DCT-MARL: A Dynamic Communication Topology-Based MARL Algorithm for Connected Vehicle Platoon Control

TL;DR

This paper introduces DCT-MARL, a novel multi-agent reinforcement learning algorithm that dynamically adapts vehicle communication topology to improve the robustness and safety of connected vehicle platoons under communication uncertainties.

Contribution

It proposes a dynamic communication topology mechanism and augmented state space to enhance platoon control robustness against delays and packet loss.

Findings

Outperforms existing methods in string stability

Enhances driving comfort under communication disruptions

Demonstrates superior robustness in simulations

Abstract

With the rapid advancement of vehicular communication facilities and autonomous driving technologies, connected vehicle platooning has emerged as a promising approach to improve traffic efficiency and driving safety. Reliable Vehicle-to-Vehicle (V2V) communication is critical to achieving efficient cooperative control. However, in the real-world traffic environment, V2V communication may suffer from time-varying delay and packet loss, leading to degraded control performance and even safety risks. To mitigate the adverse effects of non-ideal communication, this paper proposes a Dynamic Communication Topology based Multi-Agent Reinforcement Learning (DCT-MARL) algorithm for robust cooperative platoon control. Specifically, the state space is augmented with historical control action and delay to enhance robustness against communication delay. To mitigate the impact of packet loss, a multi-key gated communication mechanism is introduced, which dynamically adjusts the communication topology based on the correlation between vehicles and their current communication status. Simulation results demonstrate that the proposed DCT-MARL significantly outperforms state-of-the-art methods in terms of string stability and driving comfort, validating its superior robustness and effectiveness.