Road Surface Digitization and Classification for NVH Prediction: A Simulation and Validation Approach Using Real Data

Vehicle vibration and noise are predominantly driven by road-surface excitation, making robust prediction of these phenomena based on pavement roughness a central challenge in automotive development. This paper presents a fully automated pipeline that begins with high-resolution laser-triangulation scans of actual test tracks to produce centerline elevation profiles. These profiles are processed and classified by a MATLAB routine using ISO 8608-based power-spectral-density analysis to extract the Gh0 roughness coefficient. Concurrently, in-service acoustic and chassis-vibration data, collected at two representative speeds, are transformed into feature vectors comprising statistical PSD descriptors. A regression model then learns the mapping from these features to Gh0, evaluating the feasibility of mapping vehicle-borne signatures to roughness metrics. Predicted Gh0 values drive a profile-synthesis algorithm to generate two-dimensional height grids, which are exported as CRG files and imported into a multibody simulation software (MSC ADAMS) as well as driver-in-the-loop platforms. Simulation results closely reproduce the primary excitation characteristics of the physical tracks, demonstrating a preliminary proof-of-concept pipeline for virtual road surface generation. While the cross-validated regression model indicates limited generalization on the current small dataset (R2=−0.2783), the end-to-end workflow establishes the baseline integration required for future data-driven NVH simulation. To extend applicability beyond a single test vehicle, a set of Vehicle Calibration Transforms is proposed to adapt power-spectral-density features from arbitrary vehicles into the calibrated feature domain. The complete workflow promises to streamline virtual NVH validation, reduce prototype testing, and support full NVH simulator engineering in future research.