This article introduces a model designed to assess the lifecycle maintenance costs associated with asphalt pavement, taking into account various distribution patterns influenced by autonomous vehicles. The primary objective is to establish a robust framework for managing mixed traffic flows and to facilitate the adoption of technologies pertinent to autonomous driving. The model employs lifecycle maintenance costs as the key evaluative metric, incorporating considerations such as maintenance, fuel consumption, and environmental impact to analyze the effects of autonomous vehicles on pavement maintenance expenditures. The results indicate that the integration of autonomous vehicles can lead to a substantial decrease in the overall maintenance costs of asphalt pavement. This cost reduction is primarily attributed to the more consistent driving patterns exhibited by autonomous vehicles, which diminish the frequency of necessary repairs and maintenance. By estimating the lifecycle maintenance costs of asphalt pavement under various distribution scenarios resulting from autonomous vehicles, this research demonstrates that an optimal distribution of these vehicles can significantly postpone the formation of ruts, potentially reducing maintenance costs by as much as 69.5%. Conversely, the maintenance costs associated with asphalt pavement under a zero distribution scenario are found to increase by 13.7%.