Qualcomm Incorporated

Ross Eric Kessler

Daniel Warren Mellinger, Iii

Michael Joshua Shomin

John Anthony Dougherty

Jonathan Paul Davis

Stephen Marc Chaves

Travis Van Schoyck

Paul Daniel Martin

Kristen Wagner Cerase

Jordan Scott Burklund

Michael Franco Taveira

Donald Hutson

Charles Wheeler Sweet, Iii

Aleksandr Kushleyev

Matthew Hyatt Turpin

Moussa Ben Coulibaly

Rizwan Ehtesham Ahmed

Clayton Dumstorff

Paul Ferrell

Daniel Warren Mellinger Iii

Methods, devices and systems enable controlling an autonomous vehicle by identifying a vehicle that is within a threshold distance of the autonomous vehicle, determining an autonomous capability metric (ACM) the identified vehicle, determining whether the ACM of the identified vehicle is greater than a first threshold, determining whether the ACM of the identified vehicle is less than a second threshold, and adjusting a driving parameter of the autonomous vehicle so that the autonomous vehicle is more or less reliant on the capabilities of the identified vehicle based on whether the ACM of the identified vehicle exceeds the thresholds.

Methods and systems for managing interactions between vehicles with varying levels of autonomy

Various embodiments include methods and interactive traffic control devices for interactively controlling traffic, which may include receiving refined location and state information associated with individual vehicles on a roadway, and determining customized dynamic traffic control instructions for a first one or more of the individual vehicles. The determined customized dynamic traffic control instructions may be based on the received refined location and state information and offer an optional route alternative to a set limited number of the individual vehicles. The first customized dynamic traffic control instructions may be transmitted by the interactive traffic control device to the first one or more of the individual vehicles.

Intelligent and adaptive traffic control system

Methods, devices and systems enable controlling an autonomous vehicle by identifying vehicles that are within a threshold distance of the autonomous vehicle, determining an autonomous capability metric of each of the identified vehicles, and adjusting a driving parameter of the autonomous vehicle based on the determined autonomous capability metric of each of the identified vehicles. Adjusting a driving parameter may include adjusting one or more of a minimum separation distance, a minimum following distance, a speed parameter, or an acceleration rate parameter.

Methods, devices and systems enable controlling an autonomous vehicle by identifying vehicles that are within a threshold distance of the autonomous vehicle, determining an autonomous capability metric of each of the identified vehicles, and adjusting a driving parameter of the autonomous vehicle based on the determined autonomous capability metric of each of the identified vehicles. Embodiments may further include determining, based on the determined ACMs, whether one or more identified vehicles would provide an operational advantage to the autonomous vehicle in a cooperative driving engagement, and initiating a cooperative driving engagement with the one or more identified vehicles in response to determining that the one or more identified vehicles would provide an operational advantage to the autonomous vehicle in a cooperative driving engagement.

Methods and systems for establishing cooperative driving engagements with vehicles having varying levels of autonomy

Various embodiments include processing devices and methods for managing cleaning behavior by a cleaning robot. In some embodiments, a processor of the cleaning robot may obtain user planning information and user location information from one or more information sources external to the cleaning robot. The processor may analyze the user planning information and the user location information. The processor may determine one or more cleaning parameters for the cleaning robot based on the analysis of the user planning information and the user location information. The processor may generate an instruction for the cleaning robot to schedule an operation of the cleaning robot based on the one or more cleaning parameters. The processor may execute the generated instruction to perform the operation of the cleaning robot.

Managing cleaning robot behavior

Methods, systems, and devices for robotic navigation are described. A robotic device such as a robotic vacuum or a robotic assistant may navigate a first surface. In some cases, navigating the first surface may include removing debris from the first surface. The robotic device may identify a location of a track that connects the first surface to a second surface that is vertically displaced from the first surface. The robotic device may engage the track based at least in part on the identified location. The robotic device may ascend to the second surface by activating an actuator and navigate the second surface (e.g., may remove debris from the second surface, may map the second surface, etc.).

Robotic stair lifts

Embodiments include devices and methods operating a robotic vehicle. A robotic vehicle processor may detect an object posing an imminent risk of collision with the robotic vehicle. The robotic vehicle processor may determine a classification of the detected object. The robotic vehicle processor may manage a rotation of a rotor of the robotic vehicle prior to a collision based on the classification of the object.

Collision management for a robotic vehicle

Embodiments include methods performed by a processor of a robotic vehicle for detecting and responding to defects on an on-board imaging device that includes an image sensor. Various embodiments may include causing the imaging device to capture at least one image, determining whether a defect to the imaging device is detected based at least in part on the at least one captured image, and, in response to determining that a defect to the imaging device is detected, identifying an area of the image sensor corresponding to the defect and masking image data received from the identified area of the image sensor.

Systems and methods for improving performance of a robotic vehicle by managing on-board camera defects

A lighting system for an unmanned autonomous vehicle (UAV) adapts to the environment around the UAV to ensure status notification lights are visible to an operator and/or abide by regulatory lighting requirements. A processor of the UAV may receive information from various sensors regarding environmental conditions and location of the UAV, and adjust a UAV lighting system to ensure visibility under the environmental conditions. Adjustments to the lighting system may include selection of light sources that are illuminated, the illumination intensity of particular light sources, the colors emitted by various light sources and other lighting configurations.

Environmentally aware status LEDs for use in drones

Embodiments described herein relates to an Unmanned Aerial Vehicle (UAV) having vibration dampening and isolation capabilities, the UAV including a first frame portion, a second frame portion, and a third frame portion. Each of the first frame portion, the second frame portion, and the third frame portion is separated from one another. At least one first support member inelastically coupling the first frame portion and the third frame portion. At least one second support member elastically coupling the second frame portion and one or more of the first frame portion or the third frame portion to isolate the first frame portion and the third frame portion from vibration of the second frame portion.

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Philadelphia, PA, United States of America

Ross Eric Kessler