Title
Automated Vehicles' Impact on Pavement Performance (Research)
Abstract
Over the past few years, automated vehicles (AVs) have received considerable attention around the world. Automated driving may become a reality in the next decades because of its potential benefits, including reduced pollution, energy consumption, driver stress, and the cost of congestion, as well as increasing highway capacity, safety, green mobility, and the feasibility of alternative fuel vehicles and improving transport accessibility. The deployment of AV technologies has the potential to change the transportation sector on a global scale. The current transportation infrastructure is designed primarily for human-driven vehicles. Since vehicle-related technologies are advancing faster than ever, it is essential to better understand how and when the conventional infrastructures may be affected by AVs' deployment. The transition of road infrastructures from human-driven vehicles to infrastructures that are appropriate for AVs implies an evolution in road design. From the road infrastructure point of view, one of AVs deployment's crucial aspects is its impact on road pavement performance. This PhD project aimed to study the implication of AVs' on pavement performance. To this end, we identify the differences between AVs and non-AVs that they can influence the pavement performance and evaluate their effects using the finite element modeling approach. In this sense, we evaluate the AVs' lateral wandering patterns, AV' penetration rates into the roads, AVs' lane management scenarios (e.g., dedicated lane, narrower lane, and shared lane), AVs' speed, AVs' deceleration, and braking systems effects on pavement performance. This project's results create a better understanding of pavement analysis and design for the era of co-existing AVs and non-AVs. This contributes to our knowledge of the decision-making process to choose lateral wandering patterns for AVs, vehicle speed design, lane-management policies, pavement thicknesses, and the used materials.
Period of project
16 March 2019 - 15 March 2023