Dharmaraj, Nayan Gokull (University of Moratuwa), Dias, Dileeka (University of Moratuwa), Hemachandra, Kasun Thilina (University of Moratuwa)

Enabling Scalable, Privacy-Preserving Smart Pedestrian Crossings with CSI-Based Sensing on COTS Hardware

Scheduled for presentation during the Regular Session "S06c-Safety, Sensing, and Infrastructure Design for Vulnerable Road Users" (WE-LA-T6), Wednesday, November 19, 2025, 16:40−17:00, Surfers Paradise 3

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 19, 2025

Abstract

Real-time pedestrian sensing at crossings is essential for Intelligent Transportation Systems (ITS). Yet traditional vision-based solutions and wearable devices face privacy, cost, and deployment challenges. While Wi-Fi channel state information (CSI) has shown promise in indoor environments, its potential for dynamic outdoor environments remains largely unexplored. This paper presents a device-free, CSI-based system for detecting human movement and walking direction at crossings and alert oncoming vehicles to enhance pedestrian safety. A two-stage classification framework is adopted with noise suppression and Doppler-sensitive features. Classical and deep learning models are evaluated across four outdoor locations and seven subjects, achieving 96.9% accuracy for movement detection and up to 62% accuracy for classification of movement direction. These findings demonstrate the feasibility of deploying low-cost commercial off-the-shelf (COTS) hardware such as ESP32 and Raspberry Pi for scalable, privacy-preserving ITS applications.