An unmanned aerial vehicle (UAV) carries a camera, sends data from the camera, and receives commands. The UAV is connected to a messaging platform. Pictures or video clips received from the UAV are selected and placed in messages broadcast by an account associated with the UAV. Video footage from the camera is live-streamed in a card-type message. Account holders of the messaging platform may control the UAV with commands embedded in messages and directed towards an account associated with the UAV. Controllable elements of the UAV include UAV location, camera orientation, camera subject, UAV-mounted lighting, a UAV-mounted display, a UAV-mounted projector, UAV-mounted speakers, and a detachable payload. UAV control may be determined through democratic means. Some UAV functionality may be triggered through aggregated engagements on the messaging platform. The UAV may include a display screen and/or a microphone to provide for telepresence or interview functionality.

Technologies disclosed relate to a remote intervention system for the operation of a vehicle, which can be an autonomous vehicle, a vehicle that includes driver assist features, a vehicle used for ride sharing services or the like. The system includes a vehicle sending a request for remote intervention to a remote operator when the operation of the vehicle is suspended. The request for remote intervention can include a request for object identification or a request for decision confirmation. The vehicle can update vehicle operation based in part on vehicle-based sensor data and a response to the remote intervention request from the remote operator. The remote operator can be a human operator or an AI operator.

An unmanned vehicle belongs to an unmanned vehicle group that includes a plurality of unmanned vehicles to control the unmanned vehicle in accordance with the condition of the entire unmanned vehicle group The unmanned device includes a reception means configured to receive a restriction condition pertaining to an amount of activity of the unmanned vehicle group; and a control means configured to control the unmanned vehicle on the basis of the restriction condition received by the reception means and the activity condition of the unmanned vehicle.

A rover or semi-autonomous or autonomous vehicle may use an image classifier to determine a terrain class of regions of an image of the terrain ahead of the rover or vehicle. The regions of the images are used to estimate the slope of the terrain for the different regions. The terrain class and slope are used to predict an amount of slip the rover will experience when traversing the terrain of the different regions. A heuristic mapping for the terrain class may be applied to the predicted slip amount to determine a hazard level for the rover or vehicle traversing the terrain.

Described is a system and method for the classification of agents based on agent movement patterns. In operation, the system receives position data of a moving agent from a camera or sensor. Motion data of the moving agent is then extracted and used to generate a predicted future motion of the moving agent using a set of pre-calculated Echo State Networks (ESN). Each ESN represents an agent classification and generates a predicted future motion. A prediction error is generated for each ESN by comparing the predicted future motion for each ESN with actual motion data. Finally, the agent is classified based on the ESN having the smallest prediction error.

Sensor data is received from an array of sensors configured to capture one or more objects in an external environment of an autonomous vehicle. A first sensor group is selected from the array of sensors based on proximity data or environmental contexts. First sensor data from the first sensor group is prioritized for transmission based on the proximity data or environmental contexts.

An apparatus for providing a notification of control authority transition in a vehicle is provided. The apparatus includes a speaker configured to output a sound notification, a vibration motor configured to output a vibration notification, and a control circuit configured to be electrically connected with the speaker and the vibration motor. The control circuit is configured to output a first notification using the speaker during a first time interval, when a situation to transfer control authority for the vehicle occurs, output a second notification using the speaker and the vibration motor during a second time interval, after the first time interval elapses, and output a third notification using the speaker and the vibration motor during a third time interval, after the second time interval elapses.

Aspects of the disclosure relate to an autonomous vehicle that may detected other nearby vehicles and identify them as parked or unparked. This identification may be based on visual indicia displayed by the detected vehicles as well as traffic control factors relating to the detected vehicles. Detected vehicles that are in a known parking spot may automatically be identified as parked. In addition, detected vehicles that satisfy conditions that are indications of being parked may also be identified as parked. The autonomous vehicle may then base its control strategy on whether or not a vehicle has been identified as parked or not.

A system for preconditioning a power source of an electric aircraft is presented. The apparatus includes a power source of an electric aircraft, a computing device, and a user device. The computing device is configured to receive a flight plan, determine a predicted power usage model as a function of the flight plan, and initiate a power source modification on the electric aircraft as a function of the predicted power usage model. The user device is configured to display a flight performance infographic as a function of the predicted power usage model.

The present teaching relates to system, method, medium for in-situ perception in an autonomous driving vehicle. A plurality of types of sensor data acquired continuously by a plurality of types of sensors deployed on the vehicle are first received, where the plurality of types of sensor data provide information about surrounding of the vehicle. Based on at least one model, one or more items are tracked from a first of the plurality of types of sensor data acquired by one or more of a first type of the plurality of types of sensors, wherein the one or more items appear in the surrounding of the vehicle. At least some of the one or more items are then automatically labeled on-the-fly via either cross modality validation or cross temporal validation of the one or more items and are used to locally adapt, on-the-fly, the at least one model in the vehicle.