Simulation of autonomous truck for minimizing asphalt pavement distresses

The improvement of the pavement performance by different means is essential for the smooth movement of autonomous trucks (ATs). This study focuses on minimising the pavement distress by controlling vehicular loading distribution pattern (wander), traffic distribution on lanes (lane sharing) of a road, and limiting the running duration of AT to low-temperature time only. Mechanistic-Empirical Pavement Design Software, AASHTOWare, was incorporated in this research to analyze and then minimise the generation of asphalt pavement distress from autonomous truck loading. Different loading distribution patterns and traffic distribution of autonomous trucks were devised in AASHTOWare using the load equivalency factor (LEF) and lane distribution factors. Using multilayer elastic theory, LEFs were calculated for fatigue cracking and rutting separately. The acquired performances clearly showed significant improvement in pavement distress for a small increase of standard deviation of wheel wander and uniform distribution of traffic loading and for equally distributed ATs on the road lanes. In addition, an attempt has been made to optimise pavement distress in putting all ATs in a low-temperature duration of a day. Placing all ATs in a certain period of a day is beneficial for reducing asphalt pavement distresses and can bring a fruitful solution to prevent the early deterioration of the pavements.