Qualcomm Incorporated

John Anthony Dougherty

Daniel Warren Mellinger, Iii

Michael Joshua Shomin

Stephen Marc Chaves

Ross Eric Kessler

Paul Daniel Martin

Travis Van Schoyck

Michael Franco Taveira

Jonathan Paul Davis

Kristen Wagner Cerase

Aleksandr Kushleyev

Matthew Hyatt Turpin

Jordan Scott Burklund

Rizwan Ehtesham Ahmed

Donald Hutson

Charles Wheeler Sweet, Iii

Christopher Brunner

Jayakrishnan Unnikrishnan

Radhika Dilip Gowaikar

Moussa Ben Coulibaly

Fu-Chun Yeh

Daniel Warren Mellinger Iii

Various embodiments include methods and vehicles, such as a semi-autonomous vehicle, for safely operating a vehicle based on vehicle-control gestures made by an occupant. Exemplary implementations may include determining a first vehicle action by applying a first passenger profile to a detected first vehicle-control gesture performed by a first passenger, determining whether the first vehicle action is safe to perform, and operating the vehicle to implement the first vehicle action in response to determining that the first vehicle action is safe for the vehicle and occupants. The first passenger profile may be selected from a plurality of passenger profiles to normalize vehicle-control gestures received from the first passenger. The vehicle control gesture may be ignored or a vehicle action differing from but similar to the first vehicle action may be implemented in response to determining that the first vehicle action is unsafe for the vehicle or occupants.

Gesture-based control for semi-autonomous vehicle

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

Methods, systems, and devices for automatically customizing operation of a robotic vehicle are described. The method may include identifying an operator, retrieving an operator profile and associated metadata for the operator from a database, where the metadata includes operator habit information, and configuring the robotic vehicle based on existing preference-based and performance-based settings, where the existing preference-based and performance-based settings are based on the metadata. The methods may include identifying operator habit information during operation of the robotic vehicle, deriving updated preference-based and performance-based settings for the operator based on the identified operator habit information, and providing, to the database, modifications to the metadata associated with the operator profile of the operator.

Systems and methods for automatically customizing operation of a robotic vehicle

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

Disclosed are techniques for annotating image frames using information from a light detection and ranging (LiDAR) sensor. An exemplary method includes receiving, from the LiDAR sensor, at least one LiDAR frame, receiving, from a camera sensor, at least one image frame, removing LiDAR points that represent a ground surface of the environment, identifying LiDAR points of interest in the at least one LiDAR frame, segmenting the LiDAR points of interest to identify at least one object of interest in the at least one LiDAR frame, and annotating the at least one image frame with a three-dimensional oriented bounding box of the at least one object of interest detected in the at least one image frame by projecting the three-dimensional oriented bounding boxes from the at least one LiDAR frame to the at least one image frame using cross-calibration transforms between the LiDAR sensor and the camera.

Using light detection and ranging (LIDAR) to train camera and imaging radar deep learning networks

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

Some embodiments include methods for customizing operation of the robotic vehicle for an operator. Such embodiments may include identifying a current operator of the robotic vehicle, configuring the robotic vehicle based on metadata associated with an operator profile for the operator, determining whether the operator has changed, and if so, identifying the new operator, deriving updated preference-based settings and performance-based settings for the new operator, and updating configurations of the robotic vehicle accordingly. Some embodiments may include identifying a current operator, retrieving an operator profile and associated metadata for the operator from a central database, configuring the robotic vehicle based on preference-based and performance-based settings, analyzing input data during operation, storing identified real-time operator habit information during operation of the robotic vehicle, deriving updated preference-based and performance-based settings for the operator; and uploading to the central database modifications to the metadata associated with the operator profile of the operator.

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

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.

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Philadelphia, PA, United States of America

John Anthony Dougherty