Optimized Wireless Sensing and Deep Learning for Enhanced Human-Vehicle Recognition

In the realm of traffic parameter measurement, wireless sensing-based human-vehicle recognition methods have been pivotal due to their low cost and non-invasive nature. Traditionally, these methods have relied on the 2.4 GHz frequency band, often neglecting the rich potential of the sub-GHz bands. Furthermore, the energy attributes of wireless signals are influenced by both antenna height and carrier frequency, yet few studies have explored their impact on human-vehicle recognition performance. Addressing this critical research gap, this study introduces an innovative convolutional neural network-based method that leverages both sub-GHz bands and variable antenna heights. Specifically, this paper focuses on two key aspects: received signal strength signal-to-noise ratio analysis and wireless sensing-based human-vehicle recognition performance analysis. Experimental results demonstrate that the optimal human-vehicle recognition performance is achieved with 2.4 GHz wireless signals and an antenna height of 0.8 m, resulting in an average vehicle recognition accuracy of 95.96%. Besides, the dataset with various carrier frequencies and antenna heights has been publicly available at https://github.com/TZ-mx/mixed-RSS-dataset.

I. Introduction

Intelligent Traffic Systems (ITSs) have become indispensable in the framework of smart cities, greatly enhancing urban traffic flow dispatching efficiency [1], [2]. With advancements in intelligent traffic technology, ITSs now facilitate various smart functions such as adaptive traffic light control and street light brightness management, contributing to improved travel efficiency and reduced carbon emissions [3], [4], [5]. Central to the evolution of ITSs is the human-vehicle recognition (HVR) method, a cornerstone of traffic parameter measurement systems and a current research focus [6]. The accuracy of HVR methods is pivotal in determining the efficacy of traffic speed measurement and traffic flow statistics.