# Vulnerable Road User Detection Using Smartphone Sensors and Recurrence Quantification Analysis

arXiv: https://arxiv.org/abs/2006.06941 · HTML: https://mlsanity.com/paper/2006.06941/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2020.

## Built on by (most cited first, 3 of 3)

- [Vulnerable Road User Detection and Safety Enhancement: A Comprehensive Survey](https://mlsanity.com/paper/2405.19202/) (2024)
- [Automated Pavement Cracks Detection and Classification Using Deep Learning](https://mlsanity.com/paper/2406.07674/) (2024)
- [Advancing Roadway Sign Detection with YOLO Models and Transfer Learning](https://mlsanity.com/paper/2406.09437/) (2024)

## Abstract (arXiv)

With the fast advancements of the Autonomous Vehicle (AV) industry, detection of Vulnerable Road Users (VRUs) using smartphones is critical for safety applications of Cooperative Intelligent Transportation Systems (C-ITSs). This study explores the use of low-power smartphone sensors and the Recurrence Quantification Analysis (RQA) features for this task. These features are computed over a thresholded similarity matrix extracted from nine channels: accelerometer, gyroscope, and rotation vector in each direction (x, y, and z). Given the high-power consumption of GPS, GPS data is excluded. RQA features are added to traditional time domain features to investigate the classification accuracy when using binary, four-class, and five-class Random Forest classifiers. Experimental results show a promising performance when only using RQA features with a resulted accuracy of 98. 34% and a 98. 79% by adding time domain features. Results outperform previous reported accuracy, demonstrating that RQA features have high classifying capability with respect to VRU detection.