Truck Platooning via Zeno-Free Event-Triggered Communication Based on Reinforcement Learning

Abstract

Truck platooning depends on frequent vehicle-to-vehicle communication to achieve platoon formation, which can create potentially substantial communication and computational burdens. To balance formation performance and communication efficiency, this paper proposes a Proximal Policy Optimization-based Event-Triggered Mechanism (PPO-ETM) for truck platoon formation. Under a predecessor-following topology, each follower uses locally available information and independently determines whether its state should be transmitted. PPO optimizes communication decisions by jointly considering formation errors and transmission costs, while a controller generates acceleration and steering commands. An analytically designed event-triggered filter is further incorporated to guarantee a strictly positive Minimum Inter-Event Time, thereby excluding Zeno behavior. Simulation results show that the proposed framework enables initially dispersed trucks to converge to and maintain the desired formation while avoiding redundant information transmissions. Comparative studies demonstrate that PPO-ETM achieves a favorable balance among formation accuracy, communication efficiency, and learning performance, outperforming other Reinforcement Learning methods. These results indicate that the proposed framework provides a decentralized and scalable solution for communication-constrained truck platooning.