Communication-Efficient MARL for Platoon Stability and Energy-Efficiency Co-Optimization in Cooperative Adaptive Cruise Control of CAVs

Cooperative adaptive cruise control (CACC) is a critical function that faces significant challenges in maintaining platoon stability and achieving energy efficiency, especially in real-world operations. The CACC of connected and autonomous vehicles (CAVs) based on the multi-agent reinforcement learning (MARL) algorithm is studied to optimize platoon stability and energy efficiency simultaneously. Then the effectiveness of communication information is the key to guaranteeing learning performance in real-world driving, and thus this paper has proposed a communication-efficient MARL by incorporating the quantified stochastic gradient descent (QSGD) and a binary differential consensus (BDC) method into a fully-decentralized MARL framework. We evaluate this BDC-MARL algorithm against several typical non-communicative and communicative MARL algorithms, including IA2C, FPrint, and DIAL, focusing on metrics such as platoon stability, fuel economy, and driving comfort. Our results demonstrate that BDC-MARL achieves superior energy savings, with improvements of up to 5.8%, an average velocity of 15.26 m/s, and an inter-vehicle spacing of 20.76 m. Additionally, we perform comprehensive analyses of communicative information-sharing efficiency and scalability across varying platoon sizes, further validating the practical effectiveness through real-world scenarios using data from the open-source OpenACC.