Development of a Finite Element Model to Analyze the Lateral Drift of Autonomous Vehicles and Its Impacts on Rutting Depth on Flexible Pavements Under Mixed Traffic Scenarios

Abstract

The continuous development of technologies, including intelligent networking, machine vision, and new energy vehicles, has made autonomous driving vehicles a research focus. Autonomous vehicles significantly affect road performance under mixed traffic flow conditions with their excellent lateral control ability and driving stability. A finite element model is established to analyze the impact of these modes on pavement deformation under maximum air temperature in Nanjing, China. Based on the simulation results, it has been shown that lateral control of autonomous vehicles significantly reduces pavement deformation. The proportion of autonomous vehicles affects the difference in distribution modes, and the uniform distribution mode is the most favorable for horizontal distribution. Additionally, the edge lane experiences less pavement deformation than the center lane and varies more when the distribution pattern changes. By comparing the deformation of the center and edge lanes, the edge lane experiences a significant reduction in deformation under the uniform distribution mode, with a maximum reduction of 73.7%. Comparing the pavement deformation of center and edge lanes under normal distribution mode and uniform distribution mode shows that safe solutions for the lateral distribution of autonomous vehicles depend on the proportion of autonomous vehicles. The optimal driving speed for autonomous vehicles is 80 km/h under the uniform distribution model, while under the normal distribution model it is 90 km/h or 110 km/h. These findings provide a theoretical basis for the lateral control of autonomous vehicles in the future and propose safe solutions for the lateral distribution of autonomous vehicles.