Computer Vision-Based Contactless Estimation of Asphalt Pavement Surface Friction

Details

• Creators:
  Al-Qadi, Imad L. ; Golparvar-Fard, Mani ; Fakhreddine, Mohammad ; Lang, Hong ; Villamil, William D.
• Corporate Creators:
  Illinois Center for Transportation ; University of Illinois at Urbana-Champaign
• Corporate Contributors:
  Illinois. Department of Transportation ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  • Pavement maintenance
  • Friction
  • Asphalt pavements
  • Photogrammetry
  • Machine learning
  • Hydroplaning
  • Transportation safety
  • Estimates
  • Microtexture
  • Hot mix asphalt
  More +
• Publication/ Report Number:
  FHWA-ICT-26-017 ; UILU-2026-2019
• DOI:
  https://doi.org/10.36501/0197-9191/26-019
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report 7/1/22-9/30/26
• Corporate Publisher:
  Illinois Center for Transportation
• Description:
  This study developed a contactless and automated technique to estimate hot-mix asphalt pavement surface friction by combining computer vision image-based 3D reconstruction with machine learning. Existing continuous friction estimation equipment, including high-speed laser scanners, are costly to purchase and operate-limiting routine, frequent monitoring. This study addressed this limitation through the development and utilization of cost-effective and scalable equipment. A Structure-from-Motion computer vision framework was developed to reconstruct pavement macrotexture in three dimensions and compute mean profile depth (MPD). Ground-control-based measurements agreed with a reference laser scanner within 15% across all test locations. A high-speed prototype using industrial cameras and a real-time kinematic positioning system was built. It was able to collect data up to 35 mi/hr and perform 3D reconstruction on cloud environments, increasing processing speed. The prototype was validated against a laser scanner at a controlled test track. The results demonstrated that contactless texture measurement and friction prediction are feasible and provide a foundation for more frequent, lower-cost monitoring of pavement surface characteristics, supporting proactive pavement management, safety screening, and more cost-effective maintenance decisions. The developed device measures texture and estimates friction for $61/mile, about 65% less than the $140/mile needed to collect both with current methods, and it costs only $73,500 to buy, compared with about $1,100,000 for a combined laser scanner and a transverse profiler. In addition, a data-driven model was developed to predict sideways friction measured by a Sideway-force Coefficient Routine Investigation Machine using pavement texture, traffic, roadway geometry, operating speed, and surface temperature. Three datasets collected in Florida and Illinois were used to evaluate the influence of dataset size, available features, and data distribution. Among the tree-based ensemble models evaluated, the Extra Trees model performed best, explaining approximately 85% of the variance, with MPD, traffic, operating speed, and surface temperature identified as the most influential variables. Finally, a finite element framework coupling a 3D tire model, texture-based friction model, and water film was developed to evaluate hydroplaning. The framework demonstrated that coarser macrotextures resulted in relatively better tire-pavement contact at higher speeds because of their improved surface drainage capacity.
• Format:
  PDF
• Funding:
  R27-247
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/93746/dot_93746_DS1.pdf
• File Type:
  [PDF - 10.13 MB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:dc2988c40b6b93df4e45c1016fb72684393229a01727802b0830f156b43d4984780472653b653592ffcb710dfff38616af09e3d9f6c83ed7e5fd83ce7f68fcf7