A method of determining a flight path for an aerial vehicle, includes controlling the aerial vehicle to fly along a first route, identifying, during the flight along the first route and with aid of one or more processors, a change in a state of signal transmission occurring at a first location, in response to identifying the change, determining, by the one or more processors, a second location different from the first location, determining a second route to the second location, and controlling, by the one or more processors, the aerial vehicle to fly to and land at the second location. The change of the state of signal transmission indicates an abnormal state in a signal transmission between the aerial vehicle and a control device.

In some embodiments, apparatuses and methods are provided herein useful to delivering merchandise using autonomous ground vehicles (AGVs) in cooperation with unmanned aerial vehicles (UAVs). In some embodiments, the system includes: an AGV having a motorized locomotion system, a storage area to hold merchandise, a sensor to detect obstacles, a transceiver, and a control circuit to operate the AGV; a UAV having a motorized flight system, a gripper mechanism to grab merchandise, a transceiver, an optical sensor to capture images; and a control circuit to operate the UAV. In some forms, the system may include a control circuit that instructs movement of the AGV along a delivery route; determines if the AGV has stopped due to an obstacle; and in certain circumstances, instructs the UAV to retrieve merchandise from the AGV, calculates a delivery route for the UAV to the delivery location, and instructs the UAV to deliver the merchandise.

The apparatus for controlling group driving according to as aspect may include an inter-vehicle communication unit for communicating with a leader vehicle to receive the driving state and traveling track of the leader vehicle, a leader vehicle learning unit for learning a driving pattern of the leader vehicle based on the driving state of the leader vehicle received through the inter-vehicle communication unit, an autonomous drive unit for autonomously driving the follower vehicle in accordance with the traveling track of the leader vehicle, and a follow-up control unit for receiving the driving state of the leader vehicle to learn the driving pattern of the leader vehicle, controlling the autonomous drive unit to follow the traveling track of the leader vehicle, and performing the autonomous driving by applying the driving pattern of the leader vehicle.

A transportation system includes an artificial intelligence (AI) system for processing a sensory input from a wearable device in a self-driving vehicle to determine an emotional state of a rider and optimizing a vehicle operating parameter to improve the rider emotional state. The AI system includes a recurrent neural network to indicate a change in the emotional state of the rider by a recognition of patterns of emotional state indicative wearable sensor data from a set of wearable sensors worn by the rider. The patterns are indicative of a first degree of a favorable emotional state of the rider and/or a second degree of an unfavorable emotional state of the rider. The AI system further includes a radial basis function neural network to optimize, for achieving a target emotional state of the rider, the vehicle operating parameter in response to the indication of the change in the rider emotional state.

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.

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.

Disclosed are a mover robot system and a controlling method for the same, in which a manipulation of a control screen where a remote control is performed is restricted if a mover robot is located in an area other than a driving area, and a locked screen requesting an input of a preset use code is displayed on a terminal, whereby a display of the locked screen is maintained or released in accordance with the input code.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for unmanned aerial vehicle modular command priority determination and filtering system. One of the methods includes enabling control of the UAV by a first control source that provides modular commands to the UAV, each modular command being a command associated with performance of one or more actions by the UAV. Modular commands from a second control source requesting control of the UAV are received. The second control source is determined to be in control of the UAV based on priority information associated with each control source. Control of the UAV is enabled by the second control source, and modular commands are implemented.