Amirkabir University of Technology AUT Journal of Mathematics and Computing 2783-2449 1 2 2020 09 01 Roadside acoustic sensors to support vulnerable pedestrians via their smartphones 135 143 3312 10.22060/ajmc.2019.15479.1017 EN Mehdi Ghatee Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran 0000-0002-9558-8286 Masoomeh Khalili Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran Mehdi Teimouri Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran Mohammad Mahdi Bejani Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran Journal Article 2018 12 18 This paper proposes a smartphone-based warning system to evaluate the risk of a motor vehicle for vulnerable pedestrians (VP). The acoustic sensors are embedded in the roadside to receive vehicle sounds and they are classified into heavy vehicles, light vehicles with low speed, light vehicles with high speed, and no vehicle classes. For this aim, we extract new features by Mel-frequency Cepstrum Coefficients (MFCC) and Linear Predictive Coefficients (LPC) algorithms. We use different classification algorithms and show that MLP neural network achieves at least $96.77$% accuracy criterion. To install this system, directional microphones are embedded on the roadside and the risk is classified. Then, for every microphone, a danger area is defined and the warning alarms have been sent to every VPs’ smartphones covered in this danger area. https://ajmc.aut.ac.ir/article_3312_334467d41d5cf21e234465a1530ba647.pdf