CV2X-LOCA：面向自动驾驶汽车的路侧单元协同定位框架
CV2X-LOCA: Roadside Unit-Enabled Cooperative Localization Framework for Autonomous Vehicles
AI总结：
针对自动驾驶汽车在城市峡谷等GNSS拒止环境下的高精度定位难题，提出首个基于路侧单元的协同定位框架CV2X-LOCA，仅利用C-V2X信道状态信息即可在动态网络和噪声条件下实现车道级定位，并为RSUs部署提供投资决策参考。
摘要：
准确且鲁棒的定位系统对于自动驾驶汽车（AVs）在城市环境中实现安全驾驶至关重要。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定位车辆是有效的，但在城市峡谷中（如多层桥梁的下层、高层建筑旁的街道、隧道等）实现高精度定位仍然是一个挑战。本文研究了蜂窝车联网（C-V2X）无线通信在改善GNSS拒止环境下AVs定位性能方面的潜力。具体而言，我们提出了首个路侧单元（RSU）赋能的协同定位框架，即CV2X-LOCA，该框架仅使用C-V2X信道状态信息即可实现车道级定位精度。CV2X-LOCA包含四个关键部分：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。这些模块共同应对动态C-V2X网络中存在的挑战。大量仿真和现场实验表明，即使在高速移动和稀疏RSUs覆盖环境的噪声条件下，CV2X-LOCA在车辆定位方面也达到了最先进的性能。研究结果还为交通机构关于成本效益地部署RSUs的未来投资决策提供了见解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
