  Upcoming Intelligent Transportation Systems (ITSs) will transform roads from
static resources to dynamic Cyber Physical Systems (CPSs) in order to satisfy
the requirements of future vehicular traffic in smart city environments.
Up-to-date information serves as the basis for changing street directions as
well as guiding individual vehicles to a fitting parking slot. In this context,
not only abstract indicators like traffic flow and density are required, but
also data about mobility parameters and class information of individual
vehicles. Consequently, accurate and reliable systems that are capable of
providing these kinds of information in real-time are highly demanded. In this
paper, we present a system for classifying vehicles based on their
radio-fingerprints which applies cutting-edge machine learning models and can
be non-intrusively installed into the existing road infrastructure in an ad-hoc
manner. In contrast to other approaches, it is able to provide accurate
classification results without causing privacy-violations or being vulnerable
to challenging weather conditions. Moreover, it is a promising candidate for
large-scale city deployments due to its cost-efficient installation and
maintenance properties. The proposed system is evaluated in a comprehensive
field evaluation campaign 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 89.15% for a fine-grained
classification task with nine different classes.
