AI 中文总结
研究车路协同网络中交通状态估计问题，核心方法是结合基础设施传感器与联网车辆，通过V2X通信及分布式卡尔曼滤波器等进行估计，主要贡献是能准确重建交通状态，检测冲击波动态，还分析了多种因素对估计精度的影响。
AI 中文摘要
本文提出了一种分布式交通状态估计框架，将基础设施传感器和联网车辆作为协同感知节点。利用车联网（V2X）通信，相邻节点交换局部估计值，并通过为二阶宏观交通流模型设计的分布式卡尔曼滤波器进行更新。一个共识步骤融合网络中的异构信息，而投影步骤确保交通状态在物理上的一致性。我们在HighD和NGSIM数据以及捕获瞬态拥堵的微观SUMO模拟上评估了该方法。结果表明，即使在基础设施感知稀疏和车辆连接间歇性的情况下，也能准确重建高速公路交通状态并检测非线性冲击波动态。统计分析进一步显示了联网车辆渗透率、V2X通信范围和基础设施部署如何影响估计精度。特别是，在10%的联网车辆渗透率、300 - 400米的V2X范围和稀疏的基础设施部署下，车路组合配置始终优于仅依赖基础设施或仅依赖联网车辆的方法。
英文摘要
This paper proposes a distributed traffic state estimation framework that combines infrastructure sensors and connected vehicles as cooperative sensing nodes. Using Vehicle-to-Everything (V2X) communication, nearby nodes exchange local estimates and update them through a distributed Kalman filter designed for a second-order macroscopic traffic flow model. A consensus step fuses heterogeneous information across the network, while projection steps enforce physically consistent traffic states. We evaluate the method on HighD and NGSIM data, and on microscopic SUMO simulations that capture transient congestion. The results show accurate reconstruction of highway traffic states and detection of nonlinear shockwave dynamics, even with sparse infrastructure sensing and intermittent vehicular connectivity. A statistical analysis further shows how CV penetration rate, V2X communication range, and infrastructure deployment affect estimation accuracy. In particular, with 10% CV penetration, V2X ranges of 300-400 m, and sparse infrastructure deployment, the combined infrastructure-vehicle configuration consistently outperforms approaches that rely only on infrastructure or only on connected vehicles.
