Autonomous trucks’ (ATs) lateral distribution and asphalt pavement performance

ABSTRACT

Recent research studies have provided useful insights into the impact of the Autonomous Trucks ’s (AT) wheel wander on pavement performance. However, only a single road structure, asphalt mix stiffness and asphalt mix thickness are considered in these studies. With these in mind, the present study aims to extend previous limited research results and present a more comprehensive insight into the relationship between AT wandering and the performance of flexible pavement asphalt layers with different thicknesses and asphalt stiffness. For the investigation, a pavement analysis was done on flexible pavement cross-sections with variations in the base and subgrade mechanical characteristics. In addition, for each cross-section the HMA thickness and stiffness varied. The ATs’ lateral position was modelled by four distinct distribution patterns: the zero, normal, uniform and Laplace . A static and dynamic pavement analysis was performed with the Mechanistic Empirical Pavement Design Guide (MEPDG) to determine pavement performance in terms of fatigue cracking and rutting of the asphalt layer. Analysis results showed that the zero lateral distribution of ATs hurts pavement performance. On the other hand, when ATs wheel paths are distributed uniformly in the lateral direction within the lane, the accumulated damage is significantly reduced.

KEYWORDS:

• Autonomous trucks
• wheel wander
• fatigue cracking
• rutting
• pavement performance

Acknowledgements

“This research is co-financed by Greece and the European Union (European Social Fund- ESF) through the Operational Programme «Human Resources Development, Education and Lifelong Learning» in the context of the project “Reinforcement of Postdoctoral Researchers - 2nd Cycle” (MIS-5033021), implemented by the State Scholarships Foundation (ΙΚΥ).” There ia aslo a visual identity provided in our submitted article.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by State Scholarships Foundation: [grant number 5033021].