Modular Platooning and Formation Control

Yanlin Zhou, George Pu, Fan Lu, Xiyao Ma, Xiaolin Li

Keywords: Deep Reinforcement Learning, Vehicle Platooning, Formation Control, Obstacle Avoidance

Abstract: Moving vehicles in formation, or platooning, can dramatically increase road capacity. While traditional control methods can manage fleets of vehicles, they do not address the issues of dynamic road conditions and scalability (i.e., sophisticated control law redesign and physics modeling). We propose a modular framework that averts daunting retrains of an image-to-action neural network, provides flexibility in transferring to different robots/cars, while also being more transparent than previous approaches. First, a convolutional neural network was trained to localize in an indoor setting with dynamic foreground/background. Then, we design a new deep reinforcement learning algorithm named Momentum Policy Gradient (MPG) for continuous control tasks and prove its convergence. MPG is sucessfully applied to the platooning problem with obstacle avoidance and intra-group collision avoidance.