Leveraging the Channel as a Sensor: Real-time Vehicle Classification Using Multidimensional Radio-fingerprinting

TL;DR

This paper introduces a non-intrusive, radio-fingerprint based vehicle classification system that leverages existing infrastructure, achieving over 99% success in binary classification and 89.15% accuracy in nine-class scenarios in real-world tests.

Contribution

It presents a novel radio-fingerprinting approach combined with machine learning for real-time vehicle classification without privacy issues or weather sensitivity.

Findings

Binary classification success ratio over 99%

Overall accuracy of 89.15% for nine vehicle classes

Suitable for large-scale, cost-efficient city deployment

Abstract

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.