Because most automated vehicles (AVs) are programmed to follow a set path and maintain a lateral position in the center of the lane, over time significant pavement rutting will occur. This study directly measured AV lateral wandering patterns. It was found that the wandering patterns of both AVs and human-driven vehicles could be modeled with a normal distribution but have significantly different standard deviations, with AV lateral wandering being at least 3 times smaller than the wandering of human-driven vehicles. Modeling with the Texas Mechanistic-Empirical Flexible Pavement Design System (TxME) found that the AVs with smaller lateral wandering would shorten pavement fatigue life by 22 percent and increase pavement rut depth by 30 percent, which leads to a much higher risk of hydroplaning. Researchers also calculated the maximum tolerable rut depths at different hydroplaning speeds. AVs have a much smaller tolerable rut depth than human-driven vehicles due to greater water film thickness in the rutted wheel paths. To reduce the negative impact of AVs on roadway safety and pavement life, this research recommends an optimal AV wandering pattern, a uniform distribution, which results in prolonged pavement life and decreased hydroplaning potential.
- This project evaluated impact of automated vehicle (AV) lateral wandering patterns on roadway safety and pavement life. It was found that the AVs with smaller lateral wandering would shorten pavement fatigue life by 22 percent and increase pavement rut depth by 30 percent, which leads to a much higher risk of hydroplaning. AVs have a much smaller tolerable rut depth than human-driven vehicles due to greater water film thickness in the rutted wheel paths.
EWD & T2 Products
Course Module (zip): The course module developed for this project consists of 6 files: a white paper (docx), an animation on rutting-CAV lateral wandering (pptx), an animation on rutting and hydroplaning (pptx), a PPT presentation on the project (pptx), a description of the rutting animation (docx), and a description of the hydroplaning animation (docx).
Student Impact Statement (pdf): Two students were funded under this project (Aman Sharma, Master’s student at TAMU and Kenneth X. Vélez Rodríguez, PhD student at VT). This file contains a statement of the impact this project made on these students’ education and workforce development.
Below is a webinar performed by the research team on February 18th, 2020.
Zhou, F., Hu, S., Chrysler, S. T., Kim, Y., Damnjanovic, I., Talebpour, A., and Espejo, A. (2019). Optimization of Lateral Wandering of Automated Vehicles to Reduce Hydroplaning Potential and to Improve Pavement Life. Journal of the Transportation Research Board, Vol. 2673, Issue 11, 2019, 81-89. https://doi.org/10.1177/0361198119853560
Zhou, F., Hu, S., Chrysler, S., Kim, Y., Damnjanovic, I., Talebpour, A., & Espejo, A. (2018, July). Optimization of Lateral Wandering of Automated Vehicles to Reduce Hydroplaning Potential and to Improve Pavement Life. To be presented at TRB, Washington D.C., 2019 (Accepted)
The final datasets for this project are located in the Safe-D Collection on the VTTI Dataverse; DOI: 10.15787/VTT1/1QXWSN.
Research Investigators (PI*)
Start Date: 2017-05-10
End Date: 2019-08-30
Grant Number: 69A3551747115
Total Funding: $420,134
Source Organization: Safe-D National UTC
Project Number: 02-008
Safe-D Theme Areas
Safe-D Application Areas
Planning for Safety
Operations and Design
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC 20590 United States
Texas A&M University
Texas A&M Transportation Institute
College Station, Texas 77843-3135
Virginia Polytechnic Institute and State University
Virginia Tech Transportation Institute
3500 Transportation Research Plaza
Blacksburg, Virginia 24061