Systems and methods for determining object prioritization and predicting future object locations for an autonomous vehicle are provided. A method can include obtaining, by a computing system comprising one or more processors, state data descriptive of at least a current or past state of a plurality of objects that are perceived by an autonomous vehicle. The method can further include determining, by the computing system, a priority classification for each object in the plurality of objects based at least in part on the respective state data for each object. The method can further include determining, by the computing system, an order at which the computing system determines a predicted future state for each object based at least in part on the priority classification for each object and determining, by the computing system, the predicted future state for each object based at least in part on the determined order.

Example implementations relate to a method of dynamically updating a transport task of a UAV. The method includes receiving, at a transport-provider computing system, an item provider request for transportation of a plurality of packages from a loading location at a given future time. The method also includes assigning, by the transport-provider computing system, a respective transport task to each of a plurality of UAVs, where the respective transport task comprises an instruction to deploy to the loading location to pick up one or more of the plurality of packages. Further, the method includes identifying, by the transport-provider system, a first package while or after a first UAV picks up the first package. Yet further, the method includes based on the identifying of the first package, providing, by the transport-provider system, a task update to the first UAV to update the respective transport task of the first UAV.

In some implementations, a UAV flight system can dynamically adjust UAV flight operations based on radio frequency (RF) signal data. For example, the flight system can determine an initial flight plan for inspecting a RF transmitter and configure a UAV to perform an aerial inspection of the RF transmitter. Once airborne, the UAV can collect RF signal data and the flight system can automatically adjust the flight plan to avoid RF signal interference and/or damage to the UAV based on the collected RF signal data. In some implementations, the UAV can collect RF signal data and generate a three-dimensional received signal strength map that describes the received signal strength at various locations within a volumetric area around the RF transmitter. In some implementations, the UAV can collect RF signal data and determine whether a RF signal transmitter is properly aligned.

Embodiments herein can determine an optimal route for an autonomous electric vehicle. The system may score viable routes between the start and end locations of a trip using a numeric or other scale that denotes how viable the route is for autonomy. The score is adjusted using a variety of factors where a learning process leverages both offline and online data. The scored routes are not based simply on the shortest distance between the start and end points but determine the best route based on the driving context for the vehicle and the user.

Aspects of the technology relate to exception handling for a vehicle. For instance, a current trajectory for the vehicle and sensor data corresponding to one or more objects may be received. Based on the received sensor data, projected trajectories of the one or more objects may be determined. Potential collisions with the one or more objects may be determined based on the projected trajectories and the current trajectory. One of the potential collisions that is earliest in time may be identified. Based on the one of the potential collisions, a safety-time-horizon (STH) may be identified. When a runtime exception occurs, before performing a precautionary maneuver to avoid a collision, waiting no longer than the STH for the runtime exception to resolve.

An unmanned aerial vehicle (UAV) logs first UAV information at a first frequency. The UAV triggers a camera associated with the UAV to capture an image. In response to triggering the camera to capture the image, the UAV logs second UAV information at a second frequency that is higher than the first frequency. A device that is separate from the UAV identifies a location of the UAV corresponding to the image based on a capture timestamp of the image received from the camera, the first UAV information, and the second UAV information. The device generates a geo-rectified imagery based on the image and the location of the UAV.

Systems and methods for controlling an autonomous vehicle are provided. In one example embodiment, a computer-implemented method includes obtaining sensor data indicative of a surrounding environment of the autonomous vehicle, the surrounding environment including one or more occluded sensor zones. The method includes determining that a first occluded sensor zone of the occluded sensor zone(s) is occupied based at least in part on the sensor data. The method includes, in response to determining that the first occluded sensor zone is occupied, controlling the autonomous vehicle to travel clear of the first occluded sensor zone.

One variation of a method for accessing supplemental data from other vehicles includes, at an autonomous vehicle: recording a scan image of a scene around the autonomous vehicle at a first time; detecting insufficient perception data in a region of the scan image; in response to detecting insufficient perception data in the region, defining a ground area of interest containing the region and wirelessly broadcasting a query for perception data representing objects within the ground area of interest; in response to receiving supplemental perception data—representing objects within the ground area of interest detected by the second vehicle at approximately the first time—from a second vehicle proximal the scene, incorporating the supplemental perception data into the scan image to form a composite scan image; selecting a navigational action based on objects in the scene represented by the composite scan image; and autonomously executing the navigational action.

A control system for controlling at least one remote device includes a communication module for transmitting control instructions to the remote device; a processor unit for generating said control instructions and sending them to said communication module; and a user interface for detecting information from a user. The user interface includes at least one muscle activity sensor for detecting muscular activity information from the user by measuring the electrical activity of at least one of the user’s muscles, and the user interface generates muscular activity signals representative of detected muscular activity and sends them to the processor unit, and the processor unit generates the control instructions as a function of the muscular activity signals received by the processor unit.

Disclosed is a coverage path planning method for multiple unmanned surface mapping vehicles, comprising: simultaneously creating submaps and an overall map; outputting its own position information and obstacle information, transmitting to BL.sub.l.sup.i and updating BL.sub.l.sup.m; defining a behavior strategy list (BS); determining the BS with priority for path planning, outputting a to or th state if any criterion is satisfied; when trapped in a local optimum, updating map layers layer-by-layer going upwards, searching for tp in the corresponding layers, performing a BS determination, and outputting a tr instruction; if no target point is found even at the highest layer, checking each CS.sub.P.sub.i∈{FN.sub.i,UFN.sub.i}, and determining a termination. As such, the coverage rate and coverage effect of multiple unmanned surface mapping vehicles in a complex environment can be increased, thus increasing the operational efficiency of the unmanned surface mapping vehicles.