Assessing road roughness using UAV-derived dense point clouds

Abstract

The quality and safety of road networks are of paramount importance in modern transportation infrastructure. Road surface conditions, particularly road roughness, significantly impact vehicular travel safety, user comfort, vehicle operating costs, and overall road infrastructure maintenance. Traditional methods for road roughness analysis, such as manual inspections or image annotation, often present limitations in terms of data completeness, efficiency, and cost-effectiveness, especially for extensive road networks. This study investigates the potential of Unmanned Aerial Vehicles (UAVs) equipped with Structure-from-Motion (SfM) derived point clouds to transform road roughness assessment. By leveraging the capabilities of UAVs, including rapid data acquisition and high-resolution imagery, and employing SfM to generate detailed point clouds, this research aims to provide a comprehensive analysis of road surface conditions. The study, conducted on a road segment within the Harran University Osmanbey campus, systematically examines road roughness at different kernel sizes: 30 cm (smaller), 50 cm (moderate), and 75 cm (larger). Through this investigation, insights are gained into how different scales of analysis influence roughness measurements. The findings highlight the potential of UAV-derived point clouds as a promising avenue for road roughness analysis, offering transportation authorities and road administrators an efficient and cost-effective means of maintaining and enhancing road networks. The integration of this technology could lead to the development of safer, more efficient, and economically sustainable road transportation systems, benefiting both road users and infrastructure managers. As research and technological advancements in UAV-based road roughness assessment continue to progress, the potential for revolutionizing road management practices becomes increasingly apparent, ultimately leading to improved road quality and enhanced travel experiences for road users.

Keywords

• UAV
• Sfm
• Point Cloud
• Road Insfracsture
• Rougness

References

1. King, B. A. (2014). The effect of road roughness on traffic speed and road safety. Master’s Thesis. University of Southern Queensland.
2. Bester, C. J. (2003). The effect of road roughness on safety. 82nd Annual Meeting of the Transportation Research Board, Washington, DC.
3. Davies, R. B., Cenek, P. D., & Henderson, R. J. (2005). The effect of skid resistance and texture on crash risk. Proceedings Surface Friction Roads and Runways, Christchurch, 1-4.
4. Wu, J., & Song, X. (2020). Review on smart highways critical technology. Journal of Shandong University (Engineering Science), 50, 52-69
5. Ihs, A. (2005). The influence of road surface condition on traffic safety and ride comfort. 6th International Conference on Managing Pavements 19–24 October 2004. Brisbane Convention & Exhibition Centre, Queensland Australia, 11-21.
6. Kumar, P., Lewis, P., McElhinney, C. P., & Rahman, A. A. (2015). An algorithm for automated estimation of road roughness from mobile laser scanning data. The Photogrammetric Record, 30(149), 30-45. https://doi.org/10.1111/phor.12090
7. Hesami, R., & McManus, K. J. (2009). Signal processing approach to road roughness analysis and measurement. TENCON 2009-2009 IEEE Region 10 Conference, 1-6. https://doi.org/10.1109/TENCON.2009.5396085
8. Zhou, Y., Guo, X., Hou, F., & Wu, J. (2022). Review of intelligent road defects detection technology. Sustainability, 14(10), 6306. https://doi.org/10.3390/su14106306

9. Yiğit, A. Y., & Uysal, M. (2021). Yüksek çözünürlüklü insansız hava aracı (İHA) görüntülerinden karayolların tespiti. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 10(3), 1040-1054. https://doi.org/10.17798/bitlisfen.900817
10. Uysal, M., Toprak, A. S., & Polat, N. (2015). DEM generation with UAV Photogrammetry and accuracy analysis in Sahitler Hill. Measurement, 73, 539-543. https://doi.org/10.1016/j.measurement.2015.06.010
11. Akca, S., & Polat, N. (2022). Semantic segmentation and quantification of trees in an orchard using UAV orthophoto. Earth Science Informatics, 15(4), 2265-2274. https://doi.org/10.1007/s12145-022-00871-y
12. Smith, A., & Sarlo, R. (2022). Automated extraction of structural beam lines and connections from point clouds of steel buildings. Computer‐Aided Civil and Infrastructure Engineering, 37(1), 110-125. https://doi.org/10.1111/mice.12699
13. Snavely, N., Seitz, S. M., & Szeliski, R. (2008). Modeling the world from internet photo collections. International journal of computer vision, 80, 189-210. https://doi.org/10.1007/s11263-007-0107-3
14. Toprak, A. S., Polat, N., & Uysal, M. (2019). 3D modeling of lion tombstones with UAV photogrammetry: a case study in ancient Phrygia (Turkey). Archaeological and Anthropological Sciences, 11(5), 1973-1976. https://doi.org/10.1007/s12520-018-0649-z
15. https://www.cloudcompare.org/doc/wiki/index.php/Roughness
16. Gillespie, T. D. (2021). Fundamentals of vehicle dynamics. SAE international.