane defining a departure angle less than or equal to ninety degrees. If the condition precedent exists, the vehicle may enter the feeder lane. If the condition precedent does not exist, the vehicle may move on to another feeder lane.

Parking-lot-navigation system and method

A machine learning model is trained by defining a scenario including models of vehicles and a typical driving environment. A model of a subject vehicle is added to the scenario and sensor locations are defined on the subject vehicle. A perception of the scenario by sensors at the sensor locations is simulated. The scenario further includes a model of a lane-splitting vehicle. The location of the lane-splitting vehicle and the simulated outputs of the sensors perceiving the scenario are input to a machine learning algorithm that trains a model to detect the location of a lane-splitting vehicle based on the sensor outputs. A vehicle controller then incorporates the machine learning model and estimates the presence and/or location of a lane-splitting vehicle based on actual sensor outputs input to the machine learning model.

Training algorithm for collision avoidance

A method of recommending radio stations including the step of generating a plurality of driver models. Each driver model may correspond to a different driver and documents audio content listened to by that driver while driving. The plurality of driver models may be aggregated to generate a recommendation model correlating radio stations, audio content, and geographic regions. Thereafter, a request for recommendations may be received from a driver while the driver is in a familiar or unfamiliar geographic region. A driver model corresponding to the driver may be compared against other data contained within the recommendation model in order to identify one or more radio stations that present audio content within the geographic region that best matches the audio content documented within the driver model corresponding to the driver. The one or more radio stations may then be communicated to the driver.

Radio-station-recommendation system and method

Techniques and examples pertaining to vehicle water wading safety are described. A processor implementable to a vehicle approaching a waterbody may receive data related to the waterbody from one or more above-water or under-water sensors. The processor may determine a top surface and a bottom profile of the waterbody, and calculate one or more critical trajectories of water-sensitive components of the vehicle if the vehicle is to wade through the waterbody by traversing the bottom profile. The processor may then determine the wading safety based on the critical trajectories and the top surface of the waterbody. The processor may further determine a wading route, and autonomously drive the vehicle to wade the waterbody via the optimal wading route.

Growing community of inventors

Sunnyvale, CA, United States of America

Harpreetsingh Banvait