Transportation systems have artificial intelligence including neural networks for recognition and classification of objects and behavior including natural language processing and computer vision systems. The transportation systems involve sets of complex chemical processes, mechanical systems, and interactions with behaviors of operators. System-level interactions and behaviors are classified, predicted and optimized using neural networks and other artificial intelligence systems through selective deployment, as well as hybrids and combinations of the artificial intelligence systems, neural networks, expert systems, cognitive systems, genetic algorithms and deep learning.

Transportation systems have artificial intelligence including neural networks for recognition and classification of objects and behavior including natural language processing and computer vision systems. The transportation systems involve sets of complex chemical processes, mechanical systems, and interactions with behaviors of operators. System-level interactions and behaviors are classified, predicted and optimized using neural networks and other artificial intelligence systems through selective deployment, as well as hybrids and combinations of the artificial intelligence systems, neural networks, expert systems, cognitive systems, genetic algorithms and deep learning.

Techniques disclosed provide for enhanced V2X communications by defining information Elements (IE) for V2X messaging between V2X entities. For a transmitting vehicle that sends a V2X message to a receiving vehicle, these IEs are indicative of a detected vehicle model type detected by the transmitting vehicle of a detected vehicle; a pitch rate of the transmitting vehicle, a detected vehicle, or a detected object; a roll rate of the transmitting vehicle, a detected vehicle, or a detected object; a yaw rate of a detected vehicle, or a detected object; a pitch rate confidence; a roll rate confidence; an indication of whether a rear brake light of a detected vehicle is on; or an indication of whether a turning signal of a detected vehicle is on; or any combination thereof. With this information, the receiving vehicle is able to make more intelligent maneuvers than otherwise available through traditional V2X messaging.

An unmanned aerial vehicle (UAV) control system and a UAV control method are provided. The UAV control method includes: storing a reporting configuration by a UAV; communicatively connecting to the UAV and storing at least one historical status information corresponding to the UAV by a server; reporting to the server at least one current status information according to the reporting configuration by the UAV; calculating a variance between the at least one historical status information and the at least one current status information by the server; and updating the reporting configuration of the UAV according to the variance by the server.

Devices, system, and method of vehicular multiple-link wireless communication. A vehicular communication bonding unit creates a bonded wireless communication connection that transports data-packets of a source data-stream from a remote server to a vehicular transceiver, by transporting the data-packets over at least two wireless communication links. The vehicular communication bonding unit utilizes a vehicular cellular transceiver to receive a first batch of the data-packets over a first cellular communication link that connects between the vehicular cellular transceiver and the remote server. The vehicular communication bonding unit further utilizes least one end-user device, of an occupant of a vehicle, to receive a second batch of the data-packets of the particular data-stream, over a second cellular communication link that connects between the end-user device and the remote server.

Transportation systems have artificial intelligence including neural networks for recognition and classification of objects and behavior including natural language processing and computer vision systems. The transportation systems involve sets of complex chemical processes, mechanical systems, and interactions with behaviors of operators. System-level interactions and behaviors are classified, predicted and optimized using neural networks and other artificial intelligence systems through selective deployment, as well as hybrids and combinations of the artificial intelligence systems, neural networks, expert systems, cognitive systems, genetic algorithms and deep learning.

The embodiments relate to a robot and a server communicating with the robot, the robot being driven by using at least one among a driving wheel, a propeller, and a manipulator moving at least one joint.

A controller may receive, from a sensor device, machine velocity data indicating a velocity of a machine controlled by a remote control device. The controller may determine, based on the machine velocity data, a distance to be traveled by the machine until the machine stops traveling after a communication, between the machine and the remote control device, is interrupted. The controller may generate, based on the distance, an overlay to indicate the distance. The controller may provide the overlay, for display, with a video feed of an environment surrounding the machine.

A remote control device is a remote control device configured to control one or more mobile objects via a network, which includes a receiver configured to receive mobile object information including a first state quantity of a state quantity of the mobile object and surrounding information around the mobile object, a trajectory generation unit configured to generate a target trajectory of the mobile object on the basis of the surrounding information, a mobile object estimation unit configured to estimate transmission latency of the network, a gain setting unit configured to set a control gain on the basis of the transmission latency, a control amount calculation unit configured to calculate a control amount for causing the mobile object to follow the target trajectory on the basis of the mobile object information and the control gain, and a transmitter configured to transmit the control amount to the mobile object.

An inventory management system managing a plurality of inventory items stored in a storage area. The inventory management system includes an autonomous vehicle configured to move within the storage area and a computing device communicatively coupled to the autonomous vehicle. The autonomous vehicle includes a beacon configured to facilitate detection of a current position of the autonomous vehicle based on signal triangulation, a sensor configured to detect information indicative of a number of inventory items at the current position of the autonomous vehicle, and a wireless data link configured to transmit a signal indicative of the number of inventory items. The computing device may detect a position of the autonomous vehicle based on the signal transmitted from the beacon, and update an inventory of the storage area based on the signal indicative of the number of inventory items.