用于自行车和车辆检测和分类的低成本模块化无线电层析成像系统
A Low Cost Modular Radio Tomography System for Bicycle and Vehicle Detection and Classification
摘要：
无处不在的物联网（IoT）驱动的车辆检测和分类系统的推进部署将成功地将现有的道路基础设施转变为高度动态和互连的网络物理系统（CPS）。尽管近年来提出了许多不同的传感器系统，但这些解决方案只能满足一部分要求，包括成本效益、鲁棒性、准确性和隐私保护。本文提供了一种模块化的系统方法，利用无线电断层扫描的衰减模式和高度准确的信道信息，可靠和强大的检测和不同的道路用户的分类。为此，我们使用无线局域网（WLAN）和超宽带（UWB）收发器模块，其提供信道状态信息（CSI）或信道脉冲响应（CIR）数据。由于所提出的系统利用现成的和节能的嵌入式系统，它允许在现有的道路基础设施中的成本效益的特设部署。我们已经评估了所提出的系统的性能骑自行车和其他机动车辆的实验现场部署。在这个问题上，主要的重点一直是准确检测骑自行车的人在自行车道上。然而，我们也进行了初步的评估测试测量不同的机动车辆使用类似的系统配置为骑自行车的人。总之，该系统在检测骑自行车的人方面达到了100%的准确率，并对骑自行车的人和汽车进行了98%以上的分类。
英文摘要：
The advancing deployment of ubiquitous Internet of Things (IoT)-powered vehicle detection and classification systems will successively turn the existing road infrastructure into a highly dynamical and interconnected Cyber-physical System (CPS). Though many different sensor systems have been proposed in recent years, these solutions can only meet a subset of requirements, including cost-efficiency, robustness, accuracy, and privacy preservation. This paper provides a modular system approach that exploits radio tomography in terms of attenuation patterns and highly accurate channel information for reliable and robust detection and classification of different road users. Hereto, we use Wireless Local Area Network (WLAN) and Ultra-Wideband (UWB) transceiver modules providing either Channel State Information (CSI) or Channel Impulse Response (CIR) data. Since the proposed system utilizes off-the-shelf and power-efficient embedded systems, it allows for a cost-efficient ad-hoc deployment in existing road infrastructures. We have evaluated the proposed system's performance for cyclists and other motorized vehicles with an experimental live deployment. In this concern, the primary focus has been on the accurate detection of cyclists on a bicycle path. However, we also have conducted preliminary evaluation tests measuring different motorized vehicles using a similar system configuration as for the cyclists. In summary, the system achieves up to 100% accuracy for detecting cyclists and more than 98% classifying cyclists and cars.
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。
