A Low Cost Modular Radio Tomography System for Bicycle and Vehicle Detection and Classification

TL;DR

This paper introduces a cost-effective, modular radio tomography system using WLAN and UWB transceivers for accurate detection and classification of bicycles and vehicles, suitable for deployment in existing road infrastructure.

Contribution

It presents a novel modular radio tomography approach leveraging off-the-shelf transceivers for reliable vehicle and cyclist detection and classification.

Findings

Achieves up to 100% accuracy in cyclist detection

Over 98% accuracy in classifying cyclists and cars

Demonstrated effective live deployment in real-world scenarios

Abstract

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.