In this paper, a self-adaptive anti-misalignment model for transceivers in hybrid radio frequency (RF) communication and visible light communication (VLC) vehicle-to-vehicle (V2V) network is firstly proposed to solve the transceiver misalignment (TM) between vehicles in adjacent lanes caused by vehicle states' changes. By the information on roads and the state of vehicles, traffic scenarios are divided into three categories: the static traffic scenario, the low-speed traffic scenario and the high-speed traffic scenario. By the vehicle behavior characteristics, the relationship between TMs and vehicle states in different traffic scenarios is established. By the relationship, the self-adaptive anti-misalignment model for transceivers is constructed. By the model, the TM can be predicted and the communication mode can be selected. By simulation, the effectiveness of the model is demonstrated. The simulation results show that the model has comparative advantages of preventing the VLC links' interruption and reducing communication modes’ handover numbers in different traffic scenarios.

在本文中，首先提出了一种用于混合射频（RF）通信和可见光通信（VLC）车对车（V2V）网络中收发器的自适应抗错位模型，以解决收发器之间的错位（TM）问题。车辆状态变化引起的相邻车道车辆。根据道路信息和车辆状态，交通场景分为三类：静态交通场景、低速交通场景和高速交通场景。通过车辆行为特征，建立不同交通场景下TMs与车辆状态之间的关系。根据该关系，构建了收发器的自适应防错位模型。通过该模型，可以预测TM并选择通信模式。通过模拟，证明了模型的有效性。仿真结果表明，该模型在防止VLC链路中断和减少不同流量场景下通信模式切换次数方面具有比较优势。