A system and method are provided for location-targeting the provision of media distributed by a mobile platform. The method provides a mobile platform with an attached media projection subsystem, and an identifier associated with the media projection subsystem. The media projection subsystem is selectively enabled, the geographic location of the mobile platform is determined, and the identifier and the enablement of the media projection system are verified. Verification information, including the mobile platform (media projection subsystem) location, identifier, and enablement of the media projection subsystem is communicated to a server and stored in a non-transitory memory. A targeting application may direct the system to a target location in cooperation with analyzing the verification information, weighted for factors such as proximate vehicular traffic, line of sight, proximate pedestrian traffic, proximity to cultural events, proximity to cultural facilities, the time of day, and the length of time the media is being projected.

A system and method are provided for location-targeting the provision of media distributed by a mobile platform. The method provides a mobile platform with an attached media projection subsystem, and an identifier associated with the media projection subsystem. The media projection subsystem is selectively enabled, the geographic location of the mobile platform is determined, and the identifier and the enablement of the media projection system are verified. Verification information, including the mobile platform (media projection subsystem) location, identifier, and enablement of the media projection subsystem is communicated to a server and stored in a non-transitory memory. A targeting application may direct the system to a target location in cooperation with analyzing the verification information, weighted for factors such as proximate vehicular traffic, line of sight, proximate pedestrian traffic, proximity to cultural events, proximity to cultural facilities, the time of day, and the length of time the media is being projected.

A machine localization system includes a work machine including an extendable implement, a first pressure sensor coupled to the work machine, a second pressure sensor located at a known elevation, and a computing system operably coupled to the work machine, the first pressure sensor, and the second pressure sensor. The computing system is configured to receive a first pressure measurement from the first pressure sensor and a second pressure measurement from the second pressure sensor, determine a maximum operating height of the extendable implement based on a difference between the first pressure measurement and the second pressure measurement, and configure the extendable implement to not exceed the maximum operating height.

A machine localization system includes a work machine including an extendable implement, a first pressure sensor coupled to the work machine, a second pressure sensor located at a known elevation, and a computing system operably coupled to the work machine, the first pressure sensor, and the second pressure sensor. The computing system is configured to receive a first pressure measurement from the first pressure sensor and a second pressure measurement from the second pressure sensor, determine a maximum operating height of the extendable implement based on a difference between the first pressure measurement and the second pressure measurement, and configure the extendable implement to not exceed the maximum operating height.

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.

A transportation system includes an artificial intelligence 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 artificial intelligence system detects the rider emotional state in the self-driving vehicle by recognition of patterns of emotional state indicative data from a set of wearable sensors worn by the rider. The patterns are indicative of at least one of a favorable emotional state and an unfavorable emotional state of the rider. The artificial intelligence system is to optimize, for achieving at least one of maintaining a detected favorable emotional state of the rider and achieving a favorable emotional state of a rider subsequent to a detection of an unfavorable emotional state, the operating parameter of the vehicle in response to the detected emotional state of the rider.

A vehicle includes a display disposed to facilitate presenting an augmentation of content in an environment of a rider of the vehicle; a circuit for registering at least one of location and orientation of the vehicle; a machine learning circuit that determines at least one augmentation parameter by processing at least one input relating to at least one of the rider and the vehicle; and a reality augmentation circuit that, responsive to the at least one of the location or the orientation of the vehicle, generates an augmentation element for presenting in the display, the generating based at least in part on the at least one augmentation parameter.

A system and a method are disclosed that generate for display to a remote operator a user interface comprising a map, the map comprising visual representations of a source area, a plurality of candidate robots, and a plurality of candidate destination areas. The system receives, via the user interface, a selection of a visual representation of a candidate robot of the plurality of candidate robots, and detects a drag-and-drop gesture within the user interface of the visual representation of the candidate robot being dragged-and-dropped to a visual representation of a candidate destination area of the plurality of candidate destination areas. Responsive to detecting the drag-and-drop gesture, the system generates a mission, where the mission causes the candidate robot to autonomously transport an object from the source area to the candidate destination area.

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.