One for all: Decentralized optimization of lateral position of autonomous trucks in a platoon to improve roadway infrastructure sustainability

Abstract

Introduction of autonomous and connected trucks (ACTs) is expected to result in drastic changes in operational characteristics of freight shipments, which may in turn have significant impacts on highway safety, vehicle fuel consumption, and infrastructure durability. One such important change is the formation of truck platoons which can be defined as the convoy of trucks travelling in a very close distance. Reducing congestion, regulating traffic, and improving fuel efficiency are some of reported and expected benefits of platooning. Yet such platooning operations may accelerate the damage accumulation within pavement structures because the lateral position of successive trucks within a lane is expected to be similar (i.e., channelized traffic) and the time between two consecutive axle loads (i.e., resting period) is expected to be reduced. Therefore, this study develops a platooning-control strategy for a fleet of ACTs such that the lateral position of trucks and spacing between them can be explicitly optimized to minimize damage to the pavement. Pavement damage is simulated using recently developed pavement performance models. On the other hand, fluid dynamics models were developed to compute fuel-cost due to aerodynamic drags. Three numerical optimization algorithms, genetic algorithms, particle swarm optimization and pattern search algorithm were used to solve the objective function. The proposed control strategy efficiency is demonstrated through a case study; relative costs to agencies and users could be reduced by 9%.