  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.
