论文标题
作为交通传感器的频道：基于无线电指纹的车辆检测和分类
The Channel as a Traffic Sensor: Vehicle Detection and Classification based on Radio Fingerprinting
论文作者
论文摘要
无处不在的物联网（IoT） - 基于自动的车辆分类系统将催化未来智能城市中数据驱动的交通流优化，并将将道路基础设施本身转变为动态感知的网络物理系统（CPS）。尽管已经提出了各种不同的交通传感系统，但现有的解决方案尚无法同时满足多种要求，例如准确性，鲁棒性，成本效益和隐私保护。在本文中，我们提出了一种新颖的方法，该方法利用无线电信号的无线电指纹 - 多维衰减模式 - 用于准确且健壮的车辆检测和分类。提出的系统可以以高度成本效益的方式部署，因为它依赖于安装在现有DelineAtor帖子中的现成的嵌入式设备。在一项全面的现场评估活动中，在德国高速公路上的实验实时部署中分析了基于无线电指纹的方法的性能，在那里，它能够实现与七个不同类别的分类任务的二进制分类成功率超过99％的二进制分类成功率，总体准确性为93.83％。
Ubiquitously deployed Internet of Things (IoT)- based automatic vehicle classification systems will catalyze data-driven traffic flow optimization in future smart cities and will transform the road infrastructure itself into a dynamically sensing Cyber-physical System (CPS). Although a wide range of different traffic sensing systems has been proposed, the existing solutions are not yet able to simultaneously satisfy the multitude of requirements, e.g., accuracy, robustness, cost-efficiency, and privacy preservation. In this paper, we present a novel approach, which exploits radio fingerprints - multidimensional attenuation patterns of wireless signals - for accurate and robust vehicle detection and classification. The proposed system can be deployed in a highly cost-efficient manner as it relies on off-the-shelf embedded devices which are installed into existing delineator posts. In a comprehensive field evaluation campaign, the performance of the radio fingerprinting-based approach is analyzed within an experimental live deployment on a German highway, where it is able to achieve a binary classification success ratio of more than 99% and an overall accuracy of 93.83% for a classification task with seven different classes.
