Existing Unmanned Aerial Vehicle (UAV) clusters lack a formalized model, fail to consider external interference factors, and overlook the need for dynamic cluster maintenance to ensure stability and coordination in open scenarios. To address these issues, we propose an UAV collaborative cluster formation method suitable for open scenarios, which can form an energy-efficient, reliable, and stable UAV cluster even in external interferences. First, we present a UAV node promotion method based on mobility similarity and connectivity. Then, we formalize a collaborative UAV cluster model based on the energy efficiency, reliability, and stability among UAV nodes. Next, we propose a formation method for UAV clusters based on Pareto optimality and provide a maintenance method for clusters. Extensive simulation results demonstrate that the proposed method significantly outperforms state-of-the-art (SOTA) approaches. Specifically, in open scenarios, our method improves average cluster efficiency by up to 6.9%, enhances average cluster reliability by 12.4%, enhances average cluster stability by 14.2%, and extends the average cluster survival time and the average node survival time by 18.1% and 17.2% compared to the best-performing baseline, verifying the superior effectiveness and robustness of the proposed framework.

The dynamic topologies and sensitive information exchanged among autonomous vehicle groups make them prime targets for attackers. In particular, in a collusive attack scenario, malicious nodes can collaborate to manipulate the trust evaluation system, thereby compromising the security of the entire vehicle group. To handle this limitation, this work proposes a privacy-preserving method for forming autonomous vehicle groups in a collusive attack scenario. First, we introduce a distributed trust evaluation algorithm based on a federated learning topology, which preserves local data privacy while facilitating reliable inter-vehicle trust computation. Then, we propose a PageRank-based detection mechanism that analyzes the trust propagation network to identify potential collusive attackers. Finally, we present a privacy-preserving method for autonomous vehicle group formation. Experimental results show that our proposed approach significantly improves the security and stability of autonomous vehicle groups compared to existing methods.

Forming a stable autonomous vehicle group is extremely challenging in an urban scene, which is disturbed by many environmental factors, e.g., manned vehicles, roadside obstacles, traffic lights, and pedestrians. Existing work focuses on autonomous vehicle group formation (AVGF) in a highway scene only. Its outcomes cannot be directly applied to an urban scene because of different environmental factors and poor communication quality. This work presents an autonomous vehicle group model in an urban scene. First, it proposes a prediction method to analyze the impact of environmental factors on communications among autonomous vehicles. Then, it defines preperception degree, vehicle activity, and mobility similarity of autonomous vehicles and selects leader vehicles based on them. Next, it measures connectivity, coupling, and timeliness increments of a vehicle group, based on which a vehicle group model is formulated. Finally, it solves the proposed vehicle group model by using a modified distributed multiobjective optimization method, proves its convergence, and analyzes its time complexity. The simulation results on synthetic and real roads show that the proposed prediction method achieves lower errors than XGBoost and a multilayer perceptron, and the proposed vehicle group model outperforms two AVGF methods and a dynamic clustering method for vehicular ad-hoc network.