Response-based methods to measure road surface irregularity: a state-of-the-art review

Nguyen, Teron; Lechner, Bernhard; Wong, Yiik Diew · 2019 · OpenAlex-citations

DOI: 10.1186/s12544-019-0380-6

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Summary

This review paper addresses the growing need for efficient, cost-effective methods to monitor road surface irregularity, driven by the advent of smart technologies, the Internet of Things, and inexpensive onboard sensors. Traditional contact and non-contact profiling methods are often expensive and labor-intensive. In contrast, response-based methods indirectly assess road conditions by measuring vehicle dynamics (displacements, velocities, accelerations) using sensors like accelerometers and gyroscopes. The authors aim to provide a holistic overview of state-of-the-art response-based techniques, highlight key differences between methodologies, and propose focus areas for future research. The review covers three primary applications: road profile reconstruction for vehicle dynamics control, pothole detection for precise localization, and roughness index estimation for pavement maintenance. The authors conducted a systematic literature review, collecting data primarily from the Scopus database and partially from Google Scholar. The search focused on articles published in the last 15 years (post-2005) using keywords related to road roughness, potholes, accelerometers, and estimation techniques. From an initial pool of 161 documents, 130 were selected for detailed analysis after excluding irrelevant studies, such as those focused on bridge-vehicle interaction. The reviewed literature was categorized by application: 37% focused on road profile reconstruction, 39% on pothole detection, and 24% on roughness index estimation. The methods were further classified into model-based approaches (e.g., Kalman filters, sliding mode observers), data-driven machine learning techniques, and transfer function or signal processing methods. The findings indicate that machine learning and data-driven methods have been intensively used with promising results, though their reliance on large datasets limits some applications compared to analytical methods. For road profile reconstruction, recent algorithms increasingly utilize passive suspension and quarter-vehicle models, requiring fewer parameters and offering speed independence for real-time applications. Techniques like Q-parameterization and adaptive Kalman filters show improved performance and lower computational costs. For pothole detection and roughness estimation, research is shifting toward GPS accuracy, data aggregation, and crowdsourcing platforms using smartphones and fleet vehicles. While threshold-based and signal processing methods are common for detection, they often struggle with setting accurate thresholds under varying vehicle speeds and suspension types. Despite advancements, the review notes a lack of a comprehensive system comparable to the International Roughness Index for large-scale pavement management. The significance of this work lies in its synthesis of diverse methodologies, providing a clear roadmap for the research community. It highlights that while response-based methods offer significant cost reductions and enable adaptive vehicle control, gaps remain in creating robust, standardized systems for pavement management. The authors conclude that future research should focus on integrating air-suspension systems, improving machine learning algorithms to reduce data dependence, and developing fleet-based approaches that can reliably estimate road irregularities across large networks. This review serves as a critical reference for developing next-generation probe data performance management systems.

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