Authors: Pınar Boyraz
Addresses: Mechanical Engineering Department, Istanbul Technical University, Inonu Cd. No: 65, 34437 Beyoglu Istanbul, Turkey
Abstract: A low-cost acoustic road-type classification system is proposed to be used in road-tyre friction force estimation in active safety applications. The system employs audio signal processing and extracts features such as linear predictive coefficients (LPC), mel-frequency cepstrum coefficients (MFCC) and power spectrum coefficients (PSC). The features are extracted using time windows of 0.02, 0.05 and 0.1 seconds in order to find the best representative window for the signal properties which should also be as short as possible for active safety systems. In order to find the best feature space, a variance analysis based approach is considered to represent the road types as distinguished classes. Optimised feature space is classified using artificial neural networks (ANN). The results show that the designed ANN can classify the road types with 91% accuracy at worst condition. To demonstrate the value of the system, a case study including traction control application is reported.
Keywords: acoustic signal processing; road type estimation; active vehicle safety; intelligent vehicle safety; road-tyre friction force; feature extraction; active safety systems; artificial neural networks; ANNs; traction control.
International Journal of Vehicle Safety, 2014 Vol.7 No.2, pp.209 - 222
Received: 17 Jun 2013
Accepted: 30 Oct 2013
Published online: 26 Feb 2014 *