An accurate and robust localization system is crucial for autonomous vehicles (AVs) to enable safe driving in urban scenes. While existing global navigation satellite system (GNSS)-based methods are effective at locating vehicles in open-sky regions, achieving high-accuracy positioning in urban canyons such as lower layers of multi-layer bridges, streets beside tall buildings, tunnels, etc., remains a challenge. In this paper, we investigate the potential of cellular-vehicle-to-everything (C-V2X) wireless communications in improving the localization performance of AVs under GNSS-denied environments. Specifically, we propose the first roadside unit (RSU)-enabled cooperative localization framework, namely CV2X-LOCA, that only uses C-V2X channel state information to achieve lane-level positioning accuracy. CV2X-LOCA consists of four key parts: data processing module, coarse positioning module, environment parameter correcting module, and vehicle trajectory filtering module. These modules jointly handle challenges present in dynamic C-V2X networks. Extensive simulation and field experiments show that CV2X-LOCA achieves state-of-the-art performance for vehicle localization even under noisy conditions with high-speed movement and sparse RSUs coverage environments. The study results also provide insights into future investment decisions for transportation agencies regarding deploying RSUs cost-effectively.

翻译：精确且鲁棒的定位系统是实现城市环境下自动驾驶车辆安全行驶的关键。尽管现有的基于全球导航卫星系统（GNSS）的方法在开阔天空区域能够有效定位车辆，但在城市峡谷场景（如多层桥梁下层、高楼旁街道、隧道等）中实现高精度定位仍是一项挑战。本文研究了蜂窝车联网（C-V2X）无线通信技术在GNSS拒止环境下提升自动驾驶车辆定位性能的潜力。具体而言，我们首次提出了基于路侧单元（RSU）的协同定位框架CV2X-LOCA，该方法仅利用C-V2X信道状态信息即可实现车道级定位精度。CV2X-LOCA包含四个关键模块：数据处理模块、粗定位模块、环境参数校正模块和车辆轨迹滤波模块。这些模块协同处理动态C-V2X网络中的现存挑战。大量仿真与现场实验表明，即使在高速移动与稀疏RSU覆盖的嘈杂条件下，CV2X-LOCA仍能实现车辆定位的当前最优性能。本研究结果亦为交通管理部门关于经济高效部署RSU的未来投资决策提供了洞见。