论文标题
交通信号处的队列长度估计：带范围测量传感器的连接车辆
Queue Length Estimation at Traffic Signals: Connected Vehicles with Range Measurement Sensors
论文作者
论文摘要
如今，车辆已成为丰富的数据来源，因为它们配备了本地化或跟踪以及无线通信技术。随着对自动驾驶技术或自动驾驶技术的兴趣日益增加，车辆还配备了范围测量传感器（例如Lidar，立体声摄像头和超声波），以检测周围环境中的其他车辆和物体。可以想象，此类车辆可以与运输基础设施元素（例如，交通信号控制器）共享数据，以实现不同的移动性和安全应用。然后，这些连接车辆的数据可用于实时估计系统状态。本文从配备范围测量传感器的连接车辆中开发了队列长度估计器。为队列长度估算而开发了简单的插件模型，而无需通过扩展以前的配方来实现地面真相队列长度。所提出的方法易于实现，并且可以在具有已知相长度的流量信号处采用循环队列。通过微观流量模拟的数据评估了派生模型。从数值实验中，具有范围传感器的QLE模型在差异与均值比率的25％和低于20％的市场渗透率下的变异系数中提高了25％。
Today vehicles are becoming a rich source of data as they are equipped with localization or tracking and with wireless communications technologies. With the increasing interest in automated- or self- driving technologies, vehicles are also being equipped with range measuring sensors (e.g., LIDAR, stereo cameras, and ultrasonic) to detect other vehicles and objects in the surrounding environment. It is possible to envision that such vehicles could share their data with the transportation infrastructure elements (e.g., a traffic signal controller) to enable different mobility and safety applications. Data from these connected vehicles could then be used to estimate the system state in real-time. This paper develops queue length estimators from connected vehicles equipped with range measurement sensors. Simple plug-and-play models are developed for queue length estimations without needing ground truth queue lengths by extending the previous formulations. The proposed method is simple to implement and can be adopted to cyclic queues at traffic signals with known phase lengths. The derived models are evaluated with data from microscopic traffic simulations. From numerical experiments, the QLE model with range sensors improves the errors as much as 25% in variance-to-mean ratio and 5\% in coefficient of variation at low less than 20% market penetration rates.
