Ford Global Technolgoies, LLC

Ford Motor Company Limited

Shounak Athavale

Anuja Anil Shirsat

Romil Shah

Vidhya Iyer

Sakthi Narayanan Balasubramanian

Sonal Doomra

Shant Tokatyan

Naman Kohli

Kiril Spasovski

Eric H Wingfield

Tejaswi Koduri

Sagar Kumar Regmi

Owen Carpenter

Matthew Castaldini

Candice Xu

Meiyappan Kannappa

Kevin Marx

Alberto Gabriel Rivera

Srilaxmi Kanna

Flavia Rodrigues

Alberto Rivera

Randy Nunez

Systems and methods for random vehicle movement to prevent theft are disclosed. The vehicle system may automatically reposition to make it seem like the user is home and to reduce battery drain and flat spots on the tire. The system may randomize the start time based on the preferences of the user. At the randomly decided time, the vehicle may start up on its own and reposition itself. In addition, the system may learn a driving behavior pattern of the vehicle via machine learning based on driving behavior associated with the vehicle, such that the vehicle may perform vehicle movement based on the learned driving behavior pattern.

Systems and methods for random vehicle movement for vehicle safety

The disclosure provides systems and methods for tracking luggage. Luggage is tracked by associating the luggage with a user account during a loading action and during an unloading action. The system further includes systems and methods for retrieving lost luggage.

Systems and methods for tracking luggage in a vehicle

Example embodiments described in this disclosure are generally directed to systems and methods for preventing vehicular mishaps. In an example method, an object detector mounted on a building or a roadside fixture, detects an object in a detection coverage area of the object detector. The object is undetectable by a collision avoidance system of a vehicle. The object detector conveys information about the object to a supervisory computer. The information can include, for example, a location and/or a direction of travel of the object (if the object is moving). The supervisory computer evaluates the information and transmits an alert to the collision avoidance system of the vehicle, so as to prevent a collision between the vehicle and the object. In an example situation, where the object is a pedestrian, the supervisory computer may also transmit a warning alert to a personal communication device of the pedestrian.

Systems and methods to prevent vehicular mishaps

Example vehicle security systems and methods are described. In one implementation, a method receives a destination and a driving route to the destination for an autonomous vehicle to follow. A vehicle security system identifies a passkey associated with the driving route and communicates the passkey to a user designated to meet the autonomous vehicle at the destination. The vehicle security system confirms that the user designated to meet the autonomous vehicle possesses the passkey.

Vehicle security systems and methods

A first image is obtained prior to a second image. Upon dividing the second image into a plurality of zones, light characteristic values for each of the plurality of zones are determined based on at least one of a brightness value, a contrast value, and an intensity value for the respective zones. A first difference score for one of the zones is determined based on a difference between corresponding light characteristic values for the one zone in the first and second images. Upon identifying an object in the one zone of the first and second images, a second difference score for the one zone is determined based on determining a difference between respective confidence scores associated with the corresponding identified objects. Upon determining at least one of the first or second difference score is greater than a respective threshold, the vehicle is operated based on the first image.

Enhanced vehicle operation

Vehicle damage identification and incident management systems and methods are provided herein. An example method can include determining occurrence of a damage event for a first vehicle based on a vehicle sensor signal, determining a location on the first vehicle where damage has occurred using the vehicle sensor signal, activating a camera on a side of the first vehicle corresponding with, or adjacent to, the location on the first vehicle where damage has occurred, determining, from camera images, identifying information related to an object captured in the camera images, and transmitting a message to a recipient that includes the identifying information.

Vehicle damage identification and incident management systems and methods

Upon determining a first vehicle is moving on a two-way road in a first direction of travel in an only lane of the two-way road, vehicle sensor data is input to a first neural network that identifies a yield area along the two-way road via image segmentation. A second vehicle is detected traveling toward the first vehicle on the only lane of the two-way road. Then, upon determining the first vehicle is to yield to the second vehicle, one or more vehicle components are actuated to move the first vehicle to the yield area.

Vehicle yield decision

A method comprising: associating, by a computing device, at least one first item to a first passenger based on the at least one first item being received in an area of a vehicle; associating, by the computing device, at last one second item to a second passenger based on the at least one second item being received in the area of the vehicle; monitoring, by at least two image capture devices, the area of the vehicle; and determining, by the computing device and based on the first passenger taking the at least one second item from the area, an alert.

Systems and methods of preventing removal of items from vehicles by improper parties

From detecting a marker detected in an image, a physical object on which the marker is provided can be identified. Based on a determining a location of the marker in the physical world, a location and an orientation of the physical object can be determined.

Object locator with fiducial marker

Systems, methods, and devices for object detection, including manufacturing part detection, are disclosed. A method for identifying a manufacturing part includes receiving an image comprising a manufacturing part from an augmented reality device and determining a bounding perimeter encapsulating the manufacturing part. The method includes receiving a prediction label and a confidence value for the manufacturing part from a neural network. The method includes generating a message comprising the image, the prediction label, and the confidence value, and providing the message to the augmented reality device.

Manufacturing part identification using computer vision and machine learning

A controller receives images from a rearward-facing camera having an inter-lane region in the field of view thereof. A region of interest of the images is identified corresponding to the inter-lane region. The region of interest is converted to a grayscale image having a drastically reduced number of intensity levels. A difference image is determined between the grayscale image for each image relative to the grayscale image of a preceding image. If the non-zero pixels in the difference image exceed a threshold and the area of non-zero pixels in the difference images for the input images are increasing with time, a lane-splitting vehicle will be determined to be present and an alert may be generated and/or autonomous lane changes may be suppressed.

Detection of lane-splitting motorcycles

Methods and systems for detecting a medical emergency related to the driver of a vehicle are described. Various data, such as in-situ biophysical data of the driver, a medical history of the driver, and in-situ motion data of the vehicle, are employed to determine a possible medical emergency. In an event that a medical emergency is detected, the system determines the severity of the emergency and takes appropriate control of the vehicle. If the medical emergency is not life-threatening, the system may drive the vehicle to a safe area, park the vehicle, and then request help from emergency service providers (e.g., ambulance and police). If the medical emergency is severe (e.g., life-threatening), the system may transition the vehicle to an EMS (Emergency Medical Service)-privileged vehicle and autonomously drive the vehicle to an emergency service provider.

Emergency evacuation using autonomous driving

A system receives candidate strings from a speech recognition engine. Where the speech recognition indicates success, the candidate string may be reported or otherwise used. Otherwise, candidate strings may be processed by comparing collapsed forms of the candidate strings to collapsed forms of a user's contacts. The collapsed form may replace contiguous consonants with a 1 and contiguous vowels with a 0. If a candidate string has a collapsed form matching a contact's collapsed form, that contact may be used. Where multiple candidate strings match the collapsed forms of multiple contacts, the contacts may be filtered according to textual similarity to the candidate strings, proximity to a typical usage location for the contacts, and a typical usage time for the contacts.

Growing community of inventors

San Jose, CA, United States of America

Shounak Athavale