1 research outputs found
A Radio-fingerprinting-based Vehicle Classification System for Intelligent Traffic Control in Smart Cities
The measurement and provision of precise and upto-date traffic-related key
performance indicators is a key element and crucial factor for intelligent
traffic controls systems in upcoming smart cities. The street network is
considered as a highly-dynamic Cyber Physical System (CPS) where measured
information forms the foundation for dynamic control methods aiming to optimize
the overall system state. Apart from global system parameters like traffic flow
and density, specific data such as velocity of individual vehicles as well as
vehicle type information can be leveraged for highly sophisticated traffic
control methods like dynamic type-specific lane assignments. Consequently,
solutions for acquiring these kinds of information are required and have to
comply with strict requirements ranging from accuracy over cost-efficiency to
privacy preservation. In this paper, we present a system for classifying
vehicles based on their radio-fingerprint. In contrast to other approaches, the
proposed system is able to provide real-time capable and precise vehicle
classification as well as cost-efficient installation and maintenance, privacy
preservation and weather independence. The system performance in terms of
accuracy and resource-efficiency is evaluated in the field using comprehensive
measurements. Using a machine learning based approach, the resulting success
ratio for classifying cars and trucks is above 99%