Inertial sensor-based road condition evaluation in laboratory and real environments

Abstract The condition of road surfaces is a critical factor that impacts the technical state of vehicles, the distribution of passenger and freight traffic, and road safety, to name just a few aspects. This issue becomes increasingly important year by year as the automotive industry continues to develop, leading to a growing number of passenger and freight vehicles on the roads. Even though numerous articles have already been published on this topic, there is a lack of a validated, low-cost, and real-time method that accurately links inertial sensor data, vehicle speed, and road defect severity under controlled and real-world conditions. The objective of this work was to develop and validate a reliable, real-time road surface quality assessment method based on inertial sensor data and vehicle speed, using both controlled laboratory experiments and real-world outdoor measurements. The laboratory investigation identified a linear relationship within the tested model vehicle and an ethylene-vinyl-acetate (EVA)-foam track system. This finding is based on the Pearson correlation coefficient (r), which exceeded the critical value at the significance level α = 0.05 in all test cases ( r > 0.89). Based on the laboratory results, this study proposes a bivariate function to model the relationship between road quality, acceleration magnitude, and vehicle speed. A comparison presented in the paper also highlights that the proposed method exhibits a strong correlation ( r = 0.981) with a model established in a simulated environment for approximating the International Roughness Index (IRI). The proposed method offers a cost-effective, speed-aware, accurate, and scalable solution for real-time assessment of road surface quality.