Machine Learning-Based Road Surface Defect Detection from Signal Features Using Data from an Instrumented Vehicle Platform

Berkin Uluutku, Korkut Kaynardag, Daisuke Oshima, John Cotter, Fikret Necati Catbas

Connected vehicle platforms enable large-scale collection of vehicle dynamics data from production fleets, creating opportunities for passive roadway monitoring using onboard sensing systems. While existing vibration-based approaches primarily focus on pavement roughness estimation, the ability of fused onboard signals to capture defect-level characteristics remains insufficiently explored. This study investigates whether Road Surface Monitoring (RSM) signals, developed by Honda as an integrated OEM sensing approach, contain distinguishable patterns associated with specific road surface defects. A framework is developed to analyze, detect, and classify defect-related vibration signatures using these fused signals. The approach introduces the Defect Consistency Index (DCI), which measured a 29% average difference between pothole and patching signal signatures within the dataset. A threshold-based Defect Identification Algorithm (DIA) was then applied to detect defective segments, achieving 89% detection accuracy. A machine learning pipeline using shape-based features was subsequently used to classify potholes and patching, achieving up to 90% classification accuracy on the evaluated dataset. The framework was evaluated using real-world RSM data collected from a single instrumented vehicle within a limited geographic region. The results indicate that fused vibration signals contain recurring defect-related patterns that may support defect-level analysis using compact, non-visual measurements. These findings indicate the potential of connected vehicle vibration sensing for scalable roadway monitoring while highlighting the need for broader validation across vehicles, environments, and defect conditions.