Roadway Friction Screening and Measurement With Automated Vehicle Telematics and Control

Details

• Creators:
  Huang, Heye ; Beojone, Caio V. ; Ouyang, Yanfeng ; Liang, Zhaohui ; Li, Xiaopeng (Shaw)
• Corporate Creators:
  University of Wisconsin--Madison ; University of Michigan Transportation Research Institute ; Center for Connected and Automated Transportation (CCAT)
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  Automated Vehicle Control Autonomous Vehicles Computer Networks Data Analysis Friction Estimation Friction Tests High-slip-ratio Control Intelligent Vehicles Network Operation Noise Control Tires Transportation Safety

  More +
• Publication/ Report Number:
  AWD-001401
• DOI:
  https://doi.org/10.21231/8trv-9w17
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report Report (July 2024 - December 2025)
• Corporate Publisher:
  University of Michigan. Center for Connected and Automated Transportation
• Abstract:
  Accurate estimation of the tire-road friction coefficient (TRFC) is essential for ensuring safe vehicle control, particularly under adverse road conditions. Conventional approaches typically rely on naturalistic driving data from regular vehicles, which operate under mild acceleration and braking and therefore provide limited slip excitation and insufficient observability of peak TRFC. To address this limitation, we developed a high-slip-ratio control framework that enables automated vehicles (AVs) to actively excite the peak friction region during empty-haul operations while maintaining operational safety. The framework employs a simplified Magic Formula tire model to capture nonlinear slip-force dynamics, locally fitted through repeated high-slip measurements. To ensure safety in car-following scenarios, we designed a constrained optimal control strategy that balances slip excitation, trajectory tracking, and collision avoidance. In addition, a binning-based statistical projection method was introduced to robustly estimate peak TRFC in the presence of noise and local data sparsity. To further improve estimation accuracy, we also developed a network routing method that leverages spatial information from multiple vehicles and routes. The complete framework was validated through closed-loop simulations and real-vehicle experiments, demonstrating its accuracy, robustness, safety, and scalability for cost-effective roadway friction screening.
• Format:
  PDF
• Funding:
  GR000036376 UWMSN |Shaw_CCAT_YR2_Connected Intel(AAN9211)
• Collection(s):
  Autonomous Vehicles University Transportation Centers
• Main Document Checksum:
  urn:sha-512:e64567328994f7358ffeb96a7c61df4d63c935ab829bd42cbf85c97cc90a25f9f1da4174a9e50235f86c405f6f7e933b07f6f77772aa390e668366b74a0e620a
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/92645/dot_92645_DS1.pdf
• File Type:
  [PDF - 4.27 MB ]