Abstract: 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.

Abstract: Various aspects may include methods enabling a vehicle to broadcast intentions and/or motion plans to surrounding vehicles. Various aspects include methods for using intentions and/or motion plans received from one or more surrounding vehicles.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Various embodiments include devices and methods for controlling a robotic vehicle. Each electronic speed controller (ESC) of the robotic vehicle may receive open loop flight control information from a flight controller or another processing device of the robotic vehicle. In some embodiments, each ESC may store the provided open loop flight control information in a memory. In response to detecting a loss of control signals from the flight controller, each ESC may access the stored open loop flight control information and perform control of a motor associated with each ESC based on the open loop flight control information. The open loop flight control information may be a sequence of motor control instructions to be performed over a period of time, or parameterized information or vehicle state information that enables each ESC to generate a sequence of motor control instructions.

Abstract: 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.

Abstract: Various embodiments include devices and methods for mitigating the bias of a magnetometer resulting from operating various hardware components on a device such as a drone or a computing device. Various embodiments may improve the accuracy of magnetometer output by estimating the bias or magnetic interference caused by the hardware components based on a utilization or operating state of each hardware component, and adjusting the magnetometer output to compensate for the estimated bias.

Abstract: 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.

Abstract: 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.

Abstract: Various embodiments include methods, devices, and aerial robotic vehicles for adjusting a proximity threshold implemented in a collision avoidance system based on whether propeller guards are installed. Methods may include an aerial robotic vehicle processor determining whether a propeller guard is installed, setting, a proximity threshold for collision avoidance based on the determination as to whether propeller guard(s) is/are installed on the aerial robotic vehicle, and controlling one or more motors of the aerial robotic vehicle using the proximity threshold for collision avoidance. When propeller guards are installed, the proximity threshold may be set at a smaller distance than when propeller guards are not installed. The determination of whether a propeller guard is installed may be based on sensor data from one or more sensors configured to detect or indicate the presence of a propeller guard, and/or based on rotor revolution rates determined from a motor or motor controller.

Abstract: 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.

Abstract: 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.

Abstract: Various embodiments include methods, devices, and robotic vehicle processing devices implementing such methods for automatically adjusting the minimum distance that a robotic vehicle is permitted to approach an object by a collision avoidance system based upon a classification or type of object.

Abstract: Various embodiments include methods, devices, and robotic vehicle processing devices implementing such methods for automatically adjusting the minimum distance that a robotic vehicle is permitted to approach an object by a collision avoidance system (the “proximity threshold”) to compensate for unpredictability in environmental or other conditions that may compromise control or navigation of the robotic vehicle, and/or to accommodate movement unpredictability of the object. Some embodiments enable dynamic adjustments to the proximity threshold to compensate for changes in environmental and other conditions. Some embodiments include path planning that takes into account unpredictability in environmental or other conditions plus movement unpredictability of objects in the environment.

Abstract: Various embodiments include methods, devices, and robotic vehicles that adjust a proximity threshold implemented in a collision avoidance system based on whether a payload is being carried. Methods may include determining whether a payload is carried by the robotic vehicle, setting a proximity threshold for collision avoidance in response to determining that a payload is carried by the robotic vehicle, and controlling one or more motors of the robotic vehicle using the proximity threshold for collision avoidance. Some embodiments may include raising the proximity threshold when a payload is not being carried or decreasing proximity threshold when a payload is being carried. Some embodiments may include determining a classification of a payload and setting the proximity threshold based at least in part on the classification.

Abstract: 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.