Floating Car Observers (FCOs) are an innovative method to collect traffic data by deploying sensor-equipped vehicles to detect and locate other vehicles. We demonstrate that even a small penetration rate of FCOs can identify a significant amount of vehicles at a given intersection. This is achieved through the emulation of detection within a microscopic traffic simulation. Additionally, leveraging data from previous moments can enhance the detection of vehicles in the current frame. Our findings indicate that, with a 20-second observation window, it is possible to recover up to 20\% of vehicles that are not visible by FCOs in the current timestep. To exploit this, we developed a data-driven strategy, utilizing sequences of Bird's Eye View (BEV) representations of detected vehicles and deep learning models. This approach aims to bring currently undetected vehicles into view in the present moment, enhancing the currently detected vehicles. Results of different spatiotemporal architectures show that up to 41\% of the vehicles can be recovered into the current timestep at their current position. This enhancement enriches the information initially available by the FCO, allowing an improved estimation of traffic states and metrics (e.g. density and queue length) for improved implementation of traffic management strategies.

翻译：浮动车观测器（FCOs）是一种通过部署搭载传感器的车辆来检测和定位其他车辆，从而收集交通数据的创新方法。我们证明，即使FCO的渗透率较低，也能在给定交叉口识别出大量车辆。这是通过在微观交通仿真中模拟检测过程来实现的。此外，利用先前时刻的数据可增强当前帧中车辆的检测能力。研究结果表明，在20秒的观测窗口内，最多可恢复当前时间步中FCO未观测到的20%车辆。为利用这一特性，我们提出了一种数据驱动策略，采用检测车辆鸟瞰图（BEV）序列与深度学习模型。该方法旨在将当前时刻未被检测到的车辆引入视野，从而增强当前检测到的车辆。不同时空架构的实验结果显示，最多可将41%的车辆恢复至当前时间步的当前位置。该增强技术丰富了FCO初始可用信息，有助于更准确地估计交通状态与指标（例如密度和排队长度），从而改进交通管理策略的实施。