An autonomous vehicle includes a control system, an array of sensors, processing logic, and a switch. The processing logic generates operation instructions based on sensor data and the control system controls the autonomous vehicle based on the operation instructions. The array of sensors generate the sensor data that is related to objects in an external environment. The switch is coupled between the sensors and the processing logic to buffer the processing logic from the sensor data. The switch is further coupled between the processing logic and the control system to provide the operation instructions from the processing logic to the control system. The switch includes a prioritization engine that prioritizes an order of transmission, from the switch to the processing logic, of the first sensor data over the second sensor data based on received vehicle operation data.

A system for transportation includes a vehicle having a user interface, and a robotic process automation system wherein a set of data is captured for each user in a set of users as each user interacts with the user interface, and wherein an artificial intelligence system is trained using the set of data to interact with the vehicle to automatically undertake actions with the vehicle on behalf of the user.

A method of optimizing an operating state of a vehicle includes classifying, using a first neural network of a hybrid neural network, social media data sourced from a plurality of social media sources as affecting a transportation system. The method further includes predicting, using a second neural network of the hybrid neural network, one or more effects of the classified social media data on the transportation system. The method further includes optimizing, using a third neural network of the hybrid neural network, a state of at least one vehicle of the transportation system, wherein the optimizing addresses an influence of the predicted one or more effects on the at least one vehicle.

Apparatus, systems, processes, and computer-readable mediums for facilitating the use of drones are described. For one embodiment, such a system includes a user element having a user application computer program configured to instruct a user interface device to facilitate use of user data and use of mission parameter(s) for a proposed drone mission. An owner element includes an owner application computer program configured to facilitate use of owner data and use of at least one drone parameter. A fleet system element is communicatively coupled to the user element and to the owner element and includes a computer system processor configured to facilitate use of a fleet record and use of at least one fleet parameter.

An unmanned vehicle control method includes acquiring a delivery request for an item, the delivery request comprising delivery information of the item, and determining, according to the delivery information, predicted travelling data associated with delivering the item and at least one of network coverage or network connection quality associated with the predicted travelling data. The method further includes allocating network resources according to the at least one of the network coverage or the network connection quality of the predicted travelling data, and generating a remote driving control instruction according to the predicted travelling data. The method further includes transmitting the remote driving control instruction to an unmanned vehicle using the allocated network resources, so as to cause the unmanned vehicle to drive based on the remote driving control instruction, the unmanned vehicle being configured to transport the item.

A user terminal generates a virtual drone camera image, as an estimated captured image where it is assumed that a virtual drone camera mounted on a drone has captured an image of a planned landing position on the basis of a captured image obtained by capturing the planned landing position of the drone with the user terminal, and transmits the generated virtual drone camera image to the drone. The drone collates the virtual drone camera image with the image captured by the drone camera and lands at the planned landing position in the image captured by the drone camera. The user terminal generates a corresponding pixel positional relationship formula indicating a correspondence relationship between a pixel position on the captured image of the user terminal and a pixel position on the captured image of the virtual drone camera, and generates the virtual drone camera image using the generated relationship formula.

A vehicle includes an on-board controller. A position of the vehicle is detected by a positioning sensor and a CPU of the on-board controller Based on the position of the vehicle and area information stored in a memory, the CPU determines whether the vehicle is in a free driving zone, an alternative driving zone, or a remote driving zone. The CPU sets a driving mode to either one of a free driving mode, an alternative driving mode, and a remote driving mode based on a signal of the driving mode of the vehicle inputted by a main key of a vehicle main body and information indicating which driving zone the vehicle is in. A CPU of the vehicle main body controls an operation of the vehicle in accordance with the set driving mode. The on-board controller may be provided by a mobile terminal.

Connected and automated vehicles (CAVs) have shown the potential to improve safety, increase road throughput, and optimize energy efficiency and emissions in several complicated traffic scenarios. This invention describes a mixed-integer programming (MIP) optimization method for global multi-vehicle decision making and motion planning of CAVs in a highly dynamic environment that consists of multiple human-driven, i.e., conventional or manual, vehicles and multiple conflict zones, such as merging points and intersections. The proposed approach ensures safety, high throughput and energy efficiency by solving a global multi-vehicle constrained optimization problem. The solution provides a feasible and optimal time schedule through road segments and conflict zones for the automated vehicles, by using information from the position, velocity, and destination of the manual vehicles, which cannot be directly controlled. Despite MIP having combinatorial complexity, the proposed formulation remains feasible for real-time implementation in the infrastructure, such as in mobile edge computers (MECs).

A geographic position of an agricultural machine is captured. Agricultural data is received that corresponds to a geographic position. Georeferenced visual indicia are displayed that are indicative of the received agricultural data.

A control system for remotely controlling a power machine is provided. The remote control system can include a hand-held remote control device configured to be in communication with a mobile device. The hand-held remote control device can include a plurality of operator input modules configured for manual actuation. The mobile device can be configured to be operatively connected to the power machine by a wireless communication system. The mobile device can be configured to receive an input from the hand-held remote control device, and output a command signal to a control system on the power machine based on the received input to command one or more functions of the power machine.