Unmanned Aerial Vehicles (UAVs) can cooperate through formations to perform tasks. Wireless communication allows UAVs to exchange information, but for the situations requiring high security, electromagnetic silence is needed to avoid potential threats. The passive UAV formation maintenance strategies can fulfill the requirement of electromagnetic silence at the cost of heavy real-time computing and precise locations of UAVs. To pursue high real-time performance without the localization of UAVs, this paper proposes a scalable distributed control algorithm for bearing-only passive UAV formation maintenance. By minimizing necessary communication, pure angle information is applied to maintain UAV formations through distributed control, without the knowledge of the UAVs’ precise locations. The convergency of the proposed algorithm is proven strictly and the converging radius is derived. Through simulation, the proposed algorithm is proven to be suitable for a general case and demonstrates fast convergence speed, strong anti-interference capability, and high scalability.

Mobile communication technologies accelerate the development of intelligent transportation by providing connectivity among vehicles and other vehicles, roadside infrastructures, and cloud platforms, thus creating opportunities to achieve carbon neutrality by promoting intelligent shared mobility. Here, we construct a city-level carbon emission model to quantitatively analyze the relationship between emissions and shared mobility based on a series of trips in several cities. Additionally, vehicle dispatching and urban road planning at low carbon cost are considered. We test our model on a dataset of over 17.6 million trips in five metropolises. Our method yields a reduction in carbon emissions compared with that obtained with the original method and dataset. This difference mainly results from the decrease in vehicle miles traveled via ridesharing and the reduction in energy consumption resulting from vehicle platooning. Although the analysis results depend on a number of assumptions, they are proven robust and could become even more relevant with the ongoing development of autonomous mobility and network intelligence.

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