Automatic pavement macrotexture depth calculation using a statistical approach based on the tire/road noise signal by directional microphones

Abstract

This paper develops an automatic method to calculate the macrotexture depth of pavement roads, using the tire/road noise data collected by the two directional microphones mounted underneath a moving test vehicle. The directional microphones collect valid tire/road noise signal at the travel speed of 10–110 km/h, and the sampling frequency is 50 kHz. The tire/road noise signal carries significant amount of road surface information, such as macrotexture depth. Using bandpass filter, principal component analysis, speed effect elimination, Gaussian mixture model, and reversible jump Markov Chain Monte Carlo, the macrotexture depth of pavement roads can be calculated from the tire/road noise data, automatically and efficiently. Compared to the macrotexture depth results by the sand-patch method and laser profiler, the acoustic method has been successfully demonstrated in engineering applications for the accurate results of macrotexture depth with excellent repeatability, at the test vehicle’s travel speed of 10-110 km/h.