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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. 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 article, 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.

The Channel as a Traffic Sensor: Vehicle Detection and Classification Based on Radio Fingerprinting