Automated Road Damage Recognition based on the Sparse Coding Analysis of Vehicle Vibrations

¹ Department of Construction Science, Texas A&M University, College Station, TX 77843
² Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX 77843
³ Bert S. Turner Department of Construction Management, Louisiana State University, Baton Rouge, LA 70803

^* Corresponding author: dujing@tamu.edu

Abstract

Road pavement damage inspection is a critical yet challenging task. At present, road pavement damage inspection is usually done by DOTs using a manual process. Another emerging method of inspection is via the use of sensors, such as the use of LiDAR. This study proposes an automated road damage recognition method via the Sparse Coding analysis of vehicle vibrations. Sparse Coding is a class of unsupervised methods that learn data patterns based on extracted overcomplete bases. Unlike frequency domain-based analysis, e.g. Spectral Analysis, Sparse Coding analysis preserves the temporal information of the vehicle vibration that contains important patterns related to road pavement damage. A preliminary study was performed with vehicle vibration data collected in College Station, Texas. Results confirm the feasibility of the proposed method in automated road pavement damage recognition. More data points should be collected in the future to further benchmark the effectiveness of the proposed method.