Vulnerable Road User Detection Using Smartphone Sensors and Recurrence Quantification Analysis
Links to Fileshttps://ieeexplore.ieee.org/abstract/document/8917520
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Type of Work6 pages
conference papers and proceedings
Citation of Original PublicationAshqar, Huthaifa et al.; Vulnerable Road User Detection Using Smartphone Sensors and Recurrence Quantification Analysis; 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 28 November, 2019; https://doi.org/10.1109/ITSC.2019.8917520
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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.