An Autonomous Vehicle Group Model in an Urban Scene

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.