Affordable, Autonomous, and Comprehensive Road Condition Assessment Using RGB-D Sensors: Enhancing Pavement Condition Evaluation

The assessment of road conditions is important in ensuring the safety and efficiency of transportation infrastructure. However, current methods of evaluation suffer from subjectivity, delayed response, and high costs. To address these limitations, this study proposes the development of an autonomous system that utilizes crowdsourcing RGB-D data to comprehensively and efficiently assess road conditions. The system includes an affordable vehicle-based data acquisition system, allowing for the collection of 3D pavement surface data while driving. By utilizing RGB-D sensors, the system captures both 2D color and 3D depth information of the entire lane width, with precise frame registration facilitated by a high-precision global positioning system (GPS) sensor. To evaluate the pavement conditions, the Pavement Surface Evaluation Rating (PASER) for asphalt pavement is used as a case study in this study. The establishment of an expert system for pavement condition evaluation involves the classification and quantification of pavement data using deep learning and computer vision approaches. The pavement surface data is classified into eight classes, including healthy surface, open joint, manhole, crack sealant, transverse crack, longitudinal crack, alligator cracking, and pothole. Moreover, the quantification results provide detailed information on distress and offer a more accurate understanding of pavement conditions. The system also facilitates the tracking of identified defects and repair work, providing up-to-date information on pavement deterioration and maintenance. It can be used for quality control of the pavement rehabilitation processes where the road authorities can evaluate the quality of the work that is done by the contractors.