Reducing fuel consumption in platooning systems through reinforcement learning

Abstract

Fuel efficiency in platooning systems is a central topic of interest because of its significant economic and environmental impact on the transportation industry. In platoon systems, Adaptive Cruise Control (ACC) is widely adopted because it can guarantee string stability while requiring only radar or lidar measurements. A key parameter in ACC is the desired time gap between the platoon's neighboring vehicles. A small time gap results in a short inter-vehicular distance, which is fuel efficient when the vehicles are moving at constant speeds due to air drag reductions. On the other hand, when the vehicles accelerate and brake a lot, a bigger time gap is more fuel efficient. This motivates us to find a policy that minimizes fuel consumption by conveniently switching between two desired time gap parameters. Thus, one can interpret this formulation as a dynamic system controlled by a switching ACC, and the learning problem reduces to finding a switching rule that is fuel efficient. We apply a Reinforcement Learning (RL) algorithm to find a time switching policy between two desired time gap parameters of an ACC controller to reach our goal. We adopt the proximal policy optimization (PPO) algorithm to learn the appropriate transient shift times that minimize the platoon's fuel consumption when it faces stochastic traffic conditions. Numerical simulations show that the PPO algorithm outperforms both static time gap ACC and a threshold-based switching control in terms of the average fuel efficiency