Abstract Control and characterization of pavement roughness is a major quality assurance requirement. With emerging technologies in real‐time monitoring and increasingly stringent requirements to minimize localized roughness features, there is an opportunity to improve upon the traditional quarter‐car (QC) algorithm used to qualify roughness. Current methods suffer from phase lag that mislocates roughness features and require relatively long profiles to achieve high accuracy. In this study, continuous and discrete wavelet bases were modified in the frequency domain to design 116 new QC‐wavelet filters in the spatial domain that were used to analyze 30 road profiles. QC‐wavelet filters were compared to the currently used finite difference algorithm and filtering in the frequency domain. QC‐wavelet filters design based on a Daubechies and nonanalytic Morlet (i.e., db21 and morl0) wavelets outperformed the other filters and algorithms in terms of characterizing overall profiles and accurately quantifying localized features. The major advantages of the new approach include accurately estimating the position and severity of localized feature, and accurately analyzing short profile segments (i.e., <7.62 m).