Examples described may enable provision of remote assistance for an autonomous vehicle. An example method includes a computing system operating by default in a first mode and periodically transitioning from operation in the first mode to operation in a second mode. In the first mode, the system may receive environment data provided by the vehicle and representing object(s) having a detection confidence below a threshold, where the detection confidence is indicative of a likelihood of correct identification of the object(s), and responsive to the object(s) having a confidence below the threshold, provide remote assistance data comprising an instruction to control the vehicle and/or a correct identification of the object(s). In the second mode, the system may trigger user interface display of remote assistor alertness data based on pre-stored data related to an environment in which the pre-stored data was acquired, and receive a response relating to the alertness data.

A system for providing remote assistance to an autonomous vehicle is disclosed herein. The system includes at least one remote assistance button configured to be selectively activated to initiate remote assistance for the autonomous vehicle. Each remote assistance button corresponds to a dedicated remote assistance function for the autonomous vehicle. For example, the system can include remote assistance buttons for causing the autonomous vehicle to stop, decelerate, or pull over. The system includes a controller configured to detect activation of the remote assistance button(s) and to cause remote assistance to be provided to the autonomous vehicle in response to the activation. For example, the autonomous vehicle may perform an action corresponding to the activated button with input assistance from the controller but without the system taking over control of the autonomous vehicle.

An apparatus for providing control signals to a remotely piloted unmanned aerial vehicle (UAV) which includes a pedal input mechanically connected to a base enclosure using a linkage system which allows for the angular movement of the pedal input. The apparatus further includes a potentiometer which provides a first control signal to a central controller indicating a detected angular position of the pedal. The central controller includes circuitry to translate the received first signal into a control signal which the central controller may transmit to one or more UAVs via a transceiver and antenna.

A method performed by a first server communicatively connected to a network for supporting tele-operated driving, ToD, is provided. The method includes receiving a request from client device for ToD support. This request includes a trigger and information for supporting a tele-operated driving.

The first server may further process the request to identify a second server for providing support for the ToD, wherein the processing is based on parameters of the network for providing ToD support. The first server may further establish a ToD session between the second server and the client.

The first server may also predict whether a ToD event is likely in a future time period based on prediction information.

A cleaning robot includes a navigator to move a main body, a remote controller to output a modulated infrared ray in accordance with a control command of a user and to form a light spot, a light receiver to receive the infrared ray from the remote controller, and a controller to control the navigator such that the main body tracks the light spot when the modulated infrared ray is received in accordance with the control command. Because the cleaning robot tracks a position indicated by the remote controller, a user may conveniently move the cleaning robot.

The present disclosure relates to a computer-implemented method for stitching images representing the surroundings of an automated vehicle into a stitched view and an image stitching system for an automated vehicle for use in said method. The method comprises the steps of: providing, by means of respective image capturing units, two images representing surroundings of the automated vehicle, wherein the two images share an overlapping region of the surroundings from different viewpoints of the respective image capturing units; determining an image transformation between the two images based on pre-calculated calibration information, or feature matching of discernable features of the surroundings visible in said two images; stitching the two images into a stitched view with a respective image seam between the two images based on said image transformation; displaying the stitched view to an operator of the automated vehicle, and receiving, by means of an operator input device, operator input data indicating the operator's viewpoint in the stitched view; determining a region of interest within the stitched view based on said operator input data; wherein the step of stitching the two images into a stitched view involves determining a set of stitching solutions between the two images and selecting a stitching solution that results in a stitched view with a stitching seam that is displaced away a distance from a point in the region of interest in a direction towards the outside of the region of interest.

The embodiments of present disclosure herein address unresolved problem of cognitive navigation strategies for a telepresence robotic system. This includes giving instruction remotely over network to go to a point in an indoor space, to go an area, to go to an object. Also, human robot interaction to give and understand interaction is not integrated in a common telepresence framework. The embodiments herein provide a telepresence robotic system empowered with a smart navigation which is based on in situ intelligent visual semantic mapping of the live scene captured by a robot. It further presents an edge-centric software architecture of a teledrive comprising a speech recognition based HRI, a navigation module and a real-time WebRTC based communication framework that holds the entire telepresence robotic system together. Additionally, the disclosure provides a robot independent API calls via device driver ROS, making the offering hardware independent and capable of running in any robot.

The present specification provides systems, devices and methods for controlling an uncrewed aerial vehicle (UAV) at a public safety incident. An example method contemplates placing a UAV in a shadow mode that follows a firefighter’s movements throughout the PSI while monitoring voice activity while the UAV performs tasks such as sending images from a camera to a central server. Potential voice commands are extracted from the voice activity and associated with tasks being performed by the UAV. A machine learning dataset is built from those associations such that at future incidents the UAV can operate in a freelance mode based on detected voice commands or other contextual factors.

Methods, systems, and devices for data capture trigger configuration for asset tracking are provided. Another example method capturing raw data involves obtaining a rich data capture trigger that defines when a controller of an asset tracking device onboard an asset is to identify and log an unsimplified block of raw data in raw data on the asset tracking device for rich data analysis, transmitting data capture instructions to the asset tracking device that contains the rich data capture trigger, and receiving the simplified set of raw data and the unsimplified block of raw data from the asset tracking device.

A wearable device includes a skin-attachable device to be attached to a skin of a user to acquire user data, an electronic device that supplies power to the skin-attachable device, and a connection device including a first cable connected to the skin-attachable device and a second cable connected to the first cable and detachably attached to the electronic device.