Vulnerable Road User Detection Using Smartphone Sensors and Recurrence Quantification Analysis

TL;DR

This paper presents a novel approach for Vulnerable Road User detection using smartphone sensors and Recurrence Quantification Analysis, achieving high accuracy and outperforming previous methods in safety-critical transportation systems.

Contribution

It introduces the use of RQA features from smartphone sensor data for VRU detection, enhancing classification accuracy without relying on GPS.

Findings

Achieved 98.34% accuracy with RQA features alone.

Adding traditional time domain features increased accuracy to 98.79%.

Outperformed previous VRU detection methods.

Abstract

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.