The improvement of the pavement performance by different means is essential for smooth movement of autonomous truck (AT). This study focuses on minimization of pavement distresses by controlling strategies of 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 minimize the generation of asphalt pavement distresses 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 multi-layer elastic theory LEFs were calculated for fatigue cracking and rutting separately. The acquired performances clearly showed significant improvement in pavement distresses for small increase of standard deviation of wheel wander and uniform distribution of traffic loading and for equally distributed traffic on road lanes. In addition, attempt has been made to optimize pavement distresses in putting all AT in low temperature duration of a day. Placing all ATs in certain period of a day might be beneficial for reducing asphalt pavement distresses and may bring fruitful solution to prevent the early deterioration of pavement.