  We propose a new warning system based on smartphones that evaluates the risk
of motor vehicle for vulnerable pedestrian (VP). The acoustic sensors are
embedded in roadside to receive vehicles sounds and they are classified into
heavy vehicle, light vehicle with low speed, light vehicle 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% in accuracy criterion. To install this system,
directional microphones are embedded on roadside and the risk is classified
there. 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.
