  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.
