A computing device for a vehicle is provided. The computing device includes one or more processors for controlling operation of the computing device, and a memory for storing data and program instructions usable by the one or more processors, wherein the one or more processors are configured to execute instructions stored in the memory to receive sensor data relating to operation of an autonomous driving assistance system, process the received sensor data to derive a value for an assistance system data quality parameter, compare the quality parameter value to a reference, and generate a notification including a result of the comparison.

A method (100) for managing a group of Unmanned Aerial Vehicles (UAVs) operable to fly in a formation is disclosed, each UAV being programmed with a task to be performed by the UAV. The method is performed in a controller UAV of the group and comprises receiving UAV status information from UAVs in the group (110), obtaining information on a current formation of the group (120) and combining the received UAV status information with the information on current group formation to form a representation of a first state of the group (130). The method further comprises using a machine learning model to predict, on the basis of the first state of the group, an optimal formation transition to a new formation (140) and to instruct the UAVs in the group to perform the predicted optimal formation transition (150). An optimal formation transition is a transition to a formation that will minimise predicted total energy consumption for all UAVs in the group to complete their tasks.

A method (20) performed in a network entity (4, 11, 12) is provided for directing an unmanned aerial vehicle (2) to a destination. The method (20) comprises obtaining (21) route information for at least a first vehicle (3a, 3b) and for the unmanned aerial vehicle (2), establishing (22), based on the route information, that a criterion for co-traveling with the first vehicle (3a, 3b) is fulfilled, and transmitting (23), to the unmanned vehicle (2), information enabling the unmanned aerial vehicle (2) to co-travel with the first vehicle (3a, 3b). Methods in an unmanned aerial vehicle and in a network entity, and an unmanned aerial vehicle, network entity, computer programs and computer program products are also provided.

A robot operates using electrical power. An application subsystem provides application functionality such as processing speech, performing video calls, retrieving information responsive to a user request, presenting audio or video content, and so forth. A mobility subsystem includes sensors, software, and hardware that allows the robot to move about an environment. The application subsystem operates using electrical power from a first battery while the mobility subsystem operates using electrical power from a second battery. An electrical interconnect allows for the transfer of electrical power between the subsystems. A power management module uses information about previous tasks performed by the robot that involved the application subsystem and the mobility subsystem to develop power training data. This power training data is then used to estimate power consumption for future task requests. Based on available charge of the respective batteries, power may be transferred from the second battery to operate the application subsystem.

A vehicle system having processors configured to determine permissible regions of a trip where the vehicle system is permitted for automatic control. The permissible regions of the trip are determined based on one or more of parameters of a route, a trend of operating parameters of the vehicle system, or a trip plan that designates one or more operational settings of the vehicle system at different locations, different times, or different distances along a route. The processors also are configured to control transition of the vehicle system between manual control and the automatic control in the permissible regions by alerting an operator of the vehicle system, automatically switching between the manual control and the automatic control, or modifying conditions on which the transition occur.

The systems and methods described herein disclose vehicle positioning and benefit distribution in a vehicle platoon. As described here, vehicles are organized in the platoon based on maximum benefit to the platoon as a whole. The systems and methods then determine the difference in benefit received between each platoon member and equalize between members. The systems and methods can include determining cumulative travel benefits for a group of vehicles. A platoon organization can then be created to achieve the cumulative travel benefits. A platoon can then be created using the platoon organization. The benefit distribution of the platoon can then be determined. Then, the actualized benefits can be apportioned to the one or more platoon members based on the benefit distribution.

A platoon driving control system of a vehicle includes a processor, a navigation, and a driving controller communicatively connected to one another. The processor is configured to estimate a charging amount of a power source that drives a driving device or an available driving distance of each vehicle included in a platoon. The navigation is configured to set a driving route based on a destination of the platoon and search for a charging station for the power source based on the set driving route. The processor is further configured to determine a charging strategy of the power source of the platoon based on the charging amount or the available driving distance of each vehicle, the driving route set by the navigation, and the searched charging station. The driving controller is configured to control driving of the platoon based on the driving route set by the navigation and the charging strategy.

A method for assisting in a flight management of an aircraft calculates a local cost function CF(xi, hj) at various altitudes hj along a planned vertical reference flight trajectory over a discrete set of points P(xi, hj) which forms a two-dimensional grid in which the planned vertical reference flight trajectory varies, the local cost function CF(xi, hj) being calculated locally at each point P(xi, hj) according to aircraft data and environmental data predicted at said local point P(xi, hj). Then, for each point P(xi, hj) of the grid, the method determines a neighbourhood including the point P(xi, hj), and associates a colour K(xi, hj) therewith that is dependent on the value of the local cost function CF(xi, hj) using a predetermined bijective lookup transformation. Next, the method displays the coloured grid formed by the coloured neighbourhoods.

A system and method for autonomous vehicle control to minimize energy cost are disclosed. A particular embodiment includes: generating a plurality of potential routings and related vehicle motion control operations for an autonomous vehicle to cause the autonomous vehicle to transit from a current position to a desired destination; generating predicted energy consumption rates for each of the potential routings and related vehicle motion control operations using a vehicle energy consumption model; scoring each of the plurality of potential routings and related vehicle motion control operations based on the corresponding predicted energy consumption rates; selecting one of the plurality of potential routings and related vehicle motion control operations having a score within an acceptable range; and outputting a vehicle motion control output representing the selected one of the plurality of potential routings and related vehicle motion control operations.

The disclosure generally pertains to minimizing battery power consumption while employing local wireless communication to activate an operation of an autonomous vehicle. In an example implementation, a vehicle controller of an autonomous vehicle transitions to a powered-down state after the autonomous vehicle is parked at a parking spot that lacks cellular communication coverage. The vehicle controller may transition to a powered-up state at a scheduled time to execute an autonomous operation based on a directive stored in a cloud-based device. In no directive has been stored, the vehicle controller wakes up periodically in a partially powered-up state and transmits a query in a local wireless communications format to the cloud-based device to check for a directive. If no directive is present, the vehicle controller transitions back to the powered-down state. If a directive is present, the vehicle controller transitions to a fully powered-up state to execute the autonomous operation.