Roadway Friction Screening and Measurement with Automated Vehicle Telematics and Control

Center for Connected and Automated Transportation/Research/Research from the University of Wisconsin/Roadway Friction Screening and Measurement with Automated Vehicle Telematics and Control

Roadway Friction Screening and Measurement with Automated Vehicle Telematics and Control

In Progress
Headshot of Xiaopeng Li. The link directs to their profile page
Xiaopeng Li
Headshot of Yanfeng Ouyang. The link directs to their profile page.
Yanfeng Ouyang
Headshot of Heye Huang. The link directs to their profile page.
Heye Huang
The University of Wisconsin-Madison Logo. The link directs to the funded research led by this institution.
Illinois Center for Transportation (University of Illinois at Urbana Champaign) Logo. The link directs to the funded research led by this institution.

Principal Investigator(s):

Xiaopeng Li, Professor of Civil and Environmental Engineering – University of Wisconsin-Madison
Yanfeng Ouyang, George Krambles Endowed Professor in Rail and Public Transit – The University of Illinois at Urbana-Champaign
Paul F. Kent Endowed Faculty Scholar – The University of Illinois at Urbana-Champaign
Donald Biggar Willett Faculty Scholar – The University of Illinois at Urbana-Champaign
Director – Chinese-American Railway Transportation Joint Research Center
Associate Director for Mobility – Illinois Center for Transportation
Heye Huang, Research Associate – University of Wisconsin-Madison

Project Abstract:
Measuring roadway friction is crucial for roadway safety, particularly on wet surfaces and challenging road geometries. Roadway friction, influenced by pavement design, aggregate type, traffic loading, surface treatment, and weather, fluctuates over time. Traditionally, state agencies perform costly, periodic friction measurements using specialized devices and vehicles. This project introduces an advanced road friction screening system using telematics data from both regular and automated vehicles (RVs and AVs), enabling comprehensive network-level friction analysis. The system utilizes Physics-Enhanced Residual Learning (PERL) for AV control to maintain optimal slip ratios and peak Tire-Road Friction Coefficient (TRFC) values, ensuring accurate friction measurements without causing excessive sliding or tire wear. It leverages cooperative perception from connected vehicles to improve accuracy and extend coverage by mitigating sensor errors. Additionally, the system employs smart routing to optimize data collection routes for connected AVs, enhancing coverage and the efficiency of the screening process. The system’s potential to improve road safety, cooperative driving automation (CDA) applications, and infrastructure management will be demonstrated through field tests with real-world scenarios in UW-Madison’s Level 3 testbed and possibly later at Mcity.

Institution(s): University of Wisconsin-Madison
University of Illinois at Urbana-Champaign

Award Year: 2024

Research Focus: Safety, Mobility

Project Form(s):

Project Information Form
Data Management Plan