Systems and methods for improving aerial ride quality based on user feedback accessed from an aerial vehicle and devices associated with passengers are provided. A network system receives, from one or more devices associated with a passenger on an aerial vehicle, feedback data regarding a flight, whereby the feedback data is associated with an issue experienced by the passenger. The network system then identifies a root cause of the issue experienced by the passenger on the aerial vehicle. The identifying may include correlating the feedback data with other data associated with the flight. A mitigation action to mitigate the issue is determined. The network system may determine whether to trigger the mitigation action based on a corresponding threshold and can trigger the mitigation action to occur accordingly.

Systems and methods for improving aerial ride quality based on user feedback accessed from an aerial vehicle and devices associated with passengers are provided. A network system receives, from one or more devices associated with a passenger on an aerial vehicle, feedback data regarding a flight, whereby the feedback data is associated with an issue experienced by the passenger. The network system then identifies a root cause of the issue experienced by the passenger on the aerial vehicle. The identifying may include correlating the feedback data with other data associated with the flight. A mitigation action to mitigate the issue is determined. The network system may determine whether to trigger the mitigation action based on a corresponding threshold and can trigger the mitigation action to occur accordingly.

Systems and methods for improving aerial ride quality based on user feedback accessed from an aerial vehicle and devices associated with passengers are provided. A network system receives, from one or more devices associated with a passenger on an aerial vehicle, feedback data regarding a flight, whereby the feedback data is associated with an issue experienced by the passenger. The network system then identifies a root cause of the issue experienced by the passenger on the aerial vehicle. The identifying may include correlating the feedback data with other data associated with the flight. A mitigation action to mitigate the issue is determined. The network system may determine whether to trigger the mitigation action based on a corresponding threshold and can trigger the mitigation action to occur accordingly.

A display system displays, on a display, positions of a work vehicle and one or more unmanned aerial vehicles flying around the work vehicle. The display system includes a processor configured or programmed to obtain position information of the work vehicle and the unmanned aerial vehicles, and, based on the position information, display the positions of the work vehicle and the unmanned aerial vehicles in a field shown on the display.

A display system displays, on a display, positions of a work vehicle and one or more unmanned aerial vehicles flying around the work vehicle. The display system includes a processor configured or programmed to obtain position information of the work vehicle and the unmanned aerial vehicles, and, based on the position information, display the positions of the work vehicle and the unmanned aerial vehicles in a field shown on the display.

A system to monitor vehicle accidents using a network of aerial-based monitoring systems, terrestrial-based monitoring systems and in-vehicle monitoring systems is described. Aerial vehicles used for this surveillance include manned and unmanned aircraft, satellites and lighter than air craft. Aerial vehicles can also be deployed from vehicles. The deployment is triggered by sensors registering a pattern in the data that is indicative of an accident that has happened or an accident about to happen.

A system to monitor vehicle accidents using a network of aerial-based monitoring systems, terrestrial-based monitoring systems and in-vehicle monitoring systems is described. Aerial vehicles used for this surveillance include manned and unmanned aircraft, satellites and lighter than air craft. Aerial vehicles can also be deployed from vehicles. The deployment is triggered by sensors registering a pattern in the data that is indicative of an accident that has happened or an accident about to happen.

Systems, methods, and other embodiments described herein relate to identifying and adapting exciting shot-paths within a camera mode through acquiring data from a vehicle and an aerial device. In one embodiment, a method includes estimating an activity using context from situational data acquired about a vehicle and an environment surrounding the vehicle. The method also includes identifying shot-paths for the activity from estimated paths and viewing angles of an aerial device. The method also includes calculating excitement factors for the shot-paths using a model and selecting at least one of the shot-paths according to the excitement factors. The method also includes, on a condition that the at least one of the shot-paths satisfies feasibility conditions, adapting the shot-paths for the activity by monitoring the situational data and factoring the excitement factors.

Systems, methods, and other embodiments described herein relate to identifying and adapting exciting shot-paths within a camera mode through acquiring data from a vehicle and an aerial device. In one embodiment, a method includes estimating an activity using context from situational data acquired about a vehicle and an environment surrounding the vehicle. The method also includes identifying shot-paths for the activity from estimated paths and viewing angles of an aerial device. The method also includes calculating excitement factors for the shot-paths using a model and selecting at least one of the shot-paths according to the excitement factors. The method also includes, on a condition that the at least one of the shot-paths satisfies feasibility conditions, adapting the shot-paths for the activity by monitoring the situational data and factoring the excitement factors.

The present invention defines and constructs internet of things-based green traffic system for civil-military integration (GTS), which relates to the fields of intelligent transportation, intelligent cabin, Internet of Things, Internet, communication network, big data, driverless, joint control, magnetic levitation, magnetic drive, auto-payment, sensing, positioning, identification, national defense security, social security, and so on. Adoption of risk segregation, joint control, de-consolidation transport, de-signalization, de-signing, full intersection interchanges, station/stop insertion, intelligent control, fully enclosed all-weather all-day operation, unlimited speed, unmanned, one-stop arrival, online parking, general use by military, police and civilians, disposal of passenger and cargo priority franchise, intelligent control linkage to the system of intelligent cabin real-time monitoring and automatic control and optimization of the line, resource utilization and system balance, and other programs to ensure that the system safety and efficiency, optimal operation, to solve peak bottlenecks, low capacity, accidents, affected by the weather, waste of resources and other traffic problems, to create a very simplified standardization, resource sharing, global access to green transport green travel.