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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Vulnerable Road User Detection Using Smartphone Sensors and Recurrence Quantification Analysis


    Beteiligte:


    Erscheinungsdatum :

    2019-10-01


    Format / Umfang :

    509532 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Urban Vulnerable Road User Localization using GNSS, Inertial Sensors and Ultra-Wideband Ranging

    de Ponte Muller, Fabian / Diaz, Estefania Munoz / Perul, Johan et al. | IEEE | 2020


    Vulnerable road user safety in APROSYS

    Hardy, Roger | Tema Archiv | 2007


    ACTIVE SAFETY FOR VULNERABLE ROAD USERS BASED ON SMARTPHONE POSITION DATA

    Liebner, M. / Klanner, F. / Stiller, C. et al. | British Library Conference Proceedings | 2013


    Improving 3D Vulnerable Road User Detection With Point Augmentation

    Lu, Weihao / Zhao, Dezong / Premebida, Cristiano et al. | IEEE | 2023