The development of vehicle-to-vehicle (V2V) communication facil-itates the study of cooperative positioning (CP) techniques for vehicular applications. The CP methods can improve the posi-tioning availability and accuracy by inter-vehicle ranging and data exchange between vehicles. However, the inter-vehicle rang-ing can be easily interrupted due to many factors such as obsta-cles in-between two cars. Without inter-vehicle ranging, the other cooperative data such as vehicle positions will be wasted, leading to performance degradation of range-based CP methods. To fully utilize the cooperative data and mitigate the impact of inter-vehicle ranging loss, a novel cooperative positioning method aided by plane constraints is proposed in this paper. The positioning results received from cooperative vehicles are used to construct the road plane for each vehicle. The plane parameters are then introduced into CP scheme to impose constraints on positioning solutions. The state-of-art factor graph optimization (FGO) algo-rithm is employed to integrate the plane constraints with raw data of Global Navigation Satellite Systems (GNSS) as well as inter-vehicle ranging measurements. The proposed CP method has the ability to resist the interruptions of inter-vehicle ranging since the plane constraints are computed by just using position-related data. A vehicle can still benefit from the position data of cooperative vehicles even if the inter-vehicle ranging is unavaila-ble. The experimental results indicate the superiority of the pro-posed CP method in positioning performance over the existing methods, especially when the inter-ranging interruptions occur.

翻译：车车通信技术的发展推动了协同定位技术在车辆应用中的研究。通过车辆间测距与数据交换，协同定位方法可提升定位可用性与精度。然而，受车辆间障碍物等因素影响，车辆间测距容易中断。若缺少车辆间测距，车辆位置等其他协同数据将无法被有效利用，导致基于测距的协同定位方法性能下降。为充分利用协同数据并减轻车辆间测距丢失的影响，本文提出一种新型平面约束辅助的协同定位方法。通过协同车辆接收的定位结果构建各车辆的道路平面参数，并将这些参数引入协同定位方案以对定位解施加约束。采用当前最先进的因子图优化算法，将平面约束与全球导航卫星系统的原始数据及车辆间测距测量值进行融合。由于平面约束仅通过位置相关数据计算得出，所提协同定位方法能够抵御车辆间测距中断的影响——即使车辆间测距不可用，车辆仍能从协同车辆的位置数据中获益。实验结果表明，所提方法在定位性能上优于现有方法，尤其在车辆间测距中断场景下优势更为显著。